import configparser
import pandas as pd
from sqlalchemy import create_engine
from urllib.parse import quote_plus
# 读取配置文件
config = configparser.ConfigParser()
config.read('config.ini')

# 获取数据库连接配置
source_config = {
    'host': config.get('source_db', 'host'),
    'user': config.get('source_db', 'user'),
    'password': config.get('source_db', 'password'),
    'database': config.get('source_db', 'database'),
    'port': config.getint('source_db', 'port')
}

target_config = {
    'host': config.get('target_db', 'host'),
    'user': config.get('target_db', 'user'),
    'password': config.get('target_db', 'password'),
    'database': config.get('target_db', 'database'),
    'port': config.getint('target_db', 'port')
}

# 创建数据库引擎
source_engine = create_engine(
    f"mysql+pymysql://{source_config['user']}:{quote_plus(source_config['password'])}@{source_config['host']}:{source_config['port']}/{source_config['database']}"
)
target_engine = create_engine(
    f"mysql+pymysql://{target_config['user']}:{quote_plus(target_config['password'])}@{target_config['host']}:{target_config['port']}/{target_config['database']}"
)

# 定义状态映射
status_mapping = {
    16: 0,  # 待入库 → 0
    17: 1,  # 在库 → 1
    18: 2,  # 在用 → 2
    19: 3,  # 退料检修 → 3
    23: 7,  # 待报废 → 7
    24: 8,  # 已报废 → 8
    64: 18,  # 丢失 → 18
    133: 4  # 待审批 → 4
}


def normalize_date_string(s):
    if pd.isnull(s):
        return None
    if isinstance(s, str):
        s = s.replace('－', '-').strip()
        try:
            return pd.to_datetime(s, errors='coerce')  # 自动识别格式
        except:
            return None
    return pd.to_datetime(s, errors='coerce')  # 如果已经是日期，仍然尝试转化

def process_machines():
    """处理ma_machines表数据到ma_machine"""
    try:
        # 读取源数据
        df = pd.read_sql("SELECT * FROM ma_machine", source_engine)

        # 按照规则转换数据
        result = pd.DataFrame()
        result['ma_id'] = df['ID'] + 70000  # ID加70000
        result['type_id'] = df['MATYPE_ID'] + 6000  # 类型ID加6000
        result['ma_status'] = df['STATUS'].map(status_mapping)  # 状态映射
        result['ma_code'] = df['CODE']
        result['out_fac_time'] = df['OUTFACTORT_TIME'].apply(normalize_date_string)
        result['out_fac_code'] = df['OUTCODE']
        result['ma_vender'] = df['SUPPLIER_ID'] + 500  # 供应商ID加500
        result['assets_code'] = df['ASSET_NUM']
        result['rfid_code'] = df['EPC_CODE']
        result['this_check_time'] = df['REPAIR_TIME'].apply(normalize_date_string)
        result['check_man'] = df['EXAMINER']
        result['next_check_time'] = df['NEXT_REPAIR_TIME'].apply(normalize_date_string)
        result['inspect_status'] = df['TESTRESULT']
        result['ex_code'] = df['EX_CODE']
        result['ex_url'] = df['EX_URL']
        result['ex_up_time'] = df['EX_UP_TIME'].apply(normalize_date_string)
        result['ex_name'] = df['EX_NAME']

        # 写入目标表
        result.to_sql('ma_machine', target_engine,
                      if_exists='append', index=False)

        print(f"成功转换并导入 {len(result)} 条记录到 ma_machine")
        return True

    except Exception as e:
        print(f"处理 ma_machines 时发生错误: {str(e)}")
        return False


if __name__ == "__main__":
    process_machines()