logistics/物流t投递审核.py

"""
fetch_order_data函数只是获取源数据,是一个sql语句，可以更改
cal_min_fee 函数是分别以一票一件和一票多件计算出最小的费用和渠道
analyze_orders 订单层面的业务逻辑判断，防止出现混合渠道投递，卡派订单包含多个不同快递追踪单号，多渠道订单总重量小于1000KG(因为1000KG以内一个卡派可以搞定，不应该出现多渠道)
analyze_logistics 真正的物流投递层面去分析，先判断投递渠道和最优渠道是否一致，再判断偶发估算费用和最优渠道费用是否一致
"""
import pandas as pd
from utils.gtools import MySQLconnect
from utils.logisticsBill import BillFactory, Billing
from utils.countryOperator import OperateCountry
from utils.Package import Package, Package_group
from utils.logistics_name_config import logistics_name
# 获取数据
def fetch_order_data():
    """从数据库获取原始订单数据"""
    with MySQLconnect('ods') as db:
        sql = """
        SELECT
        ol.order_date,
        ol.fund_status,
        oe.`包裹状态`,
            oe.包裹号 AS package,
            oe.单号 AS order_id,
                        oe.运输方式,
                        oe.`目的国`,
            ol.postcode AS postcode,
                        oe.`快递分区`,
            oe.快递跟踪号,
            ecm.类型 AS 渠道类型, -- 包裹类型
            pvi.length AS 长,
            pvi.width AS 宽,
            pvi.hight AS 高,
            pvi.weight AS 重量,
            pfi.express_fee AS 基础估算,
            pfi.express_additional_fee AS 偶发估算,
            pfi.express_fee + pfi.express_additional_fee AS 总估算,
            
            oe.快递公司
        FROM
            ods.order_express oe
            LEFT JOIN ods.express_company ecm ON oe.快递公司 = ecm.快递公司
            LEFT JOIN ods.package_vol_info pvi ON oe.包裹号 = pvi.package
            LEFT JOIN ods.package_fee_info pfi ON oe.包裹号 = pfi.package
            LEFT JOIN ods.order_list ol ON oe.单号 = ol.order_id
        WHERE
            oe.包裹状态 REGEXP '已经投递|发货仓出库'
            AND oe.`快递公司` NOT REGEXP "--"
            AND `卡板发货时间` REGEXP "--"
            AND ol.fund_status NOT REGEXP '等待|全额退款'
            AND ol.site_name REGEXP 'litfad|kwoking|lakiq'
            AND oe.投递时间 >= DATE_SUB(NOW(), INTERVAL 3 DAY) 
						AND pvi.length>0 AND pvi.width >0 AND pvi.hight>0 AND pvi.weight>0
            and oe.目的国 regexp 'United States'
        """

        return pd.read_sql(sql, db.engine())
    

def cal_min_fee(raw_data: pd.DataFrame):
    """
    处理物流费用数据并实现业务逻辑判断
    1.用
    """    
    df = raw_data.copy()
    # 包裹层面审核
    for order_id, group in df.groupby('order_id'):
        package_group = Package_group()
        opCountry = OperateCountry(group['目的国'].iloc[0])
        express_fee = 0
        for index, row in group.iterrows():
            # 计算一票一件
            packages=Package_group()
            package = Package(row['package'], row['长'], row['宽'], row['高'], row['重量'])
            packages.add_package(package)
            bill_express = Billing("1",opCountry,packages,row['postcode'],company_name=None,head_type=1,beizhu="") 
            if bill_express.tail_amount[0] == 0 or bill_express.tail_amount[0] >=9999:
                df.loc[index,"快递尾端费用"] = "不可派"
                express_fee = 999999
            else:
                df.loc[index,"快递尾端费用"] = bill_express.tail_amount[0]
                df.loc[index,"快递尾端渠道"] = bill_express.company_name
                express_fee += bill_express.tail_amount[0]
            # 计算一票多件
            package_group.add_package(package)
        # 计算一票多件
        if len(package_group) > 1:
            bill_ltl = Billing("1",opCountry,package_group,row['postcode'],company_name=None,head_type=1,beizhu="") 
            df.loc[df['order_id']==order_id,'卡派尾端费用'] = bill_ltl.tail_amount[0]/len(package_group)
            df.loc[df['order_id']==order_id,'卡派尾端渠道'] = bill_ltl.company_name
            min_fee = min(bill_ltl.tail_amount[0],express_fee)
        else:
            min_fee = express_fee
        if min_fee == express_fee:
            df.loc[df['order_id']==order_id,'最优总物流费用'] = min_fee
            df.loc[df['order_id']==order_id,'最优渠道类型'] = "快递"
        else:
            df.loc[df['order_id']==order_id,'最优总物流费用'] = min_fee
            df.loc[df['order_id']==order_id,'最优渠道类型'] = "卡派"
        df.loc[df['order_id']==order_id,'尾端货币'] = bill_ltl.tail_amount[1]
    return df

# 订单层面审核，防止出现混合渠道投递，卡派订单包含多个不同快递单号，多渠道订单总重量小于1000KG
def analyze_orders(raw_data: pd.DataFrame):
    """
    处理订单数据并实现业务逻辑判断
    返回聚合后的订单数据和分析结果，包裹信息按指定字典格式输出
    
    """
    data = raw_data.copy()

    # 1. 预处理 - 处理空值
    data.fillna({
        '渠道类型': '未知类型',
        '基础估算': 0,
        '偶发估算': 0,
        '总估算': 0,
        '重量': 0,
        '长': 0,
        '宽': 0,
        '高': 0,
        'postcode': '未知'
    }, inplace=True)

    # 2. 按订单聚合数据
    def create_package_details(group):
        """创建包裹详情字典，严格按照要求的格式"""
        details = {}
        for i, (_, row) in enumerate(group.iterrows(), 1):
            details[f"包裹{i}"] = {
                "宽": f"{float(row['宽']):.2f}",
                "长": f"{float(row['长']):.2f}",
                "高": f"{float(row['高']):.2f}",
                "重量": f"{float(row['重量']):.2f}"
            }
        return details

    grouped = data.groupby('order_id')

    aggregated = pd.DataFrame({
        '包裹数量': grouped.size(),
        '总重量': grouped['重量'].sum(),
        '总基础估算': grouped['基础估算'].sum(),
        '总附加估算': grouped['偶发估算'].sum(),
        '总物流估算': grouped['总估算'].sum(),
        '包裹数据': grouped.apply(create_package_details),  # 使用新函数
        '快递公司列表': grouped['快递公司'].unique(),
        '渠道类型列表': grouped['渠道类型'].unique(),
        '邮编列表': grouped['postcode'].first(),
        '快递跟踪号': grouped['快递跟踪号'].unique()
    }).reset_index()

    # 3. 实现业务逻辑判断（保持不变）
    def determine_order_type(row):
        if len(row['渠道类型列表']) > 1:
            return '混合'
        elif len(row['渠道类型列表']) == 1:
            return row['渠道类型列表'][0]
        else:
            return '未知类型'

    def determine_channel_type(row):
        if len(row['快递公司列表']) > 1:
            return '多渠道'
        else:
            return '单渠道'

    aggregated['订单类型'] = aggregated.apply(determine_order_type, axis=1)
    aggregated['渠道种类'] = aggregated.apply(determine_channel_type, axis=1)

    # 4. 实现业务规则检查（保持不变）
    def apply_business_rules(row):
        actions = []
        status = '正常'
        comments = []

        if row['订单类型'] == '卡派' and len(row['快递跟踪号']) > 1:
            # tracking_nos = [list(p.values())[0] for p in row['包裹数据'].values()]
            # if len(set(tracking_nos)) > 1:
            #     status = '异常'
            status = '异常'
            comments.append('卡派订单包含多个不同快递单号')
        elif row['订单类型'] == '混合':
            status = '异常'
            comments.append('出现混合渠道类型订单，需要核查')

        if row['渠道种类'] == '多渠道':
            if row['总重量'] < 1000:
                comments.append(f'多渠道订单总重量{row["总重量"]:.2f}KG < 1000KG')

        return pd.Series({
            '状态': status,
            '建议操作': '; '.join(actions) if actions else '下一步',
            '备注': ' | '.join(comments) if comments else ''
        })

    rule_results = aggregated.apply(apply_business_rules, axis=1)
    aggregated = pd.concat([aggregated, rule_results], axis=1)

    # 5. 整理最终输出列
    final_columns = [
        'order_id', '订单类型', '渠道种类',
        '包裹数量', '总重量',
        '总基础估算', '总附加估算', '总物流估算',
        '快递公司列表', '邮编列表',
        '包裹数据' ,'状态', '备注','快递跟踪号'# 使用新列名
    ]

    return aggregated[final_columns]

# 物流费用层面审核
def analyze_logistics(df: pd.DataFrame):
    """
    1.判断实际投递物流渠道和cal_min_fee计算的最优物流渠道是否一致
    2.物流渠道一致的情况下，判断费用是否一样
    """
    # 1. 计算最优渠道和费用
    df= cal_min_fee(df)
    # 判断渠道是否一致
    df['最优渠道'] = df.apply(lambda row: row['快递尾端渠道'] if row['最优渠道类型'] == "快递" else row['卡派尾端渠道'], axis=1)
    df['渠道一致'] = df.apply(lambda row: row['最优渠道'] == logistics_name.get(row['快递公司']), axis=1)
    # 2. 计算费用是否一致
    def all_estimate(row):
        if row['总估算'] is None or row['总估算'] ==0:
            return "暂无系统估算值"
        if row['最优总物流费用'] is None or row['最优总物流费用'] ==0:
            return "暂无最优费用"
        if row['尾端货币'] == "USD":
            all_estimate= row['总估算']/7
        elif row['尾端货币'] == "GBP":
            all_estimate = row['总估算']/9
        elif row['尾端货币'] == "EUR":
            all_estimate = row['总估算']/8
        elif row['尾端货币'] == "AUD":
            all_estimate = row['总估算']/5
        elif row['尾端货币'] == "CAD":
            all_estimate = row['总估算']/5
        elif row['尾端货币'] == "JPY":
            all_estimate = row['总估算']/0.05
        return all_estimate
    
    
    df['费用一致'] = df.apply(lambda row: False if isinstance(all_estimate(row), str) else abs(all_estimate(row) - row['最优总物流费用']) < 1,axis=1)

    df['费用差(当地货币)'] = df.apply(lambda row: "费用有误" if isinstance(all_estimate(row), str) else row['最优总物流费用'] - all_estimate(row),axis=1)
    return df

def main():
    # 获取数据
    raw_data = fetch_order_data()
    print('已获取数据')
    # 订单层面审核
    order_result = analyze_orders(raw_data)
    print('已完成订单层面审核')
    order_result.to_excel(r'D:\test\logistics\拦截数据\order_analysis.xlsx', index=False)
    # 计算最优渠道和费用
    raw_data = analyze_logistics(raw_data)
    print('已完成物流费用层面审核')
    raw_data.to_excel(r'D:\test\logistics\拦截数据\logistics_analysis.xlsx', index=False)
    
if __name__ == '__main__':

    main()