290 lines
10 KiB
Python
290 lines
10 KiB
Python
import configparser
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import pandas as pd
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import sqlalchemy
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from sqlalchemy import create_engine
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from urllib.parse import quote_plus
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# 读取配置文件
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config = configparser.ConfigParser()
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config.read('config.ini')
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# 获取数据库连接配置
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source_config = {
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'host': config.get('source_db', 'host'),
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'user': config.get('source_db', 'user'),
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'password': config.get('source_db', 'password'),
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'database': config.get('source_db', 'database'),
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'port': config.getint('source_db', 'port')
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}
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target_config = {
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'host': config.get('target_db', 'host'),
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'user': config.get('target_db', 'user'),
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'password': config.get('target_db', 'password'),
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'database': config.get('target_db', 'database'),
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'port': config.getint('target_db', 'port')
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}
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def analyze_sql_and_output():
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# 2. 建立数据库连接
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# 创建数据库引擎
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source_engine = create_engine(
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f"mysql+pymysql://{source_config['user']}:{quote_plus(source_config['password'])}@{source_config['host']}:{source_config['port']}/{source_config['database']}"
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)
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target_engine = create_engine(
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f"mysql+pymysql://{target_config['user']}:{quote_plus(target_config['password'])}@{target_config['host']}:{target_config['port']}/{target_config['database']}"
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)
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# 3. 定义SQL查询语句
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collar_sql = """
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SELECT
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bat.AGREEMENT_ID AS agreement_id,
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bmc.ID as lease_id,
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mm.TYPE as type_id,
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mm.ID as ma_id,
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ttm.CREATE_TIME as start_time,
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1 as num,
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IF(bma.IS_BALANCE = 0,1,0) as is_slt,
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bma.SETTLEMENT_TIME as slt_time,
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mt.IS_COUNT,
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mt.lease_price
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FROM
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ba_ma_collar bmc
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LEFT JOIN ba_agreement_task bat on bmc.ID = bat.TASK_ID
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LEFT JOIN ba_ma_agreement bma on bat.AGREEMENT_ID = bma.ID
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LEFT JOIN tm_task_ma ttm on bmc.ID = ttm.TASK_ID
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LEFT JOIN ma_machines mm on ttm.MA_ID = mm.ID
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LEFT JOIN ma_type mt on mm.TYPE = mt.ID
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WHERE bmc.company_id =1 AND mt.IS_COUNT = 0
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UNION
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SELECT
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bat.AGREEMENT_ID AS agreement_id,
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bmc.ID as lease_id,
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ttot.MA_TYPE_ID as type_id,
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null as ma_id,
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ttot.CREATE_TIME as start_time,
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ttot.NUM as num,
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IF(bma.IS_BALANCE = 0,1,0) as is_slt,
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bma.SETTLEMENT_TIME as slt_time,
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mt.IS_COUNT,
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mt.lease_price
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FROM
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ba_ma_collar bmc
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LEFT JOIN ba_agreement_task bat on bmc.ID = bat.TASK_ID
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LEFT JOIN ba_ma_agreement bma on bat.AGREEMENT_ID = bma.ID
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LEFT JOIN tm_task_out_type ttot on bmc.ID = ttot.TASK_ID
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LEFT JOIN ma_type mt on ttot.MA_TYPE_ID = mt.ID
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WHERE bmc.company_id =1 AND ttot.IS_COUNT =1;
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"""
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back_sql = """
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SELECT
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bat.AGREEMENT_ID AS agreement_id,
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bmb.ID as back_id,
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mm.TYPE as type_id,
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mm.ID as ma_id,
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ttm.CREATE_TIME as end_time,
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1 as num,
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mt.IS_COUNT
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FROM
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ba_ma_back bmb
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LEFT JOIN ba_agreement_task bat on bmb.ID = bat.TASK_ID
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LEFT JOIN ba_ma_agreement bma on bat.AGREEMENT_ID = bma.ID
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LEFT JOIN tm_task_ma ttm on bmb.ID = ttm.TASK_ID
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LEFT JOIN ma_machines mm on ttm.MA_ID = mm.ID
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LEFT JOIN ma_type mt on mm.TYPE = mt.ID
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WHERE mt.IS_COUNT = 0
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UNION
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SELECT
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bat.AGREEMENT_ID AS agreement_id,
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bmb.id as back_id,
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ttmt.MA_TYPE_ID as type_id,
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null as maId,
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bmb.CREATE_TIME as end_time,
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ROUND(ttmt.MACHINES_NUM,3) as num,
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ttmt.IS_COUNT
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FROM
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ba_ma_back bmb
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LEFT JOIN ba_agreement_task bat on bmb.ID = bat.TASK_ID
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LEFT JOIN ba_ma_agreement bma on bat.AGREEMENT_ID = bma.ID
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LEFT JOIN tm_task_ma_type ttmt on bmb.ID = ttmt.TASK_ID
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LEFT JOIN pm_user pu on bmb.CREATOR = pu.ID
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WHERE ttmt.IS_COUNT =1
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"""
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price_sql = """
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SELECT
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ph.id,
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ph.type_id,
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ph.start_time,
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ph.end_time,
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ph.lease_price
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FROM
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lease_price_his ph
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WHERE is_active =1
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"""
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# 4. 执行SQL查询获取原始数据
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collar_df = pd.read_sql(collar_sql, source_engine)
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back_df = pd.read_sql(back_sql, source_engine)
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price_df = pd.read_sql(price_sql, source_engine)
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# 5. 处理数据，按照规则进行匹配
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result_list = []
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# 处理IS_COUNT=0的情况
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count0_collar = collar_df[collar_df['IS_COUNT'] == 0]
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for _, row in count0_collar.iterrows():
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# 在退料查询中查找相同的agreement_id和ma_id
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matched_back = back_df[(back_df['agreement_id'] == row['agreement_id']) &
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(back_df['ma_id'] == row['ma_id'])]
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if not matched_back.empty:
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end_time = matched_back.iloc[0]['end_time']
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back_id = matched_back.iloc[0]['back_id']
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status = '已退料'
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else:
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end_time = None
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back_id = None
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status = '未退料'
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# 检查租赁单价区间
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lease_price = row['lease_price']
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price_match = price_df[(price_df['type_id'] == row['type_id']) &
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(price_df['start_time'] <= row['start_time']) &
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(price_df['end_time'] >= row['start_time'])]
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if not price_match.empty:
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lease_price = price_match.iloc[0]['lease_price']
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# 构建结果行
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result_row = {
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'agreement_id': row['agreement_id'],
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'type_id': row['type_id'],
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'ma_id': row['ma_id'],
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'num': row['num'],
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'start_time': row['start_time'],
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'end_time': end_time,
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'status': status,
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'lease_id': row['lease_id'],
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'back_id': back_id,
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'lease_price': lease_price,
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'buy_price': None, # 根据实际情况补充
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'is_slt': row['is_slt'],
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'slt_time': row['slt_time']
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}
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result_list.append(result_row)
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# 处理IS_COUNT=1的情况
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count1_collar = collar_df[collar_df['IS_COUNT'] == 1]
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for _, row in count1_collar.iterrows():
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# 按时间顺序正序查找相同的agreement_id和type_id
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matched_backs = back_df[(back_df['agreement_id'] == row['agreement_id']) &
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(back_df['type_id'] == row['type_id'])].sort_values('end_time')
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remaining_num = float(row['num'])
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for _, back_row in matched_backs.iterrows():
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if float(remaining_num) <= 0:
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break
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if back_row['num'] == remaining_num:
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# 数量相等，直接匹配
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result_row = {
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'agreement_id': row['agreement_id'],
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'type_id': row['type_id'],
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'ma_id': row['ma_id'],
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'num': remaining_num,
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'start_time': row['start_time'],
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'end_time': back_row['end_time'],
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'status': '已退料',
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'lease_id': row['lease_id'],
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'back_id': back_row['back_id'],
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'lease_price': row['lease_price'],
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'buy_price': None,
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'is_slt': row['is_slt'],
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'slt_time': row['slt_time']
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}
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result_list.append(result_row)
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remaining_num = 0.0
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elif float(back_row['num']) < remaining_num:
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# 退料数量小于领料数量，拆分
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result_row1 = {
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'agreement_id': row['agreement_id'],
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'type_id': row['type_id'],
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'ma_id': row['ma_id'],
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'num': back_row['num'],
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'start_time': row['start_time'],
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'end_time': back_row['end_time'],
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'status': '已退料',
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'lease_id': row['lease_id'],
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'back_id': back_row['back_id'],
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'lease_price': row['lease_price'],
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'buy_price': None,
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'is_slt': row['is_slt'],
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'slt_time': row['slt_time']
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}
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result_list.append(result_row1)
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remaining_num -= float(back_row['num'])
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else:
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# 退料数量大于领料数量，匹配剩余部分
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result_row = {
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'agreement_id': row['agreement_id'],
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'type_id': row['type_id'],
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'ma_id': row['ma_id'],
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'num': remaining_num,
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'start_time': row['start_time'],
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'end_time': back_row['end_time'],
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'status': '已退料',
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'lease_id': row['lease_id'],
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'back_id': back_row['back_id'],
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'lease_price': row['lease_price'],
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'buy_price': None,
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'is_slt': row['is_slt'],
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'slt_time': row['slt_time']
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}
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result_list.append(result_row)
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remaining_num = 0
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# 处理未匹配完的领料
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if float(remaining_num) > 0:
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result_row = {
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'agreement_id': row['agreement_id'],
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'type_id': row['type_id'],
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'ma_id': row['ma_id'],
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'num': remaining_num,
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'start_time': row['start_time'],
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'end_time': None,
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'status': '未退料',
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'lease_id': row['lease_id'],
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'back_id': None,
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'lease_price': row['lease_price'],
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'buy_price': None,
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'is_slt': row['is_slt'],
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'slt_time': row['slt_time']
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}
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result_list.append(result_row)
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# 6. 创建结果DataFrame并输出到目标表
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result_df = pd.DataFrame(result_list)
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# 检查租赁单价区间并更新lease_price
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for idx, row in result_df.iterrows():
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if pd.isna(row['start_time']):
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continue
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price_match = price_df[(price_df['type_id'] == row['type_id']) &
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(price_df['start_time'] <= row['start_time']) &
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(price_df['end_time'] >= row['start_time'])]
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if not price_match.empty:
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result_df.at[idx, 'lease_price'] = price_match.iloc[0]['lease_price']
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# 输出到目标表
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result_df.to_sql('slt_agreement_info', target_engine, if_exists='replace', index=False)
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print("数据处理完成，已输出到slt_agreement_info表")
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# 执行函数
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analyze_sql_and_output()
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