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feature/AutoFit-Optimize-20260821
lvjunjie 2 weeks ago
parent e8278d6c87
commit 4a5500d927
3 changed files with 424 additions and 7 deletions
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Bin/Config/Lang/cn/nmNum_cn.ts
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@ -638,6 +638,389 @@ Reason: %1</source>
<translation>代理评分服务器标准错误输出（stderr）：%1</translation>
</message>
</context>
<context>
<name>nmCalculationAutoFitLM</name>
<message>
<source>=== User Stop Request Received ===</source>
<translation>=== 用户停止请求已接收 ===</translation>
</message>
<message>
<source>Gracefully stopping LM automatic fitting...</source>
<translation>正在平稳停止 LM 自动拟合...</translation>
</message>
<message>
<source>Waiting for current solver evaluation to stop...</source>
<translation>正在等待当前求解器评估停止...</translation>
</message>
<message>
<source>LM automatic fitting stop request processed</source>
<translation>LM 自动拟合停止请求已处理</translation>
</message>
<message>
<source>Stop request received but optimization is not running</source>
<translation>收到停止请求，但优化未运行</translation>
</message>
<message>
<source>LM fitting trace: %1</source>
<translation>LM 拟合跟踪：%1</translation>
</message>
<message>
<source>LM fitting trace metadata: %1</source>
<translation>LM 拟合跟踪元数据：%1</translation>
</message>
<message>
<source>=== LM Run Summary ===</source>
<translation>=== LM 运行摘要 ===</translation>
</message>
<message>
<source>Stop reason: %1</source>
<translation>停止原因：%1</translation>
</message>
<message>
<source>Result: %1, final error=%2, iterations=%3, evaluations=%4 (successful=%5, failed=%6)</source>
<translation>结果：%1，最终误差=%2，迭代次数=%3，评估次数=%4（成功=%5，失败=%6）</translation>
</message>
<message>
<source>SUCCESS</source>
<translation>成功</translation>
</message>
<message>
<source>FAILED</source>
<translation>失败</translation>
</message>
<message>
<source>Artifacts: trace=%1</source>
<translation>产物：跟踪=%1</translation>
</message>
<message>
<source>Artifacts: trace_meta=%1</source>
<translation>产物：跟踪元数据=%1</translation>
</message>
<message>
<source>ERROR: Failed to load configuration from data manager</source>
<translation>错误：从数据管理器加载配置失败</translation>
</message>
<message>
<source>Algorithm: LM</source>
<translation>算法：LM</translation>
</message>
<message>
<source>Enabled parameters count: %1</source>
<translation>启用参数数量：%1</translation>
</message>
<message>
<source>ERROR: No parameters enabled for optimization</source>
<translation>错误：没有启用优化参数</translation>
</message>
<message>
<source>ERROR: Target LogLog data is empty or insufficient</source>
<translation>错误：目标双对数数据为空或不足</translation>
</message>
<message>
<source>ERROR: Target LogLog data arrays have inconsistent sizes</source>
<translation>错误：目标双对数数据数组大小不一致</translation>
</message>
<message>
<source>ERROR: Target well name is empty</source>
<translation>错误：目标井名称为空</translation>
</message>
<message>
<source>Target data validation passed (%1 data points)</source>
<translation>目标数据验证通过（%1 个数据点）</translation>
</message>
<message>
<source>=== Evaluating Initial Solution (Elite Protection) ===</source>
<translation>=== 评估初始解 ===</translation>
</message>
<message>
<source>Initial parameters: </source>
<translation>初始参数：</translation>
</message>
<message>
<source>Starting initial solution evaluation...</source>
<translation>开始初始解评估...</translation>
</message>
<message>
<source>Initial solution evaluation successful</source>
<translation>初始解评估成功</translation>
</message>
<message>
<source>Initial Error: %1</source>
<translation>初始误差：%1</translation>
</message>
<message>
<source>Initial solution evaluation failed</source>
<translation>初始解评估失败</translation>
</message>
<message>
<source>Exception during initial solution evaluation</source>
<translation>初始解评估期间出现异常</translation>
</message>
<message>
<source>Critical exception in automatic fitting: %1</source>
<translation>自动拟合发生严重异常：%1</translation>
</message>
<message>
<source>CRITICAL ERROR: %1</source>
<translation>严重错误：%1</translation>
</message>
<message>
<source>Unknown critical exception in automatic fitting</source>
<translation>自动拟合发生未知严重异常</translation>
</message>
<message>
<source>CRITICAL ERROR: Unknown exception in automatic fitting</source>
<translation>严重错误：自动拟合发生未知异常</translation>
</message>
<message>
<source>Applying optimized parameters to model...</source>
<translation>正在将优化参数应用到模型...</translation>
</message>
<message>
<source>Final full-field calculation skipped after user stop</source>
<translation>用户停止后已跳过最终全场计算</translation>
</message>
<message>
<source>Running final full-field calculation with optimized parameters...</source>
<translation>正在使用优化参数运行最终全场计算...</translation>
</message>
<message>
<source>Final full-field calculation completed successfully</source>
<translation>最终全场计算成功完成</translation>
</message>
<message>
<source>Final full-field calculation stopped by user</source>
<translation>最终全场计算已被用户停止</translation>
</message>
<message>
<source>Optimized parameters were found, but the final full-field calculation failed</source>
<translation>已找到优化参数，但最终全场计算失败</translation>
</message>
<message>
<source>ERROR: Final full-field calculation failed</source>
<translation>错误：最终全场计算失败</translation>
</message>
<message>
<source>=== Optimization Results ===</source>
<translation>=== 优化结果 ===</translation>
</message>
<message>
<source>Final error: %1</source>
<translation>最终误差：%1</translation>
</message>
<message>
<source>Total iterations: %1</source>
<translation>总迭代次数：%1</translation>
</message>
<message>
<source>Total evaluations: %1 (successful: %2)</source>
<translation>总评估次数：%1（成功：%2）</translation>
</message>
<message>
<source>Optimized parameters: </source>
<translation>优化参数：</translation>
</message>
<message>
<source>Parameters and full-field results applied successfully to data manager</source>
<translation>参数和全场计算结果已成功应用到数据管理器</translation>
</message>
<message>
<source>Optimized parameters applied to data manager</source>
<translation>优化参数已应用到数据管理器</translation>
</message>
<message>
<source>ERROR: Failed to apply final parameters: %1</source>
<translation>错误：应用最终参数失败：%1</translation>
</message>
<message>
<source>ERROR: Unknown error applying final parameters</source>
<translation>错误：应用最终参数时出现未知错误</translation>
</message>
<message>
<source>Target achieved. Best error: %1, Iterations: %2</source>
<translation>达到目标。最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING SUCCESSFUL ===</source>
<translation>=== LM 自动拟合成功 ===</translation>
</message>
<message>
<source>Automatic fitting converged to a stable solution. Best error: %1, Iterations: %2</source>
<translation>自动拟合已收敛到稳定解。最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING CONVERGED ===</source>
<translation>=== LM 自动拟合已收敛 ===</translation>
</message>
<message>
<source>Automatic fitting reached a local optimum. Best error: %1, Iterations: %2</source>
<translation>自动拟合达到局部最优。最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING - LOCAL OPTIMUM ===</source>
<translation>=== LM 自动拟合 - 局部最优 ===</translation>
</message>
<message>
<source>Max iterations reached. Best error: %1, Iterations: %2</source>
<translation>达到最大迭代次数。最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING - MAX ITERATIONS ===</source>
<translation>=== LM 自动拟合 - 达到最大迭代次数 ===</translation>
</message>
<message>
<source>Best error: %1, Iterations: %2</source>
<translation>最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING STOPPED BY USER ===</source>
<translation>=== LM 自动拟合已被用户停止 ===</translation>
</message>
<message>
<source>Automatic fitting failed due to consecutive failures. Best error: %1, Iterations: %2</source>
<translation>自动拟合因连续失败而终止。最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING FAILED ===</source>
<translation>=== LM 自动拟合失败 ===</translation>
</message>
<message>
<source>Automatic fitting ended unexpectedly. Best error: %1, Iterations: %2</source>
<translation>自动拟合意外结束。最佳误差：%1，迭代次数：%2</translation>
</message>
<message>
<source>=== LM AUTOMATIC FITTING - UNKNOWN END ===</source>
<translation>=== LM 自动拟合 - 未知结束 ===</translation>
</message>
<message>
<source>No valid parameters are available for trust-region fitting</source>
<translation>没有可用于 LM 拟合的有效参数</translation>
</message>
<message>
<source>The initial solution and parameter-range midpoint are both invalid</source>
<translation>初始解和参数范围中点均无效</translation>
</message>
<message>
<source>=== Starting LM Main Loop ===</source>
<translation>=== 开始 LM 主循环 ===</translation>
</message>
<message>
<source>LM starting point error: %1; evaluation budget: %2</source>
<translation>LM 起点误差：%1；最大评估次数：%2</translation>
</message>
<message>
<source>No effective improvement for %1 consecutive steps; rebuilding sensitivity model for confirmation</source>
<translation>连续 %1 次无有效改善，正在重建灵敏度模型进行确认</translation>
</message>
<message>
<source>Effective improvement threshold: max(%1, %2% of baseline error); %3 consecutive ineffective steps trigger convergence confirmation</source>
<translation>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</translation>
</message>
<message>
<source>Sensitivity probe accepted: error reduced to %1</source>
<translation>已接受灵敏度试算点，误差降至 %1</translation>
</message>
<message>
<source>Sensitivity model rebuilt: %1/%2 parameter columns valid</source>
<translation>灵敏度模型重建完成：%1/%2 个参数列有效</translation>
</message>
<message>
<source>Sensitivity rebuild produced no effective improvement; local convergence detected</source>
<translation>灵敏度重建后仍无有效改善，判定为局部收敛</translation>
</message>
<message>
<source>vertical deviation</source>
<translation>上下偏差</translation>
</message>
<message>
<source>horizontal deviation</source>
<translation>左右偏差</translation>
</message>
<message>
<source>shape deviation</source>
<translation>形状偏差</translation>
</message>
<message>
<source>total error</source>
<translation>总误差</translation>
</message>
<message>
<source>Iteration %1: focus=%2, parameters=%3, error=%4, result=%5</source>
<translation>迭代 %1：关注=%2，参数个数=%3，误差=%4，结果=%5</translation>
</message>
<message>
<source>accepted</source>
<translation>接受</translation>
</message>
<message>
<source>rejected</source>
<translation>拒绝</translation>
</message>
<message>
<source>No initial solution for elite protection</source>
<translation>没有可用于精英保护的初始解</translation>
</message>
<message>
<source>=== Final Result Validation (Elite Protection) ===</source>
<translation>=== 最终结果验证 ===</translation>
</message>
<message>
<source>Comparing results: Initial=%1, Final=%2</source>
<translation>比较结果：初始=%1，最终=%2</translation>
</message>
<message>
<source>Improvement: %1 (%2%)</source>
<translation>改善：%1（%2%）</translation>
</message>
<message>
<source>Elite protection triggered: final result is invalid or worse than initial</source>
<translation>触发精英保护：最终结果无效或差于初始解</translation>
</message>
<message>
<source>Restoring initial solution as final result</source>
<translation>正在恢复初始解作为最终结果</translation>
</message>
<message>
<source>Initial solution restored successfully</source>
<translation>初始解恢复成功</translation>
</message>
<message>
<source>Final result validated - solution is not worse than initial</source>
<translation>最终结果验证通过，拟合结果不差于初始解</translation>
</message>
<message>
<source>Target error achieved</source>
<translation>达到目标误差</translation>
</message>
<message>
<source>Algorithm converged to stable solution</source>
<translation>算法已收敛到稳定解</translation>
</message>
<message>
<source>Local optimum detected</source>
<translation>检测到局部最优</translation>
</message>
<message>
<source>Maximum iterations reached</source>
<translation>达到最大迭代次数</translation>
</message>
<message>
<source>Stopped by user request</source>
<translation>根据用户请求停止</translation>
</message>
<message>
<source>Too many consecutive failures</source>
<translation>连续失败次数过多</translation>
</message>
<message>
<source>Optimization failed</source>
<translation>优化失败</translation>
</message>
<message>
<source>Unknown reason</source>
<translation>未知原因</translation>
</message>
</context>
<context>
<name>nmCalculationSolver</name>
<message>
@ -3446,6 +3829,12 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
</source>
<translation>PSO求解完成：</translation>
</message>
<message>
<source>%1 Optimization completed:
</source>
<translation>%1 拟合完成：
</translation>
</message>
<message>
<source>Best Error: %1
</source>
@ -3476,6 +3865,12 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
</source>
<translation>PSO拟合被用户强行终止：</translation>
</message>
<message>
<source>%1 Optimization stopped by user:
</source>
<translation>%1 拟合已被用户停止：
</translation>
</message>
<message>
<source>Current parameters have been applied to the model.</source>
<translation>当前参数已应用到模型。</translation>
@ -3678,6 +4073,10 @@ Supported types: Vertical, Vertical Fractured, and Horizontal Multi-Fractured We
<source>GA auto fitting started</source>
<translation>遗传法自动拟合开始</translation>
</message>
<message>
<source>%1 auto fitting started</source>
<translation>%1 自动拟合开始</translation>
</message>
<message>
<source>%1 fitting parameters set: MaxIterations=%2, TargetAccuracy=%3, TargetWell=%4</source>
<translation>%1 拟合参数设置：最大迭代次数=%2，目标精度=%3，目标井=%4</translation>

32
Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp
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@ -803,7 +803,11 @@ void nmCalculationAutoFitLM::initializeTraceFile()
writeTraceHeader();
writeTraceMetaFile();
emit logMessageGenerated(tr("LM automatic fitting trace: %1").arg(m_traceFilePath));
emit logMessageGenerated(tr("LM fitting trace: %1").arg(m_traceFilePath));
if(!m_traceMetaFilePath.isEmpty()) {
emit logMessageGenerated(
tr("LM fitting trace metadata: %1").arg(m_traceMetaFilePath));
}
}
void nmCalculationAutoFitLM::closeTraceFile()
@ -990,7 +994,7 @@ void nmCalculationAutoFitLM::emitRunSummary(bool success, StopReasonLM finalReas
emit logMessageGenerated(tr("Stop reason: %1").arg(getStopReasonDescription(finalReason)));
emit logMessageGenerated(
tr("Result: %1, final error=%2, iterations=%3, evaluations=%4 (successful=%5, failed=%6)")
.arg(success ? "SUCCESS" : "FAILED")
.arg(success ? tr("SUCCESS") : tr("FAILED"))
.arg(m_globalBestFitness, 0, 'e', 4)
.arg(m_currentIteration + 1)
.arg(m_totalEvaluations)
@ -1179,7 +1183,7 @@ bool nmCalculationAutoFitLM::startAutoFitting()
return false;
}
emit logMessageGenerated(tr("Algorithm: Finite Difference + LM"));
emit logMessageGenerated(tr("Algorithm: LM"));
const int enabledParams = getEnabledParameterCount();
emit logMessageGenerated(tr("Enabled parameters count: %1").arg(enabledParams));
@ -1209,8 +1213,8 @@ bool nmCalculationAutoFitLM::startAutoFitting()
}
emit logMessageGenerated(
tr("Candidate evaluation mode: solve all wells, retain target well '%1' only")
.arg(m_targetWellName));
tr("Target data validation passed (%1 data points)")
.arg(m_targetLogLogData[0].size()));
// resetOptimizer() 会清空运行状态，因此先保存从 DataManager 提取的初始值。
QVector<double> savedInitialValues = m_initialValues;
@ -1235,6 +1239,8 @@ bool nmCalculationAutoFitLM::startAutoFitting()
emit logMessageGenerated(paramStr);
try {
emit logMessageGenerated(
tr("Starting initial solution evaluation..."));
QTime initialEvalTimer;
initialEvalTimer.start();
m_totalEvaluations++;
@ -1747,8 +1753,9 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
}
restoreEvaluationState(current);
emit logMessageGenerated(tr("=== Starting LM Main Loop ==="));
emit logMessageGenerated(
tr("Trust-region initial error: %1; evaluation budget: %2")
tr("LM starting point error: %1; evaluation budget: %2")
.arg(current.fitness, 0, 'e', 4)
.arg(maximumEvaluations));
@ -2459,10 +2466,21 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
: QString("rejected_%1").arg(componentName),
&candidate.breakdown);
QString componentDisplayName = componentName;
if(componentName == "vertical") {
componentDisplayName = tr("vertical deviation");
} else if(componentName == "horizontal") {
componentDisplayName = tr("horizontal deviation");
} else if(componentName == "shape") {
componentDisplayName = tr("shape deviation");
} else if(componentName == "total") {
componentDisplayName = tr("total error");
}
emit logMessageGenerated(
tr("Iteration %1: focus=%2, parameters=%3, error=%4, result=%5")
.arg(iteration + 1)
.arg(componentName)
.arg(componentDisplayName)
.arg(selectedColumns.size())
.arg(candidate.fitness, 0, 'e', 4)
.arg(accepted ? tr("accepted") : tr("rejected")));

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