feat(nmNum): 完善分析成果保存与参数汇总

- 保存恢复计算方法、采样配置、拟合设置和分析结果。
- 保留筛选阈值及直方图设置，汇总拟合参数范围。
- 修正中文参数保存的 UTF-8 编码。

Bin/XmlFiles/ModelParaDefinesNM_cn.xml

@@ -145,6 +145,19 @@
     <ParaItem Name="Sensitivity_h_Higher" Alias="储层厚度 - 上限" Unit="m" Default="10" Max="10000" Min="1e-05" Digit="6" Desc="filtered h higher bound" />
     <ParaItem Name="Sensitivity_phi_Lower" Alias="孔隙度 - 下限" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="filtered phi lower bound" />
     <ParaItem Name="Sensitivity_phi_Higher" Alias="孔隙度 - 上限" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="filtered phi higher bound" />
+    <!-- 第四种：按同一误差阈值筛选各组拟合最优值。 -->
+    <ParaItem Name="Sensitivity_Fit_K_Minimum" Alias="渗透率 - 最小值" Unit="mD" Default="25" Max="10000" Min="0" Digit="6" Desc="筛选后拟合参数渗透率最小值" />
+    <ParaItem Name="Sensitivity_Fit_K_Maximum" Alias="渗透率 - 最大值" Unit="mD" Default="25" Max="10000" Min="0" Digit="6" Desc="筛选后拟合参数渗透率最大值" />
+    <ParaItem Name="Sensitivity_Fit_Skin_Minimum" Alias="表皮系数 - 最小值" Unit="" Default="0" Max="1000" Min="-100" Digit="6" Desc="筛选后拟合参数表皮系数最小值" />
+    <ParaItem Name="Sensitivity_Fit_Skin_Maximum" Alias="表皮系数 - 最大值" Unit="" Default="0" Max="1000" Min="-100" Digit="6" Desc="筛选后拟合参数表皮系数最大值" />
+    <ParaItem Name="Sensitivity_Fit_C_Minimum" Alias="井筒储集系数 - 最小值" Unit="m^3/MPa" Default="0" Max="1000" Min="0" Digit="6" Desc="筛选后拟合参数井筒储集系数最小值" />
+    <ParaItem Name="Sensitivity_Fit_C_Maximum" Alias="井筒储集系数 - 最大值" Unit="m^3/MPa" Default="0" Max="1000" Min="0" Digit="6" Desc="筛选后拟合参数井筒储集系数最大值" />
+    <ParaItem Name="Sensitivity_Fit_phi_Minimum" Alias="孔隙度 - 最小值" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="筛选后拟合参数孔隙度最小值" />
+    <ParaItem Name="Sensitivity_Fit_phi_Maximum" Alias="孔隙度 - 最大值" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="筛选后拟合参数孔隙度最大值" />
+    <ParaItem Name="Sensitivity_Fit_Dfc_Minimum" Alias="裂缝导流能力 - 最小值" Unit="mD.m" Default="1000" Max="1e+30" Min="0" Digit="6" Desc="筛选后拟合参数裂缝导流能力最小值" />
+    <ParaItem Name="Sensitivity_Fit_Dfc_Maximum" Alias="裂缝导流能力 - 最大值" Unit="mD.m" Default="1000" Max="1e+30" Min="0" Digit="6" Desc="筛选后拟合参数裂缝导流能力最大值" />
+    <ParaItem Name="Sensitivity_Fit_FractureHalfLength_Minimum" Alias="裂缝半长 - 最小值" Unit="m" Default="50" Max="10000" Min="1e-05" Digit="6" Desc="筛选后拟合参数裂缝半长最小值" />
+    <ParaItem Name="Sensitivity_Fit_FractureHalfLength_Maximum" Alias="裂缝半长 - 最大值" Unit="m" Default="50" Max="10000" Min="1e-05" Digit="6" Desc="筛选后拟合参数裂缝半长最大值" />
   </ParaGroup>
 
 </Root>

Bin/XmlFiles/ModelParaDefinesNM_en.xml

@@ -146,6 +146,19 @@
     <ParaItem Name="Sensitivity_h_Higher" Alias="h - Higher" Unit="m" Default="10" Max="10000" Min="1e-05" Digit="6" Desc="filtered h higher bound" />
     <ParaItem Name="Sensitivity_phi_Lower" Alias="φ - Lower" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="filtered phi lower bound" />
     <ParaItem Name="Sensitivity_phi_Higher" Alias="φ - Higher" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="filtered phi higher bound" />
+    <!-- 第四种：按同一误差阈值筛选各组拟合最优值。 -->
+    <ParaItem Name="Sensitivity_Fit_K_Minimum" Alias="Permeability - Minimum" Unit="mD" Default="25" Max="10000" Min="0" Digit="6" Desc="筛选后拟合参数渗透率最小值" />
+    <ParaItem Name="Sensitivity_Fit_K_Maximum" Alias="Permeability - Maximum" Unit="mD" Default="25" Max="10000" Min="0" Digit="6" Desc="筛选后拟合参数渗透率最大值" />
+    <ParaItem Name="Sensitivity_Fit_Skin_Minimum" Alias="Skin - Minimum" Unit="" Default="0" Max="1000" Min="-100" Digit="6" Desc="筛选后拟合参数表皮系数最小值" />
+    <ParaItem Name="Sensitivity_Fit_Skin_Maximum" Alias="Skin - Maximum" Unit="" Default="0" Max="1000" Min="-100" Digit="6" Desc="筛选后拟合参数表皮系数最大值" />
+    <ParaItem Name="Sensitivity_Fit_C_Minimum" Alias="Wellbore storage - Minimum" Unit="m^3/MPa" Default="0" Max="1000" Min="0" Digit="6" Desc="筛选后拟合参数井筒储集系数最小值" />
+    <ParaItem Name="Sensitivity_Fit_C_Maximum" Alias="Wellbore storage - Maximum" Unit="m^3/MPa" Default="0" Max="1000" Min="0" Digit="6" Desc="筛选后拟合参数井筒储集系数最大值" />
+    <ParaItem Name="Sensitivity_Fit_phi_Minimum" Alias="Porosity - Minimum" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="筛选后拟合参数孔隙度最小值" />
+    <ParaItem Name="Sensitivity_Fit_phi_Maximum" Alias="Porosity - Maximum" Unit="" Default="0.1" Max="1" Min="1e-05" Digit="6" Desc="筛选后拟合参数孔隙度最大值" />
+    <ParaItem Name="Sensitivity_Fit_Dfc_Minimum" Alias="Fracture conductivity - Minimum" Unit="mD.m" Default="1000" Max="1e+30" Min="0" Digit="6" Desc="筛选后拟合参数裂缝导流能力最小值" />
+    <ParaItem Name="Sensitivity_Fit_Dfc_Maximum" Alias="Fracture conductivity - Maximum" Unit="mD.m" Default="1000" Max="1e+30" Min="0" Digit="6" Desc="筛选后拟合参数裂缝导流能力最大值" />
+    <ParaItem Name="Sensitivity_Fit_FractureHalfLength_Minimum" Alias="Fracture half length - Minimum" Unit="m" Default="50" Max="10000" Min="1e-05" Digit="6" Desc="筛选后拟合参数裂缝半长最小值" />
+    <ParaItem Name="Sensitivity_Fit_FractureHalfLength_Maximum" Alias="Fracture half length - Maximum" Unit="m" Default="50" Max="10000" Min="1e-05" Digit="6" Desc="筛选后拟合参数裂缝半长最大值" />
   </ParaGroup>
 
 </Root>

Include/nmNum/nmData/nmDataSensitive.h

@@ -11,6 +11,7 @@
 #include "nmData_global.h"
 #include "nmDataBase.h"
 #include "nmDataAttribute.h"
+#include "nmDataAutomaticFitting.h"
 #include <rapidjson/document.h>
 
 class NM_DATA_EXPORT nmDataSensitive : public nmDataBase
@@ -51,7 +52,7 @@ public:
 		nmDataAttribute m_maxValue;    // Maximum
 		// 表格中的数值列表
 		QVector<double> m_values;
-        // Monte-Carlo 分布设置与确定性取值独立，仅保存在内存中。
+        // Monte-Carlo 分布设置与确定性取值独立，随分析配置保存。
         enum Distribution { NORMAL, LOGNORMAL, TRUNCATED_NORMAL, TRUNCATED_LOGNORMAL, UNIFORM, TRIANGULAR };
         Distribution m_distribution;
         bool m_distributionInitialized;
@@ -106,7 +107,7 @@ public:
         RunResult() : m_nRunIndex(0), m_dPressureRmse(-1.0), m_bSucceeded(false), m_bCancelled(false), m_bVisible(true) {}
         quint64 m_nRunIndex;
         QVector<double> m_vecParameters;
-        QVector<double> m_vecFittedParameters; // 本组 LM 最优值，与批次拟合参数顺序一致，仅存内存。
+        QVector<double> m_vecFittedParameters; // 本组 LM 最优值，与批次拟合参数顺序一致。
         QVector<QVector<double> > m_vecPressure;
         QVector<QVector<double> > m_vecLogLog;
         QVector<QVector<double> > m_vecSemiLog;
@@ -119,10 +120,11 @@ public:
 
     struct NM_DATA_EXPORT BatchResult
     {
-        BatchResult() : m_calcType(CALC_DETERMINISTIC), m_dFilterThreshold(-1.0), m_nHistogramVariable(-1), m_bCompleted(false), m_bStopped(false) {}
+        BatchResult() : m_calcType(CALC_DETERMINISTIC), m_dFilterThreshold(-1.0), m_nHistogramVariable(-1), m_nHistogramBins(20), m_bCompleted(false), m_bStopped(false) {}
         CalculationType m_calcType;
         QList<VariableSampling> m_variables; // 求解时冻结的配置，独立于之后的参数编辑。
         QList<nmDataAttribute> m_fitParameters; // 名称、基准单位和本批分析开始时的模型值。
+        QStringList m_fitParameterCodes; // 对应 XML 的参数编码，与拟合参数同序。
         QString m_sWellCode;
         QString m_sWellName;
         QString m_sAxisXName;
@@ -132,11 +134,12 @@ public:
         QVector<QVector<double> > m_vecHistoryLogLog;
         QVector<QVector<double> > m_vecBaseLogLog; // 批次启动时原模型已有的数值解。
         std::vector<RunResult> m_runs;
-        // 散点筛选阈值只随当前批次保存在内存中，负值表示尚未筛选。
+        // 散点筛选阈值随当前批次保存，负值表示尚未筛选。
         double m_dFilterThreshold;
-        int m_nHistogramVariable; // -1 为误差，其余为拟合参数下标，仅存内存。
+        int m_nHistogramVariable; // -1 为误差，其余为拟合参数下标。
+        int m_nHistogramBins; // 直方图分箱数，与统计变量一起恢复。
         bool statisticValue(const RunResult& run, int variable, double& value) const;
-        bool filteredParameterBounds(int index, double& minimum, double& maximum) const;
+        bool filteredParameterBounds(int index, double& minimum, double& maximum, bool fitted = false) const;
         bool m_bCompleted;
         bool m_bStopped;
         QString m_sFailureReason;
@@ -157,6 +160,11 @@ public:
 	// === Calculation type ===
 	CalculationType getCalculationType() const;
 	void setCalculationType(CalculationType type);
+    // 第四种仍复用 Monte-Carlo 采样，独立保存改善开关与确认的拟合设置。
+    bool isImproveEnabled() const;
+    void setImproveEnabled(bool enabled);
+    QSharedPointer<const nmSensitivityFitSettings> getImproveSettings() const;
+    void setImproveSettings(const QSharedPointer<const nmSensitivityFitSettings>& settings);
 
 	// === 总模型数 ===
 	quint64 getTotalModelCount() const;
@@ -186,6 +194,8 @@ public:
 
 private:
 	CalculationType m_calcType;
+    bool m_improveEnabled;
+    QSharedPointer<const nmSensitivityFitSettings> m_improveSettings;
 	quint64 m_totalModelCount;
 	bool m_usesBaseUnits;
 	QList<VariableSampling> m_variables;

Include/nmNum/nmSubWxs/nmWxSensitive.h

@@ -27,7 +27,6 @@ class SensitivityResultsModel;
 class iGuiPlot;
 class QMenu;
 class QAction;
-struct nmSensitivityFitSettings;
 
 class NM_SUB_WXS_EXPORT nmWxSensitivityHistogramOptions : public iDlgBase
 {
@@ -153,7 +152,6 @@ private:
 	// UI 控件
 	QComboBox *calculationTypeCombo;
     QPushButton *m_improveSettingsButton;
-    QSharedPointer<nmSensitivityFitSettings> m_improveSettings;
 	QTreeWidget *variablesTree;
 	QStackedWidget *detailStack;
 	QLabel *modelCountLabel;

Src/nmNum/nmCalculation/nmCalculationDllPebiSolverTask.cpp

@@ -996,6 +996,7 @@ nmCalculationDllPebiSolverTask* nmCalculationDllPebiSolverTask::createSensitivit
                 const char* names[] = { "Permeability", "Skin", "Wellbore storage", "Porosity",
                     "Fracture conductivity", "Fracture half length" };
                 const char* units[] = { "mD", "", "m^3/MPa", "", "mD.m", "m" };
+                const char* codes[] = { "K", "Skin", "C", "phi", "Dfc", "FractureHalfLength" };
                 nmDataWellBase* well = m_pDataManager->findWellByName(improveSettings->targetWell);
                 if(well) {
                     // 启动前补齐拟合目标井的历史双对数，后台各组共用这份目标数据。
@@ -1021,6 +1022,7 @@ nmCalculationDllPebiSolverTask* nmCalculationDllPebiSolverTask::createSensitivit
                     for(int j = 0; j < 6; ++j) if(selected[j]) {
                         task->m_pSensitivityResult->m_fitParameters.append(nmDataAttribute(
                             QCoreApplication::translate("nmWxAutomaticFitting", names[j]), values[j], units[j]));
+                        task->m_pSensitivityResult->m_fitParameterCodes.append(codes[j]);
                     }
                 }
             }

Src/nmNum/nmData/nmDataAttribute.cpp

@@ -54,7 +54,7 @@ rapidjson::Value nmDataAttribute::ToJsonValue(rapidjson::Document::AllocatorType
 	rapidjson::Value attributeValue(rapidjson::kObjectType);
 
 	// 添加属性名称，QString转UTF8并移入JSON
-        attributeValue.AddMember("Name", rapidjson::Value(m_name.toStdString().c_str(), allocator).Move(), allocator);
+        attributeValue.AddMember("Name", rapidjson::Value(m_name.toUtf8().constData(), allocator).Move(), allocator);
 
 	// 根据QVariant类型添加“值”
 	if (m_value.type() == QVariant::Double) {
@@ -62,11 +62,11 @@ rapidjson::Value nmDataAttribute::ToJsonValue(rapidjson::Document::AllocatorType
 	} else if (m_value.type() == QVariant::Int) {
 		attributeValue.AddMember("Value", m_value.toInt(), allocator);
 	} else { // 字符串及其他类型转UTF8字符串
-        attributeValue.AddMember("Value", rapidjson::Value(m_value.toString().toStdString().c_str(), allocator).Move(), allocator);
+        attributeValue.AddMember("Value", rapidjson::Value(m_value.toString().toUtf8().constData(), allocator).Move(), allocator);
 	}
 
 	// 添加单位，QString转UTF8并移入JSON
-        attributeValue.AddMember("Unit", rapidjson::Value(m_unit.toStdString().c_str(), allocator).Move(), allocator);
+        attributeValue.AddMember("Unit", rapidjson::Value(m_unit.toUtf8().constData(), allocator).Move(), allocator);
 
 	// 如果最小值有效，添加“最小值”
 	if (m_minValue.isValid()) {
@@ -75,7 +75,7 @@ rapidjson::Value nmDataAttribute::ToJsonValue(rapidjson::Document::AllocatorType
 		} else if (m_minValue.type() == QVariant::Int) {
 			attributeValue.AddMember("MinValue", m_minValue.toInt(), allocator);
 		} else { // 字符串及其他类型转UTF8字符串
-                        attributeValue.AddMember("MinValue", rapidjson::Value(m_minValue.toString().toStdString().c_str(), allocator).Move(), allocator);
+                        attributeValue.AddMember("MinValue", rapidjson::Value(m_minValue.toString().toUtf8().constData(), allocator).Move(), allocator);
 		}
 	}
 
@@ -86,7 +86,7 @@ rapidjson::Value nmDataAttribute::ToJsonValue(rapidjson::Document::AllocatorType
 		} else if (m_maxValue.type() == QVariant::Int) {
 			attributeValue.AddMember("MaxValue", m_maxValue.toInt(), allocator);
 		} else { // 字符串及其他类型转UTF8字符串
-            attributeValue.AddMember("MaxValue", rapidjson::Value(m_maxValue.toString().toStdString().c_str(), allocator).Move(), allocator);
+            attributeValue.AddMember("MaxValue", rapidjson::Value(m_maxValue.toString().toUtf8().constData(), allocator).Move(), allocator);
 		}
 	}
 
@@ -94,7 +94,7 @@ rapidjson::Value nmDataAttribute::ToJsonValue(rapidjson::Document::AllocatorType
 	if (!m_listValueSelections.isEmpty()) {
 		rapidjson::Value selectionsArray(rapidjson::kArrayType); // 创建JSON数组
 		foreach (const QString& selection , m_listValueSelections) { // 遍历列表
-                        selectionsArray.PushBack(rapidjson::Value(selection.toStdString().c_str(), allocator).Move(), allocator);
+                        selectionsArray.PushBack(rapidjson::Value(selection.toUtf8().constData(), allocator).Move(), allocator);
 		}
 		attributeValue.AddMember("ValueSelections", selectionsArray, allocator); // 将数组添加到JSON对象
 	}
@@ -103,7 +103,7 @@ rapidjson::Value nmDataAttribute::ToJsonValue(rapidjson::Document::AllocatorType
 	if (!m_listUnitSelections.isEmpty()) {
 		rapidjson::Value unitSelectionsArray(rapidjson::kArrayType); // 创建JSON数组
 		foreach (const QString& unitSelection , m_listUnitSelections) { // 遍历列表
-                        unitSelectionsArray.PushBack(rapidjson::Value(unitSelection.toStdString().c_str(), allocator).Move(), allocator);
+                        unitSelectionsArray.PushBack(rapidjson::Value(unitSelection.toUtf8().constData(), allocator).Move(), allocator);
 		}
 		attributeValue.AddMember("UnitSelections", unitSelectionsArray, allocator); // 将数组添加到JSON对象
 	}

Src/nmNum/nmData/nmDataSensitive.cpp

@@ -18,16 +18,17 @@ bool nmDataSensitive::BatchResult::statisticValue(const RunResult& run, int vari
     return qIsFinite(value);
 }
 
-bool nmDataSensitive::BatchResult::filteredParameterBounds(int index, double& minimum, double& maximum) const
+bool nmDataSensitive::BatchResult::filteredParameterBounds(int index, double& minimum, double& maximum, bool fitted) const
 {
-    // 所有参数共用同一批误差严格小于阈值的样本，不受曲线可见性影响。
+    // 采样和拟合参数共用误差严格小于阈值的运行，不受曲线可见性影响。
     bool found = false;
     for(size_t i = 0; i < m_runs.size(); ++i) {
         const RunResult& run = m_runs[i];
+        const QVector<double>& parameters = fitted ? run.m_vecFittedParameters : run.m_vecParameters;
         if(!run.m_bSucceeded || run.m_bCancelled || !qIsFinite(run.m_dPressureRmse) ||
            run.m_dPressureRmse < 0.0 || !(run.m_dPressureRmse < m_dFilterThreshold) ||
-           index < 0 || index >= run.m_vecParameters.size()) continue;
+           index < 0 || index >= parameters.size()) continue;
-        const double value = run.m_vecParameters[index];
+        const double value = parameters[index];
         if(!qIsFinite(value)) continue;
         minimum = found ? qMin(minimum, value) : value;
         maximum = found ? qMax(maximum, value) : value;
@@ -121,6 +122,13 @@ QString sensitiveTextFromJson(const rapidjson::Value& value, const char* key)
     return value.HasMember(key) && value[key].IsString()
             ? QString::fromUtf8(value[key].GetString()) : QString();
 }
+
+double sensitiveNumberFromJson(const rapidjson::Value& value, const char* key, double fallback)
+{
+    // 新增数值字段缺失或无效时沿用默认值，兼容之前保存的成果。
+    return value.HasMember(key) && value[key].IsNumber() && qIsFinite(value[key].GetDouble())
+            ? value[key].GetDouble() : fallback;
+}
 }
 
 rapidjson::Value nmDataSensitive::BatchResult::ToJsonValue(
@@ -140,6 +148,18 @@ rapidjson::Value nmDataSensitive::BatchResult::ToJsonValue(
     value.AddMember("FailureReason", rapidjson::Value(m_sFailureReason.toUtf8().constData(), allocator).Move(), allocator);
     value.AddMember("HistoryLogLog", sensitiveCurvesToJson(m_vecHistoryLogLog, allocator), allocator);
     value.AddMember("BaseLogLog", sensitiveCurvesToJson(m_vecBaseLogLog, allocator), allocator);
+    // 统计图和结果参数页直接使用已保存的误差、拟合值及筛选设置。
+    value.AddMember("FilterThreshold", qIsFinite(m_dFilterThreshold) ? m_dFilterThreshold : -1.0, allocator);
+    value.AddMember("HistogramVariable", m_nHistogramVariable, allocator);
+    value.AddMember("HistogramBins", m_nHistogramBins, allocator);
+    rapidjson::Value fitting(rapidjson::kArrayType);
+    for(int i = 0; i < m_fitParameters.size(); ++i) {
+        rapidjson::Value parameter(rapidjson::kObjectType);
+        parameter.AddMember("Attribute", m_fitParameters[i].ToJsonValue(allocator), allocator);
+        parameter.AddMember("Code", rapidjson::Value(m_fitParameterCodes.value(i).toUtf8().constData(), allocator).Move(), allocator);
+        fitting.PushBack(parameter, allocator);
+    }
+    value.AddMember("FitParameters", fitting, allocator);
     rapidjson::Value variables(rapidjson::kArrayType);
     for(int i = 0; i < m_variables.size(); ++i)
         variables.PushBack(m_variables[i].ToJsonValue(allocator), allocator);
@@ -150,6 +170,8 @@ rapidjson::Value nmDataSensitive::BatchResult::ToJsonValue(
         rapidjson::Value item(rapidjson::kObjectType);
         item.AddMember("RunIndex", run.m_nRunIndex, allocator);
         item.AddMember("Parameters", sensitiveNumbersToJson(run.m_vecParameters, allocator), allocator);
+        item.AddMember("FittedParameters", sensitiveNumbersToJson(run.m_vecFittedParameters, allocator), allocator);
+        item.AddMember("PressureRmse", qIsFinite(run.m_dPressureRmse) ? run.m_dPressureRmse : -1.0, allocator);
         item.AddMember("Pressure", sensitiveCurvesToJson(run.m_vecPressure, allocator), allocator);
         item.AddMember("LogLog", sensitiveCurvesToJson(run.m_vecLogLog, allocator), allocator);
         item.AddMember("SemiLog", sensitiveCurvesToJson(run.m_vecSemiLog, allocator), allocator);
@@ -169,8 +191,8 @@ void nmDataSensitive::BatchResult::FromJsonValue(const rapidjson::Value& value)
     *this = BatchResult();
     if(!value.IsObject()) return;
     if(value.HasMember("CalculationType") && value["CalculationType"].IsInt() &&
-       value["CalculationType"].GetInt() == CALC_DETERMINISTIC_MULTIVARIATE)
+       value["CalculationType"].GetInt() >= CALC_DETERMINISTIC && value["CalculationType"].GetInt() <= CALC_MONTE_CARLO)
-        m_calcType = CALC_DETERMINISTIC_MULTIVARIATE;
+        m_calcType = static_cast<CalculationType>(value["CalculationType"].GetInt());
     m_sWellCode = sensitiveTextFromJson(value, "WellCode");
     m_sWellName = sensitiveTextFromJson(value, "WellName");
     m_sAxisXName = sensitiveTextFromJson(value, "AxisXName");
@@ -182,6 +204,28 @@ void nmDataSensitive::BatchResult::FromJsonValue(const rapidjson::Value& value)
     m_bStopped = value.HasMember("Stopped") && value["Stopped"].IsBool() && value["Stopped"].GetBool();
     m_vecHistoryLogLog = sensitiveCurvesFromJson(value, "HistoryLogLog");
     m_vecBaseLogLog = sensitiveCurvesFromJson(value, "BaseLogLog");
+    m_dFilterThreshold = sensitiveNumberFromJson(value, "FilterThreshold", -1.0);
+    if(value.HasMember("FitParameters") && value["FitParameters"].IsArray()) {
+        const rapidjson::Value& fitting = value["FitParameters"];
+        for(rapidjson::SizeType i = 0; i < fitting.Size(); ++i) {
+            // 保留参数位置，确保每组拟合值与名称、单位和 XML 编码一一对应。
+            nmDataAttribute parameter;
+            QString code;
+            if(fitting[i].IsObject()) {
+                if(fitting[i].HasMember("Attribute") && fitting[i]["Attribute"].IsObject())
+                    parameter.FromJsonValue(fitting[i]["Attribute"]);
+                code = sensitiveTextFromJson(fitting[i], "Code");
+            }
+            m_fitParameters.append(parameter);
+            m_fitParameterCodes.append(code);
+        }
+    }
+    if(value.HasMember("HistogramVariable") && value["HistogramVariable"].IsInt()) {
+        const int index = value["HistogramVariable"].GetInt();
+        if(index >= -1 && index < m_fitParameters.size()) m_nHistogramVariable = index;
+    }
+    if(value.HasMember("HistogramBins") && value["HistogramBins"].IsInt())
+        m_nHistogramBins = qBound(1, value["HistogramBins"].GetInt(), 1000);
     if(value.HasMember("Variables") && value["Variables"].IsArray()) {
         const rapidjson::Value& variables = value["Variables"];
         for(rapidjson::SizeType i = 0; i < variables.Size(); ++i) {
@@ -199,6 +243,8 @@ void nmDataSensitive::BatchResult::FromJsonValue(const rapidjson::Value& value)
         RunResult run;
         run.m_nRunIndex = item["RunIndex"].GetUint64();
         if(item.HasMember("Parameters")) sensitiveNumbersFromJson(item["Parameters"], run.m_vecParameters);
+        if(item.HasMember("FittedParameters")) sensitiveNumbersFromJson(item["FittedParameters"], run.m_vecFittedParameters);
+        run.m_dPressureRmse = sensitiveNumberFromJson(item, "PressureRmse", -1.0);
         run.m_vecPressure = sensitiveCurvesFromJson(item, "Pressure");
         run.m_vecLogLog = sensitiveCurvesFromJson(item, "LogLog");
         run.m_vecSemiLog = sensitiveCurvesFromJson(item, "SemiLog");
@@ -234,10 +280,10 @@ rapidjson::Value nmDataSensitive::VariableSampling::ToJsonValue(
 
 	// 字符串字段
 	obj.AddMember("VarGroup",
-		rapidjson::Value(m_varGroup.toStdString().c_str(), allocator).Move(),
+		rapidjson::Value(m_varGroup.toUtf8().constData(), allocator).Move(),
 		allocator);
 	obj.AddMember("VarName",
-		rapidjson::Value(m_varName.toStdString().c_str(), allocator).Move(),
+		rapidjson::Value(m_varName.toUtf8().constData(), allocator).Move(),
 		allocator);
 
 	// 简单类型
@@ -245,6 +291,14 @@ rapidjson::Value nmDataSensitive::VariableSampling::ToJsonValue(
 	obj.AddMember("Mode", static_cast<int>(m_mode), allocator);
 	obj.AddMember("Log", m_log, allocator);
 	obj.AddMember("Number", m_number, allocator);
+    // 六种分布使用同一组字段，恢复时不重新生成用户设置的范围。
+    obj.AddMember("Distribution", int(m_distribution), allocator);
+    obj.AddMember("DistributionInitialized", m_distributionInitialized, allocator);
+    obj.AddMember("Mean", m_mean, allocator);
+    obj.AddMember("StdDev", m_stdDev, allocator);
+    obj.AddMember("Peak", m_peak, allocator);
+    obj.AddMember("DistributionMinimum", m_distributionMin, allocator);
+    obj.AddMember("DistributionMaximum", m_distributionMax, allocator);
 
 	// nmDataAttribute 成员
 	obj.AddMember("ModelValue", m_modelValue.ToJsonValue(allocator), allocator);
@@ -264,6 +318,20 @@ rapidjson::Value nmDataSensitive::VariableSampling::ToJsonValue(
 // 从 RapidJSON Value 反序列化到 VariableSampling
 void nmDataSensitive::VariableSampling::FromJsonValue(const rapidjson::Value& jsonValue)
 {
+    // 旧文件未保存分布时保留默认状态，由分析窗口按模型值初始化。
+    *this = VariableSampling();
+    if(jsonValue.HasMember("Distribution") && jsonValue["Distribution"].IsInt()) {
+        const int distribution = jsonValue["Distribution"].GetInt();
+        if(distribution >= NORMAL && distribution <= TRIANGULAR)
+            m_distribution = static_cast<Distribution>(distribution);
+    }
+    m_distributionInitialized = jsonValue.HasMember("DistributionInitialized") &&
+        jsonValue["DistributionInitialized"].IsBool() && jsonValue["DistributionInitialized"].GetBool();
+    m_mean = sensitiveNumberFromJson(jsonValue, "Mean", m_mean);
+    m_stdDev = sensitiveNumberFromJson(jsonValue, "StdDev", m_stdDev);
+    m_peak = sensitiveNumberFromJson(jsonValue, "Peak", m_peak);
+    m_distributionMin = sensitiveNumberFromJson(jsonValue, "DistributionMinimum", m_distributionMin);
+    m_distributionMax = sensitiveNumberFromJson(jsonValue, "DistributionMaximum", m_distributionMax);
 	if (jsonValue.HasMember("VarGroup") && jsonValue["VarGroup"].IsString()) {
 		m_varGroup = QString::fromUtf8(jsonValue["VarGroup"].GetString());
 	}
@@ -492,6 +560,7 @@ bool nmDataSensitive::VariableSampling::distributionQuantile(double probability,
 
 nmDataSensitive::nmDataSensitive()
 	: m_calcType(CALC_DETERMINISTIC)
+    , m_improveEnabled(false)
 	, m_totalModelCount(0)
 	, m_usesBaseUnits(false)
 	, m_variables()
@@ -510,6 +579,8 @@ nmDataSensitive& nmDataSensitive::operator=(const nmDataSensitive& other)
 {
 	if (this != &other) {
 		m_calcType        = other.m_calcType;
+        m_improveEnabled = other.m_improveEnabled;
+        m_improveSettings = other.m_improveSettings;
 		m_totalModelCount = other.m_totalModelCount;
 		m_usesBaseUnits   = other.m_usesBaseUnits;
 		m_variables       = other.m_variables;
@@ -536,6 +607,19 @@ rapidjson::Value nmDataSensitive::ToJsonValue(
 	root.AddMember("CalculationType",
 		static_cast<int>(m_calcType),
 		allocator);
+    // 与曲线一起保存采样和拟合配置，重开项目后仍可查看同一批预览。
+    root.AddMember("ImproveEnabled", m_improveEnabled, allocator);
+    root.AddMember("MonteCarloCount", m_monteCarloCount, allocator);
+    root.AddMember("MonteCarloValues", sensitiveCurvesToJson(m_monteCarloValues, allocator), allocator);
+    if(!m_improveSettings.isNull()) {
+        const nmSensitivityFitSettings& settings = *m_improveSettings;
+        rapidjson::Value fitting(rapidjson::kObjectType);
+        fitting.AddMember("Confirmed", settings.confirmed, allocator);
+        fitting.AddMember("Parameters", settings.parameters.ToJsonValue(allocator), allocator);
+        fitting.AddMember("TargetWell", rapidjson::Value(settings.targetWell.toUtf8().constData(), allocator).Move(), allocator);
+        fitting.AddMember("InitialValues", sensitiveNumbersToJson(settings.initialValues, allocator), allocator);
+        root.AddMember("ImproveSettings", fitting, allocator);
+    }
 
 	// 总模型数
 	root.AddMember("TotalModelCount",
@@ -557,6 +641,14 @@ rapidjson::Value nmDataSensitive::ToJsonValue(
 // 从 RapidJSON Value 反序列化数据到 nmDataSensitive
 void nmDataSensitive::FromJsonValue(const rapidjson::Value& jsonValue)
 {
+    // 先恢复默认值，旧成果缺失新增字段时不沿用上一项目的数据。
+    *this = nmDataSensitive();
+    if(!jsonValue.IsObject()) return;
+    m_improveEnabled = jsonValue.HasMember("ImproveEnabled") &&
+        jsonValue["ImproveEnabled"].IsBool() && jsonValue["ImproveEnabled"].GetBool();
+    if(jsonValue.HasMember("MonteCarloCount") && jsonValue["MonteCarloCount"].IsInt() &&
+       jsonValue["MonteCarloCount"].GetInt() > 0)
+        m_monteCarloCount = jsonValue["MonteCarloCount"].GetInt();
     // 旧项目没有 Results 时保持空结果。
     m_results.clear();
     if(jsonValue.HasMember("Results") && jsonValue["Results"].IsObject()) {
@@ -567,10 +659,8 @@ void nmDataSensitive::FromJsonValue(const rapidjson::Value& jsonValue)
 		jsonValue["UsesBaseUnits"].IsBool() && jsonValue["UsesBaseUnits"].GetBool();
 	if (jsonValue.HasMember("CalculationType") && jsonValue["CalculationType"].IsInt()) {
 		int t = jsonValue["CalculationType"].GetInt();
-		if (t == CALC_DETERMINISTIC_MULTIVARIATE)
+		if (t >= CALC_DETERMINISTIC && t <= CALC_MONTE_CARLO)
-			m_calcType = CALC_DETERMINISTIC_MULTIVARIATE;
+			m_calcType = static_cast<CalculationType>(t);
-		else
-			m_calcType = CALC_DETERMINISTIC;
 	}
 
 	if (jsonValue.HasMember("TotalModelCount") && jsonValue["TotalModelCount"].IsUint64()) {
@@ -589,6 +679,30 @@ void nmDataSensitive::FromJsonValue(const rapidjson::Value& jsonValue)
 			m_variables.append(vs);
 		}
 	}
+    // 恢复完整样本，列数必须与参数表对应，避免预览或生成时错用参数。
+    m_monteCarloValues = sensitiveCurvesFromJson(jsonValue, "MonteCarloValues");
+    bool validSamples = m_monteCarloValues.size() == m_monteCarloCount;
+    for(int row = 0; validSamples && row < m_monteCarloValues.size(); ++row) {
+        const QVector<double>& values = m_monteCarloValues[row];
+        validSamples = values.size() == m_variables.size();
+        for(int column = 0; validSamples && column < values.size(); ++column)
+            validSamples = qIsFinite(values[column]);
+    }
+    if(!validSamples) clearMonteCarloSamples();
+    if(jsonValue.HasMember("ImproveSettings") && jsonValue["ImproveSettings"].IsObject()) {
+        const rapidjson::Value& fitting = jsonValue["ImproveSettings"];
+        QSharedPointer<nmSensitivityFitSettings> settings(new nmSensitivityFitSettings());
+        settings->targetWell = sensitiveTextFromJson(fitting, "TargetWell");
+        const bool hasParameters = fitting.HasMember("Parameters") && fitting["Parameters"].IsObject();
+        if(hasParameters) settings->parameters.FromJsonValue(fitting["Parameters"]);
+        if(fitting.HasMember("InitialValues")) sensitiveNumbersFromJson(fitting["InitialValues"], settings->initialValues);
+        bool valid = hasParameters && settings->initialValues.size() == 6 && !settings->targetWell.isEmpty();
+        for(int i = 0; valid && i < settings->initialValues.size(); ++i)
+            valid = qIsFinite(settings->initialValues[i]);
+        settings->confirmed = valid && fitting.HasMember("Confirmed") &&
+            fitting["Confirmed"].IsBool() && fitting["Confirmed"].GetBool();
+        if(settings->confirmed) m_improveSettings = settings;
+    }
 }
 
 // === Calculation type ===
@@ -604,6 +718,30 @@ void nmDataSensitive::setCalculationType(CalculationType type)
 	m_calcType = type;
 }
 
+bool nmDataSensitive::isImproveEnabled() const
+{
+    // 开关独立于拟合设置，切回第三种后仍可保留上次确认的设置。
+    return m_improveEnabled;
+}
+
+void nmDataSensitive::setImproveEnabled(bool enabled)
+{
+    // 只记录是否启用改善，不改变 Monte-Carlo 的采样方式。
+    m_improveEnabled = enabled;
+}
+
+QSharedPointer<const nmSensitivityFitSettings> nmDataSensitive::getImproveSettings() const
+{
+    // 设置按只读引用共享，窗口重新打开时恢复，编辑时另建副本。
+    return m_improveSettings;
+}
+
+void nmDataSensitive::setImproveSettings(const QSharedPointer<const nmSensitivityFitSettings>& settings)
+{
+    // 只接收拟合窗口确认后的完整配置。
+    m_improveSettings = settings;
+}
+
 // === 总模型数 ===
 quint64 nmDataSensitive::getTotalModelCount() const
 {

Src/nmNum/nmSubWnd/nmSubWndMain.cpp

@@ -2201,7 +2201,6 @@ void nmSubWndMain::updateSensitiveHistogramPlot()
         QVBoxLayout* layout = new QVBoxLayout(page);
         layout->setContentsMargins(0, 0, 0, 0);
         QHBoxLayout* options = new QHBoxLayout;
-        page->setProperty("nmHistogramBins", 20);
         QPushButton* scatter = new QPushButton(tr("Scatter plot"), page);
         scatter->setObjectName("nmScatterButton");
         QPushButton* settings = new QPushButton(tr("Histogram options"), page);
@@ -2230,7 +2229,7 @@ void nmSubWndMain::updateSensitiveHistogramPlot()
         connect(settings, SIGNAL(clicked()), this, SLOT(onSensitiveHistogramOptions()));
     }
     iGuiPlot* plot = window->widget()->findChild<iGuiPlot*>();
-    const int binCount = window->widget()->property("nmHistogramBins").toInt();
+    const int binCount = batch->m_nHistogramBins;
     // 拟合参数与误差共用分箱绘制，轴名称和单位随本批选择同步切换。
     const int variable = batch->m_nHistogramVariable;
     plot->setAxisX(variable < 0 ? tr("Goodness of fit") : batch->m_fitParameters[variable].getName(),
@@ -2338,7 +2337,7 @@ void nmSubWndMain::onSensitiveHistogramOptions()
     if(!window || batch.isNull()) return;
     nmWxSensitivityHistogramOptions* dialog = window->findChild<nmWxSensitivityHistogramOptions*>();
     if(!dialog) {
-        dialog = new nmWxSensitivityHistogramOptions(batch, window->widget()->property("nmHistogramBins").toInt(), window->widget());
+        dialog = new nmWxSensitivityHistogramOptions(batch, batch->m_nHistogramBins, window->widget());
         connect(dialog, SIGNAL(settingsChanged(int,int)), this, SLOT(onSensitiveHistogramSettingsChanged(int,int)));
         connect(manager, SIGNAL(sigSensitiveResultsChanged()), dialog, SLOT(reject()));
     }
@@ -2349,17 +2348,18 @@ void nmSubWndMain::onSensitiveHistogramOptions()
 
 void nmSubWndMain::onSensitiveHistogramSettingsChanged(int bins, int variable)
 {
-    // 直方图变量仅存于本批内存结果，散点图独立选择自己的纵轴。
+    // 分箱数与统计变量随批次保存，散点图独立选择自己的纵轴。
     nmDataAnalyzeManager* manager = m_pResultRefreshDataManager.data();
     if(!manager || !manager->getSensitiveData()) return;
     const QSharedPointer<nmDataSensitive::BatchResult> batch = manager->getSensitiveData()->getResults();
     if(batch.isNull()) return;
     batch->m_nHistogramVariable = variable;
+    batch->m_nHistogramBins = bins;
     iSubWndFitting* fitting = m_pResultRefreshFitting.data();
     if(!fitting || !fitting->m_pMdiArea) return;
     QMdiSubWindow* window = fitting->m_pMdiArea->findChild<QMdiSubWindow*>("nmSensitivityHistogram");
     if(!window) return;
-    window->widget()->setProperty("nmHistogramBins", bins);
+    fitting->setModified(true);
     updateSensitiveHistogramPlot();
 }
 
@@ -2371,6 +2371,8 @@ void nmSubWndMain::onSensitiveFilterChanged()
     QWidget* page = fitting->getFitSubRstWxOf(FSRT_NmRst, false);
     nmWxParaPropertyPebi* parameters = page ? page->findChild<nmWxParaPropertyPebi*>() : nullptr;
     if(parameters) parameters->refreshSensitivityResults();
+    // 只有用户调整阈值才标记修改，加载成果时的面板刷新不触发保存提示。
+    if(qobject_cast<nmWxSensitivityScatter*>(sender())) fitting->setModified(true);
 }
 
 void nmSubWndMain::onSensitiveScatter()

Src/nmNum/nmSubWxs/nmWxParaPropertyPebi.cpp

@@ -154,6 +154,14 @@ void appendSensitivityParas(nmDataAnalyzeManager* manager, QStringList& paras,
         appendResultPara(paras, values, groupCode, lower, minimum);
         appendResultPara(paras, values, groupCode, higher, maximum);
     }
+    // 第四种在采样范围后追加各组拟合最优值的范围，沿用同一阈值和 XML 分组。
+    for(int i = 0; i < batch->m_fitParameterCodes.size(); ++i) {
+        double minimum = 0.0, maximum = 0.0;
+        if(!batch->filteredParameterBounds(i, minimum, maximum, true)) continue;
+        const QString name = "Sensitivity_Fit_" + batch->m_fitParameterCodes[i];
+        appendResultPara(paras, values, groupCode, name + "_Minimum", minimum);
+        appendResultPara(paras, values, groupCode, name + "_Maximum", maximum);
+    }
 }
 
 QString resultModelTitlePrefix()

Src/nmNum/nmSubWxs/nmWxSensitive.cpp

@@ -1523,7 +1523,7 @@ void nmWxSensitive::loadDataToUI(bool refreshRangesOnly)
 {
     // 加载过程中阻止联动刷新，先恢复设置，再一次生成各参数表格。
     if (!refreshRangesOnly)
-        calculationTypeCombo->setCurrentIndex(int(m_sensitiveData.getCalculationType()));
+        calculationTypeCombo->setCurrentIndex(m_sensitiveData.isImproveEnabled() ? 3 : int(m_sensitiveData.getCalculationType()));
     m_monteCarloCount->setValue(m_sensitiveData.getMonteCarloCount());
     for (int i = 0; i < m_sensitiveData.getVariableCount(); ++i) {
         nmDataSensitive::VariableSampling& variable = m_sensitiveData.getVariable(i);
@@ -1611,6 +1611,7 @@ void nmWxSensitive::updateModelCount()
     if (m_loading) return;
     m_sensitiveData.setCalculationType(
         static_cast<nmDataSensitive::CalculationType>(calculationTypeCombo->itemData(calculationTypeCombo->currentIndex()).toInt()));
+    m_sensitiveData.setImproveEnabled(calculationTypeCombo->currentIndex() == 3);
     for (int group = 0; group < variablesTree->topLevelItemCount(); ++group) {
         QTreeWidgetItem* parent = variablesTree->topLevelItem(group);
         for (int child = 0; child < parent->childCount(); ++child) {
@@ -1635,8 +1636,8 @@ void nmWxSensitive::updateModelCount()
     m_monteCarloCount->setVisible(monteCarlo);
     // 第四种确认设置且至少选择一个拟合参数后才开放生成，空选择直接禁用。
     bool hasFittingParameters = false;
-    if(!m_improveSettings.isNull()) {
+    if(!m_sensitiveData.getImproveSettings().isNull()) {
-        const nmDataAutomaticFitting& fitting = m_improveSettings->parameters;
+        const nmDataAutomaticFitting& fitting = m_sensitiveData.getImproveSettings()->parameters;
         hasFittingParameters = fitting.getPermeabilitySelected() || fitting.getSkinSelected() ||
             fitting.getWellboreStorageSelected() || fitting.getPorositySelected() ||
             fitting.getFractureConductivitySelected() || fitting.getFractureHalfLengthSelected();
@@ -2489,8 +2490,8 @@ void nmWxSensitive::onGenerateClicked()
         }
         iAnalRun* analRun = fitting ? fitting->getAnalRun() : nullptr;
         // 第四种可选择其他井拟合，其历史曲线也要使用本次的拟压力配置。
-        nmDataWellBase* fitWell = (calculationTypeCombo->currentIndex() == 3) && !m_improveSettings.isNull()
+        nmDataWellBase* fitWell = m_sensitiveData.isImproveEnabled() && !m_sensitiveData.getImproveSettings().isNull()
-            ? m_pDataManager->findWellByName(m_improveSettings->targetWell) : nullptr;
+            ? m_pDataManager->findWellByName(m_sensitiveData.getImproveSettings()->targetWell) : nullptr;
         if(!analRun || !analRun->configPsAbouts(true, fitting->getModelOption(),
                                               false, fitting->getAllWxPtr()) ||
            !m_pDataManager->updateWellHistoryData(well) ||
@@ -2516,7 +2517,7 @@ void nmWxSensitive::onGenerateClicked()
         nmCalculationDllPebiSolverTask source(QString(), m_pDataManager, well->getWellName());
         // 第四种直接交给后台任务逐组拟合，仍使用主窗口原有进度框。
         task.reset(source.createSensitivityBatch(m_sensitiveData, &error,
-            (calculationTypeCombo->currentIndex() == 3) ? m_improveSettings.data() : nullptr));
+            m_sensitiveData.isImproveEnabled() ? m_sensitiveData.getImproveSettings().data() : nullptr));
     } catch(const std::bad_alloc&) {
         error = tr("Insufficient memory to prepare solver input.");
     }
@@ -2628,7 +2629,7 @@ void nmWxSensitive::onImproveSettingsClicked()
 {
     // 在副本上编辑，只有确定才替换设置；取消保留上次确认的内容。
     nmSensitivityFitSettings settings;
-    if(!m_improveSettings.isNull()) settings = *m_improveSettings;
+    if(!m_sensitiveData.getImproveSettings().isNull()) settings = *m_sensitiveData.getImproveSettings();
     // 将分析窗口当前勾选的采样参数传给拟合窗口，用于禁用重叠选项。
     updateModelCount();
     QStringList sampledParameters;
@@ -2636,7 +2637,7 @@ void nmWxSensitive::onImproveSettingsClicked()
         if(variable.m_enabled) sampledParameters.append(variable.m_varName);
     nmWxAutomaticFitting dialog(this, &settings, sampledParameters);
     if(dialog.exec() == QDialog::Accepted) {
-        m_improveSettings = QSharedPointer<nmSensitivityFitSettings>(new nmSensitivityFitSettings(settings));
+        m_sensitiveData.setImproveSettings(QSharedPointer<const nmSensitivityFitSettings>(new nmSensitivityFitSettings(settings)));
         updateModelCount();
     }
 }