From 83a29152c9fe86615a0e95c4b104b1e015fa6331 Mon Sep 17 00:00:00 2001
From: lvjunjie <lvjunjie@abc.com>
Date: Fri, 14 Aug 2026 15:40:16 +0800
Subject: [PATCH] =?UTF-8?q?=E8=B0=83=E6=95=B4=E5=B1=80=E9=83=A8=E6=94=B6?=
 =?UTF-8?q?=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
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diff --git a/Bin/Config/Lang/cn/nmNum_cn.ts b/Bin/Config/Lang/cn/nmNum_cn.ts
index 1bb1e95..75fd2ee 100644
--- a/Bin/Config/Lang/cn/nmNum_cn.ts
+++ b/Bin/Config/Lang/cn/nmNum_cn.ts
@@ -49,6 +49,18 @@ Reason: %1</source>
 </context>
 <context>
     <name>nmCalculationAutoFitPSO</name>
+    <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>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>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>=== User Stop Request Received ===</source>
         <translation>=== 用户停止请求已接收 ===</translation>
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;