feat: 添加 JD-R 理论分析模块与 SHAP 可解释性分析功能

- 后端新增 JD-R（工作要求-资源）理论维度数据生成，包含工作要求、工作资源、
    个人资源、中介变量共 16 个新特征列
  - 新增 JD-R 分析服务与 API（维度统计、倦怠投入分析、双路径中介分析、
    分组轮廓、风险分布）
  - 新增 SHAP 可解释性分析模块（全局重要性、局部解释、特征交互、依赖图）
  - 预测服务增加风险分类模型加载与概率预测能力
  - 前端新增 JD-R 分析页面（JDRAnalysis.vue），含雷达图、散点图、路径分析等可视化
  - 预测页面增加风险概率展示与 SHAP 特征解释
  - 路由与导航菜单同步更新

backend/services/predict_service.py

@@ -32,6 +32,8 @@ MODEL_INFO = {
 class PredictService:
     def __init__(self):
         self.models = {}
+        self.classifiers = {}
+        self.classification_metrics = {}
         self.scaler = None
         self.feature_names = None
         self.selected_features = None
@@ -94,6 +96,21 @@ class PredictService:
         if valid_metrics:
             self.default_model = max(valid_metrics.items(), key=lambda item: item[1]['r2'])[0]
 
+        # 加载风险分类模型
+        for name in ['random_forest', 'gradient_boosting', 'lightgbm', 'xgboost']:
+            path = os.path.join(config.MODELS_DIR, f'risk_{name}_classifier.pkl')
+            if os.path.exists(path):
+                try:
+                    self.classifiers[name] = joblib.load(path)
+                except Exception:
+                    pass
+        cls_metrics_path = os.path.join(config.MODELS_DIR, 'classification_metrics.pkl')
+        if os.path.exists(cls_metrics_path):
+            try:
+                self.classification_metrics = joblib.load(cls_metrics_path)
+            except Exception:
+                pass
+
     def get_available_models(self):
         self._ensure_models_loaded()
         models = []
@@ -131,10 +148,15 @@ class PredictService:
 
         risk_level, risk_label = self._get_risk_level(predicted_hours)
         confidence = max(0.5, self.model_metrics.get(model_type, {}).get('r2', 0.82))
+
+        # 风险分类概率
+        risk_probability = self._get_risk_probability(features, model_type)
+
         return {
             'predicted_hours': round(predicted_hours, 2),
             'risk_level': risk_level,
             'risk_label': risk_label,
+            'risk_probability': risk_probability,
             'confidence': round(confidence, 2),
             'model_used': model_type,
             'model_name_cn': MODEL_INFO.get(model_type, {}).get('name_cn', model_type),
@@ -198,11 +220,65 @@ class PredictService:
             'predicted_hours': round(max(0.5, base_hours), 2),
             'risk_level': risk_level,
             'risk_label': risk_label,
+            'risk_probability': {'low': 0.0, 'medium': 1.0, 'high': 0.0},
             'confidence': 0.72,
             'model_used': 'default',
             'model_name_cn': '默认规则',
         }
 
+    def _get_risk_probability(self, features, model_type):
+        """获取分类器预测的风险概率"""
+        classifier = self.classifiers.get(model_type)
+        if classifier is None:
+            classifier = self.classifiers.get('random_forest')
+        if classifier is None:
+            return {'low': 0.0, 'medium': 1.0, 'high': 0.0}
+        try:
+            proba = classifier.predict_proba([features])[0]
+            classes = list(classifier.classes_)
+            result = {'low': 0.0, 'medium': 0.0, 'high': 0.0}
+            label_map = {0: 'low', 1: 'medium', 2: 'high'}
+            for idx, cls in enumerate(classes):
+                if cls in label_map:
+                    result[label_map[cls]] = round(float(proba[idx]), 4)
+            return result
+        except Exception:
+            return {'low': 0.0, 'medium': 1.0, 'high': 0.0}
+
+    def predict_risk_classification(self, data, model_type=None):
+        """使用分类模型直接预测风险等级"""
+        self._ensure_models_loaded()
+        model_type = model_type or self.default_model
+        classifier = self.classifiers.get(model_type)
+        if classifier is None:
+            classifier = self.classifiers.get('random_forest')
+        if classifier is None or self.scaler is None:
+            return None
+
+        features = self._prepare_features(data)
+        try:
+            pred_class = int(classifier.predict([features])[0])
+            proba = classifier.predict_proba([features])[0]
+            label_map = {0: 'low', 1: 'medium', 2: 'high'}
+            risk_labels_map = {'low': '低风险', 'medium': '中风险', 'high': '高风险'}
+            risk_level = label_map.get(pred_class, 'medium')
+
+            classes = list(classifier.classes_)
+            probabilities = {'low': 0.0, 'medium': 0.0, 'high': 0.0}
+            for idx, cls in enumerate(classes):
+                if cls in label_map:
+                    probabilities[label_map[cls]] = round(float(proba[idx]), 4)
+
+            return {
+                'risk_level': risk_level,
+                'risk_label': risk_labels_map[risk_level],
+                'risk_probability': probabilities,
+                'model_used': model_type,
+                'classification_metrics': self.classification_metrics.get(model_type, {}),
+            }
+        except Exception:
+            return None
+
     def get_model_info(self):
         self._ensure_models_loaded()
         return {