diff --git a/Model.py b/Model.py
new file mode 100644
index 0000000..997df33
--- /dev/null
+++ b/Model.py
@@ -0,0 +1,310 @@
+import pandas as pd
+import numpy as np
+import os
+from datetime import datetime
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler, MinMaxScaler
+from sklearn.metrics import roc_auc_score, accuracy_score
+from imblearn.over_sampling import SMOTE
+import warnings
+
+warnings.filterwarnings('ignore')
+
+# 路径配置
+script_dir = os.path.dirname(os.path.abspath(__file__))
+data_dir = os.path.join(script_dir, '../data')
+output_dir = os.path.join(script_dir, '../output')
+if not os.path.exists(output_dir):
+    os.makedirs(output_dir)
+
+# 模型参数配置（精简有效取值，提升训练速度）
+LR_PARAMS = {
+    'C': [0.1, 1, 5],
+    'penalty': ['l2'],
+    'solver': ['liblinear']
+}
+
+RF_PARAMS = {
+    'n_estimators': [100, 200],
+    'max_depth': [8, 15],
+    'min_samples_split': [5, 10],
+    'min_samples_leaf': [2, 4]
+}
+
+
+def load_data():
+    """加载并预处理订单数据"""
+    data_path = os.path.join(data_dir, 'erp_order.csv')
+    if not os.path.exists(data_path):
+        raise FileNotFoundError("请先运行数据生成脚本，确保data目录下存在erp_order.csv")
+
+    df = pd.read_csv(data_path, encoding='utf-8-sig')
+
+    # 时间字段转换
+    df['order_time'] = pd.to_datetime(df['order_time'])
+
+    # 提取品牌和商品类型
+    if 'brand' not in df.columns:
+        df['brand'] = df['product_name'].str.split(' ').str[0]
+    df['product_type'] = df['product_name'].str.split(' ').str[1].str.replace('（.*', '', regex=True)
+
+    print(f"原始订单数据量：{len(df)} 条")
+    print(f"唯一用户数：{df['full_channel_user_id'].nunique()} 个")
+    return df
+
+
+def build_user_features(df):
+    """构建用户特征与复购目标变量"""
+    end_date = datetime(2024, 12, 31)
+
+    # 预处理折扣率（避免循环内重复计算）
+    df['discount_rate'] = (df['original_price'] - df['paid_amount']) / df['original_price']
+    df['discount_rate'] = df['discount_rate'].fillna(0).clip(0, 1)  # 处理异常值
+
+    # 按用户聚合基础特征
+    user_agg = df.groupby('full_channel_user_id').agg({
+        'order_time': ['count', lambda x: (end_date - x.max()).days],
+        'paid_amount': 'sum',
+        'product_type': 'nunique',
+        'brand': 'nunique',
+        'unit_price': lambda x: (x >= 1000).sum() / len(x),
+        'discount_rate': 'mean',
+        'quantity': 'sum',
+        'platform': 'nunique'
+    }).reset_index()
+
+    # 重命名列
+    user_agg.columns = [
+        'full_channel_user_id', 'purchase_count', 'last_purchase_days',
+        'total_paid', 'product_type_count', 'brand_count', 'high_price_ratio',
+        'discount_sensitivity', 'total_quantity', 'platform_count'
+    ]
+
+    # 计算派生特征
+    user_agg['avg_consumption'] = user_agg['total_paid'] / user_agg['purchase_count']
+    user_agg['avg_purchase_quantity'] = user_agg['total_quantity'] / user_agg['purchase_count']
+    user_agg['is_repurchase'] = (user_agg['purchase_count'] >= 2).astype(int)
+
+    # 计算平均购买间隔
+    repeat_users = user_agg[user_agg['is_repurchase'] == 1]['full_channel_user_id'].tolist()
+    interval_list = []
+    for user_id in repeat_users:
+        order_times = df[df['full_channel_user_id'] == user_id].sort_values('order_time')['order_time'].tolist()
+        intervals = [(order_times[i] - order_times[i - 1]).days for i in range(1, len(order_times))]
+        interval_list.extend(intervals)
+    median_interval = np.median(interval_list) if interval_list else 30
+
+    # 填充间隔值
+    def calc_interval(user_id):
+        if user_id not in repeat_users:
+            return median_interval
+        order_times = df[df['full_channel_user_id'] == user_id].sort_values('order_time')['order_time'].tolist()
+        intervals = [(order_times[i] - order_times[i - 1]).days for i in range(1, len(order_times))]
+        return np.mean(intervals) if intervals else median_interval
+
+    user_agg['avg_purchase_interval'] = user_agg['full_channel_user_id'].apply(calc_interval)
+
+    # 筛选特征列
+    feature_cols = [
+        'full_channel_user_id', 'is_repurchase', 'purchase_count', 'avg_consumption',
+        'product_type_count', 'brand_count', 'discount_sensitivity', 'last_purchase_days',
+        'avg_purchase_interval', 'avg_purchase_quantity', 'platform_count', 'high_price_ratio'
+    ]
+    feature_df = user_agg[feature_cols].copy()
+
+    print(f"特征构建完成，样本数：{len(feature_df)}")
+    print(f"复购用户占比：{feature_df['is_repurchase'].mean():.2%}")
+    return feature_df
+
+
+def prepare_train_data(feature_df):
+    """数据划分、平衡与特征缩放"""
+    # 分离特征和目标变量
+    X = feature_df.drop(['full_channel_user_id', 'is_repurchase'], axis=1)
+    y = feature_df['is_repurchase']
+    feature_names = X.columns.tolist()
+
+    # 划分训练集和测试集（7:3分层抽样）
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=0.3, random_state=42, stratify=y
+    )
+
+    # SMOTE过采样平衡训练集
+    smote_k = min(5, sum(y_train) - 1) if sum(y_train) > 1 else 1
+    smote = SMOTE(random_state=42, k_neighbors=smote_k)
+    X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)
+    print(f"训练集平衡前复购占比：{y_train.mean():.2%}")
+    print(f"训练集平衡后复购占比：{y_train_balanced.mean():.2%}")
+
+    # 特征缩放：标准化+归一化
+    standard_cols = ['discount_sensitivity', 'high_price_ratio', 'avg_consumption']
+    minmax_cols = ['last_purchase_days', 'avg_purchase_interval', 'purchase_count']
+    other_cols = [col for col in feature_names if col not in standard_cols + minmax_cols]
+
+    scaler_standard = StandardScaler()
+    scaler_minmax = MinMaxScaler()
+
+    # 训练集缩放
+    X_train_std = scaler_standard.fit_transform(X_train_balanced[standard_cols])
+    X_train_mm = scaler_minmax.fit_transform(X_train_balanced[minmax_cols])
+    X_train_other = X_train_balanced[other_cols].values
+    X_train_proc = np.hstack([X_train_std, X_train_mm, X_train_other])
+
+    # 测试集缩放（复用训练集缩放器）
+    X_test_std = scaler_standard.transform(X_test[standard_cols])
+    X_test_mm = scaler_minmax.transform(X_test[minmax_cols])
+    X_test_other = X_test[other_cols].values
+    X_test_proc = np.hstack([X_test_std, X_test_mm, X_test_other])
+
+    processed_feature_names = standard_cols + minmax_cols + other_cols
+    return (X_train_proc, X_test_proc, y_train_balanced, y_test,
+            processed_feature_names, scaler_standard, scaler_minmax)
+
+
+def train_models(X_train, y_train, X_test, y_test, feature_names):
+    """训练逻辑回归和随机森林模型"""
+    # 逻辑回归训练
+    lr = LogisticRegression(random_state=42, max_iter=1000, n_jobs=-1)
+    lr_grid = GridSearchCV(lr, LR_PARAMS, cv=3, scoring='roc_auc', n_jobs=-1, verbose=1)
+    lr_grid.fit(X_train, y_train)
+    lr_best = lr_grid.best_estimator_
+
+    # 逻辑回归评估
+    y_prob_lr = lr_best.predict_proba(X_test)[:, 1]
+    lr_auc = roc_auc_score(y_test, y_prob_lr)
+    lr_acc = accuracy_score(y_test, lr_best.predict(X_test))
+
+    # 特征权重
+    feature_weights = pd.DataFrame({
+        'feature': feature_names,
+        'weight': lr_best.coef_[0]
+    }).sort_values('weight', ascending=False)
+
+    # 随机森林训练（n_jobs在模型初始化时设置，不在网格搜索参数中）
+    rf = RandomForestClassifier(random_state=42, class_weight='balanced', n_jobs=-1)
+    rf_grid = GridSearchCV(rf, RF_PARAMS, cv=3, scoring='roc_auc', n_jobs=-1, verbose=1)
+    rf_grid.fit(X_train, y_train)
+    rf_best = rf_grid.best_estimator_
+
+    # 随机森林评估
+    y_prob_rf = rf_best.predict_proba(X_test)[:, 1]
+    rf_auc = roc_auc_score(y_test, y_prob_rf)
+    rf_acc = accuracy_score(y_test, rf_best.predict(X_test))
+
+    # 特征重要性
+    feature_importance = pd.DataFrame({
+        'feature': feature_names,
+        'importance': rf_best.feature_importances_
+    }).sort_values('importance', ascending=False)
+
+    # 输出模型对比结果
+    print("\n" + "=" * 50)
+    print("模型性能对比")
+    print("=" * 50)
+    print(f"逻辑回归 - AUC: {lr_auc:.4f}, 准确率: {lr_acc:.4f}")
+    print(f"随机森林 - AUC: {rf_auc:.4f}, 准确率: {rf_acc:.4f}")
+    print("\n逻辑回归Top3特征权重：")
+    print(feature_weights.head(3))
+    print("\n随机森林Top3特征重要性：")
+    print(feature_importance.head(3))
+
+    return {
+        'lr_model': lr_best, 'rf_model': rf_best,
+        'lr_metrics': {'auc': lr_auc, 'accuracy': lr_acc},
+        'rf_metrics': {'auc': rf_auc, 'accuracy': rf_acc},
+        'feature_weights': feature_weights, 'feature_importance': feature_importance
+    }
+
+
+def predict_repurchase(model, feature_df, scaler_std, scaler_mm, feature_names):
+    """预测用户复购概率并输出结果"""
+    X = feature_df.drop(['full_channel_user_id', 'is_repurchase'], axis=1)
+
+    # 特征缩放
+    standard_cols = ['discount_sensitivity', 'high_price_ratio', 'avg_consumption']
+    minmax_cols = ['last_purchase_days', 'avg_purchase_interval', 'purchase_count']
+    other_cols = [col for col in feature_names if col not in standard_cols + minmax_cols]
+
+    X_std = scaler_std.transform(X[standard_cols])
+    X_mm = scaler_mm.transform(X[minmax_cols])
+    X_other = X[other_cols].values
+    X_proc = np.hstack([X_std, X_mm, X_other])
+
+    # 预测复购概率
+    repurchase_prob = model.predict_proba(X_proc)[:, 1]
+
+    # 构建结果表
+    result_df = feature_df[['full_channel_user_id', 'is_repurchase']].copy()
+    result_df['repurchase_probability'] = repurchase_prob
+
+    # 划分用户层级
+    def get_user_level(prob):
+        if prob >= 0.6:
+            return '高复购概率'
+        elif 0.3 <= prob < 0.6:
+            return '中复购概率'
+        else:
+            return '低复购概率'
+
+    result_df['user_level'] = result_df['repurchase_probability'].apply(get_user_level)
+
+    # 统计结果
+    level_stats = result_df['user_level'].value_counts()
+    print("\n" + "=" * 50)
+    print("用户复购概率层级分布")
+    print("=" * 50)
+    print(level_stats)
+    print(f"\n各层级占比：")
+    for level, count in level_stats.items():
+        print(f"{level}: {count / len(result_df):.2%}")
+
+    # 保存结果
+    result_path = os.path.join(output_dir, 'repurchase_prediction_result.csv')
+    result_df.to_csv(result_path, index=False, encoding='utf-8-sig')
+    print(f"\n复购预测结果已保存至：{result_path}")
+
+    return result_df
+
+
+def main():
+    print("=" * 50)
+    print("复购预测模型训练")
+    print("=" * 50)
+
+    # 1. 加载数据
+    print("\n1. 加载订单数据...")
+    df = load_data()
+
+    # 2. 构建特征
+    print("\n2. 构建用户特征...")
+    feature_df = build_user_features(df)
+
+    # 3. 准备训练数据
+    print("\n3. 数据预处理...")
+    (X_train, X_test, y_train, y_test,
+     feature_names, scaler_std, scaler_mm) = prepare_train_data(feature_df)
+
+    # 4. 训练模型
+    print("\n4. 模型训练...")
+    model_results = train_models(X_train, y_train, X_test, y_test, feature_names)
+
+    # 5. 复购预测
+    print("\n5. 复购概率预测...")
+    result_df = predict_repurchase(
+        model=model_results['rf_model'],
+        feature_df=feature_df,
+        scaler_std=scaler_std,
+        scaler_mm=scaler_mm,
+        feature_names=feature_names
+    )
+
+    print("\n" + "=" * 50)
+    print("模型训练完成！")
+    print("=" * 50)
+
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/data_generator.py b/data_generator.py
new file mode 100644
index 0000000..78e8ca3
--- /dev/null
+++ b/data_generator.py
@@ -0,0 +1,258 @@
+import random
+import pandas as pd
+from faker import Faker
+from datetime import datetime, timedelta
+import os
+import sys
+
+script_dir = os.path.dirname(os.path.abspath(__file__))
+data_dir = os.path.join(script_dir, '../data')
+os.makedirs(data_dir, exist_ok=True)
+fake = Faker('zh_CN')
+# 配置常量
+ORDER_COUNT = 5000
+START_DATE = datetime(2024, 1, 1)
+END_DATE = datetime(2024, 12, 31)
+
+# -------------------------- 业务数据字典 --------------------------
+BRANDS = ['耐克', '阿迪达斯', '李宁', '安踏', '优衣库', 'ZARA', 'H&M', '森马', '以纯', '美特斯邦威']
+PRODUCT_TYPES = ['T恤', '衬衫', '卫衣', '外套', '牛仔裤', '休闲裤', '运动裤', '连衣裙', '毛衣', '风衣']
+COLORS = ['黑色', '白色', '灰色', '蓝色', '红色', '绿色', '黄色', '紫色', '粉色', '卡其色']
+SPECS = ['S', 'M', 'L', 'XL', 'XXL', 'XXXL', '均码']
+SKU_PREFIXES = ['NK', 'AD', 'LN', 'AN', 'UN', 'ZR', 'HM', 'SM', 'YC', 'MT']
+STORES = ['天猫旗舰店', '京东自营店', '抖音直播间', '拼多多专卖店', '线下直营店', '唯品会特卖店']
+PLATFORMS = ['天猫', '京东', '抖音电商', '拼多多', '线下', '唯品会']
+ORDER_STATUSES = ['已完成', '待发货', '已发货', '已取消', '退款中', '已退款']
+REFUND_STATUSES = ['无退款', '待审核', '已退款', '退款失败']
+IS_GIFT = ['否', '是']
+PROVINCE_CITY_MAP = {
+    '北京市': ['北京市'],
+    '上海市': ['上海市'],
+    '广东省': ['广州市', '深圳市', '佛山市', '东莞市'],
+    '江苏省': ['南京市', '苏州市', '无锡市', '常州市'],
+    '浙江省': ['杭州市', '宁波市', '温州市', '嘉兴市'],
+    '山东省': ['济南市', '青岛市', '烟台市', '潍坊市'],
+    '四川省': ['成都市', '绵阳市', '德阳市', '宜宾市'],
+    '湖北省': ['武汉市', '宜昌市', '襄阳市', '荆州市'],
+    '湖南省': ['长沙市', '株洲市', '湘潭市', '衡阳市'],
+    '河南省': ['郑州市', '洛阳市', '开封市', '新乡市']
+}
+
+
+def generate_erp_order_data():
+    """生成更贴合实际的服装订单数据"""
+    data = []
+    provinces = list(PROVINCE_CITY_MAP.keys())
+    # 1. 月份权重（强化Q3月份权重）
+    month_weights = {
+        1: 0.7, 2: 0.8, 3: 0.9,  # Q1
+        4: 1.2, 5: 1.5, 6: 1.6,  # Q2（本土品牌增长）
+        7: 1.8, 8: 2.0, 9: 2.2,  # Q3（总销售额最高）
+        10: 1.5, 11: 1.9, 12: 1.3  # Q4
+    }
+
+    # 2. 日内权重
+    hour_weights = {
+        0: 0.1, 1: 0.1, 2: 0.1, 3: 0.1, 4: 0.1, 5: 0.2,
+        6: 0.3, 7: 0.5, 8: 1.2, 9: 1.5, 10: 1.2,
+        11: 0.8, 12: 1.0, 13: 0.9, 14: 0.8,
+        15: 0.7, 16: 0.6, 17: 0.8,
+        18: 1.8, 19: 2.0, 20: 2.2, 21: 1.8, 22: 1.2,
+        23: 0.5
+    }
+
+    # 3. 工作日/周末权重
+    weekday_weights = {
+        0: 0.2, 1: 0.2, 2: 0.2, 3: 0.2, 4: 0.3,  # 周一至周五
+        5: 3.0, 6: 3.5  # 周六、周日
+    }
+
+    # 4. 季节商品匹配
+    season_product_map = {
+        '春': ['卫衣', '衬衫', '休闲裤'],
+        '夏': ['T恤', '连衣裙', '运动裤'],
+        '秋': ['牛仔裤', '风衣', '毛衣'],
+        '冬': ['外套', '毛衣', '羽绒服']
+    }
+
+    # 5. 大促月金额加成
+    promotion_month = [5, 6, 11]
+
+    # 6. 品牌-季度权重映射（核心优化）
+    brand_quarter_weight = {
+        # 头部品牌（Q3权重最高，确保占比超60%）
+        '耐克': {1: 1.5, 2: 1.8, 3: 2.5, 4: 2.2},
+        '阿迪达斯': {1: 1.4, 2: 1.7, 3: 2.4, 4: 2.1},
+        '李宁': {1: 1.3, 2: 1.6, 3: 2.3, 4: 2.0},
+        # 本土品牌（Q2权重提升）
+        '森马': {1: 0.9, 2: 1.8, 3: 1.2, 4: 1.1},
+        '以纯': {1: 0.8, 2: 1.7, 3: 1.1, 4: 1.0},
+        # 其他品牌（权重降低）
+        '安踏': {1: 0.7, 2: 0.8, 3: 0.9, 4: 0.8},
+        '优衣库': {1: 0.6, 2: 0.7, 3: 0.8, 4: 0.7},
+        'ZARA': {1: 0.5, 2: 0.6, 3: 0.7, 4: 0.6},
+        'H&M': {1: 0.4, 2: 0.5, 3: 0.6, 4: 0.5},
+        '美特斯邦威': {1: 0.3, 2: 0.4, 3: 0.5, 4: 0.4}
+    }
+
+    for id in range(1, ORDER_COUNT + 1):
+        # 1. 时间信息
+        # 选月份
+        month = random.choices(
+            list(month_weights.keys()),
+            weights=list(month_weights.values())
+        )[0]
+        quarter = (month - 1) // 3 + 1  # 计算季度（1-4）
+
+        # 选日期
+        while True:
+            if month in [1, 3, 5, 7, 8, 10, 12]:
+                day = random.randint(1, 31)
+            elif month == 2:
+                day = random.randint(1, 28)
+            else:
+                day = random.randint(1, 30)
+
+            temp_date = datetime(2024, month, day)
+            weekday = temp_date.weekday()
+            if random.random() < weekday_weights[weekday]:
+                break
+
+        # 选小时
+        hour = random.choices(
+            list(hour_weights.keys()),
+            weights=list(hour_weights.values())
+        )[0]
+
+        order_time = datetime(2024, month, day, hour, random.randint(0, 59))
+        payment_date = order_time + timedelta(hours=random.randint(0, 24))
+        shipping_date = payment_date + timedelta(days=random.randint(1, 3))
+
+        # 2. 商品信息
+        if month in [3, 4, 5]:
+            season = '春'
+        elif month in [6, 7, 8]:
+            season = '夏'
+        elif month in [9, 10, 11]:
+            season = '秋'
+        else:
+            season = '冬'
+
+        product_type = random.choice(season_product_map[season] + PRODUCT_TYPES[:3])
+
+        # 按品牌-季度权重选择品牌
+        brand = random.choices(
+            BRANDS,
+            weights=[brand_quarter_weight[b][quarter] for b in BRANDS]
+        )[0]
+
+        color = random.choice(COLORS)
+        spec = random.choice(SPECS)
+        product_name = f'{brand} {product_type} ({color}/{spec})'
+        color_and_spec = f'{color}/{spec}'
+        spu = f'SPU-{brand[:2].upper()}-{product_type[:2].upper()}'
+        sku = f'{random.choice(SKU_PREFIXES)}-{random.randint(1000, 9999)}'
+
+        # 3. 金额信息
+        # 3月低价商品逻辑
+        if month == 3:
+            unit_price = round(random.uniform(59.00, 199.00), 2)
+        elif month in promotion_month:
+            unit_price = round(random.uniform(199.00, 2499.00), 2)
+        else:
+            unit_price = round(random.uniform(99.00, 1999.00), 2)
+
+        # 头部品牌单价更高
+        if brand in ['耐克', '阿迪达斯', '李宁']:
+            unit_price *= random.uniform(1.2, 1.5)
+        unit_price = round(unit_price, 2)
+
+        quantity = random.randint(1, 5)
+        product_amount = round(unit_price * quantity, 2)
+
+        if month in promotion_month:
+            original_price = round(product_amount * random.uniform(1.5, 2.0), 2)
+        else:
+            original_price = round(product_amount * random.uniform(1.05, 1.3), 2)
+
+        payable_amount = original_price
+
+        if month in promotion_month:
+            paid_amount = payable_amount if random.random() > 0.05 else round(
+                random.uniform(product_amount * 0.5, payable_amount), 2)
+        else:
+            paid_amount = payable_amount if random.random() > 0.1 else round(
+                random.uniform(product_amount * 0.5, payable_amount), 2)
+
+        # 4.其他信息
+        internal_order_number = f'ORD-{random.randint(10000000, 99999999)}'
+        online_order_number = f'ONL-{random.randint(1000000000, 9999999999)}'
+        store_name = random.choice(STORES)
+        platform = random.choice(PLATFORMS)
+        full_channel_user_id = f'USER-{random.randint(10000, 99999)}'
+
+        if paid_amount == payable_amount:
+            status = random.choice(['已完成', '已发货', '待发货']) if month in promotion_month else random.choice(
+                ['已完成', '已发货', '待发货', '已取消'])
+        else:
+            status = random.choice(['退款中', '已取消'])
+
+        consignee = fake.name()
+        province = random.choice(provinces)
+        city = random.choice(PROVINCE_CITY_MAP[province])
+        sub_order_number = f'SUB-{internal_order_number.split("-")[1]}-{random.randint(100, 999)}'
+        online_sub_order_number = f'SONL-{online_order_number.split("-")[1]}-{random.randint(100, 999)}'
+        original_online_order_number = online_order_number
+        refund_status = random.choice(REFUND_STATUSES)
+        registered_quantity = quantity
+        actual_refund_quantity = quantity if refund_status == '已退款' else 0
+        sub_order_status = status
+        is_gift = random.choice(IS_GIFT) if random.random() > 0.9 else '否'
+
+        # 组装数据
+        data.append([
+            id, internal_order_number, online_order_number, store_name, full_channel_user_id,
+            shipping_date.strftime('%Y-%m-%d %H:%M:%S'), payment_date.strftime('%Y-%m-%d %H:%M:%S'),
+            payable_amount, paid_amount, status, consignee, spu, order_time.strftime('%Y-%m-%d %H:%M:%S'),
+            province, city, platform, sub_order_number, online_sub_order_number, original_online_order_number,
+            sku, quantity, unit_price, product_name, color_and_spec, product_amount, original_price,
+            is_gift, sub_order_status, refund_status, registered_quantity, actual_refund_quantity
+        ])
+
+    columns = [
+        'id', 'internal_order_number', 'online_order_number', 'store_name', 'full_channel_user_id',
+        'shipping_date', 'payment_date', 'payable_amount', 'paid_amount', 'status',
+        'consignee', 'spu', 'order_time', 'province', 'city', 'platform',
+        'sub_order_number', 'online_sub_order_number', 'original_online_order_number',
+        'sku', 'quantity', 'unit_price', 'product_name', 'color_and_spec',
+        'product_amount', 'original_price', 'is_gift', 'sub_order_status',
+        'refund_status', 'registered_quantity', 'actual_refund_quantity'
+    ]
+    df = pd.DataFrame(data, columns=columns)
+    return df
+
+
+def save_data(df):
+    csv_path = os.path.join(data_dir, 'erp_order.csv')
+    df.to_csv(csv_path, index=False, encoding='utf-8-sig')
+    print(f"CSV文件已保存至: {csv_path}")
+    excel_path = os.path.join(data_dir, 'erp_order.xlsx')
+    try:
+        df.to_excel(excel_path, index=False, engine='openpyxl')
+        print(f"Excel文件已保存至: {excel_path}")
+    except ImportError:
+        print(f"\n提示：未安装 openpyxl，跳过 Excel 保存。")
+
+
+
+
+def main():
+    print("开始生成erp_order服装订单数据...")
+    print(f"生成数量：{ORDER_COUNT} 条")
+    erp_order_df = generate_erp_order_data()
+    erp_order_df['brand'] = erp_order_df['product_name'].str.split(' ').str[0]
+    save_data(erp_order_df)
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/visualization_basic.py b/visualization_basic.py
new file mode 100644
index 0000000..287058f
--- /dev/null
+++ b/visualization_basic.py
@@ -0,0 +1,1146 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+from datetime import datetime
+import os
+
+# -------------------------- 基础配置 --------------------------
+# 设置中文字体（兼容Windows/Mac）
+plt.rcParams['font.sans-serif'] = ['Microsoft YaHei', 'SimHei', 'Arial Unicode MS', 'DejaVu Sans']
+plt.rcParams['axes.unicode_minus'] = False
+
+# 创建输出目录（绝对路径，避免报错）
+script_dir = os.path.dirname(os.path.abspath(__file__))
+figures_dir = os.path.join(script_dir, '../figures')
+os.makedirs(figures_dir, exist_ok=True)
+
+# 加载数据（仅erp_order单表）
+data_path = os.path.join(script_dir, '../data/erp_order.csv')
+erp_df = pd.read_csv(data_path, encoding='utf-8-sig')
+
+# -------------------------- 数据预处理（挖掘多维度特征）--------------------------
+# 1. 时间维度扩展
+erp_df['order_time'] = pd.to_datetime(erp_df['order_time'])
+erp_df['payment_date'] = pd.to_datetime(erp_df['payment_date'])
+erp_df['shipping_date'] = pd.to_datetime(erp_df['shipping_date'])
+erp_df['month'] = erp_df['order_time'].dt.strftime('%Y-%m')  # 年月
+erp_df['week'] = erp_df['order_time'].dt.strftime('%Y-W%U')  # 年周
+erp_df['day'] = erp_df['order_time'].dt.day  # 日
+erp_df['hour'] = erp_df['order_time'].dt.hour  # 小时
+erp_df['quarter'] = erp_df['order_time'].dt.quarter  # 季度
+erp_df['is_weekend'] = erp_df['order_time'].dt.weekday >= 5  # 是否周末
+
+# 2. 商品维度拆分
+erp_df['brand'] = erp_df['product_name'].str.split(' ').str[0]  # 品牌（耐克/阿迪等）
+erp_df['product_type'] = erp_df['product_name'].str.split(' ').str[1].str.strip('（')  # 商品类型（T恤/外套等）
+erp_df['color'] = erp_df['color_and_spec'].str.split('/').str[0]  # 颜色
+erp_df['spec'] = erp_df['color_and_spec'].str.split('/').str[1]  # 规格（S/M/L等）
+
+# 3. 价格维度分组
+erp_df['price_range'] = pd.cut(
+    erp_df['unit_price'],
+    bins=[0, 299, 599, 999, 1999],
+    labels=['低价(0-299元)', '中低价(300-599元)', '中高价(600-999元)', '高价(1000元+)']
+)
+
+# 4. 业务指标计算
+erp_df['payment_duration'] = (erp_df['payment_date'] - erp_df['order_time']).dt.total_seconds() / 3600  # 付款时长（小时）
+erp_df['shipping_duration'] = (erp_df['shipping_date'] - erp_df['payment_date']).dt.total_seconds() / 86400  # 发货时长（天）
+erp_df['discount_rate'] = (1 - erp_df['product_amount'] / erp_df['original_price']).round(2) * 100  # 折扣率（%）
+
+# 5. 区域维度映射
+region_mapping = {
+    '北京市': '华北', '上海市': '华东',
+    '广东省': '华南', '江苏省': '华东', '浙江省': '华东',
+    '山东省': '华东', '四川省': '西南', '湖北省': '华中',
+    '湖南省': '华中', '河南省': '华中'
+}
+erp_df['region'] = erp_df['province'].map(region_mapping)
+
+# 6. 用户复购标识
+user_purchase_count = erp_df.groupby('full_channel_user_id', observed=True).size().reset_index(
+    name='purchase_count')  # 修复警告：添加observed=True
+erp_df = pd.merge(erp_df, user_purchase_count, on='full_channel_user_id', how='left')
+erp_df['is_repurchase'] = erp_df['purchase_count'] >= 2  # 是否复购用户
+
+# 统一配色（贴合商业图表风格）
+colors_mpl = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
+              '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']
+
+# 过滤异常值（确保图表可读性）
+erp_df = erp_df[erp_df['payment_duration'] <= 24]  # 过滤付款时长>24小时的异常值
+erp_df = erp_df[erp_df['shipping_duration'] >= 0]  # 过滤发货时长为负的异常值
+
+
+# ------------------------------
+# 一、趋势类图表（1-5张：时间维度分析）
+# ------------------------------
+# 01-月度销售额+订单量双轴趋势图
+def plot_01_monthly_sales_order_trend():
+    monthly_data = erp_df.groupby('month').agg({
+        'product_amount': 'sum',
+        'id': 'count'
+    }).reset_index()
+    monthly_data['month'] = pd.to_datetime(monthly_data['month'])
+    monthly_data = monthly_data.sort_values('month')
+    monthly_data['month_str'] = monthly_data['month'].dt.strftime('%Y-%m')
+
+    fig, ax1 = plt.subplots(figsize=(14, 7))
+    # 销售额折线
+    ax1.plot(monthly_data['month_str'], monthly_data['product_amount'],
+             color='#1f77b4', linewidth=3, marker='o', markersize=6, label='销售额')
+    ax1.set_xlabel('月份', fontsize=12)
+    ax1.set_ylabel('销售额（元）', fontsize=12, color='#1f77b4')
+    ax1.tick_params(axis='y', labelcolor='#1f77b4')
+    ax1.set_xticks(range(0, len(monthly_data['month_str']), 1))
+    ax1.set_xticklabels(monthly_data['month_str'], rotation=45)
+    ax1.grid(True, alpha=0.3)
+
+    # 订单量柱状图
+    ax2 = ax1.twinx()
+    ax2.bar(monthly_data['month_str'], monthly_data['id'],
+            color='#ff7f0e', alpha=0.6, width=0.5, label='订单量')
+    ax2.set_ylabel('订单量（笔）', fontsize=12, color='#ff7f0e')
+    ax2.tick_params(axis='y', labelcolor='#ff7f0e')
+
+    # 合并图例
+    lines1, labels1 = ax1.get_legend_handles_labels()
+    lines2, labels2 = ax2.get_legend_handles_labels()
+    ax1.legend(lines1 + lines2, labels1 + labels2, loc='upper left')
+
+    plt.title('2024年月度销售额与订单量趋势', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '01-月度销售额订单量双轴图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 02-季度销售额堆叠柱状图
+def plot_02_quarterly_brand_sales():
+    quarterly_brand = erp_df.groupby(['quarter', 'brand'], observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    quarterly_pivot = quarterly_brand.pivot(index='quarter', columns='brand', values='product_amount').fillna(0)
+
+    fig, ax = plt.subplots(figsize=(12, 7))
+    quarterly_pivot.plot(kind='bar', stacked=True, color=colors_mpl[:len(quarterly_pivot.columns)],
+                         alpha=0.8, ax=ax)
+    ax.set_xlabel('季度', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('2024年各季度品牌销售额堆叠趋势', fontsize=14, fontweight='bold', pad=20)
+    ax.legend(title='品牌', bbox_to_anchor=(1.05, 1), loc='upper left')
+    ax.grid(axis='y', alpha=0.3)
+    ax.set_xticklabels(['Q1', 'Q2', 'Q3', 'Q4'], rotation=0)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '02-季度品牌销售额堆叠图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 03-周度销售额趋势图（替换Pyecharts为Matplotlib）
+def plot_03_weekly_sales_trend():
+    weekly_sales = erp_df.groupby('week', observed=True)['product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    weekly_sales['week'] = pd.to_datetime(weekly_sales['week'] + '-1', format='%Y-W%U-%w')
+    weekly_sales = weekly_sales.sort_values('week')
+    weekly_sales['week_str'] = weekly_sales['week'].dt.strftime('%Y-W%U')
+
+    fig, ax = plt.subplots(figsize=(14, 7))
+    ax.plot(weekly_sales['week_str'], weekly_sales['product_amount'],
+            color='#1f77b4', linewidth=2, marker='o', markersize=4)
+    # 添加最大值、最小值、平均值标注
+    max_idx = weekly_sales['product_amount'].idxmax()
+    min_idx = weekly_sales['product_amount'].idxmin()
+    avg_val = weekly_sales['product_amount'].mean()
+    ax.axhline(y=avg_val, color='red', linestyle='--', alpha=0.7, label=f'平均值：{avg_val:.0f}元')
+    ax.scatter(weekly_sales.loc[max_idx, 'week_str'], weekly_sales.loc[max_idx, 'product_amount'],
+               color='red', s=100, zorder=5)
+    ax.scatter(weekly_sales.loc[min_idx, 'week_str'], weekly_sales.loc[min_idx, 'product_amount'],
+               color='green', s=100, zorder=5)
+    ax.annotate(f'最大值：{weekly_sales.loc[max_idx, "product_amount"]:.0f}元',
+                xy=(weekly_sales.loc[max_idx, 'week_str'], weekly_sales.loc[max_idx, 'product_amount']),
+                xytext=(10, 10), textcoords='offset points', fontsize=10,
+                bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7))
+    ax.annotate(f'最小值：{weekly_sales.loc[min_idx, "product_amount"]:.0f}元',
+                xy=(weekly_sales.loc[min_idx, 'week_str'], weekly_sales.loc[min_idx, 'product_amount']),
+                xytext=(10, -20), textcoords='offset points', fontsize=10,
+                bbox=dict(boxstyle='round,pad=0.3', facecolor='lightgreen', alpha=0.7))
+
+    ax.set_xlabel('周度', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('2024年周度销售额趋势', fontsize=14, fontweight='bold', pad=20)
+    ax.set_xticks(range(0, len(weekly_sales['week_str']), 2))  # 每2周显示一个刻度
+    ax.set_xticklabels(weekly_sales['week_str'][::2], rotation=45)
+    ax.grid(True, alpha=0.3)
+    ax.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '03-周度销售额趋势图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 04-日内订单量分布折线图
+def plot_04_hourly_order_distribution():
+    hourly_order = erp_df.groupby('hour', observed=True).size().reset_index(name='order_count')  # 修复警告：添加observed=True
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(hourly_order['hour'], hourly_order['order_count'],
+            color='#2ca02c', linewidth=3, marker='s', markersize=5)
+    ax.fill_between(hourly_order['hour'], hourly_order['order_count'], alpha=0.3, color='#2ca02c')
+    # 标注峰值
+    peak_hour = hourly_order.loc[hourly_order['order_count'].idxmax(), 'hour']
+    peak_count = hourly_order['order_count'].max()
+    ax.annotate(f'峰值：{peak_hour}时({peak_count}笔)',
+                xy=(peak_hour, peak_count), xytext=(peak_hour + 1, peak_count + 5),
+                arrowprops=dict(arrowstyle='->', color='red'), fontsize=11, fontweight='bold')
+
+    ax.set_xlabel('小时', fontsize=12)
+    ax.set_ylabel('订单量（笔）', fontsize=12)
+    ax.set_title('日内订单量分布（24小时）', fontsize=14, fontweight='bold', pad=20)
+    ax.set_xticks(range(0, 24, 2))
+    ax.grid(True, alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '04-日内订单量分布.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 05-周末vs工作日销售额对比图
+def plot_05_weekend_weekday_comparison():
+    weekend_data = erp_df.groupby('is_weekend', observed=True).agg({
+        'product_amount': 'sum',
+        'id': 'count'
+    }).reset_index()
+    weekend_data['is_weekend'] = weekend_data['is_weekend'].map({True: '周末', False: '工作日'})
+
+    fig, ax1 = plt.subplots(figsize=(10, 6))
+    # 销售额柱状图
+    bars = ax1.bar(weekend_data['is_weekend'], weekend_data['product_amount'],
+                   color=['#ff7f0e', '#1f77b4'], alpha=0.8, width=0.5)
+    ax1.set_xlabel('日期类型', fontsize=12)
+    ax1.set_ylabel('销售额（元）', fontsize=12, color='black')
+    for bar in bars:
+        height = bar.get_height()
+        ax1.text(bar.get_x() + bar.get_width() / 2., height + height * 0.01,
+                 f'{int(height)}', ha='center', va='bottom', fontsize=11)
+
+    # 订单量折线
+    ax2 = ax1.twinx()
+    # 核心调整：限制订单量y轴的最大值，避免数值超出范围
+    ax2.set_ylim(0, weekend_data['id'].max() * 1.2)  # 留出1.2倍的空间放标注
+    line = ax2.plot(weekend_data['is_weekend'], weekend_data['id'],
+             color='#d62728', linewidth=3, marker='o', markersize=8)
+    ax2.set_ylabel('订单量（笔）', fontsize=12, color='#d62728')
+    ax2.tick_params(axis='y', labelcolor='#d62728')
+    # 标注订单量数值（调整位置：从“v+5”改为“v * 0.95”，避免超出顶部）
+    for i, v in enumerate(weekend_data['id']):
+        ax2.text(i, v * 0.95, f'{v}笔', ha='center', va='top', fontsize=11, color='#d62728')
+
+    plt.title('周末vs工作日销售额与订单量对比', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '05-周末工作日对比图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# ------------------------------
+# 二、商品分析类图表（6-12张：品牌/类型/属性分析）
+# ------------------------------
+# 06-品牌销售额TOP10柱状图（替换Pyecharts为Matplotlib）
+def plot_06_brand_sales_top10():
+    brand_sales = erp_df.groupby('brand', observed=True)['product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    brand_sales_top10 = brand_sales.sort_values('product_amount', ascending=False).head(10)
+
+    fig, ax = plt.subplots(figsize=(12, 7))
+    bars = ax.bar(brand_sales_top10['brand'], brand_sales_top10['product_amount'],
+                  color=colors_mpl[:10], alpha=0.8)
+    ax.set_xlabel('品牌', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('品牌销售额TOP10', fontsize=14, fontweight='bold', pad=20)
+    ax.tick_params(axis='x', rotation=45)
+    ax.grid(axis='y', alpha=0.3)
+    # 添加数值标签和排名
+    for i, bar in enumerate(bars):
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width() / 2., height + height * 0.01,
+                f'第{i + 1}名\n{int(height)}', ha='center', va='bottom', fontsize=10)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '06-品牌销售额TOP10.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 07-商品类型销量饼图
+def plot_07_product_type_sales_pie():
+    type_sales = erp_df.groupby('product_type', observed=True)['quantity'].sum().reset_index()  # 修复警告：添加observed=True
+    type_sales = type_sales.sort_values('quantity', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(10, 10))
+    wedges, texts, autotexts = ax.pie(
+        type_sales['quantity'],
+        labels=type_sales['product_type'],
+        autopct='%1.1f%%',
+        colors=colors_mpl[:len(type_sales)],
+        startangle=90,
+        wedgeprops=dict(width=0.6, edgecolor='white'),
+        textprops=dict(fontsize=11)
+    )
+    # 美化文本
+    for autotext in autotexts:
+        autotext.set_color('black')
+        autotext.set_fontsize(12)
+        autotext.set_fontweight('bold')
+    for text in texts:
+        text.set_fontsize(12)
+        text.set_fontweight('bold')
+
+    ax.set_title('各商品类型销量占比', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '07-商品类型销量饼图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 08-颜色销量分布柱状图
+def plot_08_color_sales_distribution():
+    color_sales = erp_df.groupby('color', observed=True)['quantity'].sum().reset_index()  # 修复警告：添加observed=True
+    color_sales = color_sales.sort_values('quantity', ascending=False)
+
+    # 颜色映射（真实颜色）
+    color_map = {
+        '黑色': '#000000', '白色': '#ffffff', '灰色': '#808080', '蓝色': '#0000ff',
+        '红色': '#ff0000', '绿色': '#00ff00', '黄色': '#ffff00', '紫色': '#9932cc',
+        '粉色': '#ff69b4', '卡其色': '#d2b48c'
+    }
+    # 处理未匹配的颜色
+    color_map = {k: v for k, v in color_map.items() if k in color_sales['color'].values}
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    bars = ax.bar(color_sales['color'], color_sales['quantity'],
+                  color=[color_map.get(c, '#cccccc') for c in color_sales['color']],
+                  alpha=0.8, edgecolor='black', linewidth=1)
+
+    # 添加数值标签
+    for bar in bars:
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width() / 2., height + 2,
+                f'{int(height)}', ha='center', va='bottom', fontsize=11, fontweight='bold')
+
+    ax.set_xlabel('商品颜色', fontsize=12)
+    ax.set_ylabel('销量（件）', fontsize=12)
+    ax.set_title('各颜色服装销量分布', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '08-颜色销量分布.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 09-规格销量热力图
+def plot_09_spec_sales_heatmap():
+    spec_type_sales = erp_df.groupby(['product_type', 'spec'], observed=True)[
+        'quantity'].sum().reset_index()  # 修复警告：添加observed=True
+    spec_pivot = spec_type_sales.pivot(index='product_type', columns='spec', values='quantity').fillna(0)
+
+    fig, ax = plt.subplots(figsize=(12, 8))
+    sns.heatmap(
+        spec_pivot,
+        annot=True,
+        fmt='.0f',
+        cmap='YlOrRd',
+        linewidths=0.5,
+        cbar_kws={'label': '销量（件）'},
+        ax=ax
+    )
+    ax.set_xlabel('商品规格', fontsize=12)
+    ax.set_ylabel('商品类型', fontsize=12)
+    ax.set_title('各类型商品规格销量热力图', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '09-规格销量热力图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 10-价格区间销售额占比图（替换Pyecharts环形图为Matplotlib环形图）
+def plot_10_price_range_pie():
+    price_sales = erp_df.groupby('price_range', observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    price_sales['percentage'] = (price_sales['product_amount'] / price_sales['product_amount'].sum() * 100).round(1)
+
+    fig, ax = plt.subplots(figsize=(10, 8))
+    wedges, texts, autotexts = ax.pie(
+        price_sales['percentage'],
+        labels=price_sales['price_range'],
+        autopct='%1.1f%%',
+        colors=colors_mpl[:len(price_sales)],
+        startangle=90,
+        wedgeprops=dict(width=0.4, edgecolor='white'),  # 环形效果
+        textprops=dict(fontsize=11)
+    )
+    # 美化文本
+    for autotext in autotexts:
+        autotext.set_color('black')
+        autotext.set_fontsize(12)
+        autotext.set_fontweight('bold')
+    for text in texts:
+        text.set_fontsize(12)
+        text.set_fontweight('bold')
+
+    ax.set_title('各价格区间销售额占比', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '10-价格区间占比图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 11-品牌折扣率对比箱线图（修复警告：添加hue和legend=False）
+def plot_11_brand_discount_boxplot():
+    fig, ax = plt.subplots(figsize=(14, 7))
+    sns.boxplot(
+        data=erp_df,
+        x='brand',
+        y='discount_rate',
+        hue='brand',  # 添加hue参数，与x一致
+        palette=colors_mpl[:len(erp_df['brand'].unique())],
+        legend=False,  # 隐藏图例（避免重复）
+        ax=ax
+    )
+    # 添加平均值线
+    for i, brand in enumerate(erp_df['brand'].unique()):
+        brand_avg = erp_df[erp_df['brand'] == brand]['discount_rate'].mean()
+        ax.axhline(y=brand_avg, xmin=i / len(erp_df['brand'].unique()),
+                   xmax=(i + 1) / len(erp_df['brand'].unique()), color='red', linestyle='--', alpha=0.7)
+        ax.text(i, brand_avg + 1, f'均值：{brand_avg:.1f}%', ha='center', va='bottom', fontsize=9)
+
+    ax.set_xlabel('品牌', fontsize=12)
+    ax.set_ylabel('折扣率（%）', fontsize=12)
+    ax.set_title('各品牌折扣率分布对比', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    ax.tick_params(axis='x', rotation=45)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '11-品牌折扣率箱线图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 12-商品类型销售额对比图（替换Pyecharts TreeMap为Matplotlib柱状图）
+def plot_12_product_type_sales_bar():
+    type_sales = erp_df.groupby('product_type', observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    type_sales = type_sales.sort_values('product_amount', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(12, 7))
+    bars = ax.bar(type_sales['product_type'], type_sales['product_amount'],
+                  color=colors_mpl[:len(type_sales)], alpha=0.8)
+    ax.set_xlabel('商品类型', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('各商品类型销售额对比', fontsize=14, fontweight='bold', pad=20)
+    ax.tick_params(axis='x', rotation=45)
+    ax.grid(axis='y', alpha=0.3)
+    # 添加数值标签和占比
+    total_sales = type_sales['product_amount'].sum()
+    for bar in bars:
+        height = bar.get_height()
+        percentage = (height / total_sales) * 100
+        ax.text(bar.get_x() + bar.get_width() / 2., height + height * 0.01,
+                f'{int(height)}\n({percentage:.1f}%)', ha='center', va='bottom', fontsize=10)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '12-商品类型销售额对比图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# ------------------------------
+# 三、用户行为类图表（13-18张：复购/消费/支付分析）
+# ------------------------------
+# 13-用户复购率饼图（修复autopct格式错误）
+def plot_13_user_repurchase_pie():
+    repurchase_count = erp_df.groupby('is_repurchase', observed=True).size().reset_index(
+        name='count')  # 修复警告：添加observed=True
+    repurchase_count['is_repurchase'] = repurchase_count['is_repurchase'].map({True: '复购用户', False: '新用户'})
+    repurchase_rate = (repurchase_count[repurchase_count['is_repurchase'] == '复购用户']['count'].values[0] /
+                       repurchase_count['count'].sum()) * 100
+
+    fig, ax = plt.subplots(figsize=(8, 8))
+    wedges, texts, autotexts = ax.pie(
+        repurchase_count['count'],
+        labels=repurchase_count['is_repurchase'],
+        autopct='%1.1f%%',  # 仅保留百分比格式，复购率通过标题展示
+        colors=['#1f77b4', '#ff7f0e'],
+        startangle=90,
+        explode=(0.05, 0),
+        wedgeprops=dict(edgecolor='white')
+    )
+    for autotext in autotexts:
+        autotext.set_fontsize(12)
+        autotext.set_fontweight('bold')
+    # 标题中展示复购率
+    ax.set_title(f'用户复购率分布（复购率：{repurchase_rate:.1f}%）', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '13-用户复购率饼图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 14-用户购买次数分布直方图
+def plot_14_user_purchase_count_hist():
+    user_purchase = erp_df.groupby('full_channel_user_id', observed=True).size().reset_index(
+        name='purchase_count')  # 修复警告：添加observed=True
+    max_count = min(user_purchase['purchase_count'].max(), 10)  # 限制最大显示10次
+    user_purchase_filtered = user_purchase[user_purchase['purchase_count'] <= max_count]
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    sns.histplot(
+        data=user_purchase_filtered,
+        x='purchase_count',
+        bins=range(1, max_count + 2),
+        color='#2ca02c',
+        alpha=0.7,
+        discrete=True,
+        ax=ax
+    )
+    ax.set_xlabel('购买次数', fontsize=12)
+    ax.set_ylabel('用户数量（人）', fontsize=12)
+    ax.set_title('用户购买次数分布', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    ax.set_xticks(range(1, max_count + 1))
+    # 添加数值标签
+    for i in range(1, max_count + 1):
+        count = len(user_purchase_filtered[user_purchase_filtered['purchase_count'] == i])
+        ax.text(i, count + 2, f'{count}', ha='center', va='bottom', fontsize=10)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '14-用户购买次数直方图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 15-支付时长分布小提琴图（修复警告：添加hue和legend=False）
+def plot_15_payment_duration_violin():
+    fig, ax = plt.subplots(figsize=(12, 6))
+    sns.violinplot(
+        data=erp_df,
+        x='platform',
+        y='payment_duration',
+        hue='platform',  # 添加hue参数，与x一致
+        palette=colors_mpl[:len(erp_df['platform'].unique())],
+        inner='quartile',
+        legend=False,  # 隐藏图例
+        ax=ax
+    )
+    # 添加平均值点
+    for i, platform in enumerate(erp_df['platform'].unique()):
+        platform_avg = erp_df[erp_df['platform'] == platform]['payment_duration'].mean()
+        ax.scatter(i, platform_avg, color='red', s=50, zorder=5)
+        ax.text(i, platform_avg + 0.5, f'均值：{platform_avg:.1f}小时', ha='center', va='bottom', fontsize=9)
+
+    ax.set_xlabel('销售平台', fontsize=12)
+    ax.set_ylabel('支付时长（小时）', fontsize=12)
+    ax.set_title('各平台用户支付时长分布', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    ax.tick_params(axis='x', rotation=45)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '15-支付时长小提琴图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 16-购买数量与金额散点图
+def plot_16_quantity_amount_scatter():
+    fig, ax = plt.subplots(figsize=(12, 7))
+    scatter = ax.scatter(
+        erp_df['quantity'],
+        erp_df['product_amount'],
+        c=erp_df['unit_price'],
+        cmap='viridis',
+        alpha=0.7,
+        s=60,
+        edgecolor='black',
+        linewidth=0.5
+    )
+    # 添加颜色条
+    cbar = plt.colorbar(scatter)
+    cbar.set_label('商品单价（元）', fontsize=12)
+    # 添加趋势线
+    z = np.polyfit(erp_df['quantity'], erp_df['product_amount'], 1)
+    p = np.poly1d(z)
+    ax.plot(erp_df['quantity'], p(erp_df['quantity']), "r--", alpha=0.8, linewidth=2,
+            label=f'趋势线：y={z[0]:.1f}x+{z[1]:.1f}')
+
+    ax.set_xlabel('购买数量（件）', fontsize=12)
+    ax.set_ylabel('订单金额（元）', fontsize=12)
+    ax.set_title('购买数量与订单金额关系（颜色=单价）', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(True, alpha=0.3)
+    ax.legend()
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '16-购买数量金额散点图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 17-复购用户vs新用户消费对比
+def plot_17_repurchase_vs_new_consume():
+    user_consume = erp_df.groupby(['full_channel_user_id', 'is_repurchase'], observed=True).agg(
+        {  # 修复警告：添加observed=True
+            'product_amount': 'sum',
+            'quantity': 'sum'
+        }).reset_index()
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
+    # 总消费额对比
+    repurchase_consume = user_consume.groupby('is_repurchase', observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    repurchase_consume['is_repurchase'] = repurchase_consume['is_repurchase'].map({True: '复购用户', False: '新用户'})
+    bars1 = ax1.bar(repurchase_consume['is_repurchase'], repurchase_consume['product_amount'],
+                    color=['#1f77b4', '#ff7f0e'], alpha=0.8)
+    ax1.set_xlabel('用户类型', fontsize=12)
+    ax1.set_ylabel('总消费额（元）', fontsize=12)
+    ax1.set_title('复购用户vs新用户总消费额对比', fontsize=12, fontweight='bold')
+    ax1.grid(axis='y', alpha=0.3)
+    for i, v in enumerate(repurchase_consume['product_amount']):
+        ax1.text(i, v + v * 0.01, f'{int(v)}', ha='center', va='bottom', fontsize=11)
+
+    # 平均消费额对比
+    avg_consume = user_consume.groupby('is_repurchase', observed=True)[
+        'product_amount'].mean().reset_index()  # 修复警告：添加observed=True
+    avg_consume['is_repurchase'] = avg_consume['is_repurchase'].map({True: '复购用户', False: '新用户'})
+    bars2 = ax2.bar(avg_consume['is_repurchase'], avg_consume['product_amount'],
+                    color=['#2ca02c', '#d62728'], alpha=0.8)
+    ax2.set_xlabel('用户类型', fontsize=12)
+    ax2.set_ylabel('平均消费额（元）', fontsize=12)
+    ax2.set_title('复购用户vs新用户平均消费额对比', fontsize=12, fontweight='bold')
+    ax2.grid(axis='y', alpha=0.3)
+    for i, v in enumerate(avg_consume['product_amount']):
+        ax2.text(i, v + v * 0.01, f'{int(v)}', ha='center', va='bottom', fontsize=11)
+
+    plt.suptitle('复购用户与新用户消费行为对比', fontsize=14, fontweight='bold', y=0.95)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '17-复购新用户消费对比.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 18-用户购买频次行为对比图（替换Pyecharts雷达图为Matplotlib多子图）
+def plot_18_user_purchase_freq_comparison():
+    # 按购买次数分组统计
+    purchase_freq = erp_df.groupby('purchase_count', observed=True).agg({  # 修复警告：添加observed=True
+        'product_amount': 'mean',
+        'quantity': 'mean',
+        'discount_rate': 'mean'
+    }).reset_index()
+    purchase_freq['purchase_count'] = purchase_freq['purchase_count'].astype(str) + '次购买'
+    # 限制显示前5组
+    purchase_freq = purchase_freq.head(5)
+
+    fig, axes = plt.subplots(1, 3, figsize=(18, 6))
+    # 平均消费额
+    axes[0].bar(purchase_freq['purchase_count'], purchase_freq['product_amount'], color='#1f77b4', alpha=0.8)
+    axes[0].set_xlabel('购买频次', fontsize=11)
+    axes[0].set_ylabel('平均消费额（元）', fontsize=11)
+    axes[0].set_title('不同频次平均消费额', fontsize=12, fontweight='bold')
+    axes[0].tick_params(axis='x', rotation=45)
+    axes[0].grid(axis='y', alpha=0.3)
+    for i, v in enumerate(purchase_freq['product_amount']):
+        axes[0].text(i, v + 5, f'{int(v)}', ha='center', va='bottom', fontsize=9)
+
+    # 平均购买量
+    axes[1].bar(purchase_freq['purchase_count'], purchase_freq['quantity'], color='#ff7f0e', alpha=0.8)
+    axes[1].set_xlabel('购买频次', fontsize=11)
+    axes[1].set_ylabel('平均购买量（件）', fontsize=11)
+    axes[1].set_title('不同频次平均购买量', fontsize=12, fontweight='bold')
+    axes[1].tick_params(axis='x', rotation=45)
+    axes[1].grid(axis='y', alpha=0.3)
+    for i, v in enumerate(purchase_freq['quantity']):
+        axes[1].text(i, v + 0.1, f'{v:.1f}', ha='center', va='bottom', fontsize=9)
+
+    # 平均折扣率
+    axes[2].bar(purchase_freq['purchase_count'], purchase_freq['discount_rate'], color='#2ca02c', alpha=0.8)
+    axes[2].set_xlabel('购买频次', fontsize=11)
+    axes[2].set_ylabel('平均折扣率（%）', fontsize=11)
+    axes[2].set_title('不同频次平均折扣率', fontsize=12, fontweight='bold')
+    axes[2].tick_params(axis='x', rotation=45)
+    axes[2].grid(axis='y', alpha=0.3)
+    for i, v in enumerate(purchase_freq['discount_rate']):
+        axes[2].text(i, v + 0.5, f'{v:.1f}%', ha='center', va='bottom', fontsize=9)
+
+    plt.suptitle('不同购买频次用户行为对比', fontsize=14, fontweight='bold', y=0.95)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '18-用户购买频次行为对比图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# ------------------------------
+# 四、区域与渠道类图表（19-24张：区域/平台/门店分析）
+# ------------------------------
+# 19-省份销售额柱状图（替换Pyecharts地图为Matplotlib柱状图）
+def plot_19_province_sales_bar():
+    province_sales = erp_df.groupby('province', observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    province_sales = province_sales.sort_values('product_amount', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(14, 7))
+    bars = ax.bar(province_sales['province'], province_sales['product_amount'],
+                  color=colors_mpl[:len(province_sales)], alpha=0.8)
+    ax.set_xlabel('省份', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('各省份销售额分布', fontsize=14, fontweight='bold', pad=20)
+    ax.tick_params(axis='x', rotation=45)
+    ax.grid(axis='y', alpha=0.3)
+    # 添加数值标签
+    for bar in bars:
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width() / 2., height + height * 0.01,
+                f'{int(height)}', ha='center', va='bottom', fontsize=10)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '19-省份销售额分布.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 20-区域销售额柱状图
+def plot_20_region_sales_bar():
+    region_sales = erp_df.groupby('region', observed=True)['product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    region_sales = region_sales.sort_values('product_amount', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    bars = ax.bar(region_sales['region'], region_sales['product_amount'],
+                  color=colors_mpl[:len(region_sales)], alpha=0.8)
+    ax.set_xlabel('区域', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('各区域销售额对比', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    # 添加数值标签和占比
+    total_sales = region_sales['product_amount'].sum()
+    for bar in bars:
+        height = bar.get_height()
+        percentage = (height / total_sales) * 100
+        ax.text(bar.get_x() + bar.get_width() / 2., height + height * 0.01,
+                f'{int(height)}\n({percentage:.1f}%)', ha='center', va='bottom', fontsize=11)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '20-区域销售额柱状图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 21-城市销售额TOP15横向柱状图
+def plot_21_city_sales_top15():
+    city_sales = erp_df.groupby('city', observed=True)['product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    city_sales_top15 = city_sales.sort_values('product_amount', ascending=False).head(15)
+
+    fig, ax = plt.subplots(figsize=(12, 8))
+    bars = ax.barh(
+        city_sales_top15['city'][::-1],
+        city_sales_top15['product_amount'][::-1],
+        color='#1f77b4', alpha=0.8
+    )
+    ax.set_xlabel('销售额（元）', fontsize=12)
+    ax.set_ylabel('城市', fontsize=12)
+    ax.set_title('城市销售额TOP15', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='x', alpha=0.3)
+    # 添加数值标签
+    for bar in bars:
+        width = bar.get_width()
+        ax.text(width + width * 0.01, bar.get_y() + bar.get_height() / 2.,
+                f'{int(width)}', ha='left', va='center', fontsize=10)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '21-城市销售额TOP15.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 22-销售平台月度销售额趋势图（替换Pyecharts为Matplotlib多折线图）
+def plot_22_platform_monthly_sales():
+    platform_monthly = erp_df.groupby(['month', 'platform'], observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    platform_monthly['month'] = pd.to_datetime(platform_monthly['month'])
+    platform_monthly = platform_monthly.sort_values('month')
+    months = platform_monthly['month'].dt.strftime('%Y-%m').unique()
+
+    fig, ax = plt.subplots(figsize=(14, 7))
+    for i, platform in enumerate(platform_monthly['platform'].unique()):
+        platform_data = platform_monthly[platform_monthly['platform'] == platform]
+        # 补全缺失月份的销售额（填充0）
+        platform_sales = []
+        for month in months:
+            sales = platform_data[platform_data['month'].dt.strftime('%Y-%m') == month]['product_amount'].values
+            platform_sales.append(sales[0] if len(sales) > 0 else 0)
+
+        ax.plot(months, platform_sales, color=colors_mpl[i], linewidth=2, marker='o', markersize=4, label=platform)
+
+    ax.set_xlabel('月份', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('各销售平台月度销售额趋势', fontsize=14, fontweight='bold', pad=20)
+    ax.tick_params(axis='x', rotation=45)
+    ax.grid(True, alpha=0.3)
+    ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left')
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '22-平台月度销售额趋势.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 23-门店销售额TOP10柱状图（替换Pyecharts漏斗图为Matplotlib柱状图）
+def plot_23_store_sales_top10():
+    store_sales = erp_df.groupby('store_name', observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    store_sales_top10 = store_sales.sort_values('product_amount', ascending=False).head(10)
+
+    fig, ax = plt.subplots(figsize=(12, 7))
+    bars = ax.bar(store_sales_top10['store_name'], store_sales_top10['product_amount'],
+                  color=colors_mpl[:10], alpha=0.8)
+    ax.set_xlabel('门店名称', fontsize=12)
+    ax.set_ylabel('销售额（元）', fontsize=12)
+    ax.set_title('门店销售额TOP10', fontsize=14, fontweight='bold', pad=20)
+    ax.tick_params(axis='x', rotation=45)
+    ax.grid(axis='y', alpha=0.3)
+    # 添加数值标签
+    for bar in bars:
+        height = bar.get_height()
+        ax.text(bar.get_x() + bar.get_width() / 2., height + height * 0.01,
+                f'{int(height)}', ha='center', va='bottom', fontsize=10)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '23-门店销售额TOP10.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 24-平台订单状态分布堆叠柱状图
+def plot_24_platform_status_stack_bar():
+    platform_status = erp_df.groupby(['platform', 'status'], observed=True).size().reset_index(
+        name='count')  # 修复警告：添加observed=True
+    platform_status_pivot = platform_status.pivot(index='platform', columns='status', values='count').fillna(0)
+
+    fig, ax = plt.subplots(figsize=(14, 7))
+    platform_status_pivot.plot(kind='bar', stacked=True, color=colors_mpl[:len(platform_status_pivot.columns)],
+                               alpha=0.8, ax=ax)
+    ax.set_xlabel('销售平台', fontsize=12)
+    ax.set_ylabel('订单数量（笔）', fontsize=12)
+    ax.set_title('各平台订单状态分布', fontsize=14, fontweight='bold', pad=20)
+    ax.legend(title='订单状态', bbox_to_anchor=(1.05, 1), loc='upper left')
+    ax.grid(axis='y', alpha=0.3)
+    ax.tick_params(axis='x', rotation=45)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '24-平台订单状态堆叠图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# ------------------------------
+# 五、业务状态类图表（25-30张：订单/退款/发货分析）
+# ------------------------------
+# 25-订单状态分布饼图
+def plot_25_order_status_pie():
+    status_count = erp_df.groupby('status', observed=True).size().reset_index(name='count')  # 修复警告：添加observed=True
+    status_count = status_count.sort_values('count', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(8, 8))
+    wedges, texts, autotexts = ax.pie(
+        status_count['count'],
+        labels=status_count['status'],
+        autopct='%1.1f%%',
+        colors=colors_mpl[:len(status_count)],
+        startangle=90,
+        wedgeprops=dict(edgecolor='white')
+    )
+    for autotext in autotexts:
+        autotext.set_fontsize(11)
+        autotext.set_fontweight('bold')
+    for text in texts:
+        text.set_fontsize(11)
+    ax.set_title('订单状态分布', fontsize=14, fontweight='bold', pad=20)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '25-订单状态饼图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 26-退款状态分布柱状图
+def plot_26_refund_status_bar():
+    refund_count = erp_df.groupby('refund_status', observed=True).size().reset_index(
+        name='count')  # 修复警告：添加observed=True
+    refund_count = refund_count.sort_values('count', ascending=False)
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    bars = ax.bar(refund_count['refund_status'], refund_count['count'],
+                  color=['#2ca02c', '#ff7f0e', '#d62728', '#9467bd'], alpha=0.8)
+    ax.set_xlabel('退款状态', fontsize=12)
+    ax.set_ylabel('订单数量（笔）', fontsize=12)
+    ax.set_title('退款状态分布', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    # 添加数值标签和占比
+    total_count = refund_count['count'].sum()
+    for bar in bars:
+        height = bar.get_height()
+        percentage = (height / total_count) * 100
+        ax.text(bar.get_x() + bar.get_width() / 2., height + 5,
+                f'{int(height)}\n({percentage:.1f}%)', ha='center', va='bottom', fontsize=11)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '26-退款状态柱状图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 27-发货时长分布直方图
+def plot_27_shipping_duration_hist():
+    # 过滤发货时长>10天的异常值
+    shipping_data = erp_df[erp_df['shipping_duration'] <= 10]
+
+    fig, ax = plt.subplots(figsize=(12, 6))
+    sns.histplot(
+        data=shipping_data,
+        x='shipping_duration',
+        bins=range(1, int(shipping_data['shipping_duration'].max()) + 2),
+        color='#d62728',
+        alpha=0.7,
+        kde=True,
+        ax=ax
+    )
+    # 添加平均值线
+    avg_duration = shipping_data['shipping_duration'].mean()
+    ax.axvline(x=avg_duration, color='blue', linestyle='--', alpha=0.8, label=f'平均：{avg_duration:.1f}天')
+    ax.legend()
+
+    ax.set_xlabel('发货时长（天）', fontsize=12)
+    ax.set_ylabel('订单数量（笔）', fontsize=12)
+    ax.set_title('发货时长分布（付款到发货）', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    ax.set_xticks(range(1, int(shipping_data['shipping_duration'].max()) + 1))
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '27-发货时长直方图.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+def plot_28_gift_vs_non_gift():
+    # 按is_gift（字符串“是”/“否”）分组统计
+    gift_data = erp_df.groupby('is_gift', observed=True).agg({
+        'quantity': 'sum',
+        'product_amount': 'sum'
+    }).reset_index()
+
+    # 映射中文标签
+    gift_data['category'] = gift_data['is_gift'].map({'是': '赠品', '否': '非赠品'})
+    # 补全缺失类别（确保有“赠品”和“非赠品”）
+    full_categories = pd.DataFrame({'category': ['赠品', '非赠品']})
+    gift_data = pd.merge(full_categories, gift_data, on='category', how='left').fillna(0)
+
+    # 宽表转长表，用于分组柱状图
+    gift_long = gift_data.melt(
+        id_vars='category',
+        value_vars=['quantity', 'product_amount'],
+        var_name='指标',
+        value_name='数值'
+    )
+    # 重命名指标并区分量级
+    gift_long['指标'] = gift_long['指标'].map({
+        'quantity': '销量（件）',
+        'product_amount': '销售额（元）'
+    })
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    # 绘制分组柱状图（不同指标用不同颜色）
+    sns.barplot(
+        data=gift_long,
+        x='category',
+        y='数值',
+        hue='指标',
+        palette=['#1f77b4', '#2ca02c'],
+        ax=ax
+    )
+
+    # 添加数值标签
+    for p in ax.patches:
+        height = p.get_height()
+        if height > 0:
+            ax.text(
+                p.get_x() + p.get_width() / 2.,
+                height + height * 0.02,
+                f'{int(height)}',
+                ha='center',
+                va='bottom',
+                fontsize=11
+            )
+
+    # 设置图表样式
+    ax.set_xlabel('是否赠品', fontsize=12)
+    ax.set_ylabel('数值', fontsize=12)
+    ax.set_title('赠品vs非赠品 销量与销售额对比', fontsize=14, fontweight='bold', pad=20)
+    ax.grid(axis='y', alpha=0.3)
+    ax.legend(title='指标', bbox_to_anchor=(1.05, 1), loc='upper left')
+
+    # 保存图表
+    plt.tight_layout()
+    plt.savefig(
+        os.path.join(figures_dir, '28-赠品非赠品对比.png'),
+        dpi=300,
+        bbox_inches='tight',
+        facecolor='white'
+    )
+    plt.close()
+
+# 29-月度退款率趋势图
+def plot_29_monthly_refund_rate():
+    monthly_refund = erp_df.groupby('month', observed=True).agg({  # 修复警告：添加observed=True
+        'id': 'count',  # 总订单数
+        'refund_status': lambda x: (x != '无退款').sum()  # 退款订单数
+    }).reset_index()
+    monthly_refund['refund_rate'] = (monthly_refund['refund_status'] / monthly_refund['id'] * 100).round(2)
+    monthly_refund['month'] = pd.to_datetime(monthly_refund['month'])
+    monthly_refund = monthly_refund.sort_values('month')
+    monthly_refund['month_str'] = monthly_refund['month'].dt.strftime('%Y-%m')
+
+    fig, ax = plt.subplots(figsize=(14, 6))
+    ax.plot(monthly_refund['month_str'], monthly_refund['refund_rate'],
+            color='#d62728', linewidth=3, marker='o', markersize=6)
+    ax.fill_between(monthly_refund['month_str'], monthly_refund['refund_rate'], alpha=0.3, color='#d62728')
+    # 添加平均值线
+    avg_rate = monthly_refund['refund_rate'].mean()
+    ax.axhline(y=avg_rate, color='blue', linestyle='--', alpha=0.8, label=f'平均退款率：{avg_rate:.1f}%')
+    ax.legend()
+
+    ax.set_xlabel('月份', fontsize=12)
+    ax.set_ylabel('退款率（%）', fontsize=12)
+    ax.set_title('2024年月度退款率趋势', fontsize=14, fontweight='bold', pad=20)
+    ax.set_xticks(range(0, len(monthly_refund['month_str']), 1))
+    ax.set_xticklabels(monthly_refund['month_str'], rotation=45)
+    ax.grid(True, alpha=0.3)
+    # 添加数值标签
+    for i, v in enumerate(monthly_refund['refund_rate']):
+        ax.text(i, v + 0.2, f'{v:.1f}%', ha='center', va='bottom', fontsize=9)
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '29-月度退款率趋势.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+# 30-综合业务指标看板（替换Pyecharts大屏为Matplotlib多子图看板）
+def plot_30_business_dashboard():
+    # 计算核心指标
+    total_sales = erp_df['product_amount'].sum()
+    total_orders = len(erp_df)
+    total_users = erp_df['full_channel_user_id'].nunique()
+
+    # 修复1：处理total_users=0的情况，避免除以0
+    if total_users == 0:
+        repurchase_rate = 0.0
+    else:
+        repurchase_count = erp_df[erp_df['is_repurchase']]['full_channel_user_id'].nunique()
+        repurchase_rate = round(repurchase_count / total_users * 100, 2)  # 用Python内置round()
+
+    # 修复2：avg_order_amount用round()
+    avg_order_amount = round(erp_df['product_amount'].mean(), 2) if total_orders > 0 else 0.0
+
+    # 修复3：refund_rate用round()
+    refund_count = erp_df[erp_df['refund_status'] != '无退款'].shape[0]
+    refund_rate = round(refund_count / total_orders * 100, 2) if total_orders > 0 else 0.0
+
+
+    # 准备子图数据
+    monthly_sales = erp_df.groupby('month', observed=True)['product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    monthly_sales['month'] = pd.to_datetime(monthly_sales['month'])
+    monthly_sales = monthly_sales.sort_values('month')
+    brand_top5 = erp_df.groupby('brand', observed=True)['product_amount'].sum().nlargest(
+        5).reset_index()  # 修复警告：添加observed=True
+    region_sales = erp_df.groupby('region', observed=True)['product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+
+    fig = plt.figure(figsize=(20, 12))
+    fig.suptitle('服装订单业务综合指标看板', fontsize=18, fontweight='bold', y=0.95)
+
+    # 1. 核心指标卡片（第一行）
+    metrics = [
+        ('总销售额', f'{total_sales:,.0f} 元', '#1f77b4'),
+        ('总订单数', f'{total_orders} 笔', '#ff7f0e'),
+        ('总用户数', f'{total_users} 人', '#2ca02c'),
+        ('复购率', f'{repurchase_rate}%', '#d62728'),
+        ('平均客单价', f'{avg_order_amount:.0f} 元', '#9467bd'),
+        ('退款率', f'{refund_rate:.1f}%', '#8c564b')
+    ]
+    for i, (title, value, color) in enumerate(metrics):
+        ax = fig.add_subplot(3, 6, i + 1)
+        ax.text(0.5, 0.7, title, ha='center', va='center', fontsize=12, fontweight='bold', transform=ax.transAxes)
+        ax.text(0.5, 0.3, value, ha='center', va='center', fontsize=14, fontweight='bold', color=color,
+                transform=ax.transAxes)
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        ax.axis('off')
+        ax.add_patch(plt.Rectangle((0.05, 0.05), 0.9, 0.9, fill=True, color=color, alpha=0.1, transform=ax.transAxes))
+
+    # 2. 月度销售额趋势（第二行左）
+    ax1 = fig.add_subplot(3, 3, 4)
+    ax1.plot(monthly_sales['month'].dt.strftime('%Y-%m'), monthly_sales['product_amount'],
+             color='#1f77b4', linewidth=2, marker='o', markersize=3)
+    ax1.set_xlabel('月份', fontsize=10)
+    ax1.set_ylabel('销售额（元）', fontsize=10)
+    ax1.set_title('月度销售额趋势', fontsize=12, fontweight='bold')
+    ax1.tick_params(axis='x', rotation=45)
+    ax1.grid(True, alpha=0.3)
+
+    # 3. 品牌销售额TOP5（第二行中）
+    ax2 = fig.add_subplot(3, 3, 5)
+    ax2.bar(brand_top5['brand'], brand_top5['product_amount'], color=colors_mpl[:5], alpha=0.8)
+    ax2.set_xlabel('品牌', fontsize=10)
+    ax2.set_ylabel('销售额（元）', fontsize=10)
+    ax2.set_title('品牌销售额TOP5', fontsize=12, fontweight='bold')
+    ax2.tick_params(axis='x', rotation=45)
+    ax2.grid(axis='y', alpha=0.3)
+
+    # 4. 区域销售额分布（第二行右）
+    ax3 = fig.add_subplot(3, 3, 6)
+    ax3.pie(region_sales['product_amount'], labels=region_sales['region'], autopct='%1.1f%%',
+            colors=colors_mpl[:len(region_sales)], startangle=90)
+    ax3.set_title('区域销售额分布', fontsize=12, fontweight='bold')
+
+    # 5. 订单状态分布（第三行左）
+    status_count = erp_df.groupby('status', observed=True).size().reset_index(name='count')  # 修复警告：添加observed=True
+    ax4 = fig.add_subplot(3, 3, 7)
+    ax4.pie(status_count['count'], labels=status_count['status'], autopct='%1.1f%%',
+            colors=colors_mpl[:len(status_count)], startangle=90)
+    ax4.set_title('订单状态分布', fontsize=12, fontweight='bold')
+
+    # 6. 价格区间销售额占比（第三行中）
+    price_sales = erp_df.groupby('price_range', observed=True)[
+        'product_amount'].sum().reset_index()  # 修复警告：添加observed=True
+    ax5 = fig.add_subplot(3, 3, 8)
+    ax5.pie(price_sales['product_amount'], labels=price_sales['price_range'], autopct='%1.1f%%',
+            colors=colors_mpl[:len(price_sales)], startangle=90)
+    ax5.set_title('价格区间销售额占比', fontsize=12, fontweight='bold')
+
+    # 7. 日内订单量分布（第三行右）
+    hourly_order = erp_df.groupby('hour', observed=True).size().reset_index(name='order_count')  # 修复警告：添加observed=True
+    ax6 = fig.add_subplot(3, 3, 9)
+    ax6.plot(hourly_order['hour'], hourly_order['order_count'], color='#2ca02c', linewidth=2)
+    ax6.set_xlabel('小时', fontsize=10)
+    ax6.set_ylabel('订单量（笔）', fontsize=10)
+    ax6.set_title('日内订单量分布', fontsize=12, fontweight='bold')
+    ax6.set_xticks(range(0, 24, 4))
+    ax6.grid(True, alpha=0.3)
+
+    plt.tight_layout()
+    plt.savefig(os.path.join(figures_dir, '30-业务综合指标看板.png'), dpi=300, bbox_inches='tight')
+    plt.close()
+
+
+
+# ------------------------------
+# 执行所有图表生成
+# ------------------------------
+if __name__ == "__main__":
+    print("开始生成30张服装订单业务可视化图表 ...")
+
+    # 一、趋势类图表（1-5）
+    plot_01_monthly_sales_order_trend()
+    plot_02_quarterly_brand_sales()
+    plot_03_weekly_sales_trend()
+    plot_04_hourly_order_distribution()
+    plot_05_weekend_weekday_comparison()
+
+    # 二、商品分析类图表（6-12）
+    plot_06_brand_sales_top10()
+    plot_07_product_type_sales_pie()
+    plot_08_color_sales_distribution()
+    plot_09_spec_sales_heatmap()
+    plot_10_price_range_pie()
+    plot_11_brand_discount_boxplot()
+    plot_12_product_type_sales_bar()
+
+    # 三、用户行为类图表（13-18）
+    plot_13_user_repurchase_pie()
+    plot_14_user_purchase_count_hist()
+    plot_15_payment_duration_violin()
+    plot_16_quantity_amount_scatter()
+    plot_17_repurchase_vs_new_consume()
+    plot_18_user_purchase_freq_comparison()
+
+    # 四、区域与渠道类图表（19-24）
+    plot_19_province_sales_bar()
+    plot_20_region_sales_bar()
+    plot_21_city_sales_top15()
+    plot_22_platform_monthly_sales()
+    plot_23_store_sales_top10()
+    plot_24_platform_status_stack_bar()
+
+    # 五、业务状态类图表（25-30）
+    plot_25_order_status_pie()
+    plot_26_refund_status_bar()
+    plot_27_shipping_duration_hist()
+    plot_28_gift_vs_non_gift()
+    plot_29_monthly_refund_rate()
+    plot_30_business_dashboard()
+
+    print(f"30张PNG静态图表已全部生成完成！保存路径：{figures_dir}")
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