540 lines
20 KiB
Python
540 lines
20 KiB
Python
import pymysql
|
||
import numpy as np
|
||
from apscheduler.schedulers.blocking import BlockingScheduler
|
||
import os
|
||
import logging
|
||
from collections import defaultdict
|
||
import torch
|
||
from torchvision import models, transforms
|
||
from minio import Minio
|
||
from PIL import Image
|
||
import io
|
||
import seaborn as sns
|
||
import matplotlib.pyplot as plt
|
||
from scipy.sparse import csr_matrix
|
||
import matplotlib.font_manager as fm
|
||
from scipy import sparse
|
||
import pandas as pd
|
||
from datetime import datetime, timedelta
|
||
import json
|
||
|
||
from app.core.config import DB_CONFIG, TABLE_CATEGORIES, RECOMMEND_PATH_PREFIX
|
||
|
||
# 自动选择可用字体
|
||
try:
|
||
# 尝试加载常见中文字体
|
||
font_path = fm.findfont(fm.FontProperties(family=['Microsoft YaHei', 'SimHei', 'WenQuanYi Zen Hei']))
|
||
plt.rcParams['font.sans-serif'] = fm.FontProperties(fname=font_path).get_name()
|
||
except:
|
||
# 回退到英文字体
|
||
plt.rcParams['font.sans-serif'] = ['DejaVu Sans']
|
||
plt.rcParams['axes.unicode_minus'] = False
|
||
|
||
# 检查系统中可用的字体并选择支持中文的字体
|
||
font_path = fm.findfont(fm.FontProperties(family='Microsoft YaHei')) # 或其他支持中文的字体
|
||
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 设置为 Microsoft YaHei
|
||
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
|
||
|
||
# 配置日志记录
|
||
logging.basicConfig(
|
||
level=logging.INFO,
|
||
format='%(asctime)s - %(levelname)s - %(message)s',
|
||
filename='scheduler.log'
|
||
)
|
||
|
||
# MinIO 配置信息
|
||
minio_client = Minio(
|
||
"www.minio.aida.com.hk:12024", # MinIO Endpoint
|
||
access_key="admin", # Access Key
|
||
secret_key="Aidlab123123!", # Secret Key
|
||
secure=True # 使用https
|
||
)
|
||
|
||
# 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()
|
||
|
||
# 行为权重和衰减系数
|
||
BEHAVIOR_CONFIG = {
|
||
'portfolioClick': {'weight': 1, 'decay': 0.3},
|
||
'portfolioLike': {'weight': 2, 'decay': 0.2},
|
||
'secondCreation': {'weight': 3, 'decay': 0.1},
|
||
'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"""
|
||
sketch_to_iid = {sketch_path: iid for iid, sketch_path in enumerate(SYSTEM_FEATURES.keys(), start=1)}
|
||
np.save('sketch_to_iid.npy', sketch_to_iid)
|
||
print("sketch_to_iid 已保存")
|
||
|
||
|
||
# 从文件加载sketch_to_iid
|
||
def load_sketch_to_iid():
|
||
"""加载保存的sketch到iid的映射"""
|
||
if os.path.exists('sketch_to_iid.npy'):
|
||
sketch_to_iid = np.load('sketch_to_iid.npy', allow_pickle=True).item()
|
||
print("sketch_to_iid 已加载")
|
||
return sketch_to_iid
|
||
else:
|
||
# 如果文件不存在，则生成并保存
|
||
print("sketch_to_iid 文件不存在，正在生成并保存...")
|
||
save_sketch_to_iid()
|
||
return np.load('sketch_to_iid.npy', allow_pickle=True).item()
|
||
|
||
|
||
# 使用load_sketch_to_iid来获取映射
|
||
sketch_to_iid = load_sketch_to_iid()
|
||
|
||
# 在代码中其他地方使用sketch_to_iid
|
||
# print(f"Total sketches: {len(sketch_to_iid)}")
|
||
|
||
# 定义图像预处理（与ResNet训练时的预处理一致）
|
||
transform = transforms.Compose([
|
||
transforms.Resize((224, 224)), # ResNet 要求 224x224 的输入
|
||
transforms.ToTensor(), # 转换为 Tensor
|
||
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), # 标准化
|
||
])
|
||
|
||
# 加载预训练的 ResNet 模型 (ResNet50)
|
||
resnet_model = models.resnet50(pretrained=True)
|
||
modules = list(resnet_model.children())[:-1] # 移除最后的全连接层
|
||
resnet_model = torch.nn.Sequential(*modules)
|
||
resnet_model.eval() # 设置为评估模式
|
||
|
||
|
||
# 从 MinIO 获取图片并进行预处理
|
||
def get_sketch_image_from_minio(sketch_path):
|
||
"""
|
||
从 MinIO 获取 sketch 图像并预处理
|
||
"""
|
||
# 分割路径，获取桶名和文件路径
|
||
path_parts = sketch_path.split('/', 1) # 根据第一个斜杠分割，得到桶名和路径
|
||
bucket_name = path_parts[0] # 桶名
|
||
file_name = path_parts[1] # 文件路径（从第二部分开始）
|
||
|
||
try:
|
||
# 获取文件
|
||
obj = minio_client.get_object(bucket_name, file_name)
|
||
img_data = obj.read() # 读取图像数据
|
||
img = Image.open(io.BytesIO(img_data)) # 将数据转为图像对象
|
||
img = transform(img) # 对图像进行预处理
|
||
return img.unsqueeze(0) # 扩展维度以适应批量处理
|
||
except Exception as e:
|
||
print(f"Error fetching image for {sketch_path}: {e}")
|
||
return None
|
||
|
||
|
||
def extract_feature_vector_from_resnet(sketch_path):
|
||
"""
|
||
提取 sketch 图像的特征向量
|
||
"""
|
||
# 从 MinIO 获取图像并预处理
|
||
img_tensor = get_sketch_image_from_minio(sketch_path)
|
||
if img_tensor is None:
|
||
return np.zeros(2048) # 如果获取失败，返回零向量
|
||
|
||
with torch.no_grad(): # 在不需要计算梯度的情况下进行推断
|
||
feature_vector = resnet_model(img_tensor) # 获取 ResNet 的输出
|
||
return feature_vector.squeeze().cpu().numpy() # 转换为 NumPy 数组并去掉 batch 维度
|
||
|
||
|
||
def update_user_matrices():
|
||
"""每天更新用户交互次数矩阵和特征向量矩阵"""
|
||
conn = None
|
||
try:
|
||
conn = pymysql.connect(**DB_CONFIG)
|
||
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
|
||
""")
|
||
user_data = cursor.fetchall()
|
||
logging.info(f"成功读取{len(user_data)}条用户偏好记录")
|
||
|
||
# 计算矩阵
|
||
interaction_matrix, raw_counts_sparse, user_index_interaction_matrix, sketch_index_interaction_matrix, iid_to_category_interaction_matrix = calculate_interaction_matrix(user_data)
|
||
# visualize_sparse_matrix(raw_counts_sparse,'交互次数矩阵', 'interaction_frequency_matrix.png')
|
||
# visualize_sparse_matrix(interaction_matrix, '交互次数得分矩阵', 'interaction_score_matrix.png')
|
||
# plot_interaction_count_matrix(raw_counts_sparse)
|
||
# feature_matrix, iid_to_category_feature_matrix, user_index_feature_matrix, sketch_index_feature_matrix = calculate_feature_matrix(user_data)
|
||
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"{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"{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"{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)
|
||
# logging.info("矩阵更新完成")
|
||
|
||
except Exception as e:
|
||
logging.error(f"定时任务执行失败: {str(e)}", exc_info=True)
|
||
finally:
|
||
if conn:
|
||
conn.close()
|
||
|
||
|
||
def plot_interaction_count_matrix(interaction_count_matrix):
|
||
"""绘制交互次数矩阵的分布图（热图），不隐藏零值"""
|
||
try:
|
||
if not isinstance(interaction_count_matrix, csr_matrix):
|
||
sparse_matrix = csr_matrix(interaction_count_matrix)
|
||
else:
|
||
sparse_matrix = interaction_count_matrix
|
||
|
||
# 转换为密集矩阵
|
||
try:
|
||
dense_matrix = sparse_matrix.toarray()
|
||
except MemoryError:
|
||
logging.error("内存不足，无法转换为密集矩阵")
|
||
return
|
||
|
||
# 自动检测可用中文字体
|
||
try:
|
||
font_path = fm.findfont(fm.FontProperties(family='sans-serif', style='normal'))
|
||
plt.rcParams['font.sans-serif'] = fm.FontProperties(fname=font_path).get_name()
|
||
except:
|
||
plt.rcParams['font.sans-serif'] = ['DejaVu Sans'] # 回退到英文字体
|
||
plt.rcParams['axes.unicode_minus'] = False
|
||
|
||
# 处理大矩阵的显示，限制显示范围
|
||
if dense_matrix.shape[0] > 1000 or dense_matrix.shape[1] > 1000:
|
||
dense_matrix = dense_matrix[:1000, :1000] # 只绘制前1000行列
|
||
|
||
plt.figure(figsize=(15, 10))
|
||
|
||
# 使用 `cmap` 来设置颜色，零值可以使用特定颜色，调整 `vmin` 和 `vmax` 让热图更具对比
|
||
sns.heatmap(
|
||
dense_matrix,
|
||
cmap="Blues", # 可以选择不同的颜色映射，"Blues" 或 "YlGnBu"
|
||
annot=False, # 关闭标注
|
||
cbar_kws={"label": "Interaction Count"}, # 添加颜色条标签
|
||
linewidths=0.5,
|
||
vmin=0, # 设置最小值，确保零值明显
|
||
vmax=np.max(dense_matrix) # 设置最大值，保持颜色映射的合理性
|
||
)
|
||
|
||
plt.title('User-Sketch Interaction Matrix (With Zero Entries)')
|
||
plt.xlabel('Sketch Index')
|
||
plt.ylabel('User Index')
|
||
plt.savefig('interaction_heatmap_with_zeros.png', dpi=150, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
logging.info("热图已保存为 interaction_heatmap_with_zeros.png")
|
||
|
||
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
|
||
matrix = sparse.csr_matrix(matrix)
|
||
|
||
# 获取非零元素的坐标和值
|
||
rows, cols = matrix.nonzero()
|
||
values = matrix.data
|
||
|
||
# 绘制散点图
|
||
plt.figure(figsize=(24, 20))
|
||
plt.scatter(cols, rows, c=values, cmap='coolwarm', alpha=0.7, s=1)
|
||
plt.colorbar(label='Interaction Count')
|
||
plt.title(title)
|
||
plt.xlabel('Item Index')
|
||
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
|
||
all_users = set()
|
||
for account_id, path, like_count in user_data:
|
||
category = get_category_from_path(path)
|
||
if category not in TABLE_CATEGORIES.keys():
|
||
continue
|
||
all_users.add(account_id)
|
||
|
||
# 获取所有系统sketch的iid
|
||
system_sketch_iids = {sketch_to_iid[path] for path in SYSTEM_FEATURES.keys() if path in sketch_to_iid}
|
||
|
||
# 创建映射关系
|
||
user_index = {uid: idx for idx, uid in enumerate(sorted(all_users))}
|
||
sketch_index = {iid: idx for idx, iid in enumerate(sorted(system_sketch_iids))}
|
||
|
||
# 初始化双矩阵：归一化矩阵（密集）和原始计数矩阵（稀疏）
|
||
interaction_matrix = np.zeros((len(all_users), len(sketch_index))) # 归一化矩阵
|
||
data, rows, cols = [], [], [] # 用于构建稀疏矩阵的COO格式数据
|
||
|
||
# 预计算用户最大交互次数
|
||
user_max_likes = defaultdict(int)
|
||
for account_id, path, like_count in user_data:
|
||
if sketch_to_iid.get(path) in system_sketch_iids:
|
||
user_max_likes[account_id] = max(user_max_likes[account_id], like_count)
|
||
|
||
# 填充矩阵
|
||
for account_id, path, like_count in user_data:
|
||
sketch_iid = sketch_to_iid.get(path)
|
||
if sketch_iid not in system_sketch_iids:
|
||
continue
|
||
|
||
user_idx = user_index[account_id]
|
||
sketch_idx = sketch_index[sketch_iid]
|
||
|
||
# 填充稀疏矩阵数据
|
||
data.append(like_count)
|
||
rows.append(user_idx)
|
||
cols.append(sketch_idx)
|
||
|
||
# 归一化计算
|
||
max_like = user_max_likes.get(account_id, 1)
|
||
interaction_matrix[user_idx, sketch_idx] = np.log1p(1 + like_count) / np.log1p(1 + max_like)
|
||
|
||
# 构建稀疏矩阵（CSR格式适合快速行操作）
|
||
interaction_count_matrix = csr_matrix((data, (rows, cols)), shape=(len(all_users), len(sketch_index)))
|
||
|
||
return interaction_matrix, interaction_count_matrix, user_index, sketch_index, {iid: get_category_from_path(path) for path, iid in sketch_to_iid.items()}
|
||
|
||
|
||
def calculate_feature_matrix(user_data):
|
||
"""基于新表结构的特征矩阵计算，返回用户与系统草图的相似度矩阵（加权平均）"""
|
||
|
||
# 用户特征数据存储结构：{(account_id, category): {sketch_iid: [(feature_vector, weight)]}}
|
||
user_feature_weights = defaultdict(lambda: defaultdict(list))
|
||
|
||
# 初始化所有用户和系统草图集合
|
||
all_users = set()
|
||
all_system_sketches = set(SYSTEM_FEATURES.keys())
|
||
|
||
# ==== 第一遍遍历：收集特征向量和权重 ====
|
||
for account_id, path, like_count in user_data:
|
||
category = get_category_from_path(path)
|
||
if category not in TABLE_CATEGORIES.keys():
|
||
continue
|
||
|
||
sketch_iid = sketch_to_iid.get(path)
|
||
if not sketch_iid:
|
||
continue
|
||
|
||
# 记录用户
|
||
all_users.add(account_id)
|
||
|
||
# 提取特征并记录权重（like_count）
|
||
if path in SYSTEM_FEATURES: # 系统草图
|
||
feature = SYSTEM_FEATURES[path]
|
||
weight = like_count # 使用like_count作为权重
|
||
user_feature_weights[(account_id, category)][sketch_iid].append((feature, weight))
|
||
else: # 用户草图
|
||
feature = extract_feature_vector_from_resnet(path)
|
||
weight = like_count
|
||
user_feature_weights[(account_id, category)][sketch_iid].append((feature, weight))
|
||
|
||
# ==== 第二遍遍历：收集所有系统草图iid ====
|
||
system_sketch_iids = set()
|
||
for sketch_path in SYSTEM_FEATURES:
|
||
if iid := sketch_to_iid.get(sketch_path):
|
||
system_sketch_iids.add(iid)
|
||
|
||
# ==== 创建索引映射 ====
|
||
user_list = sorted(all_users)
|
||
sketch_list = sorted(system_sketch_iids)
|
||
|
||
user_index = {uid: idx for idx, uid in enumerate(user_list)}
|
||
sketch_index = {iid: idx for idx, iid in enumerate(sketch_list)}
|
||
|
||
# ==== 初始化特征矩阵 ====
|
||
feature_matrix = np.zeros((len(user_list), len(sketch_list)))
|
||
|
||
# ==== 预计算加权平均特征向量 ====
|
||
user_avg_features = {}
|
||
for (account_id, category), sketches in user_feature_weights.items():
|
||
try:
|
||
# 展平所有特征向量和权重
|
||
all_features_weights = [(vec, weight) for vec_list in sketches.values() for vec, weight in vec_list]
|
||
|
||
if len(all_features_weights) == 0:
|
||
continue
|
||
|
||
# 计算总权重
|
||
total_weight = sum(weight for _, weight in all_features_weights)
|
||
if total_weight <= 0: # 防止除零错误
|
||
total_weight = 1.0
|
||
|
||
# 加权平均计算
|
||
weighted_sum = np.zeros_like(all_features_weights[0][0]) # 获取特征向量维度
|
||
for vec, weight in all_features_weights:
|
||
weighted_sum += vec * weight
|
||
|
||
avg_vec = weighted_sum / total_weight
|
||
user_avg_features[(account_id, category)] = avg_vec
|
||
|
||
except Exception as e:
|
||
logging.warning(f"用户({account_id},{category})加权特征计算失败: {str(e)}")
|
||
continue
|
||
|
||
# ==== 计算相似度并填充矩阵 ====
|
||
for sketch_path, sys_vector in SYSTEM_FEATURES.items():
|
||
sketch_iid = sketch_to_iid.get(sketch_path)
|
||
|
||
system_sketch_category = get_category_from_path(sketch_path)
|
||
if not sketch_iid or sketch_iid not in sketch_index:
|
||
continue
|
||
|
||
sketch_col = sketch_index[sketch_iid]
|
||
|
||
# 遍历所有用户
|
||
for account_id in all_users:
|
||
user_row = user_index.get(account_id)
|
||
if user_row is None:
|
||
continue
|
||
|
||
# 获取用户加权平均特征向量
|
||
try:
|
||
# 直接通过复合键获取用户特征向量
|
||
user_vec = user_avg_features[(account_id, system_sketch_category)]
|
||
except KeyError:
|
||
# 该用户在此类别下无特征数据
|
||
continue
|
||
|
||
# 计算余弦相似度
|
||
cos_sim = cosine_similarity(user_vec, sys_vector)
|
||
feature_matrix[user_row, sketch_col] = cos_sim
|
||
|
||
return feature_matrix, user_index, sketch_index, {iid: get_category_from_path(path) for path, iid in sketch_to_iid.items()}
|
||
|
||
|
||
def get_category_from_path(path):
|
||
"""从path字段解析类别"""
|
||
try:
|
||
parts = path.split('/')
|
||
if len(parts) >= 2:
|
||
return f"{parts[2]}_{parts[3]}"
|
||
return "unknown"
|
||
except:
|
||
return "unknown"
|
||
|
||
|
||
def cosine_similarity(vec1, vec2):
|
||
"""计算余弦相似度（增加零值处理）"""
|
||
norm = np.linalg.norm(vec1) * np.linalg.norm(vec2)
|
||
return np.dot(vec1, vec2) / (norm + 1e-10) if norm != 0 else 0.0
|
||
|
||
|
||
def fetch_user_behavior_data(days=30):
|
||
"""从MySQL获取用户行为数据（整合旧查询和新需求）"""
|
||
conn = None
|
||
try:
|
||
conn = pymysql.connect(**DB_CONFIG)
|
||
|
||
# 计算日期范围
|
||
end_date = datetime.now()
|
||
start_date = end_date - timedelta(days=days)
|
||
|
||
# 整合查询（获取完整行为数据）
|
||
query = f"""
|
||
SELECT
|
||
account_id,
|
||
behavior_type,
|
||
gender,
|
||
category,
|
||
url,
|
||
create_time
|
||
FROM user_behavior
|
||
WHERE create_time BETWEEN '{start_date}' AND '{end_date}'
|
||
"""
|
||
|
||
df = pd.read_sql(query, conn)
|
||
logging.info(f"成功读取{len(df)}条用户行为记录")
|
||
return df
|
||
|
||
except Exception as e:
|
||
logging.error(f"数据库查询失败: {str(e)}")
|
||
return pd.DataFrame()
|
||
|
||
finally:
|
||
if conn:
|
||
conn.close()
|
||
|
||
|
||
def calculate_heat(row, current_date):
|
||
"""计算单个行为的热度值（每次行为独立计算，不考虑聚合次数）"""
|
||
# 计算时间差（天）
|
||
days_passed = (current_date - row['create_time']).days
|
||
|
||
# 获取行为配置（默认权重为0）
|
||
config = BEHAVIOR_CONFIG.get(row['behavior_type'], {'weight': 0, 'decay': 0})
|
||
|
||
# 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
|
||
返回: (data_array, row_labels, col_labels)
|
||
"""
|
||
with open(file_path) as f:
|
||
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
|
||
)
|
||
|
||
def update_heat_matrices():
|
||
"""每日计算并存储热度矩阵（gender_category × path）"""
|
||
current_date = datetime.now()
|
||
|
||
# 获取数据
|
||
df = fetch_user_behavior_data(30)
|
||
if df.empty:
|
||
logging.warning("无有效数据，跳过今日计算")
|
||
return None
|
||
|
||
# 计算热度值
|
||
df['heat'] = df.apply(calculate_heat, axis=1, current_date=current_date)
|
||
df['gender_category'] = df['gender'] + '_' + df['category']
|
||
|
||
# 构建热度向量
|
||
heat_vectors = {}
|
||
grouped = df.groupby(['gender_category', 'url'])['heat'].sum()
|
||
for (gender_category, url), heat in grouped.items():
|
||
heat_vectors.setdefault(gender_category, {})[url] = heat
|
||
|
||
# 存储结果
|
||
save_path = 'heat_vectors_data'
|
||
os.makedirs(save_path, exist_ok=True)
|
||
date_str = current_date.strftime('%Y%m%d')
|
||
|
||
# vectors_file = f"{save_path}/heat_vectors_{date_str}.json"
|
||
vectors_file = f"{save_path}/heat_vectors.json"
|
||
with open(vectors_file, 'w', encoding='utf-8') as f:
|
||
json.dump({
|
||
'update_time': current_date.strftime('%Y-%m-%d %H:%M:%S'),
|
||
'data': heat_vectors
|
||
}, f, ensure_ascii=False, indent=2)
|
||
|
||
logging.info(f"成功存储热度向量，共{len(heat_vectors)}个分组，日期: {date_str}")
|
||
return heat_vectors
|
||
|
||
|
||
if __name__ == "__main__":
|
||
try:
|
||
# update_user_matrices()
|
||
update_heat_matrices()
|
||
# scheduler = BlockingScheduler()
|
||
# scheduler.add_job(update_user_matrices, 'cron', hour=12, timezone='Asia/Shanghai')
|
||
# logging.info("定时任务已启动，每天12:00执行")
|
||
# scheduler.start()
|
||
except KeyboardInterrupt:
|
||
logging.info("定时任务已停止")
|
||
except Exception as e:
|
||
logging.error(f"调度器启动失败: {str(e)}", exc_info=True)
|