feat : 代码梳理 移除所有敏感密钥 通过环境变量方式配置

app/service/recommend/scheduled_task.py

@@ -18,7 +18,8 @@ import pandas as pd
 from datetime import datetime, timedelta
 import json
 
-from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX
+from app.core.config import TABLE_CATEGORIES, settings
+from app.core.mysql_config import DB_CONFIG
 
 # 自动选择可用字体
 try:
@@ -51,7 +52,7 @@ minio_client = Minio(
 )
 
 # 预加载系统sketch特征向量
-SYSTEM_FEATURES = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item()
+SYSTEM_FEATURES = np.load(f'{settings.RECOMMEND_PATH_PREFIX}sketch_feature_dict.npy', allow_pickle=True).item()
 
 # 行为权重和衰减系数
 BEHAVIOR_CONFIG = {
@@ -61,6 +62,7 @@ BEHAVIOR_CONFIG = {
     'sketchLike': {'weight': 4, 'decay': 0}  # 不衰减
 }
 
+
 # 保存sketch_to_iid到文件
 def save_sketch_to_iid():
     """保存sketch到iid的映射"""
@@ -147,11 +149,11 @@ def update_user_matrices():
         cursor = conn.cursor()
 
         # 修改后的查询语句（移除category过滤）
-        cursor.execute(""" 
-            SELECT account_id, path, COUNT(*) as like_count 
-            FROM user_preference_log_test 
-            GROUP BY account_id, path
-        """)
+        cursor.execute("""
+                       SELECT account_id, path, COUNT(*) as like_count
+                       FROM user_preference_log_test
+                       GROUP BY account_id, path
+                       """)
         user_data = cursor.fetchall()
         logging.info(f"成功读取{len(user_data)}条用户偏好记录")
 
@@ -164,17 +166,17 @@ def update_user_matrices():
         feature_matrix, user_index_feature_matrix, sketch_index_feature_matrix, iid_to_category_feature_matrix = calculate_feature_matrix(user_data)
         # visualize_sparse_matrix(feature_matrix, '系统sketch与用户category平均特征向量关联度矩阵', 'correlation_matrix.png')
         # 存储矩阵
-        np.save(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", interaction_matrix)
-        np.save(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", feature_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}interaction_matrix.npy", interaction_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}feature_matrix.npy", feature_matrix)
         #
-        np.save(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", iid_to_category_interaction_matrix)
-        np.save(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", user_index_interaction_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", iid_to_category_interaction_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", user_index_interaction_matrix)
         #
-        np.save(f"{RECOMMEND_PATH_PREFIX}iid_to_category_feature_matrix.npy", iid_to_category_feature_matrix)
-        np.save(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", user_index_feature_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}iid_to_category_feature_matrix.npy", iid_to_category_feature_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", user_index_feature_matrix)
         #
-        np.save(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy", sketch_index_interaction_matrix)
-        np.save(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", sketch_index_feature_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy", sketch_index_interaction_matrix)
+        np.save(f"{settings.RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", sketch_index_feature_matrix)
         # logging.info("矩阵更新完成")
 
     except Exception as e:
@@ -235,6 +237,7 @@ def plot_interaction_count_matrix(interaction_count_matrix):
     except Exception as e:
         logging.error(f"绘图失败: {str(e)}", exc_info=True)
 
+
 def visualize_sparse_matrix(matrix, title='Non-zero Interactions (Scatter Plot)', filename="scatter_figure_interaction.png"):
     if not sparse.issparse(matrix):
         # 转换为稀疏矩阵
@@ -253,6 +256,7 @@ def visualize_sparse_matrix(matrix, title='Non-zero Interactions (Scatter Plot)'
     plt.ylabel('Item Index')
     plt.savefig(filename)
 
+
 def calculate_interaction_matrix(user_data):
     """基于新表结构的交互次数矩阵计算（仅系统sketch）"""
     # 获取所有用户ID
@@ -475,6 +479,7 @@ def calculate_heat(row, current_date):
     # 计算热度值 = 权重 * e^(-衰减系数 * 天数)
     return config['weight'] * np.exp(-config['decay'] * days_passed)
 
+
 def load_heat_matrix_as_array(file_path):
     """
     直接加载为二维numpy数组
@@ -484,10 +489,11 @@ def load_heat_matrix_as_array(file_path):
         saved = json.load(f)
     return (
         np.array(saved['data']),  # 二维矩阵
-        saved['row_labels'],      # 行标签列表
-        saved['col_labels']       # 列标签列表
+        saved['row_labels'],  # 行标签列表
+        saved['col_labels']  # 列标签列表
     )
 
+
 def update_heat_matrices():
     """每日计算并存储热度矩阵（gender_category × path）"""
     current_date = datetime.now()