refactor(nmNum): 移除分层采样并按对数时间跨度确定采样点数

- 删除 LM 分层采样选项、逐层加密逻辑及专用诊断字段
- 每个对数时间数量级取 20 个间隔，采样点包含区间两端
- 整体误差、形状误差及前期指标共用采样网格
- 形状斜率跨度保持约为对数时间范围的 10%，同步适配残差维度和窗口权重
- 更新拟合记录中的采样策略描述，翻译资源留待统一调整

Bin/Config/Lang/cn/nmNum_cn.ts

@@ -1217,16 +1217,12 @@ Reason: %1</source>
         <translation>没有其他勾选参数需要调整形状</translation>
     </message>
     <message>
-        <source>Early adjustment: adjust wellbore storage, then skin, to match the early pressure-derivative slope difference of the target.</source>
-        <translation>前期预调整：先调整井储，再调整表皮，使模拟压力与导数的前期斜率差接近目标曲线。</translation>
+        <source>Early adjustment: select storage or skin by predicted first-window shape reduction; halve the step after rejection and switch after 3 consecutive rejections.</source>
+        <translation>前期预调整：按第一时间窗口形状误差的预计下降量选择井储或表皮；拒绝后步长减半，连续拒绝三次换参数。</translation>
     </message>
     <message>
-        <source>Early adjustment: adjust skin to match the early pressure-derivative slope difference of the target.</source>
-        <translation>前期预调整：调整表皮，使模拟压力与导数的前期斜率差接近目标曲线。</translation>
-    </message>
-    <message>
-        <source>Wellbore storage adjustment ended: %1; now adjusting skin.</source>
-        <translation>井储调整结束：%1；开始单独调整表皮。</translation>
+        <source>all wellbore parameters visited once</source>
+        <translation>已勾选的井储、表皮参数均已完成一轮调整</translation>
     </message>
     <message>
         <source>Early wellbore adjustment ended: %1</source>

Include/nmNum/nmCalculation/nmCalculationAutoFitLM.h

@@ -12,7 +12,7 @@
 #include "nmCalculation_global.h"
 
 // 固定对数时间窗口的误差诊断。时间边界为不含重叠区的基础边界；
-// rmsError 是局部加权均方根，energy 是对当前层残差能量的贡献。
+// rmsError 是局部加权均方根，energy 是对整体残差能量的贡献。
 struct AutoFitTimeWindowLM {
 	double timeMin;
 	double timeMax;
@@ -33,11 +33,6 @@ struct AutoFitObjectiveBreakdownLM {
 	double pressureLoss;
 	double derivativeLoss;
 	QVector<double> residualVector;
-	// 分层模式的残差属于当前层，total 属于全部目标点；固定模式坐标数组为空。
-	QVector<double> sampleCoordinates;
-	int samplingStride;
-	int fullPointCount;
-	double layerError;
 	QVector<AutoFitTimeWindowLM> timeWindows;
 	double verticalCommonBias;
 	double verticalLoss;
@@ -46,9 +41,9 @@ struct AutoFitObjectiveBreakdownLM {
 	double horizontalLoss;
 	bool horizontalReliable;
 	bool registrationAmbiguous;
-	// 固定 log-time 网格上的双曲线斜率残差，独立于数值残差的采样层级。
+	// 与整体误差共用 log-time 采样网格，斜率跨度约为完整对数时域的 10%。
 	QVector<double> shapeResiduals;
-	// 前期数值残差为 81 点，形状残差为 73 个斜率区间，均含第一窗口权重。
+	// 前期数值和形状残差沿用同一网格与斜率跨度，均含第一窗口权重。
 	// earlyParallelResiduals/Loss 沿用历史字段名，现为压力、导数分别匹配目标的形状误差。
 	// earlyParallelBias 仍记录相对斜率偏差，仅供诊断。
 	QVector<double> earlyValueResiduals;
@@ -68,9 +63,6 @@ struct AutoFitObjectiveBreakdownLM {
 		, total(1.0e10)
 		, pressureLoss(std::numeric_limits<double>::quiet_NaN())
 		, derivativeLoss(std::numeric_limits<double>::quiet_NaN())
-		, samplingStride(1)
-		, fullPointCount(80)
-		, layerError(1.0e10)
 		, verticalCommonBias(std::numeric_limits<double>::quiet_NaN())
 		, verticalLoss(std::numeric_limits<double>::quiet_NaN())
 		, verticalReliable(false)
@@ -122,7 +114,6 @@ public:
 	int getTotalEvaluations() const;
 	void resetOptimizer();
 	void setTargetWellName(const QString& wellName);
-	void setLayeredSamplingEnabled(bool enabled);
 
 signals:
 	void progressUpdated(int iteration, double bestFitness);
@@ -217,9 +208,6 @@ private:
 	double m_comparisonTimeMin;
 	double m_comparisonTimeMax;
 	QString m_targetWellName;
-	// 此开关由拟合界面传入；每轮重置层级，固定模式仍使用原来的 80 点目标。
-	bool m_layeredSampling;
-	int m_samplingStride;
 
 	int m_maxIterations;
 	double m_targetError;

Include/nmNum/nmSubWxs/nmWxAutomaticFitting.h

@@ -82,8 +82,6 @@ private:
 	QLineEdit* m_errorLimitEdit;
 	QComboBox* m_targetWellCombo;
 	QComboBox* m_algorithmCombo;
-	QLabel* m_samplingLabel;
-	QComboBox* m_samplingCombo;
 	QLabel* m_surrogateLabel;
 	QComboBox* m_surrogateCombo;
 

Src/nmNum/nmSubWxs/nmWxAutomaticFitting.cpp

@@ -729,13 +729,6 @@ void nmWxAutomaticFitting::setupControlPanel()
 	m_surrogateCombo->setCurrentIndex(automaticFittingData.getSurrogateScreeningEnabled() ? 1 : 0);
 	m_surrogateCombo->setMaximumWidth(160);
 	m_surrogateCombo->setMinimumWidth(160);
-	// 分层采样用于 LM 对比试验，每次打开默认使用原来的固定 80 点模式。
-	m_samplingLabel = new QLabel(tr("LM sampling:"));
-	m_samplingCombo = new QComboBox();
-	m_samplingCombo->addItem(tr("Fixed sampling (80 points)"));
-	m_samplingCombo->addItem(tr("Layered target sampling"));
-	m_samplingCombo->setMinimumWidth(180);
-	m_samplingCombo->setToolTip(tr("Layered sampling uses target-point subsets for directions and all valid target points for acceptance."));
 	connect(m_algorithmCombo, SIGNAL(currentIndexChanged(int)), this, SLOT(onAlgorithmChanged(int)));
 	onAlgorithmChanged(m_algorithmCombo->currentIndex());
 
@@ -793,10 +786,6 @@ void nmWxAutomaticFitting::setupControlPanel()
 	m_controlLayout->addWidget(m_surrogateCombo);
 	m_controlLayout->addSpacing(15);
 
-	m_controlLayout->addWidget(m_samplingLabel);
-	m_controlLayout->addWidget(m_samplingCombo);
-	m_controlLayout->addSpacing(15);
-
 	m_controlLayout->addWidget(iterationLabel);
 	m_controlLayout->addWidget(m_iterationEdit);
 	m_controlLayout->addSpacing(15);
@@ -818,8 +807,6 @@ void nmWxAutomaticFitting::onAlgorithmChanged(int index)
 	const bool usePSO = (index == 0);
 	m_surrogateLabel->setEnabled(usePSO);
 	m_surrogateCombo->setEnabled(usePSO);
-	m_samplingLabel->setEnabled(!usePSO);
-	m_samplingCombo->setEnabled(!usePSO);
 }
 
 void nmWxAutomaticFitting::setupButtons()
@@ -1207,7 +1194,6 @@ void nmWxAutomaticFitting::startAutoFitting(const QVector<QVector<double>>& targ
 		m_autoFitterLM = new nmCalculationAutoFitLM(this);
 		m_autoFitterLM->setTargetLogLogData(targetData);
 		m_autoFitterLM->setTargetWellName(targetWellName);
-		m_autoFitterLM->setLayeredSamplingEnabled(m_samplingCombo->currentIndex() == 1);
 	} else {
 		DEBUG_UI("Creating PSO auto fitter");
 		m_autoFitterPSO = new nmCalculationAutoFitPSO(this);