更新物流投递审核代码
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@ -649,8 +649,14 @@ class XmilesLogistics_US(TailLogistics):
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detail_amount['tail_amount'] = 99999
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return detail_amount
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postcode = int(postcode[:5])
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# 判断邮编是美东还是美西
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zone = self.postcode_table[self.postcode_table['POSTCODE'] == postcode]['地区']
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if zone.empty:
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detail_amount['tail_amount'] = 99999
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return detail_amount
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base_fee_list = set() # 将每个包裹费用都记录在列表中
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base_fee_list = [] # 将每个包裹费用都记录在列表中
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volume_weight = 0 # 计算抛重，用于计算美西转运美东价格
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for package in packages:
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volume_weight +=package.get_volume_weight(6000)
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@ -658,31 +664,26 @@ class XmilesLogistics_US(TailLogistics):
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detail_amount['tail_amount'] = 99999
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return detail_amount
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if package.lbs_weight <=90 and package.fst_inch <=96 and package.girth_inch <=130:
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base_fee_list.add(20)
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base_fee_list.append(20)
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elif package.lbs_weight <=150 and package.fst_inch <=96 and package.girth_inch <=130:
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base_fee_list.add(38)
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base_fee_list.append(38)
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elif package.lbs_weight <=150 and package.fst_inch <=108 and package.girth_inch <=165:
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base_fee_list.add(50)
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base_fee_list.append(50)
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elif package.lbs_weight <=200 and package.fst_inch <=144 and package.girth_inch <=225:
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base_fee_list.add(75)
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base_fee_list.append(75)
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else:
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base_fee_list.add(75+0.55*math.ceil(package.lbs_weight))
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base_fee_list.append(75+0.55*math.ceil(package.lbs_weight))
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# 取所有费用,最大的费用取全部费用，其他取半价然后求和
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base_fee_list = sorted(base_fee_list,reverse=True)
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# 计算base_fee_list最大值+其他费用的一半
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if len(base_fee_list) == 0:
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detail_amount['tail_amount'] = 99999
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return detail_amount
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elif len(base_fee_list) == 1:
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detail_amount["base"] = base_fee_list[0]
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else:
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detail_amount["base"] = base_fee_list[0] + sum(base_fee_list[1:])/2
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# 判断邮编是美东还是美西
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zone = self.postcode_table[self.postcode_table['POSTCODE'] == postcode]['地区']
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if zone.empty:
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detail_amount['tail_amount'] = 99999
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return detail_amount
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zone = zone.iloc[0]
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if zone =="NJ":
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detail_amount["transfer"] = 0.5*volume_weight
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@ -1,14 +1,14 @@
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import pandas as pd
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import math
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express_price = pd.read_excel(r'D:\test\logistics\data\售价尾端价格.xlsx', sheet_name='Sheet1')
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key_column = express_price.iloc[:, 8] # 第 I 列
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value_column = express_price.iloc[:, 9] # 第 J 列
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small_column = express_price.iloc[:, 10] # 第 K 列
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big_column = express_price.iloc[:, 11] # 第 L 列
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air_small_dict = dict(zip(key_column, small_column))
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air_big_dict = dict(zip(key_column, big_column))
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# 转换成字典
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ocean_price_dict = dict(zip(key_column, value_column))
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from utils.gtools import MySQLconnect,DBconnect
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# 取计算订单物流费的参数
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with DBconnect() as db:
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express_dict= pd.read_sql("SELECT * FROM us_shop_logistics",db.eng)
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# 构建字典
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air_small_dict = dict(zip(express_dict['g'], express_dict["空运usps_v2"]))
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air_big_dict = dict(zip(express_dict['g'], express_dict["空运fedex_v2"]))
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ocean_price_dict = dict(zip(express_dict['g'], express_dict["海运物流配置_v2"]))
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def ocean_order_price(packages):
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express_fee = 0 # 快递基础费
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long_fee = 0 # 超长费
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@ -19,7 +19,7 @@ def ocean_order_price(packages):
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express_type_length = ''
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for package in packages:
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for key, value in ocean_price_dict.items():
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if max(package.get_volume_weight(8.5), package.weight) <=key:
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if max(package.get_volume_weight(8500)*1000, package.weight) <=key:
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express_fee+=value
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break
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if package.fst_size>=116 or package.sed_size>=71 or package.girth>=251:
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30
物流t投递审核.py
30
物流t投递审核.py
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@ -23,7 +23,7 @@ def fetch_order_data():
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oe.单号 AS order_id,
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oe.运输方式,
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oe.`目的国`,
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oe.快递公司,
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ol.postcode AS postcode,
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oe.`快递分区`,
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oe.快递跟踪号,
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ecm.类型 AS 渠道类型, -- 包裹类型
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@ -34,7 +34,8 @@ def fetch_order_data():
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pfi.express_fee AS 基础估算,
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pfi.express_additional_fee AS 偶发估算,
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pfi.express_fee + pfi.express_additional_fee AS 总估算,
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ol.postcode AS postcode
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oe.快递公司
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FROM
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ods.order_express oe
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LEFT JOIN ods.express_company ecm ON oe.快递公司 = ecm.快递公司
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@ -47,7 +48,7 @@ def fetch_order_data():
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AND `卡板发货时间` REGEXP "--"
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AND ol.fund_status NOT REGEXP '等待|全额退款'
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AND ol.site_name REGEXP 'litfad|kwoking|lakiq'
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AND oe.投递时间 >= DATE_SUB(NOW(), INTERVAL 20 DAY)
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AND oe.投递时间 >= DATE_SUB(NOW(), INTERVAL 3 DAY)
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AND pvi.length>0 AND pvi.width >0 AND pvi.hight>0 AND pvi.weight>0
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and oe.目的国 regexp 'United States'
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"""
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@ -58,6 +59,7 @@ def fetch_order_data():
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def cal_min_fee(raw_data: pd.DataFrame):
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"""
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处理物流费用数据并实现业务逻辑判断
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1.用
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"""
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df = raw_data.copy()
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# 包裹层面审核
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@ -81,10 +83,13 @@ def cal_min_fee(raw_data: pd.DataFrame):
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# 计算一票多件
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package_group.add_package(package)
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# 计算一票多件
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if len(package_group) > 1:
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bill_ltl = Billing("1",opCountry,package_group,row['postcode'],company_name=None,head_type=1,beizhu="")
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df.loc[df['order_id']==order_id,'卡派尾端费用'] = bill_ltl.tail_amount[0]/len(package_group)
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df.loc[df['order_id']==order_id,'卡派尾端渠道'] = bill_ltl.company_name
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min_fee = min(bill_ltl.tail_amount[0],express_fee)
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else:
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min_fee = express_fee
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if min_fee == express_fee:
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df.loc[df['order_id']==order_id,'最优总物流费用'] = min_fee
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df.loc[df['order_id']==order_id,'最优渠道类型'] = "快递"
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@ -141,7 +146,7 @@ def analyze_orders(raw_data: pd.DataFrame):
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'快递公司列表': grouped['快递公司'].unique(),
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'渠道类型列表': grouped['渠道类型'].unique(),
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'邮编列表': grouped['postcode'].first(),
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'快递跟踪号数量': grouped['快递跟踪号'].unique()
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'快递跟踪号': grouped['快递跟踪号'].unique()
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}).reset_index()
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# 3. 实现业务逻辑判断（保持不变）
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@ -168,15 +173,15 @@ def analyze_orders(raw_data: pd.DataFrame):
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status = '正常'
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comments = []
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if row['订单类型'] == '卡派':
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tracking_nos = [list(p.values())[0] for p in row['包裹数据'].values()]
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if len(set(tracking_nos)) > 1:
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if row['订单类型'] == '卡派' and len(row['快递跟踪号']) > 1:
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# tracking_nos = [list(p.values())[0] for p in row['包裹数据'].values()]
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# if len(set(tracking_nos)) > 1:
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# status = '异常'
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status = '异常'
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if len(row['快递跟踪号数量']) > 1 :
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comments.append('卡派订单包含多个不同快递单号')
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elif row['订单类型'] == '混合':
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status = '异常'
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comments.append('出现混合类型订单，需要核查')
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comments.append('出现混合渠道类型订单，需要核查')
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if row['渠道种类'] == '多渠道':
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if row['总重量'] < 1000:
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@ -197,7 +202,7 @@ def analyze_orders(raw_data: pd.DataFrame):
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'包裹数量', '总重量',
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'总基础估算', '总附加估算', '总物流估算',
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'快递公司列表', '邮编列表',
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'包裹数据' ,'状态', '备注','快递跟踪号数量'# 使用新列名
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'包裹数据' ,'状态', '备注','快递跟踪号'# 使用新列名
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]
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return aggregated[final_columns]
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@ -234,8 +239,9 @@ def analyze_logistics(df: pd.DataFrame):
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return all_estimate
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df['费用一致'] = df.apply(lambda row: abs(all_estimate(row) - row['最优总物流费用'])<1, axis=1)
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df['费用差(当地货币)'] = df.apply(lambda row: row['最优总物流费用']-all_estimate(row), axis=1)
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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)
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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)
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return df
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def main():
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