From 50f149970c31d7986fe3e3088402ddb00b900ac8 Mon Sep 17 00:00:00 2001
From: lvjunjie <lvjunjie@abc.com>
Date: Mon, 21 Sep 2026 15:54:49 +0800
Subject: [PATCH] =?UTF-8?q?refactor(nmNum):=20=E7=AE=80=E5=8C=96=20LM=20?=
 =?UTF-8?q?=E5=BD=A2=E7=8A=B6=E9=98=B6=E6=AE=B5=E8=81=94=E5=90=88=E8=B0=83?=
 =?UTF-8?q?=E6=95=B4=E4=B8=8E=E5=81=9C=E6=BB=9E=E7=A1=AE=E8=AE=A4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- 移除时间窗口选参、三参数上限及子集枚举，每步联合调整全部有效非井储表皮参数
- 连续两次改善不足后刷新灵敏度，仅做一次全局联合确认，累计有效改善门槛设为 10%
- 放宽形状扩步条件，取消实际与预测改善比值限制，保留预测下降、步长和边界约束
- 确认步求解失败时有限重试，避免将失败误判为收敛
- 同步拟合轨迹元数据、尝试次数日志及中文翻译
---
 Bin/Config/Lang/cn/nmNum_cn.qm                | Bin 147204 -> 147250 bytes
 Bin/Config/Lang/cn/nmNum_cn.ts                |  20 +-
 .../nmCalculation/nmCalculationAutoFitLM.cpp  | 303 +++++-------------
 3 files changed, 83 insertions(+), 240 deletions(-)

diff --git a/Bin/Config/Lang/cn/nmNum_cn.qm b/Bin/Config/Lang/cn/nmNum_cn.qm
index 5fba495298f5119796643e552bffa0b41ea529e3..776d7652a03136f518578c753c99f83aeb04808f 100644
GIT binary patch
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diff --git a/Bin/Config/Lang/cn/nmNum_cn.ts b/Bin/Config/Lang/cn/nmNum_cn.ts
index 104dcbea..6515933d 100644
--- a/Bin/Config/Lang/cn/nmNum_cn.ts
+++ b/Bin/Config/Lang/cn/nmNum_cn.ts
@@ -1281,20 +1281,20 @@ Reason: %1</source>
         <translation>进入联合形状调整时重建灵敏度</translation>
     </message>
     <message>
-        <source>Adaptive fitting counts: %1 completed shape rounds, %2 total-stage iterations, %3 total evaluations.</source>
-        <translation>自适应拟合统计：已完成 %1 轮形状搜索，整体阶段迭代 %2 次，累计评估 %3 次。</translation>
+        <source>Adaptive fitting counts: %1 shape attempts, %2 total-stage iterations, %3 total evaluations.</source>
+        <translation>自适应拟合统计：形状尝试 %1 次，整体阶段迭代 %2 次，累计评估 %3 次。</translation>
     </message>
     <message>
-        <source>LM stage 2: adaptive shape search; confirm stagnation after 2 complete rounds without significant improvement.</source>
-        <translation>LM 阶段2：自适应形状搜索；连续两轮完整搜索无显著改善后确认停滞。</translation>
+        <source>LM stage 2: joint shape search; after 2 ineffective steps, refresh sensitivity and confirm with one global step.</source>
+        <translation>LM 第二阶段：联合调整全部可调形状参数；连续两次改善不足后，刷新灵敏度并用一次全局联合调整确认。</translation>
     </message>
     <message>
         <source>LM starting point error: %1; independent total-stage evaluation budget: %2</source>
         <translation>LM 起点误差：%1；整体阶段独立评估预算：%2</translation>
     </message>
     <message>
-        <source>Shape search round %1: shape error=%2, improvement=%3, required=%4.</source>
-        <translation>形状搜索第 %1 轮：形状误差=%2，改善量=%3，所需改善量=%4。</translation>
+        <source>Shape search step %1: shape error=%2, accumulated improvement=%3, required=%4.</source>
+        <translation>形状尝试 %1：形状误差=%2，累计改善=%3，有效改善门槛=%4。</translation>
     </message>
     <message>
         <source>Total-stage budget starts now: %1 iterations, %2 evaluations; pre-adjustment is counted separately.</source>
@@ -1305,16 +1305,16 @@ Reason: %1</source>
         <translation>无法建立有效的形状灵敏度模型。</translation>
     </message>
     <message>
-        <source>confirm stagnation after 2 complete shape rounds</source>
-        <translation>完成两轮形状搜索后确认停滞</translation>
+        <source>confirm shape stagnation after 2 ineffective steps</source>
+        <translation>连续两次形状改善不足，刷新灵敏度确认停滞</translation>
     </message>
     <message>
         <source>full sensitivity at total-stage entry</source>
         <translation>进入整体阶段时建立完整灵敏度</translation>
     </message>
     <message>
-        <source>no significant improvement in a complete round after fresh sensitivity confirmation</source>
-        <translation>重建灵敏度确认后，完整一轮搜索仍无显著改善</translation>
+        <source>no significant shape improvement after fresh sensitivity and one global step</source>
+        <translation>刷新灵敏度并进行一次全局联合调整后，形状仍无显著改善</translation>
     </message>
     <message>
         <source>no valid early sensitivity model</source>
diff --git a/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp b/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp
index 402e5b38..78ab27e9 100644
--- a/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp
+++ b/Src/nmNum/nmCalculation/nmCalculationAutoFitLM.cpp
@@ -594,95 +594,6 @@ static QVector<TrustRegionFisher> buildTrustRegionFisher(
     return information;
 }
 
-static int nextTrustRegionWindow(
-        const QVector<AutoFitTimeWindowLM>& windows,
-        const QVector<bool>& attempted)
-{
-    // 每次都按当前误差能量重新排序；本轮无效的窗口暂时跳过，-1 表示全局回退。
-    int selected = -1;
-    double largestEnergy = 0.0;
-    for(int k = 0; k < windows.size(); ++k) {
-        if(!attempted[k] && isFiniteNumber(windows[k].energy) &&
-                windows[k].energy > largestEnergy) {
-            largestEnergy = windows[k].energy;
-            selected = k;
-        }
-    }
-    return selected;
-}
-
-static QVector<int> selectTrustRegionFisherColumns(
-        const TrustRegionFisher& local,
-        const TrustRegionFisher& global,
-        const QVector<bool>& columnValid,
-        const QVector<double>& coordinates,
-        double minimumStep)
-{
-    // g^2/F 衡量修正当前残差的潜力，避免仅按影响强度选中无助于降误差的参数。
-    const int dimensions = columnValid.size();
-    double maximumDiagonal = 0.0;
-    for(int p = 0; p < dimensions; ++p) {
-        if(columnValid[p] && isFiniteNumber(local.matrix[p][p])) {
-            maximumDiagonal = qMax(maximumDiagonal, local.matrix[p][p]);
-        }
-    }
-    const double diagonalFloor = qMax(1.0e-30, maximumDiagonal * 1.0e-12);
-    QVector<double> scores(dimensions, 0.0);
-    double maximumScore = 0.0;
-    for(int p = 0; p < dimensions; ++p) {
-        if(!columnValid[p] || local.matrix[p][p] <= diagonalFloor ||
-                !isFiniteNumber(local.matrix[p][p]) ||
-                !isFiniteNumber(local.gradient[p]) ||
-                !isFiniteNumber(global.gradient[p])) {
-            continue;
-        }
-        // 全局下降方向指向边界外时暂不选该参数，避免它挤占可移动参数的位置。
-        if((coordinates[p] <= minimumStep && global.gradient[p] > 0.0) ||
-                (coordinates[p] >= 1.0 - minimumStep && global.gradient[p] < 0.0)) {
-            continue;
-        }
-        double scaledGradient = local.gradient[p] /
-            qSqrt(local.matrix[p][p] + diagonalFloor);
-        double score = scaledGradient * scaledGradient;
-        if(isFiniteNumber(score)) {
-            scores[p] = score;
-            maximumScore = qMax(maximumScore, score);
-        }
-    }
-
-    QVector<int> selected;
-    for(int selection = 0; selection < qMin(3, dimensions); ++selection) {
-        int best = -1;
-        double bestScore = maximumScore * 1.0e-12;
-        for(int p = 0; p < dimensions; ++p) {
-            if(selected.contains(p) || scores[p] <= bestScore) {
-                continue;
-            }
-            // 沿用现有全局 0.995 共线限制，不因单个窗口内响应相似而冻结参数。
-            bool correlated = false;
-            for(int i = 0; i < selected.size(); ++i) {
-                int q = selected[i];
-                double denominator = qSqrt(global.matrix[p][p]) *
-                    qSqrt(global.matrix[q][q]);
-                if(denominator > 0.0 &&
-                        qAbs(global.matrix[p][q]) / denominator > 0.995) {
-                    correlated = true;
-                    break;
-                }
-            }
-            if(!correlated) {
-                best = p;
-                bestScore = scores[p];
-            }
-        }
-        if(best < 0) {
-            break;
-        }
-        selected.append(best);
-    }
-    return selected;
-}
-
 static bool buildTrustRegionFisherStep(
         const TrustRegionFisher& global,
         const QVector<int>& selected,
@@ -1212,7 +1123,7 @@ void nmCalculationAutoFitLM::writeTraceMetaFile()
 
     QTextStream out(&metaFile);
     out << "{\n";
-    out << "  \"schema_version\": 31,\n";
+    out << "  \"schema_version\": 36,\n";
     out << "  \"strategy\": \"permeability_height_then_shape_then_joint_lm\",\n";
     out << "  \"height_acceptance\": \"reliable_vertical_loss_decrease; no_shape_or_total_loss_constraint\",\n";
     out << "  \"shape_priority\": \"storage_then_skin_then_shape_without_wellbore_then_optional_wellbore_recheck\",\n";
@@ -1228,10 +1139,10 @@ void nmCalculationAutoFitLM::writeTraceMetaFile()
     out << "  \"shape_stage_total_constraint\": false,\n";
     out << "  \"shape_wellbore_parameters_frozen\": true,\n";
     out << "  \"shape_sensitivity_refresh\": \"non_wellbore_columns_only at joint shape entry and stagnation confirmation; full Jacobian rebuilt at total-stage entry\",\n";
-    out << "  \"shape_expansion_policy\": \"one expansion per reliable shape direction, up to 2x when actual rho exceeds 0.75; retain ordinary step on failure\",\n";
-    out << "  \"shape_global_parameter_selection\": \"all_valid_non_wellbore_columns_with_positive_response\",\n";
-    out << "  \"stage2_budget\": \"adaptive, no fixed iteration or evaluation quota; full window/global rounds, 2 ineffective rounds then fresh-J confirmation\",\n";
-    out << "  \"shape_round_improvement_threshold\": \"max(0.0001, 1% of round entry shape loss)\",\n";
+    out << "  \"shape_expansion_policy\": \"one expansion per accepted shape direction, up to 2x with predicted descent and parameter bounds; no_rho_gate; retain ordinary step on failure\",\n";
+    out << "  \"shape_global_parameter_selection\": \"all_valid_non_wellbore_columns_with_positive_response_at_every_shape_step; no_window_selection\",\n";
+    out << "  \"stage2_budget\": \"adaptive, no fixed iteration or evaluation quota; 2 ineffective shape attempts then one fresh-J global confirmation\",\n";
+    out << "  \"shape_step_improvement_threshold\": \"max(0.0001, 10% of last effective shape loss); accumulate small improvements\",\n";
     out << "  \"iteration_count_scope\": \"max_iterations applies to total-stage LM; trace iteration remains cumulative\",\n";
     out << "  \"wellbore_recheck_trigger\": \"early loss exceeds initial early-stage exit by max(0.0001, 10%)\",\n";
     out << "  \"wellbore_recheck_acceptance\": \"early decrease with fixed entry shape limit max(0.0001, 5%); no_total_loss_constraint\",\n";
@@ -2033,7 +1944,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
     const char* rebuildReason = QT_TR_NOOP("initial sensitivity model");
     bool modelRebuiltAtMinimumRadius = false;
     bool stagnationConfirmationRequested = false;
-    QVector<bool> attemptedWindows(kAutoFitTimeWindowCount, false);
     bool globalFallbackAttempted = false;
     StopReasonLM stopReason = LM_MAX_ITERATIONS;
 
@@ -2210,13 +2120,13 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
 
     if(m_shouldStop) return LM_USER_STOPPED;
 
-    // 第二阶段不按迭代比例截断；普通形状按完整搜索轮次判断是否仍有显著改善。
+    // 第二阶段不按迭代比例截断；连续两次改善不足后，刷新灵敏度并仅做一次全局联合确认。
     // 普通形状调整冻结井储、表皮，只按形状验收；总误差达标只在第三阶段判断。
     bool shapeStage = true;
     bool jointShapeStarted = false;
-    double shapeRoundBaseline = current.breakdown.shapeLoss;
-    int shapeRoundNumber = 0;
-    int ineffectiveShapeRounds = 0;
+    double shapeImprovementBaseline = current.breakdown.shapeLoss;
+    int shapeStepCount = 0;
+    int ineffectiveShapeSteps = 0;
     bool shapeConfirmationRequested = false;
     const int storageColumn = m_enabledParamIndices.indexOf(2);
     const int skinColumn = m_enabledParamIndices.indexOf(1);
@@ -2283,7 +2193,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         restoreEvaluationState(current);
     };
     emit logMessageGenerated(shapeStage
-        ? tr("LM stage 2: adaptive shape search; confirm stagnation after 2 complete rounds without significant improvement.")
+        ? tr("LM stage 2: joint shape search; after 2 ineffective steps, refresh sensitivity and confirm with one global step.")
         : tr("LM stage 3: original LM fitting; accept by total error only."));
     if(earlyShapeStage) {
         emit logMessageGenerated(earlyShapeParameter == 2
@@ -2328,7 +2238,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         consecutivePoorPredictions = 0;
         modelRebuiltAtMinimumRadius = false;
         stagnationConfirmationRequested = false;
-        attemptedWindows.fill(false);
         globalFallbackAttempted = false;
         fullDataRejections = 0;
         effectiveImprovementBaseline = stageError(current);
@@ -2357,7 +2266,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         consecutiveRejectedSteps = 0;
         consecutiveSolverFailures = 0;
         stagnationConfirmationRequested = false;
-        attemptedWindows.fill(false);
         globalFallbackAttempted = false;
         trustRadius = 0.12;
         damping = 0.01;
@@ -2454,35 +2362,36 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
                       true, 0, "early_wellbore_to_shape", &current.breakdown);
     };
 
-    auto completeShapeSearchRound = [&]() {
-        if(!shapeStage || earlyShapeStage || !globalFallbackAttempted) return;
-        if(m_shouldStop) return;
-        const double required = qMax(1.0e-4, 0.01 * shapeRoundBaseline);
-        const double improvement = shapeRoundBaseline - current.breakdown.shapeLoss;
+    auto completeShapeSearchStep = [&]() {
+        if(!shapeStage || earlyShapeStage || m_shouldStop) return;
+        // 改善相对上一次有效改善累计，避免单次小步被立即否定；不再等待完整窗口轮次。
+        const double required = qMax(1.0e-4, 0.10 * shapeImprovementBaseline);
+        const double improvement = shapeImprovementBaseline - current.breakdown.shapeLoss;
         const bool improved = improvement >= required;
-        ++shapeRoundNumber;
+        ++shapeStepCount;
+        writeTraceRow(m_currentIteration, -1, "shape_step_end", current.parameters, current.fitness,
+            true, 0, QString(improved ? "improved_step_%1" : "ineffective_step_%1").arg(shapeStepCount), &current.breakdown);
+        emit logMessageGenerated(tr("Shape search step %1: shape error=%2, accumulated improvement=%3, required=%4.")
+            .arg(shapeStepCount).arg(current.breakdown.shapeLoss, 0, 'g', 6)
+            .arg(improvement, 0, 'g', 6).arg(required, 0, 'g', 6));
+        // 每次形状调整都是全局联合步，结束后清除本次尝试标记。
+        globalFallbackAttempted = false;
         if(improved) {
-            ineffectiveShapeRounds = 0;
+            shapeImprovementBaseline = current.breakdown.shapeLoss;
+            ineffectiveShapeSteps = 0;
             shapeConfirmationRequested = false;
-        } else ++ineffectiveShapeRounds;
-        writeTraceRow(m_currentIteration, -1, "shape_round_end", current.parameters, current.fitness,
-            true, 0, QString(improved ? "improved_round_%1" : "ineffective_round_%1").arg(shapeRoundNumber), &current.breakdown);
-        emit logMessageGenerated(tr("Shape search round %1: shape error=%2, improvement=%3, required=%4.")
-            .arg(shapeRoundNumber).arg(current.breakdown.shapeLoss, 0, 'g', 6)
-            .arg(improvement, 0, 'g', 6).arg(required, 0, 'g', 6));
-        if(!improved && shapeConfirmationRequested) {
-            finishShapeStage(tr("no significant improvement in a complete round after fresh sensitivity confirmation"));
             return;
         }
-        // 下一轮重新覆盖窗口和全部有效参数的全局步；单步小改善不重置本轮进度。
-        shapeRoundBaseline = current.breakdown.shapeLoss;
-        attemptedWindows.fill(false);
-        globalFallbackAttempted = false;
-        if(!improved && ineffectiveShapeRounds >= 2) {
+        ++ineffectiveShapeSteps;
+        if(shapeConfirmationRequested) {
+            finishShapeStage(tr("no significant shape improvement after fresh sensitivity and one global step"));
+            return;
+        }
+        if(ineffectiveShapeSteps >= 2) {
             jacobian.clear();
             reuseSensitivityForNextStage();
             rebuildRequested = true;
-            rebuildReason = QT_TR_NOOP("confirm stagnation after 2 complete shape rounds");
+            rebuildReason = QT_TR_NOOP("confirm shape stagnation after 2 ineffective steps");
             shapeConfirmationRequested = true;
         }
     };
@@ -2498,8 +2407,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             return false;
         }
 
-        // 有效改善后重新开放全部窗口，并按新工作点的能量排序。
-        attemptedWindows.fill(false);
+        // 有效改善后更新累计基准，并清除停滞计数。
         globalFallbackAttempted = false;
         effectiveImprovementBaseline = fitness;
         consecutiveIneffectiveSteps = 0;
@@ -2507,11 +2415,11 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         return true;
     };
 
-    // 无效尝试仍累计，但所有窗口及一次全局回退尝试完毕前，不触发停滞收敛。
-    // 完整一轮仍无改善时沿用原有重建确认，避免某个难处理窗口提前终止拟合。
+    // 普通形状按连续尝试判断停滞，并用新灵敏度下的全局联合步确认；
+    // 前期井储、表皮与整体 LM 沿用各自的无效次数处理。
     auto recordIneffectiveStep = [&]() -> bool {
         if(shapeStage && !earlyShapeStage) {
-            completeShapeSearchRound();
+            completeShapeSearchStep();
             return false;
         }
         ++consecutiveIneffectiveSteps;
@@ -2524,7 +2432,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         if(!globalFallbackAttempted) {
             return false;
         }
-        attemptedWindows.fill(false);
         globalFallbackAttempted = false;
         if(consecutiveIneffectiveSteps < maximumIneffectiveSteps) {
             return false;
@@ -2771,7 +2678,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             enterTotalStage(tr("early shape error restored within tolerance"));
         if(shapeStage && !earlyShapeStage && !jointShapeStarted) {
             jointShapeStarted = true;
-            shapeRoundBaseline = current.breakdown.shapeLoss;
+            shapeImprovementBaseline = current.breakdown.shapeLoss;
             // 初调已改变井储/表皮，普通形状必须在新的工作点重测其余可调参数的响应。
             jacobian.clear();
             reuseSensitivityForNextStage();
@@ -2840,7 +2747,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
 
         // 第三阶段独立计数；方向不可行也消耗一次局部尝试，不能无限缩步循环。
         if(!shapeStage) ++totalIterations;
-        // 每轮从最新 J 和当前残差重算窗口 Fisher，包含有限差分与割线更新的变化。
+        // 每次从最新 J 和当前阶段残差重算 Fisher，联合步使用全局信息。
         const QVector<double> objectiveResidual = earlyShapeStage
             ? current.breakdown.earlyParallelResiduals
             : (shapeStage ? current.breakdown.shapeResiduals : current.breakdown.residualVector);
@@ -2854,10 +2761,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             objectiveCoordinates.clear();
             for(int i = 0; i < 73; ++i) objectiveCoordinates.append((i + 4.0) / 80.0);
         }
-        const QVector<AutoFitTimeWindowLM> objectiveWindows = shapeStage
-            ? calculateAutoFitTimeWindows(objectiveResidual, m_comparisonTimeMin, m_comparisonTimeMax,
-                                          objectiveCoordinates, autoFitLogTimeWeights(objectiveCoordinates))
-            : current.breakdown.timeWindows;
         const QVector<TrustRegionFisher> information = buildTrustRegionFisher(
             objectiveJacobian, objectiveResidual, jacobianColumnValid, objectiveCoordinates);
         const TrustRegionFisher& global = information[kAutoFitTimeWindowCount];
@@ -2870,18 +2773,35 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         QVector<int> selectedColumns;
         QVector<double> coordinateStep;
         double predictedReduction = 0.0;
-        int selectedWindow = -1;
-
-        if(!shapeStage) {
-            // 整体阶段联合求解全部有效自由列，不按窗口得分、三参数上限或共线阈值删列。
-            // 边界列也参与耦合求解，是否向内移动由联合解及其边界投影决定。
-            for(int column = 0; column < dimensions; ++column) {
-                // 仅跳过完全没有总残差响应的列，保留弱敏感列及相互相关的列。
-                if(stageColumnValid[column] && global.matrix[column][column] > 0.0)
-                    selectedColumns.append(column);
+        const int selectedWindow = earlyShapeStage ? 0 : -1;
+
+        // 前期只开放当前井储或表皮；普通形状和整体 LM 都联合求解全部有效自由列。
+        // 保留弱敏感、相关及边界列，让参数相互配合；窗口只保留作误差诊断。
+        for(int column = 0; column < dimensions; ++column) {
+            if(stageColumnValid[column] && (earlyShapeStage || global.matrix[column][column] > 0.0))
+                selectedColumns.append(column);
+        }
+        if(earlyShapeStage) {
+            double earlyStepRadius = trustRadius;
+            if(!earlyShapeHasImproved && selectedColumns.size() == 1) {
+                // 首次调整沿用小步起调，形状变差时由后面的同向扩步循环处理。
+                const int column = selectedColumns[0];
+                const int parameterIndex = m_enabledParamIndices[column];
+                const double lower = m_parameterLower[parameterIndex];
+                const double upper = m_parameterUpper[parameterIndex];
+                const double smallStep = useTrustRegionLogScale(parameterIndex, lower, upper)
+                    ? qLn(1.05) / (qLn(upper) - qLn(lower))
+                    : (parameterIndex == 1 ? 0.02 * qMax(0.1, qAbs(current.parameters[column]))
+                                          : 0.05 * qMax(1.0e-8, qAbs(current.parameters[column]))) / (upper - lower);
+                earlyStepRadius = qMin(trustRadius, smallStep);
+            }
+            if(selectedColumns.isEmpty() || !buildEarlyGapGuidedStep(
+                    stepInformation, selectedColumns, current.coordinates, earlyGapDirection,
+                    damping, earlyStepRadius, minimumCoordinateStep, &coordinateStep, &predictedReduction)) {
+                selectedColumns.clear();
             }
-            // 当前候选已使用全局信息，停滞判断无需再逐一尝试窗口子集。
-            attemptedWindows.fill(true);
+        } else {
+            // 普通调整和停滞确认共用全局联合方向，不再遍历窗口或枚举三参数子集。
             globalFallbackAttempted = true;
             if(selectedColumns.isEmpty() || !buildTrustRegionFisherStep(
                     global, selectedColumns, current.coordinates, damping,
@@ -2889,81 +2809,6 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
                 selectedColumns.clear();
             }
         }
-        // 形状阶段先覆盖局部窗口，再用全部有效自由列求一次全局联合步。
-        while(shapeStage && selectedColumns.isEmpty()) {
-            selectedWindow = earlyShapeStage ? 0 : nextTrustRegionWindow(
-                objectiveWindows, attemptedWindows);
-            if(selectedWindow >= 0) {
-                attemptedWindows[selectedWindow] = true;
-            } else {
-                globalFallbackAttempted = true;
-            }
-            const TrustRegionFisher& local = selectedWindow >= 0
-                ? information[selectedWindow] : global;
-            QVector<int> proposed;
-            if(earlyShapeStage) {
-                // 前期只开放当前的井储或表皮参数。
-                for(int column = 0; column < dimensions; ++column) {
-                    if(stageColumnValid[column]) proposed.append(column);
-                }
-            } else if(selectedWindow < 0) {
-                // 全局步保留弱敏感及相关列，避免参数长期被窗口前三名或共线筛选排除。
-                for(int column = 0; column < dimensions; ++column) {
-                    if(stageColumnValid[column] && global.matrix[column][column] > 0.0)
-                        proposed.append(column);
-                }
-            } else {
-                proposed = selectTrustRegionFisherColumns(
-                    local, global, stageColumnValid,
-                    current.coordinates, minimumCoordinateStep);
-            }
-            // 局部窗口至多三列，比较非空子集；全局只求完整组合，不枚举全部参数子集。
-            const bool fullJointStep = !earlyShapeStage && selectedWindow < 0;
-            const int combinationCount = proposed.isEmpty() ? 0 : (fullJointStep ? 1 : (1 << proposed.size()) - 1);
-            for(int combination = 1; combination <= combinationCount; ++combination) {
-                QVector<int> columns;
-                if(fullJointStep) columns = proposed;
-                else {
-                    for(int i = 0; i < proposed.size(); ++i) {
-                        if(combination & (1 << i)) columns.append(proposed[i]);
-                    }
-                }
-                QVector<double> step;
-                double reduction = 0.0;
-                double earlyStepRadius = trustRadius;
-                if(earlyShapeStage && !earlyShapeHasImproved && columns.size() == 1) {
-                    // 确定方向后先小步：井储最多 1.05 倍，表皮为局部尺度的 2%。
-                    // 首次形状变差由后面的扩步循环处理，首次接受后恢复正常 LM 幅度。
-                    const int column = columns[0];
-                    const int parameterIndex = m_enabledParamIndices[column];
-                    const double lower = m_parameterLower[parameterIndex];
-                    const double upper = m_parameterUpper[parameterIndex];
-                    const double smallStep = useTrustRegionLogScale(parameterIndex, lower, upper)
-                        ? qLn(1.05) / (qLn(upper) - qLn(lower))
-                        : (parameterIndex == 1 ? 0.02 * qMax(0.1, qAbs(current.parameters[column]))
-                                              : 0.05 * qMax(1.0e-8, qAbs(current.parameters[column]))) / (upper - lower);
-                    earlyStepRadius = qMin(trustRadius, smallStep);
-                }
-                // 井储、表皮沿间距确定的方向调整；其他阶段保持原来的 LM 求步。
-                const bool stepBuilt = earlyShapeStage
-                    ? buildEarlyGapGuidedStep(stepInformation, columns, current.coordinates, earlyGapDirection,
-                        damping, earlyStepRadius, minimumCoordinateStep, &step, &reduction)
-                    : buildTrustRegionFisherStep(stepInformation, columns, current.coordinates,
-                        damping, trustRadius, minimumCoordinateStep, &step, &reduction);
-                if(!stepBuilt) continue;
-                const double tolerance = 1.0e-12 * qMax(predictedReduction, reduction);
-                if(selectedColumns.isEmpty() || reduction > predictedReduction + tolerance ||
-                        (qAbs(reduction - predictedReduction) <= tolerance &&
-                         columns.size() < selectedColumns.size())) {
-                    selectedColumns = columns;
-                    coordinateStep = step;
-                    predictedReduction = reduction;
-                }
-            }
-            if(earlyShapeStage || selectedWindow < 0) {
-                break;
-            }
-        }
 
         // 当前阶段没有可行下降步时，收缩半径并进入原有重建/收敛处理。
         if(selectedColumns.isEmpty()) {
@@ -2974,7 +2819,7 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
                 continue;
             }
             if(shapeStage) {
-                completeShapeSearchRound();
+                completeShapeSearchStep();
                 --iteration;
                 continue;
             }
@@ -3096,6 +2941,8 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
                 stopReason = LM_CONSECUTIVE_FAILURES;
                 break;
             }
+            // 确认步求解失败不代表形状已收敛，沿用有限重试后按求解失败退出。
+            if(shapeStage && !earlyShapeStage && shapeConfirmationRequested) continue;
             if(recordIneffectiveStep()) {
                 if(promoteSampling(false)) continue;
                 stopReason = LM_LOCAL_OPTIMUM;
@@ -3104,16 +2951,12 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
             continue;
         }
 
-        // 普通形状步真实表现可靠时，沿同一联合方向最多扩步试算一次，不区分参数类型。
+        // 普通形状步真实改善后，沿同一联合方向最多扩步一次，不再用预测兑现比例拦截。
         // 扩步只按整体形状验收，优于原步才替换，否则保留原候选。
         if(shapeStage && !earlyShapeStage && acceptable(candidate, current) && processPauseAndStop()) {
-            const double ordinaryEnergy = 0.5 * trustRegionSquaredNorm(objectiveResidual);
-            const double ordinaryReduction = ordinaryEnergy - 0.5 * trustRegionSquaredNorm(candidate.breakdown.shapeResiduals);
-            const double predictionFloor = qMax(1.0e-14, ordinaryEnergy * 1.0e-8);
             QVector<double> expandedStep;
             double expandedPrediction = 0.0;
-            if(predictedReduction > predictionFloor && ordinaryReduction / predictedReduction > 0.75 &&
-                buildExpandedTrustRegionStep(stepInformation, current.coordinates, coordinateStep,
+            if(buildExpandedTrustRegionStep(stepInformation, current.coordinates, coordinateStep,
                     maximumTrustRadius, &expandedStep, &expandedPrediction)) {
                 writeTraceRow(m_currentIteration, -1, "shape_step_base", candidate.parameters,
                     candidate.fitness, true, candidate.elapsedMs, "eligible_for_expansion", &candidate.breakdown);
@@ -3291,8 +3134,8 @@ StopReasonLM nmCalculationAutoFitLM::runTrustRegionFitting()
         m_currentIteration = completedIterations - 1;
     }
     restoreEvaluationState(current);
-    emit logMessageGenerated(tr("Adaptive fitting counts: %1 completed shape rounds, %2 total-stage iterations, %3 total evaluations.")
-        .arg(shapeRoundNumber).arg(totalIterations).arg(m_totalEvaluations));
+    emit logMessageGenerated(tr("Adaptive fitting counts: %1 shape attempts, %2 total-stage iterations, %3 total evaluations.")
+        .arg(shapeStepCount).arg(totalIterations).arg(m_totalEvaluations));
 
     if(m_shouldStop) {
         return LM_USER_STOPPED;