feat(nmNum): 新增 Monte-Carlo 参数采样与预览

- 增加 Monte-Carlo 入口及六种概率分布设置，采用拉丁超立方采样，支持自定义总次数和每次预览重新抽样。
- 显示累计概率与归一化概率密度曲线，复用运行预览列表，切换方法时保留确定性参数设置。
- 放大分析窗口，调整绘图样式、参数固定行布局及 Values 表格宽度，三角分布 Mode 与 Mean 共用位置。
- 本次仅开放采样和预览，暂不接入 Monte-Carlo 求解、统计结果及保存逻辑。

Include/nmNum/nmData/nmDataSensitive.h

@@ -21,7 +21,8 @@ public:
 	enum CalculationType
 	{
 		CALC_DETERMINISTIC = 0,
-		CALC_DETERMINISTIC_MULTIVARIATE = 1
+		CALC_DETERMINISTIC_MULTIVARIATE = 1,
+        CALC_MONTE_CARLO = 2
 	};
 
 	struct NM_DATA_EXPORT VariableSampling
@@ -50,6 +51,12 @@ 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。
+        enum Distribution { NORMAL, LOGNORMAL, TRUNCATED_NORMAL, TRUNCATED_LOGNORMAL, UNIFORM, TRIANGULAR };
+        Distribution m_distribution;
+        bool m_distributionInitialized;
+        double m_mean, m_stdDev, m_peak, m_distributionMin, m_distributionMax;
+        bool distributionQuantile(double probability, double& value) const;
 		VariableSampling();
 
 		// JSON 序列化 / 反序列化
@@ -149,6 +156,10 @@ public:
 	quint64 calculateModelCount(bool* overflow = nullptr) const;
 	// 返回与变量列表对应的基准单位值，未改变的参数保持模型值。
 	bool getRunValues(quint64 runIndex, QVector<double>& values) const;
+    // 每次打开 Monte-Carlo 预览重新分层抽样，预览滚动只读取本批结果。
+    bool resampleMonteCarlo(QString* error = nullptr);
+    int getMonteCarloCount() const;
+    void setMonteCarloCount(int count);
 	bool usesBaseUnits() const;
 	void setUsesBaseUnits(bool enabled);
     // 记录采样范围对应的求解快照，成功重新求解后再初始化范围。
@@ -172,6 +183,8 @@ private:
 	bool m_usesBaseUnits;
     QString m_modelSnapshotId;
 	QList<VariableSampling> m_variables;
+    int m_monteCarloCount;
+    QVector<QVector<double> > m_monteCarloValues;
     QSharedPointer<BatchResult> m_results;
 };
 

Include/nmNum/nmSubWxs/nmWxSensitive.h

@@ -24,6 +24,7 @@ class nmCalculationDllPebiSolverTask;
 class QCloseEvent;
 class QDialog;
 class SensitivityResultsModel;
+class SensitivityDistributionPlot;
 
 class NM_SUB_WXS_EXPORT nmWxSensitive : public iDlgBase
 {
@@ -73,6 +74,8 @@ private:
 	void createDetailPages();
 	QWidget *createVariablePage(const QString &varName);
 	void updateValuesForPage(int idx);
+    void updateDistributionPage(int idx);
+    bool readDistributionPage(int idx, nmDataSensitive::VariableSampling& variable) const;
 	QIcon createTriangleIcon();  // 创建三角箭头图标
 
 	void loadDataToUI(bool refreshRangesOnly = false);  // 从数据加载到界面
@@ -87,6 +90,7 @@ private:
 	QTreeWidget *variablesTree;
 	QStackedWidget *detailStack;
 	QLabel *modelCountLabel;
+    QSpinBox *m_monteCarloCount;
 	QPushButton *generateButton;
 	QPushButton *cancelButton;
 	QPushButton *previewButton;
@@ -103,6 +107,10 @@ private:
 	QList<QLineEdit*> m_maxSpins;
 	QList<QTableWidget*> m_valueTables;
 	QList<QLabel*> m_unitLabels;
+    QList<QLabel*> m_numLabels, m_minLabels, m_maxLabels;
+    QList<QComboBox*> m_distributionCombos;
+    QList<QWidget*> m_distributionPanels;
+    QList<SensitivityDistributionPlot*> m_distributionPlots;
 
 	// 跟踪当前选中的值索引
 	int m_selectedValueIndex;

Src/nmNum/nmData/nmDataSensitive.cpp

@@ -1,10 +1,33 @@
 #include "nmDataSensitive.h"
 #include <cmath>
 #include <limits>
+#include <random>
+#include <QDateTime>
 #include <QtCore/qnumeric.h>
 
 namespace
 {
+double normalCdf(double z)
+{
+    // 标准正态累积分布近似，最大绝对误差约 7.5e-8，兼容 VS2010。
+    const double x = std::fabs(z), t = 1.0 / (1.0 + 0.2316419 * x);
+    const double tail = 0.3989422804014327 * std::exp(-0.5 * x * x) * t *
+        (0.319381530 + t * (-0.356563782 + t * (1.781477937 + t * (-1.821255978 + t * 1.330274429))));
+    return z < 0.0 ? tail : 1.0 - tail;
+}
+
+double normalQuantile(double probability)
+{
+    // 在累积分布上二分求逆，避免引入新的数学库。
+    double low = -10.0, high = 10.0;
+    for(int i = 0; i < 60; ++i) {
+        const double mid = (low + high) * 0.5;
+        if(normalCdf(mid) < probability) low = mid;
+        else high = mid;
+    }
+    return (low + high) * 0.5;
+}
+
 rapidjson::Value sensitiveNumbersToJson(const QVector<double>& values,
                                        rapidjson::Document::AllocatorType& allocator)
 {
@@ -164,6 +187,8 @@ nmDataSensitive::VariableSampling::VariableSampling()
 	, m_mode(MODE_AUTOMATIC)
 	, m_log(false)
 	, m_number(0)
+    , m_distribution(NORMAL), m_distributionInitialized(false), m_mean(0.0), m_stdDev(1.0), m_peak(0.0)
+    , m_distributionMin(0.0), m_distributionMax(1.0)
 {
     m_modelValue = nmDataAttribute("Model value", "", "");
     m_minValue   = nmDataAttribute("Minimum",    "", "");
@@ -384,11 +409,52 @@ QVector<double> nmDataSensitive::VariableSampling::samplingValues() const
 }
 
 
+bool nmDataSensitive::VariableSampling::distributionQuantile(double probability, double& value) const
+{
+    // 六种分布都由分位数取值；对数正态的均值、标准差仍使用参数原单位。
+    if(!qIsFinite(probability) || probability <= 0.0 || probability >= 1.0) return false;
+    const bool bounded = m_distribution >= TRUNCATED_NORMAL;
+    const double low = m_distributionMin, high = m_distributionMax;
+    if(bounded && (!qIsFinite(low) || !qIsFinite(high) || low >= high)) return false;
+    if(m_distribution == UNIFORM) value = low + probability * (high - low);
+    else if(m_distribution == TRIANGULAR) {
+        if(!qIsFinite(m_peak) || m_peak < low || m_peak > high) return false;
+        const double split = (m_peak - low) / (high - low);
+        value = probability < split
+            ? low + std::sqrt(probability * (high - low) * (m_peak - low))
+            : high - std::sqrt((1.0 - probability) * (high - low) * (high - m_peak));
+    } else {
+        if(m_distribution < NORMAL || m_distribution > TRUNCATED_LOGNORMAL ||
+           !qIsFinite(m_mean) || !qIsFinite(m_stdDev) || m_stdDev <= 0.0) return false;
+        const bool logarithmic = m_distribution == LOGNORMAL || m_distribution == TRUNCATED_LOGNORMAL;
+        if(logarithmic && (m_mean <= 0.0 || (bounded && low < 0.0))) return false;
+        double mean = m_mean, sigma = m_stdDev;
+        if(logarithmic) {
+            const double ratio = m_stdDev / m_mean;
+            const double variance = std::log(1.0 + ratio * ratio);
+            mean = std::log(m_mean) - variance * 0.5;
+            sigma = std::sqrt(variance);
+        }
+        if(!qIsFinite(mean) || !qIsFinite(sigma) || sigma <= 0.0) return false;
+        if(bounded) {
+            const double a = logarithmic && low == 0.0 ? 0.0
+                : normalCdf(((logarithmic ? std::log(low) : low) - mean) / sigma);
+            const double b = normalCdf(((logarithmic ? std::log(high) : high) - mean) / sigma);
+            if(!(b > a)) return false;
+            probability = a + probability * (b - a);
+        }
+        value = mean + sigma * normalQuantile(probability);
+        if(logarithmic) value = std::exp(value);
+    }
+    return qIsFinite(value);
+}
+
 nmDataSensitive::nmDataSensitive()
 	: m_calcType(CALC_DETERMINISTIC)
 	, m_totalModelCount(0)
 	, m_usesBaseUnits(false)
 	, m_variables()
+    , m_monteCarloCount(50)
 {
 }
 
@@ -407,6 +473,8 @@ nmDataSensitive& nmDataSensitive::operator=(const nmDataSensitive& other)
 		m_usesBaseUnits   = other.m_usesBaseUnits;
         m_modelSnapshotId = other.m_modelSnapshotId;
 		m_variables       = other.m_variables;
+        m_monteCarloCount = other.m_monteCarloCount;
+        m_monteCarloValues = other.m_monteCarloValues;
         m_results         = other.m_results;
 	}
 	return *this;
@@ -513,6 +581,11 @@ quint64 nmDataSensitive::calculateModelCount(bool* overflow) const
 {
 	// 未选择变量时为零；两种确定性方法分别累计和、积。
 	if (overflow) *overflow = false;
+    if(m_calcType == CALC_MONTE_CARLO) {
+        for(int i = 0; i < m_variables.size(); ++i)
+            if(m_variables[i].m_enabled) return qMax(0, m_monteCarloCount);
+        return 0;
+    }
 	quint64 count = 0;
 	bool selected = false;
 	for (int i = 0; i < m_variables.size(); ++i) {
@@ -540,6 +613,11 @@ bool nmDataSensitive::getRunValues(quint64 runIndex, QVector<double>& values) co
 	bool overflow = false;
 	const quint64 count = calculateModelCount(&overflow);
 	if (overflow || runIndex >= count) return false;
+    if(m_calcType == CALC_MONTE_CARLO) {
+        if(m_monteCarloValues.size() != int(count)) return false;
+        values = m_monteCarloValues[int(runIndex)];
+        return true;
+    }
 	values.clear();
 	for (int i = 0; i < m_variables.size(); ++i)
 		values.append(m_variables[i].m_modelValue.getValue().toDouble());
@@ -570,6 +648,51 @@ bool nmDataSensitive::getRunValues(quint64 runIndex, QVector<double>& values) co
 	return false;
 }
 
+int nmDataSensitive::getMonteCarloCount() const
+{
+    // Monte-Carlo 总次数由用户指定，不使用单参数次数的和或积。
+    return m_monteCarloCount;
+}
+
+void nmDataSensitive::setMonteCarloCount(int count)
+{
+    // 次数变化后旧预览失效，下一次预览统一重新抽样。
+    if(count != m_monteCarloCount) m_monteCarloValues.clear();
+    m_monteCarloCount = count;
+}
+
+bool nmDataSensitive::resampleMonteCarlo(QString* error)
+{
+    // 各参数的累积概率均分为 N 层，每层抽一点，再独立打乱，组成 N 组参数。
+    if(error) error->clear();
+    m_monteCarloValues.clear();
+    const int count = int(calculateModelCount());
+    if(m_calcType != CALC_MONTE_CARLO || count <= 0) return false;
+    std::mt19937 random(uint(QDateTime::currentMSecsSinceEpoch()) ^ uint(qrand()));
+    std::uniform_real_distribution<double> uniform(0.0, 1.0);
+    QVector<QVector<double> > runs(count, QVector<double>(m_variables.size()));
+    for(int column = 0; column < m_variables.size(); ++column) {
+        const VariableSampling& variable = m_variables[column];
+        QVector<double> samples(count, variable.m_modelValue.getValue().toDouble());
+        if(variable.m_enabled) {
+            for(int row = 0; row < count; ++row) {
+                const double jitter = qBound(1e-12, uniform(random), 1.0 - 1e-12);
+                if(!variable.distributionQuantile((row + jitter) / count, samples[row])) {
+                    if(error) *error = tr("%1: invalid distribution parameters.").arg(variable.m_varName);
+                    return false;
+                }
+            }
+            for(int row = count - 1; row > 0; --row) {
+                std::uniform_int_distribution<int> index(0, row);
+                qSwap(samples[row], samples[index(random)]);
+            }
+        }
+        for(int row = 0; row < count; ++row) runs[row][column] = samples[row];
+    }
+    m_monteCarloValues.swap(runs);
+    return true;
+}
+
 bool nmDataSensitive::usesBaseUnits() const
 {
 	return m_usesBaseUnits;

Src/nmNum/nmSubWxs/nmWxSensitive.cpp

@@ -24,6 +24,7 @@
 #include <QDialog>
 #include <QDialogButtonBox>
 #include <QApplication>
+#include <QDesktopWidget>
 #include <QTableWidget>
 #include <QTableView>
 #include <QAbstractTableModel>
@@ -66,6 +67,8 @@
 
 namespace
 {
+const char* distributionFields[] = {"mean", "stdDev", "peak", "minimum", "maximum"};
+
 QString formatSamplingValue(double value, int digits)
 {
     // 仅对显示值取有效数字，较大或较小的数自动使用科学计数法。
@@ -292,6 +295,119 @@ private:
     QMap<int, bool> m_pendingVisibility;
 };
 
+class SensitivityDistributionPlot : public QWidget
+{
+public:
+    explicit SensitivityDistributionPlot(QWidget* parent) : QWidget(parent), m_valid(false)
+    {
+        // 分布图复用参数分位数计算，避免绘图与实际采样使用两套分布定义。
+        setMinimumSize(340, 300);
+    }
+    void setVariable(const nmDataSensitive::VariableSampling& variable, bool valid)
+    {
+        m_variable = variable;
+        m_valid = valid;
+        update();
+    }
+protected:
+    void paintEvent(QPaintEvent*) override
+    {
+        // 蓝线为累积概率，红色虚线为按峰值归一化的概率密度，与 Saphir 的显示一致。
+        QPainter painter(this);
+        painter.fillRect(rect(), palette().color(QPalette::Window));
+        painter.setRenderHint(QPainter::Antialiasing);
+        QVector<double> x(201), cumulativeValues(201);
+        const bool bounded = m_variable.m_distribution >= nmDataSensitive::VariableSampling::TRUNCATED_NORMAL;
+        bool valid = m_valid;
+        for(int i = 0; valid && i < x.size(); ++i) {
+            // 两端加密以平滑显示尾部，有界分布的横轴保留精确上下限。
+            const double probability = (1.0 - std::cos(3.141592653589793 * i / 200.0)) * 0.5;
+            cumulativeValues[i] = bounded ? probability : 0.0001 + 0.9998 * probability;
+            valid = m_variable.distributionQuantile(qBound(1e-10, cumulativeValues[i], 1.0 - 1e-10), x[i]);
+            if(bounded && i == 0) x[i] = m_variable.m_distributionMin;
+            if(bounded && i == 200) x[i] = m_variable.m_distributionMax;
+            valid = valid && convertSamplingValue(m_variable.m_varName, x[i], samplingBaseUnit(m_variable.m_varName),
+                                                  m_variable.m_modelValue.getUnit(), x[i]);
+        }
+        if(!valid || x.last() <= x.first()) {
+            painter.drawText(rect(), Qt::AlignCenter, nmWxSensitive::tr("Invalid distribution parameters"));
+            return;
+        }
+        // 坐标外侧沿用窗口底色，绘图区留白，标题放在纵轴左侧。
+        const QRectF plot(76, 12, width() - 96, height() - 66);
+        painter.fillRect(plot, Qt::white);
+        const QRectF dataArea = plot.adjusted(12, 16, -12, -16);
+        // 横轴采用 1、2、5 的整齐步长，按分布范围自适应，不限制实际采样值。
+        const double roughStep = (x.last() - x.first()) / 5.0;
+        const double scale = std::pow(10.0, std::floor(std::log10(roughStep)));
+        const double fraction = roughStep / scale;
+        const double step = (fraction <= 1.0 ? 1.0 : fraction <= 2.0 ? 2.0 : fraction <= 5.0 ? 5.0 : 10.0) * scale;
+        const double axisMin = std::floor(x.first() / step) * step;
+        const double axisMax = std::ceil(x.last() / step) * step;
+        QVector<double> density(201);
+        double peak = 0.0;
+        for(int i = 0; i < x.size(); ++i) {
+            const int a = qMax(0, i - 1), b = qMin(200, i + 1);
+            density[i] = x[b] > x[a] ? (cumulativeValues[b] - cumulativeValues[a]) / (x[b] - x[a]) : 0.0;
+            if(m_variable.m_distribution == nmDataSensitive::VariableSampling::TRIANGULAR &&
+               ((i == 0 && m_variable.m_peak > m_variable.m_distributionMin) ||
+                (i == 200 && m_variable.m_peak < m_variable.m_distributionMax))) density[i] = 0.0;
+            peak = qMax(peak, density[i]);
+        }
+        // 蓝色点状主网格配合黑色主、次刻度，概率轴按 0.1 标注。
+        const int intervals = qRound((axisMax - axisMin) / step);
+        for(int i = 0; i <= intervals * 5; ++i) {
+            const double value = axisMin + i * step / 5.0;
+            const double px = dataArea.left() + (value - axisMin) / (axisMax - axisMin) * dataArea.width();
+            if(i % 5 == 0) {
+                painter.setPen(QPen(QColor(100, 120, 255), 1, Qt::DotLine));
+                painter.drawLine(QPointF(px, plot.top()), QPointF(px, plot.bottom()));
+                painter.setPen(Qt::black);
+                painter.drawText(QRectF(px - 36, plot.bottom() + 8, 72, 20), Qt::AlignCenter,
+                    formatSamplingValue(value, 4));
+            }
+            painter.setPen(Qt::black);
+            painter.drawLine(QPointF(px, plot.bottom()), QPointF(px, plot.bottom() + (i % 5 == 0 ? 7 : 4)));
+        }
+        for(int i = 0; i <= 50; ++i) {
+            const double ratio = i / 50.0;
+            const double py = dataArea.bottom() - ratio * dataArea.height();
+            if(i % 10 == 0) {
+                painter.setPen(QPen(QColor(100, 120, 255), 1, Qt::DotLine));
+                painter.drawLine(QPointF(plot.left(), py), QPointF(plot.right(), py));
+            }
+            painter.setPen(Qt::black);
+            painter.drawLine(QPointF(plot.left(), py), QPointF(plot.left() - (i % 5 == 0 ? 7 : 4), py));
+            if(i % 5 == 0)
+                painter.drawText(QRectF(28, py - 10, plot.left() - 40, 20), Qt::AlignRight | Qt::AlignVCenter,
+                    QString::number(ratio));
+        }
+        QPolygonF cumulative, probability;
+        for(int i = 0; i < x.size(); ++i) {
+            const double px = dataArea.left() + (x[i] - axisMin) / (axisMax - axisMin) * dataArea.width();
+            cumulative << QPointF(px, dataArea.bottom() - cumulativeValues[i] * dataArea.height());
+            probability << QPointF(px, dataArea.bottom() - (peak > 0.0 ? density[i] / peak : 0.0) * dataArea.height());
+        }
+        painter.setPen(QPen(Qt::blue, 1.5));
+        painter.drawPolyline(cumulative);
+        painter.setPen(QPen(Qt::red, 1, Qt::DashLine));
+        painter.drawPolyline(probability);
+        painter.setPen(Qt::black);
+        painter.drawRect(plot);
+        painter.save();
+        painter.translate(17, plot.center().y());
+        painter.rotate(-90);
+        painter.drawText(QRectF(-plot.height() / 2, -10, plot.height(), 20), Qt::AlignCenter,
+            nmWxSensitive::tr("Probability distribution function"));
+        painter.restore();
+        painter.drawText(QRectF(plot.left(), height() - 22, plot.width(), 20), Qt::AlignCenter,
+            m_variable.m_varName + " [" + m_variable.m_modelValue.getUnit() + "]");
+    }
+private:
+    nmDataSensitive::VariableSampling m_variable;
+    bool m_valid;
+};
+
 // ========= 右对齐委托：保证显示和编辑时都是右对齐 =========
 class RightAlignDelegate : public QStyledItemDelegate
 {
@@ -768,6 +884,21 @@ void nmWxSensitive::refreshModelValues()
         variable.getMinValue().setValue(minimum);
         variable.getMaxValue().setValue(maximum);
         variable.setValues(values);
+        if(!variable.m_distributionInitialized || resetRanges) {
+            // Saphir 默认正态均值为模型值、标准差为其三分之一；Skin=0 时使用 5/3。
+            const double model = attribute->getValue().toDouble();
+            variable.m_mean = model;
+            variable.m_stdDev = model == 0.0 ? 5.0 / 3.0 : std::fabs(model) / 3.0;
+            variable.m_distributionMin = minimum;
+            variable.m_distributionMax = maximum;
+            // Monte-Carlo 的 Skin 分布允许负值，不能沿用求解器截断后的范围。
+            if(name == "Skin") {
+                variable.m_distributionMin = model == 0.0 ? -1.0 : qMin(model * 0.9, model * 1.1);
+                variable.m_distributionMax = model == 0.0 ? 1.0 : qMax(model * 0.9, model * 1.1);
+            }
+            variable.m_peak = qBound(variable.m_distributionMin, model * 1.01, variable.m_distributionMax);
+            variable.m_distributionInitialized = true;
+        }
         // Saphir 在重新打开对话框时同步无效 Log 范围的实际状态。
         if (variable.getMode() == nmDataSensitive::VariableSampling::MODE_AUTOMATIC &&
             (minimum <= 0.0 || maximum <= 0.0)) variable.setLog(false);
@@ -796,6 +927,7 @@ 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()));
+    m_monteCarloCount->setValue(m_sensitiveData.getMonteCarloCount());
     for (int i = 0; i < m_sensitiveData.getVariableCount(); ++i) {
         nmDataSensitive::VariableSampling& variable = m_sensitiveData.getVariable(i);
         const QString name = variable.getVarName();
@@ -833,6 +965,14 @@ void nmWxSensitive::loadDataToUI(bool refreshRangesOnly)
         setSamplingEditValue(m_modelSpins[idx], model);
         setSamplingEditValue(m_minSpins[idx], minimum);
         setSamplingEditValue(m_maxSpins[idx], maximum);
+        m_distributionCombos[idx]->setCurrentIndex(int(variable.m_distribution));
+        const double distributionValues[] = {variable.m_mean, variable.m_stdDev, variable.m_peak,
+                                            variable.m_distributionMin, variable.m_distributionMax};
+        for(int field = 0; field < 5; ++field) {
+            double display = distributionValues[field];
+            convertSamplingValue(name, display, samplingBaseUnit(name), unit, display);
+            setSamplingEditValue(m_distributionPanels[idx]->findChild<QLineEdit*>(distributionFields[field]), display);
+        }
         double displayMin = variable.getModelValue().getMinValue().toDouble();
         double displayMax = variable.getModelValue().getMaxValue().toDouble();
         convertSamplingValue(name, displayMin, samplingBaseUnit(name), unit, displayMin);
@@ -888,11 +1028,16 @@ void nmWxSensitive::updateModelCount()
         }
     }
     bool overflow = false;
+    m_sensitiveData.setMonteCarloCount(m_monteCarloCount->value());
     const quint64 count = m_sensitiveData.calculateModelCount(&overflow);
     m_sensitiveData.setTotalModelCount(count);
     modelCountLabel->setText(overflow ? tr("Too many models") : QString::number(count));
     previewButton->setEnabled(!overflow && count > 0);
-    generateButton->setEnabled(m_pDataManager && !overflow && count > 0 && !m_calculationRunning);
+    const bool monteCarlo = m_sensitiveData.getCalculationType() == nmDataSensitive::CALC_MONTE_CARLO;
+    modelCountLabel->setVisible(!monteCarlo);
+    m_monteCarloCount->setVisible(monteCarlo);
+    generateButton->setEnabled(!monteCarlo && m_pDataManager && !overflow && count > 0 && !m_calculationRunning);
+    generateButton->setToolTip(monteCarlo ? tr("Monte-Carlo sampling preview is available; calculation is not yet available.") : QString());
 }
 
 bool nmWxSensitive::saveDataFromUI()
@@ -906,6 +1051,16 @@ bool nmWxSensitive::saveDataFromUI()
         const QString name = variable.getVarName();
         const int idx = findVariableIndex(name);
         if (idx < 0 || !isSupportedSamplingVariable(name)) continue;
+        if(candidate.getCalculationType() == nmDataSensitive::CALC_MONTE_CARLO) {
+            double median = 0.0;
+            const bool valid = readDistributionPage(idx, variable) && variable.distributionQuantile(0.5, median);
+            if(!valid && variable.getEnabled()) {
+                QMessageBox::warning(this, tr("Sensitivity"), tr("%1: invalid distribution parameters.").arg(name));
+                detailStack->setCurrentIndex(idx + 1);
+                return false;
+            }
+            continue;
+        }
         variable.setMode(static_cast<nmDataSensitive::VariableSampling::Mode>(m_modeCombos[idx]->currentIndex()));
         variable.setLog(m_logChecks[idx]->isChecked());
         const QString unit = m_unitLabels[idx]->text();
@@ -965,6 +1120,9 @@ void nmWxSensitive::setupUI()
 {
     setWindowTitle(tr("Sensitivity"));
     setMinimumSize(900, 550);
+    // 默认放大窗口，同时为屏幕边缘和窗口标题栏留出空间。
+    const QSize available = QApplication::desktop()->availableGeometry(this).size() - QSize(60, 80);
+    resize(QSize(1250, 760).boundedTo(available));
 
     QVBoxLayout *mainLayout = new QVBoxLayout(this);
 
@@ -974,7 +1132,7 @@ void nmWxSensitive::setupUI()
     calculationTypeCombo = new QComboBox(this);
     calculationTypeCombo->addItem(tr("Deterministic"));
     calculationTypeCombo->addItem(tr("Deterministic multivariate"));
-    //calculationTypeCombo->addItem(tr("Monte-Carlo"));
+    calculationTypeCombo->addItem(tr("Monte-Carlo"));
     //calculationTypeCombo->addItem(tr("Monte-Carlo + Improve"));
 
     calcTypeLayout->addWidget(calcTypeLabel);
@@ -1061,6 +1219,7 @@ void nmWxSensitive::setupUI()
     // 左边宽度基本固定，右边自适应
     centerSplitter->setStretchFactor(0, 0);
     centerSplitter->setStretchFactor(1, 1);
+    centerSplitter->setSizes(QList<int>() << 260 << 960);
 
     mainLayout->addWidget(centerSplitter, 1);
 
@@ -1069,9 +1228,16 @@ void nmWxSensitive::setupUI()
 
     QLabel *totalLabel = new QLabel(tr("Total number of models:"), this);
     modelCountLabel = new QLabel(tr("0"), this);
+    m_monteCarloCount = new QSpinBox(this);
+    m_monteCarloCount->setObjectName("sensitivityMonteCarloCount");
+    m_monteCarloCount->setRange(1, 100000);
+    m_monteCarloCount->setValue(m_sensitiveData.getMonteCarloCount());
+    m_monteCarloCount->setButtonSymbols(QAbstractSpinBox::NoButtons);
+    m_monteCarloCount->setVisible(false);
 
     bottomLayout->addWidget(totalLabel);
     bottomLayout->addWidget(modelCountLabel);
+    bottomLayout->addWidget(m_monteCarloCount);
     previewButton = new QPushButton(this);
     previewButton->setIcon(QIcon(QCoreApplication::applicationDirPath().section('/', 0, -2)
                                 + "/Res/Icon/Eyes.png"));
@@ -1094,6 +1260,7 @@ void nmWxSensitive::setupUI()
 
 void nmWxSensitive::createConnections()
 {
+    connect(m_monteCarloCount, SIGNAL(valueChanged(int)), this, SLOT(onVariableParamChanged()));
     connect(generateButton, SIGNAL(clicked()),
             this,           SLOT(onGenerateClicked()));
     connect(cancelButton,   SIGNAL(clicked()),
@@ -1267,6 +1434,12 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 	QHBoxLayout *modeRowLayout = new QHBoxLayout(modeRowWidget);
 	modeRowLayout->setContentsMargins(0, 0, 0, 0);
 	modeRowLayout->addWidget(modeCombo);
+    QComboBox* distributionCombo = new QComboBox(content);
+    distributionCombo->setObjectName("sensitivityDistribution_" + varName);
+    distributionCombo->addItems(QStringList() << tr("Normal") << tr("Lognormal")
+        << tr("Truncated normal") << tr("Truncated lognormal") << tr("Uniform") << tr("Triangular"));
+    distributionCombo->setFixedWidth(180);
+    modeRowLayout->addWidget(distributionCombo);
 	modeRowLayout->addWidget(logCheck);
 	modeRowLayout->addStretch();
 
@@ -1285,9 +1458,9 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 	paramLayout->addWidget(numSpin,  row, 1);
 	++row;
 
-	// 空行（Number 和 Model value 之间）
+	// 模式、数量与下方采样参数之间留出约三行空白。
 	int gapRow = row;
-	paramLayout->setRowMinimumHeight(gapRow, 12);
+	paramLayout->setRowMinimumHeight(gapRow, numSpin->sizeHint().height() * 3);
 	++row;
 
 	// Model value + 单位
@@ -1310,6 +1483,34 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 	paramLayout->addWidget(modelRowWidget, row, 1);
 	++row;
 
+    // Monte-Carlo 使用独立分布字段，切回确定性方法时保留原范围与手动表格。
+    QWidget* distributionPanel = new QWidget(content);
+    QGridLayout* distributionLayout = new QGridLayout(distributionPanel);
+    distributionLayout->setContentsMargins(0, 0, 0, 0);
+    distributionLayout->setHorizontalSpacing(6);
+    distributionLayout->setVerticalSpacing(0);
+    distributionLayout->setColumnMinimumWidth(0, modelLabel->sizeHint().width());
+    const int fieldHeight = modelSpin->sizeHint().height();
+    // Mean 与三角分布的 Mode 共用一行，其余字段保持固定位置。
+    distributionPanel->setFixedHeight(fieldHeight * 4 + 6 * 3);
+    const QStringList distributionLabels = QStringList() << tr("Mean:") << tr("Std. dev.:")
+        << tr("Mode:") << tr("Minimum:") << tr("Maximum:");
+    for(int field = 0; field < 5; ++field) {
+        QLineEdit* edit = new QLineEdit(distributionPanel);
+        edit->setObjectName(distributionFields[field]);
+        edit->setFixedSize(qMax(150, modelSpin->sizeHint().width()), fieldHeight);
+        QLabel* label = new QLabel(distributionLabels[field], distributionPanel);
+        label->setObjectName(QString(distributionFields[field]) + "Label");
+        label->setFixedHeight(fieldHeight);
+        const int fieldRow = field == 2 ? 0 : field > 2 ? field - 1 : field;
+        distributionLayout->setRowMinimumHeight(fieldRow, fieldHeight + (fieldRow < 3 ? 6 : 0));
+        distributionLayout->addWidget(label, fieldRow, 0, Qt::AlignTop);
+        distributionLayout->addWidget(edit, fieldRow, 1, Qt::AlignTop);
+        connect(edit, SIGNAL(textChanged(QString)), this, SLOT(onVariableParamChanged()));
+    }
+    distributionLayout->setColumnStretch(2, 1);
+    paramLayout->addWidget(distributionPanel, row++, 0, 1, 2);
+
 	// Minimum
 	QLabel *minLabel = new QLabel(tr("Minimum:"), content);
 	QLineEdit *minSpin = new QLineEdit(content);
@@ -1359,12 +1560,11 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 	valuesTable->setHorizontalHeaderLabels(headers);
 
 	valuesTable->verticalHeader()->setVisible(false);
-	valuesTable->setColumnWidth(0, 15);
-	valuesTable->setColumnWidth(1, 130);
-
 	QHeaderView *hh = valuesTable->horizontalHeader();
 	hh->setResizeMode(QHeaderView::Fixed);
 	hh->setHighlightSections(false);
+	// 箭头列固定，数值列在下方设置为填满加宽后的表格。
+	valuesTable->setColumnWidth(0, 15);
 	valuesTable->setStyleSheet(
 		"QHeaderView::section {"
 		"    border-top: 0px;"
@@ -1380,7 +1580,7 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 		"}"
 		);
 
-	valuesTable->horizontalHeader()->setStretchLastSection(false);
+	valuesTable->horizontalHeader()->setStretchLastSection(true);
 	valuesTable->setEditTriggers(QAbstractItemView::DoubleClicked);
 	valuesTable->setSelectionBehavior(QAbstractItemView::SelectRows);
 	valuesTable->setSelectionMode(QAbstractItemView::NoSelection);
@@ -1390,10 +1590,8 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 
 	valuesTable->setItemDelegateForColumn(1, new RightAlignDelegate(valuesTable));
 
-	int tableWidth = valuesTable->frameWidth() * 2
-		+ valuesTable->verticalHeader()->sizeHint().width()
-		+ valuesTable->columnWidth(0)
-		+ valuesTable->columnWidth(1);
+	// 初始空表的列宽尚未稳定，直接固定容器宽度，不从未显示的表头反算。
+	const int tableWidth = 190;
 	valuesTable->setFixedWidth(tableWidth);
 
 	QWidget *tableContainer = new QWidget(content);
@@ -1406,6 +1604,8 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 	contentLayout->addWidget(paramWidget, 1);
 	contentLayout->addStretch();
 	contentLayout->addWidget(tableContainer, 0, Qt::AlignRight);
+    SensitivityDistributionPlot* distributionPlot = new SensitivityDistributionPlot(content);
+    contentLayout->addWidget(distributionPlot, 2);
 
 	panelLayout->addWidget(content);
 	pageLayout->addWidget(samplingPanel);
@@ -1420,6 +1620,13 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 	m_maxSpins.append(maxSpin);
 	m_valueTables.append(valuesTable);
 	m_unitLabels.append(unitLabel);
+    m_numLabels.append(numLabel);
+    m_minLabels.append(minLabel);
+    m_maxLabels.append(maxLabel);
+    m_distributionCombos.append(distributionCombo);
+    m_distributionPanels.append(distributionPanel);
+    m_distributionPlots.append(distributionPlot);
+    connect(distributionCombo, SIGNAL(currentIndexChanged(int)), this, SLOT(onVariableParamChanged()));
 
 	connect(numSpin,   SIGNAL(valueChanged(int)),
 		this,      SLOT(onVariableParamChanged()));
@@ -1445,6 +1652,23 @@ QWidget *nmWxSensitive::createVariablePage(const QString &varName)
 void nmWxSensitive::updateValuesForPage(int idx)
 {
     if (idx < 0 || idx >= m_numSpins.size()) return;
+    const bool monteCarlo = calculationTypeCombo->currentIndex() == nmDataSensitive::CALC_MONTE_CARLO;
+    m_modeCombos[idx]->setVisible(!monteCarlo);
+    m_logChecks[idx]->setVisible(!monteCarlo);
+    m_numLabels[idx]->setVisible(!monteCarlo);
+    m_numSpins[idx]->setVisible(!monteCarlo);
+    m_minLabels[idx]->setVisible(!monteCarlo);
+    m_maxLabels[idx]->setVisible(!monteCarlo);
+    m_minSpins[idx]->setVisible(!monteCarlo);
+    m_maxSpins[idx]->setVisible(!monteCarlo);
+    m_valueTables[idx]->parentWidget()->setVisible(!monteCarlo);
+    m_distributionCombos[idx]->setVisible(monteCarlo);
+    m_distributionPanels[idx]->setVisible(monteCarlo);
+    m_distributionPlots[idx]->setVisible(monteCarlo);
+    // Monte-Carlo 参数区只占所需宽度，窗口增宽时优先扩展分布图。
+    QHBoxLayout* contentLayout = static_cast<QHBoxLayout*>(m_distributionPlots[idx]->parentWidget()->layout());
+    contentLayout->setStretch(0, monteCarlo ? 0 : 1);
+    if(monteCarlo) { updateDistributionPage(idx); return; }
     // 自动模式由范围生成只读表格；手动模式保留每行值，不因选中行而重算。
     QTableWidget* table = m_valueTables[idx];
     const bool automatic = m_modeCombos[idx]->currentIndex() == 0;
@@ -1498,6 +1722,49 @@ void nmWxSensitive::updateValuesForPage(int idx)
     }
 }
 
+bool nmWxSensitive::readDistributionPage(int idx, nmDataSensitive::VariableSampling& variable) const
+{
+    // 字段转换沿用现有参数单位，仅校验当前分布实际使用的字段。
+    variable.m_distribution = static_cast<nmDataSensitive::VariableSampling::Distribution>(
+        m_distributionCombos[idx]->currentIndex());
+    variable.m_modelValue.setUnit(m_unitLabels[idx]->text());
+    double* fields[] = {&variable.m_mean, &variable.m_stdDev, &variable.m_peak,
+                       &variable.m_distributionMin, &variable.m_distributionMax};
+    const int distribution = variable.m_distribution;
+    for(int field = 0; field < 5; ++field) {
+        const bool used = field < 2 ? distribution < nmDataSensitive::VariableSampling::UNIFORM
+            : field == 2 ? distribution == nmDataSensitive::VariableSampling::TRIANGULAR
+            : distribution >= nmDataSensitive::VariableSampling::TRUNCATED_NORMAL;
+        if(!used) continue;
+        bool ok = false;
+        double value = samplingEditValue(m_distributionPanels[idx]->findChild<QLineEdit*>(distributionFields[field]), &ok);
+        if(!ok || !qIsFinite(value) || !convertSamplingValue(variable.m_varName, value,
+            m_unitLabels[idx]->text(), samplingBaseUnit(variable.m_varName), *fields[field])) return false;
+    }
+    return true;
+}
+
+void nmWxSensitive::updateDistributionPage(int idx)
+{
+    // 只隐藏所选分布不用的字段，保留各行位置；编辑时更新曲线，不提前抽样。
+    const int distribution = m_distributionCombos[idx]->currentIndex();
+    for(int field = 0; field < 5; ++field) {
+        QWidget* edit = m_distributionPanels[idx]->findChild<QLineEdit*>(distributionFields[field]);
+        const bool used = field < 2 ? distribution < nmDataSensitive::VariableSampling::UNIFORM
+            : field == 2 ? distribution == nmDataSensitive::VariableSampling::TRIANGULAR
+            : distribution >= nmDataSensitive::VariableSampling::TRUNCATED_NORMAL;
+        edit->setVisible(used);
+        m_distributionPanels[idx]->findChild<QLabel*>(QString(distributionFields[field]) + "Label")->setVisible(used);
+    }
+    for(int i = 0; i < m_sensitiveData.getVariableCount(); ++i) {
+        nmDataSensitive::VariableSampling variable = m_sensitiveData.getVariable(i);
+        if(findVariableIndex(variable.m_varName) != idx) continue;
+        const bool valid = readDistributionPage(idx, variable);
+        m_distributionPlots[idx]->setVariable(variable, valid);
+        break;
+    }
+}
+
 void nmWxSensitive::onSamplingEditingFinished()
 {
     // 输入结束后截断越界范围，再按六位有效数字显示，计算仍使用原始精度。
@@ -1532,7 +1799,8 @@ void nmWxSensitive::onVariableParamChanged()
     for (int i = 0; i < m_numSpins.size(); ++i) {
         if (sender() == m_numSpins[i] || sender() == m_minSpins[i] ||
             sender() == m_maxSpins[i] || sender() == m_modeCombos[i] ||
-            sender() == m_logChecks[i]) {
+            sender() == m_logChecks[i] || sender() == m_distributionCombos[i] ||
+            sender()->parent() == m_distributionPanels[i]) {
             m_selectedValueIndex = qMin(m_selectedValueIndex, m_numSpins[i]->value() - 1);
             updateValuesForPage(i);
             break;
@@ -1584,6 +1852,8 @@ void nmWxSensitive::onTreeItemChanged(QTreeWidgetItem* item, int column)
 
 void nmWxSensitive::onGenerateClicked()
 {
+    // 本阶段只开放 Monte-Carlo 参数采样与预览，尚不启动数值计算。
+    if(calculationTypeCombo->currentIndex() == nmDataSensitive::CALC_MONTE_CARLO) return;
     if(m_calculationRunning || !m_pDataManager || !saveDataFromUI()) return;
     QString error;
     QScopedPointer<nmCalculationDllPebiSolverTask> task;
@@ -1787,7 +2057,9 @@ void nmWxSensitive::onHideAllResults()
 void nmWxSensitive::onCalculationTypeChanged(int index)
 {
     Q_UNUSED(index);
-    // 两种确定性方法共用参数设置，仅切换总次数及组合规则。
+    // 复用现有参数页，切换方法只改变可见控件，不覆盖另一种方法的设置。
+    if(m_loading) return;
+    for(int i = 0; i < m_numSpins.size(); ++i) updateValuesForPage(i);
     updateModelCount();
 }
 
@@ -1809,6 +2081,13 @@ void nmWxSensitive::onTreeItemClicked(QTreeWidgetItem* item, int column)
 void nmWxSensitive::onPreviewClicked()
 {
     if (!saveDataFromUI()) return;
+    if(m_sensitiveData.getCalculationType() == nmDataSensitive::CALC_MONTE_CARLO) {
+        QString error;
+        if(!m_sensitiveData.resampleMonteCarlo(&error)) {
+            QMessageBox::warning(this, tr("Sensitivity"), error);
+            return;
+        }
+    }
     // Saphir 风格的连续运行列表，组合数再大也不显示分页控件。
     QDialog dialog(this);
     dialog.setWindowTitle(tr("Sensitivity Runs"));