From eb6ef39007083daf1e6f942a484e1eb6936ec764 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Wed, 5 Aug 2026 11:34:25 +0800 Subject: [PATCH 01/12] =?UTF-8?q?=E4=BC=98=E5=8C=96=E5=A4=9A=E4=BA=95?= =?UTF-8?q?=E8=87=AA=E5=8A=A8=E6=8B=9F=E5=90=88=E6=B1=82=E8=A7=A3=E6=B5=81?= =?UTF-8?q?=E7=A8=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nmCalculation/nmCalculationAutoFitPSO.h | 1 + .../nmCalculationDllPebiSolverTask.h | 13 ++ .../nmCalculation/nmCalculationAutoFitPSO.cpp | 187 +++++++++++++----- .../nmCalculationDllPebiSolverTask.cpp | 80 ++++++-- 4 files changed, 217 insertions(+), 64 deletions(-) diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h index e59db09..3418b04 100644 --- a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h +++ b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h @@ -166,6 +166,7 @@ private: // ===== 求解器相关 ===== QVector > runSolver(); QVector> runSolverDll(); + bool runFinalFullSolver(); QVector> runSolverExe(); // ===== 数据处理 ===== diff --git a/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h b/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h index 1de9f71..3efc33f 100644 --- a/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h +++ b/Include/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.h @@ -2,6 +2,8 @@ #define NMCALCULATIONDLLPEBISOLVERTASK_H #include +#include +#include #include #include #include @@ -22,6 +24,13 @@ class NMCALCULATION_EXPORT nmCalculationDllPebiSolverTask : public QThread { // 返回 false 时调用方会丢弃本次结果, 防止复用上一粒子留下的旧曲线. bool wasSuccessful() const; + // 自动拟合粒子评价只提取目标井曲线，不写回共享数据和网格压力场。 + // 井名为空时保持原有完整结果保存模式。 + void setAutoFitTargetWell(const QString& wellName); + QVector > getAutoFitResultPressure() const; + QVector > getAutoFitResultLogLog() const; + QVector > getAutoFitResultSemiLog() const; + private: bool execute(); @@ -37,6 +46,10 @@ class NMCALCULATION_EXPORT nmCalculationDllPebiSolverTask : public QThread { QString m_sPostprocessingDir; // run() 在线程内保存 execute() 结果, 等待线程结束的调用方只读取该状态. bool m_lastRunSucceeded; + QString m_autoFitTargetWellName; + QVector > m_autoFitResultPressure; + QVector > m_autoFitResultLogLog; + QVector > m_autoFitResultSemiLog; private slots: //void slotTaskUpdateProgress(); diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index d0deda2..936d9fc 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -721,8 +721,8 @@ void nmCalculationAutoFitPSO::setTargetLogLogData(const QVector void nmCalculationAutoFitPSO::stopFitting() { // 用户点击停止时走这里。停止策略是“请求式停止”： - // 先置 m_shouldStop，让主循环/求解器等待逻辑自然退出；短时间内还在评价时再重置计数。 - // 这样可以减少 DLL 任务被硬中断导致的数据状态残留。 + // 只置 m_shouldStop，让主循环/求解器等待逻辑退出并自行维护任务计数。 + // 不在这里清理临时目录或强制清零计数，避免与正在返回的 DLL 任务竞争。 if(m_simulationMode && m_simulationTimer) { m_simulationTimer->stop(); } @@ -736,25 +736,28 @@ void nmCalculationAutoFitPSO::stopFitting() m_shouldStop = true; - // 等待当前评估完成，缩短超时时间 + if(m_simulationMode) { + m_isRunning = false; + closeTraceFile(); + cleanupTemporaryDirectory(); + emit logMessageGenerated(tr("PSO simulation stop request processed")); + return; + } + + // 给当前评价一个短暂的自然退出时间。若仍在运行， + // runSolverDll() 会在下一个等待周期检查 m_shouldStop 并结束任务。 int waitCount = 0; - while(m_evaluationInProgress > 0 && waitCount < 30) { // 减少等待时间 + while(m_evaluationInProgress > 0 && waitCount < 30) { QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50); msleep(50); waitCount++; } - // 超时时强制重置 if(m_evaluationInProgress > 0) { - DEBUG_OUT("Force resetting evaluation counter"); - emit logMessageGenerated(tr("Force stopping current evaluation...")); - m_evaluationInProgress = 0; + emit logMessageGenerated(tr("Waiting for current solver evaluation to stop...")); } - // 确保运行标志被清除 - m_isRunning = false; - emit logMessageGenerated(tr("PSO optimization stop request processed")); DEBUG_OUT("Stop request processed"); } else { @@ -762,8 +765,6 @@ void nmCalculationAutoFitPSO::stopFitting() emit logMessageGenerated(tr("Stop request received but optimization is not running")); } - closeTraceFile(); - cleanupTemporaryDirectory(); } bool nmCalculationAutoFitPSO::isRunning() const @@ -2903,6 +2904,15 @@ bool nmCalculationAutoFitPSO::startAutoFitting() emit logMessageGenerated(tr("Target data validation passed (%1 data points)").arg(m_targetLogLogData[0].size())); + if(m_targetWellName.isEmpty()) { + m_lastError = "Target well name is empty"; + emit logMessageGenerated(tr("ERROR: Target well name is empty")); + return false; + } + + emit logMessageGenerated(tr("Particle evaluation mode: solve all wells, retain target well '%1' only") + .arg(m_targetWellName)); + // 使用保存的初始值进行精英保护。resetOptimizer() 会清空部分运行状态， // 所以先把用户当前模型参数缓存下来，后面再恢复用于初始解评价和粒子初始化。 QVector savedInitialValues = m_initialValues; @@ -3286,13 +3296,37 @@ bool nmCalculationAutoFitPSO::startAutoFitting() return false; } - m_isRunning = false; + bool finalFullSolverSucceeded = true; + bool finalFullSolverExecuted = false; // 应用最终参数 if(!m_globalBestPosition.isEmpty()) { try { emit logMessageGenerated(tr("Applying optimized parameters to model...")); applyParametersToDataManager(m_globalBestPosition); + + if(m_shouldStop) { + // 手动停止优先保持快速返回，仅写回已确认的最优参数。 + emit logMessageGenerated(tr("Final full-field calculation skipped after user stop")); + } else { + // 粒子阶段只保留目标井临时曲线。正常结束后用最优参数完整计算一次， + // 将全部井曲线和网格压力场写回项目，该次不计入 PSO 粒子评价数。 + emit logMessageGenerated(tr("Running final full-field calculation with optimized parameters...")); + finalFullSolverExecuted = true; + finalFullSolverSucceeded = runFinalFullSolver(); + + if(finalFullSolverSucceeded) { + emit logMessageGenerated(tr("Final full-field calculation completed successfully")); + } else if(m_shouldStop) { + finalFullSolverExecuted = false; + finalFullSolverSucceeded = true; + emit logMessageGenerated(tr("Final full-field calculation stopped by user")); + } else { + emit logMessageGenerated(tr("ERROR: Final full-field calculation failed")); + m_lastError = tr("Optimized parameters were found, but the final full-field calculation failed"); + } + } + saveOptimizationResult(); // 输出最终优化结果 @@ -3311,16 +3345,24 @@ bool nmCalculationAutoFitPSO::startAutoFitting() emit logMessageGenerated(finalParams); - emit logMessageGenerated(tr("Parameters applied successfully to data manager")); + if(finalFullSolverExecuted && finalFullSolverSucceeded) { + emit logMessageGenerated(tr("Parameters and full-field results applied successfully to data manager")); + } else if(!finalFullSolverExecuted) { + emit logMessageGenerated(tr("Optimized parameters applied to data manager")); + } } catch(const std::exception& e) { + finalFullSolverSucceeded = false; emit logMessageGenerated(tr("ERROR: Failed to apply final parameters: %1").arg(e.what())); m_lastError = QString("Failed to apply final parameters: %1").arg(e.what()); } catch(...) { + finalFullSolverSucceeded = false; emit logMessageGenerated(tr("ERROR: Unknown error applying final parameters")); m_lastError = "Failed to apply final parameters due to unknown error"; } } + m_isRunning = false; + // 判断系统确定最终结果 bool success; QString message; @@ -3363,6 +3405,11 @@ bool nmCalculationAutoFitPSO::startAutoFitting() emit logMessageGenerated(tr("=== PSO OPTIMIZATION - UNKNOWN END ===")); } + if(!finalFullSolverSucceeded) { + success = false; + message = m_lastError; + } + emitRunSummary(success, finalReason); // 先发送最终进度更新，确保进度条达到100% @@ -3961,7 +4008,7 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector& parameter // 1. 校验粒子参数是否在用户设置的上下界和基本物理范围内； // 2. 将参数写入 DataManager 的储层/目标井对象； // 3. 调用真实数值求解器，生成模拟结果； - // 4. 从目标井读取模拟后的 result log-log 曲线； + // 4. 从本次求解任务读取目标井 result log-log 曲线； // 5. 与目标 history log-log 曲线计算误差，误差越小代表拟合越好。 // // 返回 1e10 表示该粒子评价失败或结果不可用。PSO 会把它当成很差的解。 @@ -4122,24 +4169,11 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector& parameter return 1e10; } - // 5. 获取 LogLog 数据。runSolver() 会更新 DataManager 中目标井的计算结果， - // 这里再从目标井读取 resultLogLogData 作为模拟曲线。 - QVector> resultLogLogData; + // 5. 获取 LogLog 数据。runSolverDll() 直接从求解任务复制目标井曲线， + // 不再依赖 DataManager 中可能被其它井或上一粒子改写的共享结果。 + QVector> resultLogLogData = m_lastEvaluatedLogLogData; try { - nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName); - - if(!pTargetWell) { - DEBUG_OUT(QString("%1: Call #%2 - Target well '%3' NOT FOUND") - .arg(funcName).arg(callCount).arg(m_targetWellName)); - return 1e10; - } - - DEBUG_OUT(QString("%1: Call #%2 - Target well found: %3") - .arg(funcName).arg(callCount).arg(m_targetWellName)); - - resultLogLogData = pTargetWell->getResultLogLog(); - if(!validateLogLogData(resultLogLogData)) { DEBUG_OUT(QString("%1: Call #%2 - LogLog data VALIDATION FAILED") .arg(funcName).arg(callCount)); @@ -5145,7 +5179,7 @@ QVector> nmCalculationAutoFitPSO::runSolverDll() { // DLL 求解器路径。 // 这个函数负责把当前 DataManager 中的项目状态交给底层数值求解器， - // 并让目标井生成 result log-log 数据。evaluateFitness() 后续会从目标井读取该结果。 + // 数值求解仍包含全部计算井，以保留井间干扰；后处理只提取目标井曲线。 // // 如果这里失败，通常需要优先检查：HX_NWTM.dll、license、网格/井数据是否完整、 // 目标井是否存在，以及 DataManager 中刚写入的参数是否导致求解器异常。 @@ -5157,6 +5191,7 @@ QVector> nmCalculationAutoFitPSO::runSolverDll() } ++m_evaluationInProgress; + m_lastEvaluatedLogLogData.clear(); QVector> result; nmCalculationDllPebiSolverTask* dllTask = nullptr; @@ -5164,6 +5199,15 @@ QVector> nmCalculationAutoFitPSO::runSolverDll() DEBUG_OUT("Creating DLL solver task"); dllTask = new nmCalculationDllPebiSolverTask(m_tempDirectory); + if(m_targetWellName.isEmpty()) { + DEBUG_OUT("Target well name is empty - target-only solver cannot start"); + delete dllTask; + --m_evaluationInProgress; + return result; + } + + dllTask->setAutoFitTargetWell(m_targetWellName); + if(m_shouldStop) { DEBUG_OUT("Should stop - cleaning up and returning empty result"); delete dllTask; @@ -5223,22 +5267,9 @@ QVector> nmCalculationAutoFitPSO::runSolverDll() return result; } - // 验证结果数据是否已更新。DLL 任务会把结果写回 DataManager 中的目标井对象。 - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - //QVector wells = dataManager->getWellDataList(); - nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName); - - if(!pTargetWell) { - DEBUG_OUT("No wells found in data manager after DLL execution"); - delete dllTask; - --m_evaluationInProgress; - return result; - } - - // 验证结果数据。evaluateFitness() 最终用的是 logLogResult， - // 但这里返回 pressureResult 给 validateSolverResult() 做基本求解成功判断。 - QVector> pressureResult = pTargetWell->getResultPressure(); - QVector> logLogResult = pTargetWell->getResultLogLog(); + // 任务结束后复制其局部结果，删除任务前不再持有任务内部引用。 + QVector> pressureResult = dllTask->getAutoFitResultPressure(); + QVector> logLogResult = dllTask->getAutoFitResultLogLog(); DEBUG_OUT(QString("DLL result verification - Pressure arrays: %1, LogLog arrays: %2") .arg(pressureResult.size()).arg(logLogResult.size())); @@ -5257,8 +5288,12 @@ QVector> nmCalculationAutoFitPSO::runSolverDll() } // 数据有效性检查 - if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) { + if(pressureResult.size() >= 2 + && pressureResult[0].size() > 0 + && pressureResult[1].size() > 0 + && validateLogLogData(logLogResult)) { result = pressureResult; + m_lastEvaluatedLogLogData = logLogResult; DEBUG_OUT(QString("Got DLL solver result: %1 points").arg(result[0].size())); m_consecutiveFailures = 0; @@ -5326,6 +5361,58 @@ QVector> nmCalculationAutoFitPSO::runSolverDll() return result; } +bool nmCalculationAutoFitPSO::runFinalFullSolver() +{ + // 不设置目标井名，任务按原完整模式保存全部井和网格结果。 + if(m_evaluationInProgress > 0) { + DEBUG_OUT("Cannot start final full-field solver while another evaluation is running"); + return false; + } + + ++m_evaluationInProgress; + nmCalculationDllPebiSolverTask dllTask(m_tempDirectory); + dllTask.start(); + + const int maxWait = 3600000; + const int checkInterval = 50; + QTime waitTimer; + waitTimer.start(); + + while(waitTimer.elapsed() < maxWait) { + if(dllTask.wait(checkInterval)) { + break; + } + + // 最终完整计算可能持续较长时间，此处需处理停止按钮事件。 + QApplication::processEvents(QEventLoop::AllEvents, checkInterval); + + if(!dllTask.isRunning()) { + break; + } + + if(m_shouldStop) { + DEBUG_OUT("Final full-field solver terminated by user"); + dllTask.terminate(); + dllTask.wait(2000); + --m_evaluationInProgress; + return false; + } + } + + if(dllTask.isRunning()) { + DEBUG_OUT("Final full-field solver timeout, terminating task"); + dllTask.terminate(); + dllTask.wait(2000); + --m_evaluationInProgress; + return false; + } + + dllTask.wait(); + const bool succeeded = dllTask.wasSuccessful(); + --m_evaluationInProgress; + return succeeded; +} + //QVector> nmCalculationAutoFitPSO::runSolverExe() //{ // DEBUG_OUT("SOLVER EXE START"); diff --git a/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp b/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp index e8db3a5..9f87c0b 100644 --- a/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp @@ -227,6 +227,26 @@ bool nmCalculationDllPebiSolverTask::wasSuccessful() const return m_lastRunSucceeded; } +void nmCalculationDllPebiSolverTask::setAutoFitTargetWell(const QString& wellName) +{ + m_autoFitTargetWellName = wellName; +} + +QVector > nmCalculationDllPebiSolverTask::getAutoFitResultPressure() const +{ + return m_autoFitResultPressure; +} + +QVector > nmCalculationDllPebiSolverTask::getAutoFitResultLogLog() const +{ + return m_autoFitResultLogLog; +} + +QVector > nmCalculationDllPebiSolverTask::getAutoFitResultSemiLog() const +{ + return m_autoFitResultSemiLog; +} + bool nmCalculationDllPebiSolverTask::execute() { return this->execPebiMode(); @@ -582,6 +602,15 @@ bool nmCalculationDllPebiSolverTask::savePebiModeResult( int modelType) { nmDataAnalyzeManager* pDataInstance = nmDataAnalyzeManager::getCurrentInstance(); + if(pDataInstance == nullptr) { + return false; + } + + const bool autoFitTargetOnly = !m_autoFitTargetWellName.isEmpty(); + bool autoFitTargetFound = false; + m_autoFitResultPressure.clear(); + m_autoFitResultLogLog.clear(); + m_autoFitResultSemiLog.clear(); QVector> vvecPressure; QVector> vvecLogLog; @@ -599,16 +628,22 @@ bool nmCalculationDllPebiSolverTask::savePebiModeResult( } // 获取参与求解的井的顺序 - QVector> vecWellsOrder = nmDataAnalyzeManager::getCurrentInstance()->getCalculationWells(); + QVector> vecWellsOrder = pDataInstance->getCalculationWells(); - // 清空井名和二维位置的映射 - pDataInstance->clearWellLocations(); + // 粒子评价不修改全局井位置，完整求解仍按原流程重建映射. + if(!autoFitTargetOnly) { + pDataInstance->clearWellLocations(); + } // 遍历每口井，处理其数据 for(int wellIdx = 0; wellIdx < vecWellsOrder.size(); ++wellIdx) { NM_WELL_MODEL eWellType = vecWellsOrder[wellIdx].first; // 获取井的类型 QString sWellName = vecWellsOrder[wellIdx].second; // 获取井的名称 + if(autoFitTargetOnly && sWellName != m_autoFitTargetWellName) { + continue; + } + // 跳过裂缝（或未知井类型） if(eWellType == NM_WELL_MODEL::Unknow_Well) { continue; @@ -760,17 +795,34 @@ bool nmCalculationDllPebiSolverTask::savePebiModeResult( } } - // 将计算结果保存到对应的井数据里 - // 压力 - pWellData->setResultPressure(vvecPressure); - // 双对数 - pWellData->setResultLogLog(vvecLogLog); - // 半对数 - pWellData->setResultSemiLog(vvecSemiLog); - - // 存储当前井名称和二维位置到映射 - QPointF ptWellCoords(pWellData->getX().getValue().toDouble(), pWellData->getY().getValue().toDouble()); - pDataInstance->addWellLocation(sWellName, ptWellCoords); + if(autoFitTargetOnly) { + // 粒子评价结果保存在任务对象内，避免反复改写 DataManager. + m_autoFitResultPressure = vvecPressure; + m_autoFitResultLogLog = vvecLogLog; + m_autoFitResultSemiLog = vvecSemiLog; + autoFitTargetFound = true; + break; + } else { + // 完整模式保持原行为：写入全部井曲线和井位置. + pWellData->setResultPressure(vvecPressure); + pWellData->setResultLogLog(vvecLogLog); + pWellData->setResultSemiLog(vvecSemiLog); + + QPointF ptWellCoords(pWellData->getX().getValue().toDouble(), pWellData->getY().getValue().toDouble()); + pDataInstance->addWellLocation(sWellName, ptWellCoords); + } + } + + if(autoFitTargetOnly) { + const bool pressureValid = autoFitTargetFound + && m_autoFitResultPressure.size() >= 2 + && !m_autoFitResultPressure[0].isEmpty() + && m_autoFitResultPressure[0].size() == m_autoFitResultPressure[1].size(); + const bool logLogValid = m_autoFitResultLogLog.size() >= 3 + && !m_autoFitResultLogLog[0].isEmpty() + && m_autoFitResultLogLog[0].size() == m_autoFitResultLogLog[1].size() + && m_autoFitResultLogLog[0].size() == m_autoFitResultLogLog[2].size(); + return pressureValid && logLogValid; } // 计算有效单元数量 From 6e34d44f0b7cd7e2c4458660cd381e755cea3697 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Wed, 5 Aug 2026 16:11:30 +0800 Subject: [PATCH 02/12] =?UTF-8?q?=E4=BC=98=E5=8C=96=E6=89=B9=E9=87=8F?= =?UTF-8?q?=E9=80=89=E4=BA=95=E5=8A=A0=E8=BD=BD=E6=80=A7=E8=83=BD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit 使用集合计算井图元差集，并在批量增删期间合并 Map 重绘。 合并井列表变更触发的参数面板刷新，避免逐井完整重建。 --- Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h | 10 +++++++ Src/nmNum/nmSubWnd/nmSubWndMain.cpp | 26 ++++++++++++++--- Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp | 28 ++++++++++++++++++- 3 files changed, 59 insertions(+), 5 deletions(-) diff --git a/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h b/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h index 5488a07..d4d6938 100644 --- a/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h +++ b/Include/nmNum/nmSubWxs/nmWxParaPropertyPebi.h @@ -5,6 +5,7 @@ class nmGridRowUtils; class nmDataAnalyzeManager; +class QTimer; /// @brief PEBI求解器专用的参数输入/编辑属性对话框窗体 /// @note 与 nmWxParaProperty 结构一致，区别在于内部使用 nmGridRowUtils（NM子类） @@ -80,6 +81,8 @@ public slots: void slotWellRemoved(QString wellCode, QString wellName); /// @brief 几何对象新增、删除或改名后重建参数面板 void slotGeometryListChanged(); + /// @brief 合并同一轮事件中的多次井列表变化，只重建一次参数面板 + void slotDeferredWellListRebuild(); protected: @@ -100,6 +103,13 @@ protected: // 数据管理器引用（用于全量重建面板时获取井和几何对象列表） nmDataAnalyzeManager* m_pDataManager; + // 批量增删井时合并参数面板刷新 + QTimer* m_pWellListRebuildTimer; + +private: + + void scheduleWellListRebuild(); + signals: /// @brief 参数值变更信号（由 slotParaCtrlValueChanged 转发） diff --git a/Src/nmNum/nmSubWnd/nmSubWndMain.cpp b/Src/nmNum/nmSubWnd/nmSubWndMain.cpp index f207478..b33b517 100644 --- a/Src/nmNum/nmSubWnd/nmSubWndMain.cpp +++ b/Src/nmNum/nmSubWnd/nmSubWndMain.cpp @@ -64,6 +64,7 @@ #include "iAnalRun.h" #include +#include #include "nmSingalCenter.h" @@ -986,8 +987,18 @@ void nmSubWndMain::updateSelectedWells(QList wellObjList) } } - // 当前在画布上的所有 ZxDataWell* 数据列表 (Key Set) + // 用集合保存当前井和选中井，避免井数增加后反复调用 QList::contains 造成平方级查找。 QList pWellPlotDataList = pWellDataToPlotMap.keys(); + QSet pWellPlotDataSet; + for(int i = 0; i < pWellPlotDataList.count(); ++i) { + pWellPlotDataSet.insert(pWellPlotDataList[i]); + } + QSet selectedWellDataSet; + for(int i = 0; i < wellObjList.count(); ++i) { + if(wellObjList[i] != nullptr) { + selectedWellDataSet.insert(wellObjList[i]); + } + } // setp 2，找出需要 REMOVE (删除) 的图元 QVector vDeleteWellPlotList; @@ -996,7 +1007,7 @@ void nmSubWndMain::updateSelectedWells(QList wellObjList) for(int i = 0; i < pWellPlotDataList.count(); ++i) { ZxDataWell *pWellDataOnPlot = pWellPlotDataList[i]; // 如果该井数据不在用户选中的列表 wellObjList 中，则需要删除 - if (!wellObjList.contains(pWellDataOnPlot)) { + if (!selectedWellDataSet.contains(pWellDataOnPlot)) { // 从 Map 中获取对应的图元对象 vDeleteWellPlotList.append(pWellDataToPlotMap.value(pWellDataOnPlot)); } @@ -1009,12 +1020,16 @@ void nmSubWndMain::updateSelectedWells(QList wellObjList) for(int i = 0; i < wellObjList.count(); ++i) { ZxDataWell *pWellDataSelected = wellObjList[i]; // 确保井数据有效 - if (pWellDataSelected && !pWellPlotDataList.contains(pWellDataSelected)) { + if (pWellDataSelected && !pWellPlotDataSet.contains(pWellDataSelected)) { // 如果该井数据不在当前画布上的数据列表中，则需要添加 vAddWellPlotList.append(pWellDataSelected); } } + // 单井增删过程中会多次请求重绘；批量阶段先暂停，全部处理完成后再统一刷新。 + const bool updatesEnabled = m_pWxPlot->updatesEnabled(); + m_pWxPlot->setUpdatesEnabled(false); + // setp 4，执行删除操作 for(int i = 0; i < vDeleteWellPlotList.count(); ++i) { nmObjPointWell* pWellPlot = vDeleteWellPlotList[i]; @@ -1056,7 +1071,10 @@ void nmSubWndMain::updateSelectedWells(QList wellObjList) // 添加完毕后，重新渲染 - m_pWxPlot->update(); + m_pWxPlot->setUpdatesEnabled(updatesEnabled); + if(updatesEnabled) { + m_pWxPlot->update(); + } } // 地质图导入 diff --git a/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp b/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp index 7eb051f..f687416 100644 --- a/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp +++ b/Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp @@ -23,6 +23,7 @@ #include #include +#include #include "nmWxParaPropertyPebi.h" @@ -195,6 +196,11 @@ nmWxParaPropertyPebi::nmWxParaPropertyPebi(QWidget* parent) : m_pGridItemUtils = nullptr; m_pHelpBox = nullptr; m_pDataManager = nullptr; + // 0ms 单次定时器用于合并同一轮事件中的多次井增删通知。 + m_pWellListRebuildTimer = new QTimer(this); + m_pWellListRebuildTimer->setSingleShot(true); + connect(m_pWellListRebuildTimer, SIGNAL(timeout()), + this, SLOT(slotDeferredWellListRebuild())); setWindowTitle(tr("Numerical para property")); } @@ -376,6 +382,12 @@ void nmWxParaPropertyPebi::slotParaCtrlValueChanged(QString sPara, QVariant o) void nmWxParaPropertyPebi::rebuildAllParas() { + // 已有显式全量重建时，取消尚未执行的延迟刷新，避免重复重建面板。 + if (m_pWellListRebuildTimer != nullptr && m_pWellListRebuildTimer->isActive()) + { + m_pWellListRebuildTimer->stop(); + } + if (m_pMainLayout == nullptr || m_pDataManager == nullptr) return; @@ -779,13 +791,27 @@ void nmWxParaPropertyPebi::slotWellAdded(QString wellCode, QString wellName, QSt Q_UNUSED(wellCode); Q_UNUSED(wellName); Q_UNUSED(paras); - rebuildAllParas(); + scheduleWellListRebuild(); } void nmWxParaPropertyPebi::slotWellRemoved(QString wellCode, QString wellName) { Q_UNUSED(wellCode); Q_UNUSED(wellName); + scheduleWellListRebuild(); +} + +void nmWxParaPropertyPebi::scheduleWellListRebuild() +{ + if (m_pWellListRebuildTimer != nullptr) + { + // 批量循环内重复 start 会重置同一个单次定时器，返回事件循环后只触发一次。 + m_pWellListRebuildTimer->start(0); + } +} + +void nmWxParaPropertyPebi::slotDeferredWellListRebuild() +{ rebuildAllParas(); } From f0d90f951370cff62b4ca8334cbcc58c46328cee Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Thu, 6 Aug 2026 17:20:06 +0800 Subject: [PATCH 03/12] =?UTF-8?q?=E6=9B=B4=E6=94=B9=E6=B8=97=E9=80=8F?= =?UTF-8?q?=E7=8E=87=E7=9A=84=E5=9F=BA=E5=87=86=E5=8D=95=E4=BD=8D=E4=B8=BA?= =?UTF-8?q?Darcy?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Bin/Config/Common/ModelParaDefinesNM_cn.dat | 2 +- Bin/Config/Common/ModelParaDefinesNM_en.dat | 2 +- Bin/Config/Common/UnitDefault_cn.dat | 2 +- Bin/Config/Common/UnitDefault_en.dat | 2 +- Bin/Config/Unit/UnitDefault.dat | 2 +- Bin/XmlFiles/ModelParaDefinesNM_cn.xml | 4 ++-- Bin/XmlFiles/ModelParaDefinesNM_en.xml | 4 ++-- Bin/XmlFiles/UnitDefault_cn.xml | 2 +- Bin/XmlFiles/UnitDefault_en.xml | 2 +- 9 files changed, 11 insertions(+), 11 deletions(-) diff --git a/Bin/Config/Common/ModelParaDefinesNM_cn.dat b/Bin/Config/Common/ModelParaDefinesNM_cn.dat index 3b97d08..4ad2fde 100644 --- a/Bin/Config/Common/ModelParaDefinesNM_cn.dat +++ b/Bin/Config/Common/ModelParaDefinesNM_cn.dat @@ -1 +1 @@ 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 \ No newline at end of file diff --git a/Bin/Config/Common/ModelParaDefinesNM_en.dat b/Bin/Config/Common/ModelParaDefinesNM_en.dat index a1226c0..cac7ac4 100644 --- a/Bin/Config/Common/ModelParaDefinesNM_en.dat +++ b/Bin/Config/Common/ModelParaDefinesNM_en.dat @@ -1 +1 @@ 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z4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iV19EZmMiICAgICAgICAgICAgICAgIEFsaWFzPSJGcmFjdHVyZSBDb25kdWN0aXZpdHkiICBVbml0PSJtZC5tIiBEZWZhdWx0PSIxMDAwIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgICBEaWdpdD0iNiIgRGVzYz0iZnJhY3R1cmUgY29uZHVjdGl2aXR5IiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8UGFyYUdyb3VwIE5hbWU9IiIgQWxpYXM9IkluZGVwZW5kZW50IEZyYWN0dXJlIFBhcmFtZXRlcnMiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGX1gwIiBBbGlhcz0iU3RhcnQgWDAiICAgICAgICAgICAgICBVbml0PSJtIiAgICBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9ImZyYWN0dXJlIHN0YXJ0IFggY29vcmRpbmF0ZSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iRl9ZMCIgQWxpYXM9IlN0YXJ0IFkwIiAgICAgICAgICAgICAgVW5pdD0ibSIgICAgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmcmFjdHVyZSBzdGFydCBZIGNvb3JkaW5hdGUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkZfWDEiIEFsaWFzPSJFbmQgWDEiICAgICAgICAgICAgICAgIFVuaXQ9Im0iICAgIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0iZnJhY3R1cmUgZW5kIFggY29vcmRpbmF0ZSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iRl9ZMSIgQWxpYXM9IkVuZCBZMSIgICAgICAgICAgICAgICAgVW5pdD0ibSIgICAgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmcmFjdHVyZSBlbmQgWSBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGX0ZDIiBBbGlhcz0iRnJhY3R1cmUgQ29uZHVjdGl2aXR5IiBVbml0PSJtZC5tIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgICAgRGlnaXQ9IjYiIERlc2M9ImZyYWN0dXJlIGNvbmR1Y3Rpdml0eSIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSJGYXVsdCBQYXJhbWV0ZXJzIj4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iRlRfWDAiIEFsaWFzPSJTdGFydCBYMCIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmYXVsdCBzdGFydCBYIGNvb3JkaW5hdGUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IkZUX1kwIiBBbGlhcz0iU3RhcnQgWTAiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0iZmF1bHQgc3RhcnQgWSBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGVF9YMSIgQWxpYXM9IkVuZCBYMSIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmYXVsdCBlbmQgWCBjb29yZGluYXRlIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJGVF9ZMSIgQWxpYXM9IkVuZCBZMSIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJmYXVsdCBlbmQgWSBjb29yZGluYXRlIiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8UGFyYUdyb3VwIE5hbWU9IiIgQWxpYXM9IlJlY3RhbmdsZSBCb3VuZGFyeSBQYXJhbWV0ZXJzIj4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWE1pbiIgQWxpYXM9IkxlZnQgQm91bmRhcnkgWCIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJyZWN0YW5nbGUgYm91bmRhcnkgbWluaW11bSBYIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCUl9ZTWluIiBBbGlhcz0iQm90dG9tIEJvdW5kYXJ5IFkiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0icmVjdGFuZ2xlIGJvdW5kYXJ5IG1pbmltdW0gWSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWE1heCIgQWxpYXM9IlJpZ2h0IEJvdW5kYXJ5IFgiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0icmVjdGFuZ2xlIGJvdW5kYXJ5IG1heGltdW0gWCIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlJfWU1heCIgQWxpYXM9IlRvcCBCb3VuZGFyeSBZIiBVbml0PSJtIiBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9InJlY3RhbmdsZSBib3VuZGFyeSBtYXhpbXVtIFkiIC8+DQogIDwvUGFyYUdyb3VwPg0KDQogIDxQYXJhR3JvdXAgTmFtZT0iIiBBbGlhcz0iQ2lyY2xlIEJvdW5kYXJ5IFBhcmFtZXRlcnMiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCQ19DZW50ZXJYIiBBbGlhcz0iQ2VudGVyIFgiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0iY2lyY2xlIGJvdW5kYXJ5IGNlbnRlciBYIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCQ19DZW50ZXJZIiBBbGlhcz0iQ2VudGVyIFkiIFVuaXQ9Im0iIERlZmF1bHQ9IjAiIE1heD0iMTAwMDAiIE1pbj0iLTEwMDAwIiBEaWdpdD0iNiIgRGVzYz0iY2lyY2xlIGJvdW5kYXJ5IGNlbnRlciBZIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCQ19SYWRpdXMiICBBbGlhcz0iUmFkaXVzIiAgIFVuaXQ9Im0iIERlZmF1bHQ9IjEiIE1heD0iMTAwMDAiIE1pbj0iMWUtMDUiICBEaWdpdD0iNiIgRGVzYz0iY2lyY2xlIGJvdW5kYXJ5IHJhZGl1cyIgLz4NCiAgPC9QYXJhR3JvdXA+DQoNCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSJQb2x5Z29uIEJvdW5kYXJ5IFZlcnRleCBQYXJhbWV0ZXJzIj4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iQlBfWCIgQWxpYXM9IlggQ29vcmRpbmF0ZSIgVW5pdD0ibSIgRGVmYXVsdD0iMCIgTWF4PSIxMDAwMCIgTWluPSItMTAwMDAiIERpZ2l0PSI2IiBEZXNjPSJwb2x5Z29uIGJvdW5kYXJ5IHZlcnRleCBYIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJCUF9ZIiBBbGlhcz0iWSBDb29yZGluYXRlIiBVbml0PSJtIiBEZWZhdWx0PSIwIiBNYXg9IjEwMDAwIiBNaW49Ii0xMDAwMCIgRGlnaXQ9IjYiIERlc2M9InBvbHlnb24gYm91bmRhcnkgdmVydGV4IFkiIC8+DQogIDwvUGFyYUdyb3VwPg0KDQogIDxQYXJhR3JvdXAgTmFtZT0iIiBBbGlhcz0iUmVnaW9uIFBhcmFtZXRlcnMiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSX0xlYWthZ2UiIEFsaWFzPSJMZWFrYWdlIENvZWZmaWNpZW50IiBVbml0PSIiIERlZmF1bHQ9IjEiIE1heD0iMWUrMzAiIE1pbj0iMCIgRGlnaXQ9IjYiIERlc2M9InJlZ2lvbiBsZWFrYWdlIGNvZWZmaWNpZW50IiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8UGFyYUdyb3VwIE5hbWU9IiIgQWxpYXM9IlJlZ2lvbiBNYXJrIFBhcmFtZXRlcnMiPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSTV9Db21XIiAgICAgICBBbGlhcz0iU3RvcmFnZSBSYXRpbyIgVW5pdD0iIiBEZWZhdWx0PSIxIiBNYXg9IjEiICAgIE1pbj0iMCIgICAgIERpZ2l0PSI2IiBEZXNjPSJzdG9yYWdlIHJhdGlvIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSTV9Db21LciIgICAgICBBbGlhcz0iTW9iaWxpdHkgUmF0aW8iIFVuaXQ9IiIgRGVmYXVsdD0iMSIgTWF4PSIxMDAwIiBNaW49IjAuMDAxIiBEaWdpdD0iNiIgRGVzYz0ibW9iaWxpdHkgcmF0aW8iIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJNX05ldFRvR3Jvc3MiIEFsaWFzPSJOZXQtdG8tR3Jvc3MiIFVuaXQ9IiIgRGVmYXVsdD0iMSIgTWF4PSIxIiAgICBNaW49IjAiICAgICBEaWdpdD0iNiIgRGVzYz0ibmV0LXRvLWdyb3NzIiAvPg0KICA8L1BhcmFHcm91cD4NCg0KICA8IS0tIFJlc3VsdCBwYXJhbWV0ZXJzIGFyZSBzZWxlY3RlZCBvbmx5IGJ5IHJlYnVpbGRSZXN1bHRQYXJhcygpLiAtLT4NCiAgPFBhcmFHcm91cCBOYW1lPSIiIEFsaWFzPSJSZXN1bHQgUGFyYW1ldGVycyI+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9XX1J3IiAgIEFsaWFzPSJXZWxsIFJhZGl1cyIgICAgICAgICAgICAgVW5pdD0ibSIgICAgICBEZWZhdWx0PSIwLjEiICAgIE1heD0iMTAiICAgIE1pbj0iMC4wMDEiICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCB3ZWxsYm9yZSByYWRpdXMiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9XX0RmYyIgIEFsaWFzPSJGcmFjdHVyZSBDb25kdWN0aXZpdHkiICBVbml0PSJtZC5tIiAgIERlZmF1bHQ9IjEwMDAiICAgTWF4PSIxZSszMCIgTWluPSIwIiAgICAgIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IGZyYWN0dXJlIGNvbmR1Y3Rpdml0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X1dfU2tpbiIgQWxpYXM9IlNraW4gRmFjdG9yIiAgICAgICAgICAgIFVuaXQ9IiIgICAgICAgRGVmYXVsdD0iMCIgICAgICBNYXg9IjEwMDAiICBNaW49Ii0xMDAiICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgc2tpbiBmYWN0b3IiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9XX0MiICAgIEFsaWFzPSJXZWxsYm9yZSBTdG9yYWdlIENvZWZmIiBVbml0PSJtXjMvTVBhIiBEZWZhdWx0PSIwIiAgICAgIE1heD0iMTAwMCIgIE1pbj0iMCIgICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCB3ZWxsYm9yZSBzdG9yYWdlIGNvZWZmaWNpZW50IiAvPg0KDQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9QaSIgIEFsaWFzPSJJbml0aWFsIEZvcm1hdGlvbiBQcmVzc3VyZSIgVW5pdD0iTVBhIiAgIERlZmF1bHQ9IjQwIiAgICAgTWF4PSI1MDAiICAgTWluPSIwLjEiICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgaW5pdGlhbCBmb3JtYXRpb24gcHJlc3N1cmUiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9LIiAgIEFsaWFzPSJQZXJtZWFiaWxpdHkiICAgICAgICAgICAgICAgVW5pdD0iRGFyY3kiIERlZmF1bHQ9IjAuMDAxIiAgTWF4PSIxMCIgICAgTWluPSIwIiAgICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgcGVybWVhYmlsaXR5IiAvPgogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9oIiAgIEFsaWFzPSJSZXNlcnZvaXIgVGhpY2tuZXNzIiAgICAgICAgVW5pdD0ibSIgICAgIERlZmF1bHQ9IjEwIiAgICAgTWF4PSIxMDAwMCIgTWluPSIxZS0wNSIgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgcmVzZXJ2b2lyIHRoaWNrbmVzcyIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X3BoaSIgQWxpYXM9IlBvcm9zaXR5IiAgICAgICAgICAgICAgICAgICBVbml0PSIiICAgICAgRGVmYXVsdD0iMC4xIiAgICBNYXg9IjEiICAgICBNaW49IjFlLTA1IiBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBwb3Jvc2l0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0N0aSIgQWxpYXM9IlRvdGFsIENvbXByZXNzaWJpbGl0eSIgICAgICBVbml0PSIxL01QYSIgRGVmYXVsdD0iMC4wMDEiICBNYXg9IjEwIiAgICBNaW49IjFlLTMwIiBEaWdpdD0iMTAiIFNjaWVudGlmaWM9IjEiIERlc2M9InJlc3VsdCB0b3RhbCBjb21wcmVzc2liaWxpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9DZiIgIEFsaWFzPSJSb2NrIENvbXByZXNzaWJpbGl0eSIgICAgICAgVW5pdD0iMS9NUGEiIERlZmF1bHQ9IjAuMDAwMSIgTWF4PSIxMCIgICAgTWluPSIxZS0zMCIgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJyZXN1bHQgcm9jayBjb21wcmVzc2liaWxpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9Tb2kiIEFsaWFzPSJJbml0aWFsIE9pbCBTYXR1cmF0aW9uIiAgICAgVW5pdD0iIiAgICAgIERlZmF1bHQ9IjAuOCIgICAgTWF4PSIxIiAgICAgTWluPSIwIiAgICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgaW5pdGlhbCBvaWwgc2F0dXJhdGlvbiIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X1NnaSIgQWxpYXM9IkluaXRpYWwgR2FzIFNhdHVyYXRpb24iICAgICBVbml0PSIiICAgICAgRGVmYXVsdD0iMCIgICAgICBNYXg9IjEiICAgICBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBpbml0aWFsIGdhcyBzYXR1cmF0aW9uIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfU3dpIiBBbGlhcz0iSW5pdGlhbCBXYXRlciBTYXR1cmF0aW9uIiAgIFVuaXQ9IiIgICAgICBEZWZhdWx0PSIwLjIiICAgIE1heD0iMSIgICAgIE1pbj0iMCIgICAgIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IGluaXRpYWwgd2F0ZXIgc2F0dXJhdGlvbiIgLz4NCg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfQm8iICAgQWxpYXM9Ik9pbCBGb3JtYXRpb24gVm9sdW1lIEZhY3RvciIgICBVbml0PSJtMy9tMyIgRGVmYXVsdD0iMSIgTWF4PSIxZSszMCIgTWluPSIwIiAgICAgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgb2lsIGZvcm1hdGlvbiB2b2x1bWUgZmFjdG9yIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfTWl1byIgQWxpYXM9Ik9pbCBWaXNjb3NpdHkiICAgICAgICAgICAgICAgICBVbml0PSJtUGEucyIgRGVmYXVsdD0iMSIgTWF4PSIxZSszMCIgTWluPSIxZS0zMCIgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgb2lsIHZpc2Nvc2l0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0NvIiAgIEFsaWFzPSJPaWwgQ29tcHJlc3NpYmlsaXR5IiAgICAgICAgICAgVW5pdD0iMS9NUGEiIERlZmF1bHQ9IjAiIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgRGlnaXQ9IjEwIiBTY2llbnRpZmljPSIxIiBEZXNjPSJyZXN1bHQgb2lsIGNvbXByZXNzaWJpbGl0eSIgLz4NCiAgICA8UGFyYUl0ZW0gTmFtZT0iUmVzdWx0X0J3IiAgIEFsaWFzPSJXYXRlciBGb3JtYXRpb24gVm9sdW1lIEZhY3RvciIgVW5pdD0ibTMvbTMiIERlZmF1bHQ9IjEiIE1heD0iMWUrMzAiIE1pbj0iMCIgICAgIERpZ2l0PSI2IiAgRGVzYz0icmVzdWx0IHdhdGVyIGZvcm1hdGlvbiB2b2x1bWUgZmFjdG9yIiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfTWl1dyIgQWxpYXM9IldhdGVyIFZpc2Nvc2l0eSIgICAgICAgICAgICAgICBVbml0PSJtUGEucyIgRGVmYXVsdD0iMSIgTWF4PSIxZSszMCIgTWluPSIxZS0zMCIgRGlnaXQ9IjYiICBEZXNjPSJyZXN1bHQgd2F0ZXIgdmlzY29zaXR5IiAvPg0KICAgIDxQYXJhSXRlbSBOYW1lPSJSZXN1bHRfQ3ciICAgQWxpYXM9IldhdGVyIENvbXByZXNzaWJpbGl0eSIgICAgICAgICBVbml0PSIxL01QYSIgRGVmYXVsdD0iMCIgTWF4PSIxZSszMCIgTWluPSIwIiAgICBEaWdpdD0iMTAiIFNjaWVudGlmaWM9IjEiIERlc2M9InJlc3VsdCB3YXRlciBjb21wcmVzc2liaWxpdHkiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9CZyIgICBBbGlhcz0iR2FzIEZvcm1hdGlvbiBWb2x1bWUgRmFjdG9yIiAgIFVuaXQ9Im0zL20zIiBEZWZhdWx0PSIxIiBNYXg9IjFlKzMwIiBNaW49IjAiICAgICBEaWdpdD0iNiIgIERlc2M9InJlc3VsdCBnYXMgZm9ybWF0aW9uIHZvbHVtZSBmYWN0b3IiIC8+DQogICAgPFBhcmFJdGVtIE5hbWU9IlJlc3VsdF9NaXVnIiBBbGlhc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\ No newline at end of file diff --git a/Bin/Config/Common/UnitDefault_cn.dat b/Bin/Config/Common/UnitDefault_cn.dat index 7e3dd09..6271b6b 100644 --- a/Bin/Config/Common/UnitDefault_cn.dat +++ b/Bin/Config/Common/UnitDefault_cn.dat @@ -1 +1 @@ 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\ No newline at end of file diff --git a/Bin/Config/Common/UnitDefault_en.dat b/Bin/Config/Common/UnitDefault_en.dat index 1d2140c..a988b43 100644 --- a/Bin/Config/Common/UnitDefault_en.dat +++ b/Bin/Config/Common/UnitDefault_en.dat @@ -1 +1 @@ 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\ No newline at end of file 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\ No newline at end of file diff --git a/Bin/Config/Unit/UnitDefault.dat b/Bin/Config/Unit/UnitDefault.dat index 1d2140c..7637503 100644 --- a/Bin/Config/Unit/UnitDefault.dat +++ b/Bin/Config/Unit/UnitDefault.dat @@ -1 +1 @@ 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\ No newline at end of file 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\ No newline at end of file diff --git a/Bin/XmlFiles/ModelParaDefinesNM_cn.xml b/Bin/XmlFiles/ModelParaDefinesNM_cn.xml index 367fb1c..f26f2ef 100644 --- a/Bin/XmlFiles/ModelParaDefinesNM_cn.xml +++ b/Bin/XmlFiles/ModelParaDefinesNM_cn.xml @@ -17,7 +17,7 @@ - + @@ -90,7 +90,7 @@ - + diff --git a/Bin/XmlFiles/ModelParaDefinesNM_en.xml b/Bin/XmlFiles/ModelParaDefinesNM_en.xml index fb7e17f..ad27365 100644 --- a/Bin/XmlFiles/ModelParaDefinesNM_en.xml +++ b/Bin/XmlFiles/ModelParaDefinesNM_en.xml @@ -18,7 +18,7 @@ - + @@ -91,7 +91,7 @@ - + diff --git a/Bin/XmlFiles/UnitDefault_cn.xml b/Bin/XmlFiles/UnitDefault_cn.xml index e96401b..28882e7 100644 --- a/Bin/XmlFiles/UnitDefault_cn.xml +++ b/Bin/XmlFiles/UnitDefault_cn.xml @@ -166,7 +166,7 @@ - + diff --git a/Bin/XmlFiles/UnitDefault_en.xml b/Bin/XmlFiles/UnitDefault_en.xml index b6fa119..3c4111b 100644 --- a/Bin/XmlFiles/UnitDefault_en.xml +++ b/Bin/XmlFiles/UnitDefault_en.xml @@ -46,7 +46,7 @@ - + From 5539150b5b137b614ed7010a247f1c322fb3c4c3 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 7 Aug 2026 14:47:47 +0800 Subject: [PATCH 04/12] =?UTF-8?q?1.=E8=87=AA=E5=8A=A8=E6=8B=9F=E5=90=88?= =?UTF-8?q?=E5=8F=82=E6=95=B0=E8=8C=83=E5=9B=B4=E8=AE=BE=E7=BD=AE=E4=B8=8E?= =?UTF-8?q?=E6=A0=A1=E9=AA=8C=20=202.=E7=A7=BB=E9=99=A4GA=E7=9B=B8?= =?UTF-8?q?=E5=85=B3=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Bin/Config/Lang/cn/MPA_cn.qm | Bin 48283 -> 47628 bytes Bin/Config/Lang/cn/MPA_cn.ts | 6 - Bin/Config/Lang/cn/WTAI_cn.ts | 4 - Bin/Config/Lang/cn/nmNum_cn.qm | Bin 103081 -> 93646 bytes Bin/Config/Lang/cn/nmNum_cn.ts | 389 +-- Include/mAlg/mAlgDefines/mAlgDefines.h | 3 +- .../nmCalculation/nmCalculationAutoFitGA.h | 267 -- Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h | 19 +- .../nmSubWxs/nmWxAutomaticFittingStart.h | 12 - .../nmCalculation/nmCalculationAutoFitGA.cpp | 2911 ----------------- 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b/Bin/Config/Lang/cn/nmNum_cn.ts index 5879529..465cceb 100644 --- a/Bin/Config/Lang/cn/nmNum_cn.ts +++ b/Bin/Config/Lang/cn/nmNum_cn.ts @@ -47,345 +47,6 @@ Reason: %1 压力(MPa) - - nmCalculationAutoFitGA - - === GA Automatic Fitting Started === - === GA自动拟合开始 === - - - Algorithm: Genetic Algorithm - 算法：遗传算法 - - - ERROR: Failed to load configuration from data manager - 错误：从数据管理器加载配置失败 - - - Enabled parameters count: %1 - 启用参数数量：%1 - - - ERROR: No parameters enabled for optimization - 错误：没有启用优化参数 - - - ERROR: Target LogLog data is empty or insufficient - 错误：目标双对数数据为空或不足 - - - ERROR: Target LogLog data arrays have inconsistent sizes - 错误：目标双对数数据数组大小不一致 - - - Target data validation passed (%1 data points) - 目标数据验证通过（%1 个数据点） - - - === Evaluating Initial Solution (Elite Protection) === - === 评估初始解 === - - - Initial parameters: - 初始参数： - - - Starting initial solution evaluation... - 开始初始解评估... - - - ERROR: m_userInitialSolution is empty! - 错误：初始解为空！ - - - Initial param[%1] = %2 - 初始参数[%1] = %2 - - - evaluateGenes returned: %1 - 评估基因 返回：%1 - - - Taking SUCCESS branch (fitness < 1e9) - 进入成功分支 - - - Initial solution evaluation successful - 初始解评估成功 - - - Initial fitness (error): %1 - 初始误差：%1 - - - Elite protection activated - initial solution will be preserved if no significant improvement found - 如果未找到显著改进，将保留初始解. - - - Taking FAILURE branch (fitness >= 1e9) - 进入失败分支 - - - Initial solution evaluation failed - starting with random initialization - 初始解评估失败 - 使用随机初始化开始 - - - Exception during initial solution evaluation - 初始解评估期间出现异常 - - - Population initialized: %1 individuals, %2 dimensions - 种群初始化：%1 个个体，%2 个维度 - - - === Starting GA Main Loop === - === 开始GA主循环 === - - - --- Generation %1/%2 --- - --- 第 %1/%2 代 --- - - - Current best error: %1 - 当前最佳误差：%1 - - - Total evaluations: %1 (successful: %2, failures: %3) - 总评估次数：%1（成功：%2，失败：%3） - - - Optimization stopped by user request - 优化因用户请求而停止 - - - Generation %1 completed: best = %2, avg = %3, worst = %4 - 第 %1 代完成：最佳 = %2，平均 = %3，最差 = %4 - - - === TARGET ACHIEVED === - === 达到目标 === - - - Target error achieved! Current error: %1 < Target: %2 - 达到目标误差！当前误差：%1 < 目标：%2 - - - Optimization completed successfully after %1 generations - 达到目标误差！当前误差：%1 < 目标：%2 - - - === TRUE CONVERGENCE DETECTED === - === 检测到真正收敛 === - - - Algorithm has converged to a stable solution - 算法已收敛到稳定解 - - - Final error: %1 after %2 generations - 最终误差：%1，经过 %2 代 - - - Solution quality: %1 (1.0 = target achieved) - 解质量：%1 - - - === LOCAL OPTIMUM DETECTED === - === 检测到局部最优 === - - - Algorithm appears to be trapped in local optimum - 算法似乎陷入局部最优 - - - Current error: %1 after %2 generations - 当前误差：%1，经过 %2 代 - - - Suggestion: Try restarting with different parameters or larger search space - 建议：尝试使用不同参数或更大搜索空间重新开始 - - - === CONSECUTIVE FAILURES === - === 连续失败 === - - - Too many consecutive failed generations (%1/%2) - 连续失败代数过多（%1/%2） - - - Optimization status: diversity=%1 - 优化状态：多样性=%1 - - - Generation %1 completed - Current best: %2 - 第 %1 代完成 - 当前最佳：%2 - - - CRITICAL ERROR: %1 - 严重错误: %1 - - - CRITICAL ERROR: Unknown exception in GA main loop - 严重错误：GA主循环中的未知异常 - - - Applying optimized parameters to model... - 正在将优化参数应用到模型... - - - === Optimization Results === - === 优化结果 === - - - Final error: %1 - 最终误差: %1 - - - Total generations: %1 - 总代数：%1 - - - Total evaluations: %1 (successful: %2) - 总评估次数: %1 (成功: %2) - - - Optimized parameters: - 优化参数: - - - Parameters applied successfully to data manager - 参数已成功应用到数据管理器 - - - ERROR: Failed to apply final parameters: %1 - 错误: 应用最终参数失败: %1 - - - ERROR: Unknown error applying final parameters - 错误: 应用最终参数时出现未知错误 - - - === GA OPTIMIZATION SUCCESSFUL === - === GA优化成功 === - - - === GA OPTIMIZATION CONVERGED === - === GA优化收敛 === - - - === GA OPTIMIZATION - LOCAL OPTIMUM === - === GA优化 - 局部最优 === - - - === GA OPTIMIZATION - MAX GENERATIONS === - === GA优化 - 达到最大代数 === - - - === GA OPTIMIZATION STOPPED BY USER === - === GA优化 - 用户停止 === - - - === GA OPTIMIZATION FAILED === - === GA优化失败 === - - - === GA OPTIMIZATION - UNKNOWN END === - === GA优化 - 未知结束 === - - - Result: %1 - 结果: %1 - - - === User Stop Request Received === - === 用户停止请求已接收 === - - - Gracefully stopping GA optimization... - 在停止GA优化... - - - Force stopping current evaluation... - 强制停止当前评估... - - - GA optimization stop request processed - GA优化停止请求已处理 - - - Stop request received but optimization is not running - 收到停止请求但优化未运行 - - - Individual %1 improved: %2 -> %3 - 个体 %1 改进：%2 -> %3 - - - Individual %1: evaluation failed - 个体 %1：评估失败 - - - Individual %1: Exception: %2 - 个体 %1：异常：%2 - - - Individual %1: Unknown exception - 个体 %1：未知异常 - - - WARNING: No successful evaluations in generation %1 (consecutive failures: %2) - 警告：第 %1 代中没有成功评估（连续失败：%2） - - - Current generation stats: %1 successful, %2 failed out of %3 individuals (success rate: %4%) - 当前代统计：%3 个个体中 %1 个成功，%2 个失败（成功率：%4%） - - - ERROR: Too many consecutive failed generations (%1/%2) - stopping optimization - 错误：连续失败代数过多（%1/%2）- 停止优化 - - - WARNING: Low success rate (%1%) in generation %2, but continuing optimization - 警告：第 %2 代中成功率低（%1%），但继续优化 - - - No initial solution for elite protection - 没有初始解用于迭代 - - - === Final Result Validation (Elite Protection) === - === 最终结果验证 === - - - Comparing results: Initial=%1, Final=%2 - 比较结果: 初始=%1, 最终=%2 - - - Improvement: %1 (%2%) - 改进: %1 (%2%) - - - Elite protection triggered: insufficient improvement - 改进不足 - - - Threshold: %1%, Actual: %2% - 阈值: %1%, 实际: %2% - - - Restoring initial solution as final result - 恢复初始解作为最终结果 - - - Initial solution restored successfully - 初始解恢复成功 - - - Final result validated - significant improvement achieved - 最终结果已验证 - 实现显著改进 - - nmCalculationAutoFitPSO @@ -3698,6 +3359,38 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We Warning 警告 + + Invalid parameter range + 参数范围无效 + + + The parameter table is unavailable. + 参数表不可用。 + + + The parameter row is invalid. + 参数行无效。 + + + The range values for %1 are incomplete. + %1 的范围值不完整。 + + + The minimum value, initial value, and maximum value of %1 must be finite numbers. + %1 的最小值、初始值和最大值必须是有限数值。 + + + The physical range of %1 is invalid. + %1 的物理范围无效。 + + + The values of %1 exceed the physical range [%2, %3]. + %1 的参数值超出物理范围 [%2, %3]。 + + + The values of %1 must satisfy: minimum <= initial value <= maximum. + %1 的参数值必须满足：最小值 <= 初始值 <= 最大值。 + Please select a target well! 请选择一口目标井！ @@ -3740,11 +3433,6 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We Optimized parameters have been applied to the model. 拟合参数已应用到模型。 - - GA Optimization completed: - - GA求解完成： - Optimization Completed 拟合完成 @@ -3774,27 +3462,14 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We Optimization Stopped 拟合停止 - - GA Optimization stopped by user: - - GA拟合被用户强行终止： - PSO algorithm selected. 用户选择PSO。 - - GA algorithm selected. - 用户选择GA - PSO (Particle Swarm) PSO - - GA (Genetic Algorithm) - GA - Darcy diff --git a/Include/mAlg/mAlgDefines/mAlgDefines.h b/Include/mAlg/mAlgDefines/mAlgDefines.h index 7c96b83..4eeaa12 100644 --- a/Include/mAlg/mAlgDefines/mAlgDefines.h +++ b/Include/mAlg/mAlgDefines/mAlgDefines.h @@ -292,8 +292,7 @@ enum Fit_Method { FM_GaussNewton = 0, //高斯牛顿 FM_GaussNewtonEx, //归一化高斯牛顿 - FM_Genetic, //遗传算法 - FM_ParticleSwarm, //粒子群算法(Particle Swarm Optimization) + FM_ParticleSwarm = 3, //粒子群算法(Particle Swarm Optimization)，保留原有枚举值 FM_Unknown }; diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitGA.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitGA.h deleted file mode 100644 index 64f9e4d..0000000 --- a/Include/nmNum/nmCalculation/nmCalculationAutoFitGA.h +++ /dev/null @@ -1,267 +0,0 @@ -#ifndef NMCALCULATIONAUTOFITGA_H -#define NMCALCULATIONAUTOFITGA_H - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#include "nmCalculation_global.h" - -class nmDataWellBase; - -enum StopReasonGA { - GA_CONTINUE_OPTIMIZATION = 0, - GA_TARGET_ACHIEVED, - GA_TRUE_CONVERGENCE, - GA_LOCAL_OPTIMUM, - GA_MAX_ITERATIONS, - GA_USER_STOPPED, - GA_CONSECUTIVE_FAILURES, - GA_OPTIMIZATION_FAILED -}; - -struct GAIndividual -{ - QVector genes; // 基因（参数值） - double fitness; // 适应度值 - bool isEvaluated; // 是否已评估 - - GAIndividual() : fitness(1e10), isEvaluated(false) {} -}; - -class NMCALCULATION_EXPORT nmCalculationAutoFitGA : public QObject -{ - Q_OBJECT - -public: - explicit nmCalculationAutoFitGA(QObject* parent = nullptr); - virtual ~nmCalculationAutoFitGA(); - - // ==================== 公共接口方法 ==================== - void setTargetLogLogData(const QVector>& targetData); - bool startAutoFitting(); - void stopFitting(); - bool isRunning() const; - int getCurrentGeneration() const; - QVector getBestSolution() const; - double getBestFitness() const; - QString getLastError() const; - void resetOptimizer(); - - void setGATargetWellName(const QString& wellName); - -signals: - void progressUpdated(int generation, double bestFitness); - void fittingFinished(bool success, const QString& message); - void logMessageGenerated(const QString& message); - - private slots: - void updateProgress(); - -private: - - // 临时目录管理 - void initializeTemporaryDirectory(); - void cleanupTemporaryDirectory(); - bool removeDirectoryRecursively(const QString& path); - - // ==================== 数据加载方法 ==================== - // 从数据管理器加载所有配置 - bool loadAllConfigFromDataManager(); - // 加载优化配置 - void loadOptimizationConfig(); - // 加载参数边界 - void loadParameterBounds(); - // 提取用户初始值 - void extractUserInitialValues(); - // ==================== 遗传算法核心方法 ==================== - // 初始化种群 - void initializePopulation(); - // 评估基因 - double evaluateGenes(const QVector& genes); - // 评估个体 - double evaluateIndividual(GAIndividual& individual); - // 评估种群 - void evaluatePopulation(); - // 选择操作 - int tournamentSelection(); - int rouletteWheelSelection(); - - // 交叉操作 - void crossover(const GAIndividual& parent1, const GAIndividual& parent2, - GAIndividual& offspring1, GAIndividual& offspring2); - void singlePointCrossover(const GAIndividual& parent1, const GAIndividual& parent2, - GAIndividual& offspring1, GAIndividual& offspring2); - void uniformCrossover(const GAIndividual& parent1, const GAIndividual& parent2, - GAIndividual& offspring1, GAIndividual& offspring2); - - // 变异操作 - void mutate(GAIndividual& individual); - void gaussianMutation(GAIndividual& individual); - void polynomialMutation(GAIndividual& individual); - - // 精英保留 - void applyElitism(QVector& newPopulation); - // 更新种群统计 - void updatePopulationStatistics(); - // 收敛检查 - bool checkConvergence(); - // 自适应参数更新 - void adaptiveParameterUpdate(int generation); - - // ==================== 智能收敛判断方法 ==================== - // 分析优化状态 - StopReasonGA analyzeOptimizationStatus(); - // 检查真收敛 - bool checkTrueConvergence() const; - // 检查局部最优陷阱 - bool checkLocalOptimumTrap() const; - // 计算种群多样性 - double calculatePopulationDiversity() const; - // 计算适应度方差 - double calculateFitnessVariance(int windowSize) const; - // 计算长期改进 - double calculateLongTermImprovement(int windowSize) const; - // 更新收敛指标 - void updateConvergenceMetrics(); - // 最终结果验证和保护 - void validateAndProtectFinalResult(); - - // ==================== 参数处理方法 ==================== - // 参数验证 - bool validateParameters(const QVector& parameters) const; - // 双对数数据验证 - bool validateLogLogData(const QVector>& logLogData) const; - // 初始值验证 - bool validateInitialValues() const; - // 应用参数到数据管理器 - void applyParametersToDataManager(const QVector& parameters); - // 更新储层参数 - void updateReservoirParameters(const QVector& parameters); - // 更新井参数 - void updateWellParameters(const QVector& parameters); - // 更新井到数据管理器 - void updateWellToDataManager(nmDataWellBase* pWell); - // 参数边界约束 - void clampToLimits(QVector& parameters) const; - - // ==================== 求解器相关方法 ==================== - // 运行求解器 - QVector> runSolver(); - // 运行EXE求解器 - QVector> runSolverExe(); - // 运行Dll求解器 - QVector> runSolverDll(); - // 验证求解器结果 - bool validateSolverResult(const QVector>& result) const; - - // ==================== 数据处理方法 ==================== - // 插值数据 - QVector interpolateData(const QVector& source, - const QVector& targetX) const; - // 计算双对数曲线误差 - double calculateLogLogCurveError(const QVector>& target, - const QVector>& result) const; - // 计算曲线误差 - double calculateCurveError(const QVector& curve1, - const QVector& curve2) const; - - // ==================== 工具方法 ==================== - // 生成0-1随机数 - double random01() const; - // 高斯随机数 - double gaussianRandom(double mean, double stddev) const; - // 获取启用参数数量 - int getEnabledParameterCount() const; - // 保存优化结果 - void saveOptimizationResult(); - - -private: - // ==================== 常量定义 ==================== - static const double MIN_FITNESS_IMPROVEMENT; - static const double MUTATION_STRENGTH; - static const int CONVERGENCE_CHECK_INTERVAL; - static const int MAX_STAGNATION_GENERATIONS; - - // ==================== 核心状态变量 ==================== - bool m_isRunning; // 是否正在运行 - bool m_shouldStop; // 是否应该停止 - bool m_isPaused; // 是否暂停 - int m_currentGeneration; // 当前代数 - - // 适应度统计 - double m_bestFitness; // 最优适应度 - double m_worstFitness; // 最差适应度 - double m_averageFitness; // 平均适应度 - double m_previousBestFitness; // 上一代最优适应度 - - // ==================== GA算法参数 ==================== - int m_populationSize; // 种群大小 - int m_maxGenerations; // 最大代数 - double m_targetError; // 目标误差 - double m_crossoverRate; // 交叉概率 - double m_mutationRate; // 变异概率 - double m_elitismRate; // 精英保留比例 - int m_tournamentSize; // 锦标赛选择大小 - bool m_useUniformCrossover; // 是否使用均匀交叉 - - // ==================== 种群和个体 ==================== - QVector m_population; // 当前种群 - QVector m_eliteIndividuals; // 精英个体 - GAIndividual m_bestIndividual; // 全局最优个体 - - // ==================== 评估统计 ==================== - int m_totalEvaluations; // 总评估次数 - int m_successfulEvaluations; // 成功评估次数 - int m_evaluationInProgress; // 正在进行的评估计数 - int m_consecutiveFailures; // 连续失败次数 - - // ==================== 精英保护相关 ==================== - QVector m_initialValues; // 用户初始参数值 - QVector m_userInitialSolution; // 用户初始解 - double m_userInitialFitness; // 用户初始适应度 - int m_consecutiveFailedGenerations; // 连续失败代数 - int m_maxConsecutiveFailures; // 最大允许连续失败数 - bool m_hasValidUserSolution; // 是否有有效的用户解 - double m_improvementThreshold; // 改进阈值 - - // ==================== 收敛判断相关 ==================== - double m_diversityThreshold; // 多样性阈值 - double m_convergenceVarianceThreshold; // 收敛方差阈值 - int m_trueConvergenceWindow; // 真收敛判断窗口 - int m_localOptimumWindow; // 局部最优判断窗口 - double m_nearTargetFactor; // 接近目标的因子 - double m_farTargetFactor; // 远离目标的因子 - - // ==================== 历史记录 ==================== - QVector m_convergenceHistory; // 收敛历史 - QVector m_diversityHistory; // 多样性历史 - - // ==================== 参数配置 ==================== - QVector m_parameterSelected; // 参数选择状态 - QVector m_parameterLower; // 参数下界 - QVector m_parameterUpper; // 参数上界 - QVector m_enabledParamIndices; // 启用参数索引 - - // ==================== 目标数据 ==================== - QVector> m_targetLogLogData; // 目标双对数数据 - - // ==================== 其他 ==================== - QString m_lastError; // 最后错误信息 - QTimer* m_progressTimer; // 进度更新定时器 - // DLL求解器需要的临时目录 - QString m_tempDirectory; - - QString m_targetWellName;// 目标井名称 -}; - -#endif // NMCALCULATIONAUTOFITGA_H \ No newline at end of file diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h index a245aac..30a9119 100644 --- a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h +++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h @@ -21,17 +21,10 @@ #include "nmDataWellBase.h" #include "nmDataAutomaticFitting.h" #include "nmCalculationAutoFitPSO.h" -#include "nmCalculationAutoFitGA.h" #include "nmWxAutomaticfittingStart.h" #include "nmSubWxs_global.h" -// 算法类型枚举 -enum OptimizationAlgorithm { - ALGORITHM_PSO = 0, - ALGORITHM_GA = 1 -}; - class NM_SUB_WXS_EXPORT nmWxAutomaticFitting : public iDlgBase { Q_OBJECT @@ -46,7 +39,7 @@ public: void onAccept(); void onReject(); void onWellSelected(int index); - void onAlgorithmChanged(int index); + void onParameterTableItemChanged(QTableWidgetItem* item); // 自动拟合相关槽函数 void runAutoFitting(); @@ -63,6 +56,12 @@ private: void setParameterRowVisible(QTableWidget* table, int row, bool visible); void renumberVisibleParameterRows(QTableWidget* table); void updateParameterVisibility(QTableWidget* table, NM_SOLVER_MODEL_TYPE eType); + void initializeSuggestedParameterRanges(); + void updateRangeForParameter(int parameterIndex, double centerValue, bool afterFit); + void setParameterRange(int parameterIndex, double minValue, double maxValue); + bool getPhysicalParameterRange(int parameterIndex, double& minValue, double& maxValue); + void normalizeSavedParameterRanges(); + bool validateParameterTable(QString& errorMessage, int parameterIndex = -1); void startAutoFitting(const QVector>& targetData, const QStringList& selectedParams, const QString& targetWellName); void cleanupFitting(); @@ -108,10 +107,10 @@ private: // 自动拟合相关成员 nmCalculationAutoFitPSO* m_autoFitterPSO; - nmCalculationAutoFitGA* m_autoFitterGA; QProgressDialog* m_progressDialog; QTimer* m_progressTimer; - OptimizationAlgorithm m_selectedAlgorithm; // 选中的算法类型 + bool m_autoParameterRanges; + bool m_updatingParameterRanges; // 拟合开始界面 nmWxAutomaticfittingStart* m_progressMonitor; diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h index eb37eeb..3e9067e 100644 --- a/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h +++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFittingStart.h @@ -22,12 +22,10 @@ #include #include -#include "nmCalculationAutoFitGA.h" #include "nmCalculationAutoFitPSO.h" // 前向声明 class nmCalculationAutoFitPSO; -class nmCalculationAutoFitGA; class QPainter; class QColor; class QPaintEvent; @@ -68,12 +66,6 @@ private: bool m_pseudoPressureMode; }; -// 算法类型枚举 -enum FittingAlgorithmType { - FITTING_ALGORITHM_PSO = 0, - FITTING_ALGORITHM_GA = 1 -}; - class nmWxAutomaticfittingStart : public iDlgBase { Q_OBJECT @@ -85,8 +77,6 @@ public: // PSO算法接口 void setAutoFitter(nmCalculationAutoFitPSO* autoFitter); - // GA算法接口 - void setAutoFitterGA(nmCalculationAutoFitGA* autoFitter); // 通用设置接口 void setFittingParameters(int maxIterations, double targetError, const QString& wellName); @@ -161,8 +151,6 @@ private: // 算法实例 nmCalculationAutoFitPSO* m_autoFitterPSO; - nmCalculationAutoFitGA* m_autoFitterGA; - FittingAlgorithmType m_algorithmType; // 拟合参数 int m_maxIterations; diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp deleted file mode 100644 index 45eda30..0000000 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitGA.cpp +++ /dev/null @@ -1,2911 +0,0 @@ -#include "nmCalculationAutoFitGA.h" -//#include "nmCalculationExeSolverTask.h" -#include "nmCalculationDllPebiSolverTask.h" -#include "nmDataAnalyzeManager.h" -#include "nmDataWellBase.h" -#include "nmDataVerticalWell.h" -#include "nmDataVerticalFracturedWell.h" -#include "nmDataHorizontalFracturedWell.h" -#include "nmDataReservoir.h" -#include "nmDataAutomaticFitting.h" - -#include -#include -#include -#include -#include -#include -#include - -#ifdef Q_OS_WIN -#include -#include -#define DEBUG_OUT(msg) OutputDebugStringA(QString("[AutoFit] %1\n").arg(msg).toLocal8Bit().data()) -#endif - -// 常量定义 -const double nmCalculationAutoFitGA::MIN_FITNESS_IMPROVEMENT = 1e-8; -const double nmCalculationAutoFitGA::MUTATION_STRENGTH = 0.1; -const int nmCalculationAutoFitGA::CONVERGENCE_CHECK_INTERVAL = 10; -const int nmCalculationAutoFitGA::MAX_STAGNATION_GENERATIONS = 20; - -// 无穷大和NaN检查 -static inline bool isFiniteNumber(double value) -{ -#ifdef Q_OS_WIN - return _finite(value) != 0 && !_isnan(value); -#else - return std::isfinite(value); -#endif -} - -// sleep函数 -static inline void msleep(int ms) -{ -#ifdef Q_OS_WIN - Sleep(ms); -#endif -} - -// 构造函数 -nmCalculationAutoFitGA::nmCalculationAutoFitGA(QObject* parent) - : QObject(parent) - , m_isRunning(false) - , m_shouldStop(false) - , m_isPaused(false) - , m_currentGeneration(0) - , m_bestFitness(1e10) - , m_worstFitness(-1e10) - , m_averageFitness(1e10) - , m_previousBestFitness(1e10) - , m_populationSize(40) - , m_maxGenerations(100) - , m_targetError(0.001) - , m_crossoverRate(0.8) - , m_mutationRate(0.1) - , m_elitismRate(0.1) - , m_tournamentSize(3) - , m_useUniformCrossover(true) - , m_totalEvaluations(0) - , m_successfulEvaluations(0) - , m_evaluationInProgress(0) - , m_consecutiveFailures(0) - , m_progressTimer(0) - , m_userInitialFitness(1e10) - , m_consecutiveFailedGenerations(0) - , m_maxConsecutiveFailures(3) - , m_hasValidUserSolution(false) - , m_improvementThreshold(0.05) - , m_diversityThreshold(0.05) - , m_convergenceVarianceThreshold(1e-8) - , m_trueConvergenceWindow(15) - , m_localOptimumWindow(8) - , m_nearTargetFactor(2.0) - , m_farTargetFactor(10.0) - , m_targetWellName("") -{ - DEBUG_OUT(QString("GA Constructor: this=0x%1").arg((quintptr)this, 0, 16)); - - // 初始化随机数种子 - qsrand(QTime::currentTime().msec()); - - // 初始化临时目录用于DLL求解器 - initializeTemporaryDirectory(); - - // 初始化最优个体 - m_bestIndividual.fitness = 1e10; - m_bestIndividual.isEvaluated = false; - - // 创建进度更新定时器 - m_progressTimer = new QTimer(this); - connect(m_progressTimer, SIGNAL(timeout()), this, SLOT(updateProgress())); - - DEBUG_OUT("AutoFit GA calculator initialized (data-driven mode)"); - DEBUG_OUT("GA Constructor completed"); -} - -// 析构函数 -nmCalculationAutoFitGA::~nmCalculationAutoFitGA() -{ - DEBUG_OUT(QString("GA Destructor: this=0x%1").arg((quintptr)this, 0, 16)); - - // 首先停止算法 - if(m_isRunning) { - m_shouldStop = true; // 立即设置停止标志 - - // 等待当前操作完成，增加超时时间 - int waitCount = 0; - while(m_isRunning && waitCount < 100) { - QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50); - msleep(50); - waitCount++; - } - - // 如果仍在运行则强制停止 - if(m_isRunning) { - DEBUG_OUT("Force stopping GA - timeout reached"); - m_isRunning = false; - } - } - - // 清理临时目录 - cleanupTemporaryDirectory(); - - // 断开所有信号连接，防止回调已销毁的对象 - disconnect(this, nullptr, nullptr, nullptr); - - DEBUG_OUT("GA Destructor completed"); -} - -// ==================== 目录处理方法 ==================== - -void nmCalculationAutoFitGA::initializeTemporaryDirectory() -{ - QString timestamp = QDateTime::currentDateTime().toString("yyyyMMdd_hhmmss_zzz"); - QString processId = QString::number(QCoreApplication::applicationPid()); - - m_tempDirectory = QApplication::applicationDirPath() + - "/autofit_temp_" + processId + "_" + timestamp; - - // 确保目录不存在 - int counter = 0; - QString originalPath = m_tempDirectory; - - while(QDir(m_tempDirectory).exists() && counter < 100) { - m_tempDirectory = originalPath + "_" + QString::number(counter); - counter++; - } - - if(QDir().mkpath(m_tempDirectory)) { - DEBUG_OUT(QString("Initialized temp directory: %1").arg(m_tempDirectory)); - } else { - DEBUG_OUT(QString("Warning: Failed to create temp directory: %1").arg(m_tempDirectory)); - m_tempDirectory = QApplication::applicationDirPath(); - } -} - -void nmCalculationAutoFitGA::cleanupTemporaryDirectory() -{ - if(QDir(m_tempDirectory).exists()) { - if(removeDirectoryRecursively(m_tempDirectory)) { - DEBUG_OUT("Temp directory cleaned up successfully"); - } else { - DEBUG_OUT("Warning: Failed to clean up temp directory completely"); - } - } -} - -bool nmCalculationAutoFitGA::removeDirectoryRecursively(const QString& path) -{ - QDir dir(path); - - if(!dir.exists()) { - return true; - } - - // 递归删除子目录和文件 - QFileInfoList entries = dir.entryInfoList(QDir::NoDotAndDotDot | QDir::AllEntries | QDir::Hidden); - bool allRemoved = true; - - for(int i = 0; i < entries.size(); ++i) { - const QFileInfo& entry = entries[i]; - - if(entry.isDir()) { - if(!removeDirectoryRecursively(entry.absoluteFilePath())) { - allRemoved = false; - } - } else { - QFile file(entry.absoluteFilePath()); - - // 处理只读文件 - if(!file.permissions().testFlag(QFile::WriteUser)) { - file.setPermissions(file.permissions() | QFile::WriteUser); - } - - if(!file.remove()) { - DEBUG_OUT(QString("Failed to remove file: %1").arg(entry.absoluteFilePath())); - allRemoved = false; - } - } - } - - // 删除目录本身 - if(allRemoved) { - return dir.rmdir(path); - } - - return false; -} - -// ==================== 公共接口方法 ==================== - -void nmCalculationAutoFitGA::setTargetLogLogData(const QVector>& targetData) -{ - m_targetLogLogData = targetData; - DEBUG_OUT(QString("Target LogLog data set: %1 arrays").arg(targetData.size())); - - if(targetData.size() >= 3) { - DEBUG_OUT(QString("LogLog data points: X=%1, Y1=%2, Y2=%3") - .arg(targetData[0].size()) - .arg(targetData[1].size()) - .arg(targetData[2].size())); - } -} - -bool nmCalculationAutoFitGA::startAutoFitting() -{ - if(m_isRunning) { - m_lastError = "GA fitting is already running"; - return false; - } - - DEBUG_OUT("=== GA AUTO FITTING START ==="); - - // 发送初始化日志 - emit logMessageGenerated(tr("=== GA Automatic Fitting Started ===")); - emit logMessageGenerated(tr("Algorithm: Genetic Algorithm")); - - try { - // 从数据管理器加载所有配置 - if(!loadAllConfigFromDataManager()) { - emit logMessageGenerated(tr("ERROR: Failed to load configuration from data manager")); - return false; - } - - int enabledParams = getEnabledParameterCount(); - emit logMessageGenerated(tr("Enabled parameters count: %1").arg(enabledParams)); - - if(enabledParams == 0) { - m_lastError = "No parameters enabled for optimization"; - emit logMessageGenerated(tr("ERROR: No parameters enabled for optimization")); - return false; - } - - if(m_targetLogLogData.isEmpty() || m_targetLogLogData.size() < 3) { - m_lastError = "Target LogLog data is empty or insufficient"; - emit logMessageGenerated(tr("ERROR: Target LogLog data is empty or insufficient")); - return false; - } - - // 检查数据一致性 - if(m_targetLogLogData[0].size() != m_targetLogLogData[1].size() || - m_targetLogLogData[0].size() != m_targetLogLogData[2].size()) { - m_lastError = "Target LogLog data arrays have inconsistent sizes"; - emit logMessageGenerated(tr("ERROR: Target LogLog data arrays have inconsistent sizes")); - return false; - } - - emit logMessageGenerated(tr("Target data validation passed (%1 data points)").arg(m_targetLogLogData[0].size())); - - // 使用保存的初始值进行精英保护 - QVector savedInitialValues = m_initialValues; - - // 重置状态 - resetOptimizer(); - m_isRunning = true; - m_shouldStop = false; - m_isPaused = false; - m_currentGeneration = 0; - m_consecutiveFailures = 0; - m_consecutiveFailedGenerations = 0; - - // 精英保护：评估用户初始解 - if(!savedInitialValues.isEmpty()) { - m_userInitialSolution = savedInitialValues; - emit logMessageGenerated(tr("=== Evaluating Initial Solution (Elite Protection) ===")); - - // 输出初始参数值 - QString paramStr = tr("Initial parameters: "); - for(int i = 0; i < m_userInitialSolution.size(); ++i) { - paramStr += QString("[%1]=%2 ").arg(i).arg(m_userInitialSolution[i], 0, 'f', 6); - } - emit logMessageGenerated(paramStr); - - try { - emit logMessageGenerated(tr("Starting initial solution evaluation...")); - DEBUG_OUT(QString("Before evaluateFitness: m_userInitialSolution size = %1").arg(m_userInitialSolution.size())); - - if(m_userInitialSolution.isEmpty()) { - emit logMessageGenerated(tr("ERROR: m_userInitialSolution is empty!")); - return false; - } - - for(int i = 0; i < m_userInitialSolution.size(); ++i) { - emit logMessageGenerated(tr("Initial param[%1] = %2").arg(i).arg(m_userInitialSolution[i], 0, 'f', 6)); - } - - m_userInitialFitness = evaluateGenes(m_userInitialSolution); - - emit logMessageGenerated(tr("evaluateGenes returned: %1").arg(m_userInitialFitness, 0, 'e', 10)); - - if(m_userInitialFitness < 1e9) { - emit logMessageGenerated(tr("Taking SUCCESS branch (fitness < 1e9)")); - m_hasValidUserSolution = true; - m_bestFitness = m_userInitialFitness; - m_bestIndividual.genes = m_userInitialSolution; - m_bestIndividual.fitness = m_userInitialFitness; - m_bestIndividual.isEvaluated = true; - - emit logMessageGenerated(tr("Initial solution evaluation successful")); - emit logMessageGenerated(tr("Initial fitness (error): %1").arg(m_userInitialFitness, 0, 'e', 4)); - emit logMessageGenerated(tr("Elite protection activated - initial solution will be preserved if no significant improvement found")); - } else { - emit logMessageGenerated(tr("Taking FAILURE branch (fitness >= 1e9)")); - m_hasValidUserSolution = false; - emit logMessageGenerated(tr("Initial solution evaluation failed - starting with random initialization")); - } - } catch(...) { - m_hasValidUserSolution = false; - emit logMessageGenerated(tr("Exception during initial solution evaluation")); - } - - // 恢复初始值供种群初始化使用 - m_initialValues = savedInitialValues; - } - - // 初始化种群 - initializePopulation(); - emit logMessageGenerated(tr("Population initialized: %1 individuals, %2 dimensions").arg(m_populationSize).arg(getEnabledParameterCount())); - - // GA主循环 - emit logMessageGenerated(tr("=== Starting GA Main Loop ===")); - - for(m_currentGeneration = 0; m_currentGeneration < m_maxGenerations && !m_shouldStop; ++m_currentGeneration) { - - // 每次迭代都输出标题，或者只在重要迭代输出详细信息 - bool shouldOutputDetail = (m_currentGeneration % qMax(1, m_maxGenerations / 10) == 0) || - (m_currentGeneration < 5) || - (m_currentGeneration >= m_maxGenerations - 2); - - // 每次迭代都输出标题 - emit logMessageGenerated(tr("--- Generation %1/%2 ---").arg(m_currentGeneration + 1).arg(m_maxGenerations)); - - // 只在特定迭代输出详细统计信息 - if(shouldOutputDetail) { - emit logMessageGenerated(tr("Current best error: %1").arg(m_bestFitness, 0, 'e', 4)); - emit logMessageGenerated(tr("Total evaluations: %1 (successful: %2, failures: %3)") - .arg(m_totalEvaluations).arg(m_successfulEvaluations).arg(m_totalEvaluations - m_successfulEvaluations)); - } - - // 检查暂停状态 - while(m_isPaused && !m_shouldStop) { - QApplication::processEvents(); - //msleep(100); - } - - if(m_shouldStop) { - emit logMessageGenerated(tr("Optimization stopped by user request")); - break; - } - - // 1. 评估种群 - evaluatePopulation(); - - if(m_shouldStop) break; - - // 2. 更新统计信息 - updatePopulationStatistics(); - - // 3. 发射进度信号 - emit progressUpdated(m_currentGeneration, m_bestFitness); - - if(shouldOutputDetail) { - emit logMessageGenerated(tr("Generation %1 completed: best = %2, avg = %3, worst = %4") - .arg(m_currentGeneration + 1).arg(m_bestFitness, 0, 'e', 4) - .arg(m_averageFitness, 0, 'e', 4).arg(m_worstFitness, 0, 'e', 4)); - } - - // 记录收敛历史 - m_convergenceHistory.append(m_bestFitness); - - // 更新收敛指标 - updateConvergenceMetrics(); - - // 智能收敛判断 - StopReasonGA stopReason = analyzeOptimizationStatus(); - - if(stopReason == GA_TARGET_ACHIEVED) { - emit logMessageGenerated(tr("=== TARGET ACHIEVED ===")); - emit logMessageGenerated(tr("Target error achieved! Current error: %1 < Target: %2") - .arg(m_bestFitness, 0, 'e', 4).arg(m_targetError, 0, 'e', 4)); - emit logMessageGenerated(tr("Optimization completed successfully after %1 generations") - .arg(m_currentGeneration + 1)); - break; - } - else if(stopReason == GA_TRUE_CONVERGENCE) { - emit logMessageGenerated(tr("=== TRUE CONVERGENCE DETECTED ===")); - emit logMessageGenerated(tr("Algorithm has converged to a stable solution")); - emit logMessageGenerated(tr("Final error: %1 after %2 generations") - .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1)); - emit logMessageGenerated(tr("Solution quality: %1 (1.0 = target achieved)") - .arg(m_targetError / qMax(1e-10, m_bestFitness), 0, 'f', 3)); - break; - } - else if(stopReason == GA_LOCAL_OPTIMUM) { - emit logMessageGenerated(tr("=== LOCAL OPTIMUM DETECTED ===")); - emit logMessageGenerated(tr("Algorithm appears to be trapped in local optimum")); - emit logMessageGenerated(tr("Current error: %1 after %2 generations") - .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1)); - emit logMessageGenerated(tr("Suggestion: Try restarting with different parameters or larger search space")); - break; - } - else if(stopReason == GA_CONSECUTIVE_FAILURES) { - emit logMessageGenerated(tr("=== CONSECUTIVE FAILURES ===")); - emit logMessageGenerated(tr("Too many consecutive failed generations (%1/%2)") - .arg(m_consecutiveFailedGenerations).arg(m_maxConsecutiveFailures)); - break; - } - else if(stopReason == GA_CONTINUE_OPTIMIZATION) { - // 继续优化，每10次迭代输出一次状态 - if(m_currentGeneration > 0 && m_currentGeneration % 10 == 0) { - double diversity = calculatePopulationDiversity(); - - emit logMessageGenerated(tr(" Optimization status: diversity=%1") - .arg(diversity, 0, 'f', 4)); - } - } - - // 4. 创建新一代种群 - if(m_currentGeneration < m_maxGenerations - 1) - { - QVector newPopulation; - newPopulation.reserve(m_populationSize); - - // 精英保留 - applyElitism(newPopulation); - - // 生成新个体直到填满种群 - while(newPopulation.size() < m_populationSize && !m_shouldStop) { - // 选择父代 - int parent1Index = tournamentSelection(); - int parent2Index = tournamentSelection(); - - // 确保父代不同 - while(parent1Index == parent2Index && m_population.size() > 1) { - parent2Index = tournamentSelection(); - } - - GAIndividual offspring1, offspring2; - - // 交叉 - if(random01() < m_crossoverRate) { - crossover(m_population[parent1Index], m_population[parent2Index], - offspring1, offspring2); - } else { - offspring1 = m_population[parent1Index]; - offspring2 = m_population[parent2Index]; - } - - // 变异 - if(random01() < m_mutationRate) { - mutate(offspring1); - } - if(random01() < m_mutationRate) { - mutate(offspring2); - } - - // 边界约束 - clampToLimits(offspring1.genes); - clampToLimits(offspring2.genes); - - // 添加到新种群 - if(newPopulation.size() < m_populationSize) { - newPopulation.append(offspring1); - } - if(newPopulation.size() < m_populationSize) { - newPopulation.append(offspring2); - } - } - - // 替换种群 - m_population = newPopulation; - - // 自适应参数调整 - adaptiveParameterUpdate(m_currentGeneration); - } - - // 输出迭代结束标记 - if(shouldOutputDetail) { - emit logMessageGenerated(tr("Generation %1 completed - Current best: %2") - .arg(m_currentGeneration + 1).arg(m_bestFitness, 0, 'e', 4)); - } - - // 强制处理事件，保持界面响应 - QApplication::processEvents(); - - // 在世代间稍作停顿，减少系统负载 - if(m_currentGeneration % 3 == 2) { - //msleep(200); - } - } - - // 最终结果验证和保护 - validateAndProtectFinalResult(); - - } catch(const std::exception& e) { - m_lastError = QString("Critical exception in GA main loop: %1").arg(e.what()); - emit logMessageGenerated(tr("CRITICAL ERROR: %1").arg(e.what())); - cleanupTemporaryDirectory(); - m_isRunning = false; - if(m_progressTimer) m_progressTimer->stop(); - emit fittingFinished(false, m_lastError); - return false; - } catch(...) { - m_lastError = "Unknown critical exception in GA main loop"; - emit logMessageGenerated(tr("CRITICAL ERROR: Unknown exception in GA main loop")); - cleanupTemporaryDirectory(); - m_isRunning = false; - if(m_progressTimer) m_progressTimer->stop(); - emit fittingFinished(false, m_lastError); - return false; - } - - m_isRunning = false; - if(m_progressTimer) m_progressTimer->stop(); - - // 应用最终参数 - if(!m_bestIndividual.genes.isEmpty()) { - try { - emit logMessageGenerated(tr("Applying optimized parameters to model...")); - applyParametersToDataManager(m_bestIndividual.genes); - saveOptimizationResult(); - - // 输出最终优化结果 - emit logMessageGenerated(tr("=== Optimization Results ===")); - emit logMessageGenerated(tr("Final error: %1").arg(m_bestFitness, 0, 'e', 4)); - emit logMessageGenerated(tr("Total generations: %1").arg(m_currentGeneration + 1)); - emit logMessageGenerated(tr("Total evaluations: %1 (successful: %2)") - .arg(m_totalEvaluations).arg(m_successfulEvaluations)); - - // 输出最优参数值 - QString finalParams = tr("Optimized parameters: "); - for(int i = 0; i < m_bestIndividual.genes.size(); ++i) { - finalParams += QString("[%1]=%2 ").arg(i).arg(m_bestIndividual.genes[i], 0, 'f', 6); - } - emit logMessageGenerated(finalParams); - - emit logMessageGenerated(tr("Parameters applied successfully to data manager")); - } catch(const std::exception& e) { - emit logMessageGenerated(tr("ERROR: Failed to apply final parameters: %1").arg(e.what())); - m_lastError = QString("Failed to apply final parameters: %1").arg(e.what()); - } catch(...) { - emit logMessageGenerated(tr("ERROR: Unknown error applying final parameters")); - m_lastError = "Failed to apply final parameters due to unknown error"; - } - } - - // 判断系统确定最终结果 - bool success; - QString message; - StopReasonGA finalReason = analyzeOptimizationStatus(); - - if(finalReason == GA_TARGET_ACHIEVED) { - success = true; - //message = QString("GA optimization completed successfully. Target achieved. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION SUCCESSFUL ===")); - } - else if(finalReason == GA_TRUE_CONVERGENCE) { - success = true; - //message = QString("GA optimization converged to stable solution. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION CONVERGED ===")); - } - else if(finalReason == GA_LOCAL_OPTIMUM) { - success = true; - //message = QString("GA optimization trapped in local optimum. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION - LOCAL OPTIMUM ===")); - } - else if(finalReason == GA_MAX_ITERATIONS) { - success = true; - //message = QString("GA optimization completed. Max generations reached. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION - MAX GENERATIONS ===")); - } - else if(finalReason == GA_USER_STOPPED) { - success = true; - //message = QString("GA optimization stopped by user. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION STOPPED BY USER ===")); - } - else if(finalReason == GA_CONSECUTIVE_FAILURES) { - success = false; - //message = QString("GA optimization failed due to consecutive failures. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION FAILED ===")); - } - else { - success = false; - //message = QString("GA optimization ended unexpectedly. Best error: %1, Generations: %2") - // .arg(m_bestFitness, 0, 'e', 4).arg(m_currentGeneration + 1); - emit logMessageGenerated(tr("=== GA OPTIMIZATION - UNKNOWN END ===")); - } - - emit logMessageGenerated(tr("Result: %1").arg(success ? "SUCCESS" : "FAILED")); - - emit fittingFinished(success, message); - cleanupTemporaryDirectory(); - return success; -} - -void nmCalculationAutoFitGA::stopFitting() -{ - if(m_isRunning) { - DEBUG_OUT("Stop request received, setting stop flag..."); - - // 添加停止日志 - emit logMessageGenerated(tr("=== User Stop Request Received ===")); - emit logMessageGenerated(tr("Gracefully stopping GA optimization...")); - - m_shouldStop = true; - - if(m_progressTimer) { - m_progressTimer->stop(); - } - - // 等待当前评估完成，缩短超时时间 - int waitCount = 0; - while(m_evaluationInProgress > 0 && waitCount < 30) { // 减少等待时间 - QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 50); - msleep(50); - waitCount++; - } - - // 超时时强制重置 - if(m_evaluationInProgress > 0) { - DEBUG_OUT("Force resetting evaluation counter"); - emit logMessageGenerated(tr("Force stopping current evaluation...")); - m_evaluationInProgress = 0; - } - - // 确保运行标志被清除 - m_isRunning = false; - - emit logMessageGenerated(tr("GA optimization stop request processed")); - DEBUG_OUT("Stop request processed"); - } else { - DEBUG_OUT("Stop request received but GA is not running"); - emit logMessageGenerated(tr("Stop request received but optimization is not running")); - } - cleanupTemporaryDirectory(); -} - -bool nmCalculationAutoFitGA::isRunning() const -{ - return m_isRunning; -} - -int nmCalculationAutoFitGA::getCurrentGeneration() const -{ - return m_currentGeneration; -} - -QVector nmCalculationAutoFitGA::getBestSolution() const -{ - return m_bestIndividual.genes; -} - -double nmCalculationAutoFitGA::getBestFitness() const -{ - return m_bestFitness; -} - -QString nmCalculationAutoFitGA::getLastError() const -{ - return m_lastError; -} - -void nmCalculationAutoFitGA::resetOptimizer() -{ - m_population.clear(); - m_eliteIndividuals.clear(); - m_bestIndividual = GAIndividual(); - m_bestFitness = 1e10; - m_worstFitness = -1e10; - m_averageFitness = 1e10; - m_previousBestFitness = 1e10; - m_currentGeneration = 0; - m_totalEvaluations = 0; - m_successfulEvaluations = 0; - m_convergenceHistory.clear(); - m_lastError.clear(); - m_initialValues.clear(); - m_userInitialSolution.clear(); - m_userInitialFitness = 1e10; - m_hasValidUserSolution = false; - m_diversityHistory.clear(); - - DEBUG_OUT("GA optimizer reset"); -} - -void nmCalculationAutoFitGA::setGATargetWellName(const QString& wellName) -{ - m_targetWellName = wellName; -} - -void nmCalculationAutoFitGA::updateProgress() -{ - // 这个槽函数在定时器触发时被调用，可以用来更新界面或执行周期性任务 - if(m_isRunning) { - QApplication::processEvents(); - } -} - -// ==================== 数据加载方法 ==================== -bool nmCalculationAutoFitGA::loadAllConfigFromDataManager() -{ - try { - loadOptimizationConfig(); - loadParameterBounds(); - extractUserInitialValues(); - return true; - } catch(...) { - m_lastError = "Failed to load configuration from data manager"; - return false; - } -} - -void nmCalculationAutoFitGA::loadOptimizationConfig() -{ - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy(); - - // 加载基本配置 - m_maxGenerations = fittingData.getIterationCount().getValue().toInt(); - m_targetError = fittingData.getErrorTolerance().getValue().toDouble(); - - // 设置GA默认参数 - m_populationSize = 40; - m_crossoverRate = 0.8; - m_mutationRate = 0.1; - m_elitismRate = 0.15; - m_tournamentSize = 3; - m_useUniformCrossover = true; - - DEBUG_OUT(QString("Loaded GA optimization config: generations=%1, error=%2, population=%3") - .arg(m_maxGenerations).arg(m_targetError).arg(m_populationSize)); -} - -void nmCalculationAutoFitGA::loadParameterBounds() -{ - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy(); - - // 获取参数选择状态 - m_parameterSelected.resize(8); - m_parameterSelected[0] = fittingData.getPermeabilitySelected(); - m_parameterSelected[1] = fittingData.getSkinSelected(); - m_parameterSelected[2] = fittingData.getWellboreStorageSelected(); - m_parameterSelected[3] = fittingData.getPorositySelected(); - m_parameterSelected[4] = fittingData.getThicknessSelected(); - m_parameterSelected[5] = fittingData.getCtSelected(); - m_parameterSelected[6] = fittingData.getCfSelected(); - m_parameterSelected[7] = fittingData.getSwiSelected(); - - // 获取参数边界 - m_parameterLower.resize(8); - m_parameterUpper.resize(8); - - m_parameterLower[0] = fittingData.getPermeabilityMin().getValue().toDouble(); - m_parameterUpper[0] = fittingData.getPermeabilityMax().getValue().toDouble(); - - m_parameterLower[1] = fittingData.getSkinMin().getValue().toDouble(); - m_parameterUpper[1] = fittingData.getSkinMax().getValue().toDouble(); - - m_parameterLower[2] = fittingData.getWellboreStorageMin().getValue().toDouble(); - m_parameterUpper[2] = fittingData.getWellboreStorageMax().getValue().toDouble(); - - m_parameterLower[3] = fittingData.getPorosityMin().getValue().toDouble(); - m_parameterUpper[3] = fittingData.getPorosityMax().getValue().toDouble(); - - m_parameterLower[4] = fittingData.getThicknessMin().getValue().toDouble(); - m_parameterUpper[4] = fittingData.getThicknessMax().getValue().toDouble(); - - m_parameterLower[5] = fittingData.getCtMin().getValue().toDouble(); - m_parameterUpper[5] = fittingData.getCtMax().getValue().toDouble(); - - m_parameterLower[6] = fittingData.getCfMin().getValue().toDouble(); - m_parameterUpper[6] = fittingData.getCfMax().getValue().toDouble(); - - m_parameterLower[7] = fittingData.getSwiMin().getValue().toDouble(); - m_parameterUpper[7] = fittingData.getSwiMax().getValue().toDouble(); - - // 更新启用参数索引 - m_enabledParamIndices.clear(); - for(int i = 0; i < m_parameterSelected.size(); ++i) { - if(m_parameterSelected[i]) { - m_enabledParamIndices.append(i); - } - } - DEBUG_OUT(QString("Loaded parameter bounds: %1 enabled parameters") - .arg(m_enabledParamIndices.size())); -} - -void nmCalculationAutoFitGA::extractUserInitialValues() -{ - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - nmDataReservoir reservoirData = dataManager->getReservoirDataCopy(); - //QVector wells = dataManager->getWellDataList(); - nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName); - - m_initialValues.clear(); - - // 按照启用参数的顺序提取初始值 - for(int i = 0; i < m_enabledParamIndices.size(); ++i) { - int paramIndex = m_enabledParamIndices[i]; - double initialValue = 0.0; - - switch(paramIndex) { - case 0: // 渗透率 - initialValue = reservoirData.getPermeability().getValue().toDouble(); - break; - - case 1: // 表皮系数 - if(pTargetWell) { - initialValue = pTargetWell->getPerforation(0)->getSkin().getValue().toDouble(); - } - - break; - - case 2: // 井筒储集系数 - if(pTargetWell) { - initialValue = pTargetWell->getWellboreStorage().getValue().toDouble(); - } - - break; - - case 3: // 孔隙度 - initialValue = reservoirData.getPorosity().getValue().toDouble(); - break; - - case 4: // 储层厚度 - initialValue = reservoirData.getThickness().getValue().toDouble(); - break; - - case 5: // 综合压缩系数 - initialValue = reservoirData.getCt().getValue().toDouble(); - break; - - case 6: // 岩石压缩系数 - initialValue = reservoirData.getCf().getValue().toDouble(); - break; - - case 7: // 初始含水饱和度 - initialValue = reservoirData.getSwi().getValue().toDouble(); - break; - } - - m_initialValues.append(initialValue); - } - - DEBUG_OUT(QString("Extracted %1 user initial values").arg(m_initialValues.size())); - - for(int i = 0; i < m_initialValues.size(); ++i) { - DEBUG_OUT(QString(" Initial[%1] = %2").arg(i).arg(m_initialValues[i], 0, 'e', 3)); - } - - // 验证初始值 - if(!validateInitialValues()) { - DEBUG_OUT("Warning: Some initial values are outside parameter bounds"); - } -} - -// ==================== 遗传算法核心方法 ==================== - -void nmCalculationAutoFitGA::initializePopulation() -{ - int dimensions = getEnabledParameterCount(); - if(dimensions == 0) return; - - m_population.clear(); - m_population.resize(m_populationSize); - - bool hasValidInitials = !m_initialValues.isEmpty() && m_initialValues.size() >= dimensions; - int guidedCount = hasValidInitials ? qMax(2, m_populationSize / 2) : qMax(1, m_populationSize / 3); - - DEBUG_OUT(QString("Enhanced population initialization: %1 individuals, %2 guided, %3 random") - .arg(m_populationSize).arg(guidedCount).arg(m_populationSize - guidedCount)); - - for(int i = 0; i < m_populationSize; ++i) { - GAIndividual& individual = m_population[i]; - individual.genes.resize(dimensions); - individual.fitness = 1e10; - individual.isEvaluated = false; - - // 基因初始化 - for(int j = 0; j < dimensions; ++j) { - int paramIndex = m_enabledParamIndices[j]; - double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex]; - - if(i == 0 && hasValidInitials) { - // 第一个个体：使用用户初始值 - individual.genes[j] = m_initialValues[j]; - } else if(i < guidedCount && hasValidInitials) { - // 引导搜索策略 - double searchRadius; - if(i <= guidedCount / 3) { - searchRadius = range * 0.03; - } else if(i <= guidedCount * 2 / 3) { - searchRadius = range * 0.08; - } else { - searchRadius = range * 0.15; - } - double offset = (random01() - 0.5) * searchRadius; - individual.genes[j] = m_initialValues[j] + offset; - } else { - // 随机初始化 - individual.genes[j] = m_parameterLower[paramIndex] + random01() * range; - } - } - - // 边界约束 - clampToLimits(individual.genes); - } -} - -double nmCalculationAutoFitGA::evaluateGenes(const QVector& genes) -{ - const QString funcName = QString("evaluateGenes[Gen%1]").arg(m_currentGeneration); - static int callCount = 0; - callCount++; - - try { - DEBUG_OUT(QString("%1: Call #%2 - Starting evaluation with %3 genes") - .arg(funcName).arg(callCount).arg(genes.size())); - - // 打印参数值用于对比 - QString paramStr = "Parameters: "; - - for(int i = 0; i < genes.size(); ++i) { - paramStr += QString("[%1]=%2 ").arg(i).arg(genes[i], 0, 'f', 6); - } - - DEBUG_OUT(QString("%1: %2").arg(funcName).arg(paramStr)); - - // 1. 参数有效性检查 - if(!validateParameters(genes)) { - DEBUG_OUT(QString("%1: Call #%2 - Invalid parameters").arg(funcName).arg(callCount)); - return 1e10; - } - - // 2. 检查数据管理器状态 - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - - if(!dataManager) { - DEBUG_OUT(QString("%1: Call #%2 - DataManager is null").arg(funcName).arg(callCount)); - return 1e10; - } - - // 3. 应用参数到数据管理器 - try { - DEBUG_OUT(QString("%1: Call #%2 - Applying parameters to DataManager").arg(funcName).arg(callCount)); - applyParametersToDataManager(genes); - DEBUG_OUT(QString("%1: Call #%2 - Parameters applied successfully").arg(funcName).arg(callCount)); - } catch(const std::exception& e) { - DEBUG_OUT(QString("%1: Call #%2 - Failed to apply parameters: %3").arg(funcName).arg(callCount).arg(e.what())); - return 1e10; - } catch(...) { - DEBUG_OUT(QString("%1: Call #%2 - Unknown error applying parameters").arg(funcName).arg(callCount)); - return 1e10; - } - - // 4. 运行求解器 - QVector > solverResult; - const int maxRetries = 2; - bool solverSuccess = false; - - for(int retry = 0; retry <= maxRetries; ++retry) { - if(m_shouldStop) return 1e10; - - try { - DEBUG_OUT(QString("%1: Call #%2 - Solver attempt %3/%4") - .arg(funcName).arg(callCount).arg(retry + 1).arg(maxRetries + 1)); - - // 在求解器调用前添加短暂延迟，确保状态稳定 - if(retry > 0) { - DEBUG_OUT(QString("%1: Call #%2 - Retry delay before solver attempt") - .arg(funcName).arg(callCount)); - msleep(1000); // 增加延迟时间 - } - - solverResult = runSolver(); - - if(!solverResult.isEmpty() && validateSolverResult(solverResult)) { - DEBUG_OUT(QString("%1: Call #%2 - Solver successful on attempt %3, result size: %4") - .arg(funcName).arg(callCount).arg(retry + 1).arg(solverResult[0].size())); - solverSuccess = true; - break; - } else { - DEBUG_OUT(QString("%1: Call #%2 - Solver failed on attempt %3 - empty or invalid result") - .arg(funcName).arg(callCount).arg(retry + 1)); - - if(retry < maxRetries) { - DEBUG_OUT(QString("%1: Call #%2 - Will retry solver").arg(funcName).arg(callCount)); - } - } - - } catch(const std::exception& e) { - DEBUG_OUT(QString("%1: Call #%2 - Solver exception on attempt %3: %4") - .arg(funcName).arg(callCount).arg(retry + 1).arg(e.what())); - } catch(...) { - DEBUG_OUT(QString("%1: Call #%2 - Unknown solver exception on attempt %3") - .arg(funcName).arg(callCount).arg(retry + 1)); - } - } - - if(!solverSuccess) { - DEBUG_OUT(QString("%1: Call #%2 - All solver attempts failed").arg(funcName).arg(callCount)); - return 1e10; - } - - // 5. 获取双对数结果数据 - QVector > resultLogLogData; - - try { - - nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName); - - if(pTargetWell) { - resultLogLogData = pTargetWell->getResultLogLog(); - - if(!validateLogLogData(resultLogLogData)) { - DEBUG_OUT(QString("%1: Call #%2 - Invalid result LogLog data").arg(funcName).arg(callCount)); - return 1e10; - } - - DEBUG_OUT(QString("%1: Call #%2 - LogLog result data obtained, size: %3") - .arg(funcName).arg(callCount).arg(resultLogLogData[0].size())); - } else { - DEBUG_OUT(QString("%1: Call #%2 - No wells found after solver").arg(funcName).arg(callCount)); - return 1e10; - } - } catch(const std::exception& e) { - DEBUG_OUT(QString("%1: Call #%2 - Error getting LogLog result: %3").arg(funcName).arg(callCount).arg(e.what())); - return 1e10; - } catch(...) { - DEBUG_OUT(QString("%1: Call #%2 - Unknown error getting LogLog result").arg(funcName).arg(callCount)); - return 1e10; - } - - // 6. 双对数曲线对齐和误差计算 - double error; - - try { - error = calculateLogLogCurveError(m_targetLogLogData, resultLogLogData); - - if(!isFiniteNumber(error) || error < 0) { - DEBUG_OUT(QString("%1: Call #%2 - Invalid error value: %3").arg(funcName).arg(callCount).arg(error)); - return 1e10; - } - - DEBUG_OUT(QString("%1: Call #%2 - Evaluation successful, error = %3") - .arg(funcName).arg(callCount).arg(error, 0, 'e', 6)); - - } catch(const std::exception& e) { - DEBUG_OUT(QString("%1: Call #%2 - LogLog error calculation failed: %3").arg(funcName).arg(callCount).arg(e.what())); - return 1e10; - } catch(...) { - DEBUG_OUT(QString("%1: Call #%2 - Unknown error in LogLog error calculation").arg(funcName).arg(callCount)); - return 1e10; - } - - return error; - - } catch(const std::exception& e) { - DEBUG_OUT(QString("%1: Call #%2 - Top-level exception: %3").arg(funcName).arg(callCount).arg(e.what())); - return 1e10; - } catch(...) { - DEBUG_OUT(QString("%1: Call #%2 - Unknown top-level exception").arg(funcName).arg(callCount)); - return 1e10; - } -} - -double nmCalculationAutoFitGA::evaluateIndividual(GAIndividual& individual) -{ - // 如果已经评估过，直接返回 - if(individual.isEvaluated) { - return individual.fitness; - } - - // 调用新的评估函数 - individual.fitness = evaluateGenes(individual.genes); - individual.isEvaluated = true; - - return individual.fitness; -} - -void nmCalculationAutoFitGA::evaluatePopulation() -{ - int successfulEvaluations = 0; - int totalEvaluations = 0; - int currentGenerationFailed = 0; - - for(int i = 0; i < m_population.size() && !m_shouldStop; ++i) { - GAIndividual& individual = m_population[i]; - - if(!individual.isEvaluated) { - try { - double previousFitness = individual.fitness; - individual.fitness = evaluateGenes(individual.genes); - individual.isEvaluated = true; - totalEvaluations++; - m_totalEvaluations++; - - if(individual.fitness < 1e9) { - successfulEvaluations++; - m_successfulEvaluations++; - - // 更新全局最优 - if(individual.fitness < m_bestFitness) { - m_bestFitness = individual.fitness; - m_bestIndividual = individual; - - emit logMessageGenerated(tr(" Individual %1 improved: %2 -> %3") - .arg(i + 1).arg(previousFitness, 0, 'e', 3).arg(individual.fitness, 0, 'e', 3)); - } - } else { - currentGenerationFailed++; - emit logMessageGenerated(tr(" Individual %1: evaluation failed").arg(i + 1)); - } - - // 每评估2个个体处理一次事件 - if(i % 2 == 0) { - QApplication::processEvents(); - } - - } catch(const std::exception& e) { - emit logMessageGenerated(tr(" Individual %1: Exception: %2").arg(i + 1).arg(e.what())); - individual.fitness = 1e10; - individual.isEvaluated = true; - totalEvaluations++; - currentGenerationFailed++; - m_totalEvaluations++; - } catch(...) { - emit logMessageGenerated(tr(" Individual %1: Unknown exception").arg(i + 1)); - individual.fitness = 1e10; - individual.isEvaluated = true; - totalEvaluations++; - currentGenerationFailed++; - m_totalEvaluations++; - } - } - } - - if(m_shouldStop) return; - - double currentSuccessRate = totalEvaluations > 0 ? - (double)successfulEvaluations / totalEvaluations : 0.0; - - // 更新连续失败代数计数 - if(successfulEvaluations == 0) { - m_consecutiveFailedGenerations++; - emit logMessageGenerated(tr("WARNING: No successful evaluations in generation %1 (consecutive failures: %2)") - .arg(m_currentGeneration + 1).arg(m_consecutiveFailedGenerations)); - } else { - m_consecutiveFailedGenerations = 0; // 重置连续失败计数 - } - - // 输出当前代统计 - emit logMessageGenerated(tr("Current generation stats: %1 successful, %2 failed out of %3 individuals (success rate: %4%)") - .arg(successfulEvaluations).arg(currentGenerationFailed) - .arg(totalEvaluations).arg(currentSuccessRate * 100, 0, 'f', 1)); - - // 检查是否需要停止优化 - if(m_consecutiveFailedGenerations >= m_maxConsecutiveFailures) { - m_lastError = QString("Too many consecutive failed generations (%1)").arg(m_consecutiveFailedGenerations); - emit logMessageGenerated(tr("ERROR: Too many consecutive failed generations (%1/%2) - stopping optimization") - .arg(m_consecutiveFailedGenerations).arg(m_maxConsecutiveFailures)); - return; - } - - // 警告低成功率但不立即停止 - if(currentSuccessRate < 0.5 && m_currentGeneration > 3) { - emit logMessageGenerated(tr("WARNING: Low success rate (%1%) in generation %2, but continuing optimization") - .arg(currentSuccessRate * 100, 0, 'f', 1).arg(m_currentGeneration + 1)); - } -} - -int nmCalculationAutoFitGA::tournamentSelection() -{ - int bestIndex = qrand() % m_population.size(); - double bestFitness = m_population[bestIndex].fitness; - - for(int i = 1; i < m_tournamentSize; ++i) { - int candidateIndex = qrand() % m_population.size(); - double candidateFitness = m_population[candidateIndex].fitness; - - if(candidateFitness < bestFitness) { - bestIndex = candidateIndex; - bestFitness = candidateFitness; - } - } - - return bestIndex; -} - -int nmCalculationAutoFitGA::rouletteWheelSelection() -{ - // 计算适应度总和（使用倒数，因为我们要最小化） - double totalFitness = 0.0; - double maxFitness = -1e10; - - // 找到最大适应度值 - for(int i = 0; i < m_population.size(); ++i) { - if(m_population[i].fitness > maxFitness) { - maxFitness = m_population[i].fitness; - } - } - - // 计算转换后的适应度总和 - for(int i = 0; i < m_population.size(); ++i) { - double transformedFitness = maxFitness - m_population[i].fitness + 1e-6; - totalFitness += transformedFitness; - } - - // 轮盘赌选择 - double randomValue = random01() * totalFitness; - double cumulativeFitness = 0.0; - - for(int i = 0; i < m_population.size(); ++i) { - double transformedFitness = maxFitness - m_population[i].fitness + 1e-6; - cumulativeFitness += transformedFitness; - - if(cumulativeFitness >= randomValue) { - return i; - } - } - - return m_population.size() - 1; // 备用选择 -} - -void nmCalculationAutoFitGA::crossover(const GAIndividual& parent1, const GAIndividual& parent2, - GAIndividual& offspring1, GAIndividual& offspring2) -{ - if(m_useUniformCrossover) { - uniformCrossover(parent1, parent2, offspring1, offspring2); - } else { - singlePointCrossover(parent1, parent2, offspring1, offspring2); - } -} - -void nmCalculationAutoFitGA::singlePointCrossover(const GAIndividual& parent1, const GAIndividual& parent2, - GAIndividual& offspring1, GAIndividual& offspring2) -{ - int dimensions = parent1.genes.size(); - - if(dimensions == 0) return; - - // 初始化子代 - offspring1.genes.resize(dimensions); - offspring2.genes.resize(dimensions); - offspring1.fitness = 1e10; - offspring2.fitness = 1e10; - offspring1.isEvaluated = false; - offspring2.isEvaluated = false; - - // 选择交叉点 - int crossoverPoint = qrand() % dimensions; - - // 执行交叉 - for(int i = 0; i < dimensions; ++i) { - if(i < crossoverPoint) { - offspring1.genes[i] = parent1.genes[i]; - offspring2.genes[i] = parent2.genes[i]; - } else { - offspring1.genes[i] = parent2.genes[i]; - offspring2.genes[i] = parent1.genes[i]; - } - } -} - -void nmCalculationAutoFitGA::uniformCrossover(const GAIndividual& parent1, const GAIndividual& parent2, - GAIndividual& offspring1, GAIndividual& offspring2) -{ - int dimensions = parent1.genes.size(); - - if(dimensions == 0) return; - - // 初始化子代 - offspring1.genes.resize(dimensions); - offspring2.genes.resize(dimensions); - offspring1.fitness = 1e10; - offspring2.fitness = 1e10; - offspring1.isEvaluated = false; - offspring2.isEvaluated = false; - - // 均匀交叉 - for(int i = 0; i < dimensions; ++i) { - if(random01() < 0.5) { - offspring1.genes[i] = parent1.genes[i]; - offspring2.genes[i] = parent2.genes[i]; - } else { - offspring1.genes[i] = parent2.genes[i]; - offspring2.genes[i] = parent1.genes[i]; - } - } -} - -void nmCalculationAutoFitGA::mutate(GAIndividual& individual) -{ - // 使用高斯变异作为主要方式 - gaussianMutation(individual); -} - -void nmCalculationAutoFitGA::gaussianMutation(GAIndividual& individual) -{ - for(int i = 0; i < individual.genes.size(); ++i) { - if(random01() < m_mutationRate) { - int paramIndex = m_enabledParamIndices[i]; - double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex]; - double sigma = range * MUTATION_STRENGTH; - - // 高斯变异 - double mutation = gaussianRandom(0.0, sigma); - individual.genes[i] += mutation; - - // 边界处理 - individual.genes[i] = qMax(m_parameterLower[paramIndex], - qMin(m_parameterUpper[paramIndex], individual.genes[i])); - } - } - - // 标记为未评估 - individual.isEvaluated = false; -} - -void nmCalculationAutoFitGA::polynomialMutation(GAIndividual& individual) -{ - const double eta = 20.0; // 分布指数 - - for(int i = 0; i < individual.genes.size(); ++i) { - if(random01() < m_mutationRate) { - int paramIndex = m_enabledParamIndices[i]; - double lower = m_parameterLower[paramIndex]; - double upper = m_parameterUpper[paramIndex]; - double y = individual.genes[i]; - - double delta1 = (y - lower) / (upper - lower); - double delta2 = (upper - y) / (upper - lower); - - double rnd = random01(); - double mut_pow = 1.0 / (eta + 1.0); - - double deltaq; - - if(rnd <= 0.5) { - double xy = 1.0 - delta1; - double val = 2.0 * rnd + (1.0 - 2.0 * rnd) * pow(xy, eta + 1.0); - deltaq = pow(val, mut_pow) - 1.0; - } else { - double xy = 1.0 - delta2; - double val = 2.0 * (1.0 - rnd) + 2.0 * (rnd - 0.5) * pow(xy, eta + 1.0); - deltaq = 1.0 - pow(val, mut_pow); - } - - y += deltaq * (upper - lower); - individual.genes[i] = qMax(lower, qMin(upper, y)); - } - } - - // 标记为未评估 - individual.isEvaluated = false; -} - -void nmCalculationAutoFitGA::applyElitism(QVector& newPopulation) -{ - int eliteCount = static_cast(m_populationSize * m_elitismRate); - - if(eliteCount == 0) return; - - // 对种群按适应度排序 - QVector sortedPopulation = m_population; - - // 冒泡排序（适应度从小到大） - for(int i = 0; i < sortedPopulation.size() - 1; ++i) { - for(int j = 0; j < sortedPopulation.size() - 1 - i; ++j) { - if(sortedPopulation[j].fitness > sortedPopulation[j + 1].fitness) { - GAIndividual temp = sortedPopulation[j]; - sortedPopulation[j] = sortedPopulation[j + 1]; - sortedPopulation[j + 1] = temp; - } - } - } - - // 复制精英个体到新种群 - for(int i = 0; i < eliteCount && i < sortedPopulation.size(); ++i) { - newPopulation.append(sortedPopulation[i]); - } - - DEBUG_OUT(QString("Applied elitism: %1 elite individuals preserved").arg(eliteCount)); -} - -void nmCalculationAutoFitGA::updatePopulationStatistics() -{ - if(m_population.isEmpty()) return; - - m_previousBestFitness = m_bestFitness; - - double sum = 0.0; - double minFitness = 1e10; - double maxFitness = -1e10; - int validCount = 0; - - for(int i = 0; i < m_population.size(); ++i) { - const GAIndividual& individual = m_population[i]; - - if(individual.isEvaluated && individual.fitness < 1e9) { - sum += individual.fitness; - validCount++; - - if(individual.fitness < minFitness) { - minFitness = individual.fitness; - } - - if(individual.fitness > maxFitness) { - maxFitness = individual.fitness; - } - - // 更新全局最优 - if(individual.fitness < m_bestFitness) { - // 只有显著改进时才更新 - double improvement = (m_bestFitness - individual.fitness); - double relativeImprovement = improvement / qMax(1e-10, qAbs(m_bestFitness)); - - if(relativeImprovement > m_improvementThreshold) { - m_bestFitness = individual.fitness; - m_bestIndividual = individual; - - DEBUG_OUT(QString("Global best updated with %1% improvement: %2") - .arg(relativeImprovement * 100, 0, 'f', 3) - .arg(m_bestFitness, 0, 'e', 4)); - } - } - } - } - - if(validCount > 0) { - m_averageFitness = sum / validCount; - m_worstFitness = maxFitness; - } else { - m_averageFitness = 1e10; - m_worstFitness = 1e10; - } - - // 在没有找到更好解时才检查精英保护 - if(m_hasValidUserSolution && m_userInitialFitness < m_bestFitness) { - DEBUG_OUT("Elite protection: No significant improvement found, checking initial solution"); - - // 检查初始解是否仍然是最优的 - double improvement = m_bestFitness - m_userInitialFitness; - double relativeImprovement = improvement / qMax(1e-10, qAbs(m_bestFitness)); - - if(relativeImprovement > m_improvementThreshold * 0.5) { // 使用更宽松的阈值 - DEBUG_OUT("Elite protection: Restoring user initial solution"); - m_bestFitness = m_userInitialFitness; - m_bestIndividual.genes = m_userInitialSolution; - m_bestIndividual.fitness = m_userInitialFitness; - m_bestIndividual.isEvaluated = true; - } - } -} - -bool nmCalculationAutoFitGA::checkConvergence() -{ - if(m_convergenceHistory.size() < CONVERGENCE_CHECK_INTERVAL) { - return false; - } - - // 检查最近几次世代的改进 - double recentBest = m_convergenceHistory.last(); - double oldBest = m_convergenceHistory[m_convergenceHistory.size() - CONVERGENCE_CHECK_INTERVAL]; - - double improvement = oldBest - recentBest; - - // 如果连续多代没有显著改进，认为已收敛 - if(improvement < MIN_FITNESS_IMPROVEMENT) { - // 检查是否连续停滞 - int stagnationCount = 0; - - for(int i = m_convergenceHistory.size() - 1; i >= qMax(0, m_convergenceHistory.size() - MAX_STAGNATION_GENERATIONS); --i) { - if(i > 0) { - double diff = m_convergenceHistory[i - 1] - m_convergenceHistory[i]; - - if(diff < MIN_FITNESS_IMPROVEMENT) { - stagnationCount++; - } else { - break; - } - } - } - - return stagnationCount >= MAX_STAGNATION_GENERATIONS; - } - - return false; -} - -void nmCalculationAutoFitGA::adaptiveParameterUpdate(int generation) -{ - // 自适应调整变异率 - double progress = static_cast(generation) / m_maxGenerations; - - // 早期探索，后期开发 - m_mutationRate = 0.2 * (1.0 - progress) + 0.05 * progress; - - // 自适应调整交叉率 - if(generation > 0) { - double improvement = m_previousBestFitness - m_bestFitness; - - if(improvement < MIN_FITNESS_IMPROVEMENT) { - // 如果改进很小，增加探索性 - m_mutationRate = qMin(0.3, m_mutationRate * 1.1); - m_crossoverRate = qMax(0.6, m_crossoverRate * 0.95); - } else { - // 如果有明显改进，增加开发性 - m_mutationRate = qMax(0.05, m_mutationRate * 0.9); - m_crossoverRate = qMin(0.9, m_crossoverRate * 1.05); - } - } -} - -void nmCalculationAutoFitGA::validateAndProtectFinalResult() -{ - if(!m_hasValidUserSolution) { - emit logMessageGenerated(tr("No initial solution for elite protection")); - return; - } - - emit logMessageGenerated(tr("=== Final Result Validation (Elite Protection) ===")); - - // 使用已有的评估结果 - double finalFitness = m_bestFitness; - double initialFitness = m_userInitialFitness; - - emit logMessageGenerated(tr("Comparing results: Initial=%1, Final=%2") - .arg(initialFitness, 0, 'e', 4).arg(finalFitness, 0, 'e', 4)); - - // 计算改进程度 - double improvement = initialFitness - finalFitness; - double relativeImprovement = improvement / qMax(1e-10, qAbs(initialFitness)); - - emit logMessageGenerated(tr("Improvement: %1 (%2%)") - .arg(improvement, 0, 'e', 4).arg(relativeImprovement * 100, 0, 'f', 2)); - - if(relativeImprovement < m_improvementThreshold) { - emit logMessageGenerated(tr("Elite protection triggered: insufficient improvement")); - emit logMessageGenerated(tr("Threshold: %1%, Actual: %2%") - .arg(m_improvementThreshold * 100, 0, 'f', 2) - .arg(relativeImprovement * 100, 0, 'f', 4)); - emit logMessageGenerated(tr("Restoring initial solution as final result")); - - m_bestFitness = initialFitness; - m_bestIndividual.genes = m_userInitialSolution; - m_bestIndividual.fitness = initialFitness; - m_bestIndividual.isEvaluated = true; - - emit logMessageGenerated(tr("Initial solution restored successfully")); - } else { - emit logMessageGenerated(tr("Final result validated - significant improvement achieved")); - } -} - -StopReasonGA nmCalculationAutoFitGA::analyzeOptimizationStatus() -{ - // 1. 检查用户停止 - if(m_shouldStop) { - return GA_USER_STOPPED; - } - - // 2. 检查连续失败 - if(m_consecutiveFailedGenerations >= m_maxConsecutiveFailures) { - return GA_CONSECUTIVE_FAILURES; - } - - // 3. 检查是否达到目标精度 - if(m_bestFitness < m_targetError) { - return GA_TARGET_ACHIEVED; - } - - // 4. 检查是否达到最大迭代数 - if(m_currentGeneration >= m_maxGenerations - 1) { - return GA_MAX_ITERATIONS; - } - - // 5. 需要足够的历史数据才能判断收敛 - if(m_convergenceHistory.size() < m_localOptimumWindow) { - return GA_CONTINUE_OPTIMIZATION; - } - - // 6. 检查真正的收敛 - if(m_convergenceHistory.size() >= m_trueConvergenceWindow && checkTrueConvergence()) { - return GA_TRUE_CONVERGENCE; - } - - // 7. 检查局部最优陷阱 - if(checkLocalOptimumTrap()) { - return GA_LOCAL_OPTIMUM; - } - - return GA_CONTINUE_OPTIMIZATION; -} - -bool nmCalculationAutoFitGA::checkTrueConvergence() const -{ - if(m_convergenceHistory.size() < m_trueConvergenceWindow) { - return false; - } - - // 1. 检查解质量 - 如果已经接近目标，小改进可能是真收敛 - bool nearTarget = (m_bestFitness < m_targetError * m_nearTargetFactor); - - // 2. 检查适应度稳定性 - 长期小幅波动 - double recentVariance = calculateFitnessVariance(10); - double recentMean = 0.0; - int windowSize = qMin(10, m_convergenceHistory.size()); - - // 计算最近窗口的均值 - for(int i = m_convergenceHistory.size() - windowSize; i < m_convergenceHistory.size(); ++i) { - recentMean += m_convergenceHistory[i]; - } - - recentMean /= windowSize; - - double relativeVariance = recentVariance / qMax(1e-10, recentMean * recentMean); - bool stableError = (relativeVariance < m_convergenceVarianceThreshold); - - // 3. 检查种群多样性 - 应该收敛到同一区域 - double currentDiversity = calculatePopulationDiversity(); - bool lowDiversity = (currentDiversity < m_diversityThreshold); - - // 4. 检查长期改进趋势 - double longTermImprovement = calculateLongTermImprovement(m_trueConvergenceWindow); - bool minimalLongTermImprovement = (longTermImprovement < 1e-4); // 0.01% - - // 真收敛的判断条件 - bool isConverged = nearTarget || - (stableError && lowDiversity && minimalLongTermImprovement); - - if(isConverged) { - DEBUG_OUT("=== TRUE CONVERGENCE ANALYSIS ==="); - DEBUG_OUT(QString("nearTarget=%1 (fitness=%2, target*factor=%3)") - .arg(nearTarget).arg(m_bestFitness, 0, 'e', 4) - .arg(m_targetError * m_nearTargetFactor, 0, 'e', 4)); - DEBUG_OUT(QString("stableError=%1 (relativeVariance=%2)") - .arg(stableError).arg(relativeVariance, 0, 'e', 6)); - DEBUG_OUT(QString("lowDiversity=%1 (diversity=%2, threshold=%3)") - .arg(lowDiversity).arg(currentDiversity, 0, 'f', 6).arg(m_diversityThreshold)); - DEBUG_OUT(QString("longTermImprovement=%1%") - .arg(longTermImprovement * 100, 0, 'f', 4)); - } - - return isConverged; -} - -bool nmCalculationAutoFitGA::checkLocalOptimumTrap() const -{ - if(m_convergenceHistory.size() < m_localOptimumWindow) { - return false; - } - - // 1. 检查解质量 - 如果距离目标还很远，停滞就可能是局部最优 - bool farFromTarget = (m_bestFitness > m_targetError * m_farTargetFactor); - - // 2. 检查短期改进 - 近期改进非常小 - double shortTermImprovement = calculateLongTermImprovement(m_localOptimumWindow); - bool poorShortTermImprovement = (shortTermImprovement < 1e-5); // 0.001% - - // 3. 检查种群多样性 - 可能过早聚集或无效分散 - double currentDiversity = calculatePopulationDiversity(); - bool problematicDiversity = (currentDiversity < m_diversityThreshold * 0.1) || - (currentDiversity > m_diversityThreshold * 5.0); - - // 4. 检查适应度方差 - 可能卡在平坦区域 - double fitnessVariance = calculateFitnessVariance(m_localOptimumWindow); - bool flatFitnessLandscape = (fitnessVariance < m_convergenceVarianceThreshold * 0.1); - - // 局部最优的判断条件 - bool isLocalOptimum = farFromTarget && poorShortTermImprovement && - (problematicDiversity || flatFitnessLandscape); - - if(isLocalOptimum) { - DEBUG_OUT("=== LOCAL OPTIMUM ANALYSIS ==="); - DEBUG_OUT(QString("farFromTarget=%1 (fitness=%2, target*factor=%3)") - .arg(farFromTarget).arg(m_bestFitness, 0, 'e', 4) - .arg(m_targetError * m_farTargetFactor, 0, 'e', 4)); - DEBUG_OUT(QString("poorShortTermImprovement=%1 (improvement=%2%)") - .arg(poorShortTermImprovement).arg(shortTermImprovement * 100, 0, 'f', 4)); - DEBUG_OUT(QString("problematicDiversity=%1 (diversity=%2)") - .arg(problematicDiversity).arg(currentDiversity, 0, 'f', 6)); - DEBUG_OUT(QString("fitnessVariance=%1") - .arg(fitnessVariance, 0, 'e', 6)); - } - - return isLocalOptimum; -} - -double nmCalculationAutoFitGA::calculatePopulationDiversity() const -{ - if(m_population.size() < 2) return 0.0; - - int dimensions = getEnabledParameterCount(); - - if(dimensions == 0) return 0.0; - - double totalDiversity = 0.0; - - for(int dim = 0; dim < dimensions; ++dim) { - // 计算该维度上所有个体的均值 - double mean = 0.0; - - for(int i = 0; i < m_population.size(); ++i) { - mean += m_population[i].genes[dim]; - } - - mean /= m_population.size(); - - // 计算该维度上的方差 - double variance = 0.0; - - for(int i = 0; i < m_population.size(); ++i) { - double diff = m_population[i].genes[dim] - mean; - variance += diff * diff; - } - - variance /= m_population.size(); - - // 归一化到参数范围 - int paramIndex = m_enabledParamIndices[dim]; - double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex]; - double normalizedStd = sqrt(variance) / qMax(1e-10, range); - - totalDiversity += normalizedStd; - } - - return totalDiversity / dimensions; -} - -double nmCalculationAutoFitGA::calculateFitnessVariance(int windowSize) const -{ - if(m_convergenceHistory.size() < windowSize) { - return 1e10; // 数据不足，返回大值 - } - - // 计算最近windowSize次迭代的方差 - double mean = 0.0; - int startIdx = m_convergenceHistory.size() - windowSize; - - for(int i = startIdx; i < m_convergenceHistory.size(); ++i) { - mean += m_convergenceHistory[i]; - } - - mean /= windowSize; - - double variance = 0.0; - - for(int i = startIdx; i < m_convergenceHistory.size(); ++i) { - double diff = m_convergenceHistory[i] - mean; - variance += diff * diff; - } - - variance /= windowSize; - - return variance; -} - -double nmCalculationAutoFitGA::calculateLongTermImprovement(int windowSize) const -{ - if(m_convergenceHistory.size() < windowSize) { - return 1.0; // 数据不足，假设有改进 - } - - double oldFitness = m_convergenceHistory[m_convergenceHistory.size() - windowSize]; - double improvement = (oldFitness - m_bestFitness) / qMax(1e-10, qAbs(oldFitness)); - - return improvement; -} - -void nmCalculationAutoFitGA::updateConvergenceMetrics() -{ - // 更新多样性历史 - m_diversityHistory.append(calculatePopulationDiversity()); - - // 保持历史长度合理（最多保留50个数据点） - const int maxHistorySize = 50; - - while(m_diversityHistory.size() > maxHistorySize) { - m_diversityHistory.remove(0); - } -} - -// ==================== 参数应用方法 ==================== - -bool nmCalculationAutoFitGA::validateParameters(const QVector& parameters) const -{ - if(parameters.size() != getEnabledParameterCount()) { - return false; - } - - for(int i = 0; i < parameters.size(); ++i) { - if(!isFiniteNumber(parameters[i])) { - return false; - } - - // 检查参数范围 - if(i < m_enabledParamIndices.size()) { - int paramIndex = m_enabledParamIndices[i]; - - if(paramIndex >= 0 && paramIndex < m_parameterLower.size()) { - if(parameters[i] < m_parameterLower[paramIndex] || - parameters[i] > m_parameterUpper[paramIndex]) { - return false; - } - } - } - } - - // 直接拦截会导致求解器数值崩溃的参数值 - for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) { - int paramIndex = m_enabledParamIndices[i]; - double value = parameters[i]; - - switch(paramIndex) { - case 0: // 渗透率：必须大于零 - if(value <= 1e-8) { - DEBUG_OUT(QString("Rejecting near-zero permeability: %1").arg(value)); - return false; - } - - break; - - case 2: // 井筒储集系数：必须大于零 - if(value <= 1e-10) { - DEBUG_OUT(QString("Rejecting near-zero wellbore storage: %1").arg(value)); - return false; - } - - break; - - case 3: // 孔隙度：必须在合理范围 - if(value <= 1e-6 || value >= 0.99) { - DEBUG_OUT(QString("Rejecting unrealistic porosity: %1").arg(value)); - return false; - } - - break; - - case 5: // 综合压缩系数：必须大于零 - if(value <= 1e-8) { - DEBUG_OUT(QString("Rejecting near-zero total compressibility: %1").arg(value)); - return false; - } - - break; - } - } - - return true; -} - -bool nmCalculationAutoFitGA::validateLogLogData(const QVector>& logLogData) const -{ - // 检查基本结构 - if(logLogData.size() < 3) { - DEBUG_OUT("LogLog data has less than 3 arrays"); - return false; - } - - // 检查数组大小一致性 - int size = logLogData[0].size(); - - if(size == 0) { - DEBUG_OUT("Empty LogLog data"); - return false; - } - - if(logLogData[1].size() != size || logLogData[2].size() != size) { - DEBUG_OUT(QString("LogLog data size mismatch: X=%1, Y1=%2, Y2=%3") - .arg(logLogData[0].size()) - .arg(logLogData[1].size()) - .arg(logLogData[2].size())); - return false; - } - - // 检查最小数据点数 - if(size < 5) { - DEBUG_OUT(QString("Too few LogLog data points: %1").arg(size)); - return false; - } - - // 数据有效性检查 - for(int i = 0; i < size; ++i) { - if(!isFiniteNumber(logLogData[0][i]) || - !isFiniteNumber(logLogData[1][i]) || - !isFiniteNumber(logLogData[2][i])) { - DEBUG_OUT(QString("Invalid LogLog data at index %1").arg(i)); - return false; - } - } - - return true; -} - -bool nmCalculationAutoFitGA::validateInitialValues() const -{ - if(m_initialValues.size() != m_enabledParamIndices.size()) { - DEBUG_OUT("Initial values count mismatch with enabled parameters"); - return false; - } - - bool allValid = true; - - for(int i = 0; i < m_initialValues.size(); ++i) { - int paramIndex = m_enabledParamIndices[i]; - double value = m_initialValues[i]; - - if(!isFiniteNumber(value)) { - DEBUG_OUT(QString("Initial value[%1] is not finite: %2").arg(i).arg(value)); - allValid = false; - continue; - } - - if(paramIndex < m_parameterLower.size() && paramIndex < m_parameterUpper.size()) { - double minVal = m_parameterLower[paramIndex]; - double maxVal = m_parameterUpper[paramIndex]; - - if(value < minVal || value > maxVal) { - DEBUG_OUT(QString("Initial value[%1] = %2 is outside bounds [%3, %4]") - .arg(i).arg(value).arg(minVal).arg(maxVal)); - allValid = false; - } - } - } - - return allValid; -} - -void nmCalculationAutoFitGA::applyParametersToDataManager(const QVector& parameters) -{ - if(parameters.size() != getEnabledParameterCount()) { - return; - } - - updateReservoirParameters(parameters); - updateWellParameters(parameters); -} - -void nmCalculationAutoFitGA::updateReservoirParameters(const QVector& parameters) -{ - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - nmDataReservoir reservoirData = dataManager->getReservoirDataCopy(); - - int paramIndex = 0; - for(int i = 0; i < m_parameterSelected.size(); ++i) { - if(m_parameterSelected[i] && paramIndex < parameters.size()) { - double value = parameters[paramIndex]; - - switch(i) { - case 0: // 渗透率 - reservoirData.getPermeability().setValue(value); - break; - case 3: // 孔隙度 - reservoirData.getPorosity().setValue(value); - break; - case 4: // 储层厚度 - reservoirData.getThickness().setValue(value); - break; - case 5: // 综合压缩系数 - reservoirData.getCt().setValue(value); - break; - case 6: // 岩石压缩系数 - reservoirData.getCf().setValue(value); - break; - case 7: // 初始含水饱和度 - reservoirData.getSwi().setValue(value); - break; - } - paramIndex++; - } - } - - // 更新数据管理器 - dataManager->updateReservoirData(reservoirData); -} - -void nmCalculationAutoFitGA::updateWellParameters(const QVector& parameters) -{ - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - - // 获取井列表，更新第一口井的参数 - //QVector wells = dataManager->getWellDataList(); - nmDataWellBase* pWell = dataManager->findWellByName(m_targetWellName); - if(!pWell) return; - - //nmDataWellBase* pWell = wells[0]; // 使用第一口井 - - int paramIndex = 0; - for(int i = 0; i < m_parameterSelected.size(); ++i) { - if(m_parameterSelected[i] && paramIndex < parameters.size()) { - double value = parameters[paramIndex]; - - switch(i) { - case 1: { // 表皮系数 - nmDataAttribute skinAttr = pWell->getPerforation(0)->getSkin(); - skinAttr.setValue(value); - pWell->setRateDependentSkin(skinAttr); - } - break; - case 2: { // 井筒储集系数 - nmDataAttribute wellboreAttr = pWell->getWellboreStorage(); - wellboreAttr.setValue(value); - pWell->setWellboreStorage(wellboreAttr); - } - break; - } - paramIndex++; - } - } - - // 根据井类型更新到数据管理器 - updateWellToDataManager(pWell); -} - -void nmCalculationAutoFitGA::updateWellToDataManager(nmDataWellBase* pWell) -{ - if(!pWell) return; - - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - NM_WELL_MODEL wellType = pWell->getWellType(); - - switch(wellType) { - case NM_WELL_MODEL::Vertical_Well: { - nmDataVerticalWell* pVerticalWell = dynamic_cast(pWell); - if(pVerticalWell) { - QVector wells; - wells.append(*pVerticalWell); - dataManager->updateVerticalWells(wells); - } - break; - } - case NM_WELL_MODEL::Vertical_Fractured_Well: { - nmDataVerticalFracturedWell* pVFracturedWell = dynamic_cast(pWell); - if(pVFracturedWell) { - QVector wells; - wells.append(*pVFracturedWell); - dataManager->updateVerticalFracturedWells(wells); - } - break; - } - case NM_WELL_MODEL::Horizontal_Fractured_Well: { - nmDataHorizontalFracturedWell* pHFracturedWell = dynamic_cast(pWell); - if(pHFracturedWell) { - QVector wells; - wells.append(*pHFracturedWell); - dataManager->updateHorizontalFracturedWells(wells); - } - break; - } - default: - break; - } -} - -void nmCalculationAutoFitGA::clampToLimits(QVector& parameters) const -{ - for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) { - int paramIndex = m_enabledParamIndices[i]; - if(paramIndex >= 0 && paramIndex < m_parameterLower.size()) { - parameters[i] = qMax(m_parameterLower[paramIndex], - qMin(m_parameterUpper[paramIndex], parameters[i])); - } - } -} - -// ==================== 求解器相关方法 ==================== - -QVector> nmCalculationAutoFitGA::runSolver() -{ - //return runSolverExe(); - return runSolverDll(); -} - -bool nmCalculationAutoFitGA::validateSolverResult(const QVector>& result) const -{ - // 基本检查 - if(result.size() < 2) { - DEBUG_OUT("Solver result has less than 2 arrays"); - return false; - } - - if(result[0].size() != result[1].size()) { - DEBUG_OUT(QString("Size mismatch: X=%1, Y=%2").arg(result[0].size()).arg(result[1].size())); - return false; - } - - if(result[0].size() == 0) { - DEBUG_OUT("Empty solver result"); - return false; - } - - // 检查最小数据点数 - if(result[0].size() < 10) { - DEBUG_OUT(QString("Too few data points: %1").arg(result[0].size())); - return false; - } - - // 数据有效性检查 - for(int i = 0; i < result[0].size(); ++i) { - if(!isFiniteNumber(result[0][i]) || !isFiniteNumber(result[1][i])) { - DEBUG_OUT(QString("Invalid data at index %1: X=%2, Y=%3") - .arg(i).arg(result[0][i]).arg(result[1][i])); - return false; - } - } - - return true; -} - -QVector> nmCalculationAutoFitGA::runSolverDll() -{ - DEBUG_OUT("SOLVER DLL START"); - - if(m_evaluationInProgress > 0) { - DEBUG_OUT("DLL Solver already running, skipping"); - return QVector>(); - } - - ++m_evaluationInProgress; - QVector> result; - nmCalculationDllPebiSolverTask* dllTask = nullptr; - - try { - DEBUG_OUT("Creating DLL solver task"); - dllTask = new nmCalculationDllPebiSolverTask(m_tempDirectory); - - if(m_shouldStop) { - DEBUG_OUT("Should stop - cleaning up and returning empty result"); - delete dllTask; - --m_evaluationInProgress; - return result; - } - - DEBUG_OUT("Starting DLL solver execution..."); - - // 异步执行 - dllTask->start(); - - // 等待完成 - int waitTime = 0; - const int maxWait = 30000; // 30秒超时 - const int checkInterval = 500; - - while(waitTime < maxWait) { - QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 100); - - if(!dllTask->isRunning()) { - DEBUG_OUT("DLL solver task completed"); - break; - } - - if(m_shouldStop) { - DEBUG_OUT("DLL solver task terminated by user"); - dllTask->terminate(); - break; - } - - msleep(checkInterval); - waitTime += checkInterval; - } - - // 超时处理 - if(dllTask->isRunning()) { - DEBUG_OUT("DLL solver task timeout, terminating..."); - dllTask->terminate(); - dllTask->wait(2000); - - delete dllTask; - dllTask = nullptr; - --m_evaluationInProgress; - m_consecutiveFailures++; - return result; - } - - // 验证结果数据是否已更新 - nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); - //QVector wells = dataManager->getWellDataList(); - nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName); - - if(!pTargetWell) { - DEBUG_OUT("No wells found in data manager after DLL execution"); - delete dllTask; - --m_evaluationInProgress; - return result; - } - - // 验证结果数据 - QVector> pressureResult = pTargetWell->getResultPressure(); - QVector> logLogResult = pTargetWell->getResultLogLog(); - - DEBUG_OUT(QString("DLL result verification - Pressure arrays: %1, LogLog arrays: %2") - .arg(pressureResult.size()).arg(logLogResult.size())); - - if(pressureResult.size() >= 2) { - DEBUG_OUT(QString("Pressure result - Time points: %1, Pressure points: %2") - .arg(pressureResult[0].size()).arg(pressureResult[1].size())); - - if(pressureResult[0].size() > 0) { - DEBUG_OUT(QString("Sample pressure data - Time[0]: %1, Time[last]: %2, P[0]: %3, P[last]: %4") - .arg(pressureResult[0][0]) - .arg(pressureResult[0][pressureResult[0].size()-1]) - .arg(pressureResult[1][0]) - .arg(pressureResult[1][pressureResult[1].size()-1])); - } - } - - // 数据有效性检查 - if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) { - result = pressureResult; - DEBUG_OUT(QString("Got DLL solver result: %1 points").arg(result[0].size())); - m_consecutiveFailures = 0; - - // 检查结果是否与之前不同 - static QVector lastPressureResult; - bool isDifferentFromLast = false; - - if(lastPressureResult.isEmpty() || lastPressureResult.size() != pressureResult[1].size()) { - isDifferentFromLast = true; - } else { - for(int i = 0; i < qMin(5, pressureResult[1].size()); ++i) { - if(qAbs(lastPressureResult[i] - pressureResult[1][i]) > 1e-12) { - isDifferentFromLast = true; - break; - } - } - } - - if(isDifferentFromLast) { - DEBUG_OUT("RESULT VERIFICATION: Got NEW result data from DLL"); - lastPressureResult = pressureResult[1]; - } else { - DEBUG_OUT("!!! WARNING: Result data appears to be identical to previous run !!!"); - } - - } else { - DEBUG_OUT("DLL solver result is empty or invalid"); - DEBUG_OUT(QString("Pressure result size: %1, Array sizes: %2, %3") - .arg(pressureResult.size()) - .arg(pressureResult.size() > 0 ? pressureResult[0].size() : 0) - .arg(pressureResult.size() > 1 ? pressureResult[1].size() : 0)); - m_consecutiveFailures++; - } - - } catch(const std::bad_alloc& e) { - DEBUG_OUT(QString("Memory allocation failed in DLL solver: %1").arg(e.what())); - m_consecutiveFailures++; - } catch(const std::exception& e) { - DEBUG_OUT(QString("Exception in DLL solver: %1").arg(e.what())); - m_consecutiveFailures++; - } catch(...) { - DEBUG_OUT("Unknown exception in DLL solver"); - m_consecutiveFailures++; - } - - // 清理DLL任务 - if(dllTask) { - if(dllTask->isRunning()) { - dllTask->terminate(); - dllTask->wait(3000); - } - - DEBUG_OUT("Cleaning up DLL solver task..."); - delete dllTask; - dllTask = nullptr; - } - - QApplication::processEvents(QEventLoop::AllEvents, 100); - --m_evaluationInProgress; - - DEBUG_OUT(QString("SOLVER DLL END - ResultPoints: %1") - .arg(result.isEmpty() ? 0 : result[0].size())); - - return result; -} - -//QVector> nmCalculationAutoFitGA::runSolverExe() -//{ -// DEBUG_OUT("SOLVER EXE START"); -// -// if(m_evaluationInProgress > 0) { -// DEBUG_OUT("EXE Solver already running, skipping"); -// return QVector>(); -// } -// -// ++m_evaluationInProgress; -// QVector> result; -// nmCalculationExeSolverTask* exeTask = nullptr; -// -// try { -// DEBUG_OUT("Creating EXE solver task"); -// exeTask = new nmCalculationExeSolverTask(QString()); -// -// if(m_shouldStop) { -// DEBUG_OUT("Should stop - cleaning up and returning empty result"); -// delete exeTask; -// --m_evaluationInProgress; -// return result; -// } -// -// DEBUG_OUT("Starting EXE solver execution..."); -// -// // 启动 -// bool executeSuccess = exeTask->execute(); -// -// // 检查执行状态 -// if(!executeSuccess) { -// DEBUG_OUT(QString("EXE solver execution failed: %1").arg(exeTask->getLastError())); -// DEBUG_OUT(QString("EXE solver exit code: %1").arg(exeTask->getExitCode())); -// -// // 清理并返回空结果 -// delete exeTask; -// exeTask = nullptr; -// --m_evaluationInProgress; -// m_consecutiveFailures++; -// return result; -// } -// -// DEBUG_OUT("EXE solver execution completed successfully"); -// -// // 验证结果数据是否已更新 -// nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); -// QVector wells = dataManager->getWellDataList(); -// -// if(wells.isEmpty()) { -// DEBUG_OUT("No wells found in data manager after EXE execution"); -// delete exeTask; -// --m_evaluationInProgress; -// return result; -// } -// -// // 验证结果数据的时间戳或唯一性 -// QVector> pressureResult = wells[0]->getResultPressure(); -// QVector> logLogResult = wells[0]->getResultLogLog(); -// -// DEBUG_OUT(QString("Raw result verification - Pressure arrays: %1, LogLog arrays: %2") -// .arg(pressureResult.size()).arg(logLogResult.size())); -// -// if(pressureResult.size() >= 2) { -// DEBUG_OUT(QString("Pressure result - Time points: %1, Pressure points: %2") -// .arg(pressureResult[0].size()).arg(pressureResult[1].size())); -// -// if(pressureResult[0].size() > 0) { -// DEBUG_OUT(QString("Sample pressure data - Time[0]: %1, Time[last]: %2, P[0]: %3, P[last]: %4") -// .arg(pressureResult[0][0]) -// .arg(pressureResult[0][pressureResult[0].size() - 1]) -// .arg(pressureResult[1][0]) -// .arg(pressureResult[1][pressureResult[1].size() - 1])); -// } -// } -// -// if(logLogResult.size() >= 3) { -// DEBUG_OUT(QString("LogLog result - X points: %1, Y1 points: %2, Y2 points: %3") -// .arg(logLogResult[0].size()).arg(logLogResult[1].size()).arg(logLogResult[2].size())); -// -// if(logLogResult[0].size() > 0) { -// DEBUG_OUT(QString("Sample LogLog data - X[0]: %1, X[last]: %2, Y1[0]: %3, Y2[0]: %4") -// .arg(logLogResult[0][0]) -// .arg(logLogResult[0][logLogResult[0].size() - 1]) -// .arg(logLogResult[1][0]) -// .arg(logLogResult[2][0])); -// } -// } -// -// // 数据有效性检查 -// if(pressureResult.size() >= 2 && pressureResult[0].size() > 0 && pressureResult[1].size() > 0) { -// result = pressureResult; -// DEBUG_OUT(QString("Got EXE solver result: %1 points").arg(result[0].size())); -// m_consecutiveFailures = 0; -// -// // 检查结果是否与之前不同 -// static QVector lastPressureResult; -// bool isDifferentFromLast = false; -// -// if(lastPressureResult.isEmpty() || lastPressureResult.size() != pressureResult[1].size()) { -// isDifferentFromLast = true; -// } else { -// for(int i = 0; i < qMin(5, pressureResult[1].size()); ++i) { -// if(qAbs(lastPressureResult[i] - pressureResult[1][i]) > 1e-12) { -// isDifferentFromLast = true; -// break; -// } -// } -// } -// -// if(isDifferentFromLast) { -// DEBUG_OUT("RESULT VERIFICATION: Got NEW result data from EXE"); -// lastPressureResult = pressureResult[1]; -// } else { -// DEBUG_OUT("!!! WARNING: Result data appears to be identical to previous run !!!"); -// } -// -// } else { -// DEBUG_OUT("EXE solver result is empty or invalid"); -// DEBUG_OUT(QString("Pressure result size: %1, Array sizes: %2, %3") -// .arg(pressureResult.size()) -// .arg(pressureResult.size() > 0 ? pressureResult[0].size() : 0) -// .arg(pressureResult.size() > 1 ? pressureResult[1].size() : 0)); -// m_consecutiveFailures++; -// } -// -// } catch(const std::exception& e) { -// DEBUG_OUT(QString("Exception in EXE solver: %1").arg(e.what())); -// m_consecutiveFailures++; -// } catch(...) { -// DEBUG_OUT("Unknown exception in EXE solver"); -// m_consecutiveFailures++; -// } -// -// // 清理EXE任务 -// if(exeTask) { -// DEBUG_OUT("Cleaning up EXE solver task..."); -// delete exeTask; -// exeTask = nullptr; -// } -// -// QApplication::processEvents(QEventLoop::AllEvents, 100); -// --m_evaluationInProgress; -// -// DEBUG_OUT(QString("SOLVER EXE END - ResultPoints: %1") -// .arg(result.isEmpty() ? 0 : result[0].size())); -// -// return result; -//} - -// ==================== 数据处理方法 ==================== -QVector nmCalculationAutoFitGA::interpolateData( - const QVector& source, const QVector& targetX) const -{ - QVector result; - - if(source.isEmpty() || targetX.isEmpty()) { - DEBUG_OUT("Warning: Empty data in interpolation"); - return result; - } - - // 数据清理和验证合并 - QVector validSource; - const double MAX_REASONABLE_VALUE = 1e12; - - for(int i = 0; i < source.size(); ++i) { - const QPointF& point = source[i]; - - // 检查数值有效性 - if(!isFiniteNumber(point.x()) || !isFiniteNumber(point.y())) { - DEBUG_OUT(QString("Skipping invalid data point at index %1: X=%2, Y=%3") - .arg(i).arg(point.x()).arg(point.y())); - continue; - } - - // 检查极大值 - 跳过而不是失败 - if(qAbs(point.y()) > MAX_REASONABLE_VALUE) { - DEBUG_OUT(QString("Skipping extremely large Y value at index %1: %2") - .arg(i).arg(point.y())); - continue; - } - - // 只保留有效的数据点 - validSource.append(point); - } - - // 检查清理后的数据是否足够 - if(validSource.size() < 3) { - DEBUG_OUT(QString("Insufficient valid data points after cleaning: %1") - .arg(validSource.size())); - return result; // 返回空结果，但不算失败 - } - - DEBUG_OUT(QString("Data cleaning: %1 -> %2 valid points") - .arg(source.size()).arg(validSource.size())); - - // 对清理后的数据进行排序 - QVector sortedSource = validSource; - - for(int i = 0; i < sortedSource.size() - 1; ++i) { - for(int j = 0; j < sortedSource.size() - 1 - i; ++j) { - if(sortedSource[j].x() > sortedSource[j + 1].x()) { - QPointF temp = sortedSource[j]; - sortedSource[j] = sortedSource[j + 1]; - sortedSource[j + 1] = temp; - } - } - } - - double sourceMinX = sortedSource.first().x(); - double sourceMaxX = sortedSource.last().x(); - - double targetMinX = targetX[0]; - double targetMaxX = targetX[0]; - - for(int i = 1; i < targetX.size(); ++i) { - if(targetX[i] < targetMinX) targetMinX = targetX[i]; - - if(targetX[i] > targetMaxX) targetMaxX = targetX[i]; - } - - DEBUG_OUT(QString("Source X range: [%1, %2], Target X range: [%3, %4]") - .arg(sourceMinX).arg(sourceMaxX).arg(targetMinX).arg(targetMaxX)); - - // 安全插值算法 - for(int i = 0; i < targetX.size(); ++i) { - double x = targetX[i]; - - if(!isFiniteNumber(x)) continue; - - double y = 0.0; - - // 插值逻辑（保持原有逻辑，但使用sortedSource） - if(x <= sourceMinX) { - if(sortedSource.size() >= 2) { - double dx = sortedSource[1].x() - sortedSource[0].x(); - - if(qAbs(dx) > 1e-10) { - double slope = (sortedSource[1].y() - sortedSource[0].y()) / dx; - slope = qMax(-1e6, qMin(1e6, slope)); - y = sortedSource[0].y() + slope * (x - sortedSource[0].x()); - } else { - y = sortedSource[0].y(); - } - } else { - y = sortedSource[0].y(); - } - } else if(x >= sourceMaxX) { - if(sortedSource.size() >= 2) { - int lastIdx = sortedSource.size() - 1; - double dx = sortedSource[lastIdx].x() - sortedSource[lastIdx - 1].x(); - - if(qAbs(dx) > 1e-10) { - double slope = (sortedSource[lastIdx].y() - sortedSource[lastIdx - 1].y()) / dx; - slope = qMax(-1e6, qMin(1e6, slope)); - y = sortedSource[lastIdx].y() + slope * (x - sortedSource[lastIdx].x()); - } else { - y = sortedSource[lastIdx].y(); - } - } else { - y = sortedSource.last().y(); - } - } else { - // 内插 - bool found = false; - - for(int j = 0; j < sortedSource.size() - 1; ++j) { - if(x >= sortedSource[j].x() && x <= sortedSource[j + 1].x()) { - double dx = sortedSource[j + 1].x() - sortedSource[j].x(); - - if(qAbs(dx) > 1e-10) { - double ratio = (x - sortedSource[j].x()) / dx; - y = sortedSource[j].y() + ratio * (sortedSource[j + 1].y() - sortedSource[j].y()); - } else { - y = sortedSource[j].y(); - } - - found = true; - break; - } - } - - if(!found) { - // 使用最近点 - double minDist = 1e10; - - for(int k = 0; k < sortedSource.size(); ++k) { - double dist = qAbs(sortedSource[k].x() - x); - - if(dist < minDist) { - minDist = dist; - y = sortedSource[k].y(); - } - } - } - } - - // 最终数值检查 - if(!isFiniteNumber(y)) { - y = sortedSource.size() > 0 ? sortedSource[0].y() : 1.0; - } - - y = qMax(-1e12, qMin(1e12, y)); - - result.append(QPointF(x, y)); - } - - if(result.isEmpty()) { - DEBUG_OUT("LogLog interpolation failed"); - return result; - } - - DEBUG_OUT(QString("Interpolation completed: %1 -> %2 points") - .arg(validSource.size()).arg(result.size())); - - return result; -} - -double nmCalculationAutoFitGA::calculateLogLogCurveError( - const QVector>& target, - const QVector>& result) const -{ - // 验证数据 - if(!validateLogLogData(target) || !validateLogLogData(result)) { - return 1e10; - } - - try { - // 数据对齐：找到X值的重叠区域 - double targetMinX = target[0][0]; - double targetMaxX = target[0][0]; - - for(int i = 1; i < target[0].size(); ++i) { - if(target[0][i] < targetMinX) targetMinX = target[0][i]; - - if(target[0][i] > targetMaxX) targetMaxX = target[0][i]; - } - - double resultMinX = result[0][0]; - double resultMaxX = result[0][0]; - - for(int i = 1; i < result[0].size(); ++i) { - if(result[0][i] < resultMinX) resultMinX = result[0][i]; - - if(result[0][i] > resultMaxX) resultMaxX = result[0][i]; - } - - double overlapMinX = qMax(targetMinX, resultMinX); - double overlapMaxX = qMin(targetMaxX, resultMaxX); - - if(overlapMinX >= overlapMaxX) { - DEBUG_OUT("No overlap between target and result LogLog curves"); - return 1e10; - } - - // 生成公共X网格进行插值 - QVector commonX; - int numPoints = 50; - - if(overlapMinX > 0 && overlapMaxX > 0) { - // 对数空间均匀分布 - double logMin = qLn(overlapMinX); - double logMax = qLn(overlapMaxX); - - for(int i = 0; i < numPoints; ++i) { - double logX = logMin + i * (logMax - logMin) / (numPoints - 1); - double x = qExp(logX); - - // 数值保护 - if(!isFiniteNumber(x) || x <= 0) { - continue; - } - - commonX.append(x); - } - - DEBUG_OUT("Using log-uniform grid for better early-time coverage"); - } - - if(commonX.isEmpty()) { - DEBUG_OUT("Failed to generate common X grid"); - return 1e10; - } - - // 插值目标曲线 - QVector targetCurve1, targetCurve2; - - for(int i = 0; i < target[0].size(); ++i) { - // 检查数据有效性 - if(isFiniteNumber(target[0][i]) && isFiniteNumber(target[1][i]) && - isFiniteNumber(target[2][i])) { - targetCurve1.append(QPointF(target[0][i], target[1][i])); - targetCurve2.append(QPointF(target[0][i], target[2][i])); - } - } - - if(targetCurve1.isEmpty() || targetCurve2.isEmpty()) { - DEBUG_OUT("Target curves are empty after filtering"); - return 1e10; - } - - QVector alignedTarget1 = interpolateData(targetCurve1, commonX); - QVector alignedTarget2 = interpolateData(targetCurve2, commonX); - - // 插值结果曲线 - QVector resultCurve1, resultCurve2; - - for(int i = 0; i < result[0].size(); ++i) { - // 检查数据有效性 - if(isFiniteNumber(result[0][i]) && isFiniteNumber(result[1][i]) && - isFiniteNumber(result[2][i])) { - resultCurve1.append(QPointF(result[0][i], result[1][i])); - resultCurve2.append(QPointF(result[0][i], result[2][i])); - } - } - - if(resultCurve1.isEmpty() || resultCurve2.isEmpty()) { - DEBUG_OUT("Result curves are empty after filtering"); - return 1e10; - } - - QVector alignedResult1 = interpolateData(resultCurve1, commonX); - QVector alignedResult2 = interpolateData(resultCurve2, commonX); - - // 检查插值结果 - if(alignedTarget1.isEmpty() || alignedTarget2.isEmpty() || - alignedResult1.isEmpty() || alignedResult2.isEmpty()) { - DEBUG_OUT("LogLog interpolation failed"); - return 1e10; - } - - if(alignedTarget1.size() != alignedResult1.size() || - alignedTarget2.size() != alignedResult2.size()) { - DEBUG_OUT("LogLog interpolation size mismatch"); - return 1e10; - } - - // 计算两条曲线的误差 - double error1 = calculateCurveError(alignedTarget1, alignedResult1); - double error2 = calculateCurveError(alignedTarget2, alignedResult2); - - // 检查个别误差是否有效 - if(!isFiniteNumber(error1) || error1 > 1e9) { - DEBUG_OUT(QString("Curve1 error is invalid: %1").arg(error1)); - error1 = 1e10; - } - - if(!isFiniteNumber(error2) || error2 > 1e9) { - DEBUG_OUT(QString("Curve2 error is invalid: %1").arg(error2)); - error2 = 1e10; - } - - // 组合误差 - 添加保护 - double combinedError; - - if(error1 > 1e9 && error2 > 1e9) { - combinedError = 1e10; - } else if(error1 > 1e9) { - combinedError = error2; - } else if(error2 > 1e9) { - combinedError = error1; - } else { - combinedError = 0.5 * error1 + 0.5 * error2; - } - - DEBUG_OUT(QString("LogLog errors: Curve1=%1, Curve2=%2, Combined=%3") - .arg(error1, 0, 'e', 4).arg(error2, 0, 'e', 4).arg(combinedError, 0, 'e', 4)); - - return qMin(1e9, combinedError); - - } catch(const std::exception& e) { - DEBUG_OUT(QString("Exception in LogLog error calculation: %1").arg(e.what())); - return 1e10; - } catch(...) { - DEBUG_OUT("Unknown exception in LogLog error calculation"); - return 1e10; - } -} - -double nmCalculationAutoFitGA::calculateCurveError( - const QVector& curve1, const QVector& curve2) const -{ - if(curve1.size() != curve2.size() || curve1.isEmpty()) { - return 1e10; - } - - // 预检查：确保没有无穷大值 - for(int i = 0; i < curve1.size(); ++i) { - if(!isFiniteNumber(curve1[i].y()) || !isFiniteNumber(curve2[i].y())) { - DEBUG_OUT(QString("Infinite value detected at index %1: Y1=%2, Y2=%3") - .arg(i).arg(curve1[i].y()).arg(curve2[i].y())); - return 1e10; - } - - if(qAbs(curve1[i].y()) > 1e12 || qAbs(curve2[i].y()) > 1e12) { - DEBUG_OUT(QString("Extremely large value detected at index %1") - .arg(i)); - return 1e10; - } - } - - double totalError = 0.0; - double totalWeight = 0.0; - int validPoints = 0; - - for(int i = 0; i < curve1.size(); ++i) { - double y1 = curve1[i].y(); - double y2 = curve2[i].y(); - - // 跳过异常值 - if(!isFiniteNumber(y1) || !isFiniteNumber(y2)) { - continue; - } - - // 自适应权重：根据Y值大小调整，添加上限 - double weightFactor = qMin(100.0, qAbs(y1) * 0.01); - double weight = 1.0 / (1.0 + weightFactor); - - // 相对误差和绝对误差的组合 - double yMax = qMax(qAbs(y1), qAbs(y2)); - yMax = qMax(1e-12, yMax); // 防止除零 - - double relativeError = qAbs(y1 - y2) / yMax; - double absoluteError = qAbs(y1 - y2); - - // 限制误差值 - relativeError = qMin(1e6, relativeError); - absoluteError = qMin(1e6, absoluteError); - - // 误差组合：相对误差为主，绝对误差为辅 - double pointError = 0.7 * relativeError + 0.3 * absoluteError; - - if(isFiniteNumber(pointError) && pointError < 1e10) { - totalError += weight * pointError * pointError; - totalWeight += weight; - validPoints++; - } - } - - if(totalWeight > 0 && validPoints > 0) { - double result = sqrt(totalError / totalWeight); - - // 最终检查 - if(!isFiniteNumber(result)) { - DEBUG_OUT("Final error calculation produced infinite result"); - return 1e10; - } - - return qMin(1e9, result); // 限制最大误差值 - } else { - DEBUG_OUT(QString("No valid points for error calculation: validPoints=%1") - .arg(validPoints)); - return 1e10; - } -} -// ==================== 工具方法 ==================== - -double nmCalculationAutoFitGA::random01() const -{ - return static_cast(qrand()) / RAND_MAX; -} - -double nmCalculationAutoFitGA::gaussianRandom(double mean, double stddev) const -{ - static bool hasSpare = false; - static double spare; - - if(hasSpare) { - hasSpare = false; - return spare * stddev + mean; - } - - hasSpare = true; - double u = qMax(random01(), 1e-12); - double v = random01(); - double mag = stddev * sqrt(-2.0 * log(u)); - spare = mag * cos(2.0 * 3.14159265359 * v); - - return mag * sin(2.0 * 3.14159265359 * v) + mean; -} - -int nmCalculationAutoFitGA::getEnabledParameterCount() const -{ - int count = 0; - - for(int i = 0; i < m_parameterSelected.size(); ++i) { - if(m_parameterSelected[i]) count++; - } - - return count; -} - -void nmCalculationAutoFitGA::saveOptimizationResult() -{ - DEBUG_OUT(QString("GA optimization result: fitness=%1, evaluations=%2/%3") - .arg(m_bestFitness, 0, 'e', 4) - .arg(m_successfulEvaluations) - .arg(m_totalEvaluations)); -} diff --git a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp index a4876ed..4d3b6d8 100644 --- a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp +++ b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp @@ -12,25 +12,24 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_cfSelected = false; // 默认不选中 m_swiSelected = false; // 默认不选中 - // 初始化参数最大值 - m_permeabilityMax = nmDataAttribute("Permeability Max", 10.0, "Darcy"); // 1000 mD - m_skinMax = nmDataAttribute("Skin Max", 10.0, ""); // 100 - m_wellboreStorageMax = nmDataAttribute("Wellbore Storage Max", 2.0, "m^3/MPa"); - m_porosityMax = nmDataAttribute("Porosity Max", 0.5, ""); // 50% - m_thicknessMax = nmDataAttribute("Thickness Max", 50.0, "m"); - m_ctMax = nmDataAttribute("Ct Max", 0.1, ""); // 1/MPa - m_cfMax = nmDataAttribute("Cf Max", 0.01, ""); // 1/MPa - m_swiMax = nmDataAttribute("Swi Max", 1.0, ""); - - // 初始化参数最小值 - m_permeabilityMin = nmDataAttribute("Permeability Min", 0.001, "Darcy"); // 0.001 mD - m_skinMin = nmDataAttribute("Skin Min", -10.0, ""); // 允许负表皮 - m_wellboreStorageMin = nmDataAttribute("Wellbore Storage Min", 1e-4, "m^3/MPa"); - m_porosityMin = nmDataAttribute("Porosity Min", 0.01, ""); // 1% - m_thicknessMin = nmDataAttribute("Thickness Min", 2.0, "m"); - m_ctMin = nmDataAttribute("Ct Min", 1e-3, ""); // 小正值 - m_cfMin = nmDataAttribute("Cf Min", 1e-5, ""); // 小正值 - m_swiMin = nmDataAttribute("Swi Min", 0.0, ""); + // 拟合上下界不再使用固定默认值，由自动拟合窗口按数据对象初值和物理边界生成。 + m_permeabilityMax = nmDataAttribute("Permeability Max", QVariant(), "Darcy"); + m_skinMax = nmDataAttribute("Skin Max", QVariant(), ""); + m_wellboreStorageMax = nmDataAttribute("Wellbore Storage Max", QVariant(), "m^3/MPa"); + m_porosityMax = nmDataAttribute("Porosity Max", QVariant(), ""); + m_thicknessMax = nmDataAttribute("Thickness Max", QVariant(), "m"); + m_ctMax = nmDataAttribute("Ct Max", QVariant(), ""); + m_cfMax = nmDataAttribute("Cf Max", QVariant(), ""); + m_swiMax = nmDataAttribute("Swi Max", QVariant(), ""); + + m_permeabilityMin = nmDataAttribute("Permeability Min", QVariant(), "Darcy"); + m_skinMin = nmDataAttribute("Skin Min", QVariant(), ""); + m_wellboreStorageMin = nmDataAttribute("Wellbore Storage Min", QVariant(), "m^3/MPa"); + m_porosityMin = nmDataAttribute("Porosity Min", QVariant(), ""); + m_thicknessMin = nmDataAttribute("Thickness Min", QVariant(), "m"); + m_ctMin = nmDataAttribute("Ct Min", QVariant(), ""); + m_cfMin = nmDataAttribute("Cf Min", QVariant(), ""); + m_swiMin = nmDataAttribute("Swi Min", QVariant(), ""); // 初始化迭代参数 m_iterationCount = nmDataAttribute("Iteration Count", 20, ""); diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp index 202d14c..f0f243e 100644 --- a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp +++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp @@ -4,6 +4,9 @@ #include "nmWxParameterProperty.h" #include "nmDataAnalyzeManager.h" #include "iSubWndFitting.h" +#include "iSysParaHelper.h" +#include "iParameter.h" +#include "mModuleDefines.h" #include "mGui/mGuiAnal/iAnalRun.h" #include "iGuiPlot.h" #include "ZxObjCurve.h" @@ -32,6 +35,30 @@ bool nmAutoFitUiIsFinite(double value) #endif } +// 从系统参数表读取物理边界，读取失败时保留调用方提供的兜底边界。 +bool nmAutoFitReadPhysicalRange(const char* parameterName, + double fallbackMin, double fallbackMax, double& minValue, double& maxValue) +{ + minValue = fallbackMin; + maxValue = fallbackMax; + + iSysParaHelper* paraHelper = _paraHelper; + if(!paraHelper) { + return false; + } + + iParameter* parameter = paraHelper->getPara(QString::fromLatin1(parameterName), s_Nm_Serie); + if(!parameter || !nmAutoFitUiIsFinite(parameter->m_dMin) + || !nmAutoFitUiIsFinite(parameter->m_dMax) + || parameter->m_dMin >= parameter->m_dMax) { + return false; + } + + minValue = parameter->m_dMin; + maxValue = parameter->m_dMax; + return true; +} + // 主界面气井双对数中的源曲线和导数曲线已经采用拟压力量纲. // 自动拟合直接读取这两条显示曲线, 避免井对象中的原始压力历史数据与结果曲线量纲不一致. // 两条曲线可能因无效点过滤而长度不同, 因此按时间坐标匹配共同有效点. @@ -199,14 +226,338 @@ void nmWxAutomaticFitting::updateParameterVisibility(QTableWidget* table, NM_SOL renumberVisibleParameterRows(table); } +// 获取参数的系统物理边界，并为 Swi 叠加当前储层饱和度约束。 +bool nmWxAutomaticFitting::getPhysicalParameterRange(int parameterIndex, + double& minValue, double& maxValue) +{ + static const char* parameterNames[] = { + "Result_K", "Result_W_Skin", "Result_W_C", "Result_phi", + "Result_h", "Result_Cti", "Result_Cf", "Result_Swi" + }; + // KAPPA 的边界使用 md、ft、bbl/psi；自动拟合界面使用 Darcy、m、m^3/MPa， + // 这里统一换算到界面和 PSO 实际使用的单位：K 除以 1000，h 由 ft 换成 m， + // 井筒储集系数的 4.33667154546306e34 bbl/psi 对应约 1e36 m^3/MPa。 + // Ct/Cf/Swi 沿用模型参数表边界。 + static const double physicalMin[] = { + 1.01325027383089e-18, -5.0, 0.0, 1.0e-4, 1.0e-5, 1.0e-30, 1.0e-30, 0.0 + }; + static const double physicalMax[] = { + 1.01325027383089e42, 5000.0, 1.0e36, 0.9999, 1.0e9, 10.0, 10.0, 1.0 + }; + + if(parameterIndex < 0 || parameterIndex >= 8) { + return false; + } + + minValue = physicalMin[parameterIndex]; + maxValue = physicalMax[parameterIndex]; + bool rangeRead = true; + if(parameterIndex >= 5) { + rangeRead = nmAutoFitReadPhysicalRange(parameterNames[parameterIndex], + physicalMin[parameterIndex], physicalMax[parameterIndex], minValue, maxValue); + } + + if(parameterIndex == 7) { + double soi = reservoirData.getSoi().getValue().toDouble(); + double sgi = reservoirData.getSgi().getValue().toDouble(); + if(nmAutoFitUiIsFinite(soi) && nmAutoFitUiIsFinite(sgi)) { + if(soi < 0.0 || sgi < 0.0 || soi + sgi > 1.0) { + // Soi+Sgi 超过 1 时没有可行的 Swi，固定到物理下限，避免继续搜索非法区间。 + minValue = 0.0; + maxValue = 0.0; + } else { + maxValue = qMin(maxValue, 1.0 - soi - sgi); + } + } + } + + return rangeRead; +} + +// 将一组上下界同步到表格和自动拟合数据，保证 PSO 读取到同一份配置。 +void nmWxAutomaticFitting::setParameterRange(int parameterIndex, + double minValue, double maxValue) +{ + if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= m_parameterTable->rowCount() + || !nmAutoFitUiIsFinite(minValue) || !nmAutoFitUiIsFinite(maxValue)) { + return; + } + + if(maxValue < minValue) { + qSwap(minValue, maxValue); + } + + const bool wasUpdatingRanges = m_updatingParameterRanges; + m_updatingParameterRanges = true; + + if(m_parameterTable->item(parameterIndex, 2)) { + m_parameterTable->item(parameterIndex, 2)->setText(QString::number(minValue, 'g', 10)); + } + if(m_parameterTable->item(parameterIndex, 4)) { + m_parameterTable->item(parameterIndex, 4)->setText(QString::number(maxValue, 'g', 10)); + } + + switch(parameterIndex) { + case 0: + automaticFittingData.getPermeabilityMin().setValue(minValue); + automaticFittingData.getPermeabilityMax().setValue(maxValue); + break; + case 1: + automaticFittingData.getSkinMin().setValue(minValue); + automaticFittingData.getSkinMax().setValue(maxValue); + break; + case 2: + automaticFittingData.getWellboreStorageMin().setValue(minValue); + automaticFittingData.getWellboreStorageMax().setValue(maxValue); + break; + case 3: + automaticFittingData.getPorosityMin().setValue(minValue); + automaticFittingData.getPorosityMax().setValue(maxValue); + break; + case 4: + automaticFittingData.getThicknessMin().setValue(minValue); + automaticFittingData.getThicknessMax().setValue(maxValue); + break; + case 5: + automaticFittingData.getCtMin().setValue(minValue); + automaticFittingData.getCtMax().setValue(maxValue); + break; + case 6: + automaticFittingData.getCfMin().setValue(minValue); + automaticFittingData.getCfMax().setValue(maxValue); + break; + case 7: + automaticFittingData.getSwiMin().setValue(minValue); + automaticFittingData.getSwiMax().setValue(maxValue); + break; + default: + break; + } + + m_updatingParameterRanges = wasUpdatingRanges; +} + +// 根据初值生成首次或拟合后的建议范围，并始终限制在物理边界内。 +void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, + double centerValue, bool afterFit) +{ + if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 8 + || !nmAutoFitUiIsFinite(centerValue)) { + return; + } + + double physicalMin = 0.0; + double physicalMax = 0.0; + getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax); + + double reference = centerValue; + const bool positiveParameter = parameterIndex != 1; + + // 正值参数初值为 0 时不再借用旧的界面范围，直接从物理边界选取搜索尺度。 + if(positiveParameter && reference <= 0.0) { + if(physicalMin > 0.0) { + reference = physicalMin; + } else { + reference = qMax(physicalMax * 0.01, 1.0e-12); + } + } + + if(physicalMax < physicalMin) { + return; + } + reference = qBound(physicalMin, reference, physicalMax); + const double boundedCenterValue = qBound(physicalMin, centerValue, physicalMax); + + double newMin = physicalMin; + double newMax = physicalMax; + if(parameterIndex == 1) { + const double skinHalfRange = afterFit ? 1.0 : 10.0; + newMin = qMax(physicalMin, reference - skinHalfRange); + newMax = qMin(physicalMax, reference + skinHalfRange); + } else if(reference > 0.0 && !(parameterIndex == 7 && centerValue <= 0.0)) { + const double lowerFactor = afterFit ? 0.5 : 0.1; + const double upperFactor = afterFit ? 2.0 : 10.0; + newMin = qMax(physicalMin, reference * lowerFactor); + newMax = qMin(physicalMax, reference * upperFactor); + } else if(parameterIndex == 7) { + // 没有可靠 Swi 初值时，不把搜索范围压缩到零附近。 + newMin = physicalMin; + newMax = physicalMax; + } + + // 任何自动范围都必须包含本次使用的中心值，并且不能越过物理边界。 + newMin = qMin(newMin, boundedCenterValue); + newMax = qMax(newMax, boundedCenterValue); + newMin = qMax(newMin, physicalMin); + newMax = qMin(newMax, physicalMax); + + if(newMax >= newMin) { + setParameterRange(parameterIndex, newMin, newMax); + } +} + +// 首次进入自动范围模式时，按当前表格中的初值为所有参数建立建议范围。 +void nmWxAutomaticFitting::initializeSuggestedParameterRanges() +{ + if(!m_parameterTable) { + return; + } + + for(int parameterIndex = 0; parameterIndex < 8; ++parameterIndex) { + QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3); + if(initialItem) { + bool initialOk = false; + const double initialValue = initialItem->text().toDouble(&initialOk); + if(initialOk && nmAutoFitUiIsFinite(initialValue)) { + updateRangeForParameter(parameterIndex, initialValue, false); + } else { + // 数据对象没有提供该初值时使用完整物理区间，不回退到旧的默认范围。 + double physicalMin = 0.0; + double physicalMax = 0.0; + getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax); + if(physicalMax >= physicalMin) { + setParameterRange(parameterIndex, physicalMin, physicalMax); + } + } + } + } +} + +// 校正已保存的范围：保留物理边界内的用户区间，无交集时按当前初值生成兜底区间。 +void nmWxAutomaticFitting::normalizeSavedParameterRanges() +{ + if(!m_parameterTable) { + return; + } + + for(int parameterIndex = 0; parameterIndex < 8; ++parameterIndex) { + QTableWidgetItem* minItem = m_parameterTable->item(parameterIndex, 2); + QTableWidgetItem* maxItem = m_parameterTable->item(parameterIndex, 4); + QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3); + if(!minItem || !maxItem || !initialItem) { + continue; + } + + double physicalMin = 0.0; + double physicalMax = 0.0; + getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax); + bool savedMinOk = false; + bool savedMaxOk = false; + const double savedMin = minItem->text().toDouble(&savedMinOk); + const double savedMax = maxItem->text().toDouble(&savedMaxOk); + const bool savedRangeValid = savedMinOk && savedMaxOk + && nmAutoFitUiIsFinite(savedMin) && nmAutoFitUiIsFinite(savedMax) + && savedMax >= savedMin; + + if(savedRangeValid && physicalMax >= physicalMin) { + const double clippedMin = qMax(savedMin, physicalMin); + const double clippedMax = qMin(savedMax, physicalMax); + if(clippedMax >= clippedMin) { + setParameterRange(parameterIndex, clippedMin, clippedMax); + continue; + } + } + + // 已保存范围无效或与物理边界无交集时，按数据对象初值重新生成。 + bool initialOk = false; + const double initialValue = initialItem->text().toDouble(&initialOk); + if(initialOk && nmAutoFitUiIsFinite(initialValue)) { + updateRangeForParameter(parameterIndex, initialValue, false); + } else if(physicalMax >= physicalMin) { + setParameterRange(parameterIndex, physicalMin, physicalMax); + } + } +} + +// 校验当前表格中的参数范围；parameterIndex 为 -1 时检查所有可见参数行。 +bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int parameterIndex) +{ + if(!m_parameterTable) { + errorMessage = tr("The parameter table is unavailable."); + return false; + } + if(parameterIndex < -1 || parameterIndex >= 8) { + errorMessage = tr("The parameter row is invalid."); + return false; + } + + static const char* parameterNames[] = { + "Permeability", "Skin", "Wellbore storage", "Porosity", + "Thickness", "Ct", "Cf", "Swi" + }; + + const int firstParameterIndex = parameterIndex < 0 ? 0 : parameterIndex; + const int lastParameterIndex = parameterIndex < 0 ? 8 : parameterIndex + 1; + for(int currentParameterIndex = firstParameterIndex; + currentParameterIndex < lastParameterIndex; ++currentParameterIndex) { + // 隐藏参数不参与当前模型拟合，不用它们的历史值阻塞当前设置。 + if(m_parameterTable->isRowHidden(currentParameterIndex)) { + continue; + } + + QTableWidgetItem* minItem = m_parameterTable->item(currentParameterIndex, 2); + QTableWidgetItem* initialItem = m_parameterTable->item(currentParameterIndex, 3); + QTableWidgetItem* maxItem = m_parameterTable->item(currentParameterIndex, 4); + if(!minItem || !initialItem || !maxItem) { + errorMessage = tr("The range values for %1 are incomplete.") + .arg(tr(parameterNames[currentParameterIndex])); + return false; + } + + bool minOk = false; + bool initialOk = false; + bool maxOk = false; + const double minValue = minItem->text().toDouble(&minOk); + const double initialValue = initialItem->text().toDouble(&initialOk); + const double maxValue = maxItem->text().toDouble(&maxOk); + if(!minOk || !initialOk || !maxOk + || !nmAutoFitUiIsFinite(minValue) + || !nmAutoFitUiIsFinite(initialValue) + || !nmAutoFitUiIsFinite(maxValue)) { + errorMessage = tr("The minimum value, initial value, and maximum value of %1 must be finite numbers.") + .arg(tr(parameterNames[currentParameterIndex])); + return false; + } + + double physicalMin = 0.0; + double physicalMax = 0.0; + getPhysicalParameterRange(currentParameterIndex, physicalMin, physicalMax); + if(!nmAutoFitUiIsFinite(physicalMin) || !nmAutoFitUiIsFinite(physicalMax) + || physicalMax < physicalMin) { + errorMessage = tr("The physical range of %1 is invalid.") + .arg(tr(parameterNames[currentParameterIndex])); + return false; + } + + if(minValue < physicalMin || minValue > physicalMax + || initialValue < physicalMin || initialValue > physicalMax + || maxValue < physicalMin || maxValue > physicalMax) { + errorMessage = tr("The values of %1 exceed the physical range [%2, %3].") + .arg(tr(parameterNames[currentParameterIndex])) + .arg(QString::number(physicalMin, 'g', 10)) + .arg(QString::number(physicalMax, 'g', 10)); + return false; + } + + if(minValue > maxValue || minValue > initialValue || initialValue > maxValue) { + errorMessage = tr("The values of %1 must satisfy: minimum <= initial value <= maximum.") + .arg(tr(parameterNames[currentParameterIndex])); + return false; + } + } + + return true; +} + nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent) : iDlgBase(parent) , m_autoFitterPSO(nullptr) - , m_autoFitterGA(nullptr) , m_progressDialog(nullptr) , m_progressTimer(nullptr) , m_progressMonitor(nullptr) - , m_selectedAlgorithm(ALGORITHM_PSO) // 默认选择PSO算法 + , m_autoParameterRanges(true) + // 构造期间先禁止即时校验，避免初始值写入和范围生成之间出现短暂的不一致。 + , m_updatingParameterRanges(true) { DEBUG_UI(QString("AutoFitting Constructor: this=0x%1").arg((quintptr)this, 0, 16)); @@ -233,32 +584,13 @@ nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent) nmDataAnalyzeManager* pManager = nmDataAnalyzeManager::getCurrentInstance(); reservoirData = pManager->getReservoirDataCopy(); automaticFittingData = pManager->getAutomaticFittingDataCopy(); + const bool hasSavedFittingData = pManager && pManager->getAutomaticFittingData() != nullptr; + // 自动范围始终开启；用户在表格中修改上下限后，itemChanged 会临时切换为手工范围。 + m_autoParameterRanges = true; NM_SOLVER_MODEL_TYPE solverModelType = pManager->getSolverModelType(); - // 只有尚未点击确定保存过配置时才使用相态默认值；重新打开时保留已保存的数据。 - if(pManager->getAutomaticFittingData() == nullptr) { - bool isOilOrWaterSinglePhase = solverModelType == SMT_Oil_ConstPvt || - solverModelType == SMT_Oil_VariablePvt || - solverModelType == SMT_Water_ConstPvt || - solverModelType == SMT_Water_VariablePvt; - if(isOilOrWaterSinglePhase) { - reservoirData.getPermeability().setValue(2.5e-2); - automaticFittingData.getPermeabilityMin().setValue(1.0e-3); - automaticFittingData.getPermeabilityMax().setValue(10.0); - automaticFittingData.getSkinMin().setValue(-10.0); - automaticFittingData.getSkinMax().setValue(10.0); - automaticFittingData.getWellboreStorageMin().setValue(1.0e-4); - automaticFittingData.getWellboreStorageMax().setValue(2.0); - automaticFittingData.getPorosityMin().setValue(1.0e-2); - automaticFittingData.getPorosityMax().setValue(5.0e-1); - automaticFittingData.getThicknessMin().setValue(2.0); - automaticFittingData.getThicknessMax().setValue(50.0); - automaticFittingData.getCtMin().setValue(1.0e-3); - automaticFittingData.getCtMax().setValue(1.0e-1); - automaticFittingData.getCfMin().setValue(1.0e-5); - automaticFittingData.getCfMax().setValue(1.0e-2); - } - + // 未保存过配置时只保留参数选择的相态默认值，不再覆盖数据对象中的初值或范围。 + if(!hasSavedFittingData) { if(solverModelType == SMT_Oil_ConstPvt || solverModelType == SMT_Water_ConstPvt) { // T1/T3 的综合压缩系数默认不参与拟合。 @@ -268,43 +600,6 @@ nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent) // T2/T4 的岩石压缩系数默认参与拟合。 automaticFittingData.setCfSelected(true); } - - // 气单相变化 PVT 使用 T5 数据集对应的初始值和拟合范围。 - if(solverModelType == SMT_Gas_VariablePvt) { - reservoirData.getPermeability().setValue(5.5e-6); - reservoirData.getPorosity().setValue(3.5e-2); - reservoirData.getThickness().setValue(8.0); - reservoirData.getCf().setValue(3.0e-4); - - automaticFittingData.getPermeabilityMin().setValue(4.5e-6); - automaticFittingData.getPermeabilityMax().setValue(6.2e-6); - automaticFittingData.getSkinMin().setValue(0.0); - automaticFittingData.getSkinMax().setValue(4.5); - automaticFittingData.getWellboreStorageMin().setValue(5.0e-5); - automaticFittingData.getWellboreStorageMax().setValue(1.6e-4); - automaticFittingData.getPorosityMin().setValue(8.0e-3); - automaticFittingData.getPorosityMax().setValue(4.0e-2); - automaticFittingData.getThicknessMin().setValue(7.0); - automaticFittingData.getThicknessMax().setValue(11.0); - automaticFittingData.getCfMin().setValue(1.0e-4); - automaticFittingData.getCfMax().setValue(2.0e-3); - } else if(solverModelType == SMT_Oil_ConstPvt || - solverModelType == SMT_Water_ConstPvt) { - // T1/T3 使用固定初值，不继承当前储层参数。 - reservoirData.getPorosity().setValue(2.45e-2); - reservoirData.getThickness().setValue(9.144); - reservoirData.getCt().setValue(1.0e-2); - } else if(solverModelType == SMT_Oil_VariablePvt) { - // 油单相变化 PVT 使用固定初值。 - reservoirData.getPorosity().setValue(6.0e-2); - reservoirData.getThickness().setValue(10.5); - reservoirData.getCf().setValue(1.0e-3); - } else if(solverModelType == SMT_Water_VariablePvt) { - // 水单相变化 PVT 使用固定初值。 - reservoirData.getPorosity().setValue(6.0e-2); - reservoirData.getThickness().setValue(10.5); - reservoirData.getCf().setValue(1.5e-3); - } } setupUI(); @@ -315,6 +610,12 @@ nmWxAutomaticFitting::nmWxAutomaticFitting(QWidget *parent) if(m_targetWellCombo->count() > 0) { onWellSelected(0); // 默认选中第一口井 } + if(!hasSavedFittingData) { + initializeSuggestedParameterRanges(); + } else { + normalizeSavedParameterRanges(); + } + m_updatingParameterRanges = false; DEBUG_UI("AutoFitting Constructor completed"); } @@ -333,9 +634,6 @@ nmWxAutomaticFitting::~nmWxAutomaticFitting() if (m_autoFitterPSO) { disconnect(m_autoFitterPSO, nullptr, this, nullptr); } - if (m_autoFitterGA) { - disconnect(m_autoFitterGA, nullptr, this, nullptr); - } DEBUG_UI("AutoFitting destructor - completed"); } @@ -514,6 +812,8 @@ void nmWxAutomaticFitting::setupParameterTable() // 连接选择改变信号 connect(m_parameterTable, SIGNAL(currentCellChanged(int, int, int, int)), this, SLOT(onCellSelectionChanged(int, int, int, int))); + connect(m_parameterTable, SIGNAL(itemChanged(QTableWidgetItem*)), + this, SLOT(onParameterTableItemChanged(QTableWidgetItem*))); } void nmWxAutomaticFitting::setupControlPanel() @@ -528,9 +828,7 @@ void nmWxAutomaticFitting::setupControlPanel() QLabel* algorithmLabel = new QLabel(tr("Algorithm:")); m_algorithmCombo = new QComboBox(); m_algorithmCombo->addItem(tr("PSO (Particle Swarm)")); - m_algorithmCombo->addItem(tr("GA (Genetic Algorithm)")); - m_algorithmCombo->setCurrentIndex(0); // 默认选择PSO - connect(m_algorithmCombo, SIGNAL(currentIndexChanged(int)), this, SLOT(onAlgorithmChanged(int))); + m_algorithmCombo->setCurrentIndex(0); m_algorithmCombo->setMaximumWidth(160); m_algorithmCombo->setMinimumWidth(160); @@ -681,35 +979,37 @@ void nmWxAutomaticFitting::onReverseSelection() if(!m_parameterTable->isRowHidden(7)) m_swiCheckBox->setChecked(!m_swiCheckBox->isChecked()); } -void nmWxAutomaticFitting::onAlgorithmChanged(int index) +void nmWxAutomaticFitting::onParameterTableItemChanged(QTableWidgetItem* item) { - m_selectedAlgorithm = static_cast(index); - - // 根据算法类型调整界面提示或参数 - QString algorithmInfo; - switch(m_selectedAlgorithm) { - case ALGORITHM_PSO: - algorithmInfo = tr("PSO algorithm selected."); - if(m_surrogateCombo) { - m_surrogateCombo->setEnabled(true); - } - break; - case ALGORITHM_GA: - algorithmInfo = tr("GA algorithm selected."); - if(m_surrogateCombo) { - m_surrogateCombo->setEnabled(false); - } - break; + if(!item || m_updatingParameterRanges) { + return; } - // 在状态栏或工具提示中显示算法信息 - m_algorithmCombo->setToolTip(algorithmInfo); + // 用户改动范围后，切井和拟合结果不再自动覆盖这组手工范围。 + if(item->column() == 2 || item->column() == 4) { + m_autoParameterRanges = false; + } + + if(item->column() >= 2 && item->column() <= 4 + && !m_parameterTable->isRowHidden(item->row())) { + QString validationError; + if(!validateParameterTable(validationError, item->row())) { + // 表格编辑提交后立即提示，用户不需要先点击“确定”才发现错误。 + QMessageBox::warning(this, tr("Invalid parameter range"), validationError); + } + } } void nmWxAutomaticFitting::onAccept() { - // 首先保存参数设置 - setAutomaticFittingValue(); + QString validationError; + if(!validateParameterTable(validationError)) { + QMessageBox::warning(this, tr("Invalid parameter range"), validationError); + return; + } + + // 校验通过后再保存参数设置，非法输入不会被静默裁剪。 + setAutomaticFittingValue(); // 更新完成后，通知参数界面刷新 nmWxParameterProperty::notifyUpdateTable(); @@ -878,28 +1178,6 @@ void nmWxAutomaticFitting::onWellSelected(int index) } } - // 尚未保存拟合配置时使用相态默认值；已保存时保留井上的 Skin 和井筒储集系数。 - nmDataAnalyzeManager* pManager = nmDataAnalyzeManager::getCurrentInstance(); - bool useDefaultFittingValues = pManager && pManager->getAutomaticFittingData() == nullptr; - if(useDefaultFittingValues && (pManager->getSolverModelType() == SMT_Oil_ConstPvt || - pManager->getSolverModelType() == SMT_Water_ConstPvt)) { - skinValue = 0.0; - wellboreStorageValue = 1.0e-2; - found = true; - } else if(useDefaultFittingValues && pManager->getSolverModelType() == SMT_Gas_VariablePvt) { - skinValue = 1.0; - wellboreStorageValue = 6.0e-5; - found = true; - } else if(useDefaultFittingValues && pManager->getSolverModelType() == SMT_Oil_VariablePvt) { - skinValue = -0.2; - wellboreStorageValue = 1.5e-2; - found = true; - } else if(useDefaultFittingValues && pManager->getSolverModelType() == SMT_Water_VariablePvt) { - skinValue = -0.2; - wellboreStorageValue = 1.0e-2; - found = true; - } - if (found) { // 更新表格数据 // 确保表格项存在 @@ -915,6 +1193,11 @@ void nmWxAutomaticFitting::onWellSelected(int index) // 设置井筒储集系数（Wellbore storage） m_parameterTable->item(2, 3)->setText(QString::number(wellboreStorageValue)); + + if(m_autoParameterRanges) { + updateRangeForParameter(1, skinValue, false); + updateRangeForParameter(2, wellboreStorageValue, false); + } } } @@ -1041,11 +1324,10 @@ void nmWxAutomaticFitting::startAutoFitting(const QVector>& targ // 先清理之前的实例 cleanupFitting(); - if (m_selectedAlgorithm == ALGORITHM_PSO) { - DEBUG_UI("Creating PSO auto fitter"); - m_autoFitterPSO = new nmCalculationAutoFitPSO(this); - m_autoFitterPSO->setTargetLogLogData(targetData); - m_autoFitterPSO->setPSOTargetWellName(targetWellName); + DEBUG_UI("Creating PSO auto fitter"); + m_autoFitterPSO = new nmCalculationAutoFitPSO(this); + m_autoFitterPSO->setTargetLogLogData(targetData); + m_autoFitterPSO->setPSOTargetWellName(targetWellName); //// 特定井名时使用快速路径 //if (targetWellName == "VerticalWell1") { @@ -1080,46 +1362,23 @@ void nmWxAutomaticFitting::startAutoFitting(const QVector>& targ //} - m_progressMonitor = new nmWxAutomaticfittingStart(this); - m_progressMonitor->setAutoFitter(m_autoFitterPSO); - m_progressMonitor->setPseudoPressureMode( - nmDataAnalyzeManager::getCurrentInstance()->getSolverModelType() == SMT_Gas_VariablePvt); - m_progressMonitor->setTargetLogLogData(targetData); - - connect(m_autoFitterPSO, SIGNAL(fittingFinished(bool, QString)), - this, SLOT(onFittingFinished(bool, QString))); - - int maxIterations = m_iterationEdit->text().toInt(); - double targetError = m_errorLimitEdit->text().toDouble(); - QString wellName = m_targetWellCombo->currentText(); - m_progressMonitor->setFittingParameters(maxIterations, targetError, wellName); - m_progressMonitor->setSelectedParameters(selectedParams); - - m_progressMonitor->show(); - QTimer::singleShot(100, this, SLOT(runAutoFitting())); + m_progressMonitor = new nmWxAutomaticfittingStart(this); + m_progressMonitor->setAutoFitter(m_autoFitterPSO); + m_progressMonitor->setPseudoPressureMode( + nmDataAnalyzeManager::getCurrentInstance()->getSolverModelType() == SMT_Gas_VariablePvt); + m_progressMonitor->setTargetLogLogData(targetData); - } else if (m_selectedAlgorithm == ALGORITHM_GA) { - DEBUG_UI("Creating GA auto fitter"); - m_autoFitterGA = new nmCalculationAutoFitGA(this); - m_autoFitterGA->setTargetLogLogData(targetData); - m_autoFitterGA->setGATargetWellName(targetWellName); + connect(m_autoFitterPSO, SIGNAL(fittingFinished(bool, QString)), + this, SLOT(onFittingFinished(bool, QString))); - m_progressMonitor = new nmWxAutomaticfittingStart(this); - m_progressMonitor->setAutoFitterGA(m_autoFitterGA); - m_progressMonitor->setTargetLogLogData(targetData); + int maxIterations = m_iterationEdit->text().toInt(); + double targetError = m_errorLimitEdit->text().toDouble(); + QString wellName = m_targetWellCombo->currentText(); + m_progressMonitor->setFittingParameters(maxIterations, targetError, wellName); + m_progressMonitor->setSelectedParameters(selectedParams); - connect(m_autoFitterGA, SIGNAL(fittingFinished(bool, QString)), - this, SLOT(onFittingFinished(bool, QString))); - - int maxIterations = m_iterationEdit->text().toInt(); - double targetError = m_errorLimitEdit->text().toDouble(); - QString wellName = m_targetWellCombo->currentText(); - m_progressMonitor->setFittingParameters(maxIterations, targetError, wellName); - m_progressMonitor->setSelectedParameters(selectedParams); - - m_progressMonitor->show(); - QTimer::singleShot(100, this, SLOT(runAutoFitting())); - } + m_progressMonitor->show(); + QTimer::singleShot(100, this, SLOT(runAutoFitting())); } void nmWxAutomaticFitting::runAutoFitting() @@ -1128,10 +1387,8 @@ void nmWxAutomaticFitting::runAutoFitting() m_progressMonitor->markFittingStarted(); } - if(m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO) { + if(m_autoFitterPSO) { m_autoFitterPSO->startAutoFitting(); - } else if(m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA) { - m_autoFitterGA->startAutoFitting(); } } @@ -1147,18 +1404,13 @@ void nmWxAutomaticFitting::onFittingFinished(bool success, const QString& messag disconnect(m_autoFitterPSO, SIGNAL(fittingFinished(bool, QString)), this, SLOT(onFittingFinished(bool, QString))); } - if (m_autoFitterGA) { - disconnect(m_autoFitterGA, SIGNAL(fittingFinished(bool, QString)), - this, SLOT(onFittingFinished(bool, QString))); - } - - // 更新最佳参数到表格 - updateBestParametersToTable(); - if(success) { + // 只有成功拟合的结果才用于生成下一轮范围，失败结果不污染当前配置。 + updateBestParametersToTable(); + QString resultInfo; - if(m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO) { + if(m_autoFitterPSO) { double bestFitness = m_autoFitterPSO->getBestFitness(); // 检查是否是用户停止的情况 @@ -1173,23 +1425,6 @@ void nmWxAutomaticFitting::onFittingFinished(bool success, const QString& messag resultInfo += tr("Optimized parameters have been applied to the model."); QMessageBox::information(this, tr("Optimization Completed"), resultInfo); } - - } else if(m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA) { - // GA的类似处理 - - double bestFitness = m_autoFitterGA->getBestFitness(); - - if(message.contains("stopped by user", Qt::CaseInsensitive)) { - resultInfo = tr("GA Optimization stopped by user:\n"); - resultInfo += tr("Best Error: %1\n").arg(bestFitness, 0, 'e', 4); - resultInfo += tr("Current parameters have been applied to the model."); - QMessageBox::information(this, tr("Optimization Stopped"), resultInfo); - } else { - resultInfo = tr("GA Optimization completed:\n"); - resultInfo += tr("Best Error: %1\n").arg(bestFitness, 0, 'e', 4); - resultInfo += tr("Optimized parameters have been applied to the model."); - QMessageBox::information(this, tr("Optimization Completed"), resultInfo); - } } } else { // 只有真正失败的情况才显示警告 @@ -1199,11 +1434,9 @@ void nmWxAutomaticFitting::onFittingFinished(bool success, const QString& messag void nmWxAutomaticFitting::onStopFitting() { - if(m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO && m_autoFitterPSO->isRunning()) { + if(m_autoFitterPSO && m_autoFitterPSO->isRunning()) { m_autoFitterPSO->stopFitting(); - } else if(m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA && m_autoFitterGA->isRunning()) { - m_autoFitterGA->stopFitting(); - } + } } void nmWxAutomaticFitting::cleanupFitting() @@ -1246,31 +1479,6 @@ void nmWxAutomaticFitting::cleanupFitting() DEBUG_UI("PSO fitter cleaned up"); } - if (m_autoFitterGA) { - DEBUG_UI("Stopping and disconnecting GA fitter"); - - disconnect(m_autoFitterGA, nullptr, nullptr, nullptr); - - if (m_autoFitterGA->isRunning()) { - m_autoFitterGA->stopFitting(); - - int waitCount = 0; - while (m_autoFitterGA->isRunning() && waitCount < 50) { - QApplication::processEvents(QEventLoop::ExcludeUserInputEvents, 100); - waitCount++; - } - - // 如果仍在运行则强制停止 - if (m_autoFitterGA->isRunning()) { - DEBUG_UI("Force stopping GA - timeout reached"); - } - } - - delete m_autoFitterGA; - m_autoFitterGA = nullptr; - DEBUG_UI("GA fitter cleaned up"); - } - // 清理进度监控 if (m_progressMonitor) { // 先断开进度监控的信号连接 @@ -1296,10 +1504,8 @@ void nmWxAutomaticFitting::updateBestParametersToTable() QVector bestSolution; // 获取最佳解决方案 - if (m_selectedAlgorithm == ALGORITHM_PSO && m_autoFitterPSO) { + if (m_autoFitterPSO) { bestSolution = m_autoFitterPSO->getBestSolution(); - } else if (m_selectedAlgorithm == ALGORITHM_GA && m_autoFitterGA) { - bestSolution = m_autoFitterGA->getBestSolution(); } if (bestSolution.isEmpty()) return; @@ -1315,78 +1521,19 @@ void nmWxAutomaticFitting::updateBestParametersToTable() if(m_cfCheckBox->isChecked()) enabledParams.append(6); // 岩石压缩系数 if(m_swiCheckBox->isChecked()) enabledParams.append(7); // 初始含水饱和度 - // 范围收缩比例 - double shrinkFactor = 0.3; - // 更新参数值和范围 for (int i = 0; i < bestSolution.size() && i < enabledParams.size(); ++i) { int paramIndex = enabledParams[i]; double bestValue = bestSolution[i]; + if(!nmAutoFitUiIsFinite(bestValue)) { + continue; + } // 更新初始值 m_parameterTable->item(paramIndex, 3)->setText(QString::number(bestValue, 'g', 4)); - - // 获取当前的最小值和最大值 - double currentMin = m_parameterTable->item(paramIndex, 2)->text().toDouble(); - double currentMax = m_parameterTable->item(paramIndex, 4)->text().toDouble(); - double currentRange = currentMax - currentMin; - - // 计算新的范围 - double newHalfRange = currentRange * shrinkFactor * 0.5; - double newMin = bestValue - newHalfRange; - double newMax = bestValue + newHalfRange; - - // 确保某些参数不为负数 - if (paramIndex == 0 || paramIndex == 2 || paramIndex == 3 || paramIndex == 4) { - newMin = qMax(newMin, 0.0); - } - - // 确保最小范围，避免范围过小 - double minAllowedRange = currentRange * 0.05; - if ((newMax - newMin) < minAllowedRange) { - double center = (newMax + newMin) * 0.5; - newMin = center - minAllowedRange * 0.5; - newMax = center + minAllowedRange * 0.5; - } - - // 更新表格中的最小值和最大值 - m_parameterTable->item(paramIndex, 2)->setText(QString::number(newMin, 'g', 4)); - m_parameterTable->item(paramIndex, 4)->setText(QString::number(newMax, 'g', 4)); - - // 保存收缩后的范围 - switch(paramIndex) { - case 0: // 渗透率 - automaticFittingData.getPermeabilityMin().setValue(newMin); - automaticFittingData.getPermeabilityMax().setValue(newMax); - break; - case 1: // 表皮系数 - automaticFittingData.getSkinMin().setValue(newMin); - automaticFittingData.getSkinMax().setValue(newMax); - break; - case 2: // 井筒储集系数 - automaticFittingData.getWellboreStorageMin().setValue(newMin); - automaticFittingData.getWellboreStorageMax().setValue(newMax); - break; - case 3: // 孔隙度 - automaticFittingData.getPorosityMin().setValue(newMin); - automaticFittingData.getPorosityMax().setValue(newMax); - break; - case 4: // 储层厚度 - automaticFittingData.getThicknessMin().setValue(newMin); - automaticFittingData.getThicknessMax().setValue(newMax); - break; - case 5: // 综合压缩系数 - automaticFittingData.getCtMin().setValue(newMin); - automaticFittingData.getCtMax().setValue(newMax); - break; - case 6: // 岩石压缩系数 - automaticFittingData.getCfMin().setValue(newMin); - automaticFittingData.getCfMax().setValue(newMax); - break; - case 7: // 初始含水饱和度 - automaticFittingData.getSwiMin().setValue(newMin); - automaticFittingData.getSwiMax().setValue(newMax); - break; + // 只有自动范围模式才根据拟合结果收窄下一轮搜索区间；手工范围由用户保留。 + if(m_autoParameterRanges) { + updateRangeForParameter(paramIndex, bestValue, true); } } diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp index 6e0bd2b..acb1a3f 100644 --- a/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp +++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFittingStart.cpp @@ -383,8 +383,6 @@ nmWxAutomaticfittingStart::nmWxAutomaticfittingStart(QWidget *parent) , chartGroup(nullptr) , curveChart(nullptr) , m_autoFitterPSO(nullptr) - , m_autoFitterGA(nullptr) - , m_algorithmType(FITTING_ALGORITHM_PSO) , m_maxIterations(100) , m_targetError(0.001) , m_wellName("") @@ -580,8 +578,6 @@ void nmWxAutomaticfittingStart::setupControlArea() void nmWxAutomaticfittingStart::setAutoFitter(nmCalculationAutoFitPSO* autoFitter) { m_autoFitterPSO = autoFitter; - m_autoFitterGA = nullptr; // 清空GA实例 - m_algorithmType = FITTING_ALGORITHM_PSO; // 更新算法类型显示 algorithmTypeValue->setText("PSO"); @@ -604,31 +600,6 @@ void nmWxAutomaticfittingStart::setAutoFitter(nmCalculationAutoFitPSO* autoFitte } } -void nmWxAutomaticfittingStart::setAutoFitterGA(nmCalculationAutoFitGA* autoFitter) -{ - m_autoFitterGA = autoFitter; - m_autoFitterPSO = nullptr; // 清空PSO实例 - m_algorithmType = FITTING_ALGORITHM_GA; - - // 更新算法类型显示 - algorithmTypeValue->setText("GA"); - algorithmTypeValue->setStyleSheet("QLabel { color: red; font-weight: bold; }"); - - if (m_autoFitterGA) { - connect(m_autoFitterGA, SIGNAL(progressUpdated(int, double)), - this, SLOT(onFittingProgress(int, double))); - connect(m_autoFitterGA, SIGNAL(fittingFinished(bool, QString)), - this, SLOT(onFittingFinished(bool, QString))); - connect(m_autoFitterGA, SIGNAL(logMessageGenerated(QString)), - this, SLOT(onLogMessageReceived(QString))); - - // 启用停止按钮 - stopButton->setEnabled(true); - - addLogMessage(tr("GA auto fitting started")); - } -} - void nmWxAutomaticfittingStart::setFittingParameters(int maxIterations, double targetError, const QString& wellName) { m_maxIterations = maxIterations; @@ -641,7 +612,7 @@ void nmWxAutomaticfittingStart::setFittingParameters(int maxIterations, double t progressBar->setRange(0, maxIterations); - QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA"; + const QString algorithmName = "PSO"; addLogMessage(tr("%1 fitting parameters set: MaxIterations=%2, TargetAccuracy=%3, TargetWell=%4") .arg(algorithmName).arg(maxIterations).arg(formatScientific(targetError)).arg(wellName)); } @@ -650,7 +621,7 @@ void nmWxAutomaticfittingStart::markFittingStarted() { m_startTime = QDateTime::currentDateTime(); - QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA"; + const QString algorithmName = "PSO"; QString timestamp = m_startTime.toString("yyyy-MM-dd hh:mm:ss"); addLogMessage(tr("=== %1 Fitting Session Started at %2 ===") @@ -665,7 +636,7 @@ void nmWxAutomaticfittingStart::setSelectedParameters(const QStringList& paramet // 立即更新参数表格 updateParameterTable(); - QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA"; + const QString algorithmName = "PSO"; addLogMessage(tr("%1 selected parameters: %2").arg(algorithmName).arg(parameterNames.join(", "))); } @@ -716,7 +687,7 @@ void nmWxAutomaticfittingStart::onFittingFinished(bool success, const QString& m { m_isFinished = true; - QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA"; + const QString algorithmName = "PSO"; // 更新状态 if (success) { @@ -800,25 +771,18 @@ void nmWxAutomaticfittingStart::onFittingFinished(bool success, const QString& m void nmWxAutomaticfittingStart::onStopButtonClicked() { - bool isRunning = false; - if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) { - isRunning = m_autoFitterPSO->isRunning(); - } else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) { - isRunning = m_autoFitterGA->isRunning(); - } + const bool isRunning = m_autoFitterPSO && m_autoFitterPSO->isRunning(); if (isRunning) { - QString algorithmName = (m_algorithmType == FITTING_ALGORITHM_PSO) ? "PSO" : "GA"; + const QString algorithmName = "PSO"; int ret = QMessageBox::question(this, tr("Confirm Stop"), tr("Are you sure you want to stop the %1 fitting process?").arg(algorithmName), QMessageBox::Yes | QMessageBox::No, QMessageBox::No); if (ret == QMessageBox::Yes) { - if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) { + if (m_autoFitterPSO) { m_autoFitterPSO->stopFitting(); - } else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) { - m_autoFitterGA->stopFitting(); } addLogMessage(tr("User requested to stop %1 fitting").arg(algorithmName)); } @@ -840,16 +804,11 @@ void nmWxAutomaticfittingStart::updateParameterTable() // 最优参数值 QString valueText = "N/A"; - if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) { + if (m_autoFitterPSO) { QVector bestSolution = m_autoFitterPSO->getBestSolution(); if (i < bestSolution.size()) { valueText = formatScientific(bestSolution[i]); } - } else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) { - QVector bestSolution = m_autoFitterGA->getBestSolution(); - if (i < bestSolution.size()) { - valueText = formatScientific(bestSolution[i]); - } } QTableWidgetItem* valueItem = new QTableWidgetItem(valueText); @@ -883,16 +842,8 @@ QString nmWxAutomaticfittingStart::formatScientific(double value) void nmWxAutomaticfittingStart::closeEvent(QCloseEvent *event) { - bool isRunning = false; - QString algorithmName; - - if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) { - isRunning = m_autoFitterPSO->isRunning(); - algorithmName = "PSO"; - } else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) { - isRunning = m_autoFitterGA->isRunning(); - algorithmName = "GA"; - } + const bool isRunning = m_autoFitterPSO && m_autoFitterPSO->isRunning(); + const QString algorithmName = "PSO"; if (isRunning && !m_isFinished) { int ret = QMessageBox::question(this, tr("Confirm Close"), @@ -901,10 +852,8 @@ void nmWxAutomaticfittingStart::closeEvent(QCloseEvent *event) QMessageBox::No); if (ret == QMessageBox::Yes) { - if (m_algorithmType == FITTING_ALGORITHM_PSO && m_autoFitterPSO) { + if (m_autoFitterPSO) { m_autoFitterPSO->stopFitting(); - } else if (m_algorithmType == FITTING_ALGORITHM_GA && m_autoFitterGA) { - m_autoFitterGA->stopFitting(); } event->accept(); } else { From 6abd1913c7309db2a19489c4461db8d236872340 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 7 Aug 2026 15:37:01 +0800 Subject: [PATCH 05/12] =?UTF-8?q?=E4=BF=AE=E6=94=B9=E6=8D=9F=E5=A4=B1?= =?UTF-8?q?=E5=87=BD=E6=95=B0=EF=BC=8C=E5=A2=9E=E5=8A=A0=E4=B8=8A=E4=B8=8B?= =?UTF-8?q?=EF=BC=8C=E5=B7=A6=E5=8F=B3=EF=BC=8C=E5=BD=A2=E7=8A=B6=EF=BC=8C?= =?UTF-8?q?=E6=97=A9=E4=B8=AD=E6=99=9A=E8=AF=AF=E5=B7=AE=E8=AF=8A=E6=96=AD?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nmCalculation/nmCalculationAutoFitPSO.h | 64 +++ .../nmCalculation/nmCalculationAutoFitPSO.cpp | 514 ++++++++++++++---- 2 files changed, 460 insertions(+), 118 deletions(-) diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h index 3418b04..e65d1ee 100644 --- a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h +++ b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h @@ -9,6 +9,7 @@ #include #include #include +#include #include "nmCalculation_global.h" @@ -19,6 +20,67 @@ class nmDataWellBase; class QTimer; class QProcess; +// 双对数曲线误差分解，供误差诊断和后续参数调整读取。 +// +// 所有 pressure/derivative/shape 数值均是在 log(value) 空间计算的无量纲误差。 +// verticalBias* 保留正负号：正值表示模拟曲线整体高于目标，负值表示整体低于目标。 +// horizontalPhysicalShift 是 log(time) 方向的等效平移量，正值表示模拟曲线相对目标偏右。 +// total 仍是 PSO 当前使用的 fitness（用于粒子比较和收敛判断）；其余字段只描述误差 +// 来源，不会在本次改动中直接修改粒子参数，避免损失诊断和参数更新策略互相耦合。 +struct AutoFitObjectiveBreakdown { + // valid/total 是本次评价是否有效及其最终 fitness（用于排序和收敛判断）。 + bool valid; + double total; + // pressureLoss/derivativeLoss 是压力和导数两条曲线的整体误差。 + double pressureLoss; + double derivativeLoss; + // vertical* 描述整体上下偏移；保留 bias 的符号以判断偏高或偏低。 + double verticalBiasPressure; + double verticalBiasDerivative; + double verticalLoss; + // horizontal* 描述等效的对数时间偏移；physicalShift 为正表示模拟曲线相对目标向右 + // （时间延迟），为负表示向左。 + double horizontalShift; + double horizontalPhysicalShift; + double horizontalLoss; + // shapeLoss 是去除整体上下和左右偏移后剩余的曲线形状差异。 + double shapeLoss; + // 早、中、晚分段误差用于定位误差主要出现在哪个时间阶段。 + double pressureEarlyLoss; + double pressureMiddleLoss; + double pressureLateLoss; + double derivativeEarlyLoss; + double derivativeMiddleLoss; + double derivativeLateLoss; + // coverage 是 50 点目标网格上的 min(有效点比例、连续 log-time 跨度比例)。 + // coveragePenalty 是归一化覆盖缺口的平方惩罚，并以 0.1 权重加入 total。 + double coverage; + double coveragePenalty; + + // 无效评价使用 1e10 作为统一的“差解”标记；其他字段用 NaN 表示尚未得到诊断值。 + AutoFitObjectiveBreakdown() + : valid(false) + , total(1.0e10) + , pressureLoss(std::numeric_limits::quiet_NaN()) + , derivativeLoss(std::numeric_limits::quiet_NaN()) + , verticalBiasPressure(std::numeric_limits::quiet_NaN()) + , verticalBiasDerivative(std::numeric_limits::quiet_NaN()) + , verticalLoss(std::numeric_limits::quiet_NaN()) + , horizontalShift(std::numeric_limits::quiet_NaN()) + , horizontalPhysicalShift(std::numeric_limits::quiet_NaN()) + , horizontalLoss(std::numeric_limits::quiet_NaN()) + , shapeLoss(std::numeric_limits::quiet_NaN()) + , pressureEarlyLoss(std::numeric_limits::quiet_NaN()) + , pressureMiddleLoss(std::numeric_limits::quiet_NaN()) + , pressureLateLoss(std::numeric_limits::quiet_NaN()) + , derivativeEarlyLoss(std::numeric_limits::quiet_NaN()) + , derivativeMiddleLoss(std::numeric_limits::quiet_NaN()) + , derivativeLateLoss(std::numeric_limits::quiet_NaN()) + , coverage(std::numeric_limits::quiet_NaN()) + , coveragePenalty(std::numeric_limits::quiet_NaN()) + {} +}; + // PSO粒子结构 // 这里的 position / velocity / bestPosition 只保存“用户勾选参与拟合的参数”， // 不是完整的 11 个储层/井筒参数。完整参数向量会在写 trace 或调用代理模型时 @@ -96,6 +158,7 @@ public: void stopFitting(); QVector getBestSolution() const; double getBestFitness() const; + AutoFitObjectiveBreakdown getLastObjectiveBreakdown() const; QString getLastError() const; bool isRunning() const; int getCurrentIteration() const; @@ -288,6 +351,7 @@ private: double m_previousBestFitness; // 上一轮全局最优误差，用于自适应参数更新。 QVector > m_lastEvaluatedLogLogData; // 最近一次真实求解得到的 result log-log 曲线。 QVector > m_globalBestLogLogData; // 当前全局最优对应的 result log-log 曲线。 + mutable AutoFitObjectiveBreakdown m_lastObjectiveBreakdown; // 最近一次损失评价的误差分解。 QVector > m_userInitialLogLogData; // 用户初始解对应的 result log-log 曲线，用于精英保护。 // ===== 优化配置 ===== diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index 936d9fc..ccc05ea 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -791,6 +791,12 @@ double nmCalculationAutoFitPSO::getBestFitness() const return m_globalBestFitness; } +AutoFitObjectiveBreakdown nmCalculationAutoFitPSO::getLastObjectiveBreakdown() const +{ + // 返回最近一次损失评价的误差分解，供界面或后续优化逻辑读取。 + return m_lastObjectiveBreakdown; +} + QString nmCalculationAutoFitPSO::getLastError() const { // 上一次失败的人类可读错误信息，主要给 UI 层弹窗或日志使用。 @@ -809,6 +815,7 @@ void nmCalculationAutoFitPSO::resetOptimizer() m_previousBestFitness = 1e10; m_lastEvaluatedLogLogData.clear(); m_globalBestLogLogData.clear(); + m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); m_userInitialLogLogData.clear(); m_currentIteration = 0; m_totalEvaluations = 0; @@ -4016,6 +4023,7 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector& parameter static int callCount = 0; callCount++; m_lastEvaluatedLogLogData.clear(); + m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); try { DEBUG_OUT(QString("%1: Call #%2 - Starting evaluation with %3 parameters") @@ -4989,170 +4997,440 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( const QVector >& target, const QVector >& result) const { - // 双对数曲线误差计算。 - // - // target 通常来自目标井历史曲线，result 来自当前粒子参数下的模拟曲线。 - // 两条曲线的时间点往往不完全一致，所以这里先取两者时间范围的重叠区间， - // 再在公共时间网格上插值对齐，最后分别计算压力曲线和压力导数曲线误差。 - // - // 返回值越小表示拟合越好；返回 1e10 表示曲线无效或无法比较。 - // 验证数据 + // 这里只负责“曲线比较和误差诊断”，不根据诊断结果直接修改任何拟合参数。 + // 调用方可以读取 m_lastObjectiveBreakdown 做诊断或展示；本函数本身不修改参数。 + // 在统一的对数时间网格上计算压力和导数残差，并拆分为上下、左右、形状误差。 + const double invalidLoss = 1.0e10; + const double valueFloor = 1.0e-12; // 导数接近零时的对数下限，避免 log(0)。 + const double huberDelta = qLn(1.2); // 约对应 20% 的相对偏差拐点。 + const double minimumCoverage = 0.95; // 点数比例和连续跨度比例都至少接近 95%。 + const int numPoints = 50; // 固定网格使不同候选的损失具有可比性。 + // 目标函数的主排序项为 0.5*pressureLoss + 0.5*derivativeLoss； + // coveragePenalty 只在接近覆盖边界时提供连续惩罚，上下、左右和形状分量 + // 会写入 m_lastObjectiveBreakdown，供后续按误差类型选择参数。 + m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); + if(!validateLogLogData(target) || !validateLogLogData(result)) { - return 1e10; + return invalidLoss; } try { - // 数据对齐：找到目标曲线与模拟曲线 time 轴的重叠区域。 - // 不在重叠区域内的点不参与误差，避免外推导致误差失真。 - double targetMinX = target[0][0]; - double targetMaxX = target[0][0]; + // 清洗曲线并拆成压力、导数两条曲线。导数可以为负，所以统一使用绝对值 + // 进入双对数空间；时间和压力必须为正，否则无法进行对数插值。这里的清洗 + // 只丢弃无法比较的采样点，不改变原始曲线或求解器输出。 + auto prepareCurve = [valueFloor](const QVector >& data, + QVector* pressure, + QVector* derivative) -> bool { + if(!pressure || !derivative || data.size() < 3 || + data[0].size() != data[1].size() || + data[0].size() != data[2].size()) { + return false; + } - for(int i = 1; i < target[0].size(); ++i) { - if(target[0][i] < targetMinX) targetMinX = target[0][i]; + for(int i = 0; i < data[0].size(); ++i) { + if(!isFiniteNumber(data[0][i]) || !isFiniteNumber(data[1][i]) || + !isFiniteNumber(data[2][i]) || data[0][i] <= 0.0 || + data[1][i] <= 0.0) { + continue; + } - if(target[0][i] > targetMaxX) targetMaxX = target[0][i]; - } + pressure->append(QPointF(data[0][i], data[1][i])); + derivative->append(QPointF(data[0][i], + qMax(qAbs(data[2][i]), valueFloor))); + } - double resultMinX = result[0][0]; - double resultMaxX = result[0][0]; + // 插值要求时间严格递增。重复时间点保留排序后的最后一个值， + // 避免重复横坐标导致对数插值分母为零。压力和导数分别去重， + // 这样即使某条曲线存在重复时间点，也不会污染另一条曲线的插值。 + auto sortAndUnique = [](QVector* curve) { + std::stable_sort(curve->begin(), curve->end(), + [](const QPointF& left, const QPointF& right) { + return left.x() < right.x(); + }); + + QVector unique; + unique.reserve(curve->size()); + + for(int i = 0; i < curve->size(); ++i) { + if(unique.isEmpty() || curve->at(i).x() > unique.last().x()) { + unique.append(curve->at(i)); + } else { + unique[unique.size() - 1] = curve->at(i); + } + } - for(int i = 1; i < result[0].size(); ++i) { - if(result[0][i] < resultMinX) resultMinX = result[0][i]; + *curve = unique; + }; - if(result[0][i] > resultMaxX) resultMaxX = result[0][i]; - } + sortAndUnique(pressure); + sortAndUnique(derivative); + return pressure->size() >= 3 && derivative->size() >= 3; + }; - double overlapMinX = qMax(targetMinX, resultMinX); - double overlapMaxX = qMin(targetMaxX, resultMaxX); + QVector targetPressure; + QVector targetDerivative; + QVector resultPressure; + QVector resultDerivative; - if(overlapMinX >= overlapMaxX) { - DEBUG_OUT("No overlap between target and result LogLog curves"); - return 1e10; + if(!prepareCurve(target, &targetPressure, &targetDerivative) || + !prepareCurve(result, &resultPressure, &resultDerivative)) { + return invalidLoss; } - // 生成公共 X 网格进行插值。使用对数均匀网格，是为了给早期时间段 - // 更多分辨率；试井双对数曲线的早期形态通常对参数识别很敏感。 - QVector commonX; - int numPoints = 50; + // 在 log(time)-log(value) 空间做线性插值，而不是在线性坐标直接插值。 + // 这样可以保持双对数曲线的时间尺度和数量级特征；返回值是 + // log(abs(value))，后续残差因此可以直接解释为相对幅值误差。 + auto interpolateLogValue = [valueFloor](const QVector& curve, + double x, + double* value) -> bool { + if(!value || curve.size() < 2 || x < curve.first().x() || + x > curve.last().x() || x <= 0.0) { + return false; + } - if(overlapMinX > 0 && overlapMaxX > 0) { - // 对数空间均匀分布 - double logMin = qLn(overlapMinX); - double logMax = qLn(overlapMaxX); + int right = 1; - for(int i = 0; i < numPoints; ++i) { - double logX = logMin + i * (logMax - logMin) / (numPoints - 1); - double x = qExp(logX); + while(right < curve.size() && curve[right].x() < x) { + ++right; + } - // 数值保护 - if(!isFiniteNumber(x) || x <= 0) { - continue; - } + right = qMin(right, curve.size() - 1); + int left = qMax(0, right - 1); + double leftLogX = qLn(curve[left].x()); + double rightLogX = qLn(curve[right].x()); + double denominator = rightLogX - leftLogX; + double leftLogY = qLn(qMax(qAbs(curve[left].y()), valueFloor)); + double rightLogY = qLn(qMax(qAbs(curve[right].y()), valueFloor)); - commonX.append(x); + if(qAbs(denominator) <= 1.0e-12) { + *value = leftLogY; + } else { + double ratio = (qLn(x) - leftLogX) / denominator; + *value = leftLogY + ratio * (rightLogY - leftLogY); } - DEBUG_OUT("Using log-uniform grid for better early-time coverage"); + return isFiniteNumber(*value); + }; + + const double targetMinX = targetPressure.first().x(); + const double targetMaxX = targetPressure.last().x(); + const double resultMinX = resultPressure.first().x(); + const double resultMaxX = resultPressure.last().x(); + + if(targetMinX <= 0.0 || targetMaxX <= targetMinX || + resultMinX <= 0.0 || resultMaxX <= resultMinX) { + return invalidLoss; + } + + // 目标曲线的完整时间范围作为统一比较区间。若候选曲线覆盖不足， + // 后面的 coverage 检查会拒绝它，防止候选通过缩短时间范围来降低误差。 + QVector commonX(numPoints); + QVector commonLogX(numPoints); + QVector targetLogPressure(numPoints); + QVector targetLogDerivative(numPoints); + const double targetLogMinX = qLn(targetMinX); + const double targetLogMaxX = qLn(targetMaxX); + + for(int i = 0; i < numPoints; ++i) { + double logX = targetLogMinX + + static_cast(i) * + (targetLogMaxX - targetLogMinX) / (numPoints - 1); + commonLogX[i] = logX; + commonX[i] = qExp(logX); + + if(!interpolateLogValue(targetPressure, commonX[i], + &targetLogPressure[i]) || + !interpolateLogValue(targetDerivative, commonX[i], + &targetLogDerivative[i])) { + return invalidLoss; + } } - if(commonX.isEmpty()) { - DEBUG_OUT("Failed to generate common X grid"); - return 1e10; - } + // 残差采用“模拟减目标”，因此正值表示模拟曲线在对数幅值上高于目标， + // 负值表示模拟曲线偏低。NaN 表示该网格点不在模拟曲线支持范围内， + // 后续统计会自动跳过，但覆盖率检查仍会限制候选不能靠缺失数据降低损失。 + QVector pressureResidual(numPoints, + std::numeric_limits::quiet_NaN()); + QVector derivativeResidual(numPoints, + std::numeric_limits::quiet_NaN()); + QVector pressureSlope(numPoints, 0.0); + QVector derivativeSlope(numPoints, 0.0); + int firstSupported = -1; + int lastSupported = -1; + int supportedCount = 0; + + for(int i = 0; i < numPoints; ++i) { + if(commonX[i] < resultMinX || commonX[i] > resultMaxX) { + continue; + } - // 插值目标曲线。target[1] 是压力，target[2] 是压力导数。 - QVector targetCurve1, targetCurve2; + double resultLogPressure = 0.0; + double resultLogDerivative = 0.0; - for(int i = 0; i < target[0].size(); ++i) { - // 检查数据有效性 - if(isFiniteNumber(target[0][i]) && isFiniteNumber(target[1][i]) && - isFiniteNumber(target[2][i])) { - targetCurve1.append(QPointF(target[0][i], target[1][i])); - targetCurve2.append(QPointF(target[0][i], target[2][i])); + if(!interpolateLogValue(resultPressure, commonX[i], + &resultLogPressure) || + !interpolateLogValue(resultDerivative, commonX[i], + &resultLogDerivative)) { + continue; } - } - if(targetCurve1.isEmpty() || targetCurve2.isEmpty()) { - DEBUG_OUT("Target curves are empty after filtering"); - return 1e10; + pressureResidual[i] = resultLogPressure - targetLogPressure[i]; + derivativeResidual[i] = resultLogDerivative - targetLogDerivative[i]; + ++supportedCount; + + if(firstSupported < 0) { + firstSupported = i; + } + + lastSupported = i; + } + + // 同时使用点覆盖率和连续时间跨度覆盖率，避免只覆盖少数离散点也被判定为完整。 + AutoFitObjectiveBreakdown breakdown; + breakdown.coverage = supportedCount > 0 + ? static_cast(supportedCount) / numPoints + : 0.0; + + if(firstSupported >= 0 && lastSupported >= firstSupported) { + double span = qMax(1.0e-12, targetLogMaxX - targetLogMinX); + double coveredSpan = commonLogX[lastSupported] - + commonLogX[firstSupported]; + breakdown.coverage = qMin(breakdown.coverage, + qMax(0.0, coveredSpan / span)); + } + + // 覆盖率越接近 1，惩罚越小；覆盖不足 minimumCoverage 时直接返回无效损失。 + double coverageGap = qMax(0.0, 1.0 - breakdown.coverage); + breakdown.coveragePenalty = + qPow(coverageGap / (1.0 - minimumCoverage), 2.0); + + if(breakdown.coverage < minimumCoverage) { + breakdown.total = invalidLoss; + m_lastObjectiveBreakdown = breakdown; + return invalidLoss; + } + + // 目标曲线斜率用于把“残差随时间的系统性变化”解释为左右平移。 + // 斜率用对数坐标计算，与前面的插值空间保持一致；平坦区斜率接近零， + // 不会凭空制造水平偏移量。 + for(int i = 0; i < numPoints; ++i) { + int left = i == 0 ? 0 : i - 1; + int right = i == numPoints - 1 ? numPoints - 1 : i + 1; + double denominator = commonLogX[right] - commonLogX[left]; + + if(qAbs(denominator) > 1.0e-12) { + pressureSlope[i] = + (targetLogPressure[right] - targetLogPressure[left]) / + denominator; + derivativeSlope[i] = + (targetLogDerivative[right] - targetLogDerivative[left]) / + denominator; + } } - QVector alignedTarget1 = interpolateData(targetCurve1, commonX); - QVector alignedTarget2 = interpolateData(targetCurve2, commonX); + // Huber RMS 在小残差区域保持平方损失，在异常点区域转为线性增长， + // 避免少量求解器异常点完全主导候选排序。这里没有除以目标值， + // 因为残差已经是 log(value) 差值，本身就是相对误差的表达；返回值是 + // Huber rho 均值的平方根，保持与 RMS 类似的尺度。 + auto huberRms = [huberDelta](const QVector& values, + int begin, + int end) -> double { + double sum = 0.0; + int count = 0; - // 插值结果曲线。result 与 target 使用同一 commonX，保证逐点可比。 - QVector resultCurve1, resultCurve2; + for(int i = qMax(0, begin); + i < qMin(end, static_cast(values.size())); ++i) { + if(!isFiniteNumber(values[i])) { + continue; + } - for(int i = 0; i < result[0].size(); ++i) { - // 检查数据有效性 - if(isFiniteNumber(result[0][i]) && isFiniteNumber(result[1][i]) && - isFiniteNumber(result[2][i])) { - resultCurve1.append(QPointF(result[0][i], result[1][i])); - resultCurve2.append(QPointF(result[0][i], result[2][i])); + double absoluteValue = qAbs(values[i]); + double rho = absoluteValue <= huberDelta + ? values[i] * values[i] + : 2.0 * huberDelta * absoluteValue - + huberDelta * huberDelta; + sum += rho; + ++count; } - } - if(resultCurve1.isEmpty() || resultCurve2.isEmpty()) { - DEBUG_OUT("Result curves are empty after filtering"); - return 1e10; - } + return count > 0 + ? qSqrt(sum / count) + : std::numeric_limits::quiet_NaN(); + }; + + // 用 Huber 加权迭代估计残差中心，作为整体上下偏移。相比普通平均值， + // 它对局部尖峰更稳健，同时保留偏高/偏低的方向信息。迭代只用于诊断， + // 不会把残差“校正”后再写回求解器结果。 + auto huberCenter = [huberDelta](const QVector& values) -> double { + double center = 0.0; + int count = 0; + + for(int i = 0; i < values.size(); ++i) { + if(isFiniteNumber(values[i])) { + center += values[i]; + ++count; + } + } - QVector alignedResult1 = interpolateData(resultCurve1, commonX); - QVector alignedResult2 = interpolateData(resultCurve2, commonX); + if(count == 0) { + return std::numeric_limits::quiet_NaN(); + } - // 检查插值结果 - if(alignedTarget1.isEmpty() || alignedTarget2.isEmpty() || - alignedResult1.isEmpty() || alignedResult2.isEmpty()) { - DEBUG_OUT("LogLog interpolation failed"); - return 1e10; - } + center /= count; - if(alignedTarget1.size() != alignedResult1.size() || - alignedTarget2.size() != alignedResult2.size()) { - DEBUG_OUT("LogLog interpolation size mismatch"); - return 1e10; - } + // 固定最多 8 次迭代，控制每个候选的计算开销并保持结果稳定。 + for(int iteration = 0; iteration < 8; ++iteration) { + double weightedSum = 0.0; + double weightTotal = 0.0; - // 计算两条曲线的误差。当前压力和导数各占 50%。 - // 如果后续要让导数形态更重要，可以从这里调整权重。 - double error1 = calculateCurveError(alignedTarget1, alignedResult1); - double error2 = calculateCurveError(alignedTarget2, alignedResult2); + for(int i = 0; i < values.size(); ++i) { + if(!isFiniteNumber(values[i])) { + continue; + } - // 检查个别误差是否有效 - if(!isFiniteNumber(error1) || error1 > 1e9) { - DEBUG_OUT(QString("Curve1 error is invalid: %1").arg(error1)); - error1 = 1e10; - } + double distance = qAbs(values[i] - center); + // 距离接近零时直接取权重 1，避免除零并保持中心点不被放大。 + double weight = distance <= huberDelta || distance < 1.0e-12 + ? 1.0 + : huberDelta / distance; + weightedSum += weight * values[i]; + weightTotal += weight; + } - if(!isFiniteNumber(error2) || error2 > 1e9) { - DEBUG_OUT(QString("Curve2 error is invalid: %1").arg(error2)); - error2 = 1e10; - } + if(weightTotal <= 1.0e-12) { + break; + } - // 组合误差 - 添加保护 - double combinedError; + double nextCenter = weightedSum / weightTotal; + if(qAbs(nextCenter - center) <= 1.0e-12) { + center = nextCenter; + break; + } - if(error1 > 1e9 && error2 > 1e9) { - combinedError = 1e10; - } else if(error1 > 1e9) { - combinedError = error2; - } else if(error2 > 1e9) { - combinedError = error1; - } else { - combinedError = 0.5 * error1 + 0.5 * error2; - } + center = nextCenter; + } - DEBUG_OUT(QString("LogLog errors: Curve1=%1, Curve2=%2, Combined=%3") - .arg(error1, 0, 'e', 4).arg(error2, 0, 'e', 4).arg(combinedError, 0, 'e', 4)); + return center; + }; + + // 整体压力/导数误差用于排序；verticalLoss 主要用于解释整体上下偏移。 + breakdown.pressureLoss = huberRms(pressureResidual, 0, numPoints); + breakdown.derivativeLoss = huberRms(derivativeResidual, 0, numPoints); + breakdown.verticalBiasPressure = huberCenter(pressureResidual); + breakdown.verticalBiasDerivative = huberCenter(derivativeResidual); + breakdown.verticalLoss = + 0.5 * (qAbs(breakdown.verticalBiasPressure) + + qAbs(breakdown.verticalBiasDerivative)); + + // 去除上下中心后，把残差投影到目标曲线斜率上估计左右偏移。 + // residual ~= -physicalShift * targetSlope，因此 physicalShift 取回归系数的相反数。 + // 这是局部一阶近似，用于判断方向和大小，不等同于再次优化时间轴。 + double horizontalNumerator = 0.0; + double horizontalDenominator = 0.0; + + for(int i = 0; i < numPoints; ++i) { + if(!isFiniteNumber(pressureResidual[i]) || + !isFiniteNumber(derivativeResidual[i])) { + continue; + } + + double pressureCentered = + pressureResidual[i] - breakdown.verticalBiasPressure; + double derivativeCentered = + derivativeResidual[i] - breakdown.verticalBiasDerivative; + horizontalNumerator += pressureSlope[i] * pressureCentered + + derivativeSlope[i] * derivativeCentered; + horizontalDenominator += pressureSlope[i] * pressureSlope[i] + + derivativeSlope[i] * derivativeSlope[i]; + } + + breakdown.horizontalShift = horizontalDenominator > 1.0e-12 + ? horizontalNumerator / + horizontalDenominator + : 0.0; + breakdown.horizontalPhysicalShift = -breakdown.horizontalShift; + breakdown.horizontalLoss = qAbs(breakdown.horizontalShift); + + // 从原始残差中扣除“整体上下 + 等效左右”两部分，剩余项才作为形状误差。 + // 因此 shapeLoss 较大而 vertical/horizontal 较小时，说明主要是曲率、拐点 + // 或导数变化趋势不一致，而不是简单的整体平移。 + QVector shapePressure(numPoints, + std::numeric_limits::quiet_NaN()); + QVector shapeDerivative(numPoints, + std::numeric_limits::quiet_NaN()); + + for(int i = 0; i < numPoints; ++i) { + if(isFiniteNumber(pressureResidual[i])) { + shapePressure[i] = pressureResidual[i] - + breakdown.verticalBiasPressure - + breakdown.horizontalShift * pressureSlope[i]; + } - return qMin(1e9, combinedError); + if(isFiniteNumber(derivativeResidual[i])) { + shapeDerivative[i] = derivativeResidual[i] - + breakdown.verticalBiasDerivative - + breakdown.horizontalShift * + derivativeSlope[i]; + } + } + breakdown.shapeLoss = + 0.5 * (huberRms(shapePressure, 0, numPoints) + + huberRms(shapeDerivative, 0, numPoints)); + + // 将对数时间网格分成早、中、晚三段，用于定位误差集中出现的阶段。 + // 网格本身按 log(time) 均匀分布，所以三段对应的是时间数量级，而非原始 + // 线性时间长度，适合双对数试井曲线的早期/中期/晚期判读。 + const int segment1 = numPoints / 3; + const int segment2 = (2 * numPoints) / 3; + breakdown.pressureEarlyLoss = huberRms(pressureResidual, 0, segment1); + breakdown.pressureMiddleLoss = + huberRms(pressureResidual, segment1, segment2); + breakdown.pressureLateLoss = + huberRms(pressureResidual, segment2, numPoints); + breakdown.derivativeEarlyLoss = + huberRms(derivativeResidual, 0, segment1); + breakdown.derivativeMiddleLoss = + huberRms(derivativeResidual, segment1, segment2); + breakdown.derivativeLateLoss = + huberRms(derivativeResidual, segment2, numPoints); + + // 函数入口已将 m_lastObjectiveBreakdown 重置为无效状态，因此这里直接返回 + // invalidLoss 时不会把上一候选的误差分解误报给调用方。 + // 导数、压力或形状无法形成有效统计时，整个候选都视为无效，避免 NaN + // 进入粒子排序。 + if(!isFiniteNumber(breakdown.pressureLoss) || + !isFiniteNumber(breakdown.derivativeLoss) || + !isFiniteNumber(breakdown.shapeLoss) || + !isFiniteNumber(breakdown.verticalLoss)) { + return invalidLoss; + } + + // 当前总损失作为 fitness 用于粒子比较、收敛/停止判断；上下、左右、形状和 + // 分段分量先作为诊断输出，不在本次改动中直接参与参数更新。 + breakdown.total = 0.5 * breakdown.pressureLoss + + 0.5 * breakdown.derivativeLoss + + 0.1 * breakdown.coveragePenalty; + breakdown.valid = isFiniteNumber(breakdown.total) && + breakdown.total >= 0.0; + m_lastObjectiveBreakdown = breakdown; + + DEBUG_OUT(QString("LogLog objective: pressure=%1, derivative=%2, vertical=%3, horizontal=%4, shape=%5, coverage=%6, total=%7") + .arg(breakdown.pressureLoss, 0, 'e', 4) + .arg(breakdown.derivativeLoss, 0, 'e', 4) + .arg(breakdown.verticalLoss, 0, 'e', 4) + .arg(breakdown.horizontalLoss, 0, 'e', 4) + .arg(breakdown.shapeLoss, 0, 'e', 4) + .arg(breakdown.coverage, 0, 'f', 4) + .arg(breakdown.total, 0, 'e', 4)); + + return breakdown.valid ? qMin(1.0e9, breakdown.total) : invalidLoss; } catch(const std::exception& e) { DEBUG_OUT(QString("Exception in LogLog error calculation: %1").arg(e.what())); - return 1e10; + return invalidLoss; } catch(...) { DEBUG_OUT("Unknown exception in LogLog error calculation"); - return 1e10; + return invalidLoss; } } From efda31f3885c15e25cdb9f3910a5b5e373f4e713 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Thu, 13 Aug 2026 10:14:37 +0800 Subject: [PATCH 06/12] =?UTF-8?q?=E4=BC=98=E5=8C=96=E8=87=AA=E5=8A=A8?= =?UTF-8?q?=E6=8B=9F=E5=90=88=EF=BC=8C=E5=BC=95=E5=85=A5=E7=81=B5=E6=95=8F?= =?UTF-8?q?=E5=BA=A6=E5=88=86=E6=9E=90=E4=B8=8E=E4=BF=A1=E8=B5=96=E5=9F=9F?= =?UTF-8?q?=E6=B1=82=E8=A7=A3?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nmCalculation/nmCalculationAutoFitPSO.h | 117 +- Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h | 2 +- .../nmCalculation/nmCalculationAutoFitPSO.cpp | 2414 +++++++++++++++-- Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp | 40 +- 4 files changed, 2210 insertions(+), 363 deletions(-) diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h index e65d1ee..b3e201a 100644 --- a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h +++ b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h @@ -20,64 +20,66 @@ class nmDataWellBase; class QTimer; class QProcess; -// 双对数曲线误差分解，供误差诊断和后续参数调整读取。 -// -// 所有 pressure/derivative/shape 数值均是在 log(value) 空间计算的无量纲误差。 -// verticalBias* 保留正负号：正值表示模拟曲线整体高于目标，负值表示整体低于目标。 -// horizontalPhysicalShift 是 log(time) 方向的等效平移量，正值表示模拟曲线相对目标偏右。 -// total 仍是 PSO 当前使用的 fitness（用于粒子比较和收敛判断）；其余字段只描述误差 -// 来源，不会在本次改动中直接修改粒子参数，避免损失诊断和参数更新策略互相耦合。 +// 双对数曲线误差分解。该结构同时保存用于候选排序的主目标，以及用于判断 +// 曲线上下、左右和形状偏差的诊断量。total 是唯一的接受和排序依据，诊断量 +// 只参与信赖域选参，不能再次叠加到 total，否则会重复计算同一批曲线残差。 struct AutoFitObjectiveBreakdown { - // valid/total 是本次评价是否有效及其最终 fitness（用于排序和收敛判断）。 + // valid 表示本次曲线评价完整有效；无效评价统一保留 total=1e10。 + // pressureLoss 和 derivativeLoss 均在 log(value) 空间按固定网格计算。 bool valid; double total; - // pressureLoss/derivativeLoss 是压力和导数两条曲线的整体误差。 double pressureLoss; double derivativeLoss; - // vertical* 描述整体上下偏移；保留 bias 的符号以判断偏高或偏低。 - double verticalBiasPressure; - double verticalBiasDerivative; + // 固定目标网格上的 Huber 等效残差。非代理搜索使用它建立完整 Jacobian， + // 向量平方和与 total 的平方一致。 + QVector residualVector; + + // 上下偏差使用压力和导数残差共享的 Huber 稳健中心。 + // verticalCommonBias 为正表示模拟曲线整体偏高，为负表示整体偏低； + // verticalReliable=false 时仍保留数值，但不能据此确定参数调整方向。 + double verticalCommonBias; double verticalLoss; - // horizontal* 描述等效的对数时间偏移；physicalShift 为正表示模拟曲线相对目标向右 - // （时间延迟），为负表示向左。 - double horizontalShift; + bool verticalReliable; + + // 水平偏差在 log(time) 坐标中计算。physicalShift 为正表示模拟曲线相对 + // 目标偏右，即相同曲线特征在模拟结果中出现得更晚。 double horizontalPhysicalShift; double horizontalLoss; - // shapeLoss 是去除整体上下和左右偏移后剩余的曲线形状差异。 + bool horizontalReliable; + // true 表示当前曲线无法可靠区分上下和左右误差；此时禁止使用两类有符号 + // 诊断量选参，但去除公共中心后的 shapeLoss 仍可用于局部选参。 + bool registrationAmbiguous; + + // 去除稳健公共中心和可信左右偏差后剩余的整体形状误差；verticalReliable + // 只控制能否把公共中心解释为上下参数方向，不改变 shape 的中心化公式。 double shapeLoss; - // 早、中、晚分段误差用于定位误差主要出现在哪个时间阶段。 - double pressureEarlyLoss; - double pressureMiddleLoss; - double pressureLateLoss; - double derivativeEarlyLoss; - double derivativeMiddleLoss; - double derivativeLateLoss; - // coverage 是 50 点目标网格上的 min(有效点比例、连续 log-time 跨度比例)。 - // coveragePenalty 是归一化覆盖缺口的平方惩罚，并以 0.1 权重加入 total。 + + // 兼容现有 trace 列。当前非代理搜索不再单独识别或调度晚期分量。 + double lateDerivativeSlopeBias; + double lateDerivativeTrendLoss; + bool lateDerivativeTrendReliable; + + // 模拟曲线对目标固定网格的有效覆盖率，取覆盖点比例与连续 log-time + // 跨度比例中的较小值。低于损失函数门槛时本次评价直接无效。 double coverage; - double coveragePenalty; - // 无效评价使用 1e10 作为统一的“差解”标记；其他字段用 NaN 表示尚未得到诊断值。 AutoFitObjectiveBreakdown() : valid(false) , total(1.0e10) , pressureLoss(std::numeric_limits::quiet_NaN()) , derivativeLoss(std::numeric_limits::quiet_NaN()) - , verticalBiasPressure(std::numeric_limits::quiet_NaN()) - , verticalBiasDerivative(std::numeric_limits::quiet_NaN()) + , verticalCommonBias(std::numeric_limits::quiet_NaN()) , verticalLoss(std::numeric_limits::quiet_NaN()) - , horizontalShift(std::numeric_limits::quiet_NaN()) + , verticalReliable(false) , horizontalPhysicalShift(std::numeric_limits::quiet_NaN()) , horizontalLoss(std::numeric_limits::quiet_NaN()) + , horizontalReliable(false) + , registrationAmbiguous(false) , shapeLoss(std::numeric_limits::quiet_NaN()) - , pressureEarlyLoss(std::numeric_limits::quiet_NaN()) - , pressureMiddleLoss(std::numeric_limits::quiet_NaN()) - , pressureLateLoss(std::numeric_limits::quiet_NaN()) - , derivativeEarlyLoss(std::numeric_limits::quiet_NaN()) - , derivativeMiddleLoss(std::numeric_limits::quiet_NaN()) - , derivativeLateLoss(std::numeric_limits::quiet_NaN()) + , lateDerivativeSlopeBias(std::numeric_limits::quiet_NaN()) + , lateDerivativeTrendLoss(std::numeric_limits::quiet_NaN()) + , lateDerivativeTrendReliable(false) , coverage(std::numeric_limits::quiet_NaN()) - , coveragePenalty(std::numeric_limits::quiet_NaN()) {} }; @@ -95,6 +97,8 @@ struct AutoFitParticle { QVector velocity; // 速度 QVector bestPosition; // 真实求解器确认的个体最优位置 QVector guideBestPosition; // 仅用于速度更新的引导位置；不会参与真实 gbest/最终结果 + AutoFitObjectiveBreakdown currentObjectiveBreakdown; // 当前真实评价对应的误差分解 + AutoFitObjectiveBreakdown bestObjectiveBreakdown; // pbest 对应的误差分解 double fitness; // 当前适应度 double bestFitness; // 真实求解器确认的个体最优适应度 double guideBestObjective; // guideBestPosition 对应的真实或代理目标值 @@ -200,20 +204,26 @@ private: void loadOptimizationConfig(); void loadParameterBounds(); - // ===== PSO核心算法 ===== + // ===== 自动拟合核心算法 ===== // - // 主流程： - // 1. extractUserInitialValues(): 从当前项目数据中取用户已有初始解； - // 2. initializeSwarm(): 根据初始解和上下界生成粒子群； - // 3. updateParticle(): 对单个粒子跑真实求解器并计算误差； - // 4. updateGlobalBest(): 只用真实求解器误差更新全局最优； - // 5. updateVelocityAndPosition(): 按 PSO 公式推进下一代粒子。 + // 代理开启时保留原 PSO 筛选流程；代理关闭时使用真实求解器驱动的 + // 诊断灵敏度信赖域搜索，不依赖 pbest/gbest 速度公式。 void extractUserInitialValues(); void initializeSwarm(); void updateVelocityAndPosition(); double evaluateFitness(const QVector& parameters); void updateGlobalBest(); void updateParticle(int particleIndex); + // 非代理拟合入口：建立有限差分灵敏度，按诊断分量选择参数，再用有界 + // LM/信赖域产生候选；所有候选最终都由真实求解器总误差决定是否接受。 + StopReasonPSO runTrustRegionFitting(); + // 对一个信赖域候选执行完整真实评价，并一次性返回误差、诊断量、曲线和耗时。 + // 返回 false 表示求解失败、损失无效或用户已请求停止。 + bool evaluateTrustRegionPoint(const QVector& parameters, + double* fitness, + AutoFitObjectiveBreakdown* breakdown, + QVector >* curve, + int* elapsedMs); // ===== 参数应用方法 ===== // @@ -284,7 +294,8 @@ private: double surrogateObjective, const QString& screeningDecision, const QVector& pbestPosition, - double pbestObjective); + double pbestObjective, + const AutoFitObjectiveBreakdown* objectiveBreakdown = nullptr); void writeIterationTraceRows(); QVector buildTraceParameterVector(const QVector& selectedParameters) const; void resetRunSummary(); @@ -340,19 +351,21 @@ private: bool m_isRunning; // 当前是否有一次自动拟合正在运行。 bool m_shouldStop; // 用户停止标志；主循环和求解器等待循环会定期检查它。 bool m_isPaused; // 预留暂停标志；主循环中有暂停等待逻辑。 - int m_currentIteration; // 当前 PSO 迭代序号，从 0 开始。 + int m_currentIteration; // 当前自动拟合迭代序号，从 0 开始。 QString m_lastError; // 最近一次失败原因，供 UI 展示或日志排查。 - // ===== PSO数据 ===== + // ===== 优化状态数据 ===== QVector m_initialValues; // 当前模型中提取的用户初始值，顺序与 m_enabledParamIndices 一致。 QVector m_swarm; // 粒子群，每个粒子只保存启用参数维度。 - QVector m_globalBestPosition; // 全局最优参数，仍是启用参数向量。 + QVector m_globalBestPosition; // 真实求解器确认的当前最优参数。 double m_globalBestFitness; // 全局最优真实误差，越小越好。 double m_previousBestFitness; // 上一轮全局最优误差，用于自适应参数更新。 + AutoFitObjectiveBreakdown m_globalBestObjectiveBreakdown; // 真实 gbest 对应的误差分解。 QVector > m_lastEvaluatedLogLogData; // 最近一次真实求解得到的 result log-log 曲线。 QVector > m_globalBestLogLogData; // 当前全局最优对应的 result log-log 曲线。 mutable AutoFitObjectiveBreakdown m_lastObjectiveBreakdown; // 最近一次损失评价的误差分解。 QVector > m_userInitialLogLogData; // 用户初始解对应的 result log-log 曲线，用于精英保护。 + AutoFitObjectiveBreakdown m_userInitialObjectiveBreakdown; // 用户初始解对应的误差分解。 // ===== 优化配置 ===== // @@ -377,7 +390,7 @@ private: double m_socialParam; // 群体学习因子，控制粒子靠近全局 gbest 的程度。 // ===== 统计信息 ===== - int m_totalEvaluations; // 已调用真实求解器评价的粒子总数。 + int m_totalEvaluations; // 真实求解器评价总次数，包含粒子评价和方向试算。 int m_successfulEvaluations; // 真实求解器成功且误差有效的评价次数。 QVector m_convergenceHistory; // 每代全局最优误差历史，用于收敛判断。 @@ -393,7 +406,7 @@ private: // ===== 精英保护 ===== QVector m_userInitialSolution; // 用户初始解参数，若最终改进不足会恢复它。 double m_userInitialFitness; // 用户初始解真实误差。 - double m_improvementThreshold; // 最终结果相对初始解至少需要达到的改进阈值。 + double m_improvementThreshold; // 仅用于日志区分显著改进和微小改进。 bool m_hasValidUserSolution; // 初始解是否成功跑过真实求解器。 int m_consecutiveFailedIterations; // 连续失败迭代次数 @@ -422,8 +435,8 @@ private: // 这些字段只描述代理筛选和运行复盘，不参与 PSO 数学更新。 bool m_traceEnabled; // 是否写出 trace CSV/meta 文件。 QString m_traceRunId; // 本次运行 ID，作为 trace/candidate/score 文件名的一部分。 - QString m_traceFilePath; // pso_baseline_trace_.csv 完整路径。 - QString m_traceMetaFilePath; // pso_baseline_trace_.meta.json 完整路径。 + QString m_traceFilePath; // 本次自动拟合 trace CSV 的完整路径。 + QString m_traceMetaFilePath; // 与 trace 匹配的 meta JSON 完整路径。 QFile m_traceFile; // trace CSV 文件句柄。 bool m_surrogateScreeningEnabled; // 用户配置中的 PSO acceleration 开关。 unsigned int m_psoRandomSeed; // PSO 随机种子，也用于可复现 random audit。 diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h index 30a9119..5b3848c 100644 --- a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h +++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h @@ -57,7 +57,7 @@ private: void renumberVisibleParameterRows(QTableWidget* table); void updateParameterVisibility(QTableWidget* table, NM_SOLVER_MODEL_TYPE eType); void initializeSuggestedParameterRanges(); - void updateRangeForParameter(int parameterIndex, double centerValue, bool afterFit); + void updateRangeForParameter(int parameterIndex, double centerValue); void setParameterRange(int parameterIndex, double minValue, double maxValue); bool getPhysicalParameterRange(int parameterIndex, double& minValue, double& maxValue); void normalizeSavedParameterRanges(); diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index ccc05ea..a4eb476 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -104,6 +104,17 @@ static inline bool isFiniteNumber(double value) #endif } +// 两个 Huber RMS 的差不能直接解释为被消除的独立误差。RMS 的平方才对应 +// 稳健能量，因此先计算 reduced^2-full^2，再开方恢复原量纲。这里用于分别 +// 提取“消除公共上下偏差”和“消除水平位移”实际减少的误差贡献。 +static double nestedRmsContribution(double reducedModelLoss, + double fullModelLoss) +{ + return qSqrt(qMax(0.0, + reducedModelLoss * reducedModelLoss - + fullModelLoss * fullModelLoss)); +} + static inline bool isInClosedRange(double value, double lower, double upper) { return isFiniteNumber(value) && value >= lower && value <= upper; @@ -813,10 +824,12 @@ void nmCalculationAutoFitPSO::resetOptimizer() m_globalBestPosition.clear(); m_globalBestFitness = 1e10; m_previousBestFitness = 1e10; + m_globalBestObjectiveBreakdown = AutoFitObjectiveBreakdown(); m_lastEvaluatedLogLogData.clear(); m_globalBestLogLogData.clear(); m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); m_userInitialLogLogData.clear(); + m_userInitialObjectiveBreakdown = AutoFitObjectiveBreakdown(); m_currentIteration = 0; m_totalEvaluations = 0; m_successfulEvaluations = 0; @@ -844,9 +857,8 @@ void nmCalculationAutoFitPSO::setPSOTargetWellName(const QString& wellName) void nmCalculationAutoFitPSO::initializeTraceFile() { - // 创建本次 PSO 的可复盘文件： - // - pso_baseline_trace_.csv：逐代逐粒子的参数、真实误差、代理误差和筛选决策； - // - pso_baseline_trace_.meta.json：目标曲线、流量制度、参数上下界、PSO/代理配置。 + // 创建本次自动拟合的可复盘文件。代理 PSO 与非代理信赖域使用不同前缀， + // 防止代理回放脚本把信赖域记录误当成最新 PSO 粒子记录。 // // 代理模型评分脚本也会读取 meta.json，因此 trace meta 不是单纯日志，而是C++ 与 Python 代理模型之间的运行上下文契约。 if(!m_traceEnabled) { @@ -865,8 +877,13 @@ void nmCalculationAutoFitPSO::initializeTraceFile() return; } - m_traceFilePath = traceDir.absoluteFilePath(QString("pso_baseline_trace_%1.csv").arg(m_traceRunId)); - m_traceMetaFilePath = traceDir.absoluteFilePath(QString("pso_baseline_trace_%1.meta.json").arg(m_traceRunId)); + QString tracePrefix = isSurrogateScreeningEnabled() + ? "pso_baseline_trace" + : "trust_region_trace"; + m_traceFilePath = traceDir.absoluteFilePath( + QString("%1_%2.csv").arg(tracePrefix).arg(m_traceRunId)); + m_traceMetaFilePath = traceDir.absoluteFilePath( + QString("%1_%2.meta.json").arg(tracePrefix).arg(m_traceRunId)); m_traceFile.setFileName(m_traceFilePath); if(!m_traceFile.open(QIODevice::WriteOnly | QIODevice::Text)) { @@ -878,11 +895,12 @@ void nmCalculationAutoFitPSO::initializeTraceFile() writeTraceHeader(); writeTraceMetaFile(); - DEBUG_OUT(QString("PSO baseline trace initialized: %1").arg(m_traceFilePath)); - emit logMessageGenerated(tr("PSO baseline trace: %1").arg(m_traceFilePath)); + DEBUG_OUT(QString("Automatic fitting trace initialized: %1").arg(m_traceFilePath)); + emit logMessageGenerated(tr("Automatic fitting trace: %1").arg(m_traceFilePath)); if(!m_traceMetaFilePath.isEmpty()) { - emit logMessageGenerated(tr("PSO baseline trace meta: %1").arg(m_traceMetaFilePath)); + emit logMessageGenerated( + tr("Automatic fitting trace meta: %1").arg(m_traceMetaFilePath)); } emit logMessageGenerated(tr("PSO surrogate screening: %1, model=%2, keep=%3, audit=%4, warmup=%5, min_solver=%6") @@ -997,7 +1015,8 @@ void nmCalculationAutoFitPSO::writeTraceHeader() // - solver_objective 是真实求解器误差； // - surrogate_objective 是 Python 代理评分； // - screening_decision 说明该粒子为什么跑/不跑真实求解器； - // - pbest/gbest 字段用于离线复盘 PSO 更新是否只依赖真实误差。 + // - pbest/gbest 字段用于离线复盘 PSO 更新是否只依赖真实误差； + // - 末尾诊断字段记录同一次真实评价的分量误差，便于核对引导方向和接受结果。 if(!m_traceFile.isOpen()) { return; } @@ -1035,7 +1054,26 @@ void nmCalculationAutoFitPSO::writeTraceHeader() << "gbest_h" << "gbest_Ct" << "gbest_Cf" - << "enabled_param_indices"; + << "enabled_param_indices" + << "pressure_loss" + << "derivative_loss"; + if(!isSurrogateScreeningEnabled()) { + cols << "vertical_common_bias"; + } + cols << "vertical_loss"; + if(!isSurrogateScreeningEnabled()) { + cols << "vertical_reliable" + << "horizontal_physical_shift"; + } + cols << "horizontal_loss"; + if(!isSurrogateScreeningEnabled()) { + cols << "horizontal_reliable"; + } + cols << "shape_loss" + << "late_trend_loss" + << "late_slope_bias" + << "late_trend_reliable" + << "registration_ambiguous"; QTextStream out(&m_traceFile); out << cols.join(",") << "\n"; @@ -1077,8 +1115,14 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile() QTextStream out(&metaFile); out << "{\n"; - out << " \"schema_version\": 1,\n"; - out << " \"trace_type\": \"pso_baseline_replay_meta\",\n"; + // 非代理 v5 增加带符号诊断列；代理 PSO 保留原 v3 字段和目标，避免改变 + // 已有模型的训练和回放契约。 + out << " \"schema_version\": " + << (isSurrogateScreeningEnabled() ? 3 : 5) << ",\n"; + out << " \"trace_type\": " + << jsonEscape(isSurrogateScreeningEnabled() + ? "pso_baseline_replay_meta" + : "diagnostic_trust_region_meta") << ",\n"; out << " \"run_id\": " << jsonEscape(m_traceRunId) << ",\n"; out << " \"created_at\": " << jsonEscape(QDateTime::currentDateTime().toString(Qt::ISODate)) << ",\n"; out << " \"trace_csv\": " << jsonEscape(QFileInfo(m_traceFilePath).fileName()) << ",\n"; @@ -1180,7 +1224,8 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, double surrogateObjective, const QString& screeningDecision, const QVector& pbestPosition, - double pbestObjective) + double pbestObjective, + const AutoFitObjectiveBreakdown* objectiveBreakdown) { // 写一行 trace。generation=-1/particleIndex=-1 表示用户初始解； // 普通粒子行的 phase 为 particle_solver、particle_verified_cache 或 particle_not_evaluated。 @@ -1234,6 +1279,35 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, << traceParamAt(gbestParams, 6) << csvEscape(enabledIndices.join(";")); + if(objectiveBreakdown && objectiveBreakdown->valid) { + cols << traceNumber(objectiveBreakdown->pressureLoss) + << traceNumber(objectiveBreakdown->derivativeLoss); + if(!isSurrogateScreeningEnabled()) { + cols << traceNumber(objectiveBreakdown->verticalCommonBias); + } + cols << traceNumber(objectiveBreakdown->verticalLoss); + if(!isSurrogateScreeningEnabled()) { + cols << QString::number(objectiveBreakdown->verticalReliable ? 1 : 0) + << traceNumber(objectiveBreakdown->horizontalPhysicalShift); + } + cols << traceNumber(objectiveBreakdown->horizontalLoss); + if(!isSurrogateScreeningEnabled()) { + cols << QString::number(objectiveBreakdown->horizontalReliable ? 1 : 0); + } + cols << traceNumber(objectiveBreakdown->shapeLoss) + << traceNumber(objectiveBreakdown->lateDerivativeTrendLoss) + << traceNumber(objectiveBreakdown->lateDerivativeSlopeBias) + << QString::number(objectiveBreakdown->lateDerivativeTrendReliable ? 1 : 0) + << QString::number(objectiveBreakdown->registrationAmbiguous ? 1 : 0); + } else { + // 未运行真实求解器或评价无效时保持列数一致，诊断字段写空值。 + int diagnosticColumnCount = + isSurrogateScreeningEnabled() ? 9 : 13; + for(int i = 0; i < diagnosticColumnCount; ++i) { + cols << QString(); + } + } + QTextStream out(&m_traceFile); out << cols.join(",") << "\n"; m_traceFile.flush(); @@ -1265,9 +1339,11 @@ void nmCalculationAutoFitPSO::writeIterationTraceRows() particle.lastEvaluationSuccess, particle.lastEvaluationElapsedMs, particle.surrogateObjective, - particle.screeningDecision, - particle.bestPosition, - particle.bestFitness); + particle.screeningDecision, + particle.bestPosition, + particle.bestFitness, + particle.evaluatedThisIteration + ? &particle.currentObjectiveBreakdown : nullptr); } } @@ -2855,21 +2931,19 @@ void nmCalculationAutoFitPSO::loadParameterBounds() .arg(m_enabledParamIndices.size())); } -// ==================== PSO算法核心方法 ==================== +// ==================== 自动拟合核心方法 ==================== bool nmCalculationAutoFitPSO::startAutoFitting() { - // 自动拟合的总入口。可以把这个函数当成 PSO 的“运行剧本”： - // 读取配置 -> 校验输入 -> 评价用户初始解 -> 初始化粒子群 -> - // 按代循环评价粒子 -> 更新全局最优 -> 判断停止 -> 保存结果。 + // 自动拟合总入口：代理开启时保留原 PSO 筛选流程；代理关闭时改走 + // 诊断灵敏度信赖域搜索。两条路径共用初始解评价、真实求解器和结果写回。 + StopReasonPSO finalReason = PSO_CONTINUE_OPTIMIZATION; + bool useParticleSwarm = false; + if(m_isRunning) { m_lastError = "Auto fitting is already running"; return false; } - // 发送初始化日志 - //emit logMessageGenerated(tr("=== PSO Automatic Fitting Started ===")); - emit logMessageGenerated(tr("Algorithm: Particle Swarm Optimization")); - try { // 从 DataManager 读取界面保存的自动拟合配置。 // 本类不直接依赖 UI 控件，便于后续从脚本或其他入口复用。 @@ -2878,6 +2952,11 @@ bool nmCalculationAutoFitPSO::startAutoFitting() return false; } + useParticleSwarm = isSurrogateScreeningEnabled(); + emit logMessageGenerated(useParticleSwarm + ? tr("Algorithm: Particle Swarm Optimization") + : tr("Algorithm: Diagnostic Trust-Region Search")); + if(m_simulationMode) { // 调试/演示用快速路径，不调用真实求解器。正式工况通常不走这里。 DEBUG_OUT("=== SIMULATION MODE ACTIVATED ==="); @@ -3007,6 +3086,8 @@ bool nmCalculationAutoFitPSO::startAutoFitting() m_globalBestPosition = m_userInitialSolution; m_userInitialLogLogData = m_lastEvaluatedLogLogData; m_globalBestLogLogData = m_userInitialLogLogData; + m_userInitialObjectiveBreakdown = m_lastObjectiveBreakdown; + m_globalBestObjectiveBreakdown = m_userInitialObjectiveBreakdown; emit logMessageGenerated(tr("Initial solution evaluation successful")); emit logMessageGenerated(tr("Initial Error: %1").arg(m_userInitialFitness, 0, 'e', 4)); @@ -3025,9 +3106,11 @@ bool nmCalculationAutoFitPSO::startAutoFitting() m_userInitialFitness < 1e9, initialEvalElapsedMs, std::numeric_limits::quiet_NaN(), - "initial_solution", - m_hasValidUserSolution ? m_userInitialSolution : QVector(), - m_userInitialFitness); + "initial_solution", + m_hasValidUserSolution ? m_userInitialSolution : QVector(), + m_userInitialFitness, + m_hasValidUserSolution + ? &m_userInitialObjectiveBreakdown : nullptr); } catch(...) { m_hasValidUserSolution = false; emit logMessageGenerated(tr("Exception during initial solution evaluation")); @@ -3037,12 +3120,13 @@ bool nmCalculationAutoFitPSO::startAutoFitting() m_initialValues = savedInitialValues; } - // 初始化粒子群。粒子维度等于用户勾选的参数数量，而不是固定 11 维。 - if(kUseFixedPsoSeed) { - emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed)); - } else { - emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed)); - } + if(useParticleSwarm) { + // 初始化粒子群。粒子维度等于用户勾选的参数数量，而不是固定 11 维。 + if(kUseFixedPsoSeed) { + emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed)); + } else { + emit logMessageGenerated(tr("PSO random seed: %1 ").arg(m_psoRandomSeed)); + } initializeSwarm(); emit logMessageGenerated(tr("Swarm initialized: %1 particles, %2 dimensions").arg(m_swarmSize).arg(getEnabledParameterCount())); @@ -3191,9 +3275,7 @@ bool nmCalculationAutoFitPSO::startAutoFitting() .arg(currentSuccessRate * 100, 0, 'f', 1).arg(m_currentIteration + 1)); } - // 更新全局最优。updateGlobalBest() 只读取粒子的真实 bestFitness， - // 不使用代理模型的 surrogateObjective。 - //double previousGlobalBest = m_globalBestFitness; + // 只从真实求解器确认的粒子 bestFitness 更新全局最优，不使用代理分数。 updateGlobalBest(); // 记录本代所有粒子的真实/代理误差和筛选决策，用于复盘和排障。 writeIterationTraceRows(); @@ -3282,11 +3364,40 @@ bool nmCalculationAutoFitPSO::startAutoFitting() } } + finalReason = analyzeOptimizationStatus(); + } else { + // 非代理路径不初始化粒子，也不使用 pbest/gbest 速度更新。 + finalReason = runTrustRegionFitting(); + } + // 最终结果验证和保护 validateAndProtectFinalResult(); + if(!useParticleSwarm && !m_globalBestPosition.isEmpty() && + m_globalBestObjectiveBreakdown.valid) { + // 精英保护可能恢复用户初始解，最终行必须在保护之后写入，确保 trace + // 中最后记录的就是实际回写 DataManager 的参数，而非最后一次接受候选。 + writeTraceRow(m_currentIteration, -1, + "trust_region_final", + m_globalBestPosition, + m_globalBestFitness, + m_globalBestFitness < 1.0e9, + -1, + std::numeric_limits::quiet_NaN(), + "final_result", + m_globalBestPosition, + m_globalBestFitness, + &m_globalBestObjectiveBreakdown); + } + + if(m_globalBestFitness < m_targetError) { + finalReason = PSO_TARGET_ACHIEVED; + } + } catch(const std::exception& e) { - m_lastError = QString(tr("Critical exception in PSO main loop: %1")).arg(e.what()); + m_lastError = useParticleSwarm + ? QString(tr("Critical exception in PSO main loop: %1")).arg(e.what()) + : QString(tr("Critical exception in automatic fitting: %1")).arg(e.what()); emit logMessageGenerated(tr("CRITICAL ERROR: %1").arg(e.what())); closeTraceFile(); cleanupTemporaryDirectory(); @@ -3294,7 +3405,9 @@ bool nmCalculationAutoFitPSO::startAutoFitting() emit fittingFinished(false, m_lastError); return false; } catch(...) { - m_lastError = QString(tr("Unknown critical exception in PSO main loop")); + m_lastError = useParticleSwarm + ? QString(tr("Unknown critical exception in PSO main loop")) + : QString(tr("Unknown critical exception in automatic fitting")); emit logMessageGenerated(tr("CRITICAL ERROR: Unknown exception in PSO main loop")); closeTraceFile(); cleanupTemporaryDirectory(); @@ -3373,43 +3486,68 @@ bool nmCalculationAutoFitPSO::startAutoFitting() // 判断系统确定最终结果 bool success; QString message; - StopReasonPSO finalReason = analyzeOptimizationStatus(); if(finalReason == PSO_TARGET_ACHIEVED) { success = true; message = QString(tr("Target achieved. Best error: %1, Iterations: %2")) .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION SUCCESSFUL ===")); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION SUCCESSFUL ===") + : tr("=== AUTOMATIC FITTING SUCCESSFUL ===")); } else if(finalReason == PSO_TRUE_CONVERGENCE) { success = true; - message = QString(tr("PSO optimization converged to stable solution. Best error: %1, Iterations: %2")) - .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION CONVERGED ===")); + message = useParticleSwarm + ? QString(tr("PSO optimization converged to stable solution. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1) + : QString(tr("Automatic fitting converged to a stable solution. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION CONVERGED ===") + : tr("=== AUTOMATIC FITTING CONVERGED ===")); } else if(finalReason == PSO_LOCAL_OPTIMUM) { success = true; - message = QString(tr("PSO optimization trapped in local optimum. Best error: %1, Iterations: %2")) - .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION - LOCAL OPTIMUM ===")); + message = useParticleSwarm + ? QString(tr("PSO optimization trapped in local optimum. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1) + : QString(tr("Automatic fitting reached a local optimum. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION - LOCAL OPTIMUM ===") + : tr("=== AUTOMATIC FITTING - LOCAL OPTIMUM ===")); } else if(finalReason == PSO_MAX_ITERATIONS) { success = true; message = QString(tr("Max iterations reached. Best error: %1, Iterations: %2")) .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION - MAX ITERATIONS ===")); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION - MAX ITERATIONS ===") + : tr("=== AUTOMATIC FITTING - MAX ITERATIONS ===")); } else if(finalReason == PSO_USER_STOPPED) { success = true; message = QString(tr("Best error: %1, Iterations: %2")) .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION STOPPED BY USER ===")); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION STOPPED BY USER ===") + : tr("=== AUTOMATIC FITTING STOPPED BY USER ===")); } else if(finalReason == PSO_CONSECUTIVE_FAILURES) { success = false; - message = QString(tr("PSO optimization failed due to consecutive failures. Best error: %1, Iterations: %2")) - .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION FAILED ===")); + message = useParticleSwarm + ? QString(tr("PSO optimization failed due to consecutive failures. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1) + : QString(tr("Automatic fitting failed due to consecutive failures. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION FAILED ===") + : tr("=== AUTOMATIC FITTING FAILED ===")); } else { success = false; - message = QString(tr("PSO optimization ended unexpectedly. Best error: %1, Iterations: %2")) - .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); - emit logMessageGenerated(tr("=== PSO OPTIMIZATION - UNKNOWN END ===")); + message = useParticleSwarm + ? QString(tr("PSO optimization ended unexpectedly. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1) + : QString(tr("Automatic fitting ended unexpectedly. Best error: %1, Iterations: %2")) + .arg(m_globalBestFitness, 0, 'e', 4).arg(m_currentIteration + 1); + emit logMessageGenerated(useParticleSwarm + ? tr("=== PSO OPTIMIZATION - UNKNOWN END ===") + : tr("=== AUTOMATIC FITTING - UNKNOWN END ===")); } if(!finalFullSolverSucceeded) { @@ -3544,6 +3682,8 @@ void nmCalculationAutoFitPSO::initializeSwarm() particle.velocity.resize(dimensions); particle.bestPosition.resize(dimensions); particle.guideBestPosition.resize(dimensions); + particle.currentObjectiveBreakdown = AutoFitObjectiveBreakdown(); + particle.bestObjectiveBreakdown = AutoFitObjectiveBreakdown(); particle.bestFitness = 1e10; particle.guideBestObjective = 1e10; particle.guideBestFromSurrogate = false; @@ -3609,7 +3749,1201 @@ void nmCalculationAutoFitPSO::initializeSwarm() particle.bestPosition = particle.position; particle.guideBestPosition = particle.position; + + // 第一个粒子可能直接复用用户初始解，因此同步保存初始解的误差分解， + // 后续误差引导只能使用真实求解器确认过的 breakdown。 + if(i == 0 && m_hasValidUserSolution) { + particle.currentObjectiveBreakdown = m_userInitialObjectiveBreakdown; + particle.bestObjectiveBreakdown = m_userInitialObjectiveBreakdown; + } + } +} + +// 信赖域搜索统一在 [0, 1] 内部坐标工作。正值参数使用对数坐标，使内部相同步长 +// 表示近似相同的相对变化，避免 k、C、Ct、Cf 等跨数量级参数被线性尺度支配； +// skin 可为负数、Swi 的物理意义是线性比例，因此二者保持有界线性坐标。 +static bool useTrustRegionLogScale(int parameterIndex, double lower, double upper) +{ + return parameterIndex != 1 && parameterIndex != 7 && + lower > 0.0 && upper > lower; +} + +static double toTrustRegionCoordinate(double value, + int parameterIndex, + double lower, + double upper) +{ + // 所有进入优化器的物理值先投影到用户上下界，再转换成无量纲坐标。 + // 这样有限差分步长、信赖半径和参数间相关性可以在统一尺度上比较。 + value = qMax(lower, qMin(upper, value)); + + if(useTrustRegionLogScale(parameterIndex, lower, upper)) { + return (qLn(value) - qLn(lower)) / (qLn(upper) - qLn(lower)); + } + + return upper > lower ? (value - lower) / (upper - lower) : 0.0; +} + +static double fromTrustRegionCoordinate(double coordinate, + int parameterIndex, + double lower, + double upper) +{ + // 候选内部坐标先限制在 [0,1]，再执行上述映射的逆变换，保证写回 + // DataManager 的参数始终位于用户设置的物理范围内。 + coordinate = qMax(0.0, qMin(1.0, coordinate)); + + if(useTrustRegionLogScale(parameterIndex, lower, upper)) { + return qExp(qLn(lower) + coordinate * (qLn(upper) - qLn(lower))); } + + return lower + coordinate * (upper - lower); +} + +enum TrustRegionErrorComponent +{ + TRUST_REGION_VERTICAL_COMPONENT = 0, + TRUST_REGION_HORIZONTAL_COMPONENT, + TRUST_REGION_SHAPE_COMPONENT, + TRUST_REGION_TOTAL_COMPONENT +}; + +// 一次真实求解的完整快照。除了参数和总误差，还保存内部坐标、诊断分量和 +// 双对数曲线，因此拒绝候选后可以完整恢复上一个已接受工作点。 +struct TrustRegionEvaluation +{ + QVector parameters; + QVector coordinates; + AutoFitObjectiveBreakdown breakdown; + QVector > curve; + double fitness; + int elapsedMs; + bool valid; + + TrustRegionEvaluation() + : fitness(1.0e10) + , elapsedMs(-1) + , valid(false) + {} +}; + +// LM 只使用固定长度、全部有限的稳健残差。代理路径不会进入本搜索器。 +static bool trustRegionResidualsValid( + const AutoFitObjectiveBreakdown& breakdown) +{ + // 损失函数固定使用 50 个压力点和 50 个导数点。严格校验长度，避免 + // Jacobian 沿用旧维度后访问另一候选的短残差向量。 + if(!breakdown.valid || breakdown.residualVector.size() != 100) { + return false; + } + + for(int i = 0; i < breakdown.residualVector.size(); ++i) { + if(!isFiniteNumber(breakdown.residualVector[i])) { + return false; + } + } + return true; +} + +// 计算向量二范数的平方，避免在只比较能量或计算正规方程时反复开方。 +static double trustRegionSquaredNorm(const QVector& values) +{ + double sum = 0.0; + for(int i = 0; i < values.size(); ++i) { + sum += values[i] * values[i]; + } + return sum; +} + +// 计算同维向量内积；维度不一致表示局部模型无效，返回零让调用方放弃修正。 +static double trustRegionDotProduct(const QVector& left, + const QVector& right) +{ + if(left.size() != right.size()) { + return 0.0; + } + + double sum = 0.0; + for(int i = 0; i < left.size(); ++i) { + sum += left[i] * right[i]; + } + return sum; +} + +// trace 和运行日志使用稳定的英文标识，便于现有离线脚本继续按字段筛选。 +static QString trustRegionComponentName(int component) +{ + if(component == TRUST_REGION_VERTICAL_COMPONENT) { + return "vertical"; + } + if(component == TRUST_REGION_HORIZONTAL_COMPONENT) { + return "horizontal"; + } + if(component == TRUST_REGION_SHAPE_COMPONENT) { + return "shape"; + } + return "total"; +} + +// 三类损失量纲一致，直接选择当前最大的可靠分量；都很小时退回总残差梯度。 +static int trustRegionDominantComponent( + const AutoFitObjectiveBreakdown& breakdown, + double diagnosisThreshold) +{ + int component = TRUST_REGION_TOTAL_COMPONENT; + double largestLoss = diagnosisThreshold; + + if(breakdown.verticalReliable && + isFiniteNumber(breakdown.verticalLoss) && + breakdown.verticalLoss > largestLoss) { + component = TRUST_REGION_VERTICAL_COMPONENT; + largestLoss = breakdown.verticalLoss; + } + if(breakdown.horizontalReliable && + !breakdown.registrationAmbiguous && + isFiniteNumber(breakdown.horizontalLoss) && + breakdown.horizontalLoss > largestLoss) { + component = TRUST_REGION_HORIZONTAL_COMPONENT; + largestLoss = breakdown.horizontalLoss; + } + if(isFiniteNumber(breakdown.shapeLoss) && + breakdown.shapeLoss > largestLoss) { + component = TRUST_REGION_SHAPE_COMPONENT; + } + + return component; +} + +// 求解选中参数对应的阻尼正规方程。上下和左右诊断量保留方向；形状没有 +// 天然正负，因此使用 shapeLoss 对参数的局部导数。参数最多八维，使用带 +// 部分主元的高斯消元即可处理该小矩阵，并在主元退化时明确返回失败。 +static bool solveTrustRegionLinearSystem( + QVector > matrix, + QVector rightHandSide, + QVector* solution) +{ + if(!solution || matrix.isEmpty() || + matrix.size() != rightHandSide.size()) { + return false; + } + + const int size = matrix.size(); + for(int i = 0; i < size; ++i) { + if(matrix[i].size() != size) { + return false; + } + } + + for(int column = 0; column < size; ++column) { + int pivotRow = column; + double pivotMagnitude = qAbs(matrix[column][column]); + for(int row = column + 1; row < size; ++row) { + double magnitude = qAbs(matrix[row][column]); + if(magnitude > pivotMagnitude) { + pivotMagnitude = magnitude; + pivotRow = row; + } + } + if(pivotMagnitude <= 1.0e-14) { + return false; + } + + if(pivotRow != column) { + qSwap(matrix[pivotRow], matrix[column]); + qSwap(rightHandSide[pivotRow], rightHandSide[column]); + } + + for(int row = column + 1; row < size; ++row) { + double factor = matrix[row][column] / + matrix[column][column]; + matrix[row][column] = 0.0; + for(int nextColumn = column + 1; + nextColumn < size; ++nextColumn) { + matrix[row][nextColumn] -= + factor * matrix[column][nextColumn]; + } + rightHandSide[row] -= factor * rightHandSide[column]; + } + } + + solution->fill(0.0, size); + for(int row = size - 1; row >= 0; --row) { + double value = rightHandSide[row]; + for(int column = row + 1; column < size; ++column) { + value -= matrix[row][column] * (*solution)[column]; + } + double pivot = matrix[row][row]; + if(qAbs(pivot) <= 1.0e-14) { + return false; + } + (*solution)[row] = value / pivot; + if(!isFiniteNumber((*solution)[row])) { + return false; + } + } + return true; +} + +// 计算两个 Jacobian 列向量的绝对余弦相似度。接近 1 表示两个参数在当前 +// 工作点对曲线的影响几乎相同，联合调整容易产生不可辨识方向。 +static double trustRegionJacobianColumnCorrelation( + const QVector >& jacobian, + int leftColumn, + int rightColumn) +{ + double product = 0.0; + double leftNorm = 0.0; + double rightNorm = 0.0; + for(int row = 0; row < jacobian.size(); ++row) { + if(leftColumn >= jacobian[row].size() || + rightColumn >= jacobian[row].size()) { + return 1.0; + } + double left = jacobian[row][leftColumn]; + double right = jacobian[row][rightColumn]; + product += left * right; + leftNorm += left * left; + rightNorm += right * right; + } + + if(leftNorm <= 1.0e-20 || rightNorm <= 1.0e-20) { + return 0.0; + } + return qAbs(product) / qSqrt(leftNorm * rightNorm); +} + +// 每次接受一个真实候选后，使用满足最新割线条件的秩一修正更新完整残差 +// Jacobian。这样模型吸收了刚得到的真实变化，又不必立即逐参数重新试算。 +static void updateTrustRegionJacobian( + QVector >* jacobian, + const QVector& oldResidual, + const QVector& newResidual, + const QVector& coordinateStep) +{ + if(!jacobian || jacobian->size() != oldResidual.size() || + oldResidual.size() != newResidual.size()) { + return; + } + + double denominator = trustRegionSquaredNorm(coordinateStep); + if(denominator <= 1.0e-12) { + return; + } + + for(int row = 0; row < jacobian->size(); ++row) { + if((*jacobian)[row].size() != coordinateStep.size()) { + return; + } + + double predictedChange = 0.0; + for(int column = 0; column < coordinateStep.size(); ++column) { + predictedChange += + (*jacobian)[row][column] * coordinateStep[column]; + } + double correction = + (newResidual[row] - oldResidual[row] - predictedChange) / + denominator; + for(int column = 0; column < coordinateStep.size(); ++column) { + (*jacobian)[row][column] += + correction * coordinateStep[column]; + } + } +} + +// 对上下偏差、左右偏差和形状损失的梯度执行同样的割线秩一修正，使诊断 +// 选参模型与完整残差 Jacobian 保持在同一个已接受工作点。 +static void updateTrustRegionScalarGradient( + QVector* gradient, + double oldValue, + double newValue, + const QVector& coordinateStep) +{ + if(!gradient || gradient->size() != coordinateStep.size() || + !isFiniteNumber(oldValue) || !isFiniteNumber(newValue)) { + return; + } + + double denominator = trustRegionSquaredNorm(coordinateStep); + if(denominator <= 1.0e-12) { + return; + } + + double predictedChange = trustRegionDotProduct( + *gradient, coordinateStep); + double correction = + (newValue - oldValue - predictedChange) / denominator; + for(int i = 0; i < gradient->size(); ++i) { + (*gradient)[i] += correction * coordinateStep[i]; + } +} + +bool nmCalculationAutoFitPSO::evaluateTrustRegionPoint( + const QVector& parameters, + double* fitness, + AutoFitObjectiveBreakdown* breakdown, + QVector >* curve, + int* elapsedMs) +{ + if(!fitness || !breakdown || !curve || !elapsedMs || m_shouldStop) { + return false; + } + + // evaluateFitness() 会写入 DataManager 并调用真实求解器。这里统一统计 + // 真实评价次数和耗时，同时严格要求固定残差、诊断结构和结果曲线均有效。 + QTime timer; + timer.start(); + *fitness = evaluateFitness(parameters); + *elapsedMs = timer.elapsed(); + *breakdown = m_lastObjectiveBreakdown; + *curve = m_lastEvaluatedLogLogData; + ++m_totalEvaluations; + + bool valid = isFiniteNumber(*fitness) && *fitness < 1.0e9 && + breakdown->valid && + trustRegionResidualsValid(*breakdown) && + !curve->isEmpty(); + if(valid) { + ++m_successfulEvaluations; + } + + return valid; +} + +StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() +{ + const int dimensions = getEnabledParameterCount(); + if(dimensions <= 0 || m_enabledParamIndices.size() != dimensions) { + m_lastError = tr("No valid parameters are available for trust-region fitting"); + return PSO_OPTIMIZATION_FAILED; + } + + // 真实求解次数比“外层迭代次数”更能反映耗时。预算至少允许完成一次全参数 + // 灵敏度和两次候选评价，同时避免连续重建 Jacobian 导致运行时间失控。 + const int maximumEvaluations = qMax( + m_totalEvaluations + dimensions + 2, + qMax(20, m_maxIterations * 3)); + // 下列步长均位于归一化内部坐标：0.04 表示参数范围的 4%，信赖半径 + // 限制一次联合移动的二范数，相关性门槛用于排除响应近乎共线的参数。 + const double sensitivityStep = 0.04; + const double minimumCoordinateStep = 1.0e-5; + const double minimumTrustRadius = 2.0e-3; + const double maximumTrustRadius = 0.30; + const double columnCorrelationLimit = 0.995; + const double diagnosisThreshold = 1.0e-5; + + // damping 是 LM 阻尼；拒绝或预测失准时增大，真实下降与预测一致时减小。 + // 两组累计量控制 Jacobian 重建，避免长期使用已偏离当前工作点的局部模型。 + double trustRadius = 0.12; + double damping = 1.0e-2; + int consecutiveRejectedSteps = 0; + int consecutiveSolverFailures = 0; + int acceptedSinceRebuild = 0; + double movementSinceRebuild = 0.0; + bool rebuildRequested = true; + bool modelRebuiltAtMinimumRadius = false; + StopReasonPSO stopReason = PSO_MAX_ITERATIONS; + + // jacobian 的行对应固定 100 维稳健残差，列对应用户勾选的参数。 + // 三个 gradient 单独描述诊断分量对参数的局部变化，只用于本轮选参。 + QVector > jacobian; + QVector verticalGradient(dimensions, 0.0); + QVector horizontalGradient(dimensions, 0.0); + QVector shapeGradient(dimensions, 0.0); + QVector jacobianColumnValid(dimensions, false); + + // 参数向量的顺序始终与 m_enabledParamIndices 一致，不能按完整参数索引 + // 直接访问；下面两个转换函数集中维护这层映射关系。 + auto coordinatesFromParameters = [&](const QVector& parameters) + -> QVector { + QVector coordinates(dimensions, 0.0); + for(int i = 0; i < dimensions; ++i) { + int parameterIndex = m_enabledParamIndices[i]; + coordinates[i] = toTrustRegionCoordinate( + parameters[i], parameterIndex, + m_parameterLower[parameterIndex], + m_parameterUpper[parameterIndex]); + } + return coordinates; + }; + + auto parametersFromCoordinates = [&](const QVector& coordinates) + -> QVector { + QVector parameters(dimensions, 0.0); + for(int i = 0; i < dimensions; ++i) { + int parameterIndex = m_enabledParamIndices[i]; + parameters[i] = fromTrustRegionCoordinate( + coordinates[i], parameterIndex, + m_parameterLower[parameterIndex], + m_parameterUpper[parameterIndex]); + } + return parameters; + }; + + auto restoreEvaluationState = [&](const TrustRegionEvaluation& evaluation) { + // evaluateFitness() 会把试算参数写入 DataManager。无论候选是否接受， + // 下一次计算前都恢复到唯一的已接受工作点，防止失败试算污染后续求解。 + applyParametersToDataManager(evaluation.parameters); + m_lastObjectiveBreakdown = evaluation.breakdown; + m_lastEvaluatedLogLogData = evaluation.curve; + }; + + // 只有真实总误差更小的工作点才能发布为全局最优；曲线和诊断快照必须 + // 与参数同步更新，防止界面显示或最终精英保护使用错配的数据。 + auto publishAcceptedPoint = [&](const TrustRegionEvaluation& evaluation) { + m_previousBestFitness = m_globalBestFitness; + m_globalBestPosition = evaluation.parameters; + m_globalBestFitness = evaluation.fitness; + m_globalBestObjectiveBreakdown = evaluation.breakdown; + m_globalBestLogLogData = evaluation.curve; + emit bestCurveUpdated(m_targetLogLogData, + m_globalBestLogLogData, + m_currentIteration + 1, + m_globalBestFitness); + }; + + auto processPauseAndStop = [&]() -> bool { + while(m_isPaused && !m_shouldStop) { + QApplication::processEvents(); + msleep(100); + } + QApplication::processEvents(); + return !m_shouldStop; + }; + + // current 始终代表唯一已接受工作点。优先复用启动阶段已经真实验证的 + // 用户初始解，避免在信赖域入口重复调用一次昂贵求解器。 + TrustRegionEvaluation current; + if(m_hasValidUserSolution && + m_globalBestPosition.size() == dimensions && + trustRegionResidualsValid(m_globalBestObjectiveBreakdown) && + !m_globalBestLogLogData.isEmpty()) { + current.parameters = m_globalBestPosition; + current.coordinates = coordinatesFromParameters(current.parameters); + current.breakdown = m_globalBestObjectiveBreakdown; + current.curve = m_globalBestLogLogData; + current.fitness = m_globalBestFitness; + current.elapsedMs = 0; + current.valid = true; + } else { + // 用户初始解无效时只做一次确定性的范围中点回退；所有正值参数在对数 + // 坐标取中点，避免线性中点过分偏向跨数量级范围的上界。 + current.coordinates.fill(0.5, dimensions); + current.parameters = parametersFromCoordinates(current.coordinates); + current.valid = evaluateTrustRegionPoint( + current.parameters, + ¤t.fitness, + ¤t.breakdown, + ¤t.curve, + ¤t.elapsedMs); + writeTraceRow(-1, -1, + "trust_region_midpoint", + current.parameters, + current.fitness, + current.valid, + current.elapsedMs, + std::numeric_limits::quiet_NaN(), + current.valid ? "midpoint_valid" : "midpoint_invalid", + QVector(), + 1.0e10, + current.valid ? ¤t.breakdown : nullptr); + if(!current.valid) { + m_lastError = tr("The initial solution and parameter-range midpoint are both invalid"); + return m_shouldStop + ? PSO_USER_STOPPED + : PSO_OPTIMIZATION_FAILED; + } + publishAcceptedPoint(current); + } + + restoreEvaluationState(current); + m_convergenceHistory.append(current.fitness); + emit logMessageGenerated( + tr("Trust-region initial error: %1; evaluation budget: %2") + .arg(current.fitness, 0, 'e', 4) + .arg(maximumEvaluations)); + + if(current.fitness < m_targetError) { + return PSO_TARGET_ACHIEVED; + } + + // 在同一个真实工作点逐参数做单边差分。首选可用空间更大的方向；只有该方向 + // 求解失败时才补算反方向，因此初次建模通常每个参数只增加一次真实求解。 + auto rebuildSensitivity = [&]() -> bool { + const TrustRegionEvaluation base = current; + const int residualCount = base.breakdown.residualVector.size(); + if(residualCount <= 0) { + return false; + } + + jacobian = QVector >( + residualCount, QVector(dimensions, 0.0)); + verticalGradient.fill(0.0, dimensions); + horizontalGradient.fill(0.0, dimensions); + shapeGradient.fill(0.0, dimensions); + jacobianColumnValid.fill(false, dimensions); + + TrustRegionEvaluation bestProbe; + int bestProbeColumn = -1; + double bestProbeDelta = 0.0; + // 差分步长不超过参数范围的 4%，信赖域收缩后同步减小，但保留 0.5% + // 下限，避免步长太小使求解器数值噪声淹没真实灵敏度。 + const double finiteDifferenceStep = qMin( + sensitivityStep, + qMax(5.0e-3, trustRadius * 0.5)); + + for(int column = 0; + column < dimensions && + m_totalEvaluations < maximumEvaluations && + processPauseAndStop(); + ++column) { + // 单边差分优先选择离边界空间更大的方向；首方向求解无效时才反向 + // 补算，因此正常情况下每个参数只消耗一次真实求解。 + double positiveRoom = 1.0 - base.coordinates[column]; + double negativeRoom = base.coordinates[column]; + double preferredSign = positiveRoom >= negativeRoom ? 1.0 : -1.0; + bool columnBuilt = false; + + for(int directionAttempt = 0; + directionAttempt < 2 && + !columnBuilt && + m_totalEvaluations < maximumEvaluations; + ++directionAttempt) { + double direction = directionAttempt == 0 + ? preferredSign : -preferredSign; + double availableRoom = direction > 0.0 + ? positiveRoom : negativeRoom; + double deltaMagnitude = qMin( + finiteDifferenceStep, availableRoom); + if(deltaMagnitude < minimumCoordinateStep) { + continue; + } + + TrustRegionEvaluation probe; + probe.coordinates = base.coordinates; + probe.coordinates[column] += direction * deltaMagnitude; + probe.parameters = parametersFromCoordinates(probe.coordinates); + probe.valid = evaluateTrustRegionPoint( + probe.parameters, + &probe.fitness, + &probe.breakdown, + &probe.curve, + &probe.elapsedMs); + + QString decision = probe.valid + ? "sensitivity_valid" + : (directionAttempt == 0 + ? "sensitivity_retry_opposite" + : "sensitivity_invalid"); + writeTraceRow(m_currentIteration, + column, + "trust_region_sensitivity", + probe.parameters, + probe.fitness, + probe.valid, + probe.elapsedMs, + std::numeric_limits::quiet_NaN(), + decision, + base.parameters, + base.fitness, + probe.valid ? &probe.breakdown : nullptr); + + if(!probe.valid) { + restoreEvaluationState(base); + continue; + } + + double delta = probe.coordinates[column] - + base.coordinates[column]; + if(qAbs(delta) < minimumCoordinateStep || + probe.breakdown.residualVector.size() != residualCount) { + restoreEvaluationState(base); + continue; + } + + // 第 column 列是固定残差向量相对内部参数坐标的有限差分： + // J[:,column] = (r_probe-r_base)/delta。 + for(int row = 0; row < residualCount; ++row) { + jacobian[row][column] = + (probe.breakdown.residualVector[row] - + base.breakdown.residualVector[row]) / delta; + } + + // 有符号诊断量只有在基点和试算点都可靠时才能计算方向梯度； + // shapeLoss 无方向可靠性标志，始终记录其局部变化率。 + if(base.breakdown.verticalReliable && + probe.breakdown.verticalReliable && + !base.breakdown.registrationAmbiguous && + !probe.breakdown.registrationAmbiguous) { + verticalGradient[column] = + (probe.breakdown.verticalCommonBias - + base.breakdown.verticalCommonBias) / delta; + } + if(base.breakdown.horizontalReliable && + probe.breakdown.horizontalReliable && + !base.breakdown.registrationAmbiguous && + !probe.breakdown.registrationAmbiguous) { + horizontalGradient[column] = + (probe.breakdown.horizontalPhysicalShift - + base.breakdown.horizontalPhysicalShift) / delta; + } + shapeGradient[column] = + (probe.breakdown.shapeLoss - + base.breakdown.shapeLoss) / delta; + jacobianColumnValid[column] = true; + columnBuilt = true; + + if(probe.fitness < base.fitness && + (!bestProbe.valid || + probe.fitness < bestProbe.fitness)) { + bestProbe = probe; + bestProbeColumn = column; + bestProbeDelta = delta; + } + restoreEvaluationState(base); + } + } + + int validColumnCount = 0; + for(int i = 0; i < jacobianColumnValid.size(); ++i) { + if(jacobianColumnValid[i]) { + ++validColumnCount; + } + } + if(validColumnCount == 0 || m_shouldStop) { + restoreEvaluationState(base); + return false; + } + + // 灵敏度试算本身若找到更优真实解也应保留。所有列先基于同一个 base + // 建完，再用该已知割线把 Jacobian 平移到新工作点，避免边算边移动基点。 + if(bestProbe.valid && bestProbeColumn >= 0) { + QVector acceptedStep(dimensions, 0.0); + acceptedStep[bestProbeColumn] = bestProbeDelta; + updateTrustRegionJacobian( + &jacobian, + base.breakdown.residualVector, + bestProbe.breakdown.residualVector, + acceptedStep); + if(base.breakdown.verticalReliable && + bestProbe.breakdown.verticalReliable) { + updateTrustRegionScalarGradient( + &verticalGradient, + base.breakdown.verticalCommonBias, + bestProbe.breakdown.verticalCommonBias, + acceptedStep); + } + if(base.breakdown.horizontalReliable && + bestProbe.breakdown.horizontalReliable) { + updateTrustRegionScalarGradient( + &horizontalGradient, + base.breakdown.horizontalPhysicalShift, + bestProbe.breakdown.horizontalPhysicalShift, + acceptedStep); + } + updateTrustRegionScalarGradient( + &shapeGradient, + base.breakdown.shapeLoss, + bestProbe.breakdown.shapeLoss, + acceptedStep); + + current = bestProbe; + publishAcceptedPoint(current); + restoreEvaluationState(current); + m_convergenceHistory.append(current.fitness); + writeTraceRow(m_currentIteration, + bestProbeColumn, + "trust_region_sensitivity_accept", + current.parameters, + current.fitness, + true, + 0, + std::numeric_limits::quiet_NaN(), + "accepted_cached_probe", + current.parameters, + current.fitness, + ¤t.breakdown); + emit logMessageGenerated( + tr("Sensitivity probe accepted: error reduced to %1") + .arg(current.fitness, 0, 'e', 4)); + } else { + restoreEvaluationState(current); + } + + acceptedSinceRebuild = 0; + movementSinceRebuild = 0.0; + consecutiveRejectedSteps = 0; + rebuildRequested = false; + // 若重建过程中接受了试算点，当前模型已通过割线平移而不是在新点完整 + // 重算；再遇到最小半径停滞时仍允许做一次真正的新点重建。 + modelRebuiltAtMinimumRadius = + trustRadius <= minimumTrustRadius * 1.01 && + !bestProbe.valid; + emit logMessageGenerated( + tr("Sensitivity model rebuilt: %1/%2 parameter columns valid") + .arg(validColumnCount) + .arg(dimensions)); + return true; + }; + + int completedIterations = 0; + for(int iteration = 0; + iteration < m_maxIterations && + m_totalEvaluations < maximumEvaluations && + !m_shouldStop; + ++iteration) { + m_currentIteration = iteration; + completedIterations = iteration + 1; + + if(!processPauseAndStop()) { + break; + } + if(rebuildRequested) { + if(!rebuildSensitivity()) { + stopReason = m_shouldStop + ? PSO_USER_STOPPED + : PSO_LOCAL_OPTIMUM; + break; + } + if(current.fitness < m_targetError) { + stopReason = PSO_TARGET_ACHIEVED; + break; + } + if(m_totalEvaluations >= maximumEvaluations) { + stopReason = PSO_MAX_ITERATIONS; + break; + } + } + + // 先确定当前最突出的可靠诊断误差，用其梯度回答“哪些参数最能改善 + // 当前问题”；实际 LM 方向仍由完整残差梯度和 Jacobian 共同计算。 + int dominantComponent = trustRegionDominantComponent( + current.breakdown, diagnosisThreshold); + const QVector* componentGradient = nullptr; + if(dominantComponent == TRUST_REGION_VERTICAL_COMPONENT) { + componentGradient = &verticalGradient; + } else if(dominantComponent == TRUST_REGION_HORIZONTAL_COMPONENT) { + componentGradient = &horizontalGradient; + } else if(dominantComponent == TRUST_REGION_SHAPE_COMPONENT) { + componentGradient = &shapeGradient; + } + + // 主目标采用 0.5*||r||^2，其对参数的梯度为 J^T*r。这里不再叠加 + // vertical/horizontal/shape，保证诊断分量不会改变真实接受目标。 + QVector totalGradient(dimensions, 0.0); + for(int column = 0; column < dimensions; ++column) { + if(!jacobianColumnValid[column]) { + continue; + } + for(int row = 0; row < jacobian.size(); ++row) { + totalGradient[column] += + jacobian[row][column] * + current.breakdown.residualVector[row]; + } + } + + // 每轮最多联合调整三个灵敏参数。按当前诊断梯度绝对值由大到小选取， + // 并剔除 Jacobian 响应过度共线的列，降低弱可辨识参数互相补偿的风险。 + QVector selectedColumns; + QVector alreadyConsidered(dimensions, false); + for(int selection = 0; selection < qMin(3, dimensions); ++selection) { + int bestColumn = -1; + double bestScore = 0.0; + for(int column = 0; column < dimensions; ++column) { + if(alreadyConsidered[column] || + !jacobianColumnValid[column]) { + continue; + } + + double score = componentGradient + ? qAbs((*componentGradient)[column]) + : qAbs(totalGradient[column]); + if(!isFiniteNumber(score) || score <= bestScore) { + continue; + } + + bool excessivelyCorrelated = false; + for(int selectedIndex = 0; + selectedIndex < selectedColumns.size(); + ++selectedIndex) { + if(trustRegionJacobianColumnCorrelation( + jacobian, + column, + selectedColumns[selectedIndex]) > + columnCorrelationLimit) { + excessivelyCorrelated = true; + break; + } + } + if(!excessivelyCorrelated) { + bestColumn = column; + bestScore = score; + } + } + if(bestColumn < 0 || bestScore <= 1.0e-12) { + break; + } + selectedColumns.append(bestColumn); + alreadyConsidered[bestColumn] = true; + } + + // 诊断梯度接近零时，说明该分量在当前局部无法可靠选参，退回完整残差 + // 梯度，但接受标准仍然只有真实 total，诊断值不会重复计入目标函数。 + if(selectedColumns.isEmpty() && componentGradient) { + dominantComponent = TRUST_REGION_TOTAL_COMPONENT; + componentGradient = nullptr; + alreadyConsidered.fill(false, dimensions); + for(int selection = 0; + selection < qMin(3, dimensions); + ++selection) { + int bestColumn = -1; + double bestScore = 0.0; + for(int column = 0; column < dimensions; ++column) { + if(alreadyConsidered[column] || + !jacobianColumnValid[column]) { + continue; + } + double score = qAbs(totalGradient[column]); + if(score <= bestScore) { + continue; + } + bool excessivelyCorrelated = false; + for(int selectedIndex = 0; + selectedIndex < selectedColumns.size(); + ++selectedIndex) { + if(trustRegionJacobianColumnCorrelation( + jacobian, + column, + selectedColumns[selectedIndex]) > + columnCorrelationLimit) { + excessivelyCorrelated = true; + break; + } + } + if(!excessivelyCorrelated) { + bestColumn = column; + bestScore = score; + } + } + if(bestColumn < 0 || bestScore <= 1.0e-12) { + break; + } + selectedColumns.append(bestColumn); + alreadyConsidered[bestColumn] = true; + } + } + + // 当前局部没有可用方向时先缩小半径并重建灵敏度；只有已经在最小 + // 半径完整重建后仍无方向，才把它判定为局部最优。 + if(selectedColumns.isEmpty()) { + if(trustRadius <= minimumTrustRadius * 1.01 && + modelRebuiltAtMinimumRadius) { + stopReason = PSO_LOCAL_OPTIMUM; + break; + } + trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); + damping = qMin(1.0e8, damping * 4.0); + rebuildRequested = true; + continue; + } + + // 在选中参数子空间构造 LM 正规方程： + // (J^T*J + damping*diag(J^T*J))*step = -J^T*r。 + // 对角缩放使不同参数列的灵敏度量级差异不会直接改变阻尼强弱。 + const int selectedCount = selectedColumns.size(); + QVector > normalMatrix( + selectedCount, QVector(selectedCount, 0.0)); + QVector rightHandSide(selectedCount, 0.0); + for(int left = 0; left < selectedCount; ++left) { + int leftColumn = selectedColumns[left]; + rightHandSide[left] = -totalGradient[leftColumn]; + for(int right = 0; right < selectedCount; ++right) { + int rightColumn = selectedColumns[right]; + for(int row = 0; row < jacobian.size(); ++row) { + normalMatrix[left][right] += + jacobian[row][leftColumn] * + jacobian[row][rightColumn]; + } + } + double diagonalScale = qMax( + 1.0e-10, normalMatrix[left][left]); + normalMatrix[left][left] += damping * diagonalScale; + } + + QVector selectedStep; + bool solved = solveTrustRegionLinearSystem( + normalMatrix, rightHandSide, &selectedStep); + QVector coordinateStep(dimensions, 0.0); + if(solved) { + for(int i = 0; i < selectedCount; ++i) { + coordinateStep[selectedColumns[i]] = selectedStep[i]; + } + } + + double stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep)); + if(!solved || !isFiniteNumber(stepNorm) || + stepNorm < minimumCoordinateStep) { + // 正规方程退化时使用投影最速下降方向，仍只移动本轮已选择的参数。 + coordinateStep.fill(0.0, dimensions); + double gradientNormSquared = 0.0; + for(int i = 0; i < selectedCount; ++i) { + int column = selectedColumns[i]; + double stepDirection = -totalGradient[column]; + if((current.coordinates[column] <= minimumCoordinateStep && + stepDirection < 0.0) || + (current.coordinates[column] >= + 1.0 - minimumCoordinateStep && + stepDirection > 0.0)) { + stepDirection = 0.0; + } + coordinateStep[column] = stepDirection; + gradientNormSquared += stepDirection * stepDirection; + } + double gradientNorm = qSqrt(gradientNormSquared); + if(gradientNorm > minimumCoordinateStep) { + double scale = trustRadius / gradientNorm; + for(int i = 0; i < selectedCount; ++i) { + int column = selectedColumns[i]; + coordinateStep[column] *= scale; + } + } + stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep)); + } + + // LM 解只给出局部模型建议方向；若超出当前信赖半径，保持方向不变并 + // 等比例截短，避免一次试算离开 Jacobian 有效的局部区域。 + if(stepNorm > trustRadius && stepNorm > 0.0) { + double scale = trustRadius / stepNorm; + for(int i = 0; i < coordinateStep.size(); ++i) { + coordinateStep[i] *= scale; + } + } + + // 将 LM 步长投影到用户给定的参数范围，实际用于预测下降的也是投影后步长。 + QVector candidateCoordinates = current.coordinates; + for(int i = 0; i < dimensions; ++i) { + candidateCoordinates[i] = qBound( + 0.0, + current.coordinates[i] + coordinateStep[i], + 1.0); + coordinateStep[i] = candidateCoordinates[i] - + current.coordinates[i]; + } + stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep)); + + // 用线性模型 r_new ~= r_current + J*step 预测稳健残差，再用平方能量 + // 的下降量与真实候选下降量比较，作为调整阻尼和半径的依据。 + QVector predictedResidual = + current.breakdown.residualVector; + for(int row = 0; row < jacobian.size(); ++row) { + for(int column = 0; column < dimensions; ++column) { + predictedResidual[row] += + jacobian[row][column] * coordinateStep[column]; + } + } + double predictedReduction = 0.5 * + (trustRegionSquaredNorm(current.breakdown.residualVector) - + trustRegionSquaredNorm(predictedResidual)); + + // 无实际移动或模型预测不下降时没有必要调用昂贵求解器。将它按一次 + // 拒绝处理，并在连续发生后重建灵敏度，防止继续沿失效模型试算。 + if(stepNorm < minimumCoordinateStep || + !isFiniteNumber(predictedReduction) || + predictedReduction <= 1.0e-14) { + trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); + damping = qMin(1.0e8, damping * 4.0); + ++consecutiveRejectedSteps; + if(consecutiveRejectedSteps >= 2) { + if(trustRadius <= minimumTrustRadius * 1.01 && + modelRebuiltAtMinimumRadius) { + stopReason = PSO_LOCAL_OPTIMUM; + break; + } + rebuildRequested = true; + } + continue; + } + + TrustRegionEvaluation candidate; + candidate.coordinates = candidateCoordinates; + candidate.parameters = parametersFromCoordinates(candidate.coordinates); + candidate.valid = evaluateTrustRegionPoint( + candidate.parameters, + &candidate.fitness, + &candidate.breakdown, + &candidate.curve, + &candidate.elapsedMs); + + if(!candidate.valid) { + // 求解失败的候选不能改变 current。先完整恢复上一个已接受参数和 + // 对应误差快照，再缩小信赖域；连续失败达到上限才终止整个拟合。 + ++consecutiveSolverFailures; + ++consecutiveRejectedSteps; + trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); + damping = qMin(1.0e8, damping * 4.0); + writeTraceRow(m_currentIteration, + -1, + "trust_region_candidate", + candidate.parameters, + candidate.fitness, + false, + candidate.elapsedMs, + std::numeric_limits::quiet_NaN(), + "solver_invalid", + current.parameters, + current.fitness, + nullptr); + restoreEvaluationState(current); + if(consecutiveRejectedSteps >= 2) { + rebuildRequested = true; + } + if(consecutiveSolverFailures >= m_maxConsecutiveFailures) { + stopReason = PSO_CONSECUTIVE_FAILURES; + break; + } + continue; + } + + consecutiveSolverFailures = 0; + // 有效候选即使最终被拒绝，也提供了一条真实割线，可用于修正下一轮 + // 局部模型；是否成为新工作点仍只由下面的 total 严格比较决定。 + const AutoFitObjectiveBreakdown oldBreakdown = current.breakdown; + updateTrustRegionJacobian( + &jacobian, + oldBreakdown.residualVector, + candidate.breakdown.residualVector, + coordinateStep); + if(oldBreakdown.verticalReliable && + candidate.breakdown.verticalReliable && + !oldBreakdown.registrationAmbiguous && + !candidate.breakdown.registrationAmbiguous) { + updateTrustRegionScalarGradient( + &verticalGradient, + oldBreakdown.verticalCommonBias, + candidate.breakdown.verticalCommonBias, + coordinateStep); + } + if(oldBreakdown.horizontalReliable && + candidate.breakdown.horizontalReliable && + !oldBreakdown.registrationAmbiguous && + !candidate.breakdown.registrationAmbiguous) { + updateTrustRegionScalarGradient( + &horizontalGradient, + oldBreakdown.horizontalPhysicalShift, + candidate.breakdown.horizontalPhysicalShift, + coordinateStep); + } + updateTrustRegionScalarGradient( + &shapeGradient, + oldBreakdown.shapeLoss, + candidate.breakdown.shapeLoss, + coordinateStep); + + // reductionRatio 衡量局部线性模型的可信度：接近 1 表示预测准确； + // 值较小表示虽然可能下降，但模型低估了非线性，需要收紧下一步。 + double actualReduction = 0.5 * + (current.fitness * current.fitness - + candidate.fitness * candidate.fitness); + double reductionRatio = actualReduction / predictedReduction; + bool accepted = candidate.fitness < current.fitness; + QString componentName = trustRegionComponentName(dominantComponent); + + if(accepted) { + // 真实总误差下降后才正式替换 current，并同步发布参数、曲线和诊断。 + // 模型预测可靠时减小阻尼并可扩大半径，预测较差时保守收缩。 + current = candidate; + publishAcceptedPoint(current); + restoreEvaluationState(current); + ++acceptedSinceRebuild; + movementSinceRebuild += stepNorm; + consecutiveRejectedSteps = 0; + m_convergenceHistory.append(current.fitness); + + if(reductionRatio > 0.75) { + damping = qMax(1.0e-8, damping * 0.5); + if(stepNorm >= trustRadius * 0.8) { + trustRadius = qMin( + maximumTrustRadius, trustRadius * 1.6); + } + } else if(reductionRatio > 0.25) { + damping = qMax(1.0e-8, damping * 0.8); + } else { + damping = qMin(1.0e8, damping * 2.0); + trustRadius = qMax( + minimumTrustRadius, trustRadius * 0.75); + } + + if(acceptedSinceRebuild >= 6 || + movementSinceRebuild >= 0.30) { + rebuildRequested = true; + } + modelRebuiltAtMinimumRadius = false; + } else { + // 拒绝时 candidate 只保留在 trace 中，DataManager 和内存状态都恢复 + // 到 current。连续拒绝说明割线模型可能失真，因此请求重新试算灵敏度。 + ++consecutiveRejectedSteps; + damping = qMin(1.0e8, damping * 4.0); + trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); + restoreEvaluationState(current); + if(consecutiveRejectedSteps >= 2) { + rebuildRequested = true; + } + } + + writeTraceRow(m_currentIteration, + -1, + "trust_region_candidate", + candidate.parameters, + candidate.fitness, + true, + candidate.elapsedMs, + std::numeric_limits::quiet_NaN(), + accepted + ? QString("accepted_%1").arg(componentName) + : QString("rejected_%1").arg(componentName), + current.parameters, + current.fitness, + &candidate.breakdown); + + emit logMessageGenerated( + tr("Iteration %1: focus=%2, parameters=%3, error=%4, result=%5") + .arg(iteration + 1) + .arg(componentName) + .arg(selectedColumns.size()) + .arg(candidate.fitness, 0, 'e', 4) + .arg(accepted ? tr("accepted") : tr("rejected"))); + emit progressUpdated(iteration + 1, m_globalBestFitness); + + if(current.fitness < m_targetError) { + stopReason = PSO_TARGET_ACHIEVED; + break; + } + if(trustRadius <= minimumTrustRadius * 1.01 && + consecutiveRejectedSteps >= 2) { + if(modelRebuiltAtMinimumRadius) { + stopReason = PSO_LOCAL_OPTIMUM; + break; + } + rebuildRequested = true; + } + } + + if(completedIterations > 0) { + m_currentIteration = completedIterations - 1; + } + restoreEvaluationState(current); + + if(m_shouldStop) { + return PSO_USER_STOPPED; + } + if(current.fitness < m_targetError) { + return PSO_TARGET_ACHIEVED; + } + if(stopReason == PSO_CONSECUTIVE_FAILURES || + stopReason == PSO_LOCAL_OPTIMUM || + stopReason == PSO_OPTIMIZATION_FAILED) { + return stopReason; + } + return PSO_MAX_ITERATIONS; } void nmCalculationAutoFitPSO::updateParticle(int particleIndex) @@ -3647,6 +4981,7 @@ void nmCalculationAutoFitPSO::updateParticle(int particleIndex) // 直接复用真实误差和曲线，避免一次重复 DLL 调用且不改变 PSO 数学状态。 particle.fitness = m_userInitialFitness; particle.currentLogLogData = m_userInitialLogLogData; + particle.currentObjectiveBreakdown = m_userInitialObjectiveBreakdown; particle.lastEvaluationElapsedMs = 0; particle.evaluatedThisIteration = true; particle.lastEvaluationSuccess = true; @@ -3657,6 +4992,7 @@ void nmCalculationAutoFitPSO::updateParticle(int particleIndex) evalTimer.start(); particle.fitness = evaluateFitness(particle.position); particle.currentLogLogData = m_lastEvaluatedLogLogData; + particle.currentObjectiveBreakdown = m_lastObjectiveBreakdown; particle.lastEvaluationElapsedMs = evalTimer.elapsed(); particle.evaluatedThisIteration = true; particle.lastEvaluationSuccess = (particle.fitness < 1e9); @@ -3698,6 +5034,7 @@ void nmCalculationAutoFitPSO::updateParticle(int particleIndex) particle.bestFitness = particle.fitness; particle.bestPosition = particle.position; particle.bestLogLogData = particle.currentLogLogData; + particle.bestObjectiveBreakdown = particle.currentObjectiveBreakdown; if(!preserveSurrogateGuide) { particle.guideBestPosition = particle.position; @@ -3746,6 +5083,7 @@ void nmCalculationAutoFitPSO::updateGlobalBest() m_globalBestFitness = particle.bestFitness; m_globalBestPosition = particle.bestPosition; m_globalBestLogLogData = particle.bestLogLogData; + m_globalBestObjectiveBreakdown = particle.bestObjectiveBreakdown; globalBestUpdated = true; emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness); } @@ -3765,6 +5103,7 @@ void nmCalculationAutoFitPSO::updateGlobalBest() m_globalBestFitness = m_userInitialFitness; m_globalBestPosition = m_userInitialSolution; m_globalBestLogLogData = m_userInitialLogLogData; + m_globalBestObjectiveBreakdown = m_userInitialObjectiveBreakdown; emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness); } } @@ -3841,6 +5180,7 @@ void nmCalculationAutoFitPSO::updateVelocityAndPosition() int paramIndex = m_enabledParamIndices[j]; double range = m_parameterUpper[paramIndex] - m_parameterLower[paramIndex]; double maxVel = range * VELOCITY_LIMIT_FACTOR; + particle.velocity[j] = qMax(-maxVel, qMin(maxVel, particle.velocity[j])); // 更新位置 @@ -4650,10 +5990,8 @@ void nmCalculationAutoFitPSO::saveOptimizationResult() void nmCalculationAutoFitPSO::validateAndProtectFinalResult() { - // 最终精英保护。 - // PSO 是随机启发式算法，某些工况下可能没有找到比用户初始模型更好的结果。 - // 这里用初始解误差与最终全局最优误差做比较，如果改进不足，就恢复初始解， - // 避免“自动拟合”把已有模型调坏。 + // 最终精英保护只阻止无效结果或真正变差的结果。任何真实误差下降都应保留， + // 不能再用固定百分比门槛把已经找到的更优解恢复成初始值。 if(!m_hasValidUserSolution) { emit logMessageGenerated(tr("No initial solution for elite protection")); return; @@ -4668,28 +6006,36 @@ void nmCalculationAutoFitPSO::validateAndProtectFinalResult() emit logMessageGenerated(tr("Comparing results: Initial=%1, Final=%2") .arg(initialFitness, 0, 'e', 4).arg(finalFitness, 0, 'e', 4)); - // 计算改进程度 - double improvement = initialFitness - finalFitness; - double relativeImprovement = improvement / qMax(1e-10, qAbs(initialFitness)); - - emit logMessageGenerated(tr("Improvement: %1 (%2%)") - .arg(improvement, 0, 'e', 4).arg(relativeImprovement * 100, 0, 'f', 2)); + bool finalValid = isFiniteNumber(finalFitness) && + finalFitness < 1.0e9 && + m_globalBestPosition.size() == + m_userInitialSolution.size() && + !m_globalBestLogLogData.isEmpty() && + m_globalBestObjectiveBreakdown.valid; + if(finalValid) { + double improvement = initialFitness - finalFitness; + double relativeImprovement = + improvement / qMax(1.0e-10, qAbs(initialFitness)); + emit logMessageGenerated(tr("Improvement: %1 (%2%)") + .arg(improvement, 0, 'e', 4) + .arg(relativeImprovement * 100, 0, 'f', 2)); + } - if(relativeImprovement < m_improvementThreshold) { - emit logMessageGenerated(tr("Elite protection triggered: insufficient improvement")); - emit logMessageGenerated(tr("Threshold: %1%, Actual: %2%") - .arg(m_improvementThreshold * 100, 0, 'f', 2) - .arg(relativeImprovement * 100, 0, 'f', 4)); + if(!finalValid || finalFitness > initialFitness) { + emit logMessageGenerated( + tr("Elite protection triggered: final result is invalid or worse than initial")); emit logMessageGenerated(tr("Restoring initial solution as final result")); m_globalBestFitness = initialFitness; m_globalBestPosition = m_userInitialSolution; m_globalBestLogLogData = m_userInitialLogLogData; + m_globalBestObjectiveBreakdown = m_userInitialObjectiveBreakdown; emit bestCurveUpdated(m_targetLogLogData, m_globalBestLogLogData, m_currentIteration + 1, m_globalBestFitness); emit logMessageGenerated(tr("Initial solution restored successfully")); } else { - emit logMessageGenerated(tr("Final result validated - significant improvement achieved")); + emit logMessageGenerated( + tr("Final result validated - solution is not worse than initial")); } } @@ -4997,17 +6343,16 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( const QVector >& target, const QVector >& result) const { - // 这里只负责“曲线比较和误差诊断”，不根据诊断结果直接修改任何拟合参数。 - // 调用方可以读取 m_lastObjectiveBreakdown 做诊断或展示；本函数本身不修改参数。 - // 在统一的对数时间网格上计算压力和导数残差，并拆分为上下、左右、形状误差。 + // 主目标只比较固定网格上的压力和导数残差；上下、左右和形状只负责诊断 + // 误差来源和选择参数，避免同一残差在 total 中被重复计算。整个计算过程均 + // 位于 log(time)-log(value) 坐标，因此得到的是相对尺度偏差而非原始压力量纲。 const double invalidLoss = 1.0e10; - const double valueFloor = 1.0e-12; // 导数接近零时的对数下限，避免 log(0)。 - const double huberDelta = qLn(1.2); // 约对应 20% 的相对偏差拐点。 - const double minimumCoverage = 0.95; // 点数比例和连续跨度比例都至少接近 95%。 - const int numPoints = 50; // 固定网格使不同候选的损失具有可比性。 - // 目标函数的主排序项为 0.5*pressureLoss + 0.5*derivativeLoss； - // coveragePenalty 只在接近覆盖边界时提供连续惩罚，上下、左右和形状分量 - // 会写入 m_lastObjectiveBreakdown，供后续按误差类型选择参数。 + const double valueFloor = 1.0e-12; + // Huber 转折点 qLn(1.2) 对应约 20% 的倍率偏差；小偏差保持平方惩罚， + // 更大的局部尖峰改为近似线性惩罚，避免单点支配整条曲线。 + const double huberDelta = qLn(1.2); + const double minimumCoverage = 0.95; + const int numPoints = 50; m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); if(!validateLogLogData(target) || !validateLogLogData(result)) { @@ -5015,9 +6360,8 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( } try { - // 清洗曲线并拆成压力、导数两条曲线。导数可以为负，所以统一使用绝对值 - // 进入双对数空间；时间和压力必须为正，否则无法进行对数插值。这里的清洗 - // 只丢弃无法比较的采样点，不改变原始曲线或求解器输出。 + // 无法比较的采样行先跳过；有限但非正的导数无法进入双对数空间， + // 当前数据又没有逐点有效掩码，因此遇到这种导数时判本次评价无效。 auto prepareCurve = [valueFloor](const QVector >& data, QVector* pressure, QVector* derivative) -> bool { @@ -5028,37 +6372,41 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( } for(int i = 0; i < data[0].size(); ++i) { - if(!isFiniteNumber(data[0][i]) || !isFiniteNumber(data[1][i]) || - !isFiniteNumber(data[2][i]) || data[0][i] <= 0.0 || + if(!isFiniteNumber(data[0][i]) || + !isFiniteNumber(data[1][i]) || + !isFiniteNumber(data[2][i]) || + data[0][i] <= 0.0 || data[1][i] <= 0.0) { continue; } + if(data[2][i] <= 0.0) { + return false; + } pressure->append(QPointF(data[0][i], data[1][i])); - derivative->append(QPointF(data[0][i], - qMax(qAbs(data[2][i]), valueFloor))); + derivative->append( + QPointF(data[0][i], qMax(data[2][i], valueFloor))); } - // 插值要求时间严格递增。重复时间点保留排序后的最后一个值， - // 避免重复横坐标导致对数插值分母为零。压力和导数分别去重， - // 这样即使某条曲线存在重复时间点，也不会污染另一条曲线的插值。 + // 求解器输出可能不是严格升序，且同一时刻可能出现重复记录。 + // 插值前统一排序并让后出现的记录覆盖同时间旧值，保证横坐标严格递增。 auto sortAndUnique = [](QVector* curve) { - std::stable_sort(curve->begin(), curve->end(), - [](const QPointF& left, const QPointF& right) { - return left.x() < right.x(); - }); + std::stable_sort( + curve->begin(), curve->end(), + [](const QPointF& left, const QPointF& right) { + return left.x() < right.x(); + }); QVector unique; unique.reserve(curve->size()); - for(int i = 0; i < curve->size(); ++i) { - if(unique.isEmpty() || curve->at(i).x() > unique.last().x()) { + if(unique.isEmpty() || + curve->at(i).x() > unique.last().x()) { unique.append(curve->at(i)); } else { unique[unique.size() - 1] = curve->at(i); } } - *curve = unique; }; @@ -5071,44 +6419,77 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( QVector targetDerivative; QVector resultPressure; QVector resultDerivative; - if(!prepareCurve(target, &targetPressure, &targetDerivative) || !prepareCurve(result, &resultPressure, &resultDerivative)) { return invalidLoss; } - // 在 log(time)-log(value) 空间做线性插值，而不是在线性坐标直接插值。 - // 这样可以保持双对数曲线的时间尺度和数量级特征；返回值是 - // log(abs(value))，后续残差因此可以直接解释为相对幅值误差。 - auto interpolateLogValue = [valueFloor](const QVector& curve, - double x, - double* value) -> bool { - if(!value || curve.size() < 2 || x < curve.first().x() || - x > curve.last().x() || x <= 0.0) { + // 在双对数坐标中插值。二分定位用于后面的多次水平配准试算。 + auto interpolateLogValue = [valueFloor]( + const QVector& curve, + double x, + double* value) -> bool { + if(!value || curve.size() < 2 || x <= 0.0 || + x < curve.first().x() || x > curve.last().x()) { return false; } - int right = 1; - - while(right < curve.size() && curve[right].x() < x) { - ++right; + int low = 0; + int high = curve.size() - 1; + while(low < high) { + int middle = low + (high - low) / 2; + if(curve[middle].x() < x) { + low = middle + 1; + } else { + high = middle; + } } - right = qMin(right, curve.size() - 1); - int left = qMax(0, right - 1); + int right = qBound(1, low, curve.size() - 1); + int left = right - 1; double leftLogX = qLn(curve[left].x()); double rightLogX = qLn(curve[right].x()); double denominator = rightLogX - leftLogX; - double leftLogY = qLn(qMax(qAbs(curve[left].y()), valueFloor)); - double rightLogY = qLn(qMax(qAbs(curve[right].y()), valueFloor)); + double leftLogY = + qLn(qMax(qAbs(curve[left].y()), valueFloor)); + double rightLogY = + qLn(qMax(qAbs(curve[right].y()), valueFloor)); if(qAbs(denominator) <= 1.0e-12) { *value = leftLogY; } else { double ratio = (qLn(x) - leftLogX) / denominator; - *value = leftLogY + ratio * (rightLogY - leftLogY); + *value = leftLogY + + ratio * (rightLogY - leftLogY); } + return isFiniteNumber(*value); + }; + // 覆盖率通过后若只缺少首尾少量点，用模拟曲线自身的端点斜率作短距离 + // 双对数外推。该外推只用于主损失的固定网格，不参与水平配准搜索。 + auto extrapolateEndpointLogValue = [valueFloor]( + const QVector& curve, + double x, + double* value) -> bool { + if(!value || curve.size() < 2 || x <= 0.0) { + return false; + } + int left = x < curve.first().x() + ? 0 + : curve.size() - 2; + int right = left + 1; + double leftLogX = qLn(curve[left].x()); + double rightLogX = qLn(curve[right].x()); + double denominator = rightLogX - leftLogX; + if(qAbs(denominator) <= 1.0e-12) { + return false; + } + double leftLogY = + qLn(qMax(qAbs(curve[left].y()), valueFloor)); + double rightLogY = + qLn(qMax(qAbs(curve[right].y()), valueFloor)); + double ratio = (qLn(x) - leftLogX) / denominator; + *value = leftLogY + ratio * (rightLogY - leftLogY); return isFiniteNumber(*value); }; @@ -5116,14 +6497,11 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( const double targetMaxX = targetPressure.last().x(); const double resultMinX = resultPressure.first().x(); const double resultMaxX = resultPressure.last().x(); - if(targetMinX <= 0.0 || targetMaxX <= targetMinX || resultMinX <= 0.0 || resultMaxX <= resultMinX) { return invalidLoss; } - // 目标曲线的完整时间范围作为统一比较区间。若候选曲线覆盖不足， - // 后面的 coverage 检查会拒绝它，防止候选通过缩短时间范围来降低误差。 QVector commonX(numPoints); QVector commonLogX(numPoints); QVector targetLogPressure(numPoints); @@ -5131,34 +6509,34 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( const double targetLogMinX = qLn(targetMinX); const double targetLogMaxX = qLn(targetMaxX); + // 固定使用目标曲线的完整 log-time 网格，候选之间不会因采样点不同而失去可比性。 for(int i = 0; i < numPoints; ++i) { double logX = targetLogMinX + static_cast(i) * - (targetLogMaxX - targetLogMinX) / (numPoints - 1); + (targetLogMaxX - targetLogMinX) / + (numPoints - 1); commonLogX[i] = logX; commonX[i] = qExp(logX); - if(!interpolateLogValue(targetPressure, commonX[i], - &targetLogPressure[i]) || - !interpolateLogValue(targetDerivative, commonX[i], - &targetLogDerivative[i])) { + if(!interpolateLogValue( + targetPressure, commonX[i], + &targetLogPressure[i]) || + !interpolateLogValue( + targetDerivative, commonX[i], + &targetLogDerivative[i])) { return invalidLoss; } } - // 残差采用“模拟减目标”，因此正值表示模拟曲线在对数幅值上高于目标， - // 负值表示模拟曲线偏低。NaN 表示该网格点不在模拟曲线支持范围内， - // 后续统计会自动跳过，但覆盖率检查仍会限制候选不能靠缺失数据降低损失。 - QVector pressureResidual(numPoints, - std::numeric_limits::quiet_NaN()); - QVector derivativeResidual(numPoints, - std::numeric_limits::quiet_NaN()); - QVector pressureSlope(numPoints, 0.0); - QVector derivativeSlope(numPoints, 0.0); + QVector pressureResidual( + numPoints, std::numeric_limits::quiet_NaN()); + QVector derivativeResidual( + numPoints, std::numeric_limits::quiet_NaN()); int firstSupported = -1; int lastSupported = -1; int supportedCount = 0; + // 残差定义为“模拟减目标”：正值表示模拟曲线偏高，负值表示偏低。 for(int i = 0; i < numPoints; ++i) { if(commonX[i] < resultMinX || commonX[i] > resultMaxX) { continue; @@ -5166,87 +6544,95 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( double resultLogPressure = 0.0; double resultLogDerivative = 0.0; - - if(!interpolateLogValue(resultPressure, commonX[i], - &resultLogPressure) || - !interpolateLogValue(resultDerivative, commonX[i], - &resultLogDerivative)) { - continue; + if(!interpolateLogValue( + resultPressure, commonX[i], + &resultLogPressure) || + !interpolateLogValue( + resultDerivative, commonX[i], + &resultLogDerivative)) { + // 已位于结果时间范围内却无法插值说明数据存在内部断点，不能补线。 + return invalidLoss; } - pressureResidual[i] = resultLogPressure - targetLogPressure[i]; - derivativeResidual[i] = resultLogDerivative - targetLogDerivative[i]; + pressureResidual[i] = + resultLogPressure - targetLogPressure[i]; + derivativeResidual[i] = + resultLogDerivative - targetLogDerivative[i]; ++supportedCount; - if(firstSupported < 0) { firstSupported = i; } - lastSupported = i; } - // 同时使用点覆盖率和连续时间跨度覆盖率，避免只覆盖少数离散点也被判定为完整。 + // 覆盖率同时约束“有效点数量”和“连续时间跨度”。取两者较小值可避免 + // 点数很多但只集中在局部时段的候选被误认为覆盖充分。 AutoFitObjectiveBreakdown breakdown; breakdown.coverage = supportedCount > 0 - ? static_cast(supportedCount) / numPoints + ? static_cast(supportedCount) / + numPoints : 0.0; - if(firstSupported >= 0 && lastSupported >= firstSupported) { - double span = qMax(1.0e-12, targetLogMaxX - targetLogMinX); - double coveredSpan = commonLogX[lastSupported] - - commonLogX[firstSupported]; - breakdown.coverage = qMin(breakdown.coverage, - qMax(0.0, coveredSpan / span)); + double targetSpan = + qMax(1.0e-12, targetLogMaxX - targetLogMinX); + double coveredSpan = + commonLogX[lastSupported] - commonLogX[firstSupported]; + breakdown.coverage = qMin( + breakdown.coverage, + qMax(0.0, coveredSpan / targetSpan)); } - // 覆盖率越接近 1，惩罚越小；覆盖不足 minimumCoverage 时直接返回无效损失。 - double coverageGap = qMax(0.0, 1.0 - breakdown.coverage); - breakdown.coveragePenalty = - qPow(coverageGap / (1.0 - minimumCoverage), 2.0); - + // 覆盖率只判断候选是否有效，不再加入固定惩罚，避免所有误差被整体抬高。 if(breakdown.coverage < minimumCoverage) { breakdown.total = invalidLoss; m_lastObjectiveBreakdown = breakdown; return invalidLoss; } - // 目标曲线斜率用于把“残差随时间的系统性变化”解释为左右平移。 - // 斜率用对数坐标计算，与前面的插值空间保持一致；平坦区斜率接近零， - // 不会凭空制造水平偏移量。 + // 通过门槛后最多只缺少首尾少量目标点。按模拟曲线端点趋势补齐后， + // 每个候选仍在固定 50 点上计算 Huber 均值，不能靠少算难拟合端点获益。 for(int i = 0; i < numPoints; ++i) { - int left = i == 0 ? 0 : i - 1; - int right = i == numPoints - 1 ? numPoints - 1 : i + 1; - double denominator = commonLogX[right] - commonLogX[left]; - - if(qAbs(denominator) > 1.0e-12) { - pressureSlope[i] = - (targetLogPressure[right] - targetLogPressure[left]) / - denominator; - derivativeSlope[i] = - (targetLogDerivative[right] - targetLogDerivative[left]) / - denominator; - } - } - - // Huber RMS 在小残差区域保持平方损失，在异常点区域转为线性增长， - // 避免少量求解器异常点完全主导候选排序。这里没有除以目标值， - // 因为残差已经是 log(value) 差值，本身就是相对误差的表达；返回值是 - // Huber rho 均值的平方根，保持与 RMS 类似的尺度。 - auto huberRms = [huberDelta](const QVector& values, - int begin, - int end) -> double { + if(isFiniteNumber(pressureResidual[i]) && + isFiniteNumber(derivativeResidual[i])) { + continue; + } + double resultLogPressure = 0.0; + double resultLogDerivative = 0.0; + if(!extrapolateEndpointLogValue( + resultPressure, commonX[i], + &resultLogPressure) || + !extrapolateEndpointLogValue( + resultDerivative, commonX[i], + &resultLogDerivative)) { + return invalidLoss; + } + pressureResidual[i] = + resultLogPressure - targetLogPressure[i]; + derivativeResidual[i] = + resultLogDerivative - targetLogDerivative[i]; + } + + // Huber RMS 在小残差处保持平方损失，在异常点处转为线性增长。 + auto huberRmsAround = [huberDelta]( + const QVector& values, + int begin, + int end, + double center) -> double { double sum = 0.0; int count = 0; + int validBegin = qMax(0, begin); + int validEnd = + qMin(end, static_cast(values.size())); - for(int i = qMax(0, begin); - i < qMin(end, static_cast(values.size())); ++i) { + for(int i = validBegin; i < validEnd; ++i) { if(!isFiniteNumber(values[i])) { continue; } - double absoluteValue = qAbs(values[i]); + double difference = values[i] - center; + double absoluteValue = qAbs(difference); double rho = absoluteValue <= huberDelta - ? values[i] * values[i] + ? difference * difference : 2.0 * huberDelta * absoluteValue - huberDelta * huberDelta; sum += rho; @@ -5258,41 +6644,61 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( : std::numeric_limits::quiet_NaN(); }; - // 用 Huber 加权迭代估计残差中心，作为整体上下偏移。相比普通平均值， - // 它对局部尖峰更稳健，同时保留偏高/偏低的方向信息。迭代只用于诊断， - // 不会把残差“校正”后再写回求解器结果。 - auto huberCenter = [huberDelta](const QVector& values) -> double { + auto huberRms = [&huberRmsAround]( + const QVector& values, + int begin, + int end) -> double { + return huberRmsAround(values, begin, end, 0.0); + }; + + // 将 Huber 能量转换成带符号的等效残差，使向量平方和与稳健损失一致。 + // 非代理 LM 直接对该固定长度向量建立 Jacobian。 + auto huberEquivalentResidual = [huberDelta](double value) -> double { + double absoluteValue = qAbs(value); + double rho = absoluteValue <= huberDelta + ? value * value + : 2.0 * huberDelta * absoluteValue - + huberDelta * huberDelta; + double magnitude = qSqrt(qMax(0.0, rho)); + return value < 0.0 ? -magnitude : magnitude; + }; + + // Huber 加权中心保留上下偏差的符号，并降低局部尖峰的影响。 + auto huberCenterRange = [huberDelta]( + const QVector& values, + int begin, + int end) -> double { + int validBegin = qMax(0, begin); + int validEnd = + qMin(end, static_cast(values.size())); double center = 0.0; int count = 0; - for(int i = 0; i < values.size(); ++i) { + for(int i = validBegin; i < validEnd; ++i) { if(isFiniteNumber(values[i])) { center += values[i]; ++count; } } - if(count == 0) { return std::numeric_limits::quiet_NaN(); } - center /= count; - // 固定最多 8 次迭代，控制每个候选的计算开销并保持结果稳定。 for(int iteration = 0; iteration < 8; ++iteration) { double weightedSum = 0.0; double weightTotal = 0.0; - - for(int i = 0; i < values.size(); ++i) { + for(int i = validBegin; i < validEnd; ++i) { if(!isFiniteNumber(values[i])) { continue; } double distance = qAbs(values[i] - center); - // 距离接近零时直接取权重 1，避免除零并保持中心点不被放大。 - double weight = distance <= huberDelta || distance < 1.0e-12 - ? 1.0 - : huberDelta / distance; + double weight = + distance <= huberDelta || + distance < 1.0e-12 + ? 1.0 + : huberDelta / distance; weightedSum += weight * values[i]; weightTotal += weight; } @@ -5300,133 +6706,549 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( if(weightTotal <= 1.0e-12) { break; } - double nextCenter = weightedSum / weightTotal; if(qAbs(nextCenter - center) <= 1.0e-12) { center = nextCenter; break; } - center = nextCenter; } - return center; }; - // 整体压力/导数误差用于排序；verticalLoss 主要用于解释整体上下偏移。 - breakdown.pressureLoss = huberRms(pressureResidual, 0, numPoints); - breakdown.derivativeLoss = huberRms(derivativeResidual, 0, numPoints); - breakdown.verticalBiasPressure = huberCenter(pressureResidual); - breakdown.verticalBiasDerivative = huberCenter(derivativeResidual); - breakdown.verticalLoss = - 0.5 * (qAbs(breakdown.verticalBiasPressure) + - qAbs(breakdown.verticalBiasDerivative)); - - // 去除上下中心后，把残差投影到目标曲线斜率上估计左右偏移。 - // residual ~= -physicalShift * targetSlope，因此 physicalShift 取回归系数的相反数。 - // 这是局部一阶近似，用于判断方向和大小，不等同于再次优化时间轴。 - double horizontalNumerator = 0.0; - double horizontalDenominator = 0.0; + // 压力和导数合并后只求一个公共中心，表示两条曲线共同的上下位移。 + // 分别去中心会把压力与导数之间真实的相对形状差异一并消除。 + auto huberCommonCenterRange = [&huberCenterRange]( + const QVector& pressureValues, + const QVector& derivativeValues, + int begin, + int end) -> double { + QVector combined; + int validBegin = qMax(0, begin); + int validEnd = qMin( + end, + qMin(static_cast(pressureValues.size()), + static_cast(derivativeValues.size()))); + combined.reserve(2 * qMax(0, validEnd - validBegin)); + + for(int i = validBegin; i < validEnd; ++i) { + if(isFiniteNumber(pressureValues[i])) { + combined.append(pressureValues[i]); + } + if(isFiniteNumber(derivativeValues[i])) { + combined.append(derivativeValues[i]); + } + } + return huberCenterRange(combined, 0, combined.size()); + }; + + // 两个通道按能量等权合并，返回值与单通道 Huber RMS 保持同一量纲。 + auto jointHuberRmsAround = [&huberRmsAround]( + const QVector& pressureValues, + const QVector& derivativeValues, + int begin, + int end, + double center) -> double { + double pressureLoss = huberRmsAround( + pressureValues, begin, end, center); + double derivativeLoss = huberRmsAround( + derivativeValues, begin, end, center); + if(!isFiniteNumber(pressureLoss) || + !isFiniteNumber(derivativeLoss)) { + return std::numeric_limits::quiet_NaN(); + } + return qSqrt(0.5 * + (pressureLoss * pressureLoss + + derivativeLoss * derivativeLoss)); + }; + + // 主目标始终使用未做上下或左右校正的完整曲线误差。 + breakdown.pressureLoss = + huberRms(pressureResidual, 0, numPoints); + breakdown.derivativeLoss = + huberRms(derivativeResidual, 0, numPoints); + // 压力和导数各占一半权重。缩放后 residualVector 的二范数就是 + // sqrt(0.5 * pressureLoss^2 + 0.5 * derivativeLoss^2)。 + const double residualScale = qSqrt(0.5 / numPoints); + breakdown.residualVector.reserve(2 * numPoints); for(int i = 0; i < numPoints; ++i) { - if(!isFiniteNumber(pressureResidual[i]) || - !isFiniteNumber(derivativeResidual[i])) { + breakdown.residualVector.append( + residualScale * + huberEquivalentResidual(pressureResidual[i])); + } + for(int i = 0; i < numPoints; ++i) { + breakdown.residualVector.append( + residualScale * + huberEquivalentResidual(derivativeResidual[i])); + } + + const double logGridStep = + (targetLogMaxX - targetLogMinX) / + (numPoints - 1); + const double resultLogMinX = qLn(resultMinX); + const double resultLogMaxX = qLn(resultMaxX); + // 左右配准只在所有候选位移都共同覆盖的固定区间比较，至少保留 80% + // 目标点；每个 log-time 网格间隔再细分为 8 份，提高位移分辨率。 + const int minimumRegistrationPoints = + (numPoints * 4) / 5; + const int shiftSubdivisions = 8; + int maximumShiftIntervals = 4; + int registrationBegin = 0; + int registrationEnd = numPoints; + double maximumPhysicalShift = + maximumShiftIntervals * logGridStep; + + // 所有位移候选使用同一组目标点。结果范围不足时逐步缩小最大位移， + // 但用于配准的固定公共区间不得少于目标网格的 80%。 + auto updateRegistrationRange = [&](double maximumShift) { + registrationBegin = 0; + registrationEnd = numPoints; + while(registrationBegin < registrationEnd && + commonLogX[registrationBegin] - maximumShift < + resultLogMinX - 1.0e-12) { + ++registrationBegin; + } + while(registrationEnd > registrationBegin && + commonLogX[registrationEnd - 1] + maximumShift > + resultLogMaxX + 1.0e-12) { + --registrationEnd; + } + }; + + updateRegistrationRange(maximumPhysicalShift); + while(maximumShiftIntervals > 0 && + registrationEnd - registrationBegin < + minimumRegistrationPoints) { + --maximumShiftIntervals; + maximumPhysicalShift = + maximumShiftIntervals * logGridStep; + updateRegistrationRange(maximumPhysicalShift); + } + if(registrationEnd - registrationBegin < + minimumRegistrationPoints) { + return invalidLoss; + } + + // physicalShift 为正表示模拟曲线偏右；对齐时在目标时刻右侧读取模拟值。 + auto buildShiftResidual = [&]( + double physicalShift, + int compareBegin, + int compareEnd, + QVector* shiftedPressure, + QVector* shiftedDerivative) -> bool { + if(!shiftedPressure || !shiftedDerivative) { + return false; + } + + shiftedPressure->fill( + std::numeric_limits::quiet_NaN(), + numPoints); + shiftedDerivative->fill( + std::numeric_limits::quiet_NaN(), + numPoints); + + int validBegin = qMax(0, compareBegin); + int validEnd = qMin(numPoints, compareEnd); + for(int i = validBegin; i < validEnd; ++i) { + double shiftedLogX = + commonLogX[i] + physicalShift; + if(shiftedLogX < resultLogMinX - 1.0e-12 || + shiftedLogX > + resultLogMaxX + 1.0e-12) { + return false; + } + + double shiftedX = qExp( + qBound(resultLogMinX, + shiftedLogX, + resultLogMaxX)); + double resultLogPressure = 0.0; + double resultLogDerivative = 0.0; + if(!interpolateLogValue( + resultPressure, shiftedX, + &resultLogPressure) || + !interpolateLogValue( + resultDerivative, shiftedX, + &resultLogDerivative)) { + return false; + } + + (*shiftedPressure)[i] = + resultLogPressure - targetLogPressure[i]; + (*shiftedDerivative)[i] = + resultLogDerivative - targetLogDerivative[i]; + } + return true; + }; + + // 损失相同时优先选择绝对位移更小的候选，避免平坦 profile 在数值噪声 + // 下无故偏向搜索边界。 + auto isBetterProfileValue = []( + double loss, + double shift, + double bestLoss, + double bestShift) -> bool { + const double tolerance = 1.0e-12; + return loss < bestLoss - tolerance || + (qAbs(loss - bestLoss) <= tolerance && + qAbs(shift) < qAbs(bestShift)); + }; + + const int halfShiftStepCount = + maximumShiftIntervals * shiftSubdivisions; + const double physicalShiftStep = + logGridStep / shiftSubdivisions; + double zeroShiftCenteredLoss = + std::numeric_limits::quiet_NaN(); + double bestCenteredLoss = + std::numeric_limits::infinity(); + double bestPhysicalShift = 0.0; + int bestShiftStep = 0; + double bestPressureLoss = + std::numeric_limits::infinity(); + double bestPressureShift = 0.0; + double bestDerivativeLoss = + std::numeric_limits::infinity(); + double bestDerivativeShift = 0.0; + QVector profileLosses( + 2 * halfShiftStepCount + 1, + std::numeric_limits::quiet_NaN()); + QVector profileCommonBiases( + 2 * halfShiftStepCount + 1, + std::numeric_limits::quiet_NaN()); + QVector shiftedPressureResidual; + QVector shiftedDerivativeResidual; + + // 位移和公共上下偏移联合求解，避免“先扣上下还是先扣左右”的顺序依赖。 + for(int shiftStep = -halfShiftStepCount; + shiftStep <= halfShiftStepCount; + ++shiftStep) { + double physicalShift = + shiftStep * physicalShiftStep; + if(!buildShiftResidual( + physicalShift, + registrationBegin, + registrationEnd, + &shiftedPressureResidual, + &shiftedDerivativeResidual)) { continue; } - double pressureCentered = - pressureResidual[i] - breakdown.verticalBiasPressure; - double derivativeCentered = - derivativeResidual[i] - breakdown.verticalBiasDerivative; - horizontalNumerator += pressureSlope[i] * pressureCentered + - derivativeSlope[i] * derivativeCentered; - horizontalDenominator += pressureSlope[i] * pressureSlope[i] + - derivativeSlope[i] * derivativeSlope[i]; + double commonBias = huberCommonCenterRange( + shiftedPressureResidual, + shiftedDerivativeResidual, + registrationBegin, + registrationEnd); + double centeredLoss = jointHuberRmsAround( + shiftedPressureResidual, + shiftedDerivativeResidual, + registrationBegin, + registrationEnd, + commonBias); + double pressureBias = huberCenterRange( + shiftedPressureResidual, + registrationBegin, + registrationEnd); + double derivativeBias = huberCenterRange( + shiftedDerivativeResidual, + registrationBegin, + registrationEnd); + double pressureLoss = huberRmsAround( + shiftedPressureResidual, + registrationBegin, + registrationEnd, + pressureBias); + double derivativeLoss = huberRmsAround( + shiftedDerivativeResidual, + registrationBegin, + registrationEnd, + derivativeBias); + + if(!isFiniteNumber(centeredLoss) || + !isFiniteNumber(pressureLoss) || + !isFiniteNumber(derivativeLoss)) { + continue; + } + int profileIndex = shiftStep + halfShiftStepCount; + profileLosses[profileIndex] = centeredLoss; + profileCommonBiases[profileIndex] = commonBias; + if(shiftStep == 0) { + zeroShiftCenteredLoss = centeredLoss; + } + if(isBetterProfileValue( + centeredLoss, + physicalShift, + bestCenteredLoss, + bestPhysicalShift)) { + bestCenteredLoss = centeredLoss; + bestPhysicalShift = physicalShift; + bestShiftStep = shiftStep; + } + if(isBetterProfileValue( + pressureLoss, + physicalShift, + bestPressureLoss, + bestPressureShift)) { + bestPressureLoss = pressureLoss; + bestPressureShift = physicalShift; + } + if(isBetterProfileValue( + derivativeLoss, + physicalShift, + bestDerivativeLoss, + bestDerivativeShift)) { + bestDerivativeLoss = derivativeLoss; + bestDerivativeShift = physicalShift; + } } - breakdown.horizontalShift = horizontalDenominator > 1.0e-12 - ? horizontalNumerator / - horizontalDenominator - : 0.0; - breakdown.horizontalPhysicalShift = -breakdown.horizontalShift; - breakdown.horizontalLoss = qAbs(breakdown.horizontalShift); + if(!isFiniteNumber(zeroShiftCenteredLoss) || + !isFiniteNumber(bestCenteredLoss) || + !isFiniteNumber(bestPressureLoss) || + !isFiniteNumber(bestDerivativeLoss)) { + return invalidLoss; + } - // 从原始残差中扣除“整体上下 + 等效左右”两部分，剩余项才作为形状误差。 - // 因此 shapeLoss 较大而 vertical/horizontal 较小时，说明主要是曲率、拐点 - // 或导数变化趋势不一致，而不是简单的整体平移。 - QVector shapePressure(numPoints, - std::numeric_limits::quiet_NaN()); - QVector shapeDerivative(numPoints, - std::numeric_limits::quiet_NaN()); + // horizontalGain 是“允许水平位移”相对“固定零位移”减少的稳健能量。 + // 只有改善足够明显且最优点不是边界，才把位移解释为可靠左右偏差。 + double horizontalGain = nestedRmsContribution( + zeroShiftCenteredLoss, bestCenteredLoss); + double horizontalSignalThreshold = + qMax(1.0e-5, zeroShiftCenteredLoss * 0.02); + int bestProfileIndex = bestShiftStep + halfShiftStepCount; + double nearbyProfileLoss = + std::numeric_limits::infinity(); + int leftProfileIndex = + bestProfileIndex - shiftSubdivisions; + int rightProfileIndex = + bestProfileIndex + shiftSubdivisions; + if(leftProfileIndex >= 0 && + leftProfileIndex < profileLosses.size() && + isFiniteNumber(profileLosses[leftProfileIndex])) { + nearbyProfileLoss = qMin( + nearbyProfileLoss, + profileLosses[leftProfileIndex]); + } + if(rightProfileIndex >= 0 && + rightProfileIndex < profileLosses.size() && + isFiniteNumber(profileLosses[rightProfileIndex])) { + nearbyProfileLoss = qMin( + nearbyProfileLoss, + profileLosses[rightProfileIndex]); + } + double profileContrast = isFiniteNumber(nearbyProfileLoss) + ? nestedRmsContribution( + nearbyProfileLoss, + bestCenteredLoss) + : 0.0; + bool flatRegistrationProfile = + profileContrast <= horizontalSignalThreshold; + + // 平台曲线的 profile 也可能很平，但公共 bias 在各个位移下保持稳定， + // 此时仍能可靠判断上下。只有近优位移会明显改变 bias 才说明上下/左右不可辨识。 + double minimumNearOptimalBias = + std::numeric_limits::infinity(); + double maximumNearOptimalBias = + -std::numeric_limits::infinity(); + for(int i = 0; i < profileLosses.size(); ++i) { + if(isFiniteNumber(profileLosses[i]) && + isFiniteNumber(profileCommonBiases[i]) && + profileLosses[i] <= + bestCenteredLoss + horizontalSignalThreshold) { + minimumNearOptimalBias = qMin( + minimumNearOptimalBias, + profileCommonBiases[i]); + maximumNearOptimalBias = qMax( + maximumNearOptimalBias, + profileCommonBiases[i]); + } + } + double nearOptimalBiasSpread = + isFiniteNumber(minimumNearOptimalBias) && + isFiniteNumber(maximumNearOptimalBias) + ? maximumNearOptimalBias - minimumNearOptimalBias + : std::numeric_limits::infinity(); + bool commonBiasStable = nearOptimalBiasSpread <= + qMax(1.0e-4, huberDelta * 0.05); + bool horizontalAtBoundary = + halfShiftStepCount > 0 && + qAbs(bestShiftStep) == halfShiftStepCount; + // 压力和导数通道分别求出的最佳位移若方向相反或相差过大，说明一个 + // 单一水平平移无法解释两条曲线，此时标记配准歧义并禁用左右引导。 + bool pressureShiftDetected = + qAbs(bestPressureShift) >= + 0.5 * physicalShiftStep; + bool derivativeShiftDetected = + qAbs(bestDerivativeShift) >= + 0.5 * physicalShiftStep; + bool channelShiftConflict = + pressureShiftDetected && + derivativeShiftDetected && + (bestPressureShift * bestDerivativeShift < 0.0 || + qAbs(bestPressureShift - bestDerivativeShift) > + 2.0 * logGridStep); + + breakdown.horizontalLoss = horizontalGain; + breakdown.horizontalReliable = + maximumShiftIntervals > 0 && + !horizontalAtBoundary && + !channelShiftConflict && + !flatRegistrationProfile && + horizontalGain > horizontalSignalThreshold && + qAbs(bestPhysicalShift) >= + 0.5 * physicalShiftStep; + breakdown.registrationAmbiguous = + channelShiftConflict || + (qAbs(bestPhysicalShift) >= + 0.5 * physicalShiftStep && + !breakdown.horizontalReliable) || + (flatRegistrationProfile && !commonBiasStable); + breakdown.horizontalPhysicalShift = + breakdown.horizontalReliable + ? bestPhysicalShift + : 0.0; + + // 可信水平位移确定后，在该位移实际覆盖的最大区间重新计算上下和形状。 + int diagnosticBegin = 0; + int diagnosticEnd = numPoints; + while(diagnosticBegin < diagnosticEnd && + commonLogX[diagnosticBegin] + + breakdown.horizontalPhysicalShift < + resultLogMinX - 1.0e-12) { + ++diagnosticBegin; + } + while(diagnosticEnd > diagnosticBegin && + commonLogX[diagnosticEnd - 1] + + breakdown.horizontalPhysicalShift > + resultLogMaxX + 1.0e-12) { + --diagnosticEnd; + } + if(diagnosticEnd - diagnosticBegin < + minimumRegistrationPoints || + !buildShiftResidual( + breakdown.horizontalPhysicalShift, + diagnosticBegin, + diagnosticEnd, + &shiftedPressureResidual, + &shiftedDerivativeResidual)) { + return invalidLoss; + } + + double commonBias = huberCommonCenterRange( + shiftedPressureResidual, + shiftedDerivativeResidual, + diagnosticBegin, + diagnosticEnd); + double rawAlignedLoss = jointHuberRmsAround( + shiftedPressureResidual, + shiftedDerivativeResidual, + diagnosticBegin, + diagnosticEnd, + 0.0); + double centeredAlignedLoss = jointHuberRmsAround( + shiftedPressureResidual, + shiftedDerivativeResidual, + diagnosticBegin, + diagnosticEnd, + commonBias); + if(!isFiniteNumber(commonBias) || + !isFiniteNumber(rawAlignedLoss) || + !isFiniteNumber(centeredAlignedLoss)) { + return invalidLoss; + } + + // 原始对齐误差减去公共中心后的能量差定义为上下误差贡献。只有它相对 + // 当前对齐误差足够明显，且配准无歧义时，公共 bias 才可用于有符号选参。 + breakdown.verticalCommonBias = commonBias; + breakdown.verticalLoss = nestedRmsContribution( + rawAlignedLoss, centeredAlignedLoss); + breakdown.verticalReliable = + !breakdown.registrationAmbiguous && + breakdown.verticalLoss > + qMax(1.0e-5, rawAlignedLoss * 0.02); + + QVector shapePressure( + numPoints, std::numeric_limits::quiet_NaN()); + QVector shapeDerivative( + numPoints, std::numeric_limits::quiet_NaN()); + for(int i = diagnosticBegin; i < diagnosticEnd; ++i) { + if(isFiniteNumber(shiftedPressureResidual[i])) { + shapePressure[i] = + shiftedPressureResidual[i] - commonBias; + } + if(isFiniteNumber(shiftedDerivativeResidual[i])) { + shapeDerivative[i] = + shiftedDerivativeResidual[i] - commonBias; + } + } + + // shapeLoss 是去除可信左右位移和稳健公共中心后的剩余误差。 + double shapePressureLoss = huberRms( + shapePressure, diagnosticBegin, diagnosticEnd); + double shapeDerivativeLoss = huberRms( + shapeDerivative, diagnosticBegin, diagnosticEnd); + if(!isFiniteNumber(shapePressureLoss) || + !isFiniteNumber(shapeDerivativeLoss)) { + return invalidLoss; + } + breakdown.shapeLoss = qSqrt( + 0.5 * + (shapePressureLoss * shapePressureLoss + + shapeDerivativeLoss * shapeDerivativeLoss)); + + // 现阶段不识别或单独调度晚期流动段；保留字段只为了维持现有 trace 列。 + breakdown.lateDerivativeSlopeBias = 0.0; + breakdown.lateDerivativeTrendLoss = 0.0; + breakdown.lateDerivativeTrendReliable = false; - for(int i = 0; i < numPoints; ++i) { - if(isFiniteNumber(pressureResidual[i])) { - shapePressure[i] = pressureResidual[i] - - breakdown.verticalBiasPressure - - breakdown.horizontalShift * pressureSlope[i]; - } - - if(isFiniteNumber(derivativeResidual[i])) { - shapeDerivative[i] = derivativeResidual[i] - - breakdown.verticalBiasDerivative - - breakdown.horizontalShift * - derivativeSlope[i]; - } - } - - breakdown.shapeLoss = - 0.5 * (huberRms(shapePressure, 0, numPoints) + - huberRms(shapeDerivative, 0, numPoints)); - - // 将对数时间网格分成早、中、晚三段，用于定位误差集中出现的阶段。 - // 网格本身按 log(time) 均匀分布，所以三段对应的是时间数量级，而非原始 - // 线性时间长度，适合双对数试井曲线的早期/中期/晚期判读。 - const int segment1 = numPoints / 3; - const int segment2 = (2 * numPoints) / 3; - breakdown.pressureEarlyLoss = huberRms(pressureResidual, 0, segment1); - breakdown.pressureMiddleLoss = - huberRms(pressureResidual, segment1, segment2); - breakdown.pressureLateLoss = - huberRms(pressureResidual, segment2, numPoints); - breakdown.derivativeEarlyLoss = - huberRms(derivativeResidual, 0, segment1); - breakdown.derivativeMiddleLoss = - huberRms(derivativeResidual, segment1, segment2); - breakdown.derivativeLateLoss = - huberRms(derivativeResidual, segment2, numPoints); - - // 函数入口已将 m_lastObjectiveBreakdown 重置为无效状态，因此这里直接返回 - // invalidLoss 时不会把上一候选的误差分解误报给调用方。 - // 导数、压力或形状无法形成有效统计时，整个候选都视为无效，避免 NaN - // 进入粒子排序。 if(!isFiniteNumber(breakdown.pressureLoss) || !isFiniteNumber(breakdown.derivativeLoss) || + !isFiniteNumber(breakdown.verticalCommonBias) || + !isFiniteNumber(breakdown.verticalLoss) || + !isFiniteNumber(breakdown.horizontalPhysicalShift) || + !isFiniteNumber(breakdown.horizontalLoss) || !isFiniteNumber(breakdown.shapeLoss) || - !isFiniteNumber(breakdown.verticalLoss)) { + breakdown.residualVector.size() != 2 * numPoints) { return invalidLoss; } - // 当前总损失作为 fitness 用于粒子比较、收敛/停止判断；上下、左右、形状和 - // 分段分量先作为诊断输出，不在本次改动中直接参与参数更新。 - breakdown.total = 0.5 * breakdown.pressureLoss + - 0.5 * breakdown.derivativeLoss + - 0.1 * breakdown.coveragePenalty; - breakdown.valid = isFiniteNumber(breakdown.total) && - breakdown.total >= 0.0; + if(isSurrogateScreeningEnabled()) { + // 代理模型仍按原目标比较，不能用未参与训练的新损失改变候选排序。 + breakdown.total = + 0.5 * breakdown.pressureLoss + + 0.5 * breakdown.derivativeLoss; + } else { + // 非代理总目标等于固定 100 维 Huber 等效残差的二范数；压力和 + // 导数各占一半能量。上下、左右和形状分量不参与候选排序与接受。 + breakdown.total = qSqrt( + 0.5 * breakdown.pressureLoss * breakdown.pressureLoss + + 0.5 * breakdown.derivativeLoss * breakdown.derivativeLoss); + } + breakdown.valid = + isFiniteNumber(breakdown.total) && + breakdown.total >= 0.0; m_lastObjectiveBreakdown = breakdown; - DEBUG_OUT(QString("LogLog objective: pressure=%1, derivative=%2, vertical=%3, horizontal=%4, shape=%5, coverage=%6, total=%7") - .arg(breakdown.pressureLoss, 0, 'e', 4) - .arg(breakdown.derivativeLoss, 0, 'e', 4) - .arg(breakdown.verticalLoss, 0, 'e', 4) - .arg(breakdown.horizontalLoss, 0, 'e', 4) - .arg(breakdown.shapeLoss, 0, 'e', 4) - .arg(breakdown.coverage, 0, 'f', 4) - .arg(breakdown.total, 0, 'e', 4)); - - return breakdown.valid ? qMin(1.0e9, breakdown.total) : invalidLoss; + DEBUG_OUT( + QString("LogLog objective: pressure=%1, derivative=%2, vertical=%3, horizontal=%4, shape=%5, ambiguous=%6, shift=%7, coverage=%8, total=%9") + .arg(breakdown.pressureLoss, 0, 'e', 4) + .arg(breakdown.derivativeLoss, 0, 'e', 4) + .arg(breakdown.verticalLoss, 0, 'e', 4) + .arg(breakdown.horizontalLoss, 0, 'e', 4) + .arg(breakdown.shapeLoss, 0, 'e', 4) + .arg(breakdown.registrationAmbiguous) + .arg(breakdown.horizontalPhysicalShift, 0, 'e', 4) + .arg(breakdown.coverage, 0, 'f', 4) + .arg(breakdown.total, 0, 'e', 4)); + + return breakdown.valid + ? qMin(1.0e9, breakdown.total) + : invalidLoss; } catch(const std::exception& e) { - DEBUG_OUT(QString("Exception in LogLog error calculation: %1").arg(e.what())); + DEBUG_OUT( + QString("Exception in LogLog error calculation: %1") + .arg(e.what())); return invalidLoss; } catch(...) { DEBUG_OUT("Unknown exception in LogLog error calculation"); diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp index f0f243e..2a527ee 100644 --- a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp +++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp @@ -35,6 +35,14 @@ bool nmAutoFitUiIsFinite(double value) #endif } +// 物理边界可能由多个浮点配置量计算得到，界面十进制文本再转回 double 后会有 +// 末位舍入差。这里只放宽约 8 个机器精度，不放宽实际物理范围。 +bool nmAutoFitUiNearlyEqual(double left, double right) +{ + const double scale = qMax(std::fabs(left), std::fabs(right)); + return std::fabs(left - right) <= DBL_EPSILON * 8.0 * scale; +} + // 从系统参数表读取物理边界，读取失败时保留调用方提供的兜底边界。 bool nmAutoFitReadPhysicalRange(const char* parameterName, double fallbackMin, double fallbackMax, double& minValue, double& maxValue) @@ -337,9 +345,10 @@ void nmWxAutomaticFitting::setParameterRange(int parameterIndex, m_updatingParameterRanges = wasUpdatingRanges; } -// 根据初值生成首次或拟合后的建议范围，并始终限制在物理边界内。 +// 根据当前初值生成建议搜索范围，并始终截断在系统物理边界内。skin 使用 +// 加减固定宽度，其余正值参数使用倍率范围；该规则在首次加载和拟合完成后复用。 void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, - double centerValue, bool afterFit) + double centerValue) { if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 8 || !nmAutoFitUiIsFinite(centerValue)) { @@ -371,12 +380,12 @@ void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, double newMin = physicalMin; double newMax = physicalMax; if(parameterIndex == 1) { - const double skinHalfRange = afterFit ? 1.0 : 10.0; + const double skinHalfRange = 10.0; newMin = qMax(physicalMin, reference - skinHalfRange); newMax = qMin(physicalMax, reference + skinHalfRange); } else if(reference > 0.0 && !(parameterIndex == 7 && centerValue <= 0.0)) { - const double lowerFactor = afterFit ? 0.5 : 0.1; - const double upperFactor = afterFit ? 2.0 : 10.0; + const double lowerFactor = 0.1; + const double upperFactor = 10.0; newMin = qMax(physicalMin, reference * lowerFactor); newMax = qMin(physicalMax, reference * upperFactor); } else if(parameterIndex == 7) { @@ -409,7 +418,7 @@ void nmWxAutomaticFitting::initializeSuggestedParameterRanges() bool initialOk = false; const double initialValue = initialItem->text().toDouble(&initialOk); if(initialOk && nmAutoFitUiIsFinite(initialValue)) { - updateRangeForParameter(parameterIndex, initialValue, false); + updateRangeForParameter(parameterIndex, initialValue); } else { // 数据对象没有提供该初值时使用完整物理区间，不回退到旧的默认范围。 double physicalMin = 0.0; @@ -462,7 +471,7 @@ void nmWxAutomaticFitting::normalizeSavedParameterRanges() bool initialOk = false; const double initialValue = initialItem->text().toDouble(&initialOk); if(initialOk && nmAutoFitUiIsFinite(initialValue)) { - updateRangeForParameter(parameterIndex, initialValue, false); + updateRangeForParameter(parameterIndex, initialValue); } else if(physicalMax >= physicalMin) { setParameterRange(parameterIndex, physicalMin, physicalMax); } @@ -529,9 +538,12 @@ bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int par return false; } - if(minValue < physicalMin || minValue > physicalMax - || initialValue < physicalMin || initialValue > physicalMax - || maxValue < physicalMin || maxValue > physicalMax) { + if((minValue < physicalMin && !nmAutoFitUiNearlyEqual(minValue, physicalMin)) + || (minValue > physicalMax && !nmAutoFitUiNearlyEqual(minValue, physicalMax)) + || (initialValue < physicalMin && !nmAutoFitUiNearlyEqual(initialValue, physicalMin)) + || (initialValue > physicalMax && !nmAutoFitUiNearlyEqual(initialValue, physicalMax)) + || (maxValue < physicalMin && !nmAutoFitUiNearlyEqual(maxValue, physicalMin)) + || (maxValue > physicalMax && !nmAutoFitUiNearlyEqual(maxValue, physicalMax))) { errorMessage = tr("The values of %1 exceed the physical range [%2, %3].") .arg(tr(parameterNames[currentParameterIndex])) .arg(QString::number(physicalMin, 'g', 10)) @@ -1195,8 +1207,8 @@ void nmWxAutomaticFitting::onWellSelected(int index) m_parameterTable->item(2, 3)->setText(QString::number(wellboreStorageValue)); if(m_autoParameterRanges) { - updateRangeForParameter(1, skinValue, false); - updateRangeForParameter(2, wellboreStorageValue, false); + updateRangeForParameter(1, skinValue); + updateRangeForParameter(2, wellboreStorageValue); } } } @@ -1531,9 +1543,9 @@ void nmWxAutomaticFitting::updateBestParametersToTable() // 更新初始值 m_parameterTable->item(paramIndex, 3)->setText(QString::number(bestValue, 'g', 4)); - // 只有自动范围模式才根据拟合结果收窄下一轮搜索区间；手工范围由用户保留。 + // 自动范围模式下，以拟合结果为中心复用首次建范围的规则；手工范围由用户保留。 if(m_autoParameterRanges) { - updateRangeForParameter(paramIndex, bestValue, true); + updateRangeForParameter(paramIndex, bestValue); } } From e8563506b9eae901aad504db4caddf6bf5065a0d Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 14 Aug 2026 10:26:56 +0800 Subject: [PATCH 07/12] =?UTF-8?q?=E5=A2=9E=E5=8A=A0=E5=8E=8B=E8=A3=82?= =?UTF-8?q?=E4=BA=95=E5=AF=BC=E6=B5=81=E8=83=BD=E5=8A=9B=20Dfc=20=E8=87=AA?= =?UTF-8?q?=E5=8A=A8=E6=8B=9F=E5=90=88=E5=8F=82=E6=95=B0?= MIME-Version: 1.0 Content-Type: text/plain; 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// 完整 8 个参数是否被用户勾选参与拟合。 - QVector m_parameterLower; // 完整 8 个参数的搜索下界。 - QVector m_parameterUpper; // 完整 8 个参数的搜索上界。 - QVector m_enabledParamIndices; // 被勾选参数在完整 8 维体系中的索引。 + QVector m_parameterSelected; // 完整 9 个参数是否被用户勾选参与拟合。 + QVector m_parameterLower; // 完整 9 个参数的搜索下界。 + QVector m_parameterUpper; // 完整 9 个参数的搜索上界。 + QVector m_enabledParamIndices; // 被勾选参数在完整 9 维体系中的索引。 QVector > m_targetLogLogData; // 目标井 history log-log 曲线：time/pressure/derivative。 QString m_targetWellName; // 目标井名称；读写井参数和读取模拟曲线都依赖它。 diff --git a/Include/nmNum/nmData/nmDataAutomaticFitting.h b/Include/nmNum/nmData/nmDataAutomaticFitting.h index b2da223..18bc851 100644 --- a/Include/nmNum/nmData/nmDataAutomaticFitting.h +++ b/Include/nmNum/nmData/nmDataAutomaticFitting.h @@ -75,6 +75,13 @@ public: nmDataAttribute& getSwiMin(); void setSwiMin(const nmDataAttribute& swiMin); + // Getter and Setter for fractureConductivityMax + nmDataAttribute& getFractureConductivityMax(); + void setFractureConductivityMax(const nmDataAttribute& fractureConductivityMax); + // Getter and Setter for fractureConductivityMin + nmDataAttribute& getFractureConductivityMin(); + void setFractureConductivityMin(const nmDataAttribute& fractureConductivityMin); + // Getter and Setter for iteration count nmDataAttribute& getIterationCount(); void setIterationCount(const nmDataAttribute& iterationCount); @@ -114,6 +121,9 @@ public: bool getSwiSelected() const; void setSwiSelected(bool selected); + bool getFractureConductivitySelected() const; + void setFractureConductivitySelected(bool selected); + private: // 参数最大值 nmDataAttribute m_permeabilityMax; @@ -124,6 +134,7 @@ private: nmDataAttribute m_ctMax; nmDataAttribute m_cfMax; nmDataAttribute m_swiMax; + nmDataAttribute m_fractureConductivityMax; // 参数最小值 nmDataAttribute m_permeabilityMin; @@ -134,6 +145,7 @@ private: nmDataAttribute m_ctMin; nmDataAttribute m_cfMin; nmDataAttribute m_swiMin; + nmDataAttribute m_fractureConductivityMin; // 迭代参数 nmDataAttribute m_iterationCount; // 迭代步数 @@ -150,6 +162,7 @@ private: bool m_ctSelected; // 是否选择综合压缩系数进行拟合 bool m_cfSelected; // 是否选择岩石压缩系数进行拟合 bool m_swiSelected; // 是否选择初始含水饱和度进行拟合 + bool m_fractureConductivitySelected; // 是否选择裂缝导流能力进行拟合 }; #endif // NMDATAAUTOMATICFITTING_H diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h index 5b3848c..ada66d0 100644 --- a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h +++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h @@ -91,6 +91,7 @@ private: QCheckBox* m_ctCheckBox; // 综合压缩系数 QCheckBox* m_cfCheckBox; // 岩石压缩系数 QCheckBox* m_swiCheckBox; // 初始含水饱和度 + QCheckBox* m_dfcCheckBox; // 裂缝导流能力 // 按钮 QPushButton* m_reverseBtn; diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index a4eb476..2c75f29 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -432,7 +432,7 @@ static QString findExecutableInPath(const QString& executableName) static QStringList traceParameterNames() { // trace 和 trace meta 使用的完整参数名顺序。 - // 这个顺序必须与 buildTraceParameterVector() 和 m_parameterSelected 的 0-7 索引一致。 + // 这个顺序必须与 buildTraceParameterVector() 和 m_parameterSelected 的 0-8 索引一致。 QStringList names; names << "k" << "skin" @@ -441,7 +441,8 @@ static QStringList traceParameterNames() << "h" << "Ct" << "Cf" - << "Swi"; + << "Swi" + << "Dfc"; return names; } @@ -1011,7 +1012,8 @@ void nmCalculationAutoFitPSO::closeTraceFile() void nmCalculationAutoFitPSO::writeTraceHeader() { // trace CSV 字段说明： - // - 当前粒子参数只记录代理模型关心的 k/skin/wellboreC/phi/h/Ct/Cf； + // - 代理模式保持原 k/skin/wellboreC/phi/h/Ct/Cf 契约； + // - 非代理模式额外记录 Swi/Dfc，便于复盘信赖域对两项参数的调整； // - solver_objective 是真实求解器误差； // - surrogate_objective 是 Python 代理评分； // - screening_decision 说明该粒子为什么跑/不跑真实求解器； @@ -1032,8 +1034,12 @@ void nmCalculationAutoFitPSO::writeTraceHeader() << "phi" << "h" << "Ct" - << "Cf" - << "solver_objective" + << "Cf"; + if(!isSurrogateScreeningEnabled()) { + cols << "Swi" + << "Dfc"; + } + cols << "solver_objective" << "solver_success" << "elapsed_ms" << "surrogate_objective" @@ -1045,16 +1051,24 @@ void nmCalculationAutoFitPSO::writeTraceHeader() << "pbest_phi" << "pbest_h" << "pbest_Ct" - << "pbest_Cf" - << "gbest_objective" + << "pbest_Cf"; + if(!isSurrogateScreeningEnabled()) { + cols << "pbest_Swi" + << "pbest_Dfc"; + } + cols << "gbest_objective" << "gbest_k" << "gbest_skin" << "gbest_wellboreC" << "gbest_phi" << "gbest_h" << "gbest_Ct" - << "gbest_Cf" - << "enabled_param_indices" + << "gbest_Cf"; + if(!isSurrogateScreeningEnabled()) { + cols << "gbest_Swi" + << "gbest_Dfc"; + } + cols << "enabled_param_indices" << "pressure_loss" << "derivative_loss"; if(!isSurrogateScreeningEnabled()) { @@ -1118,7 +1132,7 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile() // 非代理 v5 增加带符号诊断列；代理 PSO 保留原 v3 字段和目标，避免改变 // 已有模型的训练和回放契约。 out << " \"schema_version\": " - << (isSurrogateScreeningEnabled() ? 3 : 5) << ",\n"; + << (isSurrogateScreeningEnabled() ? 3 : 6) << ",\n"; out << " \"trace_type\": " << jsonEscape(isSurrogateScreeningEnabled() ? "pso_baseline_replay_meta" @@ -1179,10 +1193,10 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile() QVector nmCalculationAutoFitPSO::buildTraceParameterVector(const QVector& selectedParameters) const { - // 将粒子内部使用的“启用参数向量”还原成完整 8 维参数向量。 + // 将粒子内部使用的“启用参数向量”还原成完整 9 维参数向量。 // 未启用的参数从当前 DataManager 读取，启用的参数用 selectedParameters 覆盖。 // trace CSV、候选 CSV、代理训练域检查都需要这个完整向量。 - QVector fullParams(8, 0.0); + QVector fullParams(9, 0.0); nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); @@ -1198,8 +1212,26 @@ QVector nmCalculationAutoFitPSO::buildTraceParameterVector(const QVector nmDataWellBase* pTargetWell = dataManager->findWellByName(m_targetWellName); if(pTargetWell) { - fullParams[1] = pTargetWell->getPerforation(0)->getSkin().getValue().toDouble(); + nmDataPerforation* perforation = pTargetWell->getPerforation(0); + if(perforation) { + fullParams[1] = perforation->getSkin().getValue().toDouble(); + } fullParams[2] = pTargetWell->getWellboreStorage().getValue().toDouble(); + + // Dfc 只存在于两类压裂井，普通井在完整向量中保持为 0。 + if(pTargetWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) { + nmDataVerticalFracturedWell* fracturedWell = + dynamic_cast(pTargetWell); + if(fracturedWell) { + fullParams[8] = fracturedWell->getDfc().getValue().toDouble(); + } + } else if(pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) { + nmDataHorizontalFracturedWell* fracturedWell = + dynamic_cast(pTargetWell); + if(fracturedWell) { + fullParams[8] = fracturedWell->getDfc().getValue().toDouble(); + } + } } } @@ -1255,8 +1287,12 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, << traceParamAt(currentParams, 3) << traceParamAt(currentParams, 4) << traceParamAt(currentParams, 5) - << traceParamAt(currentParams, 6) - << traceNumber(solverObjective) + << traceParamAt(currentParams, 6); + if(!isSurrogateScreeningEnabled()) { + cols << traceParamAt(currentParams, 7) + << traceParamAt(currentParams, 8); + } + cols << traceNumber(solverObjective) << QString::number(solverSuccess ? 1 : 0) << QString::number(elapsedMs) << traceNumber(surrogateObjective) @@ -1268,16 +1304,24 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, << traceParamAt(pbestParams, 3) << traceParamAt(pbestParams, 4) << traceParamAt(pbestParams, 5) - << traceParamAt(pbestParams, 6) - << traceNumber(m_globalBestFitness) + << traceParamAt(pbestParams, 6); + if(!isSurrogateScreeningEnabled()) { + cols << traceParamAt(pbestParams, 7) + << traceParamAt(pbestParams, 8); + } + cols << traceNumber(m_globalBestFitness) << traceParamAt(gbestParams, 0) << traceParamAt(gbestParams, 1) << traceParamAt(gbestParams, 2) << traceParamAt(gbestParams, 3) << traceParamAt(gbestParams, 4) << traceParamAt(gbestParams, 5) - << traceParamAt(gbestParams, 6) - << csvEscape(enabledIndices.join(";")); + << traceParamAt(gbestParams, 6); + if(!isSurrogateScreeningEnabled()) { + cols << traceParamAt(gbestParams, 7) + << traceParamAt(gbestParams, 8); + } + cols << csvEscape(enabledIndices.join(";")); if(objectiveBreakdown && objectiveBreakdown->valid) { cols << traceNumber(objectiveBreakdown->pressureLoss) @@ -2873,14 +2917,14 @@ void nmCalculationAutoFitPSO::loadParameterBounds() // 读取用户勾选的拟合参数及上下界。 // // 这里构建三个核心数组： - // - m_parameterSelected[8]：完整参数体系中每个参数是否参与拟合； - // - m_parameterLower/Upper[8]：完整参数体系的搜索上下界； + // - m_parameterSelected[9]：完整参数体系中每个参数是否参与拟合； + // - m_parameterLower/Upper[9]：完整参数体系的搜索上下界； // - m_enabledParamIndices：把粒子内部紧凑向量映射回完整参数索引。 nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy(); // 获取参数选择状态 - m_parameterSelected.resize(8); + m_parameterSelected.resize(9); m_parameterSelected[0] = fittingData.getPermeabilitySelected(); m_parameterSelected[1] = fittingData.getSkinSelected(); m_parameterSelected[2] = fittingData.getWellboreStorageSelected(); @@ -2889,10 +2933,11 @@ void nmCalculationAutoFitPSO::loadParameterBounds() m_parameterSelected[5] = fittingData.getCtSelected(); m_parameterSelected[6] = fittingData.getCfSelected(); m_parameterSelected[7] = fittingData.getSwiSelected(); + m_parameterSelected[8] = fittingData.getFractureConductivitySelected(); // 获取参数边界 - m_parameterLower.resize(8); - m_parameterUpper.resize(8); + m_parameterLower.resize(9); + m_parameterUpper.resize(9); m_parameterLower[0] = fittingData.getPermeabilityMin().getValue().toDouble(); m_parameterUpper[0] = fittingData.getPermeabilityMax().getValue().toDouble(); @@ -2918,6 +2963,9 @@ void nmCalculationAutoFitPSO::loadParameterBounds() m_parameterLower[7] = fittingData.getSwiMin().getValue().toDouble(); m_parameterUpper[7] = fittingData.getSwiMax().getValue().toDouble(); + m_parameterLower[8] = fittingData.getFractureConductivityMin().getValue().toDouble(); + m_parameterUpper[8] = fittingData.getFractureConductivityMax().getValue().toDouble(); + // 更新启用参数索引 m_enabledParamIndices.clear(); @@ -3425,6 +3473,15 @@ bool nmCalculationAutoFitPSO::startAutoFitting() emit logMessageGenerated(tr("Applying optimized parameters to model...")); applyParametersToDataManager(m_globalBestPosition); + // 即使用户此时停止、不再执行最终完整计算，也要把 PEBI 缓存恢复为 + // 最终已接受的 Dfc，避免缓存仍停留在最后一个被拒绝的候选值。 + if(m_parameterSelected.size() > 8 && m_parameterSelected[8]) { + nmCalculationPebiGrid* pebiGrid = nmCalculationPebiGrid::getInstance(); + if(!pebiGrid || !pebiGrid->generateOutputPara()) { + throw std::runtime_error("Failed to refresh final fracture conductivity"); + } + } + if(m_shouldStop) { // 手动停止优先保持快速返回，仅写回已确认的最优参数。 emit logMessageGenerated(tr("Final full-field calculation skipped after user stop")); @@ -3638,6 +3695,22 @@ void nmCalculationAutoFitPSO::extractUserInitialValues() case 7: // 初始含水饱和度 initialValue = reservoirData.getSwi().getValue().toDouble(); break; + + case 8: // 裂缝导流能力 + if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) { + nmDataVerticalFracturedWell* fracturedWell = + dynamic_cast(pTargetWell); + if(fracturedWell) { + initialValue = fracturedWell->getDfc().getValue().toDouble(); + } + } else if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) { + nmDataHorizontalFracturedWell* fracturedWell = + dynamic_cast(pTargetWell); + if(fracturedWell) { + initialValue = fracturedWell->getDfc().getValue().toDouble(); + } + } + break; } m_initialValues.append(initialValue); @@ -5461,6 +5534,17 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector& parameter return 1e10; } + // Dfc 位于 PEBI 裂缝数组 crack[5]，不是每次求解都会重新组装的 Base/CS 参数。 + // 因此只有勾选 Dfc 时才刷新一次网格输出参数，保证本次真实试算使用刚写入井对象的值。 + if(m_parameterSelected.size() > 8 && m_parameterSelected[8]) { + nmCalculationPebiGrid* pebiGrid = nmCalculationPebiGrid::getInstance(); + if(!pebiGrid || !pebiGrid->generateOutputPara()) { + DEBUG_OUT(QString("%1: Call #%2 - Failed to refresh PEBI fracture conductivity") + .arg(funcName).arg(callCount)); + return 1e10; + } + } + // 4. 运行求解器。真实求解器偶发失败时允许重试，避免一次 DLL 调用异常 // 直接让整个粒子评价失败。 QVector> solverResult; @@ -5623,7 +5707,7 @@ void nmCalculationAutoFitPSO::updateReservoirParameters(const QVector& p nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); nmDataReservoir reservoirData = dataManager->getReservoirDataCopy(); - // paramIndex 是粒子 position 中的索引；i 是完整 8 个参数体系中的索引。 + // paramIndex 是粒子 position 中的索引；i 是完整 9 个参数体系中的索引。 // 只有 m_parameterSelected[i] 为 true 时，才从 parameters 中消费一个值。 int paramIndex = 0; @@ -5669,7 +5753,8 @@ void nmCalculationAutoFitPSO::updateWellParameters(const QVector& parame { // 更新目标井上的拟合参数。目前井级可拟合参数主要是： // - skin：写入第一个 perforation； - // - wellboreC：写入井筒储集系数。 + // - wellboreC：写入井筒储集系数； + // - Dfc：只写入垂直压裂井或多段压裂水平井的裂缝导流能力。 // 如果目标井不存在或没有射孔数据，这里只记录 debug，不抛异常。 nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); @@ -5705,6 +5790,27 @@ void nmCalculationAutoFitPSO::updateWellParameters(const QVector& parame pWell->setWellboreStorage(wellboreAttr); } break; + + case 8: { // 裂缝导流能力 + if(pWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) { + nmDataVerticalFracturedWell* fracturedWell = + dynamic_cast(pWell); + if(fracturedWell) { + nmDataAttribute dfc = fracturedWell->getDfc(); + dfc.setValue(value); + fracturedWell->setDfc(dfc); + } + } else if(pWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) { + nmDataHorizontalFracturedWell* fracturedWell = + dynamic_cast(pWell); + if(fracturedWell) { + nmDataAttribute dfc = fracturedWell->getDfc(); + dfc.setValue(value); + fracturedWell->setDfc(dfc); + } + } + } + break; } paramIndex++; @@ -6139,6 +6245,14 @@ bool nmCalculationAutoFitPSO::validateParameters(const QVector& paramete return false; } + break; + + case 8: // 裂缝导流能力：0 表示无限导流，不能作为连续拟合搜索点 + if(value <= 1e-10) { + DEBUG_OUT(QString("Rejecting non-positive fracture conductivity: %1").arg(value)); + return false; + } + break; } } diff --git a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp index 4d3b6d8..a7a3462 100644 --- a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp +++ b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp @@ -11,6 +11,7 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_ctSelected = true; // 默认选中 m_cfSelected = false; // 默认不选中 m_swiSelected = false; // 默认不选中 + m_fractureConductivitySelected = false; // 仅压裂井可用，默认不选中 // 拟合上下界不再使用固定默认值，由自动拟合窗口按数据对象初值和物理边界生成。 m_permeabilityMax = nmDataAttribute("Permeability Max", QVariant(), "Darcy"); @@ -21,6 +22,7 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_ctMax = nmDataAttribute("Ct Max", QVariant(), ""); m_cfMax = nmDataAttribute("Cf Max", QVariant(), ""); m_swiMax = nmDataAttribute("Swi Max", QVariant(), ""); + m_fractureConductivityMax = nmDataAttribute("Fracture Conductivity Max", QVariant(), "md.m"); m_permeabilityMin = nmDataAttribute("Permeability Min", QVariant(), "Darcy"); m_skinMin = nmDataAttribute("Skin Min", QVariant(), ""); @@ -30,6 +32,7 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_ctMin = nmDataAttribute("Ct Min", QVariant(), ""); m_cfMin = nmDataAttribute("Cf Min", QVariant(), ""); m_swiMin = nmDataAttribute("Swi Min", QVariant(), ""); + m_fractureConductivityMin = nmDataAttribute("Fracture Conductivity Min", QVariant(), "md.m"); // 初始化迭代参数 m_iterationCount = nmDataAttribute("Iteration Count", 20, ""); @@ -59,6 +62,7 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF m_ctSelected = other.m_ctSelected; m_cfSelected = other.m_cfSelected; m_swiSelected = other.m_swiSelected; + m_fractureConductivitySelected = other.m_fractureConductivitySelected; // 复制参数最大值 m_permeabilityMax = other.m_permeabilityMax; @@ -69,6 +73,7 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF m_ctMax = other.m_ctMax; m_cfMax = other.m_cfMax; m_swiMax = other.m_swiMax; + m_fractureConductivityMax = other.m_fractureConductivityMax; // 复制参数最小值 m_permeabilityMin = other.m_permeabilityMin; @@ -79,6 +84,7 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF m_ctMin = other.m_ctMin; m_cfMin = other.m_cfMin; m_swiMin = other.m_swiMin; + m_fractureConductivityMin = other.m_fractureConductivityMin; // 复制迭代参数 m_iterationCount = other.m_iterationCount; @@ -102,6 +108,7 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca fittingObject.AddMember("CtSelected", m_ctSelected, allocator); fittingObject.AddMember("CfSelected", m_cfSelected, allocator); fittingObject.AddMember("SwiSelected", m_swiSelected, allocator); + fittingObject.AddMember("FractureConductivitySelected", m_fractureConductivitySelected, allocator); // 序列化参数最大值 fittingObject.AddMember("PermeabilityMax", m_permeabilityMax.ToJsonValue(allocator), allocator); @@ -112,6 +119,7 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca fittingObject.AddMember("CtMax", m_ctMax.ToJsonValue(allocator), allocator); fittingObject.AddMember("CfMax", m_cfMax.ToJsonValue(allocator), allocator); fittingObject.AddMember("SwiMax", m_swiMax.ToJsonValue(allocator), allocator); + fittingObject.AddMember("FractureConductivityMax", m_fractureConductivityMax.ToJsonValue(allocator), allocator); // 序列化参数最小值 fittingObject.AddMember("PermeabilityMin", m_permeabilityMin.ToJsonValue(allocator), allocator); @@ -122,6 +130,7 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca fittingObject.AddMember("CtMin", m_ctMin.ToJsonValue(allocator), allocator); fittingObject.AddMember("CfMin", m_cfMin.ToJsonValue(allocator), allocator); fittingObject.AddMember("SwiMin", m_swiMin.ToJsonValue(allocator), allocator); + fittingObject.AddMember("FractureConductivityMin", m_fractureConductivityMin.ToJsonValue(allocator), allocator); // 序列化迭代参数 fittingObject.AddMember("IterationCount", m_iterationCount.ToJsonValue(allocator), allocator); @@ -159,6 +168,9 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue) if (jsonValue.HasMember("SwiSelected") && jsonValue["SwiSelected"].IsBool()) { m_swiSelected = jsonValue["SwiSelected"].GetBool(); } + if (jsonValue.HasMember("FractureConductivitySelected") && jsonValue["FractureConductivitySelected"].IsBool()) { + m_fractureConductivitySelected = jsonValue["FractureConductivitySelected"].GetBool(); + } // 反序列化参数最大值 if (jsonValue.HasMember("PermeabilityMax") && jsonValue["PermeabilityMax"].IsObject()) { @@ -185,6 +197,9 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue) if (jsonValue.HasMember("SwiMax") && jsonValue["SwiMax"].IsObject()) { m_swiMax.FromJsonValue(jsonValue["SwiMax"]); } + if (jsonValue.HasMember("FractureConductivityMax") && jsonValue["FractureConductivityMax"].IsObject()) { + m_fractureConductivityMax.FromJsonValue(jsonValue["FractureConductivityMax"]); + } // 反序列化参数最小值 if (jsonValue.HasMember("PermeabilityMin") && jsonValue["PermeabilityMin"].IsObject()) { @@ -211,6 +226,9 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue) if (jsonValue.HasMember("SwiMin") && jsonValue["SwiMin"].IsObject()) { m_swiMin.FromJsonValue(jsonValue["SwiMin"]); } + if (jsonValue.HasMember("FractureConductivityMin") && jsonValue["FractureConductivityMin"].IsObject()) { + m_fractureConductivityMin.FromJsonValue(jsonValue["FractureConductivityMin"]); + } // 反序列化迭代参数 if (jsonValue.HasMember("IterationCount") && jsonValue["IterationCount"].IsObject()) { @@ -253,6 +271,9 @@ void nmDataAutomaticFitting::setCfSelected(bool selected) { m_cfSelected = selec bool nmDataAutomaticFitting::getSwiSelected() const { return m_swiSelected; } void nmDataAutomaticFitting::setSwiSelected(bool selected) { m_swiSelected = selected; } +bool nmDataAutomaticFitting::getFractureConductivitySelected() const { return m_fractureConductivitySelected; } +void nmDataAutomaticFitting::setFractureConductivitySelected(bool selected) { m_fractureConductivitySelected = selected; } + // Getter and Setter implementations for Max values nmDataAttribute& nmDataAutomaticFitting::getPermeabilityMax() { return m_permeabilityMax; } @@ -279,6 +300,9 @@ void nmDataAutomaticFitting::setCfMax(const nmDataAttribute& cfMax) { m_cfMax = nmDataAttribute& nmDataAutomaticFitting::getSwiMax() { return m_swiMax; } void nmDataAutomaticFitting::setSwiMax(const nmDataAttribute& swiMax) { m_swiMax = swiMax; } +nmDataAttribute& nmDataAutomaticFitting::getFractureConductivityMax() { return m_fractureConductivityMax; } +void nmDataAutomaticFitting::setFractureConductivityMax(const nmDataAttribute& fractureConductivityMax) { m_fractureConductivityMax = fractureConductivityMax; } + // Getter and Setter implementations for Min values nmDataAttribute& nmDataAutomaticFitting::getPermeabilityMin() { return m_permeabilityMin; } void nmDataAutomaticFitting::setPermeabilityMin(const nmDataAttribute& permeabilityMin) { m_permeabilityMin = permeabilityMin; } @@ -304,6 +328,9 @@ void nmDataAutomaticFitting::setCfMin(const nmDataAttribute& cfMin) { m_cfMin = nmDataAttribute& nmDataAutomaticFitting::getSwiMin() { return m_swiMin; } void nmDataAutomaticFitting::setSwiMin(const nmDataAttribute& swiMin) { m_swiMin = swiMin; } +nmDataAttribute& nmDataAutomaticFitting::getFractureConductivityMin() { return m_fractureConductivityMin; } +void nmDataAutomaticFitting::setFractureConductivityMin(const nmDataAttribute& fractureConductivityMin) { m_fractureConductivityMin = fractureConductivityMin; } + // Getter and Setter implementations for iteration parameters nmDataAttribute& nmDataAutomaticFitting::getIterationCount() { return m_iterationCount; } void nmDataAutomaticFitting::setIterationCount(const nmDataAttribute& iterationCount) { m_iterationCount = iterationCount; } diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp index 2a527ee..34415d0 100644 --- a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp +++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp @@ -190,7 +190,7 @@ void nmWxAutomaticFitting::renumberVisibleParameterRows(QTableWidget* table) } } -// 根据当前模型类型控制Ct/Cf/Swi参数显示。 +// 根据当前模型类型控制 Ct/Cf/Swi，并根据目标井类型控制裂缝导流能力显示。 void nmWxAutomaticFitting::updateParameterVisibility(QTableWidget* table, NM_SOLVER_MODEL_TYPE eType) { if(!table) { @@ -231,6 +231,21 @@ void nmWxAutomaticFitting::updateParameterVisibility(QTableWidget* table, NM_SOL setParameterRowVisible(table, 6, showCf); // Cf setParameterRowVisible(table, 7, showSwi); // Swi + // Dfc 只属于垂直压裂井和多段压裂水平井。普通井隐藏并取消勾选， + // 防止切换目标井后不可见的裂缝参数仍进入拟合参数向量。 + bool showFractureConductivity = false; + nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance(); + if(manager && m_targetWellCombo) { + nmDataWellBase* targetWell = manager->findWellByName(m_targetWellCombo->currentText()); + if(targetWell) { + NM_WELL_MODEL wellType = targetWell->getWellType(); + showFractureConductivity = + wellType == NM_WELL_MODEL::Vertical_Fractured_Well || + wellType == NM_WELL_MODEL::Horizontal_Fractured_Well; + } + } + setParameterRowVisible(table, 8, showFractureConductivity); // Dfc + renumberVisibleParameterRows(table); } @@ -240,20 +255,20 @@ bool nmWxAutomaticFitting::getPhysicalParameterRange(int parameterIndex, { static const char* parameterNames[] = { "Result_K", "Result_W_Skin", "Result_W_C", "Result_phi", - "Result_h", "Result_Cti", "Result_Cf", "Result_Swi" + "Result_h", "Result_Cti", "Result_Cf", "Result_Swi", "Result_W_Dfc" }; // KAPPA 的边界使用 md、ft、bbl/psi；自动拟合界面使用 Darcy、m、m^3/MPa， // 这里统一换算到界面和 PSO 实际使用的单位：K 除以 1000，h 由 ft 换成 m， // 井筒储集系数的 4.33667154546306e34 bbl/psi 对应约 1e36 m^3/MPa。 - // Ct/Cf/Swi 沿用模型参数表边界。 + // Ct/Cf/Swi/Dfc 沿用模型参数表边界。 static const double physicalMin[] = { - 1.01325027383089e-18, -5.0, 0.0, 1.0e-4, 1.0e-5, 1.0e-30, 1.0e-30, 0.0 + 1.01325027383089e-18, -5.0, 0.0, 1.0e-4, 1.0e-5, 1.0e-30, 1.0e-30, 0.0, 0.0 }; static const double physicalMax[] = { - 1.01325027383089e42, 5000.0, 1.0e36, 0.9999, 1.0e9, 10.0, 10.0, 1.0 + 1.01325027383089e42, 5000.0, 1.0e36, 0.9999, 1.0e9, 10.0, 10.0, 1.0, 1.0e30 }; - if(parameterIndex < 0 || parameterIndex >= 8) { + if(parameterIndex < 0 || parameterIndex >= 9) { return false; } @@ -338,6 +353,10 @@ void nmWxAutomaticFitting::setParameterRange(int parameterIndex, automaticFittingData.getSwiMin().setValue(minValue); automaticFittingData.getSwiMax().setValue(maxValue); break; + case 8: + automaticFittingData.getFractureConductivityMin().setValue(minValue); + automaticFittingData.getFractureConductivityMax().setValue(maxValue); + break; default: break; } @@ -350,7 +369,7 @@ void nmWxAutomaticFitting::setParameterRange(int parameterIndex, void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, double centerValue) { - if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 8 + if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 9 || !nmAutoFitUiIsFinite(centerValue)) { return; } @@ -383,7 +402,9 @@ void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, const double skinHalfRange = 10.0; newMin = qMax(physicalMin, reference - skinHalfRange); newMax = qMin(physicalMax, reference + skinHalfRange); - } else if(reference > 0.0 && !(parameterIndex == 7 && centerValue <= 0.0)) { + } else if(reference > 0.0 + && !(parameterIndex == 7 && centerValue <= 0.0) + && !(parameterIndex == 8 && centerValue <= 0.0)) { const double lowerFactor = 0.1; const double upperFactor = 10.0; newMin = qMax(physicalMin, reference * lowerFactor); @@ -392,6 +413,10 @@ void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, // 没有可靠 Swi 初值时，不把搜索范围压缩到零附近。 newMin = physicalMin; newMax = physicalMax; + } else if(parameterIndex == 8) { + // Dfc=0 表示无限导流，不存在以零为中心的连续倍率范围。 + newMin = physicalMin; + newMax = physicalMax; } // 任何自动范围都必须包含本次使用的中心值，并且不能越过物理边界。 @@ -412,7 +437,7 @@ void nmWxAutomaticFitting::initializeSuggestedParameterRanges() return; } - for(int parameterIndex = 0; parameterIndex < 8; ++parameterIndex) { + for(int parameterIndex = 0; parameterIndex < 9; ++parameterIndex) { QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3); if(initialItem) { bool initialOk = false; @@ -439,7 +464,7 @@ void nmWxAutomaticFitting::normalizeSavedParameterRanges() return; } - for(int parameterIndex = 0; parameterIndex < 8; ++parameterIndex) { + for(int parameterIndex = 0; parameterIndex < 9; ++parameterIndex) { QTableWidgetItem* minItem = m_parameterTable->item(parameterIndex, 2); QTableWidgetItem* maxItem = m_parameterTable->item(parameterIndex, 4); QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3); @@ -454,9 +479,12 @@ void nmWxAutomaticFitting::normalizeSavedParameterRanges() bool savedMaxOk = false; const double savedMin = minItem->text().toDouble(&savedMinOk); const double savedMax = maxItem->text().toDouble(&savedMaxOk); + // 旧项目没有 Dfc 字段时，nmDataAttribute 会表现为 0~0。Dfc=0 在求解器中 + // 表示无限导流，不能作为连续拟合区间，因此按当前压裂井初值重新建范围。 const bool savedRangeValid = savedMinOk && savedMaxOk && nmAutoFitUiIsFinite(savedMin) && nmAutoFitUiIsFinite(savedMax) - && savedMax >= savedMin; + && savedMax >= savedMin + && !(parameterIndex == 8 && savedMax <= 1.0e-10); if(savedRangeValid && physicalMax >= physicalMin) { const double clippedMin = qMax(savedMin, physicalMin); @@ -485,18 +513,18 @@ bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int par errorMessage = tr("The parameter table is unavailable."); return false; } - if(parameterIndex < -1 || parameterIndex >= 8) { + if(parameterIndex < -1 || parameterIndex >= 9) { errorMessage = tr("The parameter row is invalid."); return false; } static const char* parameterNames[] = { "Permeability", "Skin", "Wellbore storage", "Porosity", - "Thickness", "Ct", "Cf", "Swi" + "Thickness", "Ct", "Cf", "Swi", "Fracture conductivity" }; const int firstParameterIndex = parameterIndex < 0 ? 0 : parameterIndex; - const int lastParameterIndex = parameterIndex < 0 ? 8 : parameterIndex + 1; + const int lastParameterIndex = parameterIndex < 0 ? 9 : parameterIndex + 1; for(int currentParameterIndex = firstParameterIndex; currentParameterIndex < lastParameterIndex; ++currentParameterIndex) { // 隐藏参数不参与当前模型拟合，不用它们的历史值阻塞当前设置。 @@ -556,6 +584,14 @@ bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int par .arg(tr(parameterNames[currentParameterIndex])); return false; } + + // 底层用 Dfc=0 表示无限导流，这是离散模型选项，不属于有限导流拟合域。 + if(currentParameterIndex == 8 && m_dfcCheckBox->isChecked() + && (minValue <= 1.0e-10 || initialValue <= 1.0e-10)) { + errorMessage = tr("The minimum value of %1 must be greater than zero for automatic fitting.") + .arg(tr(parameterNames[currentParameterIndex])); + return false; + } } return true; @@ -681,7 +717,7 @@ void nmWxAutomaticFitting::setupUI() void nmWxAutomaticFitting::setupParameterTable() { // 创建表格 - m_parameterTable = new QTableWidget(8, 6, this); + m_parameterTable = new QTableWidget(9, 6, this); // 设置表头 QStringList headers; @@ -790,6 +826,17 @@ void nmWxAutomaticFitting::setupParameterTable() m_parameterTable->setItem(7, 4, new QTableWidgetItem(QString::number(automaticFittingData.getSwiMax().getValue().toDouble()))); m_parameterTable->setItem(7, 5, new QTableWidgetItem("")); + // 裂缝导流能力 (Dfc)。该行只对压裂井显示，初值在 onWellSelected() 中 + // 从当前目标井读取，其他裂缝几何参数保持固定，不进入自动拟合。 + m_parameterTable->setItem(8, 0, new QTableWidgetItem("9")); + m_dfcCheckBox = new QCheckBox(tr("Fracture conductivity")); + m_dfcCheckBox->setChecked(automaticFittingData.getFractureConductivitySelected()); + m_parameterTable->setCellWidget(8, 1, m_dfcCheckBox); + m_parameterTable->setItem(8, 2, new QTableWidgetItem(QString::number(automaticFittingData.getFractureConductivityMin().getValue().toDouble()))); + m_parameterTable->setItem(8, 3, new QTableWidgetItem()); + m_parameterTable->setItem(8, 4, new QTableWidgetItem(QString::number(automaticFittingData.getFractureConductivityMax().getValue().toDouble()))); + m_parameterTable->setItem(8, 5, new QTableWidgetItem(tr("md.m"))); + // 设置表格行为 for(int i = 0; i < m_parameterTable->rowCount(); ++i) { for(int j = 0; j < 6; ++j) { @@ -989,6 +1036,7 @@ void nmWxAutomaticFitting::onReverseSelection() if(!m_parameterTable->isRowHidden(5)) m_ctCheckBox->setChecked(!m_ctCheckBox->isChecked()); if(!m_parameterTable->isRowHidden(6)) m_cfCheckBox->setChecked(!m_cfCheckBox->isChecked()); if(!m_parameterTable->isRowHidden(7)) m_swiCheckBox->setChecked(!m_swiCheckBox->isChecked()); + if(!m_parameterTable->isRowHidden(8)) m_dfcCheckBox->setChecked(!m_dfcCheckBox->isChecked()); } void nmWxAutomaticFitting::onParameterTableItemChanged(QTableWidgetItem* item) @@ -1107,7 +1155,7 @@ void nmWxAutomaticFitting::onAccept() m_cCheckBox->isChecked() || m_phiCheckBox->isChecked() || m_hCheckBox->isChecked() || m_ctCheckBox->isChecked() || m_cfCheckBox->isChecked() || - m_swiCheckBox->isChecked(); + m_swiCheckBox->isChecked() || m_dfcCheckBox->isChecked(); if(!hasSelectedParams) { QMessageBox::warning(this, tr("Warning"), tr("Please select at least one parameter for optimization!")); @@ -1125,6 +1173,7 @@ void nmWxAutomaticFitting::onAccept() if(m_ctCheckBox->isChecked()) selectedParameterNames << tr("Ct"); if(m_cfCheckBox->isChecked()) selectedParameterNames << tr("Cf"); if(m_swiCheckBox->isChecked()) selectedParameterNames << tr("Swi"); + if(m_dfcCheckBox->isChecked()) selectedParameterNames << tr("Fracture conductivity"); // 启动自动拟合 - 传递双对数历史数据 startAutoFitting(targetLogLogData, selectedParameterNames, selectedWellName); @@ -1141,8 +1190,10 @@ void nmWxAutomaticFitting::onWellSelected(int index) // 在分类的井数据中查找匹配的井 bool found = false; + bool fracturedWell = false; double skinValue = 0.0; double wellboreStorageValue = 0.0; + double fractureConductivityValue = 0.0; // 查找垂直井 for(int i = 0; i < m_verticalWells.size(); ++i) { @@ -1172,6 +1223,8 @@ void nmWxAutomaticFitting::onWellSelected(int index) if(m_verticalFracturedWells[i].getWellName() == selectedWellName) { skinValue = m_verticalFracturedWells[i].getPerforation(0)->getSkin().getValue().toDouble(); wellboreStorageValue = m_verticalFracturedWells[i].getWellboreStorage().getValue().toDouble(); + fractureConductivityValue = m_verticalFracturedWells[i].getDfc().getValue().toDouble(); + fracturedWell = true; found = true; break; } @@ -1184,6 +1237,8 @@ void nmWxAutomaticFitting::onWellSelected(int index) if(m_horizontalFracturedWells[i].getWellName() == selectedWellName) { skinValue = m_horizontalFracturedWells[i].getPerforation(0)->getSkin().getValue().toDouble(); wellboreStorageValue = m_horizontalFracturedWells[i].getWellboreStorage().getValue().toDouble(); + fractureConductivityValue = m_horizontalFracturedWells[i].getDfc().getValue().toDouble(); + fracturedWell = true; found = true; break; } @@ -1191,6 +1246,11 @@ void nmWxAutomaticFitting::onWellSelected(int index) } if (found) { + // 先按目标井类型刷新可见行，再写入当前井的井级初值。 + nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance(); + if(manager) { + updateParameterVisibility(m_parameterTable, manager->getSolverModelType()); + } // 更新表格数据 // 确保表格项存在 if(!m_parameterTable->item(1, 3)) { @@ -1205,10 +1265,16 @@ void nmWxAutomaticFitting::onWellSelected(int index) // 设置井筒储集系数（Wellbore storage） m_parameterTable->item(2, 3)->setText(QString::number(wellboreStorageValue)); + if(fracturedWell && m_parameterTable->item(8, 3)) { + m_parameterTable->item(8, 3)->setText(QString::number(fractureConductivityValue)); + } if(m_autoParameterRanges) { updateRangeForParameter(1, skinValue); updateRangeForParameter(2, wellboreStorageValue); + if(fracturedWell) { + updateRangeForParameter(8, fractureConductivityValue); + } } } } @@ -1224,6 +1290,7 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() automaticFittingData.setCtSelected(m_ctCheckBox->isChecked()); automaticFittingData.setCfSelected(m_cfCheckBox->isChecked()); automaticFittingData.setSwiSelected(m_swiCheckBox->isChecked()); + automaticFittingData.setFractureConductivitySelected(m_dfcCheckBox->isChecked()); automaticFittingData.setSurrogateScreeningEnabled(m_surrogateCombo && m_surrogateCombo->currentIndex() == 1); // 保存渗透率的最小值和最大值 @@ -1258,6 +1325,10 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() automaticFittingData.getSwiMin().setValue(m_parameterTable->item(7, 2)->text().toDouble()); automaticFittingData.getSwiMax().setValue(m_parameterTable->item(7, 4)->text().toDouble()); + // 保存裂缝导流能力的最小值和最大值 + automaticFittingData.getFractureConductivityMin().setValue(m_parameterTable->item(8, 2)->text().toDouble()); + automaticFittingData.getFractureConductivityMax().setValue(m_parameterTable->item(8, 4)->text().toDouble()); + // 保存迭代参数 automaticFittingData.getIterationCount().setValue(m_iterationEdit->text().toInt()); automaticFittingData.getErrorTolerance().setValue(m_errorLimitEdit->text().toDouble()); @@ -1281,6 +1352,7 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() if(!selectedWellName.isEmpty()) { double newSkinValue = m_parameterTable->item(1, 3)->text().toDouble(); double newWellboreStorageValue = m_parameterTable->item(2, 3)->text().toDouble(); + double newFractureConductivityValue = m_parameterTable->item(8, 3)->text().toDouble(); // 直接从数据管理器获取目标井 nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance(); @@ -1303,6 +1375,23 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() // 根据井类型单独更新这一口井 NM_WELL_MODEL wellType = pTargetWell->getWellType(); + // Dfc 属于压裂井对象；只修改这一项，裂缝位置、长度和段数保持原值。 + if(wellType == NM_WELL_MODEL::Vertical_Fractured_Well) { + nmDataVerticalFracturedWell* fracturedWell = dynamic_cast(pTargetWell); + if(fracturedWell) { + nmDataAttribute dfc = fracturedWell->getDfc(); + dfc.setValue(newFractureConductivityValue); + fracturedWell->setDfc(dfc); + } + } else if(wellType == NM_WELL_MODEL::Horizontal_Fractured_Well) { + nmDataHorizontalFracturedWell* fracturedWell = dynamic_cast(pTargetWell); + if(fracturedWell) { + nmDataAttribute dfc = fracturedWell->getDfc(); + dfc.setValue(newFractureConductivityValue); + fracturedWell->setDfc(dfc); + } + } + if(wellType == NM_WELL_MODEL::Vertical_Well) { nmDataVerticalWell* pVerticalWell = dynamic_cast(pTargetWell); if(pVerticalWell != nullptr) { @@ -1532,6 +1621,7 @@ void nmWxAutomaticFitting::updateBestParametersToTable() if(m_ctCheckBox->isChecked()) enabledParams.append(5); // 综合压缩系数 if(m_cfCheckBox->isChecked()) enabledParams.append(6); // 岩石压缩系数 if(m_swiCheckBox->isChecked()) enabledParams.append(7); // 初始含水饱和度 + if(m_dfcCheckBox->isChecked()) enabledParams.append(8); // 裂缝导流能力 // 更新参数值和范围 for (int i = 0; i < bestSolution.size() && i < enabledParams.size(); ++i) { From 50606d35641edc315d240deba8a44340ee6b3278 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 14 Aug 2026 10:37:57 +0800 Subject: [PATCH 08/12] =?UTF-8?q?=E8=87=AA=E5=8A=A8=E6=8B=9F=E5=90=88?= =?UTF-8?q?=E6=8D=9F=E5=A4=B1=E5=87=BD=E6=95=B0=E6=94=B9=E7=94=A8=E6=99=AE?= =?UTF-8?q?=E9=80=9A=E5=AF=B9=E6=95=B0=20RMSE?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nmCalculation/nmCalculationAutoFitPSO.h | 6 +- .../nmCalculation/nmCalculationAutoFitPSO.cpp | 129 ++++++------------ 2 files changed, 42 insertions(+), 93 deletions(-) diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h index 0bf415b..748831d 100644 --- a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h +++ b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h @@ -30,11 +30,11 @@ struct AutoFitObjectiveBreakdown { double total; double pressureLoss; double derivativeLoss; - // 固定目标网格上的 Huber 等效残差。非代理搜索使用它建立完整 Jacobian， + // 固定目标网格上的普通对数残差。非代理搜索使用它建立完整 Jacobian， // 向量平方和与 total 的平方一致。 QVector residualVector; - // 上下偏差使用压力和导数残差共享的 Huber 稳健中心。 + // 上下偏差使用压力和导数残差共享的算术平均中心。 // verticalCommonBias 为正表示模拟曲线整体偏高，为负表示整体偏低； // verticalReliable=false 时仍保留数值，但不能据此确定参数调整方向。 double verticalCommonBias; @@ -50,7 +50,7 @@ struct AutoFitObjectiveBreakdown { // 诊断量选参，但去除公共中心后的 shapeLoss 仍可用于局部选参。 bool registrationAmbiguous; - // 去除稳健公共中心和可信左右偏差后剩余的整体形状误差；verticalReliable + // 去除公共均值中心和可信左右偏差后剩余的整体形状误差；verticalReliable // 只控制能否把公共中心解释为上下参数方向，不改变 shape 的中心化公式。 double shapeLoss; diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index 2c75f29..7378599 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -104,8 +104,8 @@ static inline bool isFiniteNumber(double value) #endif } -// 两个 Huber RMS 的差不能直接解释为被消除的独立误差。RMS 的平方才对应 -// 稳健能量，因此先计算 reduced^2-full^2，再开方恢复原量纲。这里用于分别 +// 两个 RMSE 的差不能直接解释为被消除的独立误差。RMSE 的平方才对应 +// 均方能量，因此先计算 reduced^2-full^2，再开方恢复原量纲。这里用于分别 // 提取“消除公共上下偏差”和“消除水平位移”实际减少的误差贡献。 static double nestedRmsContribution(double reducedModelLoss, double fullModelLoss) @@ -3900,7 +3900,7 @@ struct TrustRegionEvaluation {} }; -// LM 只使用固定长度、全部有限的稳健残差。代理路径不会进入本搜索器。 +// LM 只使用固定长度、全部有限的普通残差。代理路径不会进入本搜索器。 static bool trustRegionResidualsValid( const AutoFitObjectiveBreakdown& breakdown) { @@ -4216,7 +4216,7 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() bool modelRebuiltAtMinimumRadius = false; StopReasonPSO stopReason = PSO_MAX_ITERATIONS; - // jacobian 的行对应固定 100 维稳健残差，列对应用户勾选的参数。 + // jacobian 的行对应固定 100 维残差，列对应用户勾选的参数。 // 三个 gradient 单独描述诊断分量对参数的局部变化，只用于本轮选参。 QVector > jacobian; QVector verticalGradient(dimensions, 0.0); @@ -4803,7 +4803,7 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() } stepNorm = qSqrt(trustRegionSquaredNorm(coordinateStep)); - // 用线性模型 r_new ~= r_current + J*step 预测稳健残差，再用平方能量 + // 用线性模型 r_new ~= r_current + J*step 预测残差，再用平方能量 // 的下降量与真实候选下降量比较，作为调整阻尼和半径的依据。 QVector predictedResidual = current.breakdown.residualVector; @@ -6462,9 +6462,6 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( // 位于 log(time)-log(value) 坐标，因此得到的是相对尺度偏差而非原始压力量纲。 const double invalidLoss = 1.0e10; const double valueFloor = 1.0e-12; - // Huber 转折点 qLn(1.2) 对应约 20% 的倍率偏差；小偏差保持平方惩罚， - // 更大的局部尖峰改为近似线性惩罚，避免单点支配整条曲线。 - const double huberDelta = qLn(1.2); const double minimumCoverage = 0.95; const int numPoints = 50; m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); @@ -6704,7 +6701,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( } // 通过门槛后最多只缺少首尾少量目标点。按模拟曲线端点趋势补齐后， - // 每个候选仍在固定 50 点上计算 Huber 均值，不能靠少算难拟合端点获益。 + // 每个候选仍在固定 50 点上计算均方根误差，不能靠少算难拟合端点获益。 for(int i = 0; i < numPoints; ++i) { if(isFiniteNumber(pressureResidual[i]) && isFiniteNumber(derivativeResidual[i])) { @@ -6726,8 +6723,8 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( resultLogDerivative - targetLogDerivative[i]; } - // Huber RMS 在小残差处保持平方损失，在异常点处转为线性增长。 - auto huberRmsAround = [huberDelta]( + // 在指定中心附近计算普通均方根误差。 + auto rmseAround = []( const QVector& values, int begin, int end, @@ -6744,12 +6741,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( } double difference = values[i] - center; - double absoluteValue = qAbs(difference); - double rho = absoluteValue <= huberDelta - ? difference * difference - : 2.0 * huberDelta * absoluteValue - - huberDelta * huberDelta; - sum += rho; + sum += difference * difference; ++count; } @@ -6758,27 +6750,15 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( : std::numeric_limits::quiet_NaN(); }; - auto huberRms = [&huberRmsAround]( + auto rmse = [&rmseAround]( const QVector& values, int begin, int end) -> double { - return huberRmsAround(values, begin, end, 0.0); + return rmseAround(values, begin, end, 0.0); }; - // 将 Huber 能量转换成带符号的等效残差，使向量平方和与稳健损失一致。 - // 非代理 LM 直接对该固定长度向量建立 Jacobian。 - auto huberEquivalentResidual = [huberDelta](double value) -> double { - double absoluteValue = qAbs(value); - double rho = absoluteValue <= huberDelta - ? value * value - : 2.0 * huberDelta * absoluteValue - - huberDelta * huberDelta; - double magnitude = qSqrt(qMax(0.0, rho)); - return value < 0.0 ? -magnitude : magnitude; - }; - - // Huber 加权中心保留上下偏差的符号，并降低局部尖峰的影响。 - auto huberCenterRange = [huberDelta]( + // 普通算术平均中心保留上下偏差的符号。 + auto meanCenterRange = []( const QVector& values, int begin, int end) -> double { @@ -6797,42 +6777,12 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( if(count == 0) { return std::numeric_limits::quiet_NaN(); } - center /= count; - - for(int iteration = 0; iteration < 8; ++iteration) { - double weightedSum = 0.0; - double weightTotal = 0.0; - for(int i = validBegin; i < validEnd; ++i) { - if(!isFiniteNumber(values[i])) { - continue; - } - - double distance = qAbs(values[i] - center); - double weight = - distance <= huberDelta || - distance < 1.0e-12 - ? 1.0 - : huberDelta / distance; - weightedSum += weight * values[i]; - weightTotal += weight; - } - - if(weightTotal <= 1.0e-12) { - break; - } - double nextCenter = weightedSum / weightTotal; - if(qAbs(nextCenter - center) <= 1.0e-12) { - center = nextCenter; - break; - } - center = nextCenter; - } - return center; + return center / count; }; // 压力和导数合并后只求一个公共中心，表示两条曲线共同的上下位移。 // 分别去中心会把压力与导数之间真实的相对形状差异一并消除。 - auto huberCommonCenterRange = [&huberCenterRange]( + auto commonMeanCenterRange = [&meanCenterRange]( const QVector& pressureValues, const QVector& derivativeValues, int begin, @@ -6853,19 +6803,19 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( combined.append(derivativeValues[i]); } } - return huberCenterRange(combined, 0, combined.size()); + return meanCenterRange(combined, 0, combined.size()); }; - // 两个通道按能量等权合并，返回值与单通道 Huber RMS 保持同一量纲。 - auto jointHuberRmsAround = [&huberRmsAround]( + // 两个通道按能量等权合并，返回值与单通道 RMSE 保持同一量纲。 + auto jointRmseAround = [&rmseAround]( const QVector& pressureValues, const QVector& derivativeValues, int begin, int end, double center) -> double { - double pressureLoss = huberRmsAround( + double pressureLoss = rmseAround( pressureValues, begin, end, center); - double derivativeLoss = huberRmsAround( + double derivativeLoss = rmseAround( derivativeValues, begin, end, center); if(!isFiniteNumber(pressureLoss) || !isFiniteNumber(derivativeLoss)) { @@ -6878,9 +6828,9 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( // 主目标始终使用未做上下或左右校正的完整曲线误差。 breakdown.pressureLoss = - huberRms(pressureResidual, 0, numPoints); + rmse(pressureResidual, 0, numPoints); breakdown.derivativeLoss = - huberRms(derivativeResidual, 0, numPoints); + rmse(derivativeResidual, 0, numPoints); // 压力和导数各占一半权重。缩放后 residualVector 的二范数就是 // sqrt(0.5 * pressureLoss^2 + 0.5 * derivativeLoss^2)。 @@ -6889,12 +6839,12 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( for(int i = 0; i < numPoints; ++i) { breakdown.residualVector.append( residualScale * - huberEquivalentResidual(pressureResidual[i])); + pressureResidual[i]); } for(int i = 0; i < numPoints; ++i) { breakdown.residualVector.append( residualScale * - huberEquivalentResidual(derivativeResidual[i])); + derivativeResidual[i]); } const double logGridStep = @@ -7049,31 +6999,31 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( continue; } - double commonBias = huberCommonCenterRange( + double commonBias = commonMeanCenterRange( shiftedPressureResidual, shiftedDerivativeResidual, registrationBegin, registrationEnd); - double centeredLoss = jointHuberRmsAround( + double centeredLoss = jointRmseAround( shiftedPressureResidual, shiftedDerivativeResidual, registrationBegin, registrationEnd, commonBias); - double pressureBias = huberCenterRange( + double pressureBias = meanCenterRange( shiftedPressureResidual, registrationBegin, registrationEnd); - double derivativeBias = huberCenterRange( + double derivativeBias = meanCenterRange( shiftedDerivativeResidual, registrationBegin, registrationEnd); - double pressureLoss = huberRmsAround( + double pressureLoss = rmseAround( shiftedPressureResidual, registrationBegin, registrationEnd, pressureBias); - double derivativeLoss = huberRmsAround( + double derivativeLoss = rmseAround( shiftedDerivativeResidual, registrationBegin, registrationEnd, @@ -7124,7 +7074,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( return invalidLoss; } - // horizontalGain 是“允许水平位移”相对“固定零位移”减少的稳健能量。 + // horizontalGain 是“允许水平位移”相对“固定零位移”减少的均方能量。 // 只有改善足够明显且最优点不是边界，才把位移解释为可靠左右偏差。 double horizontalGain = nestedRmsContribution( zeroShiftCenteredLoss, bestCenteredLoss); @@ -7183,8 +7133,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( isFiniteNumber(maximumNearOptimalBias) ? maximumNearOptimalBias - minimumNearOptimalBias : std::numeric_limits::infinity(); - bool commonBiasStable = nearOptimalBiasSpread <= - qMax(1.0e-4, huberDelta * 0.05); + bool commonBiasStable = nearOptimalBiasSpread <= 1.0e-2; bool horizontalAtBoundary = halfShiftStepCount > 0 && qAbs(bestShiftStep) == halfShiftStepCount; @@ -7249,18 +7198,18 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( return invalidLoss; } - double commonBias = huberCommonCenterRange( + double commonBias = commonMeanCenterRange( shiftedPressureResidual, shiftedDerivativeResidual, diagnosticBegin, diagnosticEnd); - double rawAlignedLoss = jointHuberRmsAround( + double rawAlignedLoss = jointRmseAround( shiftedPressureResidual, shiftedDerivativeResidual, diagnosticBegin, diagnosticEnd, 0.0); - double centeredAlignedLoss = jointHuberRmsAround( + double centeredAlignedLoss = jointRmseAround( shiftedPressureResidual, shiftedDerivativeResidual, diagnosticBegin, @@ -7297,10 +7246,10 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( } } - // shapeLoss 是去除可信左右位移和稳健公共中心后的剩余误差。 - double shapePressureLoss = huberRms( + // shapeLoss 是去除可信左右位移和公共均值中心后的剩余误差。 + double shapePressureLoss = rmse( shapePressure, diagnosticBegin, diagnosticEnd); - double shapeDerivativeLoss = huberRms( + double shapeDerivativeLoss = rmse( shapeDerivative, diagnosticBegin, diagnosticEnd); if(!isFiniteNumber(shapePressureLoss) || !isFiniteNumber(shapeDerivativeLoss)) { @@ -7333,7 +7282,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( 0.5 * breakdown.pressureLoss + 0.5 * breakdown.derivativeLoss; } else { - // 非代理总目标等于固定 100 维 Huber 等效残差的二范数；压力和 + // 非代理总目标等于固定 100 维普通残差的二范数；压力和 // 导数各占一半能量。上下、左右和形状分量不参与候选排序与接受。 breakdown.total = qSqrt( 0.5 * breakdown.pressureLoss * breakdown.pressureLoss + From e73b5ab45335b00ef7d1961b228ca7f2ec3bf434 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 14 Aug 2026 11:23:23 +0800 Subject: [PATCH 09/12] =?UTF-8?q?1.=E5=A2=9E=E5=8A=A0=E5=8E=8B=E8=A3=82?= =?UTF-8?q?=E4=BA=95=E8=A3=82=E7=BC=9D=E5=8D=8A=E9=95=BF=E6=8B=9F=E5=90=88?= 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conductivity 裂缝导流能力 + + Fracture half length + 裂缝半长 + The minimum value of %1 must be greater than zero for automatic fitting. 自动拟合时，%1 的最小值和初始值必须大于零。 diff --git a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h index 748831d..9731259 100644 --- a/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h +++ b/Include/nmNum/nmCalculation/nmCalculationAutoFitPSO.h @@ -372,12 +372,13 @@ private: // 参数索引约定： // 0 k 渗透率；1 skin 表皮系数；2 wellboreC 井筒储集； // 3 phi 孔隙度；4 h 储层厚度；5 Ct 综合压缩系数； - // 6 Cf 岩石压缩系数；7 Swi 初始含水饱和度。 + // 6 Cf 岩石压缩系数；7 Swi 初始含水饱和度； + // 8 Dfc 裂缝导流能力；9 fractureHalfLength 裂缝半长。 // m_enabledParamIndices 保存被用户勾选的参数索引，粒子的 position 维度与它一致。 - QVector m_parameterSelected; // 完整 9 个参数是否被用户勾选参与拟合。 - QVector m_parameterLower; // 完整 9 个参数的搜索下界。 - QVector m_parameterUpper; // 完整 9 个参数的搜索上界。 - QVector m_enabledParamIndices; // 被勾选参数在完整 9 维体系中的索引。 + QVector m_parameterSelected; // 完整 10 个参数是否被用户勾选参与拟合。 + QVector m_parameterLower; // 完整 10 个参数的搜索下界。 + QVector m_parameterUpper; // 完整 10 个参数的搜索上界。 + QVector m_enabledParamIndices; // 被勾选参数在完整 10 维体系中的索引。 QVector > m_targetLogLogData; // 目标井 history log-log 曲线：time/pressure/derivative。 QString m_targetWellName; // 目标井名称；读写井参数和读取模拟曲线都依赖它。 diff --git a/Include/nmNum/nmData/nmDataAutomaticFitting.h b/Include/nmNum/nmData/nmDataAutomaticFitting.h index 18bc851..98829eb 100644 --- a/Include/nmNum/nmData/nmDataAutomaticFitting.h +++ b/Include/nmNum/nmData/nmDataAutomaticFitting.h @@ -82,6 +82,13 @@ public: nmDataAttribute& getFractureConductivityMin(); void setFractureConductivityMin(const nmDataAttribute& fractureConductivityMin); + // Getter and Setter for fractureHalfLengthMax + nmDataAttribute& getFractureHalfLengthMax(); + void setFractureHalfLengthMax(const nmDataAttribute& fractureHalfLengthMax); + // Getter and Setter for fractureHalfLengthMin + nmDataAttribute& getFractureHalfLengthMin(); + void setFractureHalfLengthMin(const nmDataAttribute& fractureHalfLengthMin); + // Getter and Setter for iteration count nmDataAttribute& getIterationCount(); void setIterationCount(const nmDataAttribute& iterationCount); @@ -124,6 +131,9 @@ public: bool getFractureConductivitySelected() const; void setFractureConductivitySelected(bool selected); + bool getFractureHalfLengthSelected() const; + void setFractureHalfLengthSelected(bool selected); + private: // 参数最大值 nmDataAttribute m_permeabilityMax; @@ -135,6 +145,7 @@ private: nmDataAttribute m_cfMax; nmDataAttribute m_swiMax; nmDataAttribute m_fractureConductivityMax; + nmDataAttribute m_fractureHalfLengthMax; // 参数最小值 nmDataAttribute m_permeabilityMin; @@ -146,6 +157,7 @@ private: nmDataAttribute m_cfMin; nmDataAttribute m_swiMin; nmDataAttribute m_fractureConductivityMin; + nmDataAttribute m_fractureHalfLengthMin; // 迭代参数 nmDataAttribute m_iterationCount; // 迭代步数 @@ -163,6 +175,7 @@ private: bool m_cfSelected; // 是否选择岩石压缩系数进行拟合 bool m_swiSelected; // 是否选择初始含水饱和度进行拟合 bool m_fractureConductivitySelected; // 是否选择裂缝导流能力进行拟合 + bool m_fractureHalfLengthSelected; // 是否选择裂缝半长进行拟合 }; #endif // NMDATAAUTOMATICFITTING_H diff --git a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h index ada66d0..a8c4f76 100644 --- a/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h +++ b/Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h @@ -92,6 +92,7 @@ private: QCheckBox* m_cfCheckBox; // 岩石压缩系数 QCheckBox* m_swiCheckBox; // 初始含水饱和度 QCheckBox* m_dfcCheckBox; // 裂缝导流能力 + QCheckBox* m_fractureHalfLengthCheckBox; // 裂缝半长 // 按钮 QPushButton* m_reverseBtn; diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index 7378599..0f37602 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -432,7 +432,7 @@ static QString findExecutableInPath(const QString& executableName) static QStringList traceParameterNames() { // trace 和 trace meta 使用的完整参数名顺序。 - // 这个顺序必须与 buildTraceParameterVector() 和 m_parameterSelected 的 0-8 索引一致。 + // 这个顺序必须与 buildTraceParameterVector() 和 m_parameterSelected 的 0-9 索引一致。 QStringList names; names << "k" << "skin" @@ -442,7 +442,8 @@ static QStringList traceParameterNames() << "Ct" << "Cf" << "Swi" - << "Dfc"; + << "Dfc" + << "fractureHalfLength"; return names; } @@ -1013,7 +1014,7 @@ void nmCalculationAutoFitPSO::writeTraceHeader() { // trace CSV 字段说明： // - 代理模式保持原 k/skin/wellboreC/phi/h/Ct/Cf 契约； - // - 非代理模式额外记录 Swi/Dfc，便于复盘信赖域对两项参数的调整； + // - 非代理模式额外记录 Swi/Dfc/裂缝半长，便于复盘信赖域调整； // - solver_objective 是真实求解器误差； // - surrogate_objective 是 Python 代理评分； // - screening_decision 说明该粒子为什么跑/不跑真实求解器； @@ -1037,7 +1038,8 @@ void nmCalculationAutoFitPSO::writeTraceHeader() << "Cf"; if(!isSurrogateScreeningEnabled()) { cols << "Swi" - << "Dfc"; + << "Dfc" + << "fractureHalfLength"; } cols << "solver_objective" << "solver_success" @@ -1054,7 +1056,8 @@ void nmCalculationAutoFitPSO::writeTraceHeader() << "pbest_Cf"; if(!isSurrogateScreeningEnabled()) { cols << "pbest_Swi" - << "pbest_Dfc"; + << "pbest_Dfc" + << "pbest_fractureHalfLength"; } cols << "gbest_objective" << "gbest_k" @@ -1066,7 +1069,8 @@ void nmCalculationAutoFitPSO::writeTraceHeader() << "gbest_Cf"; if(!isSurrogateScreeningEnabled()) { cols << "gbest_Swi" - << "gbest_Dfc"; + << "gbest_Dfc" + << "gbest_fractureHalfLength"; } cols << "enabled_param_indices" << "pressure_loss" @@ -1132,7 +1136,7 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile() // 非代理 v5 增加带符号诊断列；代理 PSO 保留原 v3 字段和目标，避免改变 // 已有模型的训练和回放契约。 out << " \"schema_version\": " - << (isSurrogateScreeningEnabled() ? 3 : 6) << ",\n"; + << (isSurrogateScreeningEnabled() ? 3 : 7) << ",\n"; out << " \"trace_type\": " << jsonEscape(isSurrogateScreeningEnabled() ? "pso_baseline_replay_meta" @@ -1193,10 +1197,10 @@ void nmCalculationAutoFitPSO::writeTraceMetaFile() QVector nmCalculationAutoFitPSO::buildTraceParameterVector(const QVector& selectedParameters) const { - // 将粒子内部使用的“启用参数向量”还原成完整 9 维参数向量。 + // 将粒子内部使用的“启用参数向量”还原成完整 10 维参数向量。 // 未启用的参数从当前 DataManager 读取，启用的参数用 selectedParameters 覆盖。 // trace CSV、候选 CSV、代理训练域检查都需要这个完整向量。 - QVector fullParams(9, 0.0); + QVector fullParams(10, 0.0); nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); @@ -1224,12 +1228,14 @@ QVector nmCalculationAutoFitPSO::buildTraceParameterVector(const QVector dynamic_cast(pTargetWell); if(fracturedWell) { fullParams[8] = fracturedWell->getDfc().getValue().toDouble(); + fullParams[9] = fracturedWell->getFractureHalfLength().getValue().toDouble(); } } else if(pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) { nmDataHorizontalFracturedWell* fracturedWell = dynamic_cast(pTargetWell); if(fracturedWell) { fullParams[8] = fracturedWell->getDfc().getValue().toDouble(); + fullParams[9] = fracturedWell->getFractureHalfLength().getValue().toDouble(); } } } @@ -1290,7 +1296,8 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, << traceParamAt(currentParams, 6); if(!isSurrogateScreeningEnabled()) { cols << traceParamAt(currentParams, 7) - << traceParamAt(currentParams, 8); + << traceParamAt(currentParams, 8) + << traceParamAt(currentParams, 9); } cols << traceNumber(solverObjective) << QString::number(solverSuccess ? 1 : 0) @@ -1307,7 +1314,8 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, << traceParamAt(pbestParams, 6); if(!isSurrogateScreeningEnabled()) { cols << traceParamAt(pbestParams, 7) - << traceParamAt(pbestParams, 8); + << traceParamAt(pbestParams, 8) + << traceParamAt(pbestParams, 9); } cols << traceNumber(m_globalBestFitness) << traceParamAt(gbestParams, 0) @@ -1319,7 +1327,8 @@ void nmCalculationAutoFitPSO::writeTraceRow(int generation, << traceParamAt(gbestParams, 6); if(!isSurrogateScreeningEnabled()) { cols << traceParamAt(gbestParams, 7) - << traceParamAt(gbestParams, 8); + << traceParamAt(gbestParams, 8) + << traceParamAt(gbestParams, 9); } cols << csvEscape(enabledIndices.join(";")); @@ -2917,14 +2926,14 @@ void nmCalculationAutoFitPSO::loadParameterBounds() // 读取用户勾选的拟合参数及上下界。 // // 这里构建三个核心数组： - // - m_parameterSelected[9]：完整参数体系中每个参数是否参与拟合； - // - m_parameterLower/Upper[9]：完整参数体系的搜索上下界； + // - m_parameterSelected[10]：完整参数体系中每个参数是否参与拟合； + // - m_parameterLower/Upper[10]：完整参数体系的搜索上下界； // - m_enabledParamIndices：把粒子内部紧凑向量映射回完整参数索引。 nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); nmDataAutomaticFitting fittingData = dataManager->getAutomaticFittingDataCopy(); // 获取参数选择状态 - m_parameterSelected.resize(9); + m_parameterSelected.resize(10); m_parameterSelected[0] = fittingData.getPermeabilitySelected(); m_parameterSelected[1] = fittingData.getSkinSelected(); m_parameterSelected[2] = fittingData.getWellboreStorageSelected(); @@ -2934,10 +2943,11 @@ void nmCalculationAutoFitPSO::loadParameterBounds() m_parameterSelected[6] = fittingData.getCfSelected(); m_parameterSelected[7] = fittingData.getSwiSelected(); m_parameterSelected[8] = fittingData.getFractureConductivitySelected(); + m_parameterSelected[9] = fittingData.getFractureHalfLengthSelected(); // 获取参数边界 - m_parameterLower.resize(9); - m_parameterUpper.resize(9); + m_parameterLower.resize(10); + m_parameterUpper.resize(10); m_parameterLower[0] = fittingData.getPermeabilityMin().getValue().toDouble(); m_parameterUpper[0] = fittingData.getPermeabilityMax().getValue().toDouble(); @@ -2966,6 +2976,9 @@ void nmCalculationAutoFitPSO::loadParameterBounds() m_parameterLower[8] = fittingData.getFractureConductivityMin().getValue().toDouble(); m_parameterUpper[8] = fittingData.getFractureConductivityMax().getValue().toDouble(); + m_parameterLower[9] = fittingData.getFractureHalfLengthMin().getValue().toDouble(); + m_parameterUpper[9] = fittingData.getFractureHalfLengthMax().getValue().toDouble(); + // 更新启用参数索引 m_enabledParamIndices.clear(); @@ -3474,11 +3487,14 @@ bool nmCalculationAutoFitPSO::startAutoFitting() applyParametersToDataManager(m_globalBestPosition); // 即使用户此时停止、不再执行最终完整计算，也要把 PEBI 缓存恢复为 - // 最终已接受的 Dfc，避免缓存仍停留在最后一个被拒绝的候选值。 - if(m_parameterSelected.size() > 8 && m_parameterSelected[8]) { + // 最终已接受的裂缝参数，避免缓存仍停留在最后一个被拒绝的候选值。 + const bool fractureGridParameterSelected = + (m_parameterSelected.size() > 8 && m_parameterSelected[8]) || + (m_parameterSelected.size() > 9 && m_parameterSelected[9]); + if(fractureGridParameterSelected) { nmCalculationPebiGrid* pebiGrid = nmCalculationPebiGrid::getInstance(); if(!pebiGrid || !pebiGrid->generateOutputPara()) { - throw std::runtime_error("Failed to refresh final fracture conductivity"); + throw std::runtime_error("Failed to refresh final fracture parameters"); } } @@ -3711,6 +3727,22 @@ void nmCalculationAutoFitPSO::extractUserInitialValues() } } break; + + case 9: // 裂缝半长 + if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) { + nmDataVerticalFracturedWell* fracturedWell = + dynamic_cast(pTargetWell); + if(fracturedWell) { + initialValue = fracturedWell->getFractureHalfLength().getValue().toDouble(); + } + } else if(pTargetWell && pTargetWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) { + nmDataHorizontalFracturedWell* fracturedWell = + dynamic_cast(pTargetWell); + if(fracturedWell) { + initialValue = fracturedWell->getFractureHalfLength().getValue().toDouble(); + } + } + break; } m_initialValues.append(initialValue); @@ -5534,12 +5566,15 @@ double nmCalculationAutoFitPSO::evaluateFitness(const QVector& parameter return 1e10; } - // Dfc 位于 PEBI 裂缝数组 crack[5]，不是每次求解都会重新组装的 Base/CS 参数。 - // 因此只有勾选 Dfc 时才刷新一次网格输出参数，保证本次真实试算使用刚写入井对象的值。 - if(m_parameterSelected.size() > 8 && m_parameterSelected[8]) { + // Dfc 和裂缝半长都通过 PEBI 裂缝数组传入，不属于每次求解都会重新组装的 Base/CS 参数。 + // 勾选任一裂缝参数时刷新网格输出，保证本次真实试算使用新的导流能力和端点坐标。 + const bool fractureGridParameterSelected = + (m_parameterSelected.size() > 8 && m_parameterSelected[8]) || + (m_parameterSelected.size() > 9 && m_parameterSelected[9]); + if(fractureGridParameterSelected) { nmCalculationPebiGrid* pebiGrid = nmCalculationPebiGrid::getInstance(); if(!pebiGrid || !pebiGrid->generateOutputPara()) { - DEBUG_OUT(QString("%1: Call #%2 - Failed to refresh PEBI fracture conductivity") + DEBUG_OUT(QString("%1: Call #%2 - Failed to refresh PEBI fracture parameters") .arg(funcName).arg(callCount)); return 1e10; } @@ -5707,7 +5742,7 @@ void nmCalculationAutoFitPSO::updateReservoirParameters(const QVector& p nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); nmDataReservoir reservoirData = dataManager->getReservoirDataCopy(); - // paramIndex 是粒子 position 中的索引；i 是完整 9 个参数体系中的索引。 + // paramIndex 是粒子 position 中的索引；i 是完整 10 个参数体系中的索引。 // 只有 m_parameterSelected[i] 为 true 时，才从 parameters 中消费一个值。 int paramIndex = 0; @@ -5754,7 +5789,7 @@ void nmCalculationAutoFitPSO::updateWellParameters(const QVector& parame // 更新目标井上的拟合参数。目前井级可拟合参数主要是： // - skin：写入第一个 perforation； // - wellboreC：写入井筒储集系数； - // - Dfc：只写入垂直压裂井或多段压裂水平井的裂缝导流能力。 + // - Dfc/裂缝半长：只写入垂直压裂井或多段压裂水平井。 // 如果目标井不存在或没有射孔数据，这里只记录 debug，不抛异常。 nmDataAnalyzeManager* dataManager = nmDataAnalyzeManager::getCurrentInstance(); @@ -5811,6 +5846,24 @@ void nmCalculationAutoFitPSO::updateWellParameters(const QVector& parame } } break; + + case 9: { // 裂缝半长 + // 直接修改井对象中的属性，复用已有信号重算裂缝端点。 + if(pWell->getWellType() == NM_WELL_MODEL::Vertical_Fractured_Well) { + nmDataVerticalFracturedWell* fracturedWell = + dynamic_cast(pWell); + if(fracturedWell) { + fracturedWell->getFractureHalfLength().setValue(value); + } + } else if(pWell->getWellType() == NM_WELL_MODEL::Horizontal_Fractured_Well) { + nmDataHorizontalFracturedWell* fracturedWell = + dynamic_cast(pWell); + if(fracturedWell) { + fracturedWell->getFractureHalfLength().setValue(value); + } + } + } + break; } paramIndex++; diff --git a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp index a7a3462..af01ce0 100644 --- a/Src/nmNum/nmData/nmDataAutomaticFitting.cpp +++ b/Src/nmNum/nmData/nmDataAutomaticFitting.cpp @@ -12,6 +12,7 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_cfSelected = false; // 默认不选中 m_swiSelected = false; // 默认不选中 m_fractureConductivitySelected = false; // 仅压裂井可用，默认不选中 + m_fractureHalfLengthSelected = false; // 仅压裂井可用，默认不选中 // 拟合上下界不再使用固定默认值，由自动拟合窗口按数据对象初值和物理边界生成。 m_permeabilityMax = nmDataAttribute("Permeability Max", QVariant(), "Darcy"); @@ -23,6 +24,7 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_cfMax = nmDataAttribute("Cf Max", QVariant(), ""); m_swiMax = nmDataAttribute("Swi Max", QVariant(), ""); m_fractureConductivityMax = nmDataAttribute("Fracture Conductivity Max", QVariant(), "md.m"); + m_fractureHalfLengthMax = nmDataAttribute("Fracture Half Length Max", QVariant(), "m"); m_permeabilityMin = nmDataAttribute("Permeability Min", QVariant(), "Darcy"); m_skinMin = nmDataAttribute("Skin Min", QVariant(), ""); @@ -33,6 +35,7 @@ nmDataAutomaticFitting::nmDataAutomaticFitting() m_cfMin = nmDataAttribute("Cf Min", QVariant(), ""); m_swiMin = nmDataAttribute("Swi Min", QVariant(), ""); m_fractureConductivityMin = nmDataAttribute("Fracture Conductivity Min", QVariant(), "md.m"); + m_fractureHalfLengthMin = nmDataAttribute("Fracture Half Length Min", QVariant(), "m"); // 初始化迭代参数 m_iterationCount = nmDataAttribute("Iteration Count", 20, ""); @@ -63,6 +66,7 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF m_cfSelected = other.m_cfSelected; m_swiSelected = other.m_swiSelected; m_fractureConductivitySelected = other.m_fractureConductivitySelected; + m_fractureHalfLengthSelected = other.m_fractureHalfLengthSelected; // 复制参数最大值 m_permeabilityMax = other.m_permeabilityMax; @@ -74,6 +78,7 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF m_cfMax = other.m_cfMax; m_swiMax = other.m_swiMax; m_fractureConductivityMax = other.m_fractureConductivityMax; + m_fractureHalfLengthMax = other.m_fractureHalfLengthMax; // 复制参数最小值 m_permeabilityMin = other.m_permeabilityMin; @@ -85,6 +90,7 @@ nmDataAutomaticFitting& nmDataAutomaticFitting::operator=(const nmDataAutomaticF m_cfMin = other.m_cfMin; m_swiMin = other.m_swiMin; m_fractureConductivityMin = other.m_fractureConductivityMin; + m_fractureHalfLengthMin = other.m_fractureHalfLengthMin; // 复制迭代参数 m_iterationCount = other.m_iterationCount; @@ -109,6 +115,7 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca fittingObject.AddMember("CfSelected", m_cfSelected, allocator); fittingObject.AddMember("SwiSelected", m_swiSelected, allocator); fittingObject.AddMember("FractureConductivitySelected", m_fractureConductivitySelected, allocator); + fittingObject.AddMember("FractureHalfLengthSelected", m_fractureHalfLengthSelected, allocator); // 序列化参数最大值 fittingObject.AddMember("PermeabilityMax", m_permeabilityMax.ToJsonValue(allocator), allocator); @@ -120,6 +127,7 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca fittingObject.AddMember("CfMax", m_cfMax.ToJsonValue(allocator), allocator); fittingObject.AddMember("SwiMax", m_swiMax.ToJsonValue(allocator), allocator); fittingObject.AddMember("FractureConductivityMax", m_fractureConductivityMax.ToJsonValue(allocator), allocator); + fittingObject.AddMember("FractureHalfLengthMax", m_fractureHalfLengthMax.ToJsonValue(allocator), allocator); // 序列化参数最小值 fittingObject.AddMember("PermeabilityMin", m_permeabilityMin.ToJsonValue(allocator), allocator); @@ -131,6 +139,7 @@ rapidjson::Value nmDataAutomaticFitting::ToJsonValue(rapidjson::Document::Alloca fittingObject.AddMember("CfMin", m_cfMin.ToJsonValue(allocator), allocator); fittingObject.AddMember("SwiMin", m_swiMin.ToJsonValue(allocator), allocator); fittingObject.AddMember("FractureConductivityMin", m_fractureConductivityMin.ToJsonValue(allocator), allocator); + fittingObject.AddMember("FractureHalfLengthMin", m_fractureHalfLengthMin.ToJsonValue(allocator), allocator); // 序列化迭代参数 fittingObject.AddMember("IterationCount", m_iterationCount.ToJsonValue(allocator), allocator); @@ -171,6 +180,9 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue) if (jsonValue.HasMember("FractureConductivitySelected") && jsonValue["FractureConductivitySelected"].IsBool()) { m_fractureConductivitySelected = jsonValue["FractureConductivitySelected"].GetBool(); } + if (jsonValue.HasMember("FractureHalfLengthSelected") && jsonValue["FractureHalfLengthSelected"].IsBool()) { + m_fractureHalfLengthSelected = jsonValue["FractureHalfLengthSelected"].GetBool(); + } // 反序列化参数最大值 if (jsonValue.HasMember("PermeabilityMax") && jsonValue["PermeabilityMax"].IsObject()) { @@ -200,6 +212,9 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue) if (jsonValue.HasMember("FractureConductivityMax") && jsonValue["FractureConductivityMax"].IsObject()) { m_fractureConductivityMax.FromJsonValue(jsonValue["FractureConductivityMax"]); } + if (jsonValue.HasMember("FractureHalfLengthMax") && jsonValue["FractureHalfLengthMax"].IsObject()) { + m_fractureHalfLengthMax.FromJsonValue(jsonValue["FractureHalfLengthMax"]); + } // 反序列化参数最小值 if (jsonValue.HasMember("PermeabilityMin") && jsonValue["PermeabilityMin"].IsObject()) { @@ -229,6 +244,9 @@ void nmDataAutomaticFitting::FromJsonValue(const rapidjson::Value& jsonValue) if (jsonValue.HasMember("FractureConductivityMin") && jsonValue["FractureConductivityMin"].IsObject()) { m_fractureConductivityMin.FromJsonValue(jsonValue["FractureConductivityMin"]); } + if (jsonValue.HasMember("FractureHalfLengthMin") && jsonValue["FractureHalfLengthMin"].IsObject()) { + m_fractureHalfLengthMin.FromJsonValue(jsonValue["FractureHalfLengthMin"]); + } // 反序列化迭代参数 if (jsonValue.HasMember("IterationCount") && jsonValue["IterationCount"].IsObject()) { @@ -274,6 +292,9 @@ void nmDataAutomaticFitting::setSwiSelected(bool selected) { m_swiSelected = sel bool nmDataAutomaticFitting::getFractureConductivitySelected() const { return m_fractureConductivitySelected; } void nmDataAutomaticFitting::setFractureConductivitySelected(bool selected) { m_fractureConductivitySelected = selected; } +bool nmDataAutomaticFitting::getFractureHalfLengthSelected() const { return m_fractureHalfLengthSelected; } +void nmDataAutomaticFitting::setFractureHalfLengthSelected(bool selected) { m_fractureHalfLengthSelected = selected; } + // Getter and Setter implementations for Max values nmDataAttribute& nmDataAutomaticFitting::getPermeabilityMax() { return m_permeabilityMax; } @@ -303,6 +324,9 @@ void nmDataAutomaticFitting::setSwiMax(const nmDataAttribute& swiMax) { m_swiMax nmDataAttribute& nmDataAutomaticFitting::getFractureConductivityMax() { return m_fractureConductivityMax; } void nmDataAutomaticFitting::setFractureConductivityMax(const nmDataAttribute& fractureConductivityMax) { m_fractureConductivityMax = fractureConductivityMax; } +nmDataAttribute& nmDataAutomaticFitting::getFractureHalfLengthMax() { return m_fractureHalfLengthMax; } +void nmDataAutomaticFitting::setFractureHalfLengthMax(const nmDataAttribute& fractureHalfLengthMax) { m_fractureHalfLengthMax = fractureHalfLengthMax; } + // Getter and Setter implementations for Min values nmDataAttribute& nmDataAutomaticFitting::getPermeabilityMin() { return m_permeabilityMin; } void nmDataAutomaticFitting::setPermeabilityMin(const nmDataAttribute& permeabilityMin) { m_permeabilityMin = permeabilityMin; } @@ -331,6 +355,9 @@ void nmDataAutomaticFitting::setSwiMin(const nmDataAttribute& swiMin) { m_swiMin nmDataAttribute& nmDataAutomaticFitting::getFractureConductivityMin() { return m_fractureConductivityMin; } void nmDataAutomaticFitting::setFractureConductivityMin(const nmDataAttribute& fractureConductivityMin) { m_fractureConductivityMin = fractureConductivityMin; } +nmDataAttribute& nmDataAutomaticFitting::getFractureHalfLengthMin() { return m_fractureHalfLengthMin; } +void nmDataAutomaticFitting::setFractureHalfLengthMin(const nmDataAttribute& fractureHalfLengthMin) { m_fractureHalfLengthMin = fractureHalfLengthMin; } + // Getter and Setter implementations for iteration parameters nmDataAttribute& nmDataAutomaticFitting::getIterationCount() { return m_iterationCount; } void nmDataAutomaticFitting::setIterationCount(const nmDataAttribute& iterationCount) { m_iterationCount = iterationCount; } diff --git a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp index 34415d0..6bd7312 100644 --- a/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp +++ b/Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp @@ -43,13 +43,10 @@ bool nmAutoFitUiNearlyEqual(double left, double right) return std::fabs(left - right) <= DBL_EPSILON * 8.0 * scale; } -// 从系统参数表读取物理边界，读取失败时保留调用方提供的兜底边界。 +// 从系统参数表读取物理边界，所有自动拟合参数统一以参数表配置为准。 bool nmAutoFitReadPhysicalRange(const char* parameterName, - double fallbackMin, double fallbackMax, double& minValue, double& maxValue) + double& minValue, double& maxValue) { - minValue = fallbackMin; - maxValue = fallbackMax; - iSysParaHelper* paraHelper = _paraHelper; if(!paraHelper) { return false; @@ -245,6 +242,7 @@ void nmWxAutomaticFitting::updateParameterVisibility(QTableWidget* table, NM_SOL } } setParameterRowVisible(table, 8, showFractureConductivity); // Dfc + setParameterRowVisible(table, 9, showFractureConductivity); // 裂缝半长 renumberVisibleParameterRows(table); } @@ -255,29 +253,15 @@ bool nmWxAutomaticFitting::getPhysicalParameterRange(int parameterIndex, { static const char* parameterNames[] = { "Result_K", "Result_W_Skin", "Result_W_C", "Result_phi", - "Result_h", "Result_Cti", "Result_Cf", "Result_Swi", "Result_W_Dfc" - }; - // KAPPA 的边界使用 md、ft、bbl/psi；自动拟合界面使用 Darcy、m、m^3/MPa， - // 这里统一换算到界面和 PSO 实际使用的单位：K 除以 1000，h 由 ft 换成 m， - // 井筒储集系数的 4.33667154546306e34 bbl/psi 对应约 1e36 m^3/MPa。 - // Ct/Cf/Swi/Dfc 沿用模型参数表边界。 - static const double physicalMin[] = { - 1.01325027383089e-18, -5.0, 0.0, 1.0e-4, 1.0e-5, 1.0e-30, 1.0e-30, 0.0, 0.0 - }; - static const double physicalMax[] = { - 1.01325027383089e42, 5000.0, 1.0e36, 0.9999, 1.0e9, 10.0, 10.0, 1.0, 1.0e30 + "Result_h", "Result_Cti", "Result_Cf", "Result_Swi", "Result_W_Dfc", + "W_FractureHalfLength" }; - - if(parameterIndex < 0 || parameterIndex >= 9) { + if(parameterIndex < 0 || parameterIndex >= 10) { return false; } - minValue = physicalMin[parameterIndex]; - maxValue = physicalMax[parameterIndex]; - bool rangeRead = true; - if(parameterIndex >= 5) { - rangeRead = nmAutoFitReadPhysicalRange(parameterNames[parameterIndex], - physicalMin[parameterIndex], physicalMax[parameterIndex], minValue, maxValue); + if(!nmAutoFitReadPhysicalRange(parameterNames[parameterIndex], minValue, maxValue)) { + return false; } if(parameterIndex == 7) { @@ -294,7 +278,7 @@ bool nmWxAutomaticFitting::getPhysicalParameterRange(int parameterIndex, } } - return rangeRead; + return true; } // 将一组上下界同步到表格和自动拟合数据，保证 PSO 读取到同一份配置。 @@ -357,6 +341,10 @@ void nmWxAutomaticFitting::setParameterRange(int parameterIndex, automaticFittingData.getFractureConductivityMin().setValue(minValue); automaticFittingData.getFractureConductivityMax().setValue(maxValue); break; + case 9: + automaticFittingData.getFractureHalfLengthMin().setValue(minValue); + automaticFittingData.getFractureHalfLengthMax().setValue(maxValue); + break; default: break; } @@ -369,14 +357,16 @@ void nmWxAutomaticFitting::setParameterRange(int parameterIndex, void nmWxAutomaticFitting::updateRangeForParameter(int parameterIndex, double centerValue) { - if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 9 + if(!m_parameterTable || parameterIndex < 0 || parameterIndex >= 10 || !nmAutoFitUiIsFinite(centerValue)) { return; } double physicalMin = 0.0; double physicalMax = 0.0; - getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax); + if(!getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax)) { + return; + } double reference = centerValue; const bool positiveParameter = parameterIndex != 1; @@ -437,7 +427,7 @@ void nmWxAutomaticFitting::initializeSuggestedParameterRanges() return; } - for(int parameterIndex = 0; parameterIndex < 9; ++parameterIndex) { + for(int parameterIndex = 0; parameterIndex < 10; ++parameterIndex) { QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3); if(initialItem) { bool initialOk = false; @@ -448,8 +438,8 @@ void nmWxAutomaticFitting::initializeSuggestedParameterRanges() // 数据对象没有提供该初值时使用完整物理区间，不回退到旧的默认范围。 double physicalMin = 0.0; double physicalMax = 0.0; - getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax); - if(physicalMax >= physicalMin) { + if(getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax) + && physicalMax >= physicalMin) { setParameterRange(parameterIndex, physicalMin, physicalMax); } } @@ -464,7 +454,7 @@ void nmWxAutomaticFitting::normalizeSavedParameterRanges() return; } - for(int parameterIndex = 0; parameterIndex < 9; ++parameterIndex) { + for(int parameterIndex = 0; parameterIndex < 10; ++parameterIndex) { QTableWidgetItem* minItem = m_parameterTable->item(parameterIndex, 2); QTableWidgetItem* maxItem = m_parameterTable->item(parameterIndex, 4); QTableWidgetItem* initialItem = m_parameterTable->item(parameterIndex, 3); @@ -474,7 +464,9 @@ void nmWxAutomaticFitting::normalizeSavedParameterRanges() double physicalMin = 0.0; double physicalMax = 0.0; - getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax); + if(!getPhysicalParameterRange(parameterIndex, physicalMin, physicalMax)) { + continue; + } bool savedMinOk = false; bool savedMaxOk = false; const double savedMin = minItem->text().toDouble(&savedMinOk); @@ -513,18 +505,19 @@ bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int par errorMessage = tr("The parameter table is unavailable."); return false; } - if(parameterIndex < -1 || parameterIndex >= 9) { + if(parameterIndex < -1 || parameterIndex >= 10) { errorMessage = tr("The parameter row is invalid."); return false; } static const char* parameterNames[] = { "Permeability", "Skin", "Wellbore storage", "Porosity", - "Thickness", "Ct", "Cf", "Swi", "Fracture conductivity" + "Thickness", "Ct", "Cf", "Swi", "Fracture conductivity", + "Fracture half length" }; const int firstParameterIndex = parameterIndex < 0 ? 0 : parameterIndex; - const int lastParameterIndex = parameterIndex < 0 ? 9 : parameterIndex + 1; + const int lastParameterIndex = parameterIndex < 0 ? 10 : parameterIndex + 1; for(int currentParameterIndex = firstParameterIndex; currentParameterIndex < lastParameterIndex; ++currentParameterIndex) { // 隐藏参数不参与当前模型拟合，不用它们的历史值阻塞当前设置。 @@ -558,8 +551,8 @@ bool nmWxAutomaticFitting::validateParameterTable(QString& errorMessage, int par double physicalMin = 0.0; double physicalMax = 0.0; - getPhysicalParameterRange(currentParameterIndex, physicalMin, physicalMax); - if(!nmAutoFitUiIsFinite(physicalMin) || !nmAutoFitUiIsFinite(physicalMax) + if(!getPhysicalParameterRange(currentParameterIndex, physicalMin, physicalMax) + || !nmAutoFitUiIsFinite(physicalMin) || !nmAutoFitUiIsFinite(physicalMax) || physicalMax < physicalMin) { errorMessage = tr("The physical range of %1 is invalid.") .arg(tr(parameterNames[currentParameterIndex])); @@ -717,7 +710,7 @@ void nmWxAutomaticFitting::setupUI() void nmWxAutomaticFitting::setupParameterTable() { // 创建表格 - m_parameterTable = new QTableWidget(9, 6, this); + m_parameterTable = new QTableWidget(10, 6, this); // 设置表头 QStringList headers; @@ -837,6 +830,16 @@ void nmWxAutomaticFitting::setupParameterTable() m_parameterTable->setItem(8, 4, new QTableWidgetItem(QString::number(automaticFittingData.getFractureConductivityMax().getValue().toDouble()))); m_parameterTable->setItem(8, 5, new QTableWidgetItem(tr("md.m"))); + // 裂缝半长。该行与 Dfc 一样只对压裂井显示，初值从当前目标井读取。 + m_parameterTable->setItem(9, 0, new QTableWidgetItem("10")); + m_fractureHalfLengthCheckBox = new QCheckBox(tr("Fracture half length")); + m_fractureHalfLengthCheckBox->setChecked(automaticFittingData.getFractureHalfLengthSelected()); + m_parameterTable->setCellWidget(9, 1, m_fractureHalfLengthCheckBox); + m_parameterTable->setItem(9, 2, new QTableWidgetItem(QString::number(automaticFittingData.getFractureHalfLengthMin().getValue().toDouble()))); + m_parameterTable->setItem(9, 3, new QTableWidgetItem()); + m_parameterTable->setItem(9, 4, new QTableWidgetItem(QString::number(automaticFittingData.getFractureHalfLengthMax().getValue().toDouble()))); + m_parameterTable->setItem(9, 5, new QTableWidgetItem(tr("m"))); + // 设置表格行为 for(int i = 0; i < m_parameterTable->rowCount(); ++i) { for(int j = 0; j < 6; ++j) { @@ -1037,6 +1040,7 @@ void nmWxAutomaticFitting::onReverseSelection() if(!m_parameterTable->isRowHidden(6)) m_cfCheckBox->setChecked(!m_cfCheckBox->isChecked()); if(!m_parameterTable->isRowHidden(7)) m_swiCheckBox->setChecked(!m_swiCheckBox->isChecked()); if(!m_parameterTable->isRowHidden(8)) m_dfcCheckBox->setChecked(!m_dfcCheckBox->isChecked()); + if(!m_parameterTable->isRowHidden(9)) m_fractureHalfLengthCheckBox->setChecked(!m_fractureHalfLengthCheckBox->isChecked()); } void nmWxAutomaticFitting::onParameterTableItemChanged(QTableWidgetItem* item) @@ -1155,7 +1159,8 @@ void nmWxAutomaticFitting::onAccept() m_cCheckBox->isChecked() || m_phiCheckBox->isChecked() || m_hCheckBox->isChecked() || m_ctCheckBox->isChecked() || m_cfCheckBox->isChecked() || - m_swiCheckBox->isChecked() || m_dfcCheckBox->isChecked(); + m_swiCheckBox->isChecked() || m_dfcCheckBox->isChecked() || + m_fractureHalfLengthCheckBox->isChecked(); if(!hasSelectedParams) { QMessageBox::warning(this, tr("Warning"), tr("Please select at least one parameter for optimization!")); @@ -1174,6 +1179,7 @@ void nmWxAutomaticFitting::onAccept() if(m_cfCheckBox->isChecked()) selectedParameterNames << tr("Cf"); if(m_swiCheckBox->isChecked()) selectedParameterNames << tr("Swi"); if(m_dfcCheckBox->isChecked()) selectedParameterNames << tr("Fracture conductivity"); + if(m_fractureHalfLengthCheckBox->isChecked()) selectedParameterNames << tr("Fracture half length"); // 启动自动拟合 - 传递双对数历史数据 startAutoFitting(targetLogLogData, selectedParameterNames, selectedWellName); @@ -1194,6 +1200,7 @@ void nmWxAutomaticFitting::onWellSelected(int index) double skinValue = 0.0; double wellboreStorageValue = 0.0; double fractureConductivityValue = 0.0; + double fractureHalfLengthValue = 0.0; // 查找垂直井 for(int i = 0; i < m_verticalWells.size(); ++i) { @@ -1224,6 +1231,7 @@ void nmWxAutomaticFitting::onWellSelected(int index) skinValue = m_verticalFracturedWells[i].getPerforation(0)->getSkin().getValue().toDouble(); wellboreStorageValue = m_verticalFracturedWells[i].getWellboreStorage().getValue().toDouble(); fractureConductivityValue = m_verticalFracturedWells[i].getDfc().getValue().toDouble(); + fractureHalfLengthValue = m_verticalFracturedWells[i].getFractureHalfLength().getValue().toDouble(); fracturedWell = true; found = true; break; @@ -1238,6 +1246,7 @@ void nmWxAutomaticFitting::onWellSelected(int index) skinValue = m_horizontalFracturedWells[i].getPerforation(0)->getSkin().getValue().toDouble(); wellboreStorageValue = m_horizontalFracturedWells[i].getWellboreStorage().getValue().toDouble(); fractureConductivityValue = m_horizontalFracturedWells[i].getDfc().getValue().toDouble(); + fractureHalfLengthValue = m_horizontalFracturedWells[i].getFractureHalfLength().getValue().toDouble(); fracturedWell = true; found = true; break; @@ -1268,12 +1277,16 @@ void nmWxAutomaticFitting::onWellSelected(int index) if(fracturedWell && m_parameterTable->item(8, 3)) { m_parameterTable->item(8, 3)->setText(QString::number(fractureConductivityValue)); } + if(fracturedWell && m_parameterTable->item(9, 3)) { + m_parameterTable->item(9, 3)->setText(QString::number(fractureHalfLengthValue)); + } if(m_autoParameterRanges) { updateRangeForParameter(1, skinValue); updateRangeForParameter(2, wellboreStorageValue); if(fracturedWell) { updateRangeForParameter(8, fractureConductivityValue); + updateRangeForParameter(9, fractureHalfLengthValue); } } } @@ -1291,6 +1304,7 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() automaticFittingData.setCfSelected(m_cfCheckBox->isChecked()); automaticFittingData.setSwiSelected(m_swiCheckBox->isChecked()); automaticFittingData.setFractureConductivitySelected(m_dfcCheckBox->isChecked()); + automaticFittingData.setFractureHalfLengthSelected(m_fractureHalfLengthCheckBox->isChecked()); automaticFittingData.setSurrogateScreeningEnabled(m_surrogateCombo && m_surrogateCombo->currentIndex() == 1); // 保存渗透率的最小值和最大值 @@ -1329,6 +1343,10 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() automaticFittingData.getFractureConductivityMin().setValue(m_parameterTable->item(8, 2)->text().toDouble()); automaticFittingData.getFractureConductivityMax().setValue(m_parameterTable->item(8, 4)->text().toDouble()); + // 保存裂缝半长的最小值和最大值 + automaticFittingData.getFractureHalfLengthMin().setValue(m_parameterTable->item(9, 2)->text().toDouble()); + automaticFittingData.getFractureHalfLengthMax().setValue(m_parameterTable->item(9, 4)->text().toDouble()); + // 保存迭代参数 automaticFittingData.getIterationCount().setValue(m_iterationEdit->text().toInt()); automaticFittingData.getErrorTolerance().setValue(m_errorLimitEdit->text().toDouble()); @@ -1353,6 +1371,7 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() double newSkinValue = m_parameterTable->item(1, 3)->text().toDouble(); double newWellboreStorageValue = m_parameterTable->item(2, 3)->text().toDouble(); double newFractureConductivityValue = m_parameterTable->item(8, 3)->text().toDouble(); + double newFractureHalfLengthValue = m_parameterTable->item(9, 3)->text().toDouble(); // 直接从数据管理器获取目标井 nmDataAnalyzeManager* manager = nmDataAnalyzeManager::getCurrentInstance(); @@ -1382,6 +1401,7 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() nmDataAttribute dfc = fracturedWell->getDfc(); dfc.setValue(newFractureConductivityValue); fracturedWell->setDfc(dfc); + fracturedWell->getFractureHalfLength().setValue(newFractureHalfLengthValue); } } else if(wellType == NM_WELL_MODEL::Horizontal_Fractured_Well) { nmDataHorizontalFracturedWell* fracturedWell = dynamic_cast(pTargetWell); @@ -1389,6 +1409,7 @@ void nmWxAutomaticFitting::setAutomaticFittingValue() nmDataAttribute dfc = fracturedWell->getDfc(); dfc.setValue(newFractureConductivityValue); fracturedWell->setDfc(dfc); + fracturedWell->getFractureHalfLength().setValue(newFractureHalfLengthValue); } } @@ -1622,6 +1643,7 @@ void nmWxAutomaticFitting::updateBestParametersToTable() if(m_cfCheckBox->isChecked()) enabledParams.append(6); // 岩石压缩系数 if(m_swiCheckBox->isChecked()) enabledParams.append(7); // 初始含水饱和度 if(m_dfcCheckBox->isChecked()) enabledParams.append(8); // 裂缝导流能力 + if(m_fractureHalfLengthCheckBox->isChecked()) enabledParams.append(9); // 裂缝半长 // 更新参数值和范围 for (int i = 0; i < bestSolution.size() && i < enabledParams.size(); ++i) { From e1a8610df417d0c580451083f676ae336c0bfa96 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 14 Aug 2026 15:32:47 +0800 Subject: [PATCH 10/12] =?UTF-8?q?=E6=9B=B2=E7=BA=BF=E9=87=87=E6=A0=B7?= =?UTF-8?q?=E7=82=B9=E6=95=B0=E6=94=B9=E4=B8=BA80?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index 0f37602..79453f9 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -3936,9 +3936,9 @@ struct TrustRegionEvaluation static bool trustRegionResidualsValid( const AutoFitObjectiveBreakdown& breakdown) { - // 损失函数固定使用 50 个压力点和 50 个导数点。严格校验长度，避免 + // 损失函数固定使用 80 个压力点和 80 个导数点。严格校验长度，避免 // Jacobian 沿用旧维度后访问另一候选的短残差向量。 - if(!breakdown.valid || breakdown.residualVector.size() != 100) { + if(!breakdown.valid || breakdown.residualVector.size() != 160) { return false; } @@ -4248,7 +4248,7 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() bool modelRebuiltAtMinimumRadius = false; StopReasonPSO stopReason = PSO_MAX_ITERATIONS; - // jacobian 的行对应固定 100 维残差，列对应用户勾选的参数。 + // jacobian 的行对应固定 160 维残差，列对应用户勾选的参数。 // 三个 gradient 单独描述诊断分量对参数的局部变化，只用于本轮选参。 QVector > jacobian; QVector verticalGradient(dimensions, 0.0); @@ -6516,7 +6516,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( const double invalidLoss = 1.0e10; const double valueFloor = 1.0e-12; const double minimumCoverage = 0.95; - const int numPoints = 50; + const int numPoints = 80; m_lastObjectiveBreakdown = AutoFitObjectiveBreakdown(); if(!validateLogLogData(target) || !validateLogLogData(result)) { @@ -6754,7 +6754,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( } // 通过门槛后最多只缺少首尾少量目标点。按模拟曲线端点趋势补齐后， - // 每个候选仍在固定 50 点上计算均方根误差，不能靠少算难拟合端点获益。 + // 每个候选仍在固定 80 点上计算均方根误差，不能靠少算难拟合端点获益。 for(int i = 0; i < numPoints; ++i) { if(isFiniteNumber(pressureResidual[i]) && isFiniteNumber(derivativeResidual[i])) { @@ -7335,7 +7335,7 @@ double nmCalculationAutoFitPSO::calculateLogLogCurveError( 0.5 * breakdown.pressureLoss + 0.5 * breakdown.derivativeLoss; } else { - // 非代理总目标等于固定 100 维普通残差的二范数；压力和 + // 非代理总目标等于固定 160 维普通残差的二范数；压力和 // 导数各占一半能量。上下、左右和形状分量不参与候选排序与接受。 breakdown.total = qSqrt( 0.5 * breakdown.pressureLoss * breakdown.pressureLoss + From 83a29152c9fe86615a0e95c4b104b1e015fa6331 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 14 Aug 2026 15:40:16 +0800 Subject: [PATCH 11/12] =?UTF-8?q?=E8=B0=83=E6=95=B4=E5=B1=80=E9=83=A8?= =?UTF-8?q?=E6=94=B6=E6=95=9B=E5=88=A4=E5=AE=9A?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Bin/Config/Lang/cn/nmNum_cn.qm | Bin 93972 -> 94621 bytes Bin/Config/Lang/cn/nmNum_cn.ts | 12 +++ .../nmCalculation/nmCalculationAutoFitPSO.cpp | 90 ++++++++++++++++++ 3 files changed, 102 insertions(+) diff --git a/Bin/Config/Lang/cn/nmNum_cn.qm b/Bin/Config/Lang/cn/nmNum_cn.qm index c9e8ed0c65c5f7422395fd6f64d2faf6de240c85..9df8a2617c56f2db44d2b2b5b16f5de42f89553d 100644 GIT binary patch delta 10049 zcmZ{pcU({J|No!Y>%7iu4>K!ySrxJ>StXT>Bt%9OT110N$s4Cq_K32{D3MWQzu$Ht z-bOarvfe)S+n(RY>vg?9l@F*t;1 z>@?1#9L{YO7BX#Za43G?%$Zcixm{x+)B1yfnCCbcO;lQq^B(5JHS2B-#uAM#;GDFS zQ!4f$lK)1``3pD`Yd8Wf1mAIvQgKT8oQuOjygbT~TiJgghWODAC#Ch^0=k)~6ius&(w{!l5518M3 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of baseline error); %3 consecutive ineffective steps trigger convergence confirmation + 有效改善阈值：取 %1 与基准误差的 %2% 中较大值；连续 %3 次无有效改善后进行收敛确认 + + + No effective improvement for %1 consecutive steps; rebuilding sensitivity model for confirmation + 连续 %1 次无有效改善，正在重建灵敏度模型进行确认 + + + Sensitivity rebuild produced no effective improvement; local convergence detected + 灵敏度重建后仍无有效改善，判定为局部收敛 + === User Stop Request Received === === 用户停止请求已接收 === diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index 79453f9..94497c7 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -4235,6 +4235,11 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() const double maximumTrustRadius = 0.30; const double columnCorrelationLimit = 0.995; const double diagnosisThreshold = 1.0e-5; + // 误差下降至少达到绝对 1e-5 且相对当前有效基准 0.2% 才算有效改善。 + // 更小的下降仍保留为最佳解，但不能反复清除停滞状态、延长拟合时间。 + const double effectiveRelativeImprovement = 2.0e-3; + const double effectiveAbsoluteImprovement = 1.0e-5; + const int maximumIneffectiveSteps = 3; // damping 是 LM 阻尼；拒绝或预测失准时增大，真实下降与预测一致时减小。 // 两组累计量控制 Jacobian 重建，避免长期使用已偏离当前工作点的局部模型。 @@ -4243,9 +4248,11 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() int consecutiveRejectedSteps = 0; int consecutiveSolverFailures = 0; int acceptedSinceRebuild = 0; + int consecutiveIneffectiveSteps = 0; double movementSinceRebuild = 0.0; bool rebuildRequested = true; bool modelRebuiltAtMinimumRadius = false; + bool stagnationConfirmationRequested = false; StopReasonPSO stopReason = PSO_MAX_ITERATIONS; // jacobian 的行对应固定 160 维残差，列对应用户勾选的参数。 @@ -4367,6 +4374,54 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() .arg(current.fitness, 0, 'e', 4) .arg(maximumEvaluations)); + // 有效改善始终相对“上一次有效改善后的误差”累计判断，避免一连串微小 + // 下降每次都清零计数；累计达到门槛后才开始新的有效改善基准。 + double effectiveImprovementBaseline = current.fitness; + auto registerEffectiveImprovement = [&](double fitness) -> bool { + const double requiredImprovement = qMax( + effectiveAbsoluteImprovement, + qAbs(effectiveImprovementBaseline) * + effectiveRelativeImprovement); + const double improvement = effectiveImprovementBaseline - fitness; + if(improvement < requiredImprovement) { + return false; + } + + effectiveImprovementBaseline = fitness; + consecutiveIneffectiveSteps = 0; + stagnationConfirmationRequested = false; + return true; + }; + + // 连续三次没有有效改善时只请求一次灵敏度重建。重建完成后由主循环 + // 直接检查累计改善，仍达不到门槛就判定局部收敛，不再继续微小试探。 + auto recordIneffectiveStep = [&]() -> bool { + ++consecutiveIneffectiveSteps; + if(consecutiveIneffectiveSteps < maximumIneffectiveSteps) { + return false; + } + + if(stagnationConfirmationRequested) { + return true; + } + + consecutiveIneffectiveSteps = 0; + stagnationConfirmationRequested = true; + rebuildRequested = true; + emit logMessageGenerated( + tr("No effective improvement for %1 consecutive steps; " + "rebuilding sensitivity model for confirmation") + .arg(maximumIneffectiveSteps)); + return false; + }; + + emit logMessageGenerated( + tr("Effective improvement threshold: max(%1, %2% of baseline error); " + "%3 consecutive ineffective steps trigger convergence confirmation") + .arg(effectiveAbsoluteImprovement, 0, 'e', 2) + .arg(effectiveRelativeImprovement * 100.0, 0, 'f', 2) + .arg(maximumIneffectiveSteps)); + if(current.fitness < m_targetError) { return PSO_TARGET_ACHIEVED; } @@ -4603,6 +4658,8 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() break; } if(rebuildRequested) { + const bool confirmingStagnation = + stagnationConfirmationRequested; if(!rebuildSensitivity()) { stopReason = m_shouldStop ? PSO_USER_STOPPED @@ -4617,6 +4674,15 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() stopReason = PSO_MAX_ITERATIONS; break; } + const bool rebuildEffective = + registerEffectiveImprovement(current.fitness); + if(confirmingStagnation && !rebuildEffective) { + emit logMessageGenerated( + tr("Sensitivity rebuild produced no effective improvement; " + "local convergence detected")); + stopReason = PSO_LOCAL_OPTIMUM; + break; + } } // 先确定当前最突出的可靠诊断误差，用其梯度回答“哪些参数最能改善 @@ -4748,6 +4814,10 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5); damping = qMin(1.0e8, damping * 4.0); rebuildRequested = true; + if(recordIneffectiveStep()) { + stopReason = PSO_LOCAL_OPTIMUM; + break; + } continue; } @@ -4865,6 +4935,10 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() } rebuildRequested = true; } + if(recordIneffectiveStep()) { + stopReason = PSO_LOCAL_OPTIMUM; + break; + } continue; } @@ -4905,6 +4979,10 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() stopReason = PSO_CONSECUTIVE_FAILURES; break; } + if(recordIneffectiveStep()) { + stopReason = PSO_LOCAL_OPTIMUM; + break; + } continue; } @@ -4994,6 +5072,14 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() } } + // 候选只要更优就继续作为 current 保存；是否足以解除停滞，则统一 + // 相对上一次有效改善基准判断。拒绝和微小改善都会累计无效次数。 + const bool effectiveImprovement = + registerEffectiveImprovement(current.fitness); + if(!effectiveImprovement && recordIneffectiveStep()) { + stopReason = PSO_LOCAL_OPTIMUM; + } + writeTraceRow(m_currentIteration, -1, "trust_region_candidate", @@ -5018,6 +5104,10 @@ StopReasonPSO nmCalculationAutoFitPSO::runTrustRegionFitting() .arg(accepted ? tr("accepted") : tr("rejected"))); emit progressUpdated(iteration + 1, m_globalBestFitness); + if(stopReason == PSO_LOCAL_OPTIMUM) { + break; + } + if(current.fitness < m_targetError) { stopReason = PSO_TARGET_ACHIEVED; break; From 924f6f4ee8229c27c2bb48b9073ec0d66e2889b3 Mon Sep 17 00:00:00 2001 From: lvjunjie Date: Fri, 14 Aug 2026 15:50:28 +0800 Subject: [PATCH 12/12] =?UTF-8?q?=E5=88=A0=E9=99=A4=E5=80=99=E9=80=89?= =?UTF-8?q?=E8=AF=84=E4=BB=B7=E4=B8=AD=E7=9A=84=E5=8F=82=E6=95=B0=E4=B8=93?= =?UTF-8?q?=E5=B1=9E=E7=A1=AC=E7=BC=96=E7=A0=81=E9=98=88=E5=80=BC=E3=80=82?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../nmCalculation/nmCalculationAutoFitPSO.cpp | 53 +------------------ 1 file changed, 2 insertions(+), 51 deletions(-) diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp index 94497c7..cb510f6 100644 --- a/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp +++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitPSO.cpp @@ -6327,9 +6327,8 @@ int nmCalculationAutoFitPSO::getEnabledParameterCount() const bool nmCalculationAutoFitPSO::validateParameters(const QVector& parameters) const { - // 参数合法性检查分两层： - // 1. 与用户界面设置一致：维度、有限数、上下界； - // 2. 求解器保护：拦截会导致数值崩溃或明显无物理意义的极端值。 + // 参数物理范围已由拟合窗口统一校验；候选评价只检查维度、有限数和 + // 用户设置的上下界，避免另一套硬编码阈值与实际搜索范围冲突。 if(parameters.size() != getEnabledParameterCount()) { return false; } @@ -6352,54 +6351,6 @@ bool nmCalculationAutoFitPSO::validateParameters(const QVector& paramete } } - // 直接拦截会导致求解器数值崩溃的参数值 - for(int i = 0; i < parameters.size() && i < m_enabledParamIndices.size(); ++i) { - int paramIndex = m_enabledParamIndices[i]; - double value = parameters[i]; - - switch(paramIndex) { - case 0: // 渗透率：必须大于零 - if(value <= 1e-8) { - DEBUG_OUT(QString("Rejecting near-zero permeability: %1").arg(value)); - return false; - } - - break; - - case 2: // 井筒储集系数：必须大于零 - if(value <= 1e-10) { - DEBUG_OUT(QString("Rejecting near-zero wellbore storage: %1").arg(value)); - return false; - } - - break; - - case 3: // 孔隙度：必须在合理范围 - if(value <= 1e-6 || value >= 0.99) { - DEBUG_OUT(QString("Rejecting unrealistic porosity: %1").arg(value)); - return false; - } - - break; - - case 5: // 综合压缩系数：必须大于零 - if(value <= 1e-8) { - DEBUG_OUT(QString("Rejecting near-zero total compressibility: %1").arg(value)); - return false; - } - - break; - - case 8: // 裂缝导流能力：0 表示无限导流，不能作为连续拟合搜索点 - if(value <= 1e-10) { - DEBUG_OUT(QString("Rejecting non-positive fracture conductivity: %1").arg(value)); - return false; - } - - break; - } - } - return true; }