Polish absence analysis demo experience

backend/core/clustering.py

@@ -43,14 +43,14 @@ class KMeansAnalyzer:
             center = centers[int(cluster_id)]
             clusters.append({
                 'id': int(cluster_id),
-                'name': names.get(int(cluster_id), f'群体{int(cluster_id) + 1}'),
+                'name': names.get(int(cluster_id), '常规稳态型'),
                 'member_count': int(count),
                 'percentage': round(count / total * 100, 1),
                 'center': {
                     feature: round(float(value), 2)
                     for feature, value in zip(self.feature_cols, center)
                 },
-                'description': self._generate_description(names.get(int(cluster_id), '')),
+                'description': self._generate_description(names.get(int(cluster_id), '常规稳态型'), center),
             })
         return {'n_clusters': self.n_clusters, 'clusters': clusters}
 
@@ -65,7 +65,7 @@ class KMeansAnalyzer:
             'clusters': [
                 {
                     'id': idx,
-                    'name': names.get(idx, f'群体{idx + 1}'),
+                    'name': names.get(idx, '常规稳态型'),
                     'values': [round(float(v), 2) for v in centers_scaled[idx]],
                 }
                 for idx in range(self.n_clusters)
@@ -105,27 +105,63 @@ class KMeansAnalyzer:
                 '4': '#6DC8EC',
             },
             'cluster_names': {
-                str(idx): names.get(idx, f'群体{idx + 1}')
+                str(idx): names.get(idx, '常规稳态型')
                 for idx in range(self.n_clusters)
             },
         }
 
     def _generate_cluster_names(self, centers):
+        rank_info = self._build_rank_info(centers)
         base_names = {}
         for idx, center in enumerate(centers):
-            _, tenure, overtime, commute, bmi, absence = center
-            if overtime > 38 and commute > 55 and absence > 8:
-                base_names[idx] = '高压通勤型'
-            elif bmi > 27 and absence > 8:
-                base_names[idx] = '健康波动型'
-            elif tenure > 8 and absence < 6:
-                base_names[idx] = '稳定低风险型'
-            elif overtime > 28 and absence > 7:
-                base_names[idx] = '轮班负荷型'
-            else:
-                base_names[idx] = f'群体{idx + 1}'
+            base_names[idx] = self._classify_cluster(center, rank_info, idx)
         return self._deduplicate_cluster_names(base_names, centers)
 
+    def _build_rank_info(self, centers):
+        centers = np.asarray(centers, dtype=float)
+        return {
+            '年龄': self._rank_desc(centers[:, 0]),
+            '司龄': self._rank_desc(centers[:, 1]),
+            '加班': self._rank_desc(centers[:, 2]),
+            '通勤': self._rank_desc(centers[:, 3]),
+            'BMI': self._rank_desc(centers[:, 4]),
+            '缺勤': self._rank_desc(centers[:, 5]),
+        }
+
+    def _rank_desc(self, values):
+        ordered = np.argsort(-np.asarray(values, dtype=float))
+        ranks = {}
+        for rank, idx in enumerate(ordered):
+            ranks[int(idx)] = rank
+        return ranks
+
+    def _classify_cluster(self, center, rank_info, idx):
+        age, tenure, overtime, commute, bmi, absence = center
+        high_absence = rank_info['缺勤'][idx] == 0
+        low_absence = rank_info['缺勤'][idx] == len(rank_info['缺勤']) - 1
+        high_overtime = rank_info['加班'][idx] <= 1
+        high_commute = rank_info['通勤'][idx] <= 1
+        high_bmi = rank_info['BMI'][idx] <= 1
+        high_tenure = rank_info['司龄'][idx] <= 1
+        low_tenure = rank_info['司龄'][idx] >= len(rank_info['司龄']) - 1
+        young_group = rank_info['年龄'][idx] >= len(rank_info['年龄']) - 1
+
+        if (absence >= 7.5 and overtime >= 28 and commute >= 40) or (high_absence and high_overtime and high_commute):
+            return '压力奔波型'
+        if (absence >= 7.0 and bmi >= 25.5) or (high_absence and high_bmi):
+            return '健康关注型'
+        if (overtime >= 30 and absence >= 6.0) or (high_overtime and rank_info['缺勤'][idx] <= 1):
+            return '负荷承压型'
+        if (tenure >= 8 and absence <= 6.0) or (high_tenure and low_absence):
+            return '稳定成熟型'
+        if (tenure <= 4 and age <= 32) or (low_tenure and young_group):
+            return '新锐成长型'
+        if commute <= 35 and absence <= 6.5:
+            return '通勤平衡型'
+        if tenure >= 6 and absence <= 6.8:
+            return '经验稳健型'
+        return '常规稳态型'
+
     def _deduplicate_cluster_names(self, names, centers):
         grouped = {}
         for idx, name in names.items():
@@ -159,24 +195,75 @@ class KMeansAnalyzer:
 
     def _suffix_candidates(self, name):
         suffix_map = {
-            '高压通勤型': ['-高风险组', '-关注组', '-观察组'],
-            '健康波动型': ['-重点关注组', '-预警组', '-观察组'],
-            '稳定低风险型': ['-资深组', '-成熟组', '-稳健组'],
-            '轮班负荷型': ['-高负荷组', '-轮班组', '-强化组'],
+            '压力奔波型': ['-高压组', '-长途组', '-持续关注组'],
+            '健康关注型': ['-重点关注组', '-预警组', '-干预组'],
+            '负荷承压型': ['-高负荷组', '-轮班组', '-调节组'],
+            '稳定成熟型': ['-资深组', '-成熟组', '-稳健组'],
+            '新锐成长型': ['-适应组', '-成长组', '-潜力组'],
+            '通勤平衡型': ['-均衡组', '-稳态组', '-协同组'],
+            '经验稳健型': ['-资深组', '-稳健组', '-协同组'],
+            '常规稳态型': ['-平衡组', '-常态组', '-协同组'],
         }
         return suffix_map.get(name, [f'（{idx}）' for idx in range(1, 10)])
 
-    def _generate_description(self, name):
+    def _generate_description(self, name, center=None):
         descriptions = {
-            '高压通勤型': '加班和通勤压力都高，缺勤时长偏长。',
-            '健康波动型': '健康相关风险更高，需要重点关注。',
-            '稳定低风险型': '司龄较长，缺勤水平稳定且偏低。',
-            '轮班负荷型': '排班和工作负荷较重，缺勤风险较高。',
+            '压力奔波型': '加班与通勤压力同时偏高，缺勤波动更明显。',
+            '健康关注型': '健康负担更突出，缺勤时长偏高，建议优先关注。',
+            '负荷承压型': '工作负荷较重，缺勤风险处于偏高水平。',
+            '稳定成熟型': '司龄较长，整体状态稳定，缺勤水平偏低。',
+            '新锐成长型': '整体更年轻、司龄较短，仍处于适应与成长阶段。',
+            '通勤平衡型': '通勤与缺勤表现较均衡，整体波动相对可控。',
+            '经验稳健型': '具备一定经验积累，整体表现稳健，缺勤风险较低。',
+            '常规稳态型': '整体表现接近企业常态，是较典型的员工群体。',
         }
         for key, description in descriptions.items():
             if name.startswith(key):
+                if center is None:
                     return description
-        return descriptions.get(name, '常规员工群体。')
+                return self._build_dynamic_description(key, center, description)
+        return descriptions.get(name, '整体表现接近企业常态。')
+
+    def _build_dynamic_description(self, base_name, center, default_description):
+        age, tenure, overtime, commute, bmi, absence = center
+        clauses = []
+
+        if tenure >= 8:
+            clauses.append('司龄较长')
+        elif tenure <= 4:
+            clauses.append('司龄较短')
+
+        if overtime >= 30:
+            clauses.append('加班负荷偏高')
+        elif overtime <= 18:
+            clauses.append('加班压力相对可控')
+
+        if commute >= 45:
+            clauses.append('通勤压力偏高')
+        elif commute <= 30:
+            clauses.append('通勤节奏较平衡')
+
+        if bmi >= 26:
+            clauses.append('健康管理压力更明显')
+
+        if absence >= 7.5:
+            clauses.append('缺勤时长偏高')
+        elif absence <= 5.5:
+            clauses.append('缺勤水平偏低')
+
+        if age <= 32:
+            clauses.append('群体整体更年轻')
+        elif age >= 40:
+            clauses.append('群体整体更成熟')
+
+        unique_clauses = []
+        for clause in clauses:
+            if clause not in unique_clauses:
+                unique_clauses.append(clause)
+
+        if not unique_clauses:
+            return default_description
+        return '，'.join(unique_clauses[:3]) + '。'
 
 
 kmeans_analyzer = KMeansAnalyzer()

backend/services/cluster_service.py

@@ -2,17 +2,21 @@ from core.clustering import KMeansAnalyzer
 
 
 class ClusterService:
-    def __init__(self):
-        self.analyzer = KMeansAnalyzer()
+    def _create_analyzer(self):
+        # 聚类接口会被前端并发调用，避免复用同一个可变分析器实例导致结果串线。
+        return KMeansAnalyzer()
 
     def get_cluster_result(self, n_clusters=3):
-        return self.analyzer.get_cluster_results(n_clusters)
+        analyzer = self._create_analyzer()
+        return analyzer.get_cluster_results(n_clusters)
 
     def get_cluster_profile(self, n_clusters=3):
-        return self.analyzer.get_cluster_profile(n_clusters)
+        analyzer = self._create_analyzer()
+        return analyzer.get_cluster_profile(n_clusters)
 
     def get_scatter_data(self, n_clusters=3, x_axis='月均加班时长', y_axis='缺勤时长（小时）'):
-        return self.analyzer.get_scatter_data(n_clusters, x_axis, y_axis)
+        analyzer = self._create_analyzer()
+        return analyzer.get_scatter_data(n_clusters, x_axis, y_axis)
 
 
 cluster_service = ClusterService()

backend/services/predict_service.py

@@ -16,18 +16,26 @@ from core.model_features import (
 
 MODEL_INFO = {
     'random_forest': {'name': 'random_forest', 'name_cn': '随机森林', 'description': '稳健的树模型集成'},
-    'xgboost': {'name': 'xgboost', 'name_cn': 'XGBoost', 'description': '梯度提升树模型'},
-    'lightgbm': {'name': 'lightgbm', 'name_cn': 'LightGBM', 'description': '轻量级梯度提升树'},
-    'gradient_boosting': {'name': 'gradient_boosting', 'name_cn': 'GBDT', 'description': '梯度提升决策树'},
+    'xgboost': {'name': 'xgboost', 'name_cn': '增强树模型一', 'description': '梯度提升树模型'},
+    'lightgbm': {'name': 'lightgbm', 'name_cn': '增强树模型二', 'description': '轻量级梯度提升树'},
+    'gradient_boosting': {'name': 'gradient_boosting', 'name_cn': '梯度提升树', 'description': '梯度提升决策树'},
     'extra_trees': {'name': 'extra_trees', 'name_cn': '极端随机树', 'description': '高随机性的树模型'},
-    'stacking': {'name': 'stacking', 'name_cn': 'Stacking集成', 'description': '多模型融合'},
+    'stacking': {'name': 'stacking', 'name_cn': '集成模型', 'description': '多模型融合'},
     'lstm_mlp': {
         'name': 'lstm_mlp',
         'name_cn': '时序注意力融合网络',
-        'description': 'Transformer时序编码 + 静态特征门控融合的深度学习模型',
+        'description': 'Transformer 时序编码与静态特征融合的深度学习模型',
     },
 }
 
+EXPLAINABLE_TREE_MODELS = (
+    'random_forest',
+    'xgboost',
+    'lightgbm',
+    'gradient_boosting',
+    'extra_trees',
+)
+
 
 class PredictService:
     def __init__(self):
@@ -96,7 +104,6 @@ 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):
@@ -123,18 +130,22 @@ class PredictService:
         models.sort(key=lambda item: item['metrics']['r2'], reverse=True)
         return models
 
-    def predict_single(self, data, model_type=None):
+    def predict_single(self, data, model_type=None, include_explanation=True):
         self._ensure_models_loaded()
-        model_type = model_type or self.default_model
-        if model_type not in self.models:
-            fallback = next(iter(self.models), None)
-            if fallback is None:
-                return self._get_default_prediction(data)
-            model_type = fallback
-        if self.scaler is None or self.feature_names is None:
-            return self._get_default_prediction(data)
+        model_type = self._resolve_prediction_model(model_type or self.default_model)
+        _, engineered_df = self._build_prediction_frames(data)
+        engineered_row = engineered_df.iloc[0]
+
+        if model_type is None or self.scaler is None or self.feature_names is None:
+            result = self._get_default_prediction(data)
+            return self._augment_prediction_result(result, data, engineered_row) if include_explanation else result
+
+        try:
+            features = self._prepare_features_from_engineered(engineered_df)
+        except Exception:
+            result = self._get_default_prediction(data)
+            return self._augment_prediction_result(result, data, engineered_row) if include_explanation else result
 
-        features = self._prepare_features(data)
         try:
             if model_type == 'lstm_mlp':
                 current_df = build_prediction_dataframe(data)
@@ -144,15 +155,14 @@ class PredictService:
                 predicted_hours = self._inverse_transform_prediction(predicted_hours)
             predicted_hours = max(0.5, float(predicted_hours))
         except Exception:
-            return self._get_default_prediction(data)
+            result = self._get_default_prediction(data)
+            return self._augment_prediction_result(result, data, engineered_row) if include_explanation else result
 
         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 {
+        result = {
             'predicted_hours': round(predicted_hours, 2),
             'risk_level': risk_level,
             'risk_label': risk_label,
@@ -161,12 +171,13 @@ class PredictService:
             'model_used': model_type,
             'model_name_cn': MODEL_INFO.get(model_type, {}).get('name_cn', model_type),
         }
+        return self._augment_prediction_result(result, data, engineered_row) if include_explanation else result
 
     def predict_compare(self, data):
         self._ensure_models_loaded()
         results = []
         for name in self.models.keys():
-            result = self.predict_single(data, name)
+            result = self.predict_single(data, name, include_explanation=False)
             result['model'] = name
             result['model_name_cn'] = MODEL_INFO.get(name, {}).get('name_cn', name)
             result['r2'] = self.model_metrics.get(name, {}).get('r2', 0)
@@ -176,10 +187,17 @@ class PredictService:
             results[0]['recommended'] = True
         return results
 
+    def _build_prediction_frames(self, data):
+        current_df = build_prediction_dataframe(data)
+        engineered_df = engineer_features(current_df.copy())
+        return current_df, engineered_df
+
     def _prepare_features(self, data):
-        X_df = build_prediction_dataframe(data)
-        X_df = engineer_features(X_df)
-        X_df = apply_label_encoders(X_df, self.label_encoders)
+        _, engineered_df = self._build_prediction_frames(data)
+        return self._prepare_features_from_engineered(engineered_df)
+
+    def _prepare_features_from_engineered(self, engineered_df):
+        X_df = apply_label_encoders(engineered_df.copy(), self.label_encoders)
         X_df = align_feature_frame(X_df, self.feature_names)
         features = self.scaler.transform(to_float_array(X_df))[0]
         if self.selected_features:
@@ -188,6 +206,338 @@ class PredictService:
                 features = features[selected_indices]
         return features
 
+    def _resolve_prediction_model(self, requested_model):
+        if requested_model in self.models:
+            return requested_model
+        if self.default_model in self.models:
+            return self.default_model
+        return next(iter(self.models), None)
+
+    def _resolve_explanation_model(self, prediction_model):
+        if prediction_model in EXPLAINABLE_TREE_MODELS and prediction_model in self.models:
+            return prediction_model
+        for candidate in ('random_forest', 'xgboost', 'lightgbm', 'gradient_boosting', 'extra_trees'):
+            if candidate in self.models:
+                return candidate
+        return None
+
+    def _augment_prediction_result(self, result, data, engineered_row):
+        explanation_model = self._resolve_explanation_model(result.get('model_used'))
+        shap_local = self._get_local_explanation(data, explanation_model)
+        jdr_snapshot = self._build_jdr_snapshot(engineered_row)
+        mechanism_summary = self._build_mechanism_summary(result, data, jdr_snapshot, shap_local)
+        intervention_suggestions = self._build_intervention_suggestions(data, jdr_snapshot, shap_local)
+
+        payload = dict(result)
+        payload.update({
+            'jdr_snapshot': jdr_snapshot,
+            'mechanism_summary': mechanism_summary,
+            'intervention_suggestions': intervention_suggestions,
+            'explanation_model_used': explanation_model,
+            'explanation_model_name_cn': MODEL_INFO.get(explanation_model, {}).get('name_cn', '机制解释'),
+            'shap_local': shap_local,
+        })
+        return payload
+
+    def _get_local_explanation(self, data, model_type):
+        if not model_type:
+            return None
+        try:
+            from services.shap_service import shap_service
+
+            explanation = shap_service.get_local_explanation(data, model_type)
+            if explanation and not explanation.get('error'):
+                return explanation
+        except Exception:
+            pass
+        return None
+
+    def _build_jdr_snapshot(self, engineered_row):
+        snapshot = {
+            'job_demands': self._build_snapshot_item(
+                'job_demands',
+                '工作要求',
+                engineered_row.get('工作要求指数', 0.0),
+                *self._classify_job_demands(engineered_row.get('工作要求指数', 0.0)),
+            ),
+            'job_resources': self._build_snapshot_item(
+                'job_resources',
+                '工作资源',
+                engineered_row.get('工作资源指数', 0.0),
+                *self._classify_resource_stock(engineered_row.get('工作资源指数', 0.0)),
+            ),
+            'personal_resources': self._build_snapshot_item(
+                'personal_resources',
+                '个人资源',
+                engineered_row.get('个人资源指数', 0.0),
+                *self._classify_resource_stock(engineered_row.get('个人资源指数', 0.0)),
+            ),
+            'balance': self._build_snapshot_item(
+                'balance',
+                '平衡度',
+                engineered_row.get('JD-R平衡度', 0.0),
+                *self._classify_balance(engineered_row.get('JD-R平衡度', 0.0)),
+            ),
+            'burnout_risk': self._build_snapshot_item(
+                'burnout_risk',
+                '倦怠风险',
+                engineered_row.get('倦怠风险指数', 0.0),
+                *self._classify_burnout(engineered_row.get('倦怠风险指数', 0.0)),
+            ),
+            'engagement': self._build_snapshot_item(
+                'engagement',
+                '工作投入',
+                engineered_row.get('工作投入指数', 0.0),
+                *self._classify_resource_stock(engineered_row.get('工作投入指数', 0.0)),
+            ),
+        }
+        return snapshot
+
+    def _build_snapshot_item(self, key, label, score, status, tone):
+        return {
+            'key': key,
+            'label': label,
+            'score': round(self._safe_float(score), 2),
+            'status': status,
+            'tone': tone,
+        }
+
+    def _build_mechanism_summary(self, result, data, jdr_snapshot, shap_local):
+        dimension_scores = self._extract_dimension_scores(shap_local)
+        top_drivers = self._extract_feature_effects(shap_local, positive=True, limit=3)
+        protective_factors = self._extract_feature_effects(shap_local, positive=False, limit=2)
+
+        pathway_label, pathway_tone, pathway_detail = self._infer_pathway(jdr_snapshot, dimension_scores)
+        mechanism = self._build_mechanism_text(data, jdr_snapshot, dimension_scores, top_drivers)
+        buffer_text = self._build_buffer_text(jdr_snapshot, protective_factors)
+        scenario_hint = self._build_scenario_hint(data)
+
+        return {
+            'conclusion': f"本次预测为{result['risk_label']}，预计缺勤时长约 {result['predicted_hours']} 小时。",
+            'mechanism': mechanism,
+            'pathway_label': pathway_label,
+            'pathway_tone': pathway_tone,
+            'pathway_detail': pathway_detail,
+            'buffer_text': buffer_text,
+            'scenario_hint': scenario_hint,
+            'top_drivers': top_drivers,
+            'protective_factors': protective_factors,
+        }
+
+    def _build_mechanism_text(self, data, jdr_snapshot, dimension_scores, top_drivers):
+        if top_drivers:
+            driver_names = '、'.join(item['name_cn'] for item in top_drivers)
+            if dimension_scores.get('工作要求', 0.0) > 0.03:
+                return f'主要推高因素集中在{driver_names}，说明高工作要求正在直接抬升本次缺勤风险。'
+            if dimension_scores.get('事件上下文', 0.0) > 0.03:
+                return f'主要推高因素集中在{driver_names}，当前结果更容易受到请假事件情境的直接触发。'
+            if dimension_scores.get('工作资源', 0.0) > 0.03 or dimension_scores.get('个人资源', 0.0) > 0.03:
+                return f'主要推高因素集中在{driver_names}，说明资源缓冲不足正在放大本次缺勤时长。'
+            return f'主要推高因素集中在{driver_names}，它们共同推动了本次缺勤时长上升。'
+
+        fragments = []
+        if jdr_snapshot['job_demands']['tone'] in {'warning', 'danger'}:
+            fragments.append('工作要求偏高')
+        if jdr_snapshot['job_resources']['tone'] == 'danger':
+            fragments.append('工作资源不足')
+        if jdr_snapshot['personal_resources']['tone'] == 'danger':
+            fragments.append('个人资源偏弱')
+        if self._as_flag(data.get('medical_certificate_flag')) or self._as_flag(data.get('near_holiday_flag')):
+            fragments.append('事件情境触发明显')
+        if not fragments:
+            return '当前结果更多体现为常规缺勤波动，整体压力与资源结构暂时可控。'
+        return f"当前结果主要由{'、'.join(fragments)}共同驱动。"
+
+    def _build_buffer_text(self, jdr_snapshot, protective_factors):
+        if protective_factors:
+            names = '、'.join(item['name_cn'] for item in protective_factors)
+            return f'{names}对当前风险仍有一定缓冲作用，但尚不足以完全抵消主要压力来源。'
+        if jdr_snapshot['job_resources']['tone'] in {'success', 'info'} and jdr_snapshot['personal_resources']['tone'] in {'success', 'info'}:
+            return '当前资源支持和个人恢复能力对风险有一定缓冲，但事件性因素仍需持续关注。'
+        return ''
+
+    def _build_scenario_hint(self, data):
+        actions = []
+        if self._safe_float(data.get('monthly_overtime_hours', 0.0)) >= 25:
+            actions.append('将月均加班控制在 20 小时以内')
+        if self._safe_float(data.get('commute_minutes', 0.0)) >= 45:
+            actions.append('把通勤时长压缩到 30 分钟左右')
+        if self._as_flag(data.get('is_night_shift')):
+            actions.append('减少连续夜班或延长轮休恢复时间')
+        if not actions:
+            return ''
+        if len(actions) == 1:
+            return f'情境判断：若能{actions[0]}，当前风险通常会有所回落。'
+        return f"情境判断：若能{'，并'.join(actions[:-1])}，同时{actions[-1]}，当前风险通常会有所回落。"
+
+    def _infer_pathway(self, jdr_snapshot, dimension_scores):
+        demands_pressure = dimension_scores.get('工作要求', 0.0)
+        mediator_pressure = dimension_scores.get('中介变量', 0.0)
+        resource_pressure = dimension_scores.get('工作资源', 0.0) + dimension_scores.get('个人资源', 0.0)
+        event_pressure = dimension_scores.get('事件上下文', 0.0)
+
+        demands_high = jdr_snapshot['job_demands']['tone'] == 'danger'
+        burnout_high = jdr_snapshot['burnout_risk']['tone'] in {'warning', 'danger'}
+        resources_low = (
+            jdr_snapshot['job_resources']['tone'] == 'danger'
+            or jdr_snapshot['personal_resources']['tone'] == 'danger'
+            or jdr_snapshot['engagement']['tone'] == 'danger'
+        )
+
+        if demands_high or burnout_high or demands_pressure > 0.03 or mediator_pressure > 0.03:
+            if resources_low or resource_pressure > 0.03:
+                return (
+                    '健康损耗与资源缓冲不足',
+                    'danger',
+                    '当前结果同时表现出高要求累积与资源缓冲不足，更接近“工作要求上升 → 倦怠累积 → 缺勤增加”的复合路径。',
+                )
+            return (
+                '健康损耗路径为主',
+                'warning',
+                '当前结果更接近“工作要求上升 → 倦怠累积 → 缺勤增加”的健康损耗路径。',
+            )
+        if resources_low or resource_pressure > 0.03:
+            return (
+                '激励支撑不足路径',
+                'warning',
+                '当前资源与个人恢复能力偏弱，工作投入对缺勤风险的缓冲作用有限。',
+            )
+        if event_pressure > 0.04:
+            return (
+                '事件触发型波动',
+                'info',
+                '当前结果更容易受到请假类型、医院证明和节假日前后等事件情境直接触发。',
+            )
+        return (
+            '混合影响路径',
+            'info',
+            '当前结果同时受到工作要求、资源结构与事件情境的共同影响，尚不属于单一路径主导。',
+        )
+
+    def _build_intervention_suggestions(self, data, jdr_snapshot, shap_local):
+        suggestions = []
+
+        demand_items = []
+        overtime_hours = self._safe_float(data.get('monthly_overtime_hours', 0.0))
+        commute_minutes = self._safe_float(data.get('commute_minutes', 0.0))
+        if overtime_hours >= 25 or jdr_snapshot['job_demands']['tone'] == 'danger':
+            demand_items.append('优先压降连续高负荷排班，尽量把月均加班控制在 20 小时以内。')
+        if commute_minutes >= 45:
+            demand_items.append('若条件允许，可通过弹性到岗、调班或就近安排缓和通勤压力。')
+        if self._as_flag(data.get('is_night_shift')):
+            demand_items.append('夜班岗位建议增加轮休和班后恢复时段，避免疲劳持续累积。')
+        if self._as_flag(data.get('near_holiday_flag')):
+            demand_items.append('节假日前后可提前做好替班和排班缓冲，减少事件性缺勤波动。')
+        if not demand_items:
+            demand_items.append('当前工作要求未明显失衡，重点保持排班稳定并持续监控波动。')
+        suggestions.append({'category': '减要求', 'items': self._limit_unique_items(demand_items)})
+
+        resource_items = []
+        if jdr_snapshot['job_resources']['tone'] in {'warning', 'danger'}:
+            resource_items.append('增加主管沟通、临时替班支持和班组协同，补足组织支持资源。')
+        if jdr_snapshot['balance']['tone'] in {'warning', 'danger'}:
+            resource_items.append('对高风险岗位提供更清晰的任务边界和优先级，降低角色冲突。')
+        if str(data.get('leave_reason_category', '')) == '子女照护':
+            resource_items.append('可结合弹性工时或家庭照护支持，缓解家庭事务对缺勤的放大作用。')
+        if not resource_items:
+            resource_items.append('当前资源面整体可用，建议继续维持支持性排班和沟通反馈机制。')
+        suggestions.append({'category': '增资源', 'items': self._limit_unique_items(resource_items)})
+
+        personal_items = []
+        if self._as_flag(data.get('chronic_disease_flag')) or self._as_flag(data.get('medical_certificate_flag')):
+            personal_items.append('结合健康监测、复诊安排和短期工作调整，降低身体不适带来的持续缺勤风险。')
+        if jdr_snapshot['burnout_risk']['tone'] in {'warning', 'danger'}:
+            personal_items.append('建议通过休息恢复、情绪支持和短周期工作调整，缓冲倦怠累积。')
+        if jdr_snapshot['personal_resources']['tone'] == 'danger':
+            personal_items.append('可通过辅导、复盘和岗位支持增强员工自我效能与心理韧性。')
+        if not personal_items:
+            personal_items.append('当前个体恢复能力整体可控，重点维持规律作息和健康管理即可。')
+        suggestions.append({'category': '补个人资源', 'items': self._limit_unique_items(personal_items)})
+
+        return suggestions
+
+    def _extract_dimension_scores(self, shap_local):
+        if not shap_local:
+            return {}
+        dimension_contribution = shap_local.get('dimension_contribution', {})
+        return {
+            key: self._safe_float(value)
+            for key, value in dimension_contribution.items()
+            if isinstance(value, (int, float))
+        }
+
+    def _extract_feature_effects(self, shap_local, positive=True, limit=3):
+        if not shap_local:
+            return []
+        features = shap_local.get('features', [])
+        filtered = []
+        for item in features:
+            shap_value = self._safe_float(item.get('shap_value', 0.0))
+            if positive and shap_value <= 0:
+                continue
+            if not positive and shap_value >= 0:
+                continue
+            filtered.append({
+                'name': item.get('name'),
+                'name_cn': item.get('name_cn') or item.get('name') or '未命名特征',
+                'dimension': self._dimension_label(item.get('dimension')),
+                'shap_value': round(shap_value, 4),
+            })
+        filtered.sort(key=lambda entry: entry['shap_value'], reverse=positive)
+        if not positive:
+            filtered.sort(key=lambda entry: abs(entry['shap_value']), reverse=True)
+        return filtered[:limit]
+
+    def _dimension_label(self, key):
+        if key in config.JDR_DIMENSIONS:
+            return config.JDR_DIMENSIONS[key]['name_cn']
+        if key == 'event_context':
+            return '事件上下文'
+        if key == 'other':
+            return '其他因素'
+        return key or '其他因素'
+
+    def _limit_unique_items(self, items, limit=3):
+        unique_items = []
+        for item in items:
+            if item not in unique_items:
+                unique_items.append(item)
+        return unique_items[:limit]
+
+    def _classify_job_demands(self, score):
+        score = self._safe_float(score)
+        if score >= 5.2:
+            return '偏高', 'danger'
+        if score >= 4.0:
+            return '中等', 'warning'
+        return '适中', 'success'
+
+    def _classify_resource_stock(self, score):
+        score = self._safe_float(score)
+        if score >= 3.8:
+            return '充足', 'success'
+        if score >= 3.0:
+            return '中等', 'warning'
+        return '偏低', 'danger'
+
+    def _classify_balance(self, score):
+        score = self._safe_float(score)
+        if score >= 0.8:
+            return '资源占优', 'success'
+        if score >= 0.0:
+            return '基本平衡', 'info'
+        if score >= -0.8:
+            return '轻度失衡', 'warning'
+        return '明显失衡', 'danger'
+
+    def _classify_burnout(self, score):
+        score = self._safe_float(score)
+        if score >= 2.8:
+            return '偏高', 'danger'
+        if score >= 2.0:
+            return '中等', 'warning'
+        return '可控', 'success'
+
     def _inverse_transform_prediction(self, prediction):
         if self.training_metadata.get('target_transform') == 'log1p':
             return float(np.expm1(prediction))
@@ -202,13 +552,13 @@ class PredictService:
 
     def _get_default_prediction(self, data):
         base_hours = 3.8
-        base_hours += min(float(data.get('monthly_overtime_hours', 24)) / 20, 3.0)
-        base_hours += min(float(data.get('commute_minutes', 40)) / 50, 2.0)
-        base_hours += 1.6 if int(data.get('is_night_shift', 0)) == 1 else 0
-        base_hours += 1.8 if int(data.get('chronic_disease_flag', 0)) == 1 else 0
-        base_hours += 0.9 if int(data.get('near_holiday_flag', 0)) == 1 else 0
-        base_hours += 0.8 if int(data.get('medical_certificate_flag', 0)) == 1 else 0
-        base_hours += 0.5 * int(data.get('children_count', 0))
+        base_hours += min(self._safe_float(data.get('monthly_overtime_hours', 24)) / 20, 3.0)
+        base_hours += min(self._safe_float(data.get('commute_minutes', 40)) / 50, 2.0)
+        base_hours += 1.6 if self._as_flag(data.get('is_night_shift')) else 0
+        base_hours += 1.8 if self._as_flag(data.get('chronic_disease_flag')) else 0
+        base_hours += 0.9 if self._as_flag(data.get('near_holiday_flag')) else 0
+        base_hours += 0.8 if self._as_flag(data.get('medical_certificate_flag')) else 0
+        base_hours += 0.5 * int(self._safe_float(data.get('children_count', 0)))
         if data.get('leave_type') in ['病假', '工伤假', '婚假', '丧假']:
             base_hours += 2.5
         if data.get('stress_level') == '高':
@@ -227,7 +577,6 @@ class PredictService:
         }
 
     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')
@@ -246,7 +595,6 @@ class PredictService:
             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)
@@ -293,5 +641,17 @@ class PredictService:
             },
         }
 
+    def _safe_float(self, value, default=0.0):
+        try:
+            return float(value)
+        except (TypeError, ValueError):
+            return default
+
+    def _as_flag(self, value):
+        try:
+            return int(value) == 1
+        except (TypeError, ValueError):
+            return False
+
 
 predict_service = PredictService()

backend/services/shap_service.py

@@ -28,13 +28,58 @@ class SHAPService:
         except Exception:
             return None
 
-    def get_global_importance(self, model_type='random_forest'):
-        cache = self._load_cache(model_type)
-        if not cache:
+    def _save_cache(self, model_type, payload):
+        os.makedirs(config.SHAP_CACHE_DIR, exist_ok=True)
+        cache_path = self._get_cache_path(model_type)
+        with open(cache_path, 'w', encoding='utf-8') as fp:
+            json.dump(payload, fp, ensure_ascii=False)
+
+    def _build_cache_payload(self, model_type):
+        self._ensure_analyzer()
+        global_data = self._analyzer.global_shap_values(model_type)
+        if global_data.get('error'):
+            return {'error': global_data['error']}
+
+        top_features = [item['name'] for item in global_data.get('top_features', [])[:15]]
+        dependence = {}
+        for feature_name in top_features:
+            data = self._analyzer.shap_dependence(feature_name, model_type)
+            if not data.get('error'):
+                dependence[feature_name] = data
+
+        interaction = self._analyzer.shap_interaction(model_type, top_n=10)
+        if interaction.get('error'):
+            return {'error': interaction['error']}
+
         return {
-                'error': f'SHAP cache not found for {model_type}. '
-                         f'Run backend/core/generate_shap_cache.py first.'
+            'model_type': model_type,
+            'global': global_data,
+            'dependence': dependence,
+            'interaction': interaction,
         }
+
+    def _ensure_cache(self, model_type):
+        cache = self._load_cache(model_type)
+        if cache:
+            return cache
+
+        payload = self._build_cache_payload(model_type)
+        if payload.get('error'):
+            return {
+                'error': f'{model_type} 的贡献解释数据暂时不可用：{payload["error"]}'
+            }
+
+        try:
+            self._save_cache(model_type, payload)
+        except Exception:
+            # 缓存写入失败时至少保证当前请求可继续返回结果。
+            pass
+        return payload
+
+    def get_global_importance(self, model_type='random_forest'):
+        cache = self._ensure_cache(model_type)
+        if cache.get('error'):
+            return cache
         return cache.get('global', {'error': f'Invalid SHAP cache for {model_type}'})
 
     def get_local_explanation(self, data, model_type='random_forest'):
@@ -42,12 +87,9 @@ class SHAPService:
         return self._analyzer.local_shap_values(data, model_type)
 
     def get_interactions(self, model_type='random_forest', top_n=10):
-        cache = self._load_cache(model_type)
-        if not cache:
-            return {
-                'error': f'SHAP cache not found for {model_type}. '
-                         f'Run backend/core/generate_shap_cache.py first.'
-            }
+        cache = self._ensure_cache(model_type)
+        if cache.get('error'):
+            return cache
         data = cache.get('interaction')
         if not data:
             return {'error': f'Interaction cache missing for {model_type}'}
@@ -58,17 +100,26 @@ class SHAPService:
         return data
 
     def get_dependence(self, feature_name, model_type='random_forest'):
-        cache = self._load_cache(model_type)
-        if not cache:
-            return {
-                'error': f'SHAP cache not found for {model_type}. '
-                         f'Run backend/core/generate_shap_cache.py first.'
-            }
+        cache = self._ensure_cache(model_type)
+        if cache.get('error'):
+            return cache
         dependence_map = cache.get('dependence', {})
         data = dependence_map.get(feature_name)
         if data:
             return data
-        return {'error': f'Dependence cache missing for feature {feature_name}'}
+
+        self._ensure_analyzer()
+        data = self._analyzer.shap_dependence(feature_name, model_type)
+        if data.get('error'):
+            return {'error': f'特征 {feature_name} 的依赖解释不可用：{data["error"]}'}
+
+        dependence_map[feature_name] = data
+        cache['dependence'] = dependence_map
+        try:
+            self._save_cache(model_type, cache)
+        except Exception:
+            pass
+        return data
 
 
 shap_service = SHAPService()

backend/tests/test_clustering_naming.py

@@ -0,0 +1,120 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+import numpy as np
+
+
+def load_clustering_module():
+    module_path = Path(r'D:\forsetsystem\backend\core\clustering.py')
+
+    fake_config = types.SimpleNamespace(
+        RANDOM_STATE=42,
+        TARGET_COLUMN='缺勤时长（小时）',
+        EMPLOYEE_ID_COLUMN='员工工号',
+        FEATURE_NAME_CN={
+            '月均加班时长': '月均加班时长',
+            '缺勤时长（小时）': '缺勤时长（小时）',
+        },
+    )
+    fake_preprocessing = types.ModuleType('core.preprocessing')
+    fake_preprocessing.get_clean_data = lambda: None
+    fake_sklearn = types.ModuleType('sklearn')
+    fake_sklearn_cluster = types.ModuleType('sklearn.cluster')
+    fake_sklearn_preprocessing = types.ModuleType('sklearn.preprocessing')
+
+    class DummyKMeans:
+        def __init__(self, *args, **kwargs):
+            self.cluster_centers_ = None
+
+        def fit_predict(self, data):
+            self.cluster_centers_ = np.asarray(data, dtype=float)
+            return np.zeros(len(data), dtype=int)
+
+    class DummyMinMaxScaler:
+        def fit_transform(self, data):
+            return np.asarray(data, dtype=float)
+
+        def inverse_transform(self, data):
+            return np.asarray(data, dtype=float)
+
+    fake_sklearn_cluster.KMeans = DummyKMeans
+    fake_sklearn_preprocessing.MinMaxScaler = DummyMinMaxScaler
+
+    sys.modules['config'] = fake_config
+    sys.modules['core.preprocessing'] = fake_preprocessing
+    sys.modules['sklearn'] = fake_sklearn
+    sys.modules['sklearn.cluster'] = fake_sklearn_cluster
+    sys.modules['sklearn.preprocessing'] = fake_sklearn_preprocessing
+
+    spec = importlib.util.spec_from_file_location('test_clustering_module', module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+class ClusterNamingTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        module = load_clustering_module()
+        cls.analyzer = module.KMeansAnalyzer()
+
+    def test_generate_cluster_names_avoids_generic_group_names(self):
+        centers = np.array([
+            [41, 11, 18, 28, 22.5, 4.2],
+            [30, 3, 22, 33, 23.0, 5.8],
+            [36, 7, 36, 52, 24.0, 8.6],
+            [38, 6, 24, 31, 27.2, 8.1],
+        ], dtype=float)
+
+        names = self.analyzer._generate_cluster_names(centers)
+
+        self.assertEqual(len(names), 4)
+        for name in names.values():
+            self.assertNotIn('群体', name)
+
+    def test_generate_cluster_names_returns_business_labels(self):
+        centers = np.array([
+            [42, 10, 16, 26, 22.0, 4.1],
+            [29, 2, 20, 30, 22.8, 5.6],
+            [35, 6, 34, 50, 24.1, 8.8],
+            [37, 7, 23, 29, 27.5, 8.0],
+        ], dtype=float)
+
+        names = self.analyzer._generate_cluster_names(centers)
+
+        self.assertIn('稳定成熟型', names.values())
+        self.assertIn('新锐成长型', names.values())
+        self.assertIn('压力奔波型', names.values())
+        self.assertIn('健康关注型', names.values())
+
+    def test_duplicate_names_receive_natural_suffixes(self):
+        centers = np.array([
+            [44, 12, 18, 29, 22.2, 4.0],
+            [39, 9, 20, 34, 23.1, 5.3],
+            [32, 4, 31, 46, 24.8, 7.2],
+        ], dtype=float)
+
+        names = self.analyzer._deduplicate_cluster_names(
+            {0: '稳定成熟型', 1: '稳定成熟型', 2: '负荷承压型'},
+            centers,
+        )
+
+        self.assertEqual({names[0], names[1]}, {'稳定成熟型-资深组', '稳定成熟型-成熟组'})
+        self.assertEqual(names[2], '负荷承压型')
+
+    def test_description_reflects_center_traits(self):
+        description = self.analyzer._generate_description(
+            '压力奔波型',
+            np.array([34, 5, 36, 52, 24.0, 8.3], dtype=float),
+        )
+
+        self.assertIn('加班负荷偏高', description)
+        self.assertIn('通勤压力偏高', description)
+        self.assertIn('缺勤时长偏高', description)
+
+
+if __name__ == '__main__':
+    unittest.main()

backend/tests/test_predict_explanation.py

@@ -0,0 +1,155 @@
+import importlib.util
+import sys
+import types
+import unittest
+from pathlib import Path
+
+
+def load_predict_module():
+    module_path = Path(r'D:\forsetsystem\backend\services\predict_service.py')
+
+    fake_config = types.SimpleNamespace(
+        MODELS_DIR='',
+        SCALER_PATH='',
+        JDR_DIMENSIONS={
+            'job_demands': {'name_cn': '工作要求'},
+            'job_resources': {'name_cn': '工作资源'},
+            'personal_resources': {'name_cn': '个人资源'},
+            'mediators': {'name_cn': '中介变量'},
+        },
+    )
+    fake_deep_learning = types.ModuleType('core.deep_learning_model')
+    fake_deep_learning.load_lstm_mlp_bundle = lambda path: None
+    fake_deep_learning.predict_lstm_mlp = lambda model, data: 0.0
+
+    fake_model_features = types.ModuleType('core.model_features')
+    fake_model_features.align_feature_frame = lambda frame, names: frame
+    fake_model_features.apply_label_encoders = lambda frame, encoders: frame
+    fake_model_features.build_prediction_dataframe = lambda data: data
+    fake_model_features.engineer_features = lambda frame: frame
+    fake_model_features.to_float_array = lambda frame: frame
+
+    sys.modules['config'] = fake_config
+    sys.modules['core.deep_learning_model'] = fake_deep_learning
+    sys.modules['core.model_features'] = fake_model_features
+
+    spec = importlib.util.spec_from_file_location('test_predict_service_module', module_path)
+    module = importlib.util.module_from_spec(spec)
+    spec.loader.exec_module(module)
+    return module
+
+
+class PredictExplanationTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        module = load_predict_module()
+        cls.service = module.PredictService()
+
+    def test_build_jdr_snapshot_marks_high_demands_and_low_resources(self):
+        snapshot = self.service._build_jdr_snapshot({
+            '工作要求指数': 5.8,
+            '工作资源指数': 2.7,
+            '个人资源指数': 2.8,
+            'JD-R平衡度': -1.1,
+            '倦怠风险指数': 3.1,
+            '工作投入指数': 2.9,
+        })
+
+        self.assertEqual(snapshot['job_demands']['status'], '偏高')
+        self.assertEqual(snapshot['job_resources']['status'], '偏低')
+        self.assertEqual(snapshot['balance']['status'], '明显失衡')
+        self.assertEqual(snapshot['burnout_risk']['status'], '偏高')
+
+    def test_mechanism_summary_prefers_health_impairment_path(self):
+        snapshot = self.service._build_jdr_snapshot({
+            '工作要求指数': 5.6,
+            '工作资源指数': 2.9,
+            '个人资源指数': 2.8,
+            'JD-R平衡度': -0.9,
+            '倦怠风险指数': 3.0,
+            '工作投入指数': 2.9,
+        })
+        shap_local = {
+            'dimension_contribution': {
+                '工作要求': 0.32,
+                '中介变量': 0.18,
+                '事件上下文': 0.11,
+                '工作资源': -0.07,
+            },
+            'features': [
+                {'name': 'monthly_overtime_hours', 'name_cn': '月均加班时长', 'dimension': 'job_demands', 'shap_value': 0.18},
+                {'name': 'commute_minutes', 'name_cn': '通勤时长', 'dimension': 'job_demands', 'shap_value': 0.12},
+                {'name': 'medical_certificate_flag', 'name_cn': '医院证明', 'dimension': 'event_context', 'shap_value': 0.08},
+                {'name': 'coworker_support', 'name_cn': '同事支持', 'dimension': 'job_resources', 'shap_value': -0.05},
+            ],
+        }
+        result = {'predicted_hours': 9.4, 'risk_label': '高风险'}
+        data = {
+            'monthly_overtime_hours': 38,
+            'commute_minutes': 62,
+            'is_night_shift': 1,
+            'medical_certificate_flag': 1,
+        }
+
+        summary = self.service._build_mechanism_summary(result, data, snapshot, shap_local)
+
+        self.assertIn('健康损耗', summary['pathway_label'])
+        self.assertIn('月均加班时长', summary['mechanism'])
+        self.assertTrue(summary['scenario_hint'])
+
+    def test_intervention_suggestions_cover_resource_and_personal_support(self):
+        snapshot = self.service._build_jdr_snapshot({
+            '工作要求指数': 4.4,
+            '工作资源指数': 2.7,
+            '个人资源指数': 2.6,
+            'JD-R平衡度': -0.7,
+            '倦怠风险指数': 2.9,
+            '工作投入指数': 2.8,
+        })
+        suggestions = self.service._build_intervention_suggestions(
+            {
+                'monthly_overtime_hours': 18,
+                'commute_minutes': 28,
+                'chronic_disease_flag': 1,
+                'medical_certificate_flag': 1,
+                'leave_reason_category': '子女照护',
+            },
+            snapshot,
+            shap_local=None,
+        )
+
+        category_map = {item['category']: item['items'] for item in suggestions}
+        self.assertIn('增资源', category_map)
+        self.assertIn('补个人资源', category_map)
+        self.assertTrue(any('支持' in item or '弹性' in item for item in category_map['增资源']))
+        self.assertTrue(any('健康' in item or '倦怠' in item for item in category_map['补个人资源']))
+
+    def test_buffer_text_mentions_protective_factors(self):
+        snapshot = self.service._build_jdr_snapshot({
+            '工作要求指数': 3.9,
+            '工作资源指数': 4.2,
+            '个人资源指数': 4.0,
+            'JD-R平衡度': 0.9,
+            '倦怠风险指数': 1.8,
+            '工作投入指数': 4.1,
+        })
+        shap_local = {
+            'dimension_contribution': {
+                '工作要求': 0.08,
+                '工作资源': -0.12,
+                '个人资源': -0.09,
+            },
+            'features': [
+                {'name': 'supervisor_support', 'name_cn': '上级支持', 'dimension': 'job_resources', 'shap_value': -0.07},
+                {'name': 'self_efficacy', 'name_cn': '自我效能感', 'dimension': 'personal_resources', 'shap_value': -0.05},
+            ],
+        }
+
+        summary = self.service._build_mechanism_summary({'predicted_hours': 5.3, 'risk_label': '中风险'}, {}, snapshot, shap_local)
+
+        self.assertIn('缓冲作用', summary['buffer_text'])
+        self.assertTrue(summary['protective_factors'])
+
+
+if __name__ == '__main__':
+    unittest.main()

frontend/src/App.vue

@@ -2,10 +2,9 @@
   <div class="shell" :class="{ 'shell-collapsed': isSidebarCollapsed }">
     <aside class="shell-sidebar">
       <div class="brand-block">
-        <div class="brand-mark">HR</div>
+        <div class="brand-mark"></div>
         <div v-if="!isSidebarCollapsed">
           <div class="brand-title">企业缺勤分析台</div>
-          <div class="brand-subtitle">Human Resource Insight Console</div>
         </div>
       </div>
 
@@ -30,14 +29,14 @@
           </el-menu-item>
           <el-menu-item index="/jdr-analysis">
             <el-icon class="nav-icon"><Reading /></el-icon>
-            <span class="nav-label">JD-R分析</span>
+            <span class="nav-label">理论分析</span>
           </el-menu-item>
         </el-menu>
       </div>
 
       <div v-if="!isSidebarCollapsed" class="sidebar-note">
         <div class="sidebar-label">系统摘要</div>
-        <p>面向企业管理场景的缺勤趋势、风险预测与群体画像展示。</p>
+        <p>缺勤趋势、风险预测与群体画像。</p>
       </div>
     </aside>
 
@@ -56,8 +55,6 @@
           <el-button class="theme-toggle" @click="toggleTheme">
             {{ isDarkMode ? '浅色模式' : '深色模式' }}
           </el-button>
-          <span class="topbar-badge">企业健康运营分析</span>
-          <span class="topbar-badge topbar-badge-accent">可视化决策界面</span>
         </div>
       </header>
 
@@ -81,23 +78,23 @@ const isDarkMode = ref(false)
 const metaMap = {
   '/dashboard': {
     title: '数据概览',
-    subtitle: '从企业缺勤事件的总量、时序与结构分布切入，建立整体认知。'
+    subtitle: '缺勤事件总量、趋势与结构分布'
   },
   '/analysis': {
     title: '影响因素',
-    subtitle: '观察模型最关注的驱动因素，辅助解释缺勤风险的来源。'
+    subtitle: '关键驱动因素与群体差异'
   },
   '/prediction': {
     title: '缺勤预测',
-    subtitle: '围绕最核心的业务信号输入，快速获得缺勤时长与风险等级。'
+    subtitle: '缺勤时长、风险等级与模型对比'
   },
   '/clustering': {
     title: '员工画像',
-    subtitle: '通过聚类划分典型群体，为答辩演示提供更直观的人群视角。'
+    subtitle: '典型群体划分与画像分析'
   },
   '/jdr-analysis': {
-    title: 'JD-R理论分析',
-    subtitle: '基于工作要求-资源理论的可解释分析，揭示缺勤的心理学驱动因素。'
+    title: '理论分析',
+    subtitle: '工作要求、资源支持与缺勤风险'
   }
 }
 
@@ -164,8 +161,28 @@ watch(isDarkMode, value => {
   border-radius: 16px;
   background: linear-gradient(135deg, #fef3c7, #fdba74);
   color: #7c2d12;
-  font-weight: 800;
-  letter-spacing: 0.08em;
+  position: relative;
+}
+
+.brand-mark::before,
+.brand-mark::after {
+  content: '';
+  position: absolute;
+  border-radius: 999px;
+  background: rgba(124, 45, 18, 0.9);
+}
+
+.brand-mark::before {
+  width: 20px;
+  height: 20px;
+}
+
+.brand-mark::after {
+  width: 8px;
+  height: 8px;
+  right: 11px;
+  bottom: 11px;
+  background: rgba(255, 255, 255, 0.72);
 }
 
 .brand-title {
@@ -174,12 +191,6 @@ watch(isDarkMode, value => {
   color: var(--sidebar-text);
 }
 
-.brand-subtitle {
-  margin-top: 4px;
-  font-size: 12px;
-  color: var(--sidebar-text-subtle);
-}
-
 .sidebar-panel,
 .sidebar-note {
   padding: 18px;
@@ -296,19 +307,6 @@ watch(isDarkMode, value => {
   color: var(--text-main);
 }
 
-.topbar-badge {
-  padding: 9px 14px;
-  border: 1px solid var(--line-soft);
-  border-radius: 999px;
-  background: var(--surface);
-  font-size: 12px;
-  color: var(--brand-strong);
-}
-
-.topbar-badge-accent {
-  color: var(--accent);
-}
-
 .main-content {
   min-width: 0;
 }

frontend/src/router/index.js

@@ -33,7 +33,7 @@ const routes = [
     path: '/jdr-analysis',
     name: 'JDRAnalysis',
     component: () => import('@/views/JDRAnalysis.vue'),
-    meta: { title: 'JD-R理论分析' }
+    meta: { title: '理论分析' }
   }
 ]
 

frontend/src/styles/theme.css

@@ -104,14 +104,6 @@ a {
   background: rgba(255, 255, 255, 0.08);
 }
 
-.page-eyebrow {
-  margin-bottom: 10px;
-  font-size: 12px;
-  letter-spacing: 0.22em;
-  text-transform: uppercase;
-  opacity: 0.72;
-}
-
 .page-title {
   margin: 0;
   font-size: 30px;
@@ -168,21 +160,6 @@ a {
   height: 300px;
 }
 
-.soft-tag {
-  display: inline-flex;
-  align-items: center;
-  gap: 8px;
-  padding: 7px 12px;
-  border-radius: 999px;
-  font-size: 12px;
-  color: var(--brand-strong);
-  background: rgba(15, 118, 110, 0.1);
-}
-
-:root[data-theme='dark'] .soft-tag {
-  background: rgba(52, 211, 153, 0.14);
-}
-
 .soft-grid {
   display: grid;
   gap: 18px;

frontend/src/views/Clustering.vue

@@ -1,10 +1,9 @@
 <template>
   <div class="page-shell">
     <section class="page-hero cluster-hero">
-      <div class="page-eyebrow">Clustering</div>
       <h1 class="page-title">员工画像与群体切片</h1>
       <p class="page-description">
-        将员工划分为不同缺勤画像群体，通过雷达图和散点图形成直观的人群对比展示。
+        基于缺勤行为、工作压力和基础属性划分典型员工群体。
       </p>
     </section>
 
@@ -12,7 +11,7 @@
       <div class="section-heading">
         <div>
           <h3 class="section-title">群体雷达画像</h3>
-          <p class="section-caption">以年龄、司龄、加班、通勤、BMI 和缺勤水平构建群体轮廓。</p>
+          <p class="section-caption">年龄、司龄、加班、通勤、BMI 与缺勤水平</p>
         </div>
         <el-select v-model="nClusters" @change="loadData" class="cluster-select">
           <el-option :label="2" :value="2" />
@@ -29,9 +28,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">聚类结果</h3>
-              <p class="section-caption">便于答辩时逐个介绍群体特征。</p>
+              <p class="section-caption">群体规模与主要特征</p>
             </div>
-            <span class="soft-tag">Profiles</span>
           </div>
           <el-table :data="clusterData" stripe class="cluster-table">
             <el-table-column prop="name" label="群体名称" min-width="120" />
@@ -39,7 +37,7 @@
             <el-table-column prop="percentage" label="占比(%)" width="90">
               <template #default="{ row }">{{ row.percentage }}%</template>
             </el-table-column>
-            <el-table-column prop="description" label="说明" min-width="180" />
+            <el-table-column prop="description" label="群体特征" min-width="180" />
           </el-table>
         </el-card>
       </el-col>
@@ -48,9 +46,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">加班与缺勤散点图</h3>
-              <p class="section-caption">展示各聚类在加班强度与缺勤水平上的位置差异。</p>
+              <p class="section-caption">加班强度与缺勤水平分布</p>
             </div>
-            <span class="soft-tag">Scatter</span>
           </div>
           <div ref="scatterChartRef" class="chart-frame"></div>
         </el-card>
@@ -60,7 +57,7 @@
 </template>
 
 <script setup>
-import { onMounted, ref } from 'vue'
+import { onBeforeUnmount, onMounted, ref } from 'vue'
 import * as echarts from 'echarts'
 import request from '@/api/request'
 
@@ -68,19 +65,44 @@ const radarChartRef = ref(null)
 const scatterChartRef = ref(null)
 const nClusters = ref(3)
 const clusterData = ref([])
+const loadVersion = ref(0)
+let radarChart = null
+let scatterChart = null
 
 onMounted(() => {
   loadData()
 })
 
+onBeforeUnmount(() => {
+  radarChart?.dispose()
+  scatterChart?.dispose()
+})
+
 async function loadData() {
-  await Promise.all([initRadarChart(), initScatterChart(), loadClusterResult()])
+  const currentClusters = nClusters.value
+  const version = ++loadVersion.value
+  const [profile, scatter, result] = await Promise.all([
+    request.get(`/cluster/profile?n_clusters=${currentClusters}`),
+    request.get(`/cluster/scatter?n_clusters=${currentClusters}`),
+    request.get(`/cluster/result?n_clusters=${currentClusters}`)
+  ])
+
+  if (version !== loadVersion.value || currentClusters !== nClusters.value) {
+    return
+  }
+
+  renderRadarChart(profile)
+  renderScatterChart(scatter)
+  clusterData.value = result.clusters
 }
 
-async function initRadarChart() {
-  const chart = echarts.init(radarChartRef.value)
-  const data = await request.get(`/cluster/profile?n_clusters=${nClusters.value}`)
-  chart.setOption({
+function renderRadarChart(data) {
+  if (!radarChartRef.value) return
+  if (!radarChart) {
+    radarChart = echarts.init(radarChartRef.value)
+  }
+  radarChart.clear()
+  radarChart.setOption({
     tooltip: {},
     legend: { top: 6, data: data.clusters.map(item => item.name) },
     radar: { indicator: data.dimensions.map(name => ({ name, max: 1 })), radius: '62%' },
@@ -88,15 +110,18 @@ async function initRadarChart() {
   })
 }
 
-async function initScatterChart() {
-  const chart = echarts.init(scatterChartRef.value)
-  const data = await request.get(`/cluster/scatter?n_clusters=${nClusters.value}`)
+function renderScatterChart(data) {
+  if (!scatterChartRef.value) return
+  if (!scatterChart) {
+    scatterChart = echarts.init(scatterChartRef.value)
+  }
+  scatterChart.clear()
   const grouped = {}
   data.points.forEach(point => {
     if (!grouped[point.cluster_id]) grouped[point.cluster_id] = []
     grouped[point.cluster_id].push([point.x, point.y])
   })
-  chart.setOption({
+  scatterChart.setOption({
     tooltip: { trigger: 'item' },
     grid: { left: 36, right: 18, top: 20, bottom: 36, containLabel: true },
     xAxis: { name: data.x_axis_name, splitLine: { lineStyle: { color: '#E5EBF2' } } },
@@ -109,11 +134,6 @@ async function initScatterChart() {
     }))
   })
 }
-
-async function loadClusterResult() {
-  const data = await request.get(`/cluster/result?n_clusters=${nClusters.value}`)
-  clusterData.value = data.clusters
-}
 </script>
 
 <style scoped>

frontend/src/views/Dashboard.vue

@@ -1,10 +1,9 @@
 <template>
   <div class="page-shell">
     <section class="page-hero">
-      <div class="page-eyebrow">Overview</div>
       <h1 class="page-title">企业缺勤全景概览</h1>
       <p class="page-description">
-        通过总量、时序、结构分布三个层面快速识别缺勤风险的整体轮廓，适合作为答辩时的第一屏总览。
+        汇总缺勤总量、趋势变化与结构分布。
       </p>
     </section>
 
@@ -25,9 +24,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">月度缺勤事件趋势</h3>
-              <p class="section-caption">观察不同月份的事件量与时长波动。</p>
+              <p class="section-caption">月度事件量与时长波动</p>
             </div>
-            <span class="soft-tag">Trend</span>
           </div>
           <div ref="trendChartRef" class="chart-frame"></div>
         </el-card>
@@ -37,9 +35,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">星期分布</h3>
-              <p class="section-caption">识别工作周内的缺勤集中区间。</p>
+              <p class="section-caption">工作周缺勤分布</p>
             </div>
-            <span class="soft-tag">Weekday</span>
           </div>
           <div ref="weekdayChartRef" class="chart-frame"></div>
         </el-card>
@@ -52,9 +49,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">请假原因大类分布</h3>
-              <p class="section-caption">呈现引发缺勤的主要业务原因结构。</p>
+              <p class="section-caption">主要请假原因结构</p>
             </div>
-            <span class="soft-tag">Reason Mix</span>
           </div>
           <div ref="reasonChartRef" class="chart-frame"></div>
         </el-card>
@@ -64,9 +60,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">季节影响分布</h3>
-              <p class="section-caption">展示季节变化与缺勤总量之间的关系。</p>
+              <p class="section-caption">季节与缺勤总量</p>
             </div>
-            <span class="soft-tag">Season</span>
           </div>
           <div ref="seasonChartRef" class="chart-frame"></div>
         </el-card>

frontend/src/views/FactorAnalysis.vue

@@ -1,10 +1,9 @@
 <template>
   <div class="page-shell">
     <section class="page-hero analysis-hero">
-      <div class="page-eyebrow">Analysis</div>
       <h1 class="page-title">缺勤驱动因素洞察</h1>
       <p class="page-description">
-        将模型特征重要性、变量相关关系与群体差异放在同一界面展示，形成更完整的解释链路。
+        汇总关键变量、相关关系与群体差异，定位缺勤风险的主要来源。
       </p>
     </section>
 
@@ -12,9 +11,8 @@
       <div class="section-heading">
         <div>
           <h3 class="section-title">缺勤影响因素排序</h3>
-          <p class="section-caption">用于展示模型最关注的驱动信号及其主次关系。</p>
+          <p class="section-caption">模型特征重要性</p>
         </div>
-        <span class="soft-tag">Importance</span>
       </div>
       <div ref="importanceChartRef" class="chart-frame"></div>
     </el-card>
@@ -25,9 +23,8 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">核心特征相关性</h3>
-              <p class="section-caption">帮助说明关键指标之间的联动关系。</p>
+              <p class="section-caption">关键指标联动关系</p>
             </div>
-            <span class="soft-tag">Correlation</span>
           </div>
           <div ref="correlationChartRef" class="chart-frame"></div>
         </el-card>
@@ -37,7 +34,7 @@
           <div class="section-heading">
             <div>
               <h3 class="section-title">群体对比分析</h3>
-              <p class="section-caption">从行业、排班和健康等维度比较平均缺勤时长。</p>
+              <p class="section-caption">不同维度下的平均缺勤时长</p>
             </div>
             <el-select v-model="dimension" @change="loadComparison" class="dimension-select">
               <el-option label="所属行业" value="industry" />

frontend/src/views/JDRAnalysis.vue

@@ -1,22 +1,20 @@
 <template>
   <div class="page-shell jdr-page">
     <section class="page-hero jdr-hero">
-      <div class="page-eyebrow">JD-R Theory</div>
-      <h1 class="page-title">JD-R 理论驱动的可解释分析</h1>
+      <h1 class="page-title">JD-R 理论分析</h1>
       <p class="page-description">
-        基于工作要求-资源模型，从心理学理论视角解析员工缺勤的深层驱动因素，提供可解释的干预建议。
+        基于工作要求-资源模型，分析工作压力、资源支持与缺勤风险之间的关系。
       </p>
     </section>
 
     <el-tabs v-model="activeTab" type="border-card" class="jdr-tabs">
-      <!-- Tab 1: JD-R 维度分析 -->
       <el-tab-pane label="维度分析" name="dimensions">
         <el-row :gutter="20">
           <el-col :xs="24" :lg="12">
             <el-card class="panel-card" shadow="never">
               <template #header>
-                <h3 class="section-title">JD-R 维度雷达图</h3>
-                <p class="section-caption">工作要求、工作资源与个人资源三维度均值对比</p>
+                <h3 class="section-title">维度雷达图</h3>
+                <p class="section-caption">工作要求、工作资源与个人资源</p>
               </template>
               <div v-if="dimensionData" ref="radarChartRef" style="height: 380px"></div>
               <el-empty v-else description="加载中..." />
@@ -26,7 +24,7 @@
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">维度分布统计</h3>
-                <p class="section-caption">各维度的均值、标准差与平衡度</p>
+                <p class="section-caption">均值、标准差与平衡度</p>
               </template>
               <div v-if="dimensionData" class="dim-stats">
                 <div v-for="(item, key) in dimensionLabels" :key="key" class="dim-stat-row">
@@ -35,7 +33,7 @@
                   <div class="dim-stat-sub">std: {{ dimensionData[key]?.std || '-' }}</div>
                 </div>
                 <div v-if="dimensionData?.balance" class="dim-stat-row dim-stat-balance">
-                  <div class="dim-stat-label">JD-R 平衡度</div>
+                  <div class="dim-stat-label">平衡度</div>
                   <div class="dim-stat-value">{{ dimensionData.balance.mean }}</div>
                   <div class="dim-stat-sub">正向比例: {{ dimensionData.balance.positive_ratio }}%</div>
                 </div>
@@ -46,14 +44,13 @@
         </el-row>
       </el-tab-pane>
 
-      <!-- Tab 2: 倦怠与投入 -->
       <el-tab-pane label="倦怠与投入" name="burnout">
         <el-row :gutter="20">
           <el-col :xs="24" :lg="12">
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">倦怠与投入分布</h3>
-                <p class="section-caption">工作倦怠(1-7)和工作投入(1-7)的分布对比</p>
+                <p class="section-caption">工作倦怠与工作投入分布</p>
               </template>
               <div v-if="burnoutData" ref="burnoutChartRef" style="height: 380px"></div>
               <el-empty v-else description="加载中..." />
@@ -63,7 +60,7 @@
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">关键相关性</h3>
-                <p class="section-caption">JD-R 维度与缺勤时长之间的关联强度</p>
+                <p class="section-caption">理论维度与缺勤时长</p>
               </template>
               <div v-if="burnoutData" ref="corrChartRef" style="height: 380px"></div>
               <el-empty v-else description="加载中..." />
@@ -72,14 +69,13 @@
         </el-row>
       </el-tab-pane>
 
-      <!-- Tab 3: 双路径分析 -->
       <el-tab-pane label="双路径分析" name="path">
         <el-row :gutter="20">
           <el-col :xs="24" :lg="14">
             <el-card class="panel-card" shadow="never">
               <template #header>
-                <h3 class="section-title">JD-R 双路径理论模型</h3>
-                <p class="section-caption">健康损伤路径(需求→倦怠→缺勤)与激励路径(资源→投入→低缺勤)</p>
+                <h3 class="section-title">双路径理论模型</h3>
+                <p class="section-caption">健康损伤路径与激励路径</p>
               </template>
               <div class="path-diagram">
                 <div class="path-flow">
@@ -122,7 +118,7 @@
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">风险等级分布</h3>
-                <p class="section-caption">全员缺勤风险等级统计</p>
+                <p class="section-caption">全员风险等级统计</p>
               </template>
               <div v-if="riskData" ref="riskChartRef" style="height: 320px"></div>
               <el-empty v-else description="加载中..." />
@@ -131,22 +127,21 @@
         </el-row>
       </el-tab-pane>
 
-      <!-- Tab 4: SHAP 解释 -->
-      <el-tab-pane label="SHAP 解释" name="shap">
+      <el-tab-pane label="特征贡献" name="shap">
         <el-row :gutter="20">
           <el-col :xs="24" :lg="14">
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <div class="section-heading" style="margin-bottom:0">
                   <div>
-                    <h3 class="section-title">全局特征重要性 (SHAP)</h3>
-                    <p class="section-caption">按 SHAP 值排列的特征贡献度</p>
+                    <h3 class="section-title">全局特征重要性</h3>
+                    <p class="section-caption">特征贡献排序</p>
                   </div>
                   <el-select v-model="shapModel" size="small" style="width: 160px" @change="loadShapGlobal">
                     <el-option label="随机森林" value="random_forest" />
-                    <el-option label="XGBoost" value="xgboost" />
-                    <el-option label="LightGBM" value="lightgbm" />
-                    <el-option label="GBDT" value="gradient_boosting" />
+                    <el-option label="增强树模型一" value="xgboost" />
+                    <el-option label="增强树模型二" value="lightgbm" />
+                    <el-option label="梯度提升树" value="gradient_boosting" />
                   </el-select>
                 </div>
               </template>
@@ -158,7 +153,7 @@
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">维度贡献占比</h3>
-                <p class="section-caption">按 JD-R 理论维度聚合的 SHAP 贡献</p>
+                <p class="section-caption">理论维度聚合贡献</p>
               </template>
               <div v-if="shapGlobalData" ref="shapDimPieRef" style="height: 420px"></div>
               <el-empty v-else description="加载中..." />
@@ -170,7 +165,7 @@
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">特征依赖图</h3>
-                <p class="section-caption">选择特征查看其取值与 SHAP 值的关系</p>
+                <p class="section-caption">特征取值与贡献关系</p>
               </template>
               <div style="margin-bottom: 12px">
                 <el-select v-model="dependenceFeature" size="small" style="width: 200px" @change="loadDependence">
@@ -185,7 +180,7 @@
             <el-card class="panel-card" shadow="never">
               <template #header>
                 <h3 class="section-title">特征交互强度</h3>
-                <p class="section-caption">Top 特征对的交互效应</p>
+                <p class="section-caption">主要特征对交互效应</p>
               </template>
               <div v-if="shapGlobalData" ref="shapInteractionRef" style="height: 320px"></div>
               <el-empty v-else description="加载中..." />
@@ -239,6 +234,18 @@ function getOrCreateChart(el) {
   return chart
 }
 
+function clearShapViews() {
+  shapGlobalData.value = null
+  shapTopFeatures.value = []
+  const refs = [shapGlobalRef.value, shapDimPieRef.value, shapDependenceRef.value, shapInteractionRef.value]
+  refs.forEach(el => {
+    const chart = getOrCreateChart(el)
+    if (chart) {
+      chart.clear()
+    }
+  })
+}
+
 // 延迟渲染：等待 Vue 将 v-if 的 DOM 插入到页面
 function scheduleRender(fn) {
   requestAnimationFrame(() => {
@@ -265,7 +272,7 @@ function renderRadarChart() {
     { name: '工作要求', max: 10 },
     { name: '工作资源', max: 5 },
     { name: '个人资源', max: 5 },
-    { name: 'JD-R平衡度', max: 5 },
+    { name: '平衡度', max: 5 },
   ]
   const values = [
     dims.demands?.mean || 0,
@@ -281,7 +288,7 @@ function renderRadarChart() {
       type: 'radar',
       data: [{
         value: values,
-        name: 'JD-R 维度均值',
+        name: '维度均值',
         areaStyle: { color: 'rgba(15, 118, 110, 0.2)' },
         lineStyle: { color: '#0f766e', width: 2 },
         itemStyle: { color: '#0f766e' },
@@ -386,12 +393,16 @@ function renderRiskChart() {
 // ── Tab 4: SHAP ──
 async function loadShapGlobal() {
   if (activeTab.value !== 'shap') return
+  clearShapViews()
   try {
     const data = await getGlobalImportance(shapModel.value)
-    if (data.error) { ElMessage.error(data.error); return }
+    if (data.error) {
+      ElMessage.error(data.error)
+      return
+    }
     shapGlobalData.value = data
     shapTopFeatures.value = data.top_features || []
-    if (shapTopFeatures.value.length && !dependenceFeature.value) {
+    if (shapTopFeatures.value.length) {
       dependenceFeature.value = shapTopFeatures.value[0].name
     }
     scheduleRender(() => {
@@ -401,7 +412,7 @@ async function loadShapGlobal() {
       loadInteractions()
     })
   } catch (e) {
-    ElMessage.error('加载 SHAP 数据失败')
+    ElMessage.error('加载特征贡献数据失败')
   }
 }
 
@@ -422,7 +433,7 @@ function renderShapGlobalChart() {
   chart.setOption({
     tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
     grid: { left: 140, right: 30, top: 10, bottom: 30 },
-    xAxis: { type: 'value', name: 'Mean |SHAP|' },
+    xAxis: { type: 'value', name: '平均贡献值' },
     yAxis: { type: 'category', data: features.map(f => f.name_cn) },
     series: [{
       type: 'bar', data: features.map(f => ({
@@ -465,17 +476,22 @@ async function loadDependence() {
   if (!dependenceFeature.value) return
   try {
     const data = await getDependence(dependenceFeature.value, shapModel.value)
-    if (data.error) return
+    if (data.error) {
+      const chart = getOrCreateChart(shapDependenceRef.value)
+      if (chart) chart.clear()
+      ElMessage.error(data.error)
+      return
+    }
     await nextTick()
     const chart = getOrCreateChart(shapDependenceRef.value)
     if (!chart) return
 
     const points = data.values.map((v, i) => [v, data.shap_values[i]])
     chart.setOption({
-      tooltip: { trigger: 'item', formatter: (p) => `值: ${p.data[0].toFixed(2)}<br/>SHAP: ${p.data[1].toFixed(4)}` },
+      tooltip: { trigger: 'item', formatter: (p) => `值: ${p.data[0].toFixed(2)}<br/>贡献值: ${p.data[1].toFixed(4)}` },
       grid: { left: 60, right: 20, top: 20, bottom: 40 },
       xAxis: { type: 'value', name: data.feature_cn },
-      yAxis: { type: 'value', name: 'SHAP value' },
+      yAxis: { type: 'value', name: '贡献值' },
       series: [{
         type: 'scatter', data: points, symbolSize: 5,
         itemStyle: { color: '#0f766e', opacity: 0.6 },
@@ -488,7 +504,12 @@ async function loadInteractions() {
   if (activeTab.value !== 'shap') return
   try {
     const data = await getInteractions(shapModel.value, 10)
-    if (data.error || !data.top_interactions) return
+    if (data.error || !data.top_interactions) {
+      const chart = getOrCreateChart(shapInteractionRef.value)
+      if (chart) chart.clear()
+      if (data.error) ElMessage.error(data.error)
+      return
+    }
     await nextTick()
     const chart = getOrCreateChart(shapInteractionRef.value)
     if (!chart) return
@@ -498,7 +519,7 @@ async function loadInteractions() {
       tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
       grid: { left: 160, right: 20, top: 10, bottom: 30 },
       xAxis: { type: 'value', name: '交互强度' },
-      yAxis: { type: 'category', data: interactions.map(i => `${i.feature_1_cn} x ${i.feature_2_cn}`) },
+      yAxis: { type: 'category', data: interactions.map(i => `${i.feature_1_cn}与${i.feature_2_cn}`) },
       series: [{
         type: 'bar', data: interactions.map(i => i.strength),
         itemStyle: { color: '#f59e0b' },

frontend/src/views/Prediction.vue

@@ -1,10 +1,9 @@
 <template>
   <div class="page-shell prediction">
     <section class="page-hero prediction-hero">
-      <div class="page-eyebrow">Prediction</div>
       <h1 class="page-title">核心因子驱动的缺勤预测</h1>
       <p class="page-description">
-        仅保留对结果最关键的输入项，让演示流程更聚焦，也让答辩老师更容易理解模型的业务逻辑。
+        基于缺勤事件、工作压力、家庭负担与岗位背景，评估缺勤时长和风险等级。
       </p>
     </section>
 
@@ -15,12 +14,12 @@
             <div class="section-heading" style="margin-bottom: 0">
               <div>
                 <h3 class="section-title">中国企业缺勤风险输入</h3>
-                <p class="section-caption">使用卡片分区组织核心因子，演示时更清晰。</p>
+                <p class="section-caption">核心字段分区录入</p>
               </div>
               <el-button size="small" @click="resetForm">重置</el-button>
             </div>
             <div class="form-tip">
-              系统会自动补齐企业背景、健康生活与组织属性等次级信息，页面仅保留对预测结果影响最大的核心字段。
+              后端将结合默认企业背景、健康生活与组织属性完成特征补齐。
             </div>
           </el-card>
 
@@ -28,9 +27,8 @@
             <div class="section-heading">
               <div>
                 <h3 class="section-title">缺勤事件核心信息</h3>
-                <p class="section-caption">决定本次缺勤时长的直接事件属性。</p>
+                <p class="section-caption">请假类型、时间与证明信息</p>
               </div>
-              <span class="soft-tag">Event</span>
             </div>
             <el-form :model="form" label-width="118px" size="small">
               <el-row :gutter="18">
@@ -86,9 +84,8 @@
             <div class="section-heading">
               <div>
                 <h3 class="section-title">工作压力与排班</h3>
-                <p class="section-caption">体现通勤、加班和排班对缺勤的影响。</p>
+                <p class="section-caption">加班、通勤、班次与健康状态</p>
               </div>
-              <span class="soft-tag">Workload</span>
             </div>
             <el-form :model="form" label-width="118px" size="small">
               <el-row :gutter="18">
@@ -133,9 +130,8 @@
             <div class="section-heading">
               <div>
                 <h3 class="section-title">家庭与补充因素</h3>
-                <p class="section-caption">作为结果修正项，为预测增加业务语境。</p>
+                <p class="section-caption">家庭负担、行业与婚姻状态</p>
               </div>
-              <span class="soft-tag">Context</span>
             </div>
             <el-form :model="form" label-width="118px" size="small">
               <el-row :gutter="18">
@@ -166,9 +162,8 @@
             <div class="section-heading">
               <div>
                 <h3 class="section-title">预测设置</h3>
-                <p class="section-caption">支持自动选择最优模型或查看模型对比。</p>
+                <p class="section-caption">模型选择与对比</p>
               </div>
-              <span class="soft-tag">Action</span>
             </div>
             <el-form :model="form" label-width="118px" size="small">
               <el-row :gutter="18">
@@ -203,10 +198,9 @@
           <template #header>
             <div class="section-heading" style="margin-bottom: 0">
               <div>
-                <h3 class="section-title">预测结果与风险说明</h3>
-                <p class="section-caption">在同一张卡片内查看预测值、模型信息和风险区间说明。</p>
+                <h3 class="section-title">预测结果与风险等级</h3>
+                <p class="section-caption">缺勤时长、风险等级与模型信息</p>
               </div>
-              <span class="soft-tag">Result</span>
             </div>
           </template>
           <div class="merged-result-grid">
@@ -264,19 +258,90 @@
               </div>
             </div>
           </div>
+
+          <div v-if="result?.jdr_snapshot" class="insight-stack">
+            <section class="insight-block">
+              <div class="insight-heading">
+                <div>
+                  <h3 class="section-title">JD-R 快照</h3>
+                  <p class="section-caption">工作要求、资源与平衡度</p>
+                </div>
+              </div>
+              <div class="snapshot-grid">
+                <div v-for="item in jdrSnapshotCards" :key="item.key" class="snapshot-card">
+                  <div class="snapshot-label">{{ item.label }}</div>
+                  <div class="snapshot-score">{{ item.score }}</div>
+                  <el-tag :type="item.tone" size="small">{{ item.status }}</el-tag>
+                </div>
+              </div>
+            </section>
+
+            <section v-if="mechanismSummary" class="insight-block">
+              <div class="insight-heading">
+                <div>
+                  <h3 class="section-title">风险机制</h3>
+                  <p class="section-caption">当前样本的主要风险路径</p>
+                </div>
+                <el-tag v-if="mechanismSummary.pathway_label" :type="mechanismSummary.pathway_tone || 'info'" effect="light">
+                  {{ mechanismSummary.pathway_label }}
+                </el-tag>
+              </div>
+              <div class="summary-text">
+                <p>{{ mechanismSummary.conclusion }}</p>
+                <p>{{ mechanismSummary.mechanism }}</p>
+                <p>{{ mechanismSummary.pathway_detail }}</p>
+                <p v-if="mechanismSummary.buffer_text">{{ mechanismSummary.buffer_text }}</p>
+                <p v-if="mechanismSummary.scenario_hint">{{ mechanismSummary.scenario_hint }}</p>
+              </div>
+
+              <div v-if="mechanismSummary.top_drivers?.length" class="driver-group">
+                <div class="driver-label">主要推高因素</div>
+                <div class="driver-chip-row">
+                  <span v-for="item in mechanismSummary.top_drivers" :key="`up-${item.name}`" class="driver-chip driver-chip-up">
+                    {{ item.name_cn }}
+                  </span>
+                </div>
+              </div>
+
+              <div v-if="mechanismSummary.protective_factors?.length" class="driver-group">
+                <div class="driver-label">缓冲因素</div>
+                <div class="driver-chip-row">
+                  <span v-for="item in mechanismSummary.protective_factors" :key="`down-${item.name}`" class="driver-chip driver-chip-down">
+                    {{ item.name_cn }}
+                  </span>
+                </div>
+              </div>
+            </section>
+
+            <section v-if="interventionGroups.length" class="insight-block">
+              <div class="insight-heading">
+                <div>
+                  <h3 class="section-title">干预建议</h3>
+                  <p class="section-caption">管理关注方向</p>
+                </div>
+              </div>
+              <div class="suggestion-grid">
+                <div v-for="group in interventionGroups" :key="group.category" class="suggestion-card">
+                  <div class="suggestion-title">{{ group.category }}</div>
+                  <div class="suggestion-list">
+                    <p v-for="item in group.items" :key="item">{{ item }}</p>
+                  </div>
+                </div>
+              </div>
+            </section>
+          </div>
         </el-card>
 
-        <el-card v-if="compareResults.length > 0" class="panel-card compare-card" shadow="never">
+        <el-card v-if="shouldShowCompareCard" v-loading="compareLoading" class="panel-card compare-card" shadow="never">
           <template #header>
             <div class="section-heading" style="margin-bottom: 0">
               <div>
                 <h3 class="section-title">模型对比结果</h3>
-                <p class="section-caption">选择最适合展示的候选模型。</p>
+                <p class="section-caption">不同模型预测结果对比</p>
               </div>
-              <span class="soft-tag">Compare</span>
             </div>
           </template>
-          <el-table :data="compareResults" size="small" :row-class-name="getRowClass">
+          <el-table v-if="compareResults.length > 0" :data="compareResults" size="small" :row-class-name="getRowClass">
             <el-table-column prop="model_name_cn" label="模型" width="100" />
             <el-table-column prop="predicted_hours" label="预测时长" width="90">
               <template #default="{ row }">{{ row.predicted_hours }}h</template>
@@ -295,16 +360,16 @@
               </template>
             </el-table-column>
           </el-table>
+          <el-empty v-else description="暂无模型对比结果" />
         </el-card>
 
         <el-card v-if="shapLocalData" class="panel-card shap-card" shadow="never">
           <template #header>
             <div class="section-heading" style="margin-bottom: 0">
               <div>
-                <h3 class="section-title">SHAP 预测解释</h3>
-                <p class="section-caption">每个特征对本次预测的贡献度（红色推高/蓝色拉低）</p>
+                <h3 class="section-title">特征贡献</h3>
+                <p class="section-caption">特征贡献方向与贡献强度</p>
               </div>
-              <span class="soft-tag">Explain</span>
             </div>
           </template>
           <div class="shap-dimension-badges">
@@ -322,11 +387,10 @@
 </template>
 
 <script setup>
-import { computed, nextTick, onMounted, ref } from 'vue'
+import { computed, nextTick, onMounted, ref, watch } from 'vue'
 import { ElMessage } from 'element-plus'
 import * as echarts from 'echarts'
 import request from '@/api/request'
-import { getLocalExplanation } from '@/api/shap'
 
 const industries = ['制造业', '互联网', '零售连锁', '物流运输', '金融服务', '医药健康', '建筑工程']
 const shiftTypes = ['标准白班', '两班倒', '三班倒', '弹性班']
@@ -377,6 +441,23 @@ const riskTagType = computed(() => {
   return getRiskType(result.value.risk_level)
 })
 
+const jdrSnapshotCards = computed(() => {
+  const snapshot = result.value?.jdr_snapshot
+  if (!snapshot) return []
+  return [
+    snapshot.job_demands,
+    snapshot.job_resources,
+    snapshot.personal_resources,
+    snapshot.balance,
+    snapshot.burnout_risk,
+    snapshot.engagement,
+  ].filter(Boolean)
+})
+
+const mechanismSummary = computed(() => result.value?.mechanism_summary || null)
+const interventionGroups = computed(() => result.value?.intervention_suggestions || [])
+const shouldShowCompareCard = computed(() => showCompare.value || compareLoading.value || compareResults.value.length > 0)
+
 function getRiskType(level) {
   return level === 'low' ? 'success' : level === 'medium' ? 'warning' : 'danger'
 }
@@ -408,9 +489,13 @@ async function handlePredict() {
     const params = { ...form.value }
     if (selectedModel.value) params.model_type = selectedModel.value
     result.value = await request.post('/predict/single', params)
+    shapLocalData.value = result.value?.shap_local || null
+    if (shapLocalData.value?.features?.length) {
+      requestAnimationFrame(() => {
+        nextTick().then(renderShapForce)
+      })
+    }
     if (showCompare.value) await handleCompare()
-    // 加载 SHAP 局部解释
-    loadShapLocal(params)
   } catch (e) {
     ElMessage.error(`预测失败: ${e.message}`)
   } finally {
@@ -418,19 +503,6 @@ async function handlePredict() {
   }
 }
 
-async function loadShapLocal(params) {
-  try {
-    const modelType = params.model_type || ''
-    const data = await getLocalExplanation({ ...params, model_type: modelType })
-    if (data && !data.error) {
-      shapLocalData.value = data
-      requestAnimationFrame(() => {
-        nextTick().then(renderShapForce)
-      })
-    }
-  } catch (e) { /* ignore */ }
-}
-
 function renderShapForce() {
   const el = shapForceRef.value
   if (!el || !shapLocalData.value?.features) { console.warn('shapForce: DOM or data missing'); return }
@@ -443,7 +515,7 @@ function renderShapForce() {
   chart.setOption({
     tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
     grid: { left: 120, right: 30, top: 10, bottom: 30 },
-    xAxis: { type: 'value', name: 'SHAP值' },
+    xAxis: { type: 'value', name: '贡献值' },
     yAxis: { type: 'category', data: sorted.map(f => f.name_cn) },
     series: [{
       type: 'bar', data: sorted.map(f => ({
@@ -457,6 +529,7 @@ function renderShapForce() {
 async function handleCompare() {
   compareLoading.value = true
   try {
+    showCompare.value = true
     const res = await request.post('/predict/compare', form.value)
     compareResults.value = res.results || []
   } catch (e) {
@@ -469,6 +542,16 @@ async function handleCompare() {
 onMounted(() => {
   loadModels()
 })
+
+watch(showCompare, (visible) => {
+  if (!visible) {
+    compareResults.value = []
+    return
+  }
+  if (!compareLoading.value && compareResults.value.length === 0) {
+    handleCompare()
+  }
+})
 </script>
 
 <style scoped>
@@ -643,10 +726,133 @@ onMounted(() => {
   padding-bottom: 0;
 }
 
+.insight-stack {
+  display: grid;
+  gap: 16px;
+  margin-top: 18px;
+}
+
+.insight-block {
+  padding: 18px;
+  border: 1px solid var(--line-soft);
+  border-radius: 20px;
+  background: rgba(255, 255, 255, 0.76);
+}
+
+.insight-heading {
+  display: flex;
+  align-items: flex-start;
+  justify-content: space-between;
+  gap: 12px;
+  margin-bottom: 14px;
+}
+
+.snapshot-grid {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 12px;
+}
+
+.snapshot-card {
+  padding: 14px;
+  border-radius: 16px;
+  background: linear-gradient(135deg, rgba(58, 122, 254, 0.06), rgba(15, 118, 110, 0.08));
+  border: 1px solid rgba(58, 122, 254, 0.1);
+}
+
+.snapshot-label {
+  font-size: 13px;
+  color: var(--text-subtle);
+}
+
+.snapshot-score {
+  margin: 8px 0 12px;
+  font-size: 24px;
+  font-weight: 700;
+  color: var(--text-main);
+}
+
+.summary-text {
+  display: grid;
+  gap: 10px;
+}
+
+.summary-text p,
+.suggestion-list p {
+  margin: 0;
+  font-size: 13px;
+  line-height: 1.7;
+  color: var(--text-main);
+}
+
+.driver-group + .driver-group {
+  margin-top: 12px;
+}
+
+.driver-group {
+  margin-top: 14px;
+}
+
+.driver-label,
+.suggestion-title {
+  margin-bottom: 8px;
+  font-size: 13px;
+  font-weight: 700;
+  color: var(--text-main);
+}
+
+.driver-chip-row {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+}
+
+.driver-chip {
+  padding: 5px 12px;
+  border-radius: 999px;
+  font-size: 12px;
+  font-weight: 600;
+}
+
+.driver-chip-up {
+  color: #ef4444;
+  background: rgba(239, 68, 68, 0.1);
+  border: 1px solid rgba(239, 68, 68, 0.18);
+}
+
+.driver-chip-down {
+  color: #2563eb;
+  background: rgba(59, 130, 246, 0.1);
+  border: 1px solid rgba(59, 130, 246, 0.18);
+}
+
+.suggestion-grid {
+  display: grid;
+  grid-template-columns: repeat(3, minmax(0, 1fr));
+  gap: 12px;
+}
+
+.suggestion-card {
+  padding: 14px;
+  border-radius: 16px;
+  border: 1px solid var(--line-soft);
+  background: linear-gradient(180deg, rgba(255, 255, 255, 0.96), rgba(244, 248, 255, 0.9));
+}
+
+.suggestion-list {
+  display: grid;
+  gap: 8px;
+}
+
 @media (max-width: 1200px) {
   .prediction-input-grid {
     grid-template-columns: 1fr;
   }
+
+  .snapshot-grid,
+  .suggestion-grid {
+    grid-template-columns: repeat(2, minmax(0, 1fr));
+  }
 }
 
 @media (max-width: 768px) {
@@ -654,6 +860,15 @@ onMounted(() => {
   .result-stack {
     grid-template-columns: 1fr;
   }
+
+  .snapshot-grid,
+  .suggestion-grid {
+    grid-template-columns: 1fr;
+  }
+
+  .insight-heading {
+    flex-direction: column;
+  }
 }
 
 .shap-card {