perf(nmNum): 优化 LM 灵敏度重建时机与采样加密衔接

- 取消累计步长触发重建，连续两步实际改善不足预测的 25% 时刷新模型
- 将兜底刷新间隔调整为接受 10 步，避免微小预测量和单纯约束拒绝触发重建
- 合并临近的采样加密与灵敏度重建，提前考虑剩余求解预算
- 阶段切换时清除旧目标的预测失准计数，保留必要的失败恢复与停滞确认
- 补充重建原因日志、跟踪记录及中文翻译

Bin/Config/Lang/cn/nmNum_cn.ts

@@ -1184,6 +1184,54 @@ Reason: %1</source>
         <source>permeability reached its bound</source>
         <translation>渗透率已到达范围边界</translation>
     </message>
+    <message>
+        <source>Rebuilding sensitivity model: %1</source>
+        <translation>正在重建灵敏度模型：%1</translation>
+    </message>
+    <message>
+        <source>initial sensitivity model</source>
+        <translation>首次建立灵敏度模型</translation>
+    </message>
+    <message>
+        <source>sampling layer changed</source>
+        <translation>采样层级已改变</translation>
+    </message>
+    <message>
+        <source>no valid sensitivity model</source>
+        <translation>没有有效的灵敏度模型</translation>
+    </message>
+    <message>
+        <source>solver failure before stage switch</source>
+        <translation>阶段切换前发生求解失败</translation>
+    </message>
+    <message>
+        <source>10 accepted steps since last rebuild</source>
+        <translation>距上次重建已接受 10 步调整，进行兜底刷新</translation>
+    </message>
+    <message>
+        <source>confirm stagnation after ineffective steps</source>
+        <translation>连续无有效改善，重新确认停滞</translation>
+    </message>
+    <message>
+        <source>sampling refinement merged with pending rebuild</source>
+        <translation>提前加密采样，与本次重建合并处理</translation>
+    </message>
+    <message>
+        <source>no feasible descent step</source>
+        <translation>未找到可行的下降方向</translation>
+    </message>
+    <message>
+        <source>2 consecutive solver failures</source>
+        <translation>连续 2 次候选求解失败</translation>
+    </message>
+    <message>
+        <source>2 consecutive steps with actual improvement below 25% of prediction</source>
+        <translation>连续 2 步实际改善不足预测的 25%</translation>
+    </message>
+    <message>
+        <source>inaccurate model at minimum trust radius</source>
+        <translation>最小信赖半径下仍连续预测失准</translation>
+    </message>
 </context>
 <context>
     <name>nmCalculationSolver</name>

Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp

@@ -1041,7 +1041,7 @@ void nmCalculationAutoFitLM::writeTraceMetaFile()
 
     QTextStream out(&metaFile);
     out << "{\n";
-    out << "  \"schema_version\": 9,\n";
+    out << "  \"schema_version\": 10,\n";
     out << "  \"strategy\": \"permeability_height_then_shape_then_original_lm\",\n";
     out << "  \"shape_metric\": \"pressure_and_derivative_log_slopes_81_points_lag_8\",\n";
     out << "  \"shape_stage_total_tolerance\": \"max(0.02, 25% of stage entry total)\",\n";
@@ -1819,15 +1819,17 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
     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。
+    // 按连续预测失准刷新 Jacobian；接受步数只作兜底，不因累计移动距离强制重建。
     double trustRadius = 0.12;
     double damping = 1.0e-2;
     int consecutiveRejectedSteps = 0;
     int consecutiveSolverFailures = 0;
     int acceptedSinceRebuild = 0;
     int consecutiveIneffectiveSteps = 0;
-    double movementSinceRebuild = 0.0;
+    int consecutivePoorPredictions = 0;
     bool rebuildRequested = true;
+    // 跟踪文件保留稳定英文原因，界面输出时再翻译，避免受本地编码影响。
+    const char* rebuildReason = QT_TR_NOOP("initial sensitivity model");
     bool modelRebuiltAtMinimumRadius = false;
     bool stagnationConfirmationRequested = false;
     QVector<bool> attemptedWindows(kAutoFitTimeWindowCount, false);
@@ -2061,12 +2063,13 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         jacobian.clear();
         jacobianColumnValid.fill(false);
         rebuildRequested = true;
+        rebuildReason = QT_TR_NOOP("sampling layer changed");
         trustRadius = 0.12;
         damping = 1.0e-2;
         consecutiveRejectedSteps = 0;
         consecutiveIneffectiveSteps = 0;
         acceptedSinceRebuild = 0;
-        movementSinceRebuild = 0.0;
+        consecutivePoorPredictions = 0;
         modelRebuiltAtMinimumRadius = false;
         stagnationConfirmationRequested = false;
         attemptedWindows.fill(false);
@@ -2088,7 +2091,12 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
 
     auto enterTotalStage = [&](const QString& reason) {
         // 保留当前曲线和完整 J，只重置阶段停滞状态、阻尼及信赖半径。
-        rebuildRequested = jacobian.isEmpty() || consecutiveSolverFailures > 0;
+        rebuildRequested = jacobian.isEmpty() || consecutiveSolverFailures > 0 || acceptedSinceRebuild >= 10;
+        if(jacobian.isEmpty()) rebuildReason = QT_TR_NOOP("no valid sensitivity model");
+        else if(consecutiveSolverFailures > 0) rebuildReason = QT_TR_NOOP("solver failure before stage switch");
+        else if(acceptedSinceRebuild >= 10) rebuildReason = QT_TR_NOOP("10 accepted steps since last rebuild");
+        // 阶段目标已改变，不把形状目标下的预测失准累计到整体阶段。
+        consecutivePoorPredictions = 0;
         modelRebuiltAtMinimumRadius = false;
         shapeStage = false;
         effectiveImprovementBaseline = current.fitness;
@@ -2148,6 +2156,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
 
         stagnationConfirmationRequested = true;
         rebuildRequested = true;
+        rebuildReason = QT_TR_NOOP("confirm stagnation after ineffective steps");
         emit logMessageGenerated(
             tr("No effective improvement for %1 consecutive steps; "
                "rebuilding sensitivity model for confirmation")
@@ -2324,7 +2333,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         }
 
         acceptedSinceRebuild = 0;
-        movementSinceRebuild = 0.0;
+        consecutivePoorPredictions = 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
@@ -2356,18 +2365,34 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             enterTotalStage(tr("reserve remaining iterations and evaluations for total fitting"));
         }
         if(!shapeStage && m_layeredSampling && m_samplingStride > 1) {
-            const bool reserveFinalBudget = maximumEvaluations - m_totalEvaluations <= 2 * (dimensions + 1);
+            const int remainingEvaluations = maximumEvaluations - m_totalEvaluations;
+            // 已准备重建时，提前计入本轮差分及候选的开销，避免紧接着因预算再加密。
+            const bool reserveFinalBudget = remainingEvaluations <= 2 * (dimensions + 1) ||
+                (rebuildRequested && remainingEvaluations <= 3 * (dimensions + 1));
             const int layerDeadline = qMax(1, m_maxIterations * (m_samplingStride == 4 ? 1 : 2) / 3);
-            if(reserveFinalBudget || iteration >= layerDeadline) {
-                promoteSampling(reserveFinalBudget);
+            const bool mergeRefinement = rebuildRequested && iteration < layerDeadline &&
+                (iteration + 1 >= layerDeadline || reserveFinalBudget);
+            if(reserveFinalBudget || iteration >= layerDeadline || mergeRefinement) {
+                const bool promoted = promoteSampling(reserveFinalBudget);
                 if(samplingRefreshFailed) break;
+                if(promoted && mergeRefinement) {
+                    rebuildReason = QT_TR_NOOP("sampling refinement merged with pending rebuild");
+                }
             }
         }
         if(rebuildRequested) {
+            emit logMessageGenerated(tr("Rebuilding sensitivity model: %1").arg(tr(rebuildReason)));
+            writeTraceRow(m_currentIteration, -1, "sensitivity_rebuild", current.parameters,
+                          current.fitness, true, 0, rebuildReason, &current.breakdown);
             const bool confirmingStagnation =
                 stagnationConfirmationRequested;
             if(!rebuildSensitivity()) {
-                if(shapeStage && !m_shouldStop) { enterTotalStage(tr("no valid shape sensitivity model")); rebuildRequested = true; continue; }
+                if(shapeStage && !m_shouldStop) {
+                    enterTotalStage(tr("no valid shape sensitivity model"));
+                    rebuildRequested = true;
+                    rebuildReason = QT_TR_NOOP("no valid sensitivity model");
+                    continue;
+                }
                 if(promoteSampling(false)) continue;
                 stopReason = m_shouldStop
                              ? LM_USER_STOPPED
@@ -2507,6 +2532,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
             damping = qMin(1.0e8, damping * 4.0);
             rebuildRequested = true;
+            rebuildReason = QT_TR_NOOP("no feasible descent step");
             if(recordIneffectiveStep()) {
                 if(promoteSampling(false)) continue;
                 stopReason = LM_LOCAL_OPTIMUM;
@@ -2544,6 +2570,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             // 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;
+            consecutivePoorPredictions = 0;
             trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
             damping = qMin(1.0e8, damping * 4.0);
             writeTraceRow(m_currentIteration,
@@ -2556,8 +2583,9 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
                           "solver_invalid_" + selectionName,
                           nullptr);
             restoreEvaluationState(current);
-            if(consecutiveRejectedSteps >= 2) {
+            if(consecutiveSolverFailures >= 2) {
                 rebuildRequested = true;
+                rebuildReason = QT_TR_NOOP("2 consecutive solver failures");
             }
             if(consecutiveSolverFailures >= m_maxConsecutiveFailures) {
                 stopReason = LM_CONSECUTIVE_FAILURES;
@@ -2582,9 +2610,20 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             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 * (trustRegionSquaredNorm(objectiveResidual) -
+        const double objectiveEnergy = 0.5 * trustRegionSquaredNorm(objectiveResidual);
+        double actualReduction = objectiveEnergy - 0.5 * (
             trustRegionSquaredNorm(shapeStage ? candidate.breakdown.shapeResiduals : candidate.breakdown.residualVector));
         double reductionRatio = actualReduction / predictedReduction;
+        // 使用同一采样层、同一阶段目标比较预测和实际改善。接近收敛时的微小
+        // 预测量交给停滞逻辑处理，避免比例数值波动反复触发昂贵的全参数重建。
+        const double predictionFloor = qMax(1.0e-14, objectiveEnergy * 1.0e-8);
+        const bool poorPrediction = predictedReduction > predictionFloor &&
+            (!isFiniteNumber(reductionRatio) || reductionRatio < 0.25);
+        consecutivePoorPredictions = poorPrediction ? consecutivePoorPredictions + 1 : 0;
+        if(consecutivePoorPredictions >= 2) {
+            rebuildRequested = true;
+            rebuildReason = QT_TR_NOOP("2 consecutive steps with actual improvement below 25% of prediction");
+        }
         bool accepted = acceptable(candidate, current);
         // 粗层认为下降而完整数据不认可时累计，连续两次就提前加密。
         if(!shapeStage && m_layeredSampling && m_samplingStride > 1 && !accepted && actualReduction > 0.0) {
@@ -2599,7 +2638,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             // 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。
             acceptPoint(candidate);
             ++acceptedSinceRebuild;
-            movementSinceRebuild += stepNorm;
             consecutiveRejectedSteps = 0;
 
             if(reductionRatio > 0.75) {
@@ -2616,21 +2654,19 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
                     minimumTrustRadius, trustRadius * 0.75);
             }
 
-            if(acceptedSinceRebuild >= 6 ||
-                    movementSinceRebuild >= 0.30) {
+            if(acceptedSinceRebuild >= 10 && !rebuildRequested) {
                 rebuildRequested = true;
+                rebuildReason = QT_TR_NOOP("10 accepted steps since last rebuild");
             }
             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。
+            // 到 current。形状上限或全目标点验收也可能拒绝候选，不能仅凭拒绝
+            // 次数认定模型失准；重建由上面的预测质量判断，约束冲突先缩步。
             ++consecutiveRejectedSteps;
             damping = qMin(1.0e8, damping * 4.0);
             trustRadius = qMax(minimumTrustRadius, trustRadius * 0.5);
             restoreEvaluationState(current);
-            if(consecutiveRejectedSteps >= 2) {
-                rebuildRequested = true;
-            }
         }
 
         // 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
@@ -2687,13 +2723,14 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             break;
         }
         if(selectedWindow < 0 && trustRadius <= minimumTrustRadius * 1.01 &&
-                consecutiveRejectedSteps >= 2) {
+                consecutiveRejectedSteps >= 2 && consecutivePoorPredictions >= 2) {
             if(modelRebuiltAtMinimumRadius) {
                 if(promoteSampling(false)) continue;
                 stopReason = LM_LOCAL_OPTIMUM;
                 break;
             }
             rebuildRequested = true;
+            rebuildReason = QT_TR_NOOP("inaccurate model at minimum trust radius");
         }
     }