363 lines
14 KiB
Python
363 lines
14 KiB
Python
"""
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定频轮询脚本：从Excel文件导入关键词到baidu_keyword表
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"""
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import pandas as pd
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import logging
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import os
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import time
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import glob
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from database_config import DatabaseManager, DB_CONFIG
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from datetime import datetime
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# 配置
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POLL_INTERVAL = 60 # 轮询间隔（秒）
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UPLOAD_FOLDER = 'query_upload' # Excel文件目录
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SEED_ID = 9999 # 固定值
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SEED_NAME = '手动提交' # 固定值
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CRAWLED = 1 # 固定值
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QUERY_COLUMN = 'query' # Excel中query列名
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DEPT_COLUMN = '科室' # Excel中科室列名
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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logger = logging.getLogger(__name__)
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def read_excel_keywords_with_department(excel_path, query_column='query', department_column='科室'):
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"""
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读取Excel文件中的关键词和部门信息
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Args:
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excel_path: Excel文件路径
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query_column: query列名，默认为'query'
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department_column: 部门列名，默认为'科室'，如果为None则不读取部门信息
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Returns:
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包含(keyword, department)元组的列表，如果没有部门列则department为None
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"""
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try:
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# 读取Excel文件
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df = pd.read_excel(excel_path)
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logger.info(f"成功读取Excel文件: {excel_path}")
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logger.info(f"Excel文件包含 {len(df)} 行数据")
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logger.info(f"Excel列名: {df.columns.tolist()}")
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# 检查query列是否存在
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if query_column not in df.columns:
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logger.error(f"未找到query列: {query_column}")
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return []
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# 检查是否有部门列
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has_department = department_column and department_column in df.columns
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if department_column and not has_department:
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logger.warning(f"未找到department列: {department_column}，将不使用部门信息")
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# 获取query数据
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query_data = df[query_column].dropna()
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query_list = query_data.tolist()
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# 根据是否有部门列，组合数据
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keyword_dept_pairs = []
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if has_department:
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# 有部门列，获取部门数据
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department_data = df[department_column].dropna()
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# 对齐数据长度，取最短长度
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min_length = min(len(query_data), len(department_data))
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query_list = query_data.iloc[:min_length].tolist()
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department_list = department_data.iloc[:min_length].tolist()
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for i in range(min_length):
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keyword = str(query_list[i]).strip()
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department = str(department_list[i]).strip()
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if keyword and department: # 确保关键词和部门都不为空
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keyword_dept_pairs.append((keyword, department))
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else:
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# 没有部门列，只提取关键词
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logger.info("没有部门列，将只导入关键词，不指定科室")
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for keyword in query_list:
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keyword = str(keyword).strip()
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if keyword: # 确保关键词不为空
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keyword_dept_pairs.append((keyword, None))
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# 去除重复项，保留第一个出现的组合
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seen = set()
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unique_keyword_dept_pairs = []
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for keyword, dept in keyword_dept_pairs:
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if (keyword, dept) not in seen:
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seen.add((keyword, dept))
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unique_keyword_dept_pairs.append((keyword, dept))
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if has_department:
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logger.info(f"提取到 {len(unique_keyword_dept_pairs)} 个唯一的关键词-部门组合")
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else:
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logger.info(f"提取到 {len(unique_keyword_dept_pairs)} 个唯一的关键词")
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return unique_keyword_dept_pairs
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except Exception as e:
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logger.error(f"读取Excel文件失败: {e}", exc_info=True)
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raise
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def get_department_id(db_manager, department_name):
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"""
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根据科室名称从ai_departments表中获取对应的ID
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Args:
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db_manager: 数据库管理器实例
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department_name: 科室名称
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Returns:
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科室ID，如果未找到则抛出异常
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"""
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try:
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# 查询科室ID - 使用正确的字段名
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sql = "SELECT id FROM ai_departments WHERE department_name = %s"
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result = db_manager.execute_query(sql, (department_name,), fetch_one=True)
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if result:
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return result[0] # 返回ID
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else:
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error_msg = f"未找到科室 '{department_name}' 的ID，请先在ai_departments表中添加该科室"
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logger.error(error_msg)
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raise ValueError(error_msg)
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except Exception as e:
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logger.error(f"查询科室ID失败: {e}", exc_info=True)
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raise
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def get_author_info_by_department(db_manager, department_id):
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"""
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根据科室ID从ai_authors表中随机获取一个符合条件的作者信息
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Args:
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db_manager: 数据库管理器实例
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department_id: 科室ID
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Returns:
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(author_id, author_name) 元组，如果未找到则返回 (0, '')
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"""
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try:
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# 随机获取该科室下的一个活跃作者
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sql = "SELECT id, author_name FROM ai_authors WHERE department_id = %s AND status = 'active' AND daily_post_max > 0 ORDER BY RAND() LIMIT 1"
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result = db_manager.execute_query(sql, (department_id,), fetch_one=True)
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if result:
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return result[0], result[1]
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else:
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logger.warning(f"未找到科室ID {department_id} 下符合条件的活跃作者")
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return 0, ''
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except Exception as e:
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logger.error(f"查询作者信息失败: {e}")
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return 0, ''
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def import_keywords_to_db(db_manager, keyword_dept_pairs, seed_id=9999, seed_name='手动提交', crawled=1, batch_size=100, sleep_seconds=0.1):
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"""
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将关键词批量导入到baidu_keyword表
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Args:
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db_manager: 数据库管理器实例
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keyword_dept_pairs: 包含(keyword, department)元组的列表
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seed_id: 种子ID，默认9999
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seed_name: 种子名称，默认'手动提交'
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crawled: 是否已爬取，默认1
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batch_size: 日志批次大小，每多少条记录输出一次进度
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sleep_seconds: 每条记录间隔睡眠时间（秒），默认0.1秒
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Returns:
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成功导入的数量
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"""
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if not keyword_dept_pairs:
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logger.warning("没有关键词需要导入")
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return 0
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try:
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logger.info(f"开始导入 {len(keyword_dept_pairs)} 个关键词-部门组合到数据库...")
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logger.info("采用逐条查询+插入模式，避免重复")
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# 准备SQL语句
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check_sql = "SELECT COUNT(*) FROM baidu_keyword WHERE keyword = %s"
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insert_sql = """
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INSERT INTO baidu_keyword (keyword, seed_id, seed_name, crawled, parents_id, created_at, department, department_id, query_status, author_id, author_name)
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VALUES (%s, %s, %s, %s, 0, NOW(), %s, %s, %s, %s, %s)
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"""
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success_count = 0
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skip_count = 0
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failed_count = 0
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# 逐条处理
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for idx, (keyword, department) in enumerate(keyword_dept_pairs, 1):
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try:
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if department:
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logger.debug(f'[调试] 处理第 {idx}/{len(keyword_dept_pairs)} 条: {keyword}, 部门: {department}')
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else:
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logger.debug(f'[调试] 处理第 {idx}/{len(keyword_dept_pairs)} 条: {keyword}, 无部门信息')
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# 1. 如果有部门信息，获取科室ID和作者信息
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if department:
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dept_id = get_department_id(db_manager, department)
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author_id, author_name = get_author_info_by_department(db_manager, dept_id)
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else:
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# 没有部门信息，使用默认值（空字符串而不是None，避免数据库NOT NULL约束）
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department = ''
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dept_id = 0
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author_id = 0
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author_name = ''
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# 2. 查询关键词是否存在
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result = db_manager.execute_query(check_sql, (keyword,), fetch_one=True)
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exists = result[0] > 0 if result else False
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if exists:
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skip_count += 1
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logger.debug(f'[调试] 关键词已存在，跳过: {keyword}')
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else:
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# 3. 不存在则插入
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if department:
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logger.debug(f'[调试] 准备插入: {keyword}, 部门: {department}, 部门ID: {dept_id}, 作者ID: {author_id}, 作者名: {author_name}, query_status: manual_review')
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else:
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logger.debug(f'[调试] 准备插入: {keyword}, 无部门信息, query_status: manual_review')
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affected = db_manager.execute_update(
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insert_sql,
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(keyword, seed_id, seed_name, crawled, department, dept_id, 'manual_review', author_id, author_name),
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autocommit=True
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)
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if affected > 0:
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success_count += 1
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if department:
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logger.debug(f'[调试] 插入成功: {keyword}, 部门: {department}, 部门ID: {dept_id}, 作者ID: {author_id}, 作者名: {author_name}, query_status: manual_review')
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else:
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logger.debug(f'[调试] 插入成功: {keyword}, 无部门信息, query_status: manual_review')
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# 5. 输出进度
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if idx % batch_size == 0 or idx == len(keyword_dept_pairs):
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progress = (idx / len(keyword_dept_pairs)) * 100
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logger.info(f'[插入进度] {idx}/{len(keyword_dept_pairs)} ({progress:.1f}%) | 成功: {success_count} | 跳过: {skip_count} | 失败: {failed_count}')
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# 6. 每次执行完sleep
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time.sleep(sleep_seconds)
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except ValueError as ve:
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# 遇到科室不存在的错误，停止整个导入过程
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logger.error(f'[错误] 第 {idx} 条记录遇到错误: {ve}')
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raise ve
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except Exception as e:
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failed_count += 1
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logger.warning(f'[调试] 处理失败 [{idx}/{len(keyword_dept_pairs)}]: keyword={keyword}, 部门={department}，错误：{e}')
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logger.info(f"导入完成！成功插入: {success_count} | 跳过已存在: {skip_count} | 失败: {failed_count}")
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return success_count
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except Exception as e:
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logger.error(f"导入关键词失败: {e}", exc_info=True)
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raise
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def process_single_file(db_manager, excel_path):
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"""
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处理单个Excel文件
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Args:
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db_manager: 数据库管理器实例
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excel_path: Excel文件路径
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Returns:
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成功导入的数量
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"""
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logger.info(f"开始处理文件: {os.path.basename(excel_path)}")
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# 读取Excel文件
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keyword_dept_pairs = read_excel_keywords_with_department(excel_path, QUERY_COLUMN, DEPT_COLUMN)
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if not keyword_dept_pairs:
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logger.warning(f"文件 {os.path.basename(excel_path)} 中没有可导入的数据")
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return 0
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# 执行导入
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success_count = import_keywords_to_db(
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db_manager=db_manager,
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keyword_dept_pairs=keyword_dept_pairs,
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seed_id=SEED_ID,
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seed_name=SEED_NAME,
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crawled=CRAWLED
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)
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return success_count
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def poll_once(db_manager):
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"""
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执行一次轮询
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Returns:
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处理的文件数
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"""
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# 确保目录存在
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if not os.path.exists(UPLOAD_FOLDER):
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os.makedirs(UPLOAD_FOLDER)
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logger.info(f"创建目录: {UPLOAD_FOLDER}")
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return 0
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# 查找Excel文件
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excel_files = []
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for pattern in ['*.xlsx', '*.xls']:
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excel_files.extend(glob.glob(os.path.join(UPLOAD_FOLDER, pattern)))
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if not excel_files:
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return 0
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logger.info(f"发现 {len(excel_files)} 个Excel文件待处理")
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processed_count = 0
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for excel_path in excel_files:
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try:
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success_count = process_single_file(db_manager, excel_path)
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# 处理成功后删除文件
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os.remove(excel_path)
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logger.info(f"✓ 文件处理完成并已删除: {os.path.basename(excel_path)} (导入 {success_count} 条)")
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processed_count += 1
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except Exception as e:
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logger.error(f"✗ 处理文件失败: {os.path.basename(excel_path)}, 错误: {e}")
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# 失败时不删除文件，保留以便排查
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return processed_count
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def main():
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"""主函数 - 定频轮询模式"""
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logger.info("=" * 60)
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logger.info("定频轮询脚本启动")
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logger.info(f"轮询间隔: {POLL_INTERVAL} 秒")
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logger.info(f"监控目录: {UPLOAD_FOLDER}")
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logger.info(f"固定配置: seed_id={SEED_ID}, seed_name='{SEED_NAME}', crawled={CRAWLED}")
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logger.info("=" * 60)
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# 创建数据库管理器
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db_manager = DatabaseManager(DB_CONFIG)
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# 定频轮询
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while True:
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try:
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processed = poll_once(db_manager)
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if processed > 0:
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logger.info(f"本轮处理完成，共处理 {processed} 个文件")
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except Exception as e:
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logger.error(f"轮询出错: {e}")
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# 等待下一轮
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time.sleep(POLL_INTERVAL)
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if __name__ == '__main__':
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main() |