diff --git a/backend/0.43.0 b/backend/0.43.0
new file mode 100644
index 0000000..6b5e534
--- /dev/null
+++ b/backend/0.43.0
@@ -0,0 +1,30 @@
+Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
+Collecting shap
+  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a5/8e/cee1ee136a4e54fe2fbb63a60d72d7c25e21a4ffe6aa05779cab7669cb31/shap-0.51.0-cp311-cp311-win_amd64.whl (554 kB)
+     ---------------------------------------- 554.9/554.9 kB 6.2 MB/s  0:00:00
+Requirement already satisfied: numpy>=2 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (2.3.5)
+Requirement already satisfied: scipy in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (1.17.1)
+Requirement already satisfied: scikit-learn in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (1.8.0)
+Requirement already satisfied: pandas in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (3.0.1)
+Requirement already satisfied: tqdm>=4.27.0 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (4.67.3)
+Requirement already satisfied: packaging>20.9 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (25.0)
+Collecting slicer==0.0.8 (from shap)
+  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/63/81/9ef641ff4e12cbcca30e54e72fb0951a2ba195d0cda0ba4100e532d929db/slicer-0.0.8-py3-none-any.whl (15 kB)
+Collecting numba (from shap)
+  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/53/ff/1371cbbe955be340a46093a10b61462437e0fadc7a63290473a0e584cb03/numba-0.65.0-cp311-cp311-win_amd64.whl (2.7 MB)
+     ---------------------------------------- 2.7/2.7 MB 15.9 MB/s  0:00:00
+Collecting llvmlite (from shap)
+  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a2/50/59227d06bdc96e23322713c381af4e77420949d8cd8a042c79e0043096cc/llvmlite-0.47.0-cp311-cp311-win_amd64.whl (38.1 MB)
+     ---------------------------------------- 38.1/38.1 MB 29.2 MB/s  0:00:01
+Collecting cloudpickle (from shap)
+  Downloading https://pypi.tuna.tsinghua.edu.cn/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl (22 kB)
+Requirement already satisfied: typing-extensions in D:\anaconda\envs\ml-nlp\Lib\site-packages (from shap) (4.15.0)
+Requirement already satisfied: colorama in D:\anaconda\envs\ml-nlp\Lib\site-packages (from tqdm>=4.27.0->shap) (0.4.6)
+Requirement already satisfied: python-dateutil>=2.8.2 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from pandas->shap) (2.9.0.post0)
+Requirement already satisfied: tzdata in D:\anaconda\envs\ml-nlp\Lib\site-packages (from pandas->shap) (2025.3)
+Requirement already satisfied: six>=1.5 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from python-dateutil>=2.8.2->pandas->shap) (1.17.0)
+Requirement already satisfied: joblib>=1.3.0 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from scikit-learn->shap) (1.5.3)
+Requirement already satisfied: threadpoolctl>=3.2.0 in D:\anaconda\envs\ml-nlp\Lib\site-packages (from scikit-learn->shap) (3.6.0)
+Installing collected packages: slicer, llvmlite, cloudpickle, numba, shap
+
+Successfully installed cloudpickle-3.1.2 llvmlite-0.47.0 numba-0.65.0 shap-0.51.0 slicer-0.0.8
diff --git a/backend/=0.43.0 b/backend/=0.43.0
new file mode 100644
index 0000000..ff1997a
--- /dev/null
+++ b/backend/=0.43.0
@@ -0,0 +1,38 @@
+Collecting shap
+  Downloading shap-0.51.0-cp312-cp312-win_amd64.whl.metadata (26 kB)
+Collecting numpy>=2 (from shap)
+  Downloading numpy-2.4.4-cp312-cp312-win_amd64.whl.metadata (6.6 kB)
+Requirement already satisfied: scipy in d:\anaconda\lib\site-packages (from shap) (1.13.1)
+Requirement already satisfied: scikit-learn in d:\anaconda\lib\site-packages (from shap) (1.5.1)
+Requirement already satisfied: pandas in d:\anaconda\lib\site-packages (from shap) (2.2.2)
+Requirement already satisfied: tqdm>=4.27.0 in d:\anaconda\lib\site-packages (from shap) (4.66.5)
+Requirement already satisfied: packaging>20.9 in d:\anaconda\lib\site-packages (from shap) (24.1)
+Collecting slicer==0.0.8 (from shap)
+  Downloading slicer-0.0.8-py3-none-any.whl.metadata (4.0 kB)
+Requirement already satisfied: numba in d:\anaconda\lib\site-packages (from shap) (0.60.0)
+Requirement already satisfied: llvmlite in d:\anaconda\lib\site-packages (from shap) (0.43.0)
+Requirement already satisfied: cloudpickle in d:\anaconda\lib\site-packages (from shap) (3.0.0)
+Requirement already satisfied: typing-extensions in d:\anaconda\lib\site-packages (from shap) (4.14.1)
+Requirement already satisfied: colorama in d:\anaconda\lib\site-packages (from tqdm>=4.27.0->shap) (0.4.6)
+Collecting numpy>=2 (from shap)
+  Downloading numpy-2.0.2-cp312-cp312-win_amd64.whl.metadata (59 kB)
+Requirement already satisfied: python-dateutil>=2.8.2 in d:\anaconda\lib\site-packages (from pandas->shap) (2.9.0.post0)
+Requirement already satisfied: pytz>=2020.1 in d:\anaconda\lib\site-packages (from pandas->shap) (2024.1)
+Requirement already satisfied: tzdata>=2022.7 in d:\anaconda\lib\site-packages (from pandas->shap) (2023.3)
+Requirement already satisfied: joblib>=1.2.0 in d:\anaconda\lib\site-packages (from scikit-learn->shap) (1.4.2)
+Requirement already satisfied: threadpoolctl>=3.1.0 in d:\anaconda\lib\site-packages (from scikit-learn->shap) (3.5.0)
+Requirement already satisfied: six>=1.5 in d:\anaconda\lib\site-packages (from python-dateutil>=2.8.2->pandas->shap) (1.16.0)
+Downloading shap-0.51.0-cp312-cp312-win_amd64.whl (556 kB)
+   --------------------------------------- 556.1/556.1 kB 60.5 kB/s eta 0:00:00
+Downloading slicer-0.0.8-py3-none-any.whl (15 kB)
+Downloading numpy-2.0.2-cp312-cp312-win_amd64.whl (15.6 MB)
+   ---------------------------------------- 15.6/15.6 MB 31.3 kB/s eta 0:00:00
+Installing collected packages: slicer, numpy, shap
+  Attempting uninstall: numpy
+    Found existing installation: numpy 1.26.4
+    Uninstalling numpy-1.26.4:
+      Successfully uninstalled numpy-1.26.4
+ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
+contourpy 1.2.0 requires numpy<2.0,>=1.20, but you have numpy 2.0.2 which is incompatible.
+gensim 4.3.3 requires numpy<2.0,>=1.18.5, but you have numpy 2.0.2 which is incompatible.
+Successfully installed numpy-2.0.2 shap-0.51.0 slicer-0.0.8
diff --git a/backend/api/__init__.py b/backend/api/__init__.py
index 4bd9fd3..341a0a9 100644
--- a/backend/api/__init__.py
+++ b/backend/api/__init__.py
@@ -2,6 +2,8 @@ from .overview_routes import overview_bp
 from .analysis_routes import analysis_bp
 from .predict_routes import predict_bp
 from .cluster_routes import cluster_bp
+from .jdr_routes import jdr_bp
+from .shap_routes import shap_bp
 
 
 def register_blueprints(app):
@@ -9,3 +11,5 @@ def register_blueprints(app):
     app.register_blueprint(analysis_bp)
     app.register_blueprint(predict_bp)
     app.register_blueprint(cluster_bp)
+    app.register_blueprint(jdr_bp)
+    app.register_blueprint(shap_bp)
diff --git a/backend/api/jdr_routes.py b/backend/api/jdr_routes.py
new file mode 100644
index 0000000..4075813
--- /dev/null
+++ b/backend/api/jdr_routes.py
@@ -0,0 +1,51 @@
+from flask import Blueprint, jsonify, request
+
+from services.jdr_service import jdr_service
+
+jdr_bp = Blueprint('jdr', __name__, url_prefix='/api/jdr')
+
+
+@jdr_bp.route('/dimensions', methods=['GET'])
+def get_dimensions():
+    try:
+        result = jdr_service.get_dimension_scores()
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@jdr_bp.route('/burnout-engagement', methods=['GET'])
+def get_burnout_engagement():
+    try:
+        result = jdr_service.get_burnout_engagement_analysis()
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@jdr_bp.route('/path-analysis', methods=['GET'])
+def get_path_analysis():
+    try:
+        result = jdr_service.get_jdr_path_analysis()
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@jdr_bp.route('/profile', methods=['GET'])
+def get_profile():
+    try:
+        dimension = request.args.get('dimension', '所属行业')
+        result = jdr_service.get_jdr_profile(dimension)
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@jdr_bp.route('/risk-distribution', methods=['GET'])
+def get_risk_distribution():
+    try:
+        result = jdr_service.get_risk_distribution()
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
diff --git a/backend/api/predict_routes.py b/backend/api/predict_routes.py
index 6d87939..0f9561b 100644
--- a/backend/api/predict_routes.py
+++ b/backend/api/predict_routes.py
@@ -100,3 +100,18 @@ def get_model_info():
             'message': str(e),
             'data': None
         }), 500
+
+
+@predict_bp.route('/risk-classify', methods=['POST'])
+def risk_classify():
+    try:
+        data = request.get_json()
+        if not data:
+            return jsonify({'code': 400, 'message': 'Request body is required', 'data': None}), 400
+        model_type = data.get('model_type')
+        result = predict_service.predict_risk_classification(data, model_type)
+        if result is None:
+            return jsonify({'code': 404, 'message': 'No classifier available', 'data': None}), 404
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
diff --git a/backend/api/shap_routes.py b/backend/api/shap_routes.py
new file mode 100644
index 0000000..67270a6
--- /dev/null
+++ b/backend/api/shap_routes.py
@@ -0,0 +1,50 @@
+from flask import Blueprint, jsonify, request
+
+from services.shap_service import shap_service
+
+shap_bp = Blueprint('shap', __name__, url_prefix='/api/shap')
+
+
+@shap_bp.route('/global', methods=['GET'])
+def get_global_importance():
+    try:
+        model_type = request.args.get('model', 'random_forest')
+        result = shap_service.get_global_importance(model_type)
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@shap_bp.route('/local', methods=['POST'])
+def get_local_explanation():
+    try:
+        data = request.get_json()
+        if not data:
+            return jsonify({'code': 400, 'message': 'Request body is required', 'data': None}), 400
+        model_type = data.get('model_type', 'random_forest')
+        result = shap_service.get_local_explanation(data, model_type)
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@shap_bp.route('/interaction', methods=['GET'])
+def get_interactions():
+    try:
+        model_type = request.args.get('model', 'random_forest')
+        top_n = int(request.args.get('top_n', 10))
+        result = shap_service.get_interactions(model_type, top_n)
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
+
+
+@shap_bp.route('/dependence', methods=['GET'])
+def get_dependence():
+    try:
+        feature = request.args.get('feature', '月均加班时长')
+        model_type = request.args.get('model', 'random_forest')
+        result = shap_service.get_dependence(feature, model_type)
+        return jsonify({'code': 200, 'message': 'success', 'data': result})
+    except Exception as e:
+        return jsonify({'code': 500, 'message': str(e), 'data': None}), 500
diff --git a/backend/app.py b/backend/app.py
index 65fa0cd..8ddc068 100644
--- a/backend/app.py
+++ b/backend/app.py
@@ -39,6 +39,19 @@ def create_app():
                         '/api/cluster/result',
                         '/api/cluster/profile',
                         '/api/cluster/scatter'
+                    ],
+                    'jdr': [
+                        '/api/jdr/dimensions',
+                        '/api/jdr/burnout-engagement',
+                        '/api/jdr/path-analysis',
+                        '/api/jdr/profile',
+                        '/api/jdr/risk-distribution'
+                    ],
+                    'shap': [
+                        '/api/shap/global',
+                        '/api/shap/local',
+                        '/api/shap/interaction',
+                        '/api/shap/dependence'
                     ]
                 }
             }
diff --git a/backend/config.py b/backend/config.py
index 1e37791..020f9a9 100644
--- a/backend/config.py
+++ b/backend/config.py
@@ -147,4 +147,63 @@ FEATURE_NAME_CN = {
     '年龄分层': '年龄分层',
     '通勤分层': '通勤分层',
     '加班分层': '加班分层',
+    # JD-R 工作要求维度
+    '工作自主性': '工作自主性',
+    '情绪劳动强度': '情绪劳动强度',
+    '时间压力感知': '时间压力感知',
+    '角色模糊度': '角色模糊度',
+    '工作家庭冲突': '工作家庭冲突',
+    # JD-R 工作资源维度
+    '上级支持': '上级支持',
+    '同事支持': '同事支持',
+    '技能多样性': '技能多样性',
+    '职业发展机会': '职业发展机会',
+    '参与决策': '参与决策',
+    '组织公平感': '组织公平感',
+    # JD-R 个人资源维度
+    '自我效能感': '自我效能感',
+    '心理韧性': '心理韧性',
+    '乐观程度': '乐观程度',
+    # JD-R 中介变量
+    '工作倦怠': '工作倦怠',
+    '工作投入': '工作投入',
+    # JD-R 复合指数
+    '工作要求指数': '工作要求指数',
+    '工作资源指数': '工作资源指数',
+    '个人资源指数': '个人资源指数',
+    'JD-R平衡度': 'JD-R平衡度',
+    '倦怠风险指数': '倦怠风险指数',
+    '工作投入指数': '工作投入指数',
 }
+
+# JD-R 理论维度映射
+JDR_DIMENSIONS = {
+    'job_demands': {
+        'name_cn': '工作要求',
+        'features': ['月均加班时长', '通勤时长分钟', '是否夜班岗位', '工作自主性',
+                     '情绪劳动强度', '时间压力感知', '角色模糊度', '工作家庭冲突'],
+    },
+    'job_resources': {
+        'name_cn': '工作资源',
+        'features': ['工作自主性', '上级支持', '同事支持', '技能多样性',
+                     '职业发展机会', '参与决策', '组织公平感'],
+    },
+    'personal_resources': {
+        'name_cn': '个人资源',
+        'features': ['自我效能感', '心理韧性', '乐观程度'],
+    },
+    'mediators': {
+        'name_cn': '中介变量',
+        'features': ['工作倦怠', '工作投入'],
+    },
+}
+
+# 风险等级配置
+RISK_LEVELS = {
+    'low': {'max_hours': 4, 'label': '低风险', 'color': '#22c55e'},
+    'medium': {'min_hours': 4, 'max_hours': 8, 'label': '中风险', 'color': '#f59e0b'},
+    'high': {'min_hours': 8, 'label': '高风险', 'color': '#ef4444'},
+}
+
+# JD-R 数据版本标记
+JDR_DATA_VERSION = '1.0'
diff --git a/backend/core/generate_dataset.py b/backend/core/generate_dataset.py
index 3c8e8f5..a5fb67a 100644
--- a/backend/core/generate_dataset.py
+++ b/backend/core/generate_dataset.py
@@ -387,16 +387,181 @@ def generate_dataset(output_path=None, sample_count=12000, random_state=None):
     return df
 
 
-def ensure_dataset():
-    if not os.path.exists(config.RAW_DATA_PATH):
-        generate_dataset(config.RAW_DATA_PATH)
-        return
+def enrich_with_jdr_columns(df):
+    """为现有数据追加 JD-R（工作要求-资源）理论维度列。
 
-    try:
-        df = pd.read_csv(config.RAW_DATA_PATH)
-        validate_dataset(df)
-    except Exception:
+    在已有的员工/事件属性基础上，合成 16 个新列：
+    - 工作要求：工作自主性、情绪劳动强度、时间压力感知、角色模糊度、工作家庭冲突
+    - 工作资源：上级支持、同事支持、技能多样性、职业发展机会、参与决策、组织公平感
+    - 个人资源：自我效能感、心理韧性、乐观程度
+    - 中介变量：工作倦怠、工作投入
+    """
+    rng = np.random.default_rng(config.RANDOM_STATE + 100)
+    df = df.copy()
+    n = len(df)
+
+    # ── 辅助：条件性 Likert 生成 ──
+    def likert(mean_offset, std=0.8, low=1.0, high=5.0):
+        return np.clip(rng.normal(mean_offset, std, size=n), low, high)
+
+    # ── 预提取列 ──
+    overtime = df['月均加班时长'].values
+    commute = df['通勤时长分钟'].values
+    night = df['是否夜班岗位'].values
+    children = df['子女数量'].values
+    married_arr = (df['婚姻状态'] == '已婚').astype(int).values
+    tenure = df['司龄年数'].values
+    team_size = df['团队人数'].values
+    manager_span = df['直属上级管理跨度'].values
+    exercise = df['每周运动频次'].values
+    sleep = df['近30天睡眠时长均值'].values
+    chronic = df['是否慢性病史'].values
+    perf_a = (df['绩效等级'] == 'A').astype(int).values
+    perf_ab = df['绩效等级'].isin(['A', 'B']).astype(int).values
+    level_map = {'初级': 0, '中级': 1, '高级': 2, '主管': 3, '经理及以上': 4}
+    level_vals = df['岗位级别'].map(level_map).fillna(1).values
+    industry_vals = df['所属行业'].values
+    employment_type = df['用工类型'].values
+    job_family = df['岗位序列'].values
+    company_scale_map = {
+        '100人以下': 0, '100-499人': 1, '500-999人': 2, '1000-4999人': 3, '5000人及以上': 4
+    }
+    scale_vals = df['企业规模'].map(company_scale_map).fillna(1).values
+
+    formal_employee = (df['用工类型'] == '正式员工').astype(int).values
+    edu_map = {'中专及以下': 0, '大专': 1, '本科': 2, '硕士': 3, '博士': 4}
+    edu_vals = df['最高学历'].map(edu_map).fillna(2).values
+
+    # ── 工作要求维度 (5 列) ──
+    df['工作自主性'] = likert(
+        3.2 + level_vals * 0.25
+        + np.isin(industry_vals, ['互联网', '金融服务']).astype(int) * 0.3
+        - night * 0.4
+    ).round(1)
+
+    df['情绪劳动强度'] = likert(
+        2.8
+        + np.isin(job_family, ['客服坐席', '销售业务']).astype(int) * 0.6
+        + np.isin(industry_vals, ['医药健康', '零售连锁']).astype(int) * 0.3
+    ).round(1)
+
+    df['时间压力感知'] = likert(
+        3.0 + overtime * 0.02 + commute * 0.01
+        + np.isin(industry_vals, ['互联网', '金融服务']).astype(int) * 0.2
+    ).round(1)
+
+    df['角色模糊度'] = likert(
+        2.5
+        + np.isin(employment_type, ['劳务派遣', '外包驻场']).astype(int) * 0.5
+        - tenure * 0.05
+    ).round(1)
+
+    df['工作家庭冲突'] = likert(
+        2.6 + overtime * 0.02 + children * 0.3 + married_arr * 0.3
+    ).round(1)
+
+    # ── 工作资源维度 (6 列) ──
+    df['上级支持'] = likert(
+        3.4 - manager_span * 0.02 + level_vals * 0.2
+    ).round(1)
+
+    df['同事支持'] = likert(
+        3.3 + team_size * 0.02
+        + np.isin(job_family, ['管理', '专业技术']).astype(int) * 0.2
+    ).round(1)
+
+    df['技能多样性'] = likert(
+        3.0
+        + np.isin(job_family, ['专业技术', '管理']).astype(int) * 0.5
+        - np.isin(job_family, ['生产操作']).astype(int) * 0.3
+    ).round(1)
+
+    df['职业发展机会'] = likert(
+        3.1
+        + np.isin(industry_vals, ['互联网', '金融服务']).astype(int) * 0.4
+        + scale_vals * 0.1
+    ).round(1)
+
+    df['参与决策'] = likert(
+        2.8 + level_vals * 0.35
+    ).round(1)
+
+    df['组织公平感'] = likert(
+        3.3 + formal_employee * 0.4 + perf_ab * 0.3
+    ).round(1)
+
+    # ── 个人资源维度 (3 列) ──
+    df['自我效能感'] = likert(
+        3.3 + perf_a * 0.4 + perf_ab * 0.2 + tenure * 0.03 + edu_vals * 0.08
+    ).round(1)
+
+    df['心理韧性'] = likert(
+        3.2 + exercise * 0.1 + sleep * 0.15 + tenure * 0.02
+    ).round(1)
+
+    df['乐观程度'] = likert(
+        3.3 + perf_ab * 0.3 - chronic * 0.3 + married_arr * 0.15
+    ).round(1)
+
+    # ── 中介变量 (2 列) ──
+    # 工作倦怠 (1-7)：健康损伤过程 — 高需求→高倦怠
+    df['工作倦怠'] = np.clip(
+        rng.normal(3.0, 0.8, size=n)
+        + overtime * 0.015 + night * 0.3 + commute * 0.008
+        + df['情绪劳动强度'].values * 0.25
+        + df['时间压力感知'].values * 0.25
+        + df['工作家庭冲突'].values * 0.2
+        + df['角色模糊度'].values * 0.15
+        - df['工作自主性'].values * 0.2
+        - df['上级支持'].values * 0.15
+        - df['自我效能感'].values * 0.2
+        - df['心理韧性'].values * 0.15,
+        1.0, 7.0
+    ).round(1)
+
+    # 工作投入 (1-7)：激励过程 — 高资源→高投入
+    df['工作投入'] = np.clip(
+        rng.normal(3.5, 0.8, size=n)
+        + df['工作自主性'].values * 0.2
+        + df['上级支持'].values * 0.2
+        + df['同事支持'].values * 0.15
+        + df['技能多样性'].values * 0.15
+        + df['职业发展机会'].values * 0.15
+        + df['参与决策'].values * 0.1
+        + df['组织公平感'].values * 0.1
+        + df['自我效能感'].values * 0.2
+        + df['心理韧性'].values * 0.15
+        + df['乐观程度'].values * 0.15
+        - df['工作倦怠'].values * 0.2,
+        1.0, 7.0
+    ).round(1)
+
+    # JD-R 数据版本标记
+    df['_jdr_version'] = config.JDR_DATA_VERSION
+
+    return df
+
+
+def ensure_dataset():
+    needs_regenerate = not os.path.exists(config.RAW_DATA_PATH)
+
+    if not needs_regenerate:
+        try:
+            df = pd.read_csv(config.RAW_DATA_PATH)
+            validate_dataset(df)
+        except Exception:
+            needs_regenerate = True
+
+    if needs_regenerate:
         generate_dataset(config.RAW_DATA_PATH)
+        df = pd.read_csv(config.RAW_DATA_PATH)
+
+    # 检查是否需要 JD-R 数据丰富
+    jdr_columns = ['工作自主性', '上级支持', '自我效能感', '工作倦怠', '工作投入']
+    if not all(col in df.columns for col in jdr_columns):
+        df = enrich_with_jdr_columns(df)
+        os.makedirs(os.path.dirname(config.RAW_DATA_PATH), exist_ok=True)
+        df.to_csv(config.RAW_DATA_PATH, index=False, encoding='utf-8-sig')
 
 
 if __name__ == '__main__':
diff --git a/backend/core/model_features.py b/backend/core/model_features.py
index d66d9d5..09a2253 100644
--- a/backend/core/model_features.py
+++ b/backend/core/model_features.py
@@ -35,6 +35,11 @@ NUMERICAL_OUTLIER_COLUMNS = [
     'BMI',
     '近30天睡眠时长均值',
     '每周运动频次',
+    # JD-R 维度列
+    '工作自主性', '情绪劳动强度', '时间压力感知', '角色模糊度', '工作家庭冲突',
+    '上级支持', '同事支持', '技能多样性', '职业发展机会', '参与决策', '组织公平感',
+    '自我效能感', '心理韧性', '乐观程度',
+    '工作倦怠', '工作投入',
 ]
 DEFAULT_PREDICTION_INPUT = {
     'industry': '制造业',
@@ -82,6 +87,26 @@ DEFAULT_PREDICTION_INPUT = {
     'urgent_leave_flag': 1,
     'continuous_absence_flag': 0,
     'previous_day_overtime_flag': 1,
+    # JD-R 工作要求维度
+    'work_autonomy': 3.0,
+    'emotional_labor': 3.0,
+    'time_pressure': 3.0,
+    'role_ambiguity': 3.0,
+    'work_family_conflict': 3.0,
+    # JD-R 工作资源维度
+    'supervisor_support': 3.0,
+    'coworker_support': 3.0,
+    'skill_variety': 3.0,
+    'career_development': 3.0,
+    'decision_participation': 3.0,
+    'organizational_justice': 3.0,
+    # JD-R 个人资源维度
+    'self_efficacy': 3.0,
+    'resilience': 3.0,
+    'optimism': 3.0,
+    # JD-R 中介变量
+    'burnout': 3.5,
+    'work_engagement': 3.5,
 }
 
 
@@ -171,6 +196,50 @@ def engineer_features(df):
     )
     df['管理负荷指数'] = df['团队人数'] * 0.4 + df['直属上级管理跨度'] * 0.25
 
+    # ── JD-R 复合指数 ──
+    autonomy = df.get('工作自主性', pd.Series(3.0, index=df.index))
+    df['工作要求指数'] = (
+        df['月均加班时长'] * 0.20
+        + df['通勤时长分钟'] * 0.08
+        + df['是否夜班岗位'] * 1.5
+        + (5 - autonomy) * 0.3
+        + df.get('情绪劳动强度', pd.Series(3.0, index=df.index)) * 0.25
+        + df.get('时间压力感知', pd.Series(3.0, index=df.index)) * 0.25
+        + df.get('角色模糊度', pd.Series(3.0, index=df.index)) * 0.20
+        + df.get('工作家庭冲突', pd.Series(3.0, index=df.index)) * 0.20
+    ) / 2
+
+    df['工作资源指数'] = (
+        autonomy * 0.18
+        + df.get('上级支持', pd.Series(3.0, index=df.index)) * 0.18
+        + df.get('同事支持', pd.Series(3.0, index=df.index)) * 0.14
+        + df.get('技能多样性', pd.Series(3.0, index=df.index)) * 0.14
+        + df.get('职业发展机会', pd.Series(3.0, index=df.index)) * 0.14
+        + df.get('参与决策', pd.Series(3.0, index=df.index)) * 0.10
+        + df.get('组织公平感', pd.Series(3.0, index=df.index)) * 0.12
+    )
+
+    df['个人资源指数'] = (
+        df.get('自我效能感', pd.Series(3.0, index=df.index)) * 0.35
+        + df.get('心理韧性', pd.Series(3.0, index=df.index)) * 0.35
+        + df.get('乐观程度', pd.Series(3.0, index=df.index)) * 0.30
+    )
+
+    df['JD-R平衡度'] = df['工作资源指数'] - df['工作要求指数'] * 0.5
+
+    df['倦怠风险指数'] = (
+        df.get('工作倦怠', pd.Series(3.5, index=df.index)) * 0.40
+        + df['工作要求指数'] * 0.30
+        - df['工作资源指数'] * 0.20
+        - df['个人资源指数'] * 0.10
+    )
+
+    df['工作投入指数'] = (
+        df.get('工作投入', pd.Series(3.5, index=df.index)) * 0.40
+        + df['工作资源指数'] * 0.30
+        + df['个人资源指数'] * 0.30
+    )
+
     df['工龄分层'] = pd.cut(df['司龄年数'], bins=[0, 2, 5, 10, 40], labels=['1', '2', '3', '4'])
     df['年龄分层'] = pd.cut(df['年龄'], bins=[18, 25, 32, 40, 60], labels=['1', '2', '3', '4'])
     df['通勤分层'] = pd.cut(df['通勤时长分钟'], bins=[0, 25, 45, 70, 180], labels=['1', '2', '3', '4'])
@@ -299,6 +368,26 @@ def build_prediction_dataframe(data):
             'previous_day_overtime_flag',
             DEFAULT_PREDICTION_INPUT['previous_day_overtime_flag'],
         ),
+        # JD-R 工作要求维度
+        '工作自主性': data.get('work_autonomy', DEFAULT_PREDICTION_INPUT['work_autonomy']),
+        '情绪劳动强度': data.get('emotional_labor', DEFAULT_PREDICTION_INPUT['emotional_labor']),
+        '时间压力感知': data.get('time_pressure', DEFAULT_PREDICTION_INPUT['time_pressure']),
+        '角色模糊度': data.get('role_ambiguity', DEFAULT_PREDICTION_INPUT['role_ambiguity']),
+        '工作家庭冲突': data.get('work_family_conflict', DEFAULT_PREDICTION_INPUT['work_family_conflict']),
+        # JD-R 工作资源维度
+        '上级支持': data.get('supervisor_support', DEFAULT_PREDICTION_INPUT['supervisor_support']),
+        '同事支持': data.get('coworker_support', DEFAULT_PREDICTION_INPUT['coworker_support']),
+        '技能多样性': data.get('skill_variety', DEFAULT_PREDICTION_INPUT['skill_variety']),
+        '职业发展机会': data.get('career_development', DEFAULT_PREDICTION_INPUT['career_development']),
+        '参与决策': data.get('decision_participation', DEFAULT_PREDICTION_INPUT['decision_participation']),
+        '组织公平感': data.get('organizational_justice', DEFAULT_PREDICTION_INPUT['organizational_justice']),
+        # JD-R 个人资源维度
+        '自我效能感': data.get('self_efficacy', DEFAULT_PREDICTION_INPUT['self_efficacy']),
+        '心理韧性': data.get('resilience', DEFAULT_PREDICTION_INPUT['resilience']),
+        '乐观程度': data.get('optimism', DEFAULT_PREDICTION_INPUT['optimism']),
+        # JD-R 中介变量
+        '工作倦怠': data.get('burnout', DEFAULT_PREDICTION_INPUT['burnout']),
+        '工作投入': data.get('work_engagement', DEFAULT_PREDICTION_INPUT['work_engagement']),
     }
     return pd.DataFrame([feature_row])
 
diff --git a/backend/core/shap_analysis.py b/backend/core/shap_analysis.py
new file mode 100644
index 0000000..0f22d60
--- /dev/null
+++ b/backend/core/shap_analysis.py
@@ -0,0 +1,399 @@
+import os
+
+import joblib
+import numpy as np
+import pandas as pd
+
+import config
+
+try:
+    import shap
+    SHAP_AVAILABLE = True
+except ImportError:
+    SHAP_AVAILABLE = False
+
+
+class SHAPAnalyzer:
+    """基于 SHAP 值的可解释性分析器，按 JD-R 维度聚合解释结果。"""
+
+    def __init__(self):
+        self.explainers = {}
+        self.models = {}
+        self.scaler = None
+        self.feature_names = None
+        self.selected_features = None
+        self.label_encoders = {}
+        self.background_data = None
+        self._initialized = False
+
+    def _ensure_initialized(self):
+        if self._initialized:
+            return
+
+        # 加载回归模型（SHAP 分析基于回归模型）
+        models_dir = config.MODELS_DIR
+        model_files = {
+            'random_forest': 'random_forest_model.pkl',
+            'xgboost': 'xgboost_model.pkl',
+            'lightgbm': 'lightgbm_model.pkl',
+            'gradient_boosting': 'gradient_boosting_model.pkl',
+            'extra_trees': 'extra_trees_model.pkl',
+        }
+        for name, filename in model_files.items():
+            path = os.path.join(models_dir, filename)
+            if os.path.exists(path):
+                try:
+                    self.models[name] = joblib.load(path)
+                except Exception:
+                    pass
+
+        # 加载预处理工件
+        if os.path.exists(config.SCALER_PATH):
+            self.scaler = joblib.load(config.SCALER_PATH)
+        for filename, attr in [
+            ('feature_names.pkl', 'feature_names'),
+            ('selected_features.pkl', 'selected_features'),
+            ('label_encoders.pkl', 'label_encoders'),
+        ]:
+            path = os.path.join(models_dir, filename)
+            if os.path.exists(path):
+                try:
+                    setattr(self, attr, joblib.load(path))
+                except Exception:
+                    pass
+
+        self._initialized = True
+
+    def _get_tree_explainer(self, model_type='random_forest'):
+        """获取或创建 TreeExplainer"""
+        if not SHAP_AVAILABLE:
+            return None
+
+        if model_type in self.explainers:
+            return self.explainers[model_type]
+
+        model = self.models.get(model_type)
+        if model is None:
+            return None
+
+        try:
+            explainer = shap.TreeExplainer(model)
+            self.explainers[model_type] = explainer
+            return explainer
+        except Exception:
+            return None
+
+    def _get_background_sample(self, n_samples=500):
+        """获取背景数据样本"""
+        if self.background_data is not None:
+            return self.background_data
+
+        try:
+            from core.preprocessing import get_clean_data
+            from core.model_features import (
+                normalize_columns, prepare_modeling_dataframe,
+                apply_outlier_bounds, fit_outlier_bounds,
+                engineer_features, extract_xy, fit_label_encoders,
+                apply_label_encoders, align_feature_frame, to_float_array,
+                NUMERICAL_OUTLIER_COLUMNS, ORDINAL_COLUMNS,
+            )
+
+            raw_df = normalize_columns(get_clean_data())
+            df = prepare_modeling_dataframe(raw_df)
+
+            bounds = fit_outlier_bounds(df, NUMERICAL_OUTLIER_COLUMNS)
+            df = apply_outlier_bounds(df, bounds)
+            df = engineer_features(df)
+            X_df, _ = extract_xy(df)
+            X_df, encoders = fit_label_encoders(X_df, ORDINAL_COLUMNS)
+
+            if self.feature_names:
+                X_df = align_feature_frame(X_df, self.feature_names)
+
+            if n_samples < len(X_df):
+                X_df = X_df.sample(n=n_samples, random_state=config.RANDOM_STATE)
+
+            if self.scaler is not None:
+                X = self.scaler.transform(to_float_array(X_df))
+            else:
+                X = to_float_array(X_df)
+
+            if self.selected_features and self.feature_names:
+                selected_indices = [self.feature_names.index(n) for n in self.selected_features if n in self.feature_names]
+                if selected_indices:
+                    X = X[:, selected_indices]
+
+            self.background_data = X
+            return X
+        except Exception:
+            return None
+
+    def _get_feature_display_names(self):
+        """获取特征显示名称映射"""
+        feature_names = self.selected_features or self.feature_names or []
+        return {name: config.FEATURE_NAME_CN.get(name, name) for name in feature_names}
+
+    def _map_feature_to_dimension(self, feature_name):
+        """将特征映射到 JD-R 维度"""
+        for dim_key, dim_info in config.JDR_DIMENSIONS.items():
+            if feature_name in dim_info['features']:
+                return dim_key
+        # 事件/上下文特征
+        context_features = ['缺勤月份', '星期几', '是否节假日前后', '季节',
+                            '请假类型', '请假原因大类', '是否提供医院证明',
+                            '是否临时请假', '是否连续缺勤', '前一工作日是否加班']
+        if feature_name in context_features:
+            return 'event_context'
+        return 'other'
+
+    def global_shap_values(self, model_type='random_forest'):
+        """计算全局 SHAP 重要性，按 JD-R 维度分组"""
+        if not SHAP_AVAILABLE:
+            return {'error': 'SHAP library not installed'}
+
+        self._ensure_initialized()
+        explainer = self._get_tree_explainer(model_type)
+        if explainer is None:
+            return {'error': f'No tree model available for {model_type}'}
+
+        X = self._get_background_sample()
+        if X is None:
+            return {'error': 'Failed to prepare background data'}
+
+        try:
+            shap_values = explainer.shap_values(X)
+            if isinstance(shap_values, list):
+                shap_values = shap_values[0]
+
+            mean_abs_shap = np.abs(shap_values).mean(axis=0)
+            feature_names = self.selected_features or self.feature_names or []
+            name_map = self._get_feature_display_names()
+
+            # 按维度分组
+            dimensions = {}
+            for dim_key, dim_info in config.JDR_DIMENSIONS.items():
+                dim_features = []
+                for fname in feature_names:
+                    if fname in dim_info['features']:
+                        idx = list(feature_names).index(fname)
+                        dim_features.append({
+                            'name': fname,
+                            'name_cn': name_map.get(fname, fname),
+                            'importance': round(float(mean_abs_shap[idx]), 4),
+                        })
+                if dim_features:
+                    dimensions[dim_key] = {
+                        'name_cn': dim_info['name_cn'],
+                        'features': sorted(dim_features, key=lambda x: x['importance'], reverse=True),
+                    }
+
+            # 事件上下文维度
+            context_features = []
+            for fname in feature_names:
+                if self._map_feature_to_dimension(fname) == 'event_context':
+                    idx = list(feature_names).index(fname)
+                    context_features.append({
+                        'name': fname,
+                        'name_cn': name_map.get(fname, fname),
+                        'importance': round(float(mean_abs_shap[idx]), 4),
+                    })
+            if context_features:
+                dimensions['event_context'] = {
+                    'name_cn': '事件上下文',
+                    'features': sorted(context_features, key=lambda x: x['importance'], reverse=True),
+                }
+
+            # Top 特征列表
+            top_indices = np.argsort(mean_abs_shap)[::-1][:20]
+            top_features = []
+            for idx in top_indices:
+                fname = feature_names[idx] if idx < len(feature_names) else f'f{idx}'
+                top_features.append({
+                    'name': fname,
+                    'name_cn': name_map.get(fname, fname),
+                    'importance': round(float(mean_abs_shap[idx]), 4),
+                    'dimension': self._map_feature_to_dimension(fname),
+                })
+
+            return {
+                'model_type': model_type,
+                'dimensions': dimensions,
+                'top_features': top_features,
+            }
+        except Exception as exc:
+            return {'error': str(exc)}
+
+    def local_shap_values(self, data, model_type='random_forest'):
+        """计算单条预测的 SHAP 解释"""
+        if not SHAP_AVAILABLE:
+            return {'error': 'SHAP library not installed'}
+
+        self._ensure_initialized()
+        explainer = self._get_tree_explainer(model_type)
+        if explainer is None:
+            return {'error': f'No tree model available for {model_type}'}
+
+        try:
+            from core.model_features import (
+                build_prediction_dataframe, engineer_features,
+                apply_label_encoders, align_feature_frame, to_float_array,
+            )
+
+            X_df = build_prediction_dataframe(data)
+            X_df = engineer_features(X_df)
+            X_df = apply_label_encoders(X_df, self.label_encoders)
+            if self.feature_names:
+                X_df = align_feature_frame(X_df, self.feature_names)
+            features = self.scaler.transform(to_float_array(X_df))
+            if self.selected_features and self.feature_names:
+                selected_indices = [self.feature_names.index(n) for n in self.selected_features if n in self.feature_names]
+                if selected_indices:
+                    features = features[:, selected_indices]
+
+            shap_values = explainer.shap_values(features)
+            if isinstance(shap_values, list):
+                shap_values = shap_values[0]
+
+            base_value = float(explainer.expected_value)
+            if isinstance(base_value, (list, np.ndarray)):
+                base_value = float(base_value[0])
+
+            feature_names = self.selected_features or self.feature_names or []
+            name_map = self._get_feature_display_names()
+
+            feature_contributions = []
+            dimension_contribution = {}
+            for idx, fname in enumerate(feature_names):
+                sv = float(shap_values[0][idx])
+                fv = float(features[0][idx])
+                dim = self._map_feature_to_dimension(fname)
+                feature_contributions.append({
+                    'name': fname,
+                    'name_cn': name_map.get(fname, fname),
+                    'shap_value': round(sv, 4),
+                    'feature_value': round(fv, 4),
+                    'dimension': dim,
+                })
+                dimension_contribution[dim] = dimension_contribution.get(dim, 0) + sv
+
+            feature_contributions.sort(key=lambda x: abs(x['shap_value']), reverse=True)
+
+            # 维度标签
+            dim_labels = {}
+            for dk, di in config.JDR_DIMENSIONS.items():
+                dim_labels[dk] = di['name_cn']
+            dim_labels['event_context'] = '事件上下文'
+            dim_labels['other'] = '其他'
+
+            return {
+                'base_value': round(base_value, 4),
+                'features': feature_contributions[:20],
+                'dimension_contribution': {
+                    dim_labels.get(k, k): round(v, 4)
+                    for k, v in sorted(dimension_contribution.items(), key=lambda x: abs(x[1]), reverse=True)
+                },
+            }
+        except Exception as exc:
+            return {'error': str(exc)}
+
+    def shap_interaction(self, model_type='random_forest', top_n=10):
+        """计算 SHAP 交互值"""
+        if not SHAP_AVAILABLE:
+            return {'error': 'SHAP library not installed'}
+
+        self._ensure_initialized()
+        explainer = self._get_tree_explainer(model_type)
+        if explainer is None:
+            return {'error': f'No tree model available for {model_type}'}
+
+        X = self._get_background_sample(n_samples=200)
+        if X is None:
+            return {'error': 'Failed to prepare background data'}
+
+        try:
+            interaction_values = explainer.shap_interaction_values(X)
+            if isinstance(interaction_values, list):
+                interaction_values = interaction_values[0]
+
+            mean_interaction = np.abs(interaction_values).mean(axis=0)
+            feature_names = self.selected_features or self.feature_names or []
+
+            # 获取 top_n 特征的交互
+            mean_abs = np.abs(interaction_values.mean(axis=0))
+            np.fill_diagonal(mean_abs, 0)
+            flat_idx = np.argsort(mean_abs.ravel())[::-1][:top_n * 2]
+            top_pairs = []
+            seen = set()
+            for idx in flat_idx:
+                i, j = divmod(idx, mean_abs.shape[1])
+                if i >= j:
+                    continue
+                pair_key = (min(i, j), max(i, j))
+                if pair_key in seen:
+                    continue
+                seen.add(pair_key)
+                fi = feature_names[i] if i < len(feature_names) else f'f{i}'
+                fj = feature_names[j] if j < len(feature_names) else f'f{j}'
+                name_map = self._get_feature_display_names()
+                top_pairs.append({
+                    'feature_1': fi,
+                    'feature_1_cn': name_map.get(fi, fi),
+                    'feature_2': fj,
+                    'feature_2_cn': name_map.get(fj, fj),
+                    'strength': round(float(mean_interaction[i, j]), 4),
+                })
+                if len(top_pairs) >= top_n:
+                    break
+
+            return {
+                'model_type': model_type,
+                'top_interactions': top_pairs,
+            }
+        except Exception as exc:
+            return {'error': str(exc)}
+
+    def shap_dependence(self, feature_name, model_type='random_forest'):
+        """计算单个特征的 SHAP 依赖图数据"""
+        if not SHAP_AVAILABLE:
+            return {'error': 'SHAP library not installed'}
+
+        self._ensure_initialized()
+        explainer = self._get_tree_explainer(model_type)
+        if explainer is None:
+            return {'error': f'No tree model available for {model_type}'}
+
+        X = self._get_background_sample()
+        if X is None:
+            return {'error': 'Failed to prepare background data'}
+
+        try:
+            feature_names = self.selected_features or self.feature_names or []
+            if feature_name not in feature_names:
+                return {'error': f'Feature {feature_name} not found'}
+
+            col_idx = list(feature_names).index(feature_name)
+            shap_values = explainer.shap_values(X)
+            if isinstance(shap_values, list):
+                shap_values = shap_values[0]
+
+            feature_vals = X[:, col_idx].tolist()
+            shap_vals = shap_values[:, col_idx].tolist()
+
+            # 下采样用于可视化
+            max_points = 300
+            if len(feature_vals) > max_points:
+                indices = np.random.RandomState(config.RANDOM_STATE).choice(
+                    len(feature_vals), max_points, replace=False
+                )
+                feature_vals = [feature_vals[i] for i in indices]
+                shap_vals = [shap_vals[i] for i in indices]
+
+            name_map = self._get_feature_display_names()
+            return {
+                'feature': feature_name,
+                'feature_cn': name_map.get(feature_name, feature_name),
+                'values': [round(v, 4) for v in feature_vals],
+                'shap_values': [round(v, 4) for v in shap_vals],
+            }
+        except Exception as exc:
+            return {'error': str(exc)}
diff --git a/backend/core/train_model.py b/backend/core/train_model.py
index efa6c53..782fa6f 100644
--- a/backend/core/train_model.py
+++ b/backend/core/train_model.py
@@ -7,8 +7,10 @@ from datetime import datetime
 import joblib
 import numpy as np
 from sklearn.ensemble import ExtraTreesRegressor, GradientBoostingRegressor, RandomForestRegressor
+from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
 from sklearn.feature_selection import SelectKBest, f_regression
 from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
 from sklearn.model_selection import RandomizedSearchCV, train_test_split
 from sklearn.preprocessing import RobustScaler
 
@@ -351,9 +353,81 @@ class OptimizedModelTrainer:
                 )
 
         self.save_models()
+
+        # 风险分类模型训练
+        print('\nRisk Classification Training')
+        risk_trainer = RiskClassifierTrainer(self)
+        risk_trainer.train_all(X_train, y_train, X_test, y_test)
+        risk_trainer.save()
+
         return self.model_metrics
 
 
+class RiskClassifierTrainer:
+    """风险等级分类模型训练器：低(<4h) / 中(4-8h) / 高(>8h)"""
+
+    RISK_MAP = {'low': 0, 'medium': 1, 'high': 2}
+    RISK_LABELS = ['low', 'medium', 'high']
+
+    def __init__(self, regression_trainer):
+        self.regression_trainer = regression_trainer
+        self.classifiers = {}
+        self.classification_metrics = {}
+
+    def _make_target(self, y_hours):
+        y_class = np.full(len(y_hours), 1, dtype=int)
+        y_class[y_hours < 4] = 0
+        y_class[y_hours > 8] = 2
+        return y_class
+
+    def train_all(self, X_train, y_train_hours, X_test, y_test_hours):
+        y_train_cls = self._make_target(y_train_hours)
+        y_test_cls = self._make_target(y_test_hours)
+
+        classifier_configs = {
+            'random_forest': RandomForestClassifier(
+                n_estimators=300, max_depth=14, random_state=config.RANDOM_STATE, n_jobs=-1,
+            ),
+            'gradient_boosting': GradientBoostingClassifier(
+                n_estimators=200, max_depth=4, learning_rate=0.05, random_state=config.RANDOM_STATE,
+            ),
+        }
+
+        if lgb is not None:
+            classifier_configs['lightgbm'] = lgb.LGBMClassifier(
+                n_estimators=260, max_depth=7, learning_rate=0.05,
+                random_state=config.RANDOM_STATE, n_jobs=-1, verbose=-1,
+            )
+        if xgb is not None:
+            classifier_configs['xgboost'] = xgb.XGBClassifier(
+                n_estimators=260, max_depth=6, learning_rate=0.05,
+                random_state=config.RANDOM_STATE, n_jobs=-1,
+            )
+
+        for name, clf in classifier_configs.items():
+            try:
+                clf.fit(X_train, y_train_cls)
+                y_pred = clf.predict(X_test)
+                self.classifiers[name] = clf
+                self.classification_metrics[name] = {
+                    'accuracy': round(accuracy_score(y_test_cls, y_pred), 4),
+                    'precision_macro': round(precision_score(y_test_cls, y_pred, average='macro', zero_division=0), 4),
+                    'recall_macro': round(recall_score(y_test_cls, y_pred, average='macro', zero_division=0), 4),
+                    'f1_macro': round(f1_score(y_test_cls, y_pred, average='macro', zero_division=0), 4),
+                    'confusion_matrix': confusion_matrix(y_test_cls, y_pred).tolist(),
+                }
+                m = self.classification_metrics[name]
+                print(f'  {name:20s} Acc={m["accuracy"]:.4f} F1={m["f1_macro"]:.4f}')
+            except Exception as exc:
+                print(f'  {name:20s} Skipped: {exc}')
+
+    def save(self):
+        for name, clf in self.classifiers.items():
+            path = os.path.join(config.MODELS_DIR, f'risk_{name}_classifier.pkl')
+            joblib.dump(clf, path)
+        joblib.dump(self.classification_metrics, os.path.join(config.MODELS_DIR, 'classification_metrics.pkl'))
+
+
 def train_and_save_models():
     start = time.time()
     trainer = OptimizedModelTrainer()
diff --git a/backend/outputs/eval_figures/01_模型性能对比.png b/backend/outputs/eval_figures/01_模型性能对比.png
new file mode 100644
index 0000000..190b4c2
Binary files /dev/null and b/backend/outputs/eval_figures/01_模型性能对比.png differ
diff --git a/backend/outputs/eval_figures/02_LSTM真实值_vs_预测值.png b/backend/outputs/eval_figures/02_LSTM真实值_vs_预测值.png
new file mode 100644
index 0000000..6c3b938
Binary files /dev/null and b/backend/outputs/eval_figures/02_LSTM真实值_vs_预测值.png differ
diff --git a/backend/outputs/eval_figures/03_LSTM残差分析.png b/backend/outputs/eval_figures/03_LSTM残差分析.png
new file mode 100644
index 0000000..c8f7814
Binary files /dev/null and b/backend/outputs/eval_figures/03_LSTM残差分析.png differ
diff --git a/backend/outputs/eval_figures/04_LSTM风险等级混淆矩阵.png b/backend/outputs/eval_figures/04_LSTM风险等级混淆矩阵.png
new file mode 100644
index 0000000..7fdaa8c
Binary files /dev/null and b/backend/outputs/eval_figures/04_LSTM风险等级混淆矩阵.png differ
diff --git a/backend/outputs/eval_figures/05_特征重要性_Top15.png b/backend/outputs/eval_figures/05_特征重要性_Top15.png
new file mode 100644
index 0000000..d32726a
Binary files /dev/null and b/backend/outputs/eval_figures/05_特征重要性_Top15.png differ
diff --git a/backend/outputs/eval_figures/evaluation_summary.json b/backend/outputs/eval_figures/evaluation_summary.json
new file mode 100644
index 0000000..f4408c9
--- /dev/null
+++ b/backend/outputs/eval_figures/evaluation_summary.json
@@ -0,0 +1,50 @@
+{
+  "best_model": "lstm_mlp",
+  "metrics": {
+    "lstm_mlp": {
+      "r2": 0.9272,
+      "mse": 0.3597,
+      "rmse": 0.5997,
+      "mae": 0.4735
+    },
+    "xgboost": {
+      "r2": 0.7838,
+      "mse": 1.0687,
+      "rmse": 1.0338,
+      "mae": 0.7578
+    },
+    "gradient_boosting": {
+      "r2": 0.7804,
+      "mse": 1.0854,
+      "rmse": 1.0418,
+      "mae": 0.7651
+    },
+    "random_forest": {
+      "r2": 0.7647,
+      "mse": 1.1631,
+      "rmse": 1.0785,
+      "mae": 0.7921
+    },
+    "extra_trees": {
+      "r2": 0.7577,
+      "mse": 1.1976,
+      "rmse": 1.0943,
+      "mae": 0.8045
+    }
+  },
+  "lstm_prediction_summary": {
+    "prediction_count": 2400,
+    "residual_mean": -0.0498,
+    "residual_std": 0.5976,
+    "risk_accuracy": 0.8562
+  },
+  "feature_importance_model": "xgboost",
+  "generated_files": [
+    "01_模型性能对比.png",
+    "02_LSTM真实值_vs_预测值.png",
+    "03_LSTM残差分析.png",
+    "04_LSTM风险等级混淆矩阵.png",
+    "05_特征重要性_Top15.png",
+    "lstm_predictions.csv"
+  ]
+}
\ No newline at end of file
diff --git a/backend/outputs/eval_figures/lstm_predictions.csv b/backend/outputs/eval_figures/lstm_predictions.csv
new file mode 100644
index 0000000..f5d4ebc
--- /dev/null
+++ b/backend/outputs/eval_figures/lstm_predictions.csv
@@ -0,0 +1,2401 @@
+真实值,预测值,残差,真实风险等级,预测风险等级
+4.9,4.5082,-0.3918,中风险,中风险
+7.4,7.8822,0.4822,中风险,中风险
+3.5,2.633,-0.867,低风险,低风险
+4.4,4.2099,-0.1901,中风险,中风险
+2.9,2.4485,-0.4515,低风险,低风险
+11.4,11.0718,-0.3282,高风险,高风险
+2.9,3.4642,0.5642,低风险,低风险
+6.8,5.4301,-1.3699,中风险,中风险
+6.9,6.3356,-0.5644,中风险,中风险
+8.3,8.2596,-0.0404,高风险,高风险
+9.4,9.6418,0.2418,高风险,高风险
+6.8,7.1788,0.3788,中风险,中风险
+4.2,4.2253,0.0253,中风险,中风险
+3.1,3.2756,0.1756,低风险,低风险
+3.5,5.083,1.583,低风险,中风险
+7.0,6.9437,-0.0563,中风险,中风险
+6.6,5.8894,-0.7106,中风险,中风险
+3.6,3.564,-0.036,低风险,低风险
+4.4,4.6302,0.2302,中风险,中风险
+5.1,5.2189,0.1189,中风险,中风险
+5.3,4.7826,-0.5174,中风险,中风险
+6.9,7.4634,0.5634,中风险,中风险
+1.8,2.3298,0.5298,低风险,低风险
+8.1,7.9114,-0.1886,高风险,中风险
+3.1,3.0754,-0.0246,低风险,低风险
+6.0,6.7522,0.7522,中风险,中风险
+5.8,5.2742,-0.5258,中风险,中风险
+7.8,6.8572,-0.9428,中风险,中风险
+10.5,10.9456,0.4456,高风险,高风险
+11.1,10.8667,-0.2333,高风险,高风险
+9.1,9.7198,0.6198,高风险,高风险
+5.4,5.1457,-0.2543,中风险,中风险
+8.2,7.1144,-1.0856,高风险,中风险
+8.0,7.2561,-0.7439,中风险,中风险
+6.1,6.3196,0.2196,中风险,中风险
+5.2,5.472,0.272,中风险,中风险
+3.7,4.0148,0.3148,低风险,中风险
+6.2,6.6255,0.4255,中风险,中风险
+6.2,7.1374,0.9374,中风险,中风险
+6.8,7.6603,0.8603,中风险,中风险
+8.1,7.2533,-0.8467,高风险,中风险
+6.2,6.6037,0.4037,中风险,中风险
+6.5,7.352,0.852,中风险,中风险
+6.2,5.7007,-0.4993,中风险,中风险
+5.1,5.4397,0.3397,中风险,中风险
+4.2,4.4323,0.2323,中风险,中风险
+4.5,4.4105,-0.0895,中风险,中风险
+4.1,3.3796,-0.7204,中风险,低风险
+3.5,4.073,0.573,低风险,中风险
+8.4,9.2541,0.8541,高风险,高风险
+7.0,7.728,0.728,中风险,中风险
+5.1,5.2348,0.1348,中风险,中风险
+4.8,4.9469,0.1469,中风险,中风险
+7.2,8.0235,0.8235,中风险,高风险
+4.3,4.0217,-0.2783,中风险,中风险
+3.9,4.0789,0.1789,低风险,中风险
+6.8,6.7612,-0.0388,中风险,中风险
+2.3,2.7925,0.4925,低风险,低风险
+8.7,8.4136,-0.2864,高风险,高风险
+3.0,4.0773,1.0773,低风险,中风险
+6.3,6.9341,0.6341,中风险,中风险
+5.6,5.5959,-0.0041,中风险,中风险
+6.6,6.9011,0.3011,中风险,中风险
+8.2,7.8776,-0.3224,高风险,中风险
+2.6,2.9185,0.3185,低风险,低风险
+4.6,5.1842,0.5842,中风险,中风险
+7.6,9.0139,1.4139,中风险,高风险
+4.9,3.9547,-0.9453,中风险,低风险
+4.3,3.7875,-0.5125,中风险,低风险
+5.1,4.3736,-0.7264,中风险,中风险
+7.5,7.3087,-0.1913,中风险,中风险
+8.2,8.2301,0.0301,高风险,高风险
+7.2,7.0632,-0.1368,中风险,中风险
+8.7,8.2673,-0.4327,高风险,高风险
+6.9,6.9303,0.0303,中风险,中风险
+2.6,2.5112,-0.0888,低风险,低风险
+4.9,4.9603,0.0603,中风险,中风险
+7.9,7.8011,-0.0989,中风险,中风险
+6.4,6.0714,-0.3286,中风险,中风险
+6.2,6.1496,-0.0504,中风险,中风险
+6.3,6.6469,0.3469,中风险,中风险
+4.5,4.5084,0.0084,中风险,中风险
+3.4,2.9952,-0.4048,低风险,低风险
+3.6,4.263,0.663,低风险,中风险
+2.7,2.4607,-0.2393,低风险,低风险
+6.3,6.5358,0.2358,中风险,中风险
+4.5,4.5798,0.0798,中风险,中风险
+6.4,7.0404,0.6404,中风险,中风险
+3.9,4.1301,0.2301,低风险,中风险
+1.6,2.262,0.662,低风险,低风险
+6.0,6.4072,0.4072,中风险,中风险
+7.8,7.8346,0.0346,中风险,中风险
+6.6,6.247,-0.353,中风险,中风险
+8.4,7.5607,-0.8393,高风险,中风险
+2.1,2.4139,0.3139,低风险,低风险
+7.4,7.142,-0.258,中风险,中风险
+7.7,8.028,0.328,中风险,高风险
+3.8,3.836,0.036,低风险,低风险
+4.5,4.534,0.034,中风险,中风险
+8.4,8.1226,-0.2774,高风险,高风险
+5.9,6.1531,0.2531,中风险,中风险
+2.7,2.7053,0.0053,低风险,低风险
+5.0,4.0034,-0.9966,中风险,中风险
+2.5,2.9722,0.4722,低风险,低风险
+3.7,3.4999,-0.2001,低风险,低风险
+7.7,6.5008,-1.1992,中风险,中风险
+7.9,7.0435,-0.8565,中风险,中风险
+9.2,7.9953,-1.2046,高风险,中风险
+8.3,7.8101,-0.4899,高风险,中风险
+6.6,5.5072,-1.0928,中风险,中风险
+3.6,4.2489,0.6489,低风险,中风险
+5.3,5.2604,-0.0396,中风险,中风险
+2.2,3.0071,0.8071,低风险,低风险
+2.7,3.1087,0.4087,低风险,低风险
+1.9,2.6734,0.7734,低风险,低风险
+4.6,4.6821,0.0821,中风险,中风险
+5.0,5.2539,0.2539,中风险,中风险
+3.6,3.3077,-0.2923,低风险,低风险
+3.0,3.305,0.305,低风险,低风险
+3.4,3.6367,0.2367,低风险,低风险
+4.4,4.6096,0.2096,中风险,中风险
+8.1,8.2822,0.1822,高风险,高风险
+4.5,3.9216,-0.5784,中风险,低风险
+3.7,4.0316,0.3316,低风险,中风险
+8.2,8.386,0.186,高风险,高风险
+7.8,7.3695,-0.4305,中风险,中风险
+2.5,3.0969,0.5969,低风险,低风险
+7.1,7.9575,0.8575,中风险,中风险
+2.6,2.1666,-0.4334,低风险,低风险
+3.7,2.8838,-0.8162,低风险,低风险
+5.8,6.3626,0.5626,中风险,中风险
+4.1,4.341,0.241,中风险,中风险
+7.6,6.3846,-1.2154,中风险,中风险
+4.3,4.556,0.256,中风险,中风险
+7.1,6.9753,-0.1247,中风险,中风险
+2.0,3.1995,1.1995,低风险,低风险
+8.4,7.591,-0.809,高风险,中风险
+7.0,7.6984,0.6984,中风险,中风险
+3.9,3.9281,0.0281,低风险,低风险
+3.7,3.4582,-0.2418,低风险,低风险
+3.5,2.8213,-0.6787,低风险,低风险
+4.2,3.8644,-0.3356,中风险,低风险
+8.8,9.0925,0.2925,高风险,高风险
+6.4,6.3563,-0.0437,中风险,中风险
+7.2,6.5353,-0.6647,中风险,中风险
+6.6,6.1428,-0.4572,中风险,中风险
+2.3,2.1999,-0.1001,低风险,低风险
+2.2,2.9358,0.7358,低风险,低风险
+1.4,2.065,0.665,低风险,低风险
+7.1,6.437,-0.663,中风险,中风险
+7.9,7.2416,-0.6584,中风险,中风险
+3.9,4.3038,0.4038,低风险,中风险
+3.3,3.8559,0.5559,低风险,低风险
+2.8,2.1451,-0.6549,低风险,低风险
+7.1,7.3969,0.2969,中风险,中风险
+3.5,3.5532,0.0532,低风险,低风险
+9.3,8.1544,-1.1456,高风险,高风险
+8.2,8.074,-0.126,高风险,高风险
+6.0,6.0952,0.0952,中风险,中风险
+11.7,11.7852,0.0852,高风险,高风险
+6.8,6.6442,-0.1558,中风险,中风险
+8.2,7.6492,-0.5508,高风险,中风险
+9.3,8.655,-0.645,高风险,高风险
+6.8,6.482,-0.318,中风险,中风险
+3.4,3.0409,-0.3591,低风险,低风险
+4.3,4.1721,-0.1279,中风险,中风险
+3.1,3.131,0.031,低风险,低风险
+2.7,3.0201,0.3201,低风险,低风险
+7.3,6.5633,-0.7367,中风险,中风险
+9.4,9.4091,0.0091,高风险,高风险
+2.7,2.8022,0.1022,低风险,低风险
+8.2,7.2652,-0.9348,高风险,中风险
+9.2,8.0812,-1.1188,高风险,高风险
+5.2,4.8358,-0.3642,中风险,中风险
+4.8,4.2919,-0.5081,中风险,中风险
+6.4,6.1864,-0.2136,中风险,中风险
+5.8,6.1738,0.3738,中风险,中风险
+5.6,5.2164,-0.3836,中风险,中风险
+5.1,5.1174,0.0174,中风险,中风险
+8.0,7.9557,-0.0443,中风险,中风险
+8.3,8.3216,0.0216,高风险,高风险
+7.1,6.4444,-0.6556,中风险,中风险
+4.8,4.9681,0.1681,中风险,中风险
+7.5,6.7319,-0.7681,中风险,中风险
+4.6,4.7386,0.1386,中风险,中风险
+4.9,5.4262,0.5262,中风险,中风险
+1.6,2.0476,0.4476,低风险,低风险
+7.7,8.2002,0.5002,中风险,高风险
+6.8,6.4755,-0.3245,中风险,中风险
+6.1,5.3738,-0.7262,中风险,中风险
+4.3,4.6099,0.3099,中风险,中风险
+6.5,6.8145,0.3145,中风险,中风险
+2.5,2.784,0.284,低风险,低风险
+7.4,7.2505,-0.1495,中风险,中风险
+4.9,4.4075,-0.4925,中风险,中风险
+4.6,4.1682,-0.4318,中风险,中风险
+8.7,8.5963,-0.1037,高风险,高风险
+8.6,8.412,-0.188,高风险,高风险
+5.0,5.1885,0.1885,中风险,中风险
+1.7,2.0591,0.3591,低风险,低风险
+2.9,2.6896,-0.2104,低风险,低风险
+5.6,4.8095,-0.7905,中风险,中风险
+2.7,2.801,0.101,低风险,低风险
+7.4,7.1111,-0.2889,中风险,中风险
+4.7,4.7388,0.0388,中风险,中风险
+1.4,1.8598,0.4598,低风险,低风险
+8.2,7.5851,-0.6149,高风险,中风险
+6.9,7.1556,0.2556,中风险,中风险
+7.5,6.9693,-0.5307,中风险,中风险
+3.7,3.5896,-0.1104,低风险,低风险
+4.1,3.6955,-0.4045,中风险,低风险
+4.3,3.7736,-0.5264,中风险,低风险
+7.3,6.218,-1.082,中风险,中风险
+1.9,2.3153,0.4153,低风险,低风险
+2.6,3.0977,0.4977,低风险,低风险
+2.3,2.2771,-0.0229,低风险,低风险
+9.3,10.357,1.057,高风险,高风险
+9.4,10.0163,0.6163,高风险,高风险
+9.1,8.1873,-0.9127,高风险,高风险
+2.4,3.0475,0.6475,低风险,低风险
+4.1,2.7253,-1.3747,中风险,低风险
+4.4,4.8644,0.4644,中风险,中风险
+5.7,5.3327,-0.3673,中风险,中风险
+6.9,5.6416,-1.2584,中风险,中风险
+6.5,7.3147,0.8147,中风险,中风险
+4.5,5.6972,1.1972,中风险,中风险
+4.5,4.8491,0.3491,中风险,中风险
+4.0,4.2045,0.2045,中风险,中风险
+4.3,4.5371,0.2371,中风险,中风险
+5.4,5.3615,-0.0385,中风险,中风险
+4.2,4.059,-0.141,中风险,中风险
+5.8,6.8733,1.0733,中风险,中风险
+8.2,7.7311,-0.4689,高风险,中风险
+4.8,5.1831,0.3831,中风险,中风险
+4.0,4.8618,0.8618,中风险,中风险
+6.5,5.3314,-1.1686,中风险,中风险
+3.9,3.1262,-0.7738,低风险,低风险
+3.5,3.7543,0.2543,低风险,低风险
+5.0,5.2229,0.2229,中风险,中风险
+8.3,8.5539,0.2539,高风险,高风险
+4.1,4.4564,0.3564,中风险,中风险
+6.5,6.2042,-0.2958,中风险,中风险
+7.8,7.8681,0.0681,中风险,中风险
+2.4,3.8288,1.4288,低风险,低风险
+5.6,5.1665,-0.4335,中风险,中风险
+7.2,5.5642,-1.6358,中风险,中风险
+7.2,5.4916,-1.7084,中风险,中风险
+4.5,4.1291,-0.3709,中风险,中风险
+8.1,6.8083,-1.2917,高风险,中风险
+11.3,11.0305,-0.2695,高风险,高风险
+2.2,3.1252,0.9252,低风险,低风险
+9.8,8.9408,-0.8592,高风险,高风险
+7.3,6.4842,-0.8158,中风险,中风险
+3.0,2.9206,-0.0794,低风险,低风险
+8.4,7.0215,-1.3785,高风险,中风险
+3.4,4.1183,0.7183,低风险,中风险
+2.2,2.8629,0.6629,低风险,低风险
+5.4,6.3279,0.9279,中风险,中风险
+6.6,5.598,-1.002,中风险,中风险
+8.6,8.3025,-0.2975,高风险,高风险
+3.7,4.7323,1.0323,低风险,中风险
+1.9,2.8972,0.9972,低风险,低风险
+2.5,3.2764,0.7764,低风险,低风险
+4.2,4.1635,-0.0365,中风险,中风险
+4.3,3.1655,-1.1345,中风险,低风险
+4.7,3.9662,-0.7338,中风险,低风险
+8.2,8.3033,0.1033,高风险,高风险
+5.9,5.2619,-0.6381,中风险,中风险
+2.5,3.7289,1.2289,低风险,低风险
+6.9,6.6775,-0.2225,中风险,中风险
+4.5,4.404,-0.096,中风险,中风险
+8.5,7.6963,-0.8037,高风险,中风险
+6.2,6.0941,-0.1059,中风险,中风险
+7.5,7.6215,0.1215,中风险,中风险
+6.9,7.2809,0.3809,中风险,中风险
+7.9,6.9483,-0.9517,中风险,中风险
+7.4,6.4965,-0.9035,中风险,中风险
+8.5,8.4126,-0.0874,高风险,高风险
+6.3,7.4732,1.1732,中风险,中风险
+10.0,9.7992,-0.2008,高风险,高风险
+7.6,8.3444,0.7444,中风险,高风险
+6.5,5.4693,-1.0307,中风险,中风险
+4.6,4.6941,0.0941,中风险,中风险
+8.0,7.6227,-0.3773,中风险,中风险
+5.2,5.9394,0.7394,中风险,中风险
+9.9,10.0629,0.1629,高风险,高风险
+10.9,10.7623,-0.1377,高风险,高风险
+10.8,11.025,0.225,高风险,高风险
+1.9,1.5689,-0.3311,低风险,低风险
+5.5,5.6689,0.1689,中风险,中风险
+8.4,8.3484,-0.0516,高风险,高风险
+4.5,3.7667,-0.7333,中风险,低风险
+4.9,5.0726,0.1726,中风险,中风险
+4.2,4.6748,0.4748,中风险,中风险
+5.1,5.62,0.52,中风险,中风险
+3.9,4.2069,0.3069,低风险,中风险
+6.0,5.8865,-0.1135,中风险,中风险
+5.5,5.4992,-0.0008,中风险,中风险
+7.2,7.6641,0.4641,中风险,中风险
+8.6,7.5595,-1.0405,高风险,中风险
+6.8,7.456,0.656,中风险,中风险
+6.9,7.0669,0.1669,中风险,中风险
+4.9,4.9319,0.0319,中风险,中风险
+4.3,4.2059,-0.0941,中风险,中风险
+4.2,3.3822,-0.8178,中风险,低风险
+4.8,3.9802,-0.8198,中风险,低风险
+2.6,3.367,0.767,低风险,低风险
+3.6,3.6599,0.0599,低风险,低风险
+2.1,2.4174,0.3174,低风险,低风险
+3.0,3.3427,0.3427,低风险,低风险
+8.4,8.826,0.426,高风险,高风险
+5.6,4.7616,-0.8384,中风险,中风险
+9.4,8.1587,-1.2413,高风险,高风险
+4.9,4.1125,-0.7875,中风险,中风险
+3.4,2.8011,-0.5989,低风险,低风险
+5.8,5.6066,-0.1934,中风险,中风险
+9.8,8.77,-1.03,高风险,高风险
+9.1,8.4245,-0.6755,高风险,高风险
+3.5,3.7998,0.2998,低风险,低风险
+3.7,3.9253,0.2253,低风险,低风险
+5.9,5.5293,-0.3707,中风险,中风险
+5.0,5.7591,0.7591,中风险,中风险
+4.1,4.523,0.423,中风险,中风险
+4.2,3.9254,-0.2746,中风险,低风险
+6.2,5.6986,-0.5014,中风险,中风险
+5.9,5.0715,-0.8285,中风险,中风险
+4.7,5.0611,0.3611,中风险,中风险
+2.6,2.2879,-0.3121,低风险,低风险
+4.0,4.4348,0.4348,中风险,中风险
+10.1,10.1877,0.0877,高风险,高风险
+10.0,10.1272,0.1272,高风险,高风险
+2.9,3.297,0.397,低风险,低风险
+8.1,8.1258,0.0258,高风险,高风险
+6.1,5.6524,-0.4476,中风险,中风险
+2.8,3.3363,0.5363,低风险,低风险
+3.8,3.8662,0.0662,低风险,低风险
+10.9,11.2499,0.3499,高风险,高风险
+3.8,4.2924,0.4924,低风险,中风险
+8.4,7.5454,-0.8546,高风险,中风险
+9.0,7.9935,-1.0065,高风险,中风险
+5.1,3.867,-1.233,中风险,低风险
+4.1,4.2075,0.1075,中风险,中风险
+5.6,5.0456,-0.5544,中风险,中风险
+3.6,3.6398,0.0398,低风险,低风险
+4.7,5.5776,0.8776,中风险,中风险
+4.4,3.9618,-0.4382,中风险,低风险
+4.1,4.0446,-0.0554,中风险,中风险
+2.8,3.0169,0.2169,低风险,低风险
+5.1,6.7093,1.6093,中风险,中风险
+9.8,9.1844,-0.6156,高风险,高风险
+5.0,4.5533,-0.4467,中风险,中风险
+9.0,9.8748,0.8748,高风险,高风险
+8.8,7.778,-1.022,高风险,中风险
+3.8,3.0499,-0.7501,低风险,低风险
+8.2,8.531,0.331,高风险,高风险
+8.7,8.7836,0.0836,高风险,高风险
+6.5,6.9885,0.4885,中风险,中风险
+2.5,2.8709,0.3709,低风险,低风险
+4.6,5.3351,0.7351,中风险,中风险
+7.3,7.1742,-0.1258,中风险,中风险
+6.7,7.1747,0.4747,中风险,中风险
+5.3,5.3765,0.0765,中风险,中风险
+6.1,6.3462,0.2462,中风险,中风险
+4.4,4.1904,-0.2096,中风险,中风险
+7.7,7.8332,0.1332,中风险,中风险
+8.3,7.4111,-0.8889,高风险,中风险
+3.9,4.2784,0.3784,低风险,中风险
+6.0,6.6282,0.6282,中风险,中风险
+4.2,4.3066,0.1066,中风险,中风险
+3.5,3.4369,-0.0631,低风险,低风险
+3.3,4.0117,0.7117,低风险,中风险
+3.6,3.6287,0.0287,低风险,低风险
+3.0,3.5524,0.5524,低风险,低风险
+3.8,3.4589,-0.3411,低风险,低风险
+3.1,3.4017,0.3017,低风险,低风险
+2.3,2.7425,0.4425,低风险,低风险
+5.9,5.1805,-0.7195,中风险,中风险
+4.9,4.9409,0.0409,中风险,中风险
+6.0,5.7618,-0.2382,中风险,中风险
+5.2,5.0199,-0.1801,中风险,中风险
+3.8,4.6917,0.8917,低风险,中风险
+5.9,5.2219,-0.6781,中风险,中风险
+6.8,6.7778,-0.0222,中风险,中风险
+3.7,3.6753,-0.0247,低风险,低风险
+5.3,5.4071,0.1071,中风险,中风险
+7.6,7.1891,-0.4109,中风险,中风险
+5.6,5.9612,0.3612,中风险,中风险
+5.2,5.7126,0.5126,中风险,中风险
+3.9,4.4775,0.5775,低风险,中风险
+2.9,3.0106,0.1106,低风险,低风险
+2.5,3.2726,0.7726,低风险,低风险
+3.4,2.715,-0.685,低风险,低风险
+10.0,10.0772,0.0772,高风险,高风险
+7.0,7.254,0.254,中风险,中风险
+4.4,4.7254,0.3254,中风险,中风险
+3.6,4.1689,0.5689,低风险,中风险
+8.1,8.4491,0.3491,高风险,高风险
+2.5,2.2451,-0.2549,低风险,低风险
+4.3,3.9253,-0.3747,中风险,低风险
+5.5,5.7574,0.2574,中风险,中风险
+7.3,6.8493,-0.4507,中风险,中风险
+7.5,6.2998,-1.2002,中风险,中风险
+10.5,10.6466,0.1466,高风险,高风险
+10.8,10.6098,-0.1902,高风险,高风险
+4.0,5.1026,1.1026,中风险,中风险
+2.8,3.5573,0.7573,低风险,低风险
+3.6,4.2104,0.6104,低风险,中风险
+3.8,3.4726,-0.3274,低风险,低风险
+9.8,9.0618,-0.7382,高风险,高风险
+4.4,3.729,-0.671,中风险,低风险
+4.6,3.8446,-0.7554,中风险,低风险
+3.9,3.6623,-0.2377,低风险,低风险
+5.6,6.4606,0.8606,中风险,中风险
+7.7,7.6563,-0.0437,中风险,中风险
+6.3,6.5882,0.2882,中风险,中风险
+10.2,9.9944,-0.2056,高风险,高风险
+10.1,10.113,0.013,高风险,高风险
+3.7,3.5498,-0.1502,低风险,低风险
+7.4,7.3925,-0.0075,中风险,中风险
+5.5,5.5503,0.0503,中风险,中风险
+5.8,5.8569,0.0569,中风险,中风险
+6.2,6.7054,0.5054,中风险,中风险
+6.3,6.7747,0.4747,中风险,中风险
+6.8,6.0903,-0.7097,中风险,中风险
+6.2,5.8247,-0.3753,中风险,中风险
+5.4,5.9478,0.5478,中风险,中风险
+6.4,5.9124,-0.4876,中风险,中风险
+7.7,8.1815,0.4815,中风险,高风险
+5.3,4.7763,-0.5237,中风险,中风险
+7.0,6.3625,-0.6375,中风险,中风险
+2.9,3.9296,1.0296,低风险,低风险
+3.3,3.7404,0.4404,低风险,低风险
+7.5,7.5766,0.0766,中风险,中风险
+3.0,2.999,-0.001,低风险,低风险
+6.1,4.7333,-1.3667,中风险,中风险
+8.6,7.7641,-0.8359,高风险,中风险
+2.8,2.6386,-0.1614,低风险,低风险
+5.1,4.6473,-0.4527,中风险,中风险
+9.4,8.8764,-0.5236,高风险,高风险
+6.4,6.0904,-0.3096,中风险,中风险
+5.5,4.9242,-0.5758,中风险,中风险
+7.5,7.9198,0.4198,中风险,中风险
+5.3,4.7851,-0.5149,中风险,中风险
+5.1,5.5507,0.4507,中风险,中风险
+6.8,6.7744,-0.0256,中风险,中风险
+3.4,2.7494,-0.6506,低风险,低风险
+3.2,2.9572,-0.2428,低风险,低风险
+2.8,3.2709,0.4709,低风险,低风险
+7.0,8.2283,1.2283,中风险,高风险
+6.9,7.95,1.05,中风险,中风险
+6.0,5.8516,-0.1484,中风险,中风险
+8.2,7.3519,-0.8481,高风险,中风险
+7.5,8.617,1.117,中风险,高风险
+7.7,7.1338,-0.5662,中风险,中风险
+7.0,6.3784,-0.6216,中风险,中风险
+3.3,3.4408,0.1408,低风险,低风险
+2.6,2.8048,0.2048,低风险,低风险
+6.3,6.5409,0.2409,中风险,中风险
+3.8,3.5794,-0.2206,低风险,低风险
+8.1,7.2869,-0.8131,高风险,中风险
+6.8,6.9328,0.1328,中风险,中风险
+4.2,4.4522,0.2522,中风险,中风险
+6.9,6.6397,-0.2603,中风险,中风险
+2.9,3.0424,0.1424,低风险,低风险
+7.9,7.4808,-0.4192,中风险,中风险
+5.2,5.0123,-0.1877,中风险,中风险
+6.1,5.873,-0.227,中风险,中风险
+8.7,8.4175,-0.2825,高风险,高风险
+6.0,6.2916,0.2916,中风险,中风险
+6.4,6.8324,0.4324,中风险,中风险
+2.7,2.6809,-0.0191,低风险,低风险
+10.4,10.4051,0.0051,高风险,高风险
+8.6,6.9493,-1.6507,高风险,中风险
+5.5,5.0759,-0.4241,中风险,中风险
+1.8,2.4368,0.6368,低风险,低风险
+5.5,5.2643,-0.2357,中风险,中风险
+4.4,4.5306,0.1306,中风险,中风险
+3.9,4.1661,0.2661,低风险,中风险
+2.5,2.8288,0.3288,低风险,低风险
+5.2,5.2903,0.0903,中风险,中风险
+7.4,7.0582,-0.3418,中风险,中风险
+7.2,7.3934,0.1934,中风险,中风险
+6.6,6.4081,-0.1919,中风险,中风险
+7.4,7.1011,-0.2989,中风险,中风险
+7.4,7.3768,-0.0232,中风险,中风险
+1.9,2.418,0.518,低风险,低风险
+5.5,5.2767,-0.2233,中风险,中风险
+9.4,7.9371,-1.4629,高风险,中风险
+6.5,6.7459,0.2459,中风险,中风险
+7.7,7.2503,-0.4497,中风险,中风险
+4.6,3.8209,-0.7791,中风险,低风险
+4.8,5.9279,1.1279,中风险,中风险
+3.2,4.6717,1.4717,低风险,中风险
+2.9,2.6406,-0.2594,低风险,低风险
+6.3,6.1322,-0.1678,中风险,中风险
+9.2,9.2653,0.0653,高风险,高风险
+6.1,6.1573,0.0573,中风险,中风险
+9.9,10.2231,0.3231,高风险,高风险
+7.5,7.1468,-0.3532,中风险,中风险
+4.9,5.0703,0.1703,中风险,中风险
+5.8,5.4443,-0.3557,中风险,中风险
+1.9,2.1905,0.2905,低风险,低风险
+5.7,5.6798,-0.0202,中风险,中风险
+7.4,6.4964,-0.9036,中风险,中风险
+7.9,6.8876,-1.0124,中风险,中风险
+5.5,4.7892,-0.7108,中风险,中风险
+4.7,4.58,-0.12,中风险,中风险
+8.2,7.615,-0.585,高风险,中风险
+7.5,7.6584,0.1584,中风险,中风险
+3.5,3.6041,0.1041,低风险,低风险
+6.0,6.6399,0.6399,中风险,中风险
+2.6,3.2783,0.6783,低风险,低风险
+4.6,4.3831,-0.2169,中风险,中风险
+6.3,6.1217,-0.1783,中风险,中风险
+7.9,7.0805,-0.8195,中风险,中风险
+10.6,10.5636,-0.0364,高风险,高风险
+5.1,4.8034,-0.2966,中风险,中风险
+3.5,3.5524,0.0524,低风险,低风险
+8.0,7.6028,-0.3972,中风险,中风险
+5.3,4.8973,-0.4027,中风险,中风险
+7.5,7.641,0.141,中风险,中风险
+7.9,8.1094,0.2094,中风险,高风险
+4.2,4.3921,0.1921,中风险,中风险
+4.9,4.8679,-0.0321,中风险,中风险
+6.3,7.0438,0.7438,中风险,中风险
+9.1,8.9259,-0.1741,高风险,高风险
+8.5,7.7948,-0.7052,高风险,中风险
+7.7,7.7611,0.0611,中风险,中风险
+7.1,7.8045,0.7045,中风险,中风险
+4.7,5.0137,0.3137,中风险,中风险
+7.5,7.101,-0.399,中风险,中风险
+3.9,4.9112,1.0112,低风险,中风险
+4.7,4.2684,-0.4316,中风险,中风险
+8.9,7.9735,-0.9265,高风险,中风险
+5.1,6.2201,1.1201,中风险,中风险
+7.8,7.8912,0.0912,中风险,中风险
+2.9,2.7133,-0.1867,低风险,低风险
+3.0,2.7277,-0.2723,低风险,低风险
+4.0,3.4408,-0.5592,中风险,低风险
+4.6,5.7242,1.1242,中风险,中风险
+5.1,5.4152,0.3152,中风险,中风险
+3.1,2.7828,-0.3172,低风险,低风险
+5.5,4.962,-0.538,中风险,中风险
+8.2,7.9296,-0.2704,高风险,中风险
+8.3,7.9768,-0.3232,高风险,中风险
+4.0,4.135,0.135,中风险,中风险
+3.0,3.5914,0.5914,低风险,低风险
+3.5,4.1073,0.6073,低风险,中风险
+3.6,3.6177,0.0177,低风险,低风险
+7.4,7.2088,-0.1912,中风险,中风险
+2.8,2.9782,0.1782,低风险,低风险
+5.6,5.8417,0.2417,中风险,中风险
+10.4,8.6546,-1.7454,高风险,高风险
+10.3,8.7478,-1.5522,高风险,高风险
+6.5,7.3829,0.8829,中风险,中风险
+3.8,5.042,1.242,低风险,中风险
+3.6,3.5442,-0.0558,低风险,低风险
+3.8,3.5474,-0.2526,低风险,低风险
+6.1,5.276,-0.824,中风险,中风险
+4.5,4.4611,-0.0389,中风险,中风险
+3.2,3.0615,-0.1385,低风险,低风险
+8.7,8.3011,-0.3989,高风险,高风险
+4.3,4.8626,0.5626,中风险,中风险
+5.3,5.9574,0.6574,中风险,中风险
+5.5,5.1812,-0.3188,中风险,中风险
+8.1,7.475,-0.625,高风险,中风险
+6.2,6.0412,-0.1588,中风险,中风险
+5.3,4.9753,-0.3247,中风险,中风险
+7.1,8.0024,0.9024,中风险,高风险
+7.1,6.9217,-0.1783,中风险,中风险
+6.6,6.9743,0.3743,中风险,中风险
+6.4,6.4756,0.0756,中风险,中风险
+6.5,7.148,0.648,中风险,中风险
+2.5,2.9709,0.4709,低风险,低风险
+5.9,5.7518,-0.1482,中风险,中风险
+8.6,8.8528,0.2528,高风险,高风险
+4.4,4.8309,0.4309,中风险,中风险
+4.8,4.133,-0.667,中风险,中风险
+5.1,4.1811,-0.9189,中风险,中风险
+6.6,7.0449,0.4449,中风险,中风险
+9.5,7.8301,-1.6699,高风险,中风险
+5.5,5.6193,0.1193,中风险,中风险
+2.0,2.7808,0.7808,低风险,低风险
+7.2,6.5173,-0.6827,中风险,中风险
+7.6,6.7435,-0.8565,中风险,中风险
+5.1,4.0459,-1.0541,中风险,中风险
+3.1,4.3993,1.2993,低风险,中风险
+2.8,3.1763,0.3763,低风险,低风险
+6.8,6.7303,-0.0697,中风险,中风险
+3.2,2.7341,-0.4659,低风险,低风险
+7.7,7.4911,-0.2089,中风险,中风险
+3.3,3.2938,-0.0062,低风险,低风险
+4.3,4.5916,0.2916,中风险,中风险
+8.0,7.6442,-0.3558,中风险,中风险
+9.6,8.0866,-1.5134,高风险,高风险
+7.0,7.2953,0.2953,中风险,中风险
+7.0,6.8373,-0.1627,中风险,中风险
+10.6,8.6507,-1.9493,高风险,高风险
+11.5,11.2469,-0.2531,高风险,高风险
+5.0,5.7736,0.7736,中风险,中风险
+6.5,7.6341,1.1341,中风险,中风险
+8.3,8.6412,0.3412,高风险,高风险
+7.7,6.959,-0.741,中风险,中风险
+5.1,4.4079,-0.6921,中风险,中风险
+6.8,6.8087,0.0087,中风险,中风险
+5.0,4.379,-0.621,中风险,中风险
+1.7,1.6671,-0.0329,低风险,低风险
+5.3,4.9778,-0.3222,中风险,中风险
+6.3,5.9017,-0.3983,中风险,中风险
+9.0,9.2103,0.2103,高风险,高风险
+9.1,9.2239,0.1239,高风险,高风险
+1.4,1.9362,0.5362,低风险,低风险
+6.8,6.1565,-0.6435,中风险,中风险
+7.6,8.1661,0.5661,中风险,高风险
+5.8,5.4291,-0.3709,中风险,中风险
+8.9,9.4375,0.5375,高风险,高风险
+5.9,5.4395,-0.4605,中风险,中风险
+8.6,8.9123,0.3123,高风险,高风险
+10.0,9.23,-0.77,高风险,高风险
+7.7,7.8042,0.1042,中风险,中风险
+3.1,3.5359,0.4359,低风险,低风险
+8.3,7.7078,-0.5922,高风险,中风险
+6.7,6.9369,0.2369,中风险,中风险
+4.2,5.011,0.811,中风险,中风险
+3.5,4.1105,0.6105,低风险,中风险
+7.7,8.2457,0.5457,中风险,高风险
+7.7,7.6361,-0.0639,中风险,中风险
+4.2,4.8776,0.6776,中风险,中风险
+3.4,4.0348,0.6348,低风险,中风险
+7.4,7.5436,0.1436,中风险,中风险
+7.8,7.9683,0.1683,中风险,中风险
+8.7,8.5494,-0.1506,高风险,高风险
+3.0,2.3132,-0.6868,低风险,低风险
+6.6,6.0399,-0.5601,中风险,中风险
+4.4,3.9303,-0.4697,中风险,低风险
+7.6,8.1068,0.5068,中风险,高风险
+8.1,7.1531,-0.9469,高风险,中风险
+4.4,4.3785,-0.0215,中风险,中风险
+7.9,7.2768,-0.6232,中风险,中风险
+7.7,8.2164,0.5164,中风险,高风险
+6.3,5.3751,-0.9249,中风险,中风险
+3.3,3.6586,0.3586,低风险,低风险
+3.3,3.0973,-0.2027,低风险,低风险
+4.8,4.2091,-0.5909,中风险,中风险
+3.9,3.794,-0.106,低风险,低风险
+7.2,6.5206,-0.6794,中风险,中风险
+4.0,3.6093,-0.3907,中风险,低风险
+4.0,3.1372,-0.8628,中风险,低风险
+5.7,6.5341,0.8341,中风险,中风险
+2.7,3.0084,0.3084,低风险,低风险
+5.1,5.1147,0.0147,中风险,中风险
+6.1,7.2816,1.1816,中风险,中风险
+6.8,7.2938,0.4938,中风险,中风险
+2.9,2.9992,0.0992,低风险,低风险
+5.8,4.9065,-0.8935,中风险,中风险
+8.0,6.7956,-1.2044,中风险,中风险
+6.1,6.4369,0.3369,中风险,中风险
+6.8,7.6822,0.8822,中风险,中风险
+6.4,5.9771,-0.4229,中风险,中风险
+4.0,3.4746,-0.5254,中风险,低风险
+8.0,8.2699,0.2699,中风险,高风险
+2.8,3.1859,0.3859,低风险,低风险
+3.8,3.6421,-0.1579,低风险,低风险
+5.6,5.8457,0.2457,中风险,中风险
+2.8,3.2052,0.4052,低风险,低风险
+4.3,4.7287,0.4287,中风险,中风险
+5.2,5.6034,0.4034,中风险,中风险
+7.1,7.0441,-0.0559,中风险,中风险
+7.5,7.8699,0.3699,中风险,中风险
+7.0,7.0359,0.0359,中风险,中风险
+9.3,9.5224,0.2224,高风险,高风险
+2.8,3.1131,0.3131,低风险,低风险
+4.7,4.2967,-0.4033,中风险,中风险
+6.8,6.727,-0.073,中风险,中风险
+3.5,3.8685,0.3685,低风险,低风险
+2.9,3.4563,0.5563,低风险,低风险
+3.8,4.567,0.767,低风险,中风险
+7.4,6.9408,-0.4592,中风险,中风险
+7.7,7.2645,-0.4355,中风险,中风险
+4.6,4.7883,0.1883,中风险,中风险
+7.8,6.9134,-0.8866,中风险,中风险
+3.9,3.915,0.015,低风险,低风险
+4.6,4.7247,0.1247,中风险,中风险
+3.7,3.4813,-0.2187,低风险,低风险
+4.1,5.1314,1.0314,中风险,中风险
+7.6,6.5307,-1.0693,中风险,中风险
+6.0,6.4153,0.4153,中风险,中风险
+5.1,3.8322,-1.2678,中风险,低风险
+1.3,1.5338,0.2338,低风险,低风险
+1.2,1.4197,0.2197,低风险,低风险
+8.2,8.4755,0.2755,高风险,高风险
+7.7,8.666,0.966,中风险,高风险
+8.1,9.0816,0.9816,高风险,高风险
+6.7,7.1051,0.4051,中风险,中风险
+6.3,6.3584,0.0584,中风险,中风险
+7.0,7.1824,0.1824,中风险,中风险
+7.0,7.3754,0.3754,中风险,中风险
+6.6,7.7195,1.1195,中风险,中风险
+6.2,5.4535,-0.7465,中风险,中风险
+3.0,3.2661,0.2661,低风险,低风险
+7.3,6.9632,-0.3368,中风险,中风险
+3.3,3.2613,-0.0387,低风险,低风险
+6.5,5.9582,-0.5418,中风险,中风险
+6.0,5.6114,-0.3886,中风险,中风险
+4.9,4.4155,-0.4845,中风险,中风险
+3.2,4.7948,1.5948,低风险,中风险
+8.7,8.8967,0.1967,高风险,高风险
+10.6,11.1433,0.5433,高风险,高风险
+7.3,6.4579,-0.8421,中风险,中风险
+10.8,11.0421,0.2421,高风险,高风险
+2.9,3.7173,0.8173,低风险,低风险
+3.7,3.4875,-0.2125,低风险,低风险
+5.4,6.05,0.65,中风险,中风险
+3.7,4.081,0.381,低风险,中风险
+7.3,7.2294,-0.0706,中风险,中风险
+5.5,6.3101,0.8101,中风险,中风险
+5.5,4.1263,-1.3737,中风险,中风险
+2.3,3.0707,0.7707,低风险,低风险
+4.9,5.1446,0.2446,中风险,中风险
+4.3,3.8981,-0.4019,中风险,低风险
+3.9,3.5267,-0.3733,低风险,低风险
+8.4,7.6942,-0.7058,高风险,中风险
+5.3,5.8107,0.5107,中风险,中风险
+4.7,5.3973,0.6973,中风险,中风险
+4.5,4.5047,0.0047,中风险,中风险
+8.4,7.4471,-0.9529,高风险,中风险
+6.6,5.2503,-1.3497,中风险,中风险
+4.6,5.0355,0.4355,中风险,中风险
+5.7,6.08,0.38,中风险,中风险
+7.1,7.075,-0.025,中风险,中风险
+8.1,7.2071,-0.8929,高风险,中风险
+10.1,10.1534,0.0534,高风险,高风险
+4.8,4.9972,0.1972,中风险,中风险
+6.9,5.9554,-0.9446,中风险,中风险
+3.4,4.244,0.844,低风险,中风险
+4.8,5.5619,0.7619,中风险,中风险
+7.1,6.5024,-0.5976,中风险,中风险
+7.1,7.2588,0.1588,中风险,中风险
+7.8,7.4379,-0.3621,中风险,中风险
+5.6,5.5231,-0.0769,中风险,中风险
+1.8,2.1826,0.3826,低风险,低风险
+3.7,3.6245,-0.0755,低风险,低风险
+5.4,5.7531,0.3531,中风险,中风险
+9.9,10.6926,0.7926,高风险,高风险
+6.5,7.4465,0.9465,中风险,中风险
+3.1,3.2079,0.1079,低风险,低风险
+5.4,5.6353,0.2353,中风险,中风险
+8.4,8.5584,0.1584,高风险,高风险
+2.6,3.012,0.412,低风险,低风险
+8.6,7.7362,-0.8638,高风险,中风险
+2.3,2.8636,0.5636,低风险,低风险
+3.3,3.1703,-0.1297,低风险,低风险
+6.9,6.0912,-0.8088,中风险,中风险
+5.8,5.4081,-0.3919,中风险,中风险
+2.5,3.5269,1.0269,低风险,低风险
+6.5,7.0551,0.5551,中风险,中风险
+6.8,7.2907,0.4907,中风险,中风险
+4.8,5.0436,0.2436,中风险,中风险
+6.0,7.0238,1.0238,中风险,中风险
+5.3,4.391,-0.909,中风险,中风险
+3.3,3.4052,0.1052,低风险,低风险
+3.9,3.5071,-0.3929,低风险,低风险
+8.7,7.9306,-0.7694,高风险,中风险
+2.4,3.1927,0.7927,低风险,低风险
+4.1,3.5533,-0.5467,中风险,低风险
+6.4,5.8212,-0.5788,中风险,中风险
+3.3,3.1231,-0.1769,低风险,低风险
+3.2,2.5748,-0.6252,低风险,低风险
+3.5,3.6997,0.1997,低风险,低风险
+8.1,7.9362,-0.1638,高风险,中风险
+7.0,7.589,0.589,中风险,中风险
+5.4,5.5963,0.1963,中风险,中风险
+8.6,7.3834,-1.2166,高风险,中风险
+5.8,5.4433,-0.3567,中风险,中风险
+4.7,4.1565,-0.5435,中风险,中风险
+9.4,8.6542,-0.7458,高风险,高风险
+7.3,6.8123,-0.4877,中风险,中风险
+4.3,4.8889,0.5889,中风险,中风险
+6.0,4.5614,-1.4386,中风险,中风险
+4.6,4.0682,-0.5318,中风险,中风险
+2.9,3.4373,0.5373,低风险,低风险
+6.0,5.2804,-0.7196,中风险,中风险
+5.9,4.8636,-1.0364,中风险,中风险
+3.6,3.9664,0.3664,低风险,低风险
+11.4,11.1083,-0.2917,高风险,高风险
+9.0,8.8198,-0.1802,高风险,高风险
+6.4,7.407,1.007,中风险,中风险
+7.4,6.8829,-0.5171,中风险,中风险
+3.1,4.009,0.909,低风险,中风险
+5.3,5.4265,0.1265,中风险,中风险
+2.5,2.8561,0.3561,低风险,低风险
+3.7,2.6635,-1.0365,低风险,低风险
+6.5,5.9309,-0.5691,中风险,中风险
+6.9,5.9838,-0.9162,中风险,中风险
+8.2,8.5403,0.3403,高风险,高风险
+7.3,7.4848,0.1848,中风险,中风险
+5.9,7.1104,1.2104,中风险,中风险
+5.9,5.1343,-0.7657,中风险,中风险
+9.3,9.0834,-0.2166,高风险,高风险
+9.2,8.8476,-0.3524,高风险,高风险
+4.6,5.2201,0.6201,中风险,中风险
+6.7,6.6808,-0.0192,中风险,中风险
+7.6,7.3679,-0.2321,中风险,中风险
+3.5,3.6123,0.1123,低风险,低风险
+7.8,7.9647,0.1647,中风险,中风险
+5.7,5.8101,0.1101,中风险,中风险
+1.8,2.3882,0.5882,低风险,低风险
+8.2,7.9297,-0.2703,高风险,中风险
+6.1,6.1943,0.0943,中风险,中风险
+3.2,3.3939,0.1939,低风险,低风险
+4.0,4.9216,0.9216,中风险,中风险
+6.7,8.1841,1.4841,中风险,高风险
+2.7,2.772,0.072,低风险,低风险
+6.2,7.5509,1.3509,中风险,中风险
+5.9,6.6174,0.7174,中风险,中风险
+4.2,4.3888,0.1888,中风险,中风险
+6.4,7.2047,0.8047,中风险,中风险
+5.4,4.9079,-0.4921,中风险,中风险
+2.5,2.4143,-0.0857,低风险,低风险
+4.5,3.9182,-0.5818,中风险,低风险
+2.9,2.6692,-0.2308,低风险,低风险
+9.5,10.4632,0.9632,高风险,高风险
+2.2,2.3301,0.1301,低风险,低风险
+5.2,5.731,0.531,中风险,中风险
+11.3,10.5496,-0.7504,高风险,高风险
+9.6,8.8424,-0.7576,高风险,高风险
+7.8,8.1105,0.3105,中风险,高风险
+8.8,8.5366,-0.2634,高风险,高风险
+3.9,4.7174,0.8174,低风险,中风险
+3.5,4.5066,1.0066,低风险,中风险
+3.0,3.0188,0.0188,低风险,低风险
+4.4,3.5901,-0.8099,中风险,低风险
+3.3,3.3762,0.0762,低风险,低风险
+7.4,6.9176,-0.4824,中风险,中风险
+7.0,6.9118,-0.0882,中风险,中风险
+2.9,3.0437,0.1437,低风险,低风险
+7.2,6.797,-0.403,中风险,中风险
+6.4,6.6309,0.2309,中风险,中风险
+7.5,7.7908,0.2908,中风险,中风险
+5.8,6.8608,1.0608,中风险,中风险
+7.5,7.4071,-0.0929,中风险,中风险
+4.5,5.524,1.024,中风险,中风险
+2.5,3.4128,0.9128,低风险,低风险
+5.8,5.9813,0.1813,中风险,中风险
+4.5,4.9043,0.4043,中风险,中风险
+4.6,3.8679,-0.7321,中风险,低风险
+2.0,3.0589,1.0589,低风险,低风险
+11.8,11.2709,-0.5291,高风险,高风险
+4.0,5.0275,1.0275,中风险,中风险
+6.8,7.0611,0.2611,中风险,中风险
+8.3,8.1236,-0.1764,高风险,高风险
+6.9,6.6242,-0.2758,中风险,中风险
+8.6,7.9244,-0.6756,高风险,中风险
+3.3,4.0728,0.7728,低风险,中风险
+3.8,4.2061,0.4061,低风险,中风险
+3.1,3.0302,-0.0698,低风险,低风险
+7.0,6.3859,-0.6141,中风险,中风险
+8.7,8.9957,0.2957,高风险,高风险
+7.0,6.5707,-0.4293,中风险,中风险
+3.7,3.9174,0.2174,低风险,低风险
+5.5,4.5368,-0.9632,中风险,中风险
+8.2,7.7979,-0.4021,高风险,中风险
+5.7,5.5871,-0.1129,中风险,中风险
+8.4,8.6042,0.2042,高风险,高风险
+9.3,8.8996,-0.4004,高风险,高风险
+3.7,3.4622,-0.2378,低风险,低风险
+5.0,4.459,-0.541,中风险,中风险
+8.7,8.2003,-0.4997,高风险,高风险
+6.5,6.7941,0.2941,中风险,中风险
+4.3,4.6939,0.3939,中风险,中风险
+4.6,4.3745,-0.2255,中风险,中风险
+6.9,7.0924,0.1924,中风险,中风险
+3.0,3.3414,0.3414,低风险,低风险
+8.0,7.1713,-0.8287,中风险,中风险
+9.1,7.8321,-1.2679,高风险,中风险
+2.9,3.7368,0.8368,低风险,低风险
+4.7,4.0983,-0.6017,中风险,中风险
+7.6,6.8171,-0.7829,中风险,中风险
+3.5,2.845,-0.655,低风险,低风险
+7.1,7.187,0.087,中风险,中风险
+3.0,2.8226,-0.1774,低风险,低风险
+3.5,3.938,0.438,低风险,低风险
+4.9,5.3019,0.4019,中风险,中风险
+7.5,8.0195,0.5195,中风险,高风险
+6.9,7.8843,0.9843,中风险,中风险
+4.2,4.5001,0.3001,中风险,中风险
+8.7,10.1637,1.4637,高风险,高风险
+6.2,5.2808,-0.9192,中风险,中风险
+7.5,6.6714,-0.8286,中风险,中风险
+5.2,4.0793,-1.1207,中风险,中风险
+2.1,2.7061,0.6061,低风险,低风险
+4.9,5.4846,0.5846,中风险,中风险
+7.2,7.8342,0.6342,中风险,中风险
+7.6,6.511,-1.089,中风险,中风险
+4.6,3.9942,-0.6058,中风险,低风险
+11.3,8.6766,-2.6234,高风险,高风险
+4.4,4.6531,0.2531,中风险,中风险
+4.8,5.2468,0.4468,中风险,中风险
+4.1,4.2831,0.1831,中风险,中风险
+6.8,6.5148,-0.2852,中风险,中风险
+8.0,8.5813,0.5813,中风险,高风险
+8.5,8.7099,0.2099,高风险,高风险
+5.4,5.6178,0.2178,中风险,中风险
+6.2,6.7057,0.5057,中风险,中风险
+3.7,3.9248,0.2248,低风险,低风险
+2.0,2.5946,0.5946,低风险,低风险
+7.3,7.3379,0.0379,中风险,中风险
+6.6,6.2525,-0.3475,中风险,中风险
+10.6,10.3569,-0.2431,高风险,高风险
+3.5,3.9841,0.4841,低风险,低风险
+4.8,5.2713,0.4713,中风险,中风险
+3.1,4.5207,1.4207,低风险,中风险
+2.2,1.7725,-0.4275,低风险,低风险
+4.5,3.8672,-0.6328,中风险,低风险
+1.9,1.7372,-0.1628,低风险,低风险
+3.1,3.2451,0.1451,低风险,低风险
+3.3,3.0379,-0.2621,低风险,低风险
+4.6,4.8519,0.2519,中风险,中风险
+8.2,7.3511,-0.8489,高风险,中风险
+2.5,2.9285,0.4285,低风险,低风险
+5.0,4.5853,-0.4147,中风险,中风险
+4.9,4.6006,-0.2994,中风险,中风险
+4.6,4.571,-0.029,中风险,中风险
+10.5,10.3175,-0.1825,高风险,高风险
+5.9,5.1786,-0.7214,中风险,中风险
+5.5,5.2471,-0.2529,中风险,中风险
+8.7,8.1907,-0.5093,高风险,高风险
+2.9,2.6515,-0.2485,低风险,低风险
+6.9,6.7447,-0.1553,中风险,中风险
+6.9,6.8739,-0.0261,中风险,中风险
+5.7,5.2082,-0.4918,中风险,中风险
+9.0,8.577,-0.423,高风险,高风险
+8.7,8.5275,-0.1725,高风险,高风险
+5.7,5.5734,-0.1266,中风险,中风险
+2.2,3.1078,0.9078,低风险,低风险
+4.7,3.7172,-0.9828,中风险,低风险
+5.8,3.3133,-2.4867,中风险,低风险
+2.3,2.9814,0.6814,低风险,低风险
+5.3,4.6618,-0.6382,中风险,中风险
+8.3,7.5384,-0.7616,高风险,中风险
+8.1,7.2089,-0.8911,高风险,中风险
+6.9,6.7544,-0.1456,中风险,中风险
+4.8,4.881,0.081,中风险,中风险
+5.9,4.8627,-1.0373,中风险,中风险
+8.6,8.2757,-0.3243,高风险,高风险
+7.3,7.8931,0.5931,中风险,中风险
+4.5,4.6792,0.1792,中风险,中风险
+5.6,5.5639,-0.0361,中风险,中风险
+4.1,4.378,0.278,中风险,中风险
+9.8,9.5868,-0.2132,高风险,高风险
+7.1,7.835,0.735,中风险,中风险
+9.0,10.4662,1.4662,高风险,高风险
+5.9,4.9634,-0.9366,中风险,中风险
+8.4,7.9645,-0.4355,高风险,中风险
+4.0,3.7116,-0.2884,中风险,低风险
+8.8,7.5868,-1.2132,高风险,中风险
+7.2,7.1708,-0.0292,中风险,中风险
+9.7,8.4968,-1.2032,高风险,高风险
+2.8,2.6117,-0.1883,低风险,低风险
+2.5,2.7184,0.2184,低风险,低风险
+4.9,5.4997,0.5997,中风险,中风险
+7.7,6.4141,-1.2859,中风险,中风险
+5.0,4.7573,-0.2427,中风险,中风险
+9.7,10.1213,0.4213,高风险,高风险
+8.2,8.5523,0.3523,高风险,高风险
+7.0,7.3349,0.3349,中风险,中风险
+3.0,2.976,-0.024,低风险,低风险
+6.7,6.8394,0.1394,中风险,中风险
+7.1,6.9573,-0.1427,中风险,中风险
+3.4,4.4819,1.0819,低风险,中风险
+7.9,7.4315,-0.4685,中风险,中风险
+6.2,6.1308,-0.0692,中风险,中风险
+6.0,5.097,-0.903,中风险,中风险
+2.6,2.7089,0.1089,低风险,低风险
+7.4,8.0888,0.6888,中风险,高风险
+8.9,8.3568,-0.5432,高风险,高风险
+4.8,4.8508,0.0508,中风险,中风险
+12.4,11.6756,-0.7244,高风险,高风险
+10.9,11.3769,0.4769,高风险,高风险
+10.5,10.9419,0.4419,高风险,高风险
+8.6,7.1796,-1.4204,高风险,中风险
+10.9,9.833,-1.067,高风险,高风险
+6.8,7.2017,0.4017,中风险,中风险
+9.8,11.0088,1.2088,高风险,高风险
+3.6,3.9878,0.3878,低风险,低风险
+4.8,4.5811,-0.2189,中风险,中风险
+8.9,8.2108,-0.6892,高风险,高风险
+8.7,7.1129,-1.5871,高风险,中风险
+10.7,9.6646,-1.0354,高风险,高风险
+6.1,4.887,-1.213,中风险,中风险
+6.3,6.1691,-0.1309,中风险,中风险
+8.5,8.7236,0.2236,高风险,高风险
+6.2,5.3038,-0.8962,中风险,中风险
+2.8,3.4792,0.6792,低风险,低风险
+4.4,4.7497,0.3497,中风险,中风险
+5.2,4.4174,-0.7826,中风险,中风险
+7.5,8.2917,0.7917,中风险,高风险
+3.8,4.6294,0.8294,低风险,中风险
+4.2,4.5729,0.3729,中风险,中风险
+5.0,5.001,0.001,中风险,中风险
+3.9,3.7781,-0.1219,低风险,低风险
+4.1,3.7531,-0.3469,中风险,低风险
+9.6,10.9662,1.3662,高风险,高风险
+6.0,5.1573,-0.8427,中风险,中风险
+7.6,8.2618,0.6618,中风险,高风险
+3.3,3.4555,0.1555,低风险,低风险
+6.3,5.8508,-0.4492,中风险,中风险
+8.2,7.7365,-0.4635,高风险,中风险
+4.6,4.9513,0.3513,中风险,中风险
+5.0,4.9821,-0.0179,中风险,中风险
+4.9,4.6609,-0.2391,中风险,中风险
+7.1,7.6813,0.5813,中风险,中风险
+4.0,3.7842,-0.2158,中风险,低风险
+2.3,2.5901,0.2901,低风险,低风险
+7.9,7.9185,0.0185,中风险,中风险
+4.8,5.3695,0.5695,中风险,中风险
+3.2,3.6935,0.4935,低风险,低风险
+2.1,1.8639,-0.2361,低风险,低风险
+4.9,4.8907,-0.0093,中风险,中风险
+5.4,5.1536,-0.2464,中风险,中风险
+1.8,1.733,-0.067,低风险,低风险
+2.0,1.8636,-0.1364,低风险,低风险
+3.0,2.9806,-0.0194,低风险,低风险
+4.8,4.5646,-0.2354,中风险,中风险
+10.0,9.5711,-0.4289,高风险,高风险
+6.4,6.3461,-0.0539,中风险,中风险
+4.1,4.2355,0.1355,中风险,中风险
+5.4,4.4811,-0.9189,中风险,中风险
+8.2,7.5462,-0.6538,高风险,中风险
+4.0,3.8885,-0.1115,中风险,低风险
+6.0,5.1298,-0.8702,中风险,中风险
+5.0,5.6101,0.6101,中风险,中风险
+5.7,5.1482,-0.5518,中风险,中风险
+5.6,5.5641,-0.0359,中风险,中风险
+5.8,6.0149,0.2149,中风险,中风险
+6.0,5.9529,-0.0471,中风险,中风险
+2.8,3.3153,0.5153,低风险,低风险
+4.5,4.9248,0.4248,中风险,中风险
+8.5,8.6593,0.1593,高风险,高风险
+5.9,6.1287,0.2287,中风险,中风险
+5.1,4.67,-0.43,中风险,中风险
+3.1,2.3676,-0.7324,低风险,低风险
+3.6,3.6044,0.0044,低风险,低风险
+9.7,8.2084,-1.4916,高风险,高风险
+4.2,3.6837,-0.5163,中风险,低风险
+6.5,7.179,0.679,中风险,中风险
+4.8,4.9441,0.1441,中风险,中风险
+7.2,7.2585,0.0585,中风险,中风险
+3.5,3.8094,0.3094,低风险,低风险
+2.9,3.4587,0.5587,低风险,低风险
+7.2,7.1405,-0.0595,中风险,中风险
+11.6,9.8331,-1.7669,高风险,高风险
+6.3,6.3447,0.0447,中风险,中风险
+4.1,4.2941,0.1941,中风险,中风险
+8.0,7.4103,-0.5897,中风险,中风险
+4.0,4.5709,0.5709,中风险,中风险
+2.0,2.3336,0.3336,低风险,低风险
+8.5,8.2523,-0.2477,高风险,高风险
+1.6,1.7255,0.1255,低风险,低风险
+4.3,4.0124,-0.2876,中风险,中风险
+4.7,4.5891,-0.1109,中风险,中风险
+2.9,3.9721,1.0721,低风险,低风险
+5.6,6.5926,0.9926,中风险,中风险
+3.9,4.1098,0.2098,低风险,中风险
+7.7,7.392,-0.308,中风险,中风险
+4.1,4.6932,0.5932,中风险,中风险
+4.0,3.9826,-0.0174,中风险,低风险
+4.1,4.5745,0.4745,中风险,中风险
+6.7,6.6035,-0.0965,中风险,中风险
+7.4,7.3405,-0.0595,中风险,中风险
+3.3,3.3135,0.0135,低风险,低风险
+4.0,3.7935,-0.2065,中风险,低风险
+5.3,4.8764,-0.4236,中风险,中风险
+2.7,3.137,0.437,低风险,低风险
+6.0,6.1504,0.1504,中风险,中风险
+3.5,4.65,1.15,低风险,中风险
+2.3,3.2845,0.9845,低风险,低风险
+3.0,3.6752,0.6752,低风险,低风险
+2.2,2.8225,0.6225,低风险,低风险
+3.1,3.4638,0.3638,低风险,低风险
+7.1,7.2627,0.1627,中风险,中风险
+9.5,8.6456,-0.8544,高风险,高风险
+8.8,8.4567,-0.3433,高风险,高风险
+3.9,4.9248,1.0248,低风险,中风险
+4.6,4.5669,-0.0331,中风险,中风险
+6.2,6.6926,0.4926,中风险,中风险
+5.1,5.4857,0.3857,中风险,中风险
+4.0,4.1465,0.1465,中风险,中风险
+7.3,7.1355,-0.1645,中风险,中风险
+5.4,5.2562,-0.1438,中风险,中风险
+4.1,3.9435,-0.1565,中风险,低风险
+5.5,5.1449,-0.3551,中风险,中风险
+8.6,7.3443,-1.2557,高风险,中风险
+4.0,4.3062,0.3062,中风险,中风险
+5.0,5.2564,0.2564,中风险,中风险
+9.3,8.0889,-1.2111,高风险,高风险
+10.6,11.1349,0.5349,高风险,高风险
+9.9,9.7067,-0.1933,高风险,高风险
+2.3,3.1815,0.8815,低风险,低风险
+5.3,4.9354,-0.3646,中风险,中风险
+7.5,7.1,-0.4,中风险,中风险
+3.4,3.1883,-0.2117,低风险,低风险
+4.3,4.3721,0.0721,中风险,中风险
+6.3,5.5347,-0.7653,中风险,中风险
+7.8,7.2826,-0.5174,中风险,中风险
+9.1,7.8628,-1.2372,高风险,中风险
+8.2,7.8021,-0.3979,高风险,中风险
+7.6,8.0708,0.4708,中风险,高风险
+5.2,4.9834,-0.2166,中风险,中风险
+5.0,4.2266,-0.7734,中风险,中风险
+5.6,6.1393,0.5393,中风险,中风险
+2.0,2.8046,0.8046,低风险,低风险
+3.9,4.0619,0.1619,低风险,中风险
+4.4,4.5815,0.1815,中风险,中风险
+4.4,3.3812,-1.0188,中风险,低风险
+4.2,3.2808,-0.9192,中风险,低风险
+4.9,5.0561,0.1561,中风险,中风险
+3.2,3.8923,0.6923,低风险,低风险
+5.3,5.0559,-0.2441,中风险,中风险
+6.0,6.7717,0.7717,中风险,中风险
+2.2,2.5432,0.3432,低风险,低风险
+6.4,7.2472,0.8472,中风险,中风险
+2.9,3.0021,0.1021,低风险,低风险
+6.4,6.5814,0.1814,中风险,中风险
+6.8,7.2487,0.4487,中风险,中风险
+6.9,6.7656,-0.1344,中风险,中风险
+3.1,2.8646,-0.2354,低风险,低风险
+5.3,5.0275,-0.2725,中风险,中风险
+7.3,7.0564,-0.2436,中风险,中风险
+6.5,5.9265,-0.5735,中风险,中风险
+2.7,3.2946,0.5946,低风险,低风险
+8.2,7.7335,-0.4665,高风险,中风险
+6.6,6.7199,0.1199,中风险,中风险
+2.4,2.8435,0.4435,低风险,低风险
+6.6,6.2023,-0.3977,中风险,中风险
+7.5,7.5735,0.0735,中风险,中风险
+7.5,7.5233,0.0233,中风险,中风险
+2.6,3.1194,0.5194,低风险,低风险
+3.7,3.4337,-0.2663,低风险,低风险
+7.0,7.6689,0.6689,中风险,中风险
+3.0,2.9565,-0.0435,低风险,低风险
+4.4,4.6739,0.2739,中风险,中风险
+4.2,4.2356,0.0356,中风险,中风险
+4.4,4.4341,0.0341,中风险,中风险
+6.1,6.0984,-0.0016,中风险,中风险
+10.2,10.0093,-0.1907,高风险,高风险
+3.6,3.5403,-0.0597,低风险,低风险
+3.7,3.4327,-0.2673,低风险,低风险
+6.1,6.8958,0.7958,中风险,中风险
+11.6,10.5628,-1.0372,高风险,高风险
+5.6,5.4853,-0.1147,中风险,中风险
+8.6,7.5162,-1.0838,高风险,中风险
+5.6,5.4177,-0.1823,中风险,中风险
+4.2,3.6831,-0.5169,中风险,低风险
+5.2,5.2007,0.0007,中风险,中风险
+7.1,6.8913,-0.2087,中风险,中风险
+8.3,7.8605,-0.4395,高风险,中风险
+4.1,4.1415,0.0415,中风险,中风险
+5.9,6.2927,0.3927,中风险,中风险
+7.6,7.0514,-0.5486,中风险,中风险
+7.9,7.5501,-0.3499,中风险,中风险
+3.3,3.6127,0.3127,低风险,低风险
+2.6,3.543,0.943,低风险,低风险
+8.4,7.0597,-1.3403,高风险,中风险
+5.0,4.8179,-0.1821,中风险,中风险
+3.8,4.1429,0.3429,低风险,中风险
+5.5,5.5272,0.0272,中风险,中风险
+8.7,8.4408,-0.2592,高风险,高风险
+4.1,3.8373,-0.2627,中风险,低风险
+2.7,3.2915,0.5915,低风险,低风险
+3.0,3.2908,0.2908,低风险,低风险
+5.4,5.6965,0.2965,中风险,中风险
+2.7,3.6976,0.9976,低风险,低风险
+4.6,4.8055,0.2055,中风险,中风险
+5.2,5.2574,0.0574,中风险,中风险
+9.4,10.2373,0.8373,高风险,高风险
+7.0,7.1119,0.1119,中风险,中风险
+5.8,6.6455,0.8455,中风险,中风险
+4.8,4.289,-0.511,中风险,中风险
+2.8,3.5922,0.7922,低风险,低风险
+3.4,2.2452,-1.1548,低风险,低风险
+3.6,3.1341,-0.4659,低风险,低风险
+7.5,6.524,-0.976,中风险,中风险
+3.6,3.1291,-0.4709,低风险,低风险
+6.8,5.943,-0.857,中风险,中风险
+6.8,7.7538,0.9538,中风险,中风险
+7.3,7.7083,0.4083,中风险,中风险
+6.7,7.4842,0.7842,中风险,中风险
+7.7,7.8314,0.1314,中风险,中风险
+4.6,4.5224,-0.0776,中风险,中风险
+7.0,7.8403,0.8403,中风险,中风险
+3.2,3.1748,-0.0252,低风险,低风险
+3.6,4.373,0.773,低风险,中风险
+5.7,4.9285,-0.7715,中风险,中风险
+4.6,4.5892,-0.0108,中风险,中风险
+2.8,3.5779,0.7779,低风险,低风险
+4.1,3.7982,-0.3018,中风险,低风险
+11.8,10.415,-1.385,高风险,高风险
+5.5,3.9038,-1.5962,中风险,低风险
+5.4,4.1032,-1.2968,中风险,中风险
+5.4,4.9574,-0.4426,中风险,中风险
+2.6,3.1041,0.5041,低风险,低风险
+5.7,5.2012,-0.4988,中风险,中风险
+7.6,7.1385,-0.4615,中风险,中风险
+5.6,5.4054,-0.1946,中风险,中风险
+2.9,3.2041,0.3041,低风险,低风险
+8.2,7.3627,-0.8373,高风险,中风险
+6.8,5.9985,-0.8015,中风险,中风险
+7.6,6.8315,-0.7685,中风险,中风险
+6.6,7.3227,0.7227,中风险,中风险
+6.9,7.0674,0.1674,中风险,中风险
+6.9,6.962,0.062,中风险,中风险
+2.7,2.9548,0.2548,低风险,低风险
+9.9,8.9112,-0.9888,高风险,高风险
+7.9,8.4658,0.5658,中风险,高风险
+3.8,4.8087,1.0087,低风险,中风险
+10.2,10.6643,0.4643,高风险,高风险
+7.7,6.9767,-0.7233,中风险,中风险
+1.7,2.6167,0.9167,低风险,低风险
+4.9,5.107,0.207,中风险,中风险
+9.1,8.3115,-0.7885,高风险,高风险
+2.7,2.419,-0.281,低风险,低风险
+5.0,5.105,0.105,中风险,中风险
+7.2,7.2872,0.0872,中风险,中风险
+3.9,3.3828,-0.5172,低风险,低风险
+2.8,3.1815,0.3815,低风险,低风险
+7.5,7.0738,-0.4262,中风险,中风险
+6.6,6.4647,-0.1353,中风险,中风险
+3.0,2.6193,-0.3807,低风险,低风险
+6.5,6.4985,-0.0015,中风险,中风险
+7.8,7.4472,-0.3528,中风险,中风险
+7.7,7.6836,-0.0164,中风险,中风险
+7.7,7.3502,-0.3498,中风险,中风险
+7.9,8.1027,0.2027,中风险,高风险
+7.9,7.1489,-0.7511,中风险,中风险
+4.8,3.9595,-0.8405,中风险,低风险
+7.2,7.548,0.348,中风险,中风险
+2.7,2.7555,0.0555,低风险,低风险
+6.3,7.4652,1.1652,中风险,中风险
+4.2,4.1221,-0.0779,中风险,中风险
+6.9,6.302,-0.598,中风险,中风险
+9.5,9.2718,-0.2282,高风险,高风险
+2.4,2.5695,0.1695,低风险,低风险
+5.0,5.0313,0.0313,中风险,中风险
+4.8,4.9529,0.1529,中风险,中风险
+9.0,9.1824,0.1824,高风险,高风险
+7.5,7.3035,-0.1965,中风险,中风险
+3.6,3.5915,-0.0085,低风险,低风险
+6.2,6.054,-0.146,中风险,中风险
+5.2,6.1,0.9,中风险,中风险
+9.0,8.4633,-0.5367,高风险,高风险
+8.3,8.4688,0.1688,高风险,高风险
+5.1,5.4846,0.3846,中风险,中风险
+7.0,7.8861,0.8861,中风险,中风险
+4.5,4.7526,0.2526,中风险,中风险
+8.1,7.5922,-0.5078,高风险,中风险
+8.0,7.6802,-0.3198,中风险,中风险
+7.7,6.8951,-0.8049,中风险,中风险
+11.1,11.3384,0.2384,高风险,高风险
+7.4,7.9838,0.5838,中风险,中风险
+5.1,4.5753,-0.5247,中风险,中风险
+4.7,4.3175,-0.3825,中风险,中风险
+3.8,3.1479,-0.6521,低风险,低风险
+4.3,4.7033,0.4033,中风险,中风险
+4.9,4.6983,-0.2017,中风险,中风险
+2.7,3.2844,0.5844,低风险,低风险
+5.9,5.8896,-0.0104,中风险,中风险
+5.5,5.0295,-0.4705,中风险,中风险
+3.3,3.5216,0.2216,低风险,低风险
+6.2,5.9734,-0.2266,中风险,中风险
+3.9,3.9485,0.0485,低风险,低风险
+7.8,7.6677,-0.1323,中风险,中风险
+8.1,7.709,-0.391,高风险,中风险
+6.3,5.8736,-0.4264,中风险,中风险
+3.2,3.8503,0.6503,低风险,低风险
+3.6,3.927,0.327,低风险,低风险
+4.5,4.6679,0.1679,中风险,中风险
+4.9,5.9234,1.0234,中风险,中风险
+4.8,4.6123,-0.1877,中风险,中风险
+8.3,8.641,0.341,高风险,高风险
+7.3,7.9779,0.6779,中风险,中风险
+8.4,7.865,-0.535,高风险,中风险
+4.6,5.4955,0.8955,中风险,中风险
+5.6,5.6559,0.0559,中风险,中风险
+5.3,5.4352,0.1352,中风险,中风险
+10.7,10.5844,-0.1156,高风险,高风险
+4.4,4.2859,-0.1141,中风险,中风险
+5.9,5.572,-0.328,中风险,中风险
+8.2,7.2793,-0.9207,高风险,中风险
+5.6,4.5832,-1.0168,中风险,中风险
+4.5,3.873,-0.627,中风险,低风险
+2.5,3.3649,0.8649,低风险,低风险
+8.3,8.441,0.141,高风险,高风险
+4.6,4.0741,-0.5259,中风险,中风险
+7.7,7.8259,0.1259,中风险,中风险
+5.6,5.3671,-0.2329,中风险,中风险
+4.2,4.1832,-0.0168,中风险,中风险
+9.3,9.325,0.025,高风险,高风险
+6.6,7.4711,0.8711,中风险,中风险
+5.6,5.6616,0.0616,中风险,中风险
+3.5,2.9809,-0.5191,低风险,低风险
+5.2,4.8937,-0.3063,中风险,中风险
+10.7,10.813,0.113,高风险,高风险
+10.2,10.5762,0.3762,高风险,高风险
+7.0,7.0666,0.0666,中风险,中风险
+5.3,4.7539,-0.5461,中风险,中风险
+5.0,4.5884,-0.4116,中风险,中风险
+7.2,6.8868,-0.3132,中风险,中风险
+3.7,2.8128,-0.8872,低风险,低风险
+3.5,2.9502,-0.5498,低风险,低风险
+8.1,7.0473,-1.0527,高风险,中风险
+6.7,8.0805,1.3805,中风险,高风险
+9.2,9.2719,0.0719,高风险,高风险
+4.8,4.256,-0.544,中风险,中风险
+2.8,3.7934,0.9934,低风险,低风险
+10.2,10.3547,0.1547,高风险,高风险
+5.4,4.4705,-0.9295,中风险,中风险
+5.3,5.0863,-0.2137,中风险,中风险
+7.3,7.069,-0.231,中风险,中风险
+8.0,6.2761,-1.7239,中风险,中风险
+6.2,6.577,0.377,中风险,中风险
+3.2,3.7031,0.5031,低风险,低风险
+10.0,10.0031,0.0031,高风险,高风险
+5.2,5.5905,0.3905,中风险,中风险
+4.6,4.6177,0.0177,中风险,中风险
+6.8,6.0587,-0.7413,中风险,中风险
+5.7,4.7035,-0.9965,中风险,中风险
+5.5,4.6273,-0.8727,中风险,中风险
+7.8,8.0136,0.2136,中风险,高风险
+3.8,3.3064,-0.4936,低风险,低风险
+3.5,3.8808,0.3808,低风险,低风险
+5.1,5.7905,0.6905,中风险,中风险
+10.1,9.1743,-0.9257,高风险,高风险
+4.4,3.9045,-0.4955,中风险,低风险
+6.9,7.017,0.117,中风险,中风险
+8.3,7.0033,-1.2967,高风险,中风险
+8.0,7.0451,-0.9549,中风险,中风险
+5.0,4.8498,-0.1502,中风险,中风险
+4.4,4.602,0.202,中风险,中风险
+3.3,3.5989,0.2989,低风险,低风险
+8.1,8.1161,0.0161,高风险,高风险
+8.5,8.4478,-0.0522,高风险,高风险
+2.2,3.2664,1.0664,低风险,低风险
+7.6,7.3883,-0.2117,中风险,中风险
+8.3,8.8978,0.5978,高风险,高风险
+3.5,3.7194,0.2194,低风险,低风险
+9.2,7.7564,-1.4436,高风险,中风险
+9.5,8.7989,-0.7011,高风险,高风险
+8.9,8.1515,-0.7485,高风险,高风险
+6.1,5.7907,-0.3093,中风险,中风险
+7.4,7.0898,-0.3102,中风险,中风险
+5.8,4.6879,-1.1121,中风险,中风险
+7.0,7.4988,0.4988,中风险,中风险
+3.1,3.0572,-0.0428,低风险,低风险
+9.5,8.9141,-0.5859,高风险,高风险
+2.8,3.4122,0.6122,低风险,低风险
+7.8,7.7179,-0.0821,中风险,中风险
+6.8,6.9701,0.1701,中风险,中风险
+6.4,6.8176,0.4176,中风险,中风险
+3.8,3.0125,-0.7875,低风险,低风险
+7.8,6.6581,-1.1419,中风险,中风险
+4.7,3.9074,-0.7926,中风险,低风险
+8.9,7.5682,-1.3318,高风险,中风险
+8.8,8.0825,-0.7175,高风险,高风险
+5.4,4.9239,-0.4761,中风险,中风险
+3.7,3.4328,-0.2672,低风险,低风险
+8.7,7.7927,-0.9073,高风险,中风险
+11.1,10.7989,-0.3011,高风险,高风险
+4.6,4.3496,-0.2504,中风险,中风险
+2.9,3.4701,0.5701,低风险,低风险
+2.4,2.3422,-0.0578,低风险,低风险
+3.5,4.3024,0.8024,低风险,中风险
+5.6,5.9012,0.3012,中风险,中风险
+4.5,4.5516,0.0516,中风险,中风险
+3.4,4.013,0.613,低风险,中风险
+3.7,4.1231,0.4231,低风险,中风险
+5.1,5.9176,0.8176,中风险,中风险
+2.1,2.2161,0.1161,低风险,低风险
+3.0,3.7103,0.7103,低风险,低风险
+4.6,4.0764,-0.5236,中风险,中风险
+2.7,2.6748,-0.0252,低风险,低风险
+3.6,3.5857,-0.0143,低风险,低风险
+1.9,1.9334,0.0334,低风险,低风险
+4.3,4.7435,0.4435,中风险,中风险
+6.9,8.0735,1.1735,中风险,高风险
+7.3,6.686,-0.614,中风险,中风险
+4.5,4.5581,0.0581,中风险,中风险
+10.1,9.8788,-0.2212,高风险,高风险
+6.0,5.4207,-0.5793,中风险,中风险
+4.2,5.1065,0.9065,中风险,中风险
+3.1,3.6691,0.5691,低风险,低风险
+4.6,4.6475,0.0475,中风险,中风险
+3.1,2.8463,-0.2537,低风险,低风险
+10.4,10.7981,0.3981,高风险,高风险
+7.9,7.4057,-0.4943,中风险,中风险
+7.5,7.0943,-0.4057,中风险,中风险
+6.4,5.924,-0.476,中风险,中风险
+9.7,9.6914,-0.0086,高风险,高风险
+5.2,4.448,-0.752,中风险,中风险
+7.0,6.9953,-0.0047,中风险,中风险
+3.7,3.4089,-0.2911,低风险,低风险
+8.6,7.837,-0.763,高风险,中风险
+4.6,4.8274,0.2274,中风险,中风险
+3.9,4.3085,0.4085,低风险,中风险
+5.5,5.8772,0.3772,中风险,中风险
+5.9,5.4866,-0.4134,中风险,中风险
+7.8,6.7261,-1.0739,中风险,中风险
+7.4,7.8876,0.4876,中风险,中风险
+8.0,8.0319,0.0319,中风险,高风险
+9.6,8.279,-1.321,高风险,高风险
+8.1,7.8034,-0.2966,高风险,中风险
+9.8,10.4232,0.6232,高风险,高风险
+3.3,3.4312,0.1312,低风险,低风险
+4.7,5.1814,0.4814,中风险,中风险
+7.9,7.7251,-0.1749,中风险,中风险
+6.6,6.6834,0.0834,中风险,中风险
+4.6,4.7471,0.1471,中风险,中风险
+2.9,3.3243,0.4243,低风险,低风险
+4.0,4.1649,0.1649,中风险,中风险
+8.1,7.9461,-0.1539,高风险,中风险
+8.1,7.8967,-0.2033,高风险,中风险
+7.0,7.4678,0.4678,中风险,中风险
+7.4,7.0797,-0.3203,中风险,中风险
+6.7,6.1539,-0.5461,中风险,中风险
+2.4,2.4601,0.0601,低风险,低风险
+4.1,3.9713,-0.1287,中风险,低风险
+7.5,6.8125,-0.6875,中风险,中风险
+6.9,7.0453,0.1453,中风险,中风险
+3.8,4.3901,0.5901,低风险,中风险
+4.3,5.3668,1.0668,中风险,中风险
+3.8,3.2452,-0.5548,低风险,低风险
+7.6,7.4733,-0.1267,中风险,中风险
+7.0,8.2143,1.2143,中风险,高风险
+4.4,4.5763,0.1763,中风险,中风险
+3.2,3.0194,-0.1806,低风险,低风险
+3.3,2.9351,-0.3649,低风险,低风险
+8.6,8.6905,0.0905,高风险,高风险
+4.1,4.4743,0.3743,中风险,中风险
+4.9,5.1704,0.2704,中风险,中风险
+6.1,5.095,-1.005,中风险,中风险
+5.7,4.4168,-1.2832,中风险,中风险
+6.7,6.9762,0.2762,中风险,中风险
+6.3,5.8585,-0.4415,中风险,中风险
+6.5,6.0198,-0.4802,中风险,中风险
+4.7,5.012,0.312,中风险,中风险
+5.2,4.829,-0.371,中风险,中风险
+8.1,7.685,-0.415,高风险,中风险
+8.0,8.8844,0.8844,中风险,高风险
+5.6,5.765,0.165,中风险,中风险
+3.7,4.161,0.461,低风险,中风险
+8.5,8.3552,-0.1448,高风险,高风险
+7.4,7.3525,-0.0475,中风险,中风险
+3.0,3.2958,0.2958,低风险,低风险
+10.2,9.256,-0.944,高风险,高风险
+9.5,9.271,-0.229,高风险,高风险
+6.9,6.5654,-0.3346,中风险,中风险
+3.6,4.2312,0.6312,低风险,中风险
+5.3,5.131,-0.169,中风险,中风险
+4.8,4.7669,-0.0331,中风险,中风险
+6.8,6.8024,0.0024,中风险,中风险
+4.9,4.8519,-0.0481,中风险,中风险
+7.8,7.0403,-0.7597,中风险,中风险
+4.6,4.3704,-0.2296,中风险,中风险
+2.8,2.6532,-0.1468,低风险,低风险
+5.0,4.6346,-0.3654,中风险,中风险
+5.8,5.8891,0.0891,中风险,中风险
+5.2,4.6988,-0.5012,中风险,中风险
+5.9,5.3062,-0.5938,中风险,中风险
+8.7,8.4617,-0.2383,高风险,高风险
+4.8,4.1404,-0.6596,中风险,中风险
+6.3,4.7072,-1.5928,中风险,中风险
+8.1,8.5951,0.4951,高风险,高风险
+5.5,5.682,0.182,中风险,中风险
+5.6,5.497,-0.103,中风险,中风险
+4.3,4.3454,0.0454,中风险,中风险
+8.1,7.5799,-0.5201,高风险,中风险
+7.6,7.2419,-0.3581,中风险,中风险
+6.0,6.0909,0.0909,中风险,中风险
+7.5,7.7707,0.2707,中风险,中风险
+6.5,7.0944,0.5944,中风险,中风险
+6.2,6.4343,0.2343,中风险,中风险
+5.1,5.8154,0.7154,中风险,中风险
+3.5,4.06,0.56,低风险,中风险
+4.3,4.0782,-0.2218,中风险,中风险
+4.0,4.0881,0.0881,中风险,中风险
+5.4,4.9822,-0.4178,中风险,中风险
+2.7,3.3473,0.6473,低风险,低风险
+3.2,3.3916,0.1916,低风险,低风险
+5.1,5.0736,-0.0264,中风险,中风险
+4.4,4.2325,-0.1675,中风险,中风险
+9.0,7.9015,-1.0985,高风险,中风险
+8.7,8.1822,-0.5178,高风险,高风险
+2.4,3.1795,0.7795,低风险,低风险
+7.8,7.8786,0.0786,中风险,中风险
+5.4,4.1694,-1.2306,中风险,中风险
+7.0,7.5107,0.5107,中风险,中风险
+6.4,7.5032,1.1032,中风险,中风险
+6.3,7.3617,1.0617,中风险,中风险
+5.1,4.878,-0.222,中风险,中风险
+12.1,12.2164,0.1164,高风险,高风险
+4.3,4.4921,0.1921,中风险,中风险
+6.0,5.2049,-0.7951,中风险,中风险
+12.1,11.1643,-0.9357,高风险,高风险
+6.3,5.4208,-0.8792,中风险,中风险
+8.9,9.8971,0.9971,高风险,高风险
+8.9,7.7966,-1.1034,高风险,中风险
+7.9,9.1672,1.2672,中风险,高风险
+9.4,9.0824,-0.3176,高风险,高风险
+8.2,7.0973,-1.1027,高风险,中风险
+7.1,7.2713,0.1713,中风险,中风险
+6.6,6.9329,0.3329,中风险,中风险
+5.5,5.8301,0.3301,中风险,中风险
+4.8,4.8229,0.0229,中风险,中风险
+5.0,4.7623,-0.2377,中风险,中风险
+13.8,12.2082,-1.5918,高风险,高风险
+4.4,4.2254,-0.1746,中风险,中风险
+6.3,6.2968,-0.0032,中风险,中风险
+5.6,5.2136,-0.3864,中风险,中风险
+5.7,6.3566,0.6566,中风险,中风险
+9.2,7.4559,-1.7441,高风险,中风险
+4.5,4.8836,0.3836,中风险,中风险
+8.5,7.3937,-1.1063,高风险,中风险
+5.1,4.2394,-0.8606,中风险,中风险
+4.4,3.4168,-0.9832,中风险,低风险
+5.1,4.5823,-0.5177,中风险,中风险
+6.6,7.0785,0.4785,中风险,中风险
+6.3,6.0732,-0.2268,中风险,中风险
+5.3,5.6109,0.3109,中风险,中风险
+3.5,3.1812,-0.3188,低风险,低风险
+4.1,3.4617,-0.6383,中风险,低风险
+6.4,5.8036,-0.5964,中风险,中风险
+1.7,2.1373,0.4373,低风险,低风险
+3.2,4.2233,1.0233,低风险,中风险
+4.1,4.2588,0.1588,中风险,中风险
+5.9,5.5969,-0.3031,中风险,中风险
+5.7,6.0445,0.3445,中风险,中风险
+8.4,7.2013,-1.1987,高风险,中风险
+11.6,11.153,-0.447,高风险,高风险
+6.5,5.788,-0.712,中风险,中风险
+8.9,8.6065,-0.2935,高风险,高风险
+7.0,5.9147,-1.0853,中风险,中风险
+5.8,6.6368,0.8368,中风险,中风险
+4.5,3.9537,-0.5463,中风险,低风险
+2.8,3.4318,0.6318,低风险,低风险
+3.7,3.7037,0.0037,低风险,低风险
+3.8,3.8402,0.0402,低风险,低风险
+7.7,8.1305,0.4305,中风险,高风险
+7.4,7.1518,-0.2482,中风险,中风险
+9.3,10.4623,1.1623,高风险,高风险
+6.3,6.0953,-0.2047,中风险,中风险
+10.8,11.5435,0.7435,高风险,高风险
+4.8,5.0887,0.2887,中风险,中风险
+9.0,8.2456,-0.7544,高风险,高风险
+8.3,8.0669,-0.2331,高风险,高风险
+6.9,7.4197,0.5197,中风险,中风险
+8.0,7.1773,-0.8227,中风险,中风险
+6.7,5.788,-0.912,中风险,中风险
+7.7,7.6738,-0.0262,中风险,中风险
+4.4,3.528,-0.872,中风险,低风险
+6.1,5.7441,-0.3559,中风险,中风险
+3.7,2.6212,-1.0788,低风险,低风险
+5.0,4.191,-0.809,中风险,中风险
+4.0,4.2869,0.2869,中风险,中风险
+3.8,4.0253,0.2253,低风险,中风险
+8.1,6.9861,-1.1139,高风险,中风险
+7.4,7.2257,-0.1743,中风险,中风险
+2.9,3.2814,0.3814,低风险,低风险
+2.5,2.2689,-0.2311,低风险,低风险
+4.3,4.2656,-0.0344,中风险,中风险
+3.7,3.0399,-0.6601,低风险,低风险
+6.5,6.0873,-0.4127,中风险,中风险
+5.7,5.2732,-0.4268,中风险,中风险
+10.6,11.7944,1.1944,高风险,高风险
+2.8,3.1281,0.3281,低风险,低风险
+8.6,8.1493,-0.4507,高风险,高风险
+3.8,3.5334,-0.2666,低风险,低风险
+9.0,7.7979,-1.2021,高风险,中风险
+4.3,4.5981,0.2981,中风险,中风险
+4.6,4.4777,-0.1223,中风险,中风险
+8.9,8.7517,-0.1483,高风险,高风险
+4.5,4.1352,-0.3648,中风险,中风险
+3.3,3.5526,0.2526,低风险,低风险
+2.6,2.9605,0.3605,低风险,低风险
+5.6,5.7099,0.1099,中风险,中风险
+2.2,2.4774,0.2774,低风险,低风险
+5.0,5.6293,0.6293,中风险,中风险
+4.9,4.7584,-0.1416,中风险,中风险
+5.6,5.0267,-0.5733,中风险,中风险
+9.4,9.3625,-0.0375,高风险,高风险
+3.7,2.4915,-1.2085,低风险,低风险
+5.7,6.2984,0.5984,中风险,中风险
+6.2,6.6117,0.4117,中风险,中风险
+9.0,7.9742,-1.0258,高风险,中风险
+3.8,3.8314,0.0314,低风险,低风险
+4.9,4.1795,-0.7205,中风险,中风险
+6.6,7.0157,0.4157,中风险,中风险
+6.6,6.9353,0.3353,中风险,中风险
+3.2,2.7077,-0.4923,低风险,低风险
+6.0,6.0349,0.0349,中风险,中风险
+6.1,6.1065,0.0065,中风险,中风险
+4.2,4.3198,0.1198,中风险,中风险
+4.7,4.9816,0.2816,中风险,中风险
+8.0,7.1274,-0.8726,中风险,中风险
+7.1,6.9444,-0.1556,中风险,中风险
+5.6,6.0394,0.4394,中风险,中风险
+3.9,3.9399,0.0399,低风险,低风险
+4.0,4.0632,0.0632,中风险,中风险
+5.1,5.8696,0.7696,中风险,中风险
+5.6,6.0339,0.4339,中风险,中风险
+2.7,3.6431,0.9431,低风险,低风险
+7.8,7.2281,-0.5719,中风险,中风险
+7.7,6.9702,-0.7298,中风险,中风险
+3.3,3.4427,0.1427,低风险,低风险
+6.1,6.0092,-0.0908,中风险,中风险
+5.2,4.8207,-0.3793,中风险,中风险
+1.4,2.211,0.811,低风险,低风险
+6.0,6.4416,0.4416,中风险,中风险
+3.4,3.7055,0.3055,低风险,低风险
+2.7,2.5902,-0.1098,低风险,低风险
+7.0,6.7666,-0.2334,中风险,中风险
+7.6,7.6461,0.0461,中风险,中风险
+4.5,4.8786,0.3786,中风险,中风险
+5.9,6.7218,0.8218,中风险,中风险
+8.2,8.4982,0.2982,高风险,高风险
+6.5,6.6045,0.1045,中风险,中风险
+9.8,10.2314,0.4314,高风险,高风险
+6.8,6.2807,-0.5193,中风险,中风险
+7.6,7.2122,-0.3878,中风险,中风险
+3.3,3.6259,0.3259,低风险,低风险
+2.2,2.0623,-0.1377,低风险,低风险
+4.4,3.8318,-0.5682,中风险,低风险
+6.3,5.6491,-0.6509,中风险,中风险
+7.7,8.2388,0.5388,中风险,高风险
+8.8,9.1865,0.3865,高风险,高风险
+6.8,7.892,1.092,中风险,中风险
+7.3,6.9825,-0.3175,中风险,中风险
+4.9,4.582,-0.318,中风险,中风险
+3.9,4.5796,0.6796,低风险,中风险
+4.9,4.716,-0.184,中风险,中风险
+4.3,4.0473,-0.2527,中风险,中风险
+3.6,4.4035,0.8035,低风险,中风险
+7.1,7.6104,0.5104,中风险,中风险
+3.2,3.4544,0.2544,低风险,低风险
+9.7,10.4002,0.7002,高风险,高风险
+3.0,2.65,-0.35,低风险,低风险
+1.6,2.0886,0.4886,低风险,低风险
+7.2,7.584,0.384,中风险,中风险
+6.3,5.7276,-0.5724,中风险,中风险
+10.4,11.0214,0.6214,高风险,高风险
+7.4,6.5761,-0.8239,中风险,中风险
+7.9,7.1783,-0.7217,中风险,中风险
+8.1,8.0754,-0.0246,高风险,高风险
+7.5,7.7393,0.2393,中风险,中风险
+5.6,4.9828,-0.6172,中风险,中风险
+5.7,5.2033,-0.4967,中风险,中风险
+5.1,4.7933,-0.3067,中风险,中风险
+4.5,5.1913,0.6913,中风险,中风险
+3.1,3.0754,-0.0246,低风险,低风险
+1.8,2.619,0.819,低风险,低风险
+3.5,4.3661,0.8661,低风险,中风险
+6.2,6.3106,0.1106,中风险,中风险
+6.1,6.6451,0.5451,中风险,中风险
+3.2,3.0632,-0.1368,低风险,低风险
+7.6,7.8889,0.2889,中风险,中风险
+4.0,3.6521,-0.3479,中风险,低风险
+6.0,4.8627,-1.1373,中风险,中风险
+3.3,2.7987,-0.5013,低风险,低风险
+4.7,3.4569,-1.2431,中风险,低风险
+8.9,7.5371,-1.3629,高风险,中风险
+5.4,5.0913,-0.3087,中风险,中风险
+11.0,10.8763,-0.1237,高风险,高风险
+4.0,4.1427,0.1427,中风险,中风险
+6.9,5.8292,-1.0708,中风险,中风险
+7.5,5.6169,-1.8831,中风险,中风险
+3.7,4.4171,0.7171,低风险,中风险
+8.9,7.5453,-1.3547,高风险,中风险
+6.7,7.0751,0.3751,中风险,中风险
+5.4,5.1944,-0.2056,中风险,中风险
+9.2,8.2916,-0.9084,高风险,高风险
+5.9,5.8324,-0.0676,中风险,中风险
+8.8,8.9675,0.1675,高风险,高风险
+7.5,6.5728,-0.9272,中风险,中风险
+3.6,3.3119,-0.2881,低风险,低风险
+8.0,7.3251,-0.6749,中风险,中风险
+11.5,11.2597,-0.2403,高风险,高风险
+7.1,6.4265,-0.6735,中风险,中风险
+7.2,6.4133,-0.7867,中风险,中风险
+8.0,7.8152,-0.1848,中风险,中风险
+4.1,3.5582,-0.5418,中风险,低风险
+4.2,3.8199,-0.3801,中风险,低风险
+4.4,4.5528,0.1528,中风险,中风险
+6.0,6.7176,0.7176,中风险,中风险
+10.6,11.2835,0.6835,高风险,高风险
+2.9,3.1381,0.2381,低风险,低风险
+3.1,4.2192,1.1192,低风险,中风险
+2.2,2.9206,0.7206,低风险,低风险
+8.0,7.7555,-0.2445,中风险,中风险
+8.8,7.4204,-1.3796,高风险,中风险
+5.4,4.8447,-0.5553,中风险,中风险
+3.5,3.9435,0.4435,低风险,低风险
+7.5,6.7419,-0.7581,中风险,中风险
+7.6,7.0323,-0.5677,中风险,中风险
+4.9,5.2847,0.3847,中风险,中风险
+3.9,3.6912,-0.2088,低风险,低风险
+3.7,3.6745,-0.0255,低风险,低风险
+10.4,10.4509,0.0509,高风险,高风险
+8.4,8.2707,-0.1293,高风险,高风险
+8.2,7.8155,-0.3845,高风险,中风险
+4.9,5.1766,0.2766,中风险,中风险
+8.0,7.9224,-0.0776,中风险,中风险
+8.7,9.6221,0.9221,高风险,高风险
+4.2,4.3516,0.1516,中风险,中风险
+5.7,5.6809,-0.0191,中风险,中风险
+3.9,4.2152,0.3152,低风险,中风险
+9.8,8.2577,-1.5423,高风险,高风险
+7.0,6.2626,-0.7374,中风险,中风险
+7.0,7.2287,0.2287,中风险,中风险
+3.4,3.8994,0.4994,低风险,低风险
+7.3,6.8295,-0.4705,中风险,中风险
+3.4,3.45,0.05,低风险,低风险
+4.9,4.5395,-0.3605,中风险,中风险
+9.2,9.7318,0.5318,高风险,高风险
+2.6,2.5002,-0.0998,低风险,低风险
+10.0,10.713,0.713,高风险,高风险
+6.3,5.608,-0.692,中风险,中风险
+3.1,3.0412,-0.0588,低风险,低风险
+5.1,4.3522,-0.7478,中风险,中风险
+2.6,3.1011,0.5011,低风险,低风险
+6.2,6.4352,0.2352,中风险,中风险
+6.9,6.5103,-0.3897,中风险,中风险
+7.8,8.1285,0.3285,中风险,高风险
+7.8,7.8996,0.0996,中风险,中风险
+7.1,6.3302,-0.7698,中风险,中风险
+2.2,3.0062,0.8062,低风险,低风险
+3.2,3.9423,0.7423,低风险,低风险
+6.8,6.9082,0.1082,中风险,中风险
+4.4,5.2069,0.8069,中风险,中风险
+4.4,4.5555,0.1555,中风险,中风险
+4.3,4.4115,0.1115,中风险,中风险
+4.0,4.1316,0.1316,中风险,中风险
+3.7,3.3342,-0.3658,低风险,低风险
+5.6,5.9688,0.3688,中风险,中风险
+7.5,7.295,-0.205,中风险,中风险
+8.4,8.7159,0.3159,高风险,高风险
+8.5,8.7229,0.2229,高风险,高风险
+9.6,8.9013,-0.6987,高风险,高风险
+6.0,6.0898,0.0898,中风险,中风险
+5.0,5.5816,0.5816,中风险,中风险
+4.7,4.7267,0.0267,中风险,中风险
+7.3,7.348,0.048,中风险,中风险
+3.8,2.9528,-0.8472,低风险,低风险
+6.5,6.9314,0.4314,中风险,中风险
+2.9,3.0573,0.1573,低风险,低风险
+6.9,6.9735,0.0735,中风险,中风险
+3.7,4.4102,0.7102,低风险,中风险
+7.9,7.8119,-0.0881,中风险,中风险
+9.1,7.9171,-1.1829,高风险,中风险
+2.3,3.0649,0.7649,低风险,低风险
+4.0,5.0827,1.0827,中风险,中风险
+4.2,3.6627,-0.5373,中风险,低风险
+2.6,2.7114,0.1114,低风险,低风险
+5.6,5.5539,-0.0461,中风险,中风险
+6.5,5.9527,-0.5473,中风险,中风险
+9.8,9.5298,-0.2702,高风险,高风险
+8.8,8.4111,-0.3889,高风险,高风险
+7.5,7.9248,0.4248,中风险,中风险
+5.4,5.8571,0.4571,中风险,中风险
+6.9,7.5336,0.6336,中风险,中风险
+8.1,8.8548,0.7548,高风险,高风险
+4.0,4.1961,0.1961,中风险,中风险
+7.7,7.7433,0.0433,中风险,中风险
+2.7,3.2327,0.5327,低风险,低风险
+10.4,10.5106,0.1106,高风险,高风险
+3.3,3.8677,0.5677,低风险,低风险
+8.8,9.067,0.267,高风险,高风险
+13.6,12.1096,-1.4904,高风险,高风险
+4.1,4.7595,0.6595,中风险,中风险
+8.0,7.5741,-0.4259,中风险,中风险
+6.7,6.6109,-0.0891,中风险,中风险
+7.2,6.7515,-0.4485,中风险,中风险
+7.1,6.4934,-0.6066,中风险,中风险
+10.3,9.7139,-0.5861,高风险,高风险
+3.8,4.0805,0.2805,低风险,中风险
+3.5,3.5631,0.0631,低风险,低风险
+6.4,6.6842,0.2842,中风险,中风险
+7.2,7.3142,0.1142,中风险,中风险
+9.8,11.0371,1.2371,高风险,高风险
+5.9,6.2986,0.3986,中风险,中风险
+5.0,4.3555,-0.6445,中风险,中风险
+3.6,3.8638,0.2638,低风险,低风险
+8.3,8.6416,0.3416,高风险,高风险
+7.6,6.8691,-0.7309,中风险,中风险
+4.4,4.2747,-0.1253,中风险,中风险
+4.8,4.3941,-0.4059,中风险,中风险
+4.1,3.6271,-0.4729,中风险,低风险
+6.1,5.8924,-0.2076,中风险,中风险
+7.9,7.4695,-0.4305,中风险,中风险
+7.8,7.6537,-0.1463,中风险,中风险
+8.6,7.7338,-0.8662,高风险,中风险
+3.8,3.6738,-0.1262,低风险,低风险
+6.7,6.7619,0.0619,中风险,中风险
+9.4,9.6167,0.2167,高风险,高风险
+6.8,7.2271,0.4271,中风险,中风险
+8.1,8.4821,0.3821,高风险,高风险
+5.5,5.5683,0.0683,中风险,中风险
+5.0,5.4381,0.4381,中风险,中风险
+6.0,5.855,-0.145,中风险,中风险
+3.2,3.2339,0.0339,低风险,低风险
+4.9,6.0205,1.1205,中风险,中风险
+7.9,8.4558,0.5558,中风险,高风险
+7.2,7.0355,-0.1645,中风险,中风险
+5.5,5.4745,-0.0255,中风险,中风险
+8.6,8.1338,-0.4662,高风险,高风险
+8.3,8.1965,-0.1035,高风险,高风险
+8.5,7.8262,-0.6738,高风险,中风险
+8.2,8.0153,-0.1847,高风险,高风险
+5.1,4.6864,-0.4136,中风险,中风险
+2.3,2.8792,0.5792,低风险,低风险
+8.4,7.9364,-0.4636,高风险,中风险
+8.1,7.7734,-0.3266,高风险,中风险
+10.0,10.126,0.126,高风险,高风险
+5.3,5.1332,-0.1668,中风险,中风险
+3.5,3.6471,0.1471,低风险,低风险
+4.0,4.4025,0.4025,中风险,中风险
+6.1,5.8256,-0.2744,中风险,中风险
+6.9,7.5128,0.6128,中风险,中风险
+7.2,7.9362,0.7362,中风险,中风险
+8.3,7.0914,-1.2086,高风险,中风险
+5.5,5.0562,-0.4438,中风险,中风险
+5.9,5.8854,-0.0146,中风险,中风险
+5.4,5.4611,0.0611,中风险,中风险
+8.0,7.556,-0.444,中风险,中风险
+6.3,7.4108,1.1108,中风险,中风险
+2.4,3.0414,0.6414,低风险,低风险
+7.6,7.4135,-0.1865,中风险,中风险
+3.5,3.5365,0.0365,低风险,低风险
+3.6,4.274,0.674,低风险,中风险
+2.9,3.4434,0.5434,低风险,低风险
+4.2,4.021,-0.179,中风险,中风险
+8.7,7.7663,-0.9337,高风险,中风险
+11.3,10.3971,-0.9029,高风险,高风险
+7.9,7.7066,-0.1934,中风险,中风险
+3.8,3.2693,-0.5307,低风险,低风险
+3.5,4.4652,0.9652,低风险,中风险
+4.5,4.6072,0.1072,中风险,中风险
+4.0,3.6437,-0.3563,中风险,低风险
+5.7,5.0983,-0.6017,中风险,中风险
+5.6,5.2295,-0.3705,中风险,中风险
+1.8,2.3683,0.5683,低风险,低风险
+3.6,3.3305,-0.2695,低风险,低风险
+3.1,3.0831,-0.0169,低风险,低风险
+4.3,4.7561,0.4561,中风险,中风险
+9.2,7.2291,-1.9709,高风险,中风险
+7.5,7.1987,-0.3013,中风险,中风险
+9.2,7.8,-1.4,高风险,中风险
+6.0,4.7816,-1.2184,中风险,中风险
+8.5,9.0191,0.5191,高风险,高风险
+4.5,3.9447,-0.5553,中风险,低风险
+9.2,9.1493,-0.0507,高风险,高风险
+8.9,7.7396,-1.1604,高风险,中风险
+5.3,5.2665,-0.0335,中风险,中风险
+8.6,8.0199,-0.5801,高风险,高风险
+8.4,8.1383,-0.2617,高风险,高风险
+6.8,5.6186,-1.1814,中风险,中风险
+2.8,3.177,0.377,低风险,低风险
+7.2,7.2534,0.0534,中风险,中风险
+5.4,5.2919,-0.1081,中风险,中风险
+4.8,5.0139,0.2139,中风险,中风险
+4.5,4.5032,0.0032,中风险,中风险
+5.3,5.0071,-0.2929,中风险,中风险
+5.0,5.1555,0.1555,中风险,中风险
+2.1,2.6536,0.5536,低风险,低风险
+2.6,2.6194,0.0194,低风险,低风险
+7.5,7.55,0.05,中风险,中风险
+9.0,8.9582,-0.0418,高风险,高风险
+9.4,9.6948,0.2948,高风险,高风险
+3.1,5.024,1.924,低风险,中风险
+4.7,3.4947,-1.2053,中风险,低风险
+9.0,9.1519,0.1519,高风险,高风险
+8.6,7.3083,-1.2917,高风险,中风险
+7.7,6.9569,-0.7431,中风险,中风险
+3.0,3.4081,0.4081,低风险,低风险
+6.3,7.2752,0.9752,中风险,中风险
+2.3,3.527,1.227,低风险,低风险
+5.7,5.2128,-0.4872,中风险,中风险
+3.7,3.8146,0.1146,低风险,低风险
+3.6,4.9342,1.3342,低风险,中风险
+5.4,4.7401,-0.6599,中风险,中风险
+8.3,7.9506,-0.3494,高风险,中风险
+7.9,7.7074,-0.1926,中风险,中风险
+4.7,5.0184,0.3184,中风险,中风险
+7.3,7.6296,0.3296,中风险,中风险
+3.8,4.7738,0.9738,低风险,中风险
+3.0,3.2809,0.2809,低风险,低风险
+7.7,7.0046,-0.6954,中风险,中风险
+7.9,7.1835,-0.7165,中风险,中风险
+7.3,6.9341,-0.3659,中风险,中风险
+4.2,4.3074,0.1074,中风险,中风险
+8.0,8.5349,0.5349,中风险,高风险
+7.1,6.7307,-0.3693,中风险,中风险
+6.5,6.5492,0.0492,中风险,中风险
+4.1,3.723,-0.377,中风险,低风险
+6.4,5.8161,-0.5839,中风险,中风险
+3.2,3.5076,0.3076,低风险,低风险
+8.2,6.9008,-1.2992,高风险,中风险
+4.4,3.9766,-0.4234,中风险,低风险
+5.2,4.7719,-0.4281,中风险,中风险
+8.5,7.1739,-1.3261,高风险,中风险
+8.4,8.4654,0.0654,高风险,高风险
+7.5,7.5299,0.0299,中风险,中风险
+2.7,2.5041,-0.1959,低风险,低风险
+2.1,2.4184,0.3184,低风险,低风险
+5.2,4.3255,-0.8745,中风险,中风险
+7.3,7.1732,-0.1268,中风险,中风险
+8.9,9.2515,0.3515,高风险,高风险
+5.3,5.3153,0.0153,中风险,中风险
+4.9,4.9266,0.0266,中风险,中风险
+9.3,9.3448,0.0448,高风险,高风险
+5.7,5.845,0.145,中风险,中风险
+2.1,2.4295,0.3295,低风险,低风险
+2.7,2.6943,-0.0057,低风险,低风险
+7.1,7.011,-0.089,中风险,中风险
+4.9,5.1256,0.2256,中风险,中风险
+3.5,4.6441,1.1441,低风险,中风险
+7.3,8.0527,0.7527,中风险,高风险
+6.3,7.0342,0.7342,中风险,中风险
+7.9,7.4161,-0.4839,中风险,中风险
+4.6,4.0353,-0.5647,中风险,中风险
+4.7,5.5894,0.8894,中风险,中风险
+8.0,8.4632,0.4632,中风险,高风险
+7.4,6.2557,-1.1443,中风险,中风险
+6.3,6.324,0.024,中风险,中风险
+3.7,3.2476,-0.4524,低风险,低风险
+4.4,4.4424,0.0424,中风险,中风险
+4.5,4.1,-0.4,中风险,中风险
+4.6,3.8743,-0.7257,中风险,低风险
+2.5,3.5842,1.0842,低风险,低风险
+6.3,6.0206,-0.2794,中风险,中风险
+2.4,2.3598,-0.0402,低风险,低风险
+4.6,3.9707,-0.6293,中风险,低风险
+3.9,3.8504,-0.0496,低风险,低风险
+4.8,4.842,0.042,中风险,中风险
+3.9,4.2507,0.3507,低风险,中风险
+4.0,4.141,0.141,中风险,中风险
+4.2,4.1856,-0.0144,中风险,中风险
+8.6,9.6717,1.0717,高风险,高风险
+2.0,2.3971,0.3971,低风险,低风险
+5.9,4.6922,-1.2078,中风险,中风险
+5.6,5.5804,-0.0196,中风险,中风险
+4.7,4.4517,-0.2483,中风险,中风险
+3.2,4.0397,0.8397,低风险,中风险
+4.9,5.1342,0.2342,中风险,中风险
+4.1,4.3183,0.2183,中风险,中风险
+2.0,3.0007,1.0007,低风险,低风险
+4.6,4.3691,-0.2309,中风险,中风险
+4.3,3.9872,-0.3128,中风险,低风险
+1.4,2.5322,1.1322,低风险,低风险
+5.9,5.776,-0.124,中风险,中风险
+6.3,6.3711,0.0711,中风险,中风险
+3.7,4.1502,0.4502,低风险,中风险
+3.6,3.8587,0.2587,低风险,低风险
+7.5,7.8702,0.3702,中风险,中风险
+6.8,5.9239,-0.8761,中风险,中风险
+3.4,3.451,0.051,低风险,低风险
+6.4,7.6827,1.2827,中风险,中风险
+6.0,6.1463,0.1463,中风险,中风险
+6.1,5.6599,-0.4401,中风险,中风险
+5.5,5.3811,-0.1189,中风险,中风险
+5.8,5.2421,-0.5579,中风险,中风险
+1.9,3.2822,1.3822,低风险,低风险
+8.1,8.1002,0.0002,高风险,高风险
+8.2,8.2497,0.0497,高风险,高风险
+3.8,4.6531,0.8531,低风险,中风险
+5.4,5.9363,0.5363,中风险,中风险
+5.4,5.2037,-0.1963,中风险,中风险
+7.8,7.4972,-0.3028,中风险,中风险
+5.3,4.437,-0.863,中风险,中风险
+6.4,6.2112,-0.1888,中风险,中风险
+7.5,7.0307,-0.4693,中风险,中风险
+7.9,7.4264,-0.4736,中风险,中风险
+7.1,7.0672,-0.0328,中风险,中风险
+7.6,7.1718,-0.4282,中风险,中风险
+7.9,7.6978,-0.2022,中风险,中风险
+4.0,4.446,0.446,中风险,中风险
+8.5,7.7408,-0.7592,高风险,中风险
+6.0,4.7115,-1.2885,中风险,中风险
+4.8,5.016,0.216,中风险,中风险
+7.4,6.9157,-0.4843,中风险,中风险
+6.0,5.878,-0.122,中风险,中风险
+3.8,3.9375,0.1375,低风险,低风险
+2.9,3.0109,0.1109,低风险,低风险
+10.2,9.8523,-0.3477,高风险,高风险
+6.5,6.2617,-0.2383,中风险,中风险
+4.1,4.6737,0.5737,中风险,中风险
+7.4,7.3112,-0.0888,中风险,中风险
+7.4,7.0534,-0.3466,中风险,中风险
+5.6,4.6616,-0.9384,中风险,中风险
+6.2,7.1198,0.9198,中风险,中风险
+3.0,3.3699,0.3699,低风险,低风险
+6.8,6.788,-0.012,中风险,中风险
+6.5,5.7012,-0.7988,中风险,中风险
+6.2,6.2141,0.0141,中风险,中风险
+6.9,7.7016,0.8016,中风险,中风险
+1.9,3.2753,1.3753,低风险,低风险
+2.9,3.1282,0.2282,低风险,低风险
+7.7,6.4162,-1.2838,中风险,中风险
+6.6,6.7568,0.1568,中风险,中风险
+8.1,7.1326,-0.9674,高风险,中风险
+2.9,3.0367,0.1367,低风险,低风险
+4.7,5.0365,0.3365,中风险,中风险
+6.9,7.9275,1.0275,中风险,中风险
+5.7,5.109,-0.591,中风险,中风险
+7.6,8.0208,0.4208,中风险,高风险
+6.3,6.1263,-0.1737,中风险,中风险
+3.9,4.0354,0.1354,低风险,中风险
+4.9,4.2575,-0.6425,中风险,中风险
+6.6,6.3674,-0.2326,中风险,中风险
+5.7,4.6768,-1.0232,中风险,中风险
+3.5,1.9367,-1.5633,低风险,低风险
+4.1,4.4658,0.3658,中风险,中风险
+3.1,3.491,0.391,低风险,低风险
+11.6,10.6276,-0.9724,高风险,高风险
+5.1,4.8733,-0.2267,中风险,中风险
+5.7,5.5573,-0.1427,中风险,中风险
+4.8,3.9823,-0.8177,中风险,低风险
+2.0,2.0298,0.0298,低风险,低风险
+2.9,2.3003,-0.5997,低风险,低风险
+4.1,4.3224,0.2224,中风险,中风险
+8.0,7.9521,-0.0479,中风险,中风险
+7.6,7.3595,-0.2405,中风险,中风险
+3.6,3.4178,-0.1822,低风险,低风险
+4.0,3.1393,-0.8607,中风险,低风险
+7.2,6.6586,-0.5414,中风险,中风险
+7.7,7.0057,-0.6943,中风险,中风险
+1.7,3.0975,1.3975,低风险,低风险
+3.7,3.2721,-0.4279,低风险,低风险
+2.7,2.7633,0.0633,低风险,低风险
+6.7,6.1624,-0.5376,中风险,中风险
+3.7,3.6406,-0.0594,低风险,低风险
+2.8,3.4944,0.6944,低风险,低风险
+3.5,4.1259,0.6259,低风险,中风险
+6.7,6.9155,0.2155,中风险,中风险
+5.9,6.9125,1.0125,中风险,中风险
+4.5,4.536,0.036,中风险,中风险
+3.1,4.1306,1.0306,低风险,中风险
+3.9,4.143,0.243,低风险,中风险
+5.4,5.172,-0.228,中风险,中风险
+4.2,3.6094,-0.5906,中风险,低风险
+6.4,6.7337,0.3337,中风险,中风险
+9.2,8.74,-0.46,高风险,高风险
+7.3,7.242,-0.058,中风险,中风险
+5.8,5.5633,-0.2367,中风险,中风险
+8.8,7.9831,-0.8169,高风险,中风险
+4.0,4.4019,0.4019,中风险,中风险
+7.6,7.8095,0.2095,中风险,中风险
+3.7,3.4498,-0.2502,低风险,低风险
+5.3,4.7599,-0.5401,中风险,中风险
+6.4,6.6369,0.2369,中风险,中风险
+8.7,7.6686,-1.0314,高风险,中风险
+8.0,7.9549,-0.0451,中风险,中风险
+11.7,11.115,-0.585,高风险,高风险
+7.9,7.3656,-0.5344,中风险,中风险
+6.7,8.4451,1.7451,中风险,高风险
+3.1,2.9223,-0.1777,低风险,低风险
+6.9,7.0041,0.1041,中风险,中风险
+5.0,5.7284,0.7284,中风险,中风险
+5.2,5.4404,0.2404,中风险,中风险
+3.2,3.6032,0.4032,低风险,低风险
+8.2,7.7732,-0.4268,高风险,中风险
+5.0,4.2489,-0.7511,中风险,中风险
+5.6,6.0312,0.4312,中风险,中风险
+8.6,8.6111,0.0111,高风险,高风险
+4.5,3.7977,-0.7023,中风险,低风险
+9.6,8.7443,-0.8557,高风险,高风险
+5.0,5.0177,0.0177,中风险,中风险
+7.0,7.4549,0.4549,中风险,中风险
+3.5,4.3461,0.8461,低风险,中风险
+9.4,9.9073,0.5073,高风险,高风险
+11.8,11.5438,-0.2562,高风险,高风险
+10.0,9.0249,-0.9751,高风险,高风险
+2.6,3.2487,0.6487,低风险,低风险
+3.6,3.5229,-0.0771,低风险,低风险
+3.7,3.987,0.287,低风险,低风险
+7.1,7.2907,0.1907,中风险,中风险
+2.9,2.6348,-0.2652,低风险,低风险
+6.4,6.2751,-0.1249,中风险,中风险
+4.7,4.2485,-0.4515,中风险,中风险
+4.9,4.7521,-0.1479,中风险,中风险
+5.1,4.9891,-0.1109,中风险,中风险
+8.9,9.8466,0.9466,高风险,高风险
+5.9,5.904,0.004,中风险,中风险
+11.9,12.0623,0.1623,高风险,高风险
+9.6,9.5491,-0.0509,高风险,高风险
+7.2,7.134,-0.066,中风险,中风险
+11.1,10.7497,-0.3503,高风险,高风险
+5.2,5.1226,-0.0774,中风险,中风险
+5.3,5.5003,0.2003,中风险,中风险
+3.8,3.8045,0.0045,低风险,低风险
+2.5,2.4003,-0.0997,低风险,低风险
+3.7,3.5084,-0.1916,低风险,低风险
+8.6,7.8857,-0.7143,高风险,中风险
+7.7,7.1661,-0.5339,中风险,中风险
+2.6,2.6678,0.0678,低风险,低风险
+3.3,2.8549,-0.4451,低风险,低风险
+8.0,7.125,-0.875,中风险,中风险
+7.0,5.782,-1.218,中风险,中风险
+3.7,3.6481,-0.0519,低风险,低风险
+3.0,2.2898,-0.7102,低风险,低风险
+7.0,5.5194,-1.4806,中风险,中风险
+4.5,3.91,-0.59,中风险,低风险
+6.9,5.8795,-1.0205,中风险,中风险
+4.9,5.2554,0.3554,中风险,中风险
+7.7,7.3794,-0.3206,中风险,中风险
+3.7,3.4651,-0.2349,低风险,低风险
+4.5,4.5638,0.0638,中风险,中风险
+7.7,7.2287,-0.4713,中风险,中风险
+10.5,9.919,-0.581,高风险,高风险
+5.2,5.3092,0.1092,中风险,中风险
+7.7,7.0825,-0.6175,中风险,中风险
+8.3,6.3423,-1.9577,高风险,中风险
+8.6,8.3747,-0.2253,高风险,高风险
+5.2,4.7288,-0.4712,中风险,中风险
+6.8,6.6277,-0.1723,中风险,中风险
+6.2,5.6858,-0.5142,中风险,中风险
+6.8,7.0629,0.2629,中风险,中风险
+7.1,7.0131,-0.0869,中风险,中风险
+5.2,4.4659,-0.7341,中风险,中风险
+6.0,5.3598,-0.6402,中风险,中风险
+12.7,12.2786,-0.4214,高风险,高风险
+5.9,5.5446,-0.3554,中风险,中风险
+5.1,4.9234,-0.1766,中风险,中风险
+3.8,3.5335,-0.2665,低风险,低风险
+4.0,2.7888,-1.2112,中风险,低风险
+5.0,4.7062,-0.2938,中风险,中风险
+3.8,4.2334,0.4334,低风险,中风险
+6.6,7.101,0.501,中风险,中风险
+4.5,4.4122,-0.0878,中风险,中风险
+7.1,6.9436,-0.1564,中风险,中风险
+6.4,6.0484,-0.3516,中风险,中风险
+8.5,8.2597,-0.2403,高风险,高风险
+5.5,5.8301,0.3301,中风险,中风险
+5.5,5.6649,0.1649,中风险,中风险
+5.8,6.7731,0.9731,中风险,中风险
+2.9,3.3916,0.4916,低风险,低风险
+7.9,7.6938,-0.2062,中风险,中风险
+4.2,4.8324,0.6324,中风险,中风险
+10.5,11.2153,0.7153,高风险,高风险
+5.8,5.8669,0.0669,中风险,中风险
+5.1,4.701,-0.399,中风险,中风险
+7.5,7.7756,0.2756,中风险,中风险
+7.7,7.3286,-0.3714,中风险,中风险
+6.8,6.0673,-0.7327,中风险,中风险
+8.5,8.0063,-0.4937,高风险,高风险
+5.0,6.0143,1.0143,中风险,中风险
+4.2,3.9302,-0.2698,中风险,低风险
+4.2,4.1062,-0.0938,中风险,中风险
+8.1,8.5379,0.4379,高风险,高风险
+6.8,6.7988,-0.0012,中风险,中风险
+3.0,4.2247,1.2247,低风险,中风险
+5.3,5.7456,0.4456,中风险,中风险
+4.9,4.9518,0.0518,中风险,中风险
+9.2,8.6311,-0.5689,高风险,高风险
+5.0,4.3233,-0.6767,中风险,中风险
+2.9,3.2555,0.3555,低风险,低风险
+5.9,6.6079,0.7079,中风险,中风险
+3.7,4.435,0.735,低风险,中风险
+6.0,6.9081,0.9081,中风险,中风险
+1.9,2.3708,0.4708,低风险,低风险
+6.7,6.4816,-0.2184,中风险,中风险
+7.6,8.0969,0.4969,中风险,高风险
+5.7,6.1896,0.4896,中风险,中风险
+7.8,7.9252,0.1252,中风险,中风险
+2.6,2.724,0.124,低风险,低风险
+2.1,2.6096,0.5096,低风险,低风险
+7.6,7.6782,0.0782,中风险,中风险
+4.4,4.4405,0.0405,中风险,中风险
+4.9,5.2773,0.3773,中风险,中风险
+8.3,8.0637,-0.2363,高风险,高风险
+2.8,2.5391,-0.2609,低风险,低风险
+3.7,3.5863,-0.1137,低风险,低风险
+7.6,7.6172,0.0172,中风险,中风险
+3.7,3.4418,-0.2582,低风险,低风险
+5.9,5.3133,-0.5867,中风险,中风险
+7.5,7.5391,0.0391,中风险,中风险
+5.7,5.4102,-0.2898,中风险,中风险
+6.9,7.4481,0.5481,中风险,中风险
+3.0,3.1874,0.1874,低风险,低风险
+7.3,6.8489,-0.4511,中风险,中风险
+3.0,3.2864,0.2864,低风险,低风险
+3.4,3.212,-0.188,低风险,低风险
+7.1,6.7179,-0.3821,中风险,中风险
+7.2,7.3276,0.1276,中风险,中风险
+5.5,5.8626,0.3626,中风险,中风险
+4.5,5.0647,0.5647,中风险,中风险
+2.2,2.5725,0.3725,低风险,低风险
+8.2,8.9493,0.7493,高风险,高风险
+7.3,6.6404,-0.6596,中风险,中风险
+5.4,5.5328,0.1328,中风险,中风险
+3.3,3.0342,-0.2658,低风险,低风险
+6.8,7.5553,0.7553,中风险,中风险
+7.4,6.1634,-1.2366,中风险,中风险
+4.5,5.4038,0.9038,中风险,中风险
+1.9,1.7018,-0.1982,低风险,低风险
+5.6,5.1447,-0.4553,中风险,中风险
+3.5,3.921,0.421,低风险,低风险
+5.0,4.6901,-0.3099,中风险,中风险
+3.0,1.9509,-1.0491,低风险,低风险
+6.8,6.7255,-0.0745,中风险,中风险
+7.2,7.9272,0.7272,中风险,中风险
+3.2,3.8145,0.6145,低风险,低风险
+6.8,5.6916,-1.1084,中风险,中风险
+8.6,8.2425,-0.3575,高风险,高风险
+6.0,6.2126,0.2126,中风险,中风险
+6.9,7.1386,0.2386,中风险,中风险
+2.4,4.1043,1.7043,低风险,中风险
+6.5,7.2283,0.7283,中风险,中风险
+2.9,3.2707,0.3707,低风险,低风险
+7.4,7.8319,0.4319,中风险,中风险
+11.8,10.8392,-0.9608,高风险,高风险
+7.2,6.6304,-0.5696,中风险,中风险
+5.3,5.5098,0.2098,中风险,中风险
+3.6,4.2207,0.6207,低风险,中风险
+6.8,7.642,0.842,中风险,中风险
+7.3,7.8852,0.5852,中风险,中风险
+6.7,8.0325,1.3325,中风险,高风险
+7.6,6.8734,-0.7266,中风险,中风险
+9.4,8.544,-0.856,高风险,高风险
+3.9,4.6561,0.7561,低风险,中风险
+6.9,6.2554,-0.6446,中风险,中风险
+3.6,3.4055,-0.1945,低风险,低风险
+2.1,2.7292,0.6292,低风险,低风险
+7.9,7.8292,-0.0708,中风险,中风险
+8.5,8.6782,0.1782,高风险,高风险
+8.7,8.0465,-0.6535,高风险,高风险
+8.8,8.2265,-0.5735,高风险,高风险
+6.0,5.9774,-0.0226,中风险,中风险
+7.5,6.3281,-1.1719,中风险,中风险
+3.5,3.8284,0.3284,低风险,低风险
+8.2,8.5767,0.3767,高风险,高风险
+7.3,7.4578,0.1578,中风险,中风险
+9.9,10.5087,0.6087,高风险,高风险
+4.7,5.5652,0.8652,中风险,中风险
+3.8,4.0546,0.2546,低风险,中风险
+4.4,4.3417,-0.0583,中风险,中风险
+3.8,3.981,0.181,低风险,低风险
+6.5,7.0855,0.5855,中风险,中风险
+4.6,4.5276,-0.0724,中风险,中风险
+7.6,7.4544,-0.1456,中风险,中风险
+9.6,8.9994,-0.6006,高风险,高风险
+9.5,8.6017,-0.8983,高风险,高风险
+7.2,6.9219,-0.2781,中风险,中风险
+9.2,7.949,-1.251,高风险,中风险
+9.8,7.9786,-1.8214,高风险,中风险
+5.6,5.5394,-0.0606,中风险,中风险
+7.7,7.3639,-0.3361,中风险,中风险
+9.0,8.2905,-0.7095,高风险,高风险
+4.6,4.825,0.225,中风险,中风险
+7.2,6.8087,-0.3913,中风险,中风险
+9.4,9.4728,0.0728,高风险,高风险
+2.9,2.7124,-0.1876,低风险,低风险
+11.0,9.5726,-1.4274,高风险,高风险
+7.6,6.7486,-0.8514,中风险,中风险
+7.2,6.9669,-0.2331,中风险,中风险
+6.1,5.6969,-0.4031,中风险,中风险
+3.6,3.3565,-0.2435,低风险,低风险
+7.5,7.0584,-0.4416,中风险,中风险
+6.3,5.9149,-0.3851,中风险,中风险
+6.4,6.9947,0.5947,中风险,中风险
+7.8,7.7228,-0.0772,中风险,中风险
+8.1,7.7513,-0.3487,高风险,中风险
+6.7,5.7845,-0.9155,中风险,中风险
+2.2,2.7701,0.5701,低风险,低风险
+7.1,7.8578,0.7578,中风险,中风险
+5.0,5.0323,0.0323,中风险,中风险
+12.1,10.7588,-1.3412,高风险,高风险
+4.2,3.653,-0.547,中风险,低风险
+9.0,9.2638,0.2638,高风险,高风险
+8.4,9.3555,0.9555,高风险,高风险
+6.2,6.5978,0.3978,中风险,中风险
+7.0,7.1362,0.1362,中风险,中风险
+6.8,7.0977,0.2977,中风险,中风险
+6.4,6.5601,0.1601,中风险,中风险
+2.7,2.9424,0.2424,低风险,低风险
+7.4,6.2541,-1.1459,中风险,中风险
+9.5,9.1524,-0.3476,高风险,高风险
+3.5,2.9895,-0.5105,低风险,低风险
+4.8,4.6448,-0.1552,中风险,中风险
+3.9,3.8896,-0.0104,低风险,低风险
+3.4,3.527,0.127,低风险,低风险
+8.5,8.1043,-0.3957,高风险,高风险
+7.6,7.249,-0.351,中风险,中风险
+5.3,4.0332,-1.2668,中风险,中风险
+3.6,3.0049,-0.5951,低风险,低风险
+4.6,4.2841,-0.3159,中风险,中风险
+2.0,2.1397,0.1397,低风险,低风险
+8.5,7.6234,-0.8766,高风险,中风险
+7.1,6.162,-0.938,中风险,中风险
+7.4,6.2621,-1.1379,中风险,中风险
+9.6,8.9113,-0.6887,高风险,高风险
+5.7,5.2141,-0.4859,中风险,中风险
+7.9,6.709,-1.191,中风险,中风险
+9.0,7.8722,-1.1278,高风险,中风险
+7.7,7.7749,0.0749,中风险,中风险
+7.3,7.5362,0.2362,中风险,中风险
+4.0,3.83,-0.17,中风险,低风险
+4.6,4.7554,0.1554,中风险,中风险
+7.1,7.2164,0.1164,中风险,中风险
+4.3,5.1121,0.8121,中风险,中风险
+7.5,7.6367,0.1367,中风险,中风险
+8.7,7.7807,-0.9193,高风险,中风险
+8.3,7.7125,-0.5875,高风险,中风险
+4.5,4.1583,-0.3417,中风险,中风险
+3.7,4.1263,0.4263,低风险,中风险
+2.8,3.5325,0.7325,低风险,低风险
+2.9,2.8919,-0.0081,低风险,低风险
+7.1,7.1193,0.0193,中风险,中风险
+3.9,4.2049,0.3049,低风险,中风险
+9.8,10.3381,0.5381,高风险,高风险
+7.0,6.6414,-0.3586,中风险,中风险
+8.5,8.9294,0.4294,高风险,高风险
+6.7,6.4071,-0.2929,中风险,中风险
+7.8,7.9923,0.1923,中风险,中风险
+11.6,11.3595,-0.2405,高风险,高风险
+11.5,11.6589,0.1589,高风险,高风险
+11.0,11.5079,0.5079,高风险,高风险
+4.9,5.0867,0.1867,中风险,中风险
+8.2,7.8951,-0.3049,高风险,中风险
+9.8,9.1991,-0.6009,高风险,高风险
+7.9,7.5111,-0.3889,中风险,中风险
+5.6,4.7981,-0.8019,中风险,中风险
+6.9,6.821,-0.079,中风险,中风险
+6.2,7.3818,1.1818,中风险,中风险
+2.9,2.8628,-0.0372,低风险,低风险
+7.9,8.9399,1.0399,中风险,高风险
+6.7,6.4468,-0.2532,中风险,中风险
+3.7,3.5086,-0.1914,低风险,低风险
+5.5,4.5682,-0.9318,中风险,中风险
+5.9,6.0405,0.1405,中风险,中风险
+6.6,6.0472,-0.5528,中风险,中风险
+1.9,2.4701,0.5701,低风险,低风险
+3.7,3.8704,0.1704,低风险,低风险
+4.6,3.7162,-0.8838,中风险,低风险
+6.3,6.3824,0.0824,中风险,中风险
+9.8,10.1742,0.3742,高风险,高风险
+4.3,4.7728,0.4728,中风险,中风险
+5.1,4.7556,-0.3444,中风险,中风险
+4.6,4.9442,0.3442,中风险,中风险
+4.7,4.7641,0.0641,中风险,中风险
+6.8,7.0741,0.2741,中风险,中风险
+7.3,7.7248,0.4248,中风险,中风险
+7.7,7.5508,-0.1492,中风险,中风险
+5.4,5.0332,-0.3668,中风险,中风险
+3.8,3.729,-0.071,低风险,低风险
+5.8,5.6078,-0.1922,中风险,中风险
+8.2,7.468,-0.732,高风险,中风险
+3.9,4.3078,0.4078,低风险,中风险
+7.6,6.7555,-0.8445,中风险,中风险
+5.5,5.3971,-0.1029,中风险,中风险
+5.1,4.6108,-0.4892,中风险,中风险
+3.9,3.3013,-0.5987,低风险,低风险
+7.3,6.6781,-0.6219,中风险,中风险
+4.2,3.5031,-0.6969,中风险,低风险
+6.3,5.5266,-0.7734,中风险,中风险
+4.9,5.79,0.89,中风险,中风险
+5.4,3.889,-1.511,中风险,低风险
+4.6,4.3601,-0.2399,中风险,中风险
+5.1,5.4622,0.3622,中风险,中风险
+10.7,10.1721,-0.5279,高风险,高风险
+2.5,3.5917,1.0917,低风险,低风险
+4.2,4.1363,-0.0637,中风险,中风险
+5.6,6.1071,0.5071,中风险,中风险
+9.0,9.4307,0.4307,高风险,高风险
+5.1,5.267,0.167,中风险,中风险
+9.3,8.0694,-1.2306,高风险,高风险
+7.0,7.4015,0.4015,中风险,中风险
+4.1,4.9332,0.8332,中风险,中风险
+7.5,7.3161,-0.1839,中风险,中风险
+4.8,4.6539,-0.1461,中风险,中风险
+3.2,3.4086,0.2086,低风险,低风险
+3.4,3.4066,0.0066,低风险,低风险
+5.5,5.3683,-0.1317,中风险,中风险
+7.2,6.6502,-0.5498,中风险,中风险
+8.8,7.7426,-1.0574,高风险,中风险
+4.7,3.884,-0.816,中风险,低风险
+7.1,7.0029,-0.0971,中风险,中风险
+2.8,3.3349,0.5349,低风险,低风险
+5.9,6.3324,0.4324,中风险,中风险
+4.0,3.7305,-0.2695,中风险,低风险
+6.5,6.4484,-0.0516,中风险,中风险
+6.1,6.9183,0.8183,中风险,中风险
+3.3,3.1982,-0.1018,低风险,低风险
+4.2,4.0574,-0.1426,中风险,中风险
+6.1,5.5961,-0.5039,中风险,中风险
+2.5,3.0391,0.5391,低风险,低风险
+3.1,3.4012,0.3012,低风险,低风险
+5.4,5.2101,-0.1899,中风险,中风险
+7.5,6.4255,-1.0745,中风险,中风险
+4.2,3.8983,-0.3017,中风险,低风险
+4.7,4.6821,-0.0179,中风险,中风险
+3.8,4.2771,0.4771,低风险,中风险
diff --git a/backend/requirements.txt b/backend/requirements.txt
index c35d052..edd634a 100644
--- a/backend/requirements.txt
+++ b/backend/requirements.txt
@@ -12,6 +12,7 @@ xgboost==1.7.6
 lightgbm==4.1.0
 torch==2.6.0
 joblib==1.3.1
+shap>=0.43.0
 
 # Utilities
 python-dotenv==1.0.0
diff --git a/backend/services/jdr_service.py b/backend/services/jdr_service.py
new file mode 100644
index 0000000..166ed22
--- /dev/null
+++ b/backend/services/jdr_service.py
@@ -0,0 +1,212 @@
+import numpy as np
+import pandas as pd
+
+import config
+from core.model_features import engineer_features
+from core.preprocessing import get_clean_data
+
+
+class JDRService:
+    """JD-R（工作要求-资源）理论分析服务"""
+
+    def __init__(self):
+        self._df = None
+
+    def _ensure_data(self):
+        if self._df is None:
+            self._df = get_clean_data()
+            self._df = engineer_features(self._df)
+
+    def get_dimension_scores(self):
+        """JD-R 三维度统计分布"""
+        self._ensure_data()
+        df = self._df
+
+        result = {}
+        for dim_key, col_name in [
+            ('demands', '工作要求指数'),
+            ('resources', '工作资源指数'),
+            ('personal', '个人资源指数'),
+        ]:
+            if col_name not in df.columns:
+                continue
+            vals = df[col_name].dropna()
+            bins = np.linspace(vals.min(), vals.max(), 8)
+            hist, edges = np.histogram(vals, bins=bins)
+            result[dim_key] = {
+                'mean': round(float(vals.mean()), 2),
+                'std': round(float(vals.std()), 2),
+                'median': round(float(vals.median()), 2),
+                'distribution': [
+                    {'range': f'{round(edges[i], 1)}-{round(edges[i+1], 1)}', 'count': int(hist[i])}
+                    for i in range(len(hist))
+                ],
+            }
+
+        # JD-R 平衡度
+        if 'JD-R平衡度' in df.columns:
+            balance = df['JD-R平衡度'].dropna()
+            result['balance'] = {
+                'mean': round(float(balance.mean()), 2),
+                'positive_ratio': round(float((balance > 0).mean()) * 100, 1),
+            }
+
+        return result
+
+    def get_burnout_engagement_analysis(self):
+        """倦怠与投入分析"""
+        self._ensure_data()
+        df = self._df
+
+        result = {}
+
+        if '工作倦怠' in df.columns:
+            burnout = df['工作倦怠'].dropna()
+            result['burnout'] = {
+                'mean': round(float(burnout.mean()), 2),
+                'std': round(float(burnout.std()), 2),
+                'high_risk_ratio': round(float((burnout >= 5).mean()) * 100, 1),
+                'distribution': self._make_distribution(burnout, 1, 7, 7),
+            }
+
+        if '工作投入' in df.columns:
+            engagement = df['工作投入'].dropna()
+            result['engagement'] = {
+                'mean': round(float(engagement.mean()), 2),
+                'std': round(float(engagement.std()), 2),
+                'low_engagement_ratio': round(float((engagement <= 3).mean()) * 100, 1),
+                'distribution': self._make_distribution(engagement, 1, 7, 7),
+            }
+
+        # 相关性分析
+        corr_cols = {}
+        if '工作倦怠' in df.columns:
+            corr_cols['burnout'] = '工作倦怠'
+        if '工作投入' in df.columns:
+            corr_cols['engagement'] = '工作投入'
+        if '工作要求指数' in df.columns:
+            corr_cols['demands'] = '工作要求指数'
+        if '工作资源指数' in df.columns:
+            corr_cols['resources'] = '工作资源指数'
+        if config.TARGET_COLUMN in df.columns:
+            corr_cols['absence_hours'] = config.TARGET_COLUMN
+
+        if len(corr_cols) >= 2:
+            corr_df = df[[v for v in corr_cols.values()]].dropna()
+            corr_matrix = corr_df.corr()
+            correlations = {}
+            for k1, v1 in corr_cols.items():
+                for k2, v2 in corr_cols.items():
+                    if k1 != k2 and v1 in corr_matrix.index and v2 in corr_matrix.columns:
+                        correlations[f'{k1}_vs_{k2}'] = round(float(corr_matrix.loc[v1, v2]), 3)
+            result['correlations'] = correlations
+
+        return result
+
+    def get_jdr_path_analysis(self):
+        """JD-R 双路径中介分析"""
+        self._ensure_data()
+        df = self._df
+
+        result = {}
+
+        target = config.TARGET_COLUMN
+
+        # 健康损伤路径: demands -> burnout -> absence
+        if all(col in df.columns for col in ['工作要求指数', '工作倦怠', target]):
+            cols = ['工作要求指数', '工作倦怠', target]
+            sub = df[cols].dropna()
+            if len(sub) > 30:
+                r_demands_burnout = sub['工作要求指数'].corr(sub['工作倦怠'])
+                r_burnout_absence = sub['工作倦怠'].corr(sub[target])
+                r_demands_absence = sub['工作要求指数'].corr(sub[target])
+                indirect = r_demands_burnout * r_burnout_absence
+                result['health_impairment'] = {
+                    'direct_effect_demands': round(float(r_demands_absence), 3),
+                    'indirect_via_burnout': round(float(indirect), 3),
+                    'mediation_ratio': round(float(indirect / r_demands_absence) if r_demands_absence != 0 else 0, 3),
+                    'demands_to_burnout': round(float(r_demands_burnout), 3),
+                    'burnout_to_absence': round(float(r_burnout_absence), 3),
+                }
+
+        # 激励路径: resources -> engagement -> lower absence
+        if all(col in df.columns for col in ['工作资源指数', '工作投入', target]):
+            cols = ['工作资源指数', '工作投入', target]
+            sub = df[cols].dropna()
+            if len(sub) > 30:
+                r_resources_engagement = sub['工作资源指数'].corr(sub['工作投入'])
+                r_engagement_absence = sub['工作投入'].corr(sub[target])
+                r_resources_absence = sub['工作资源指数'].corr(sub[target])
+                indirect = r_resources_engagement * r_engagement_absence
+                result['motivational'] = {
+                    'direct_effect_resources': round(float(r_resources_absence), 3),
+                    'indirect_via_engagement': round(float(indirect), 3),
+                    'mediation_ratio': round(float(indirect / r_resources_absence) if r_resources_absence != 0 else 0, 3),
+                    'resources_to_engagement': round(float(r_resources_engagement), 3),
+                    'engagement_to_absence': round(float(r_engagement_absence), 3),
+                }
+
+        return result
+
+    def get_jdr_profile(self, dimension='所属行业'):
+        """按维度分组的 JD-R 轮廓"""
+        self._ensure_data()
+        df = self._df
+
+        if dimension not in df.columns:
+            return {'error': f'Dimension {dimension} not found'}
+
+        score_cols = ['工作要求指数', '工作资源指数', '个人资源指数', '工作倦怠', '工作投入']
+        existing_cols = [c for c in score_cols if c in df.columns]
+        if not existing_cols:
+            return {'error': 'JD-R scores not computed'}
+
+        group_cols = [dimension] + existing_cols
+        if config.TARGET_COLUMN in df.columns:
+            group_cols.append(config.TARGET_COLUMN)
+
+        grouped = df[group_cols].groupby(dimension).agg(['mean', 'std']).round(2)
+
+        profiles = []
+        for group_name in grouped.index:
+            profile = {'group_name': str(group_name)}
+            for col in existing_cols:
+                profile[col] = round(float(grouped.loc[group_name, (col, 'mean')]), 2)
+            if config.TARGET_COLUMN in df.columns:
+                profile['avg_absence_hours'] = round(float(grouped.loc[group_name, (config.TARGET_COLUMN, 'mean')]), 2)
+            profiles.append(profile)
+
+        return {'dimension': dimension, 'profiles': profiles}
+
+    def get_risk_distribution(self):
+        """风险等级分布"""
+        self._ensure_data()
+        df = self._df
+
+        target = config.TARGET_COLUMN
+        if target not in df.columns:
+            return {'error': 'Target column not found'}
+
+        hours = df[target]
+        levels = [
+            {'level': 'low', 'label': '低风险', 'color': '#22c55e', 'count': int((hours < 4).sum()),
+             'percentage': round(float((hours < 4).mean()) * 100, 1), 'avg_hours': round(float(hours[hours < 4].mean()), 2) if (hours < 4).any() else 0},
+            {'level': 'medium', 'label': '中风险', 'color': '#f59e0b', 'count': int(((hours >= 4) & (hours <= 8)).sum()),
+             'percentage': round(float(((hours >= 4) & (hours <= 8)).mean()) * 100, 1),
+             'avg_hours': round(float(hours[(hours >= 4) & (hours <= 8)].mean()), 2) if ((hours >= 4) & (hours <= 8)).any() else 0},
+            {'level': 'high', 'label': '高风险', 'color': '#ef4444', 'count': int((hours > 8).sum()),
+             'percentage': round(float((hours > 8).mean()) * 100, 1), 'avg_hours': round(float(hours[hours > 8].mean()), 2) if (hours > 8).any() else 0},
+        ]
+
+        return {'levels': levels, 'total': len(hours)}
+
+    def _make_distribution(self, series, low, high, n_bins):
+        bins = np.linspace(low, high, n_bins + 1)
+        hist, edges = np.histogram(series, bins=bins)
+        return [
+            {'range': f'{round(edges[i], 1)}-{round(edges[i+1], 1)}', 'count': int(hist[i])}
+            for i in range(len(hist))
+        ]
+
+
+jdr_service = JDRService()
diff --git a/backend/services/predict_service.py b/backend/services/predict_service.py
index 2e451b6..322e876 100644
--- a/backend/services/predict_service.py
+++ b/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 {
diff --git a/backend/services/shap_service.py b/backend/services/shap_service.py
new file mode 100644
index 0000000..931e646
--- /dev/null
+++ b/backend/services/shap_service.py
@@ -0,0 +1,31 @@
+from core.shap_analysis import SHAPAnalyzer
+
+
+class SHAPService:
+    """SHAP 可解释性分析服务"""
+
+    def __init__(self):
+        self._analyzer = None
+
+    def _ensure_analyzer(self):
+        if self._analyzer is None:
+            self._analyzer = SHAPAnalyzer()
+
+    def get_global_importance(self, model_type='random_forest'):
+        self._ensure_analyzer()
+        return self._analyzer.global_shap_values(model_type)
+
+    def get_local_explanation(self, data, model_type='random_forest'):
+        self._ensure_analyzer()
+        return self._analyzer.local_shap_values(data, model_type)
+
+    def get_interactions(self, model_type='random_forest', top_n=10):
+        self._ensure_analyzer()
+        return self._analyzer.shap_interaction(model_type, top_n)
+
+    def get_dependence(self, feature_name, model_type='random_forest'):
+        self._ensure_analyzer()
+        return self._analyzer.shap_dependence(feature_name, model_type)
+
+
+shap_service = SHAPService()
diff --git a/frontend/package-lock.json b/frontend/package-lock.json
new file mode 100644
index 0000000..611c26d
--- /dev/null
+++ b/frontend/package-lock.json
@@ -0,0 +1,1720 @@
+{
+  "name": "absenteeism-analysis-frontend",
+  "version": "1.0.0",
+  "lockfileVersion": 3,
+  "requires": true,
+  "packages": {
+    "": {
+      "name": "absenteeism-analysis-frontend",
+      "version": "1.0.0",
+      "dependencies": {
+        "@element-plus/icons-vue": "^2.3.2",
+        "axios": "^1.6.2",
+        "echarts": "^5.4.3",
+        "element-plus": "^2.4.4",
+        "vue": "^3.4.0",
+        "vue-router": "^4.2.5"
+      },
+      "devDependencies": {
+        "@vitejs/plugin-vue": "^4.5.2",
+        "vite": "^5.0.10"
+      }
+    },
+    "node_modules/@babel/helper-string-parser": {
+      "version": "7.27.1",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz",
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+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/helper-validator-identifier": {
+      "version": "7.28.5",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz",
+      "integrity": "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/parser": {
+      "version": "7.29.0",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.29.0.tgz",
+      "integrity": "sha512-IyDgFV5GeDUVX4YdF/3CPULtVGSXXMLh1xVIgdCgxApktqnQV0r7/8Nqthg+8YLGaAtdyIlo2qIdZrbCv4+7ww==",
+      "license": "MIT",
+      "dependencies": {
+        "@babel/types": "^7.29.0"
+      },
+      "bin": {
+        "parser": "bin/babel-parser.js"
+      },
+      "engines": {
+        "node": ">=6.0.0"
+      }
+    },
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+      "license": "MIT",
+      "dependencies": {
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+        "@babel/helper-validator-identifier": "^7.28.5"
+      },
+      "engines": {
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+      }
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+      "license": "MIT",
+      "engines": {
+        "node": ">=14"
+      }
+    },
+    "node_modules/@element-plus/icons-vue": {
+      "version": "2.3.2",
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+      "license": "MIT",
+      "peerDependencies": {
+        "vue": "^3.2.0"
+      }
+    },
+    "node_modules/@esbuild/aix-ppc64": {
+      "version": "0.21.5",
+      "resolved": "https://registry.npmjs.org/@esbuild/aix-ppc64/-/aix-ppc64-0.21.5.tgz",
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+        "ppc64"
+      ],
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+        "aix"
+      ],
+      "engines": {
+        "node": ">=12"
+      }
+    },
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+        "x64"
+      ],
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+        "android"
+      ],
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+      }
+    },
+    "node_modules/@esbuild/darwin-arm64": {
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+        "arm64"
+      ],
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+      "license": "MIT",
+      "optional": true,
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+        "darwin"
+      ],
+      "engines": {
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+      }
+    },
+    "node_modules/@esbuild/darwin-x64": {
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+      "resolved": "https://registry.npmjs.org/@esbuild/darwin-x64/-/darwin-x64-0.21.5.tgz",
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+        "x64"
+      ],
+      "dev": true,
+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "darwin"
+      ],
+      "engines": {
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+      }
+    },
+    "node_modules/@esbuild/freebsd-arm64": {
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+      "resolved": "https://registry.npmjs.org/@esbuild/freebsd-arm64/-/freebsd-arm64-0.21.5.tgz",
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+        "arm64"
+      ],
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+      "license": "MIT",
+      "optional": true,
+      "os": [
+        "freebsd"
+      ],
+      "engines": {
+        "node": ">=12"
+      }
+    },
+    "node_modules/@esbuild/freebsd-x64": {
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+        "x64"
+      ],
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+      "license": "MIT",
+      "optional": true,
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+        "freebsd"
+      ],
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+      }
+    },
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+        "@rollup/rollup-linux-riscv64-gnu": "4.59.0",
+        "@rollup/rollup-linux-riscv64-musl": "4.59.0",
+        "@rollup/rollup-linux-s390x-gnu": "4.59.0",
+        "@rollup/rollup-linux-x64-gnu": "4.59.0",
+        "@rollup/rollup-linux-x64-musl": "4.59.0",
+        "@rollup/rollup-openbsd-x64": "4.59.0",
+        "@rollup/rollup-openharmony-arm64": "4.59.0",
+        "@rollup/rollup-win32-arm64-msvc": "4.59.0",
+        "@rollup/rollup-win32-ia32-msvc": "4.59.0",
+        "@rollup/rollup-win32-x64-gnu": "4.59.0",
+        "@rollup/rollup-win32-x64-msvc": "4.59.0",
+        "fsevents": "~2.3.2"
+      }
+    },
+    "node_modules/source-map-js": {
+      "version": "1.2.1",
+      "resolved": "https://registry.npmjs.org/source-map-js/-/source-map-js-1.2.1.tgz",
+      "integrity": "sha512-UXWMKhLOwVKb728IUtQPXxfYU+usdybtUrK/8uGE8CQMvrhOpwvzDBwj0QhSL7MQc7vIsISBG8VQ8+IDQxpfQA==",
+      "license": "BSD-3-Clause",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/tslib": {
+      "version": "2.3.0",
+      "resolved": "https://registry.npmjs.org/tslib/-/tslib-2.3.0.tgz",
+      "integrity": "sha512-N82ooyxVNm6h1riLCoyS9e3fuJ3AMG2zIZs2Gd1ATcSFjSA23Q0fzjjZeh0jbJvWVDZ0cJT8yaNNaaXHzueNjg==",
+      "license": "0BSD"
+    },
+    "node_modules/vite": {
+      "version": "5.4.21",
+      "resolved": "https://registry.npmjs.org/vite/-/vite-5.4.21.tgz",
+      "integrity": "sha512-o5a9xKjbtuhY6Bi5S3+HvbRERmouabWbyUcpXXUA1u+GNUKoROi9byOJ8M0nHbHYHkYICiMlqxkg1KkYmm25Sw==",
+      "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "esbuild": "^0.21.3",
+        "postcss": "^8.4.43",
+        "rollup": "^4.20.0"
+      },
+      "bin": {
+        "vite": "bin/vite.js"
+      },
+      "engines": {
+        "node": "^18.0.0 || >=20.0.0"
+      },
+      "funding": {
+        "url": "https://github.com/vitejs/vite?sponsor=1"
+      },
+      "optionalDependencies": {
+        "fsevents": "~2.3.3"
+      },
+      "peerDependencies": {
+        "@types/node": "^18.0.0 || >=20.0.0",
+        "less": "*",
+        "lightningcss": "^1.21.0",
+        "sass": "*",
+        "sass-embedded": "*",
+        "stylus": "*",
+        "sugarss": "*",
+        "terser": "^5.4.0"
+      },
+      "peerDependenciesMeta": {
+        "@types/node": {
+          "optional": true
+        },
+        "less": {
+          "optional": true
+        },
+        "lightningcss": {
+          "optional": true
+        },
+        "sass": {
+          "optional": true
+        },
+        "sass-embedded": {
+          "optional": true
+        },
+        "stylus": {
+          "optional": true
+        },
+        "sugarss": {
+          "optional": true
+        },
+        "terser": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/vue": {
+      "version": "3.5.30",
+      "resolved": "https://registry.npmjs.org/vue/-/vue-3.5.30.tgz",
+      "integrity": "sha512-hTHLc6VNZyzzEH/l7PFGjpcTvUgiaPK5mdLkbjrTeWSRcEfxFrv56g/XckIYlE9ckuobsdwqd5mk2g1sBkMewg==",
+      "license": "MIT",
+      "dependencies": {
+        "@vue/compiler-dom": "3.5.30",
+        "@vue/compiler-sfc": "3.5.30",
+        "@vue/runtime-dom": "3.5.30",
+        "@vue/server-renderer": "3.5.30",
+        "@vue/shared": "3.5.30"
+      },
+      "peerDependencies": {
+        "typescript": "*"
+      },
+      "peerDependenciesMeta": {
+        "typescript": {
+          "optional": true
+        }
+      }
+    },
+    "node_modules/vue-router": {
+      "version": "4.6.4",
+      "resolved": "https://registry.npmjs.org/vue-router/-/vue-router-4.6.4.tgz",
+      "integrity": "sha512-Hz9q5sa33Yhduglwz6g9skT8OBPii+4bFn88w6J+J4MfEo4KRRpmiNG/hHHkdbRFlLBOqxN8y8gf2Fb0MTUgVg==",
+      "license": "MIT",
+      "dependencies": {
+        "@vue/devtools-api": "^6.6.4"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/posva"
+      },
+      "peerDependencies": {
+        "vue": "^3.5.0"
+      }
+    },
+    "node_modules/zrender": {
+      "version": "5.6.1",
+      "resolved": "https://registry.npmjs.org/zrender/-/zrender-5.6.1.tgz",
+      "integrity": "sha512-OFXkDJKcrlx5su2XbzJvj/34Q3m6PvyCZkVPHGYpcCJ52ek4U/ymZyfuV1nKE23AyBJ51E/6Yr0mhZ7xGTO4ag==",
+      "license": "BSD-3-Clause",
+      "dependencies": {
+        "tslib": "2.3.0"
+      }
+    }
+  }
+}
diff --git a/frontend/src/App.vue b/frontend/src/App.vue
index ddef2dd..f8ccbe6 100644
--- a/frontend/src/App.vue
+++ b/frontend/src/App.vue
@@ -28,6 +28,10 @@
             <el-icon class="nav-icon"><UserFilled /></el-icon>
             <span class="nav-label">员工画像</span>
           </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>
+          </el-menu-item>
         </el-menu>
       </div>
 
@@ -67,7 +71,7 @@
 <script setup>
 import { computed, onMounted, ref, watch } from 'vue'
 import { useRoute } from 'vue-router'
-import { DataAnalysis, Grid, TrendCharts, UserFilled } from '@element-plus/icons-vue'
+import { DataAnalysis, Grid, TrendCharts, UserFilled, Reading } from '@element-plus/icons-vue'
 
 const route = useRoute()
 const activeMenu = computed(() => route.path)
@@ -90,6 +94,10 @@ const metaMap = {
   '/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。'
+  },
+  '/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。'
   }
 }
 
diff --git a/frontend/src/api/jdr.js b/frontend/src/api/jdr.js
new file mode 100644
index 0000000..dcdb111
--- /dev/null
+++ b/frontend/src/api/jdr.js
@@ -0,0 +1,21 @@
+import request from './request'
+
+export function getDimensions() {
+  return request.get('/jdr/dimensions')
+}
+
+export function getBurnoutEngagement() {
+  return request.get('/jdr/burnout-engagement')
+}
+
+export function getPathAnalysis() {
+  return request.get('/jdr/path-analysis')
+}
+
+export function getProfile(dimension) {
+  return request.get(`/jdr/profile?dimension=${dimension}`)
+}
+
+export function getRiskDistribution() {
+  return request.get('/jdr/risk-distribution')
+}
diff --git a/frontend/src/api/shap.js b/frontend/src/api/shap.js
new file mode 100644
index 0000000..237eb2e
--- /dev/null
+++ b/frontend/src/api/shap.js
@@ -0,0 +1,17 @@
+import request from './request'
+
+export function getGlobalImportance(model) {
+  return request.get(`/shap/global?model=${model || 'random_forest'}`)
+}
+
+export function getLocalExplanation(data) {
+  return request.post('/shap/local', data)
+}
+
+export function getInteractions(model, topN) {
+  return request.get(`/shap/interaction?model=${model || 'random_forest'}&top_n=${topN || 10}`)
+}
+
+export function getDependence(feature, model) {
+  return request.get(`/shap/dependence?feature=${feature}&model=${model || 'random_forest'}`)
+}
diff --git a/frontend/src/router/index.js b/frontend/src/router/index.js
index 4978edc..47c8631 100644
--- a/frontend/src/router/index.js
+++ b/frontend/src/router/index.js
@@ -28,6 +28,12 @@ const routes = [
     name: 'Clustering',
     component: () => import('@/views/Clustering.vue'),
     meta: { title: '员工画像' }
+  },
+  {
+    path: '/jdr-analysis',
+    name: 'JDRAnalysis',
+    component: () => import('@/views/JDRAnalysis.vue'),
+    meta: { title: 'JD-R理论分析' }
   }
 ]
 
diff --git a/frontend/src/views/JDRAnalysis.vue b/frontend/src/views/JDRAnalysis.vue
new file mode 100644
index 0000000..6d8d660
--- /dev/null
+++ b/frontend/src/views/JDRAnalysis.vue
@@ -0,0 +1,670 @@
+<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>
+      <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。
+      </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>
+              </template>
+              <div v-if="dimensionData" ref="radarChartRef" style="height: 380px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+          <el-col :xs="24" :lg="12">
+            <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>
+              </template>
+              <div v-if="dimensionData" class="dim-stats">
+                <div v-for="(item, key) in dimensionLabels" :key="key" class="dim-stat-row">
+                  <div class="dim-stat-label">{{ item.label }}</div>
+                  <div class="dim-stat-value">{{ dimensionData[key]?.mean || '-' }}</div>
+                  <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-value">{{ dimensionData.balance.mean }}</div>
+                  <div class="dim-stat-sub">正向比例: {{ dimensionData.balance.positive_ratio }}%</div>
+                </div>
+              </div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+        </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>
+              </template>
+              <div v-if="burnoutData" ref="burnoutChartRef" style="height: 380px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+          <el-col :xs="24" :lg="12">
+            <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>
+              </template>
+              <div v-if="burnoutData" ref="corrChartRef" style="height: 380px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+        </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>
+              </template>
+              <div class="path-diagram">
+                <div class="path-flow">
+                  <div class="path-section">
+                    <div class="path-title">健康损伤路径</div>
+                    <div class="path-nodes">
+                      <div class="path-node node-demand">工作要求</div>
+                      <div class="path-arrow">&rarr;</div>
+                      <div class="path-node node-burnout">工作倦怠</div>
+                      <div class="path-arrow">&rarr;</div>
+                      <div class="path-node node-absence">缺勤时长</div>
+                    </div>
+                    <div v-if="pathData?.health_impairment" class="path-stats">
+                      <span>直接效应: {{ pathData.health_impairment.direct_effect_demands }}</span>
+                      <span>中介效应: {{ pathData.health_impairment.indirect_via_burnout }}</span>
+                      <span>中介比例: {{ (pathData.health_impairment.mediation_ratio * 100).toFixed(1) }}%</span>
+                    </div>
+                  </div>
+                  <div class="path-divider"></div>
+                  <div class="path-section">
+                    <div class="path-title">激励路径</div>
+                    <div class="path-nodes">
+                      <div class="path-node node-resource">工作资源</div>
+                      <div class="path-arrow">&rarr;</div>
+                      <div class="path-node node-engagement">工作投入</div>
+                      <div class="path-arrow">&rarr;</div>
+                      <div class="path-node node-absence-low">低缺勤</div>
+                    </div>
+                    <div v-if="pathData?.motivational" class="path-stats">
+                      <span>直接效应: {{ pathData.motivational.direct_effect_resources }}</span>
+                      <span>中介效应: {{ pathData.motivational.indirect_via_engagement }}</span>
+                      <span>中介比例: {{ (pathData.motivational.mediation_ratio * 100).toFixed(1) }}%</span>
+                    </div>
+                  </div>
+                </div>
+              </div>
+            </el-card>
+          </el-col>
+          <el-col :xs="24" :lg="10">
+            <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>
+              </template>
+              <div v-if="riskData" ref="riskChartRef" style="height: 320px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+        </el-row>
+      </el-tab-pane>
+
+      <!-- Tab 4: SHAP 解释 -->
+      <el-tab-pane label="SHAP 解释" 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>
+                  </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-select>
+                </div>
+              </template>
+              <div v-if="shapGlobalData" ref="shapGlobalRef" style="height: 420px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+          <el-col :xs="24" :lg="10">
+            <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>
+              </template>
+              <div v-if="shapGlobalData" ref="shapDimPieRef" style="height: 420px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+        </el-row>
+        <el-row :gutter="20" style="margin-top: 20px">
+          <el-col :xs="24" :lg="12">
+            <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>
+              </template>
+              <div style="margin-bottom: 12px">
+                <el-select v-model="dependenceFeature" size="small" style="width: 200px" @change="loadDependence">
+                  <el-option v-for="f in shapTopFeatures" :key="f.name" :label="f.name_cn" :value="f.name" />
+                </el-select>
+              </div>
+              <div v-if="shapGlobalData" ref="shapDependenceRef" style="height: 320px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+          <el-col :xs="24" :lg="12">
+            <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>
+              </template>
+              <div v-if="shapGlobalData" ref="shapInteractionRef" style="height: 320px"></div>
+              <el-empty v-else description="加载中..." />
+            </el-card>
+          </el-col>
+        </el-row>
+      </el-tab-pane>
+    </el-tabs>
+  </div>
+</template>
+
+<script setup>
+import { nextTick, onMounted, ref, watch } from 'vue'
+import * as echarts from 'echarts'
+import { ElMessage } from 'element-plus'
+import { getDimensions, getBurnoutEngagement, getPathAnalysis, getRiskDistribution } from '@/api/jdr'
+import { getGlobalImportance, getInteractions, getDependence } from '@/api/shap'
+
+const activeTab = ref('dimensions')
+
+// 数据
+const dimensionData = ref(null)
+const burnoutData = ref(null)
+const pathData = ref(null)
+const riskData = ref(null)
+const shapGlobalData = ref(null)
+const shapTopFeatures = ref([])
+const shapModel = ref('random_forest')
+const dependenceFeature = ref('月均加班时长')
+
+// 图表 DOM ref
+const radarChartRef = ref(null)
+const burnoutChartRef = ref(null)
+const corrChartRef = ref(null)
+const riskChartRef = ref(null)
+const shapGlobalRef = ref(null)
+const shapDimPieRef = ref(null)
+const shapDependenceRef = ref(null)
+const shapInteractionRef = ref(null)
+
+const dimensionLabels = {
+  demands: { label: '工作要求指数', color: '#ef4444' },
+  resources: { label: '工作资源指数', color: '#22c55e' },
+  personal: { label: '个人资源指数', color: '#3b82f6' },
+}
+
+function getOrCreateChart(el) {
+  if (!el) return null
+  let chart = echarts.getInstanceByDom(el)
+  if (!chart) chart = echarts.init(el)
+  return chart
+}
+
+// 延迟渲染：等待 Vue 将 v-if 的 DOM 插入到页面
+function scheduleRender(fn) {
+  requestAnimationFrame(() => {
+    nextTick().then(fn)
+  })
+}
+
+// ── Tab 1: 维度分析 ──
+async function loadDimensions() {
+  try {
+    dimensionData.value = await getDimensions()
+    scheduleRender(renderRadarChart)
+  } catch (e) {
+    ElMessage.error('加载维度数据失败')
+  }
+}
+
+function renderRadarChart() {
+  const chart = getOrCreateChart(radarChartRef.value)
+  if (!chart || !dimensionData.value) { console.warn('radarChart: DOM or data missing'); return }
+
+  const dims = dimensionData.value
+  const indicators = [
+    { 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 },
+  ]
+  const values = [
+    dims.demands?.mean || 0,
+    dims.resources?.mean || 0,
+    dims.personal?.mean || 0,
+    dims.balance?.mean || 0,
+  ]
+
+  chart.setOption({
+    tooltip: {},
+    radar: { indicator: indicators, shape: 'circle', splitNumber: 5 },
+    series: [{
+      type: 'radar',
+      data: [{
+        value: values,
+        name: 'JD-R 维度均值',
+        areaStyle: { color: 'rgba(15, 118, 110, 0.2)' },
+        lineStyle: { color: '#0f766e', width: 2 },
+        itemStyle: { color: '#0f766e' },
+      }],
+    }],
+  })
+}
+
+// ── Tab 2: 倦怠与投入 ──
+async function loadBurnout() {
+  try {
+    burnoutData.value = await getBurnoutEngagement()
+    scheduleRender(() => { renderBurnoutChart(); renderCorrChart() })
+  } catch (e) {
+    ElMessage.error('加载倦怠/投入数据失败')
+  }
+}
+
+function renderBurnoutChart() {
+  const chart = getOrCreateChart(burnoutChartRef.value)
+  if (!chart || !burnoutData.value) { console.warn('burnoutChart: DOM or data missing'); return }
+
+  const bDist = burnoutData.value.burnout?.distribution || []
+  const eDist = burnoutData.value.engagement?.distribution || []
+  const categories = bDist.map(d => d.range)
+
+  chart.setOption({
+    tooltip: { trigger: 'axis' },
+    legend: { data: ['工作倦怠', '工作投入'] },
+    xAxis: { type: 'category', data: categories },
+    yAxis: { type: 'value', name: '人数' },
+    series: [
+      {
+        name: '工作倦怠', type: 'bar', data: bDist.map(d => d.count),
+        itemStyle: { color: '#ef4444' }, barWidth: '30%',
+      },
+      {
+        name: '工作投入', type: 'bar', data: eDist.map(d => d.count),
+        itemStyle: { color: '#22c55e' }, barWidth: '30%',
+      },
+    ],
+  })
+}
+
+function renderCorrChart() {
+  const chart = getOrCreateChart(corrChartRef.value)
+  if (!chart || !burnoutData.value?.correlations) { console.warn('corrChart: DOM or data missing'); return }
+
+  const corrs = burnoutData.value.correlations
+  const items = [
+    { name: '要求→倦怠', value: corrs.demands_vs_burnout },
+    { name: '资源→投入', value: corrs.resources_vs_engagement },
+    { name: '倦怠→缺勤', value: corrs.burnout_vs_absence_hours },
+    { name: '投入→缺勤', value: corrs.engagement_vs_absence_hours },
+    { name: '要求→缺勤', value: corrs.demands_vs_absence_hours },
+    { name: '资源→缺勤', value: corrs.resources_vs_absence_hours },
+  ].filter(i => i.value !== undefined)
+
+  chart.setOption({
+    tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
+    xAxis: { type: 'value', name: '相关系数', min: -1, max: 1 },
+    yAxis: { type: 'category', data: items.map(i => i.name) },
+    series: [{
+      type: 'bar', data: items.map(i => ({
+        value: i.value,
+        itemStyle: { color: i.value >= 0 ? '#22c55e' : '#ef4444' },
+      })),
+    }],
+  })
+}
+
+// ── Tab 3: 双路径 ──
+async function loadPathAndRisk() {
+  try {
+    const [path, risk] = await Promise.all([getPathAnalysis(), getRiskDistribution()])
+    pathData.value = path
+    riskData.value = risk
+    scheduleRender(renderRiskChart)
+  } catch (e) {
+    ElMessage.error('加载路径分析失败')
+  }
+}
+
+function renderRiskChart() {
+  const chart = getOrCreateChart(riskChartRef.value)
+  if (!chart || !riskData.value?.levels) { console.warn('riskChart: DOM or data missing'); return }
+
+  const levels = riskData.value.levels
+  chart.setOption({
+    tooltip: { trigger: 'item', formatter: '{b}: {c} ({d}%)' },
+    series: [{
+      type: 'pie', radius: ['40%', '70%'],
+      data: levels.map(l => ({
+        name: l.label, value: l.count,
+        itemStyle: { color: l.color },
+      })),
+      label: { formatter: '{b}\n{d}%' },
+    }],
+  })
+}
+
+// ── Tab 4: SHAP ──
+async function loadShapGlobal() {
+  try {
+    const data = await getGlobalImportance(shapModel.value)
+    if (data.error) { ElMessage.error(data.error); return }
+    shapGlobalData.value = data
+    shapTopFeatures.value = data.top_features || []
+    if (shapTopFeatures.value.length && !dependenceFeature.value) {
+      dependenceFeature.value = shapTopFeatures.value[0].name
+    }
+    scheduleRender(() => {
+      renderShapGlobalChart()
+      renderShapDimPie()
+      loadDependence()
+      loadInteractions()
+    })
+  } catch (e) {
+    ElMessage.error('加载 SHAP 数据失败')
+  }
+}
+
+function renderShapGlobalChart() {
+  const chart = getOrCreateChart(shapGlobalRef.value)
+  if (!chart || !shapGlobalData.value?.top_features) { console.warn('shapGlobal: DOM or data missing'); return }
+
+  const features = shapGlobalData.value.top_features.slice(0, 15).reverse()
+  const dimColors = {
+    job_demands: '#ef4444',
+    job_resources: '#22c55e',
+    personal_resources: '#3b82f6',
+    mediators: '#f59e0b',
+    event_context: '#8b5cf6',
+    other: '#6b7280',
+  }
+
+  chart.setOption({
+    tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
+    grid: { left: 140, right: 30, top: 10, bottom: 30 },
+    xAxis: { type: 'value', name: 'Mean |SHAP|' },
+    yAxis: { type: 'category', data: features.map(f => f.name_cn) },
+    series: [{
+      type: 'bar', data: features.map(f => ({
+        value: f.importance,
+        itemStyle: { color: dimColors[f.dimension] || '#6b7280' },
+      })),
+    }],
+  })
+}
+
+function renderShapDimPie() {
+  const chart = getOrCreateChart(shapDimPieRef.value)
+  if (!chart || !shapGlobalData.value?.dimensions) { console.warn('shapDimPie: DOM or data missing'); return }
+
+  const dims = shapGlobalData.value.dimensions
+  const dimColorMap = {
+    job_demands: '#ef4444',
+    job_resources: '#22c55e',
+    personal_resources: '#3b82f6',
+    mediators: '#f59e0b',
+    event_context: '#8b5cf6',
+  }
+  const pieData = Object.entries(dims).map(([key, info]) => {
+    const total = info.features.reduce((s, f) => s + f.importance, 0)
+    return { name: info.name_cn, value: parseFloat(total.toFixed(4)), itemStyle: { color: dimColorMap[key] || '#6b7280' } }
+  })
+
+  chart.setOption({
+    tooltip: { trigger: 'item', formatter: '{b}: {c} ({d}%)' },
+    series: [{
+      type: 'pie', radius: ['35%', '65%'],
+      data: pieData,
+      label: { formatter: '{b}\n{d}%' },
+    }],
+  })
+}
+
+async function loadDependence() {
+  if (!dependenceFeature.value) return
+  try {
+    const data = await getDependence(dependenceFeature.value, shapModel.value)
+    if (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: ${p.data[1].toFixed(4)}` },
+      grid: { left: 60, right: 20, top: 20, bottom: 40 },
+      xAxis: { type: 'value', name: data.feature_cn },
+      yAxis: { type: 'value', name: 'SHAP value' },
+      series: [{
+        type: 'scatter', data: points, symbolSize: 5,
+        itemStyle: { color: '#0f766e', opacity: 0.6 },
+      }],
+    })
+  } catch (e) { /* ignore */ }
+}
+
+async function loadInteractions() {
+  try {
+    const data = await getInteractions(shapModel.value, 10)
+    if (data.error || !data.top_interactions) return
+    await nextTick()
+    const chart = getOrCreateChart(shapInteractionRef.value)
+    if (!chart) return
+
+    const interactions = data.top_interactions.slice(0, 8)
+    chart.setOption({
+      tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
+      grid: { left: 160, right: 20, top: 10, bottom: 30 },
+      xAxis: { type: 'value', name: '交互强度' },
+      yAxis: { type: 'category', data: interactions.map(i => `${i.feature_1_cn} x ${i.feature_2_cn}`) },
+      series: [{
+        type: 'bar', data: interactions.map(i => i.strength),
+        itemStyle: { color: '#f59e0b' },
+      }],
+    })
+  } catch (e) { /* ignore */ }
+}
+
+// Tab 切换时重新渲染图表（等待 DOM 就绪）
+watch(activeTab, async (tab) => {
+  await nextTick()
+  requestAnimationFrame(async () => {
+    await nextTick()
+    if (tab === 'dimensions') {
+      if (dimensionData.value) renderRadarChart()
+      else loadDimensions()
+    }
+    if (tab === 'burnout') {
+      if (burnoutData.value) { renderBurnoutChart(); renderCorrChart() }
+      else loadBurnout()
+    }
+    if (tab === 'path') {
+      if (riskData.value) renderRiskChart()
+      else loadPathAndRisk()
+    }
+    if (tab === 'shap') {
+      if (shapGlobalData.value) { renderShapGlobalChart(); renderShapDimPie(); loadDependence(); loadInteractions() }
+      else loadShapGlobal()
+    }
+  })
+})
+
+onMounted(() => {
+  loadDimensions()
+  loadBurnout()
+  loadPathAndRisk()
+  loadShapGlobal()
+})
+</script>
+
+<style scoped>
+.jdr-hero {
+  background: linear-gradient(135deg, rgba(15, 23, 42, 0.96), rgba(59, 130, 246, 0.92) 50%, rgba(15, 118, 110, 0.88));
+}
+
+.jdr-tabs {
+  border-radius: 18px;
+  overflow: hidden;
+}
+
+.jdr-tabs :deep(.el-tabs__content) {
+  padding: 20px;
+}
+
+.dim-stats {
+  display: flex;
+  flex-direction: column;
+  gap: 14px;
+}
+
+.dim-stat-row {
+  display: flex;
+  align-items: center;
+  gap: 14px;
+  padding: 14px 18px;
+  border: 1px solid var(--line-soft);
+  border-radius: 14px;
+  background: rgba(255, 255, 255, 0.76);
+}
+
+.dim-stat-label {
+  flex: 1;
+  font-size: 14px;
+  font-weight: 600;
+  color: var(--text-main);
+}
+
+.dim-stat-value {
+  font-size: 22px;
+  font-weight: 700;
+  color: #0f766e;
+}
+
+.dim-stat-sub {
+  font-size: 12px;
+  color: var(--text-subtle);
+}
+
+.dim-stat-balance {
+  background: linear-gradient(135deg, rgba(59, 130, 246, 0.08), rgba(255, 255, 255, 0.9));
+}
+
+.path-diagram {
+  padding: 20px 0;
+}
+
+.path-flow {
+  display: flex;
+  flex-direction: column;
+  gap: 24px;
+}
+
+.path-section {
+  padding: 24px;
+  border: 1px solid var(--line-soft);
+  border-radius: 18px;
+  background: rgba(255, 255, 255, 0.76);
+}
+
+.path-title {
+  margin-bottom: 16px;
+  font-size: 16px;
+  font-weight: 700;
+  color: var(--text-main);
+}
+
+.path-nodes {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+  gap: 12px;
+  margin-bottom: 16px;
+}
+
+.path-node {
+  padding: 12px 20px;
+  border-radius: 12px;
+  font-size: 14px;
+  font-weight: 600;
+  color: #fff;
+}
+
+.node-demand { background: #ef4444; }
+.node-burnout { background: #f59e0b; }
+.node-absence { background: #dc2626; }
+.node-resource { background: #22c55e; }
+.node-engagement { background: #0f766e; }
+.node-absence-low { background: #3b82f6; }
+
+.path-arrow {
+  font-size: 20px;
+  font-weight: 700;
+  color: var(--text-subtle);
+}
+
+.path-stats {
+  display: flex;
+  justify-content: center;
+  gap: 24px;
+  padding-top: 14px;
+  border-top: 1px solid var(--line-soft);
+  font-size: 13px;
+  color: var(--text-subtle);
+}
+
+.path-divider {
+  height: 1px;
+  background: var(--line-soft);
+  margin: 0 40px;
+}
+
+.section-heading {
+  display: flex;
+  align-items: flex-start;
+  justify-content: space-between;
+}
+
+:deep(.panel-card .el-card__header) {
+  padding-bottom: 0;
+  border-bottom: none;
+}
+</style>
diff --git a/frontend/src/views/Prediction.vue b/frontend/src/views/Prediction.vue
index 4529b40..a7352a7 100644
--- a/frontend/src/views/Prediction.vue
+++ b/frontend/src/views/Prediction.vue
@@ -297,15 +297,36 @@
           </el-table>
         </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>
+              </div>
+              <span class="soft-tag">Explain</span>
+            </div>
+          </template>
+          <div class="shap-dimension-badges">
+            <span v-for="(val, dim) in shapLocalData.dimension_contribution" :key="dim"
+              class="shap-badge" :class="val >= 0 ? 'shap-badge-positive' : 'shap-badge-negative'">
+              {{ dim }}: {{ val >= 0 ? '+' : '' }}{{ val }}
+            </span>
+          </div>
+          <div ref="shapForceRef" style="height: 320px"></div>
+        </el-card>
+
       </el-col>
     </el-row>
   </div>
 </template>
 
 <script setup>
-import { computed, onMounted, ref } from 'vue'
+import { computed, nextTick, onMounted, ref } 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']
@@ -348,6 +369,8 @@ const showCompare = ref(false)
 const selectedModel = ref('')
 const availableModels = ref([])
 const modelsLoading = ref(false)
+const shapLocalData = ref(null)
+const shapForceRef = ref(null)
 
 const riskTagType = computed(() => {
   if (!result.value) return 'info'
@@ -366,6 +389,7 @@ function resetForm() {
   form.value = { ...defaultForm }
   result.value = null
   compareResults.value = []
+  shapLocalData.value = null
 }
 
 async function loadModels() {
@@ -385,6 +409,8 @@ async function handlePredict() {
     if (selectedModel.value) params.model_type = selectedModel.value
     result.value = await request.post('/predict/single', params)
     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 {
@@ -392,6 +418,42 @@ 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 }
+  let chart = echarts.getInstanceByDom(el)
+  if (!chart) chart = echarts.init(el)
+
+  const features = shapLocalData.value.features.slice(0, 12)
+  const sorted = [...features].sort((a, b) => b.shap_value - a.shap_value)
+
+  chart.setOption({
+    tooltip: { trigger: 'axis', axisPointer: { type: 'shadow' } },
+    grid: { left: 120, right: 30, top: 10, bottom: 30 },
+    xAxis: { type: 'value', name: 'SHAP值' },
+    yAxis: { type: 'category', data: sorted.map(f => f.name_cn) },
+    series: [{
+      type: 'bar', data: sorted.map(f => ({
+        value: f.shap_value,
+        itemStyle: { color: f.shap_value >= 0 ? '#ef4444' : '#3b82f6' },
+      })),
+    }],
+  })
+}
+
 async function handleCompare() {
   compareLoading.value = true
   try {
@@ -593,4 +655,34 @@ onMounted(() => {
     grid-template-columns: 1fr;
   }
 }
+
+.shap-card {
+  margin-top: 20px;
+}
+
+.shap-dimension-badges {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 8px;
+  margin-bottom: 14px;
+}
+
+.shap-badge {
+  padding: 4px 12px;
+  border-radius: 999px;
+  font-size: 12px;
+  font-weight: 600;
+}
+
+.shap-badge-positive {
+  background: rgba(239, 68, 68, 0.1);
+  color: #ef4444;
+  border: 1px solid rgba(239, 68, 68, 0.2);
+}
+
+.shap-badge-negative {
+  background: rgba(59, 130, 246, 0.1);
+  color: #3b82f6;
+  border: 1px solid rgba(59, 130, 246, 0.2);
+}
 </style>