From 1bbd4c643f2994280f6b2915c57bcc42dfa098c2 Mon Sep 17 00:00:00 2001 From: Wenxixi <2911872386@qq.com> Date: Tue, 1 Jul 2025 18:18:37 +0800 Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=E7=89=A9=E6=B5=81=E6=8A=95?= =?UTF-8?q?=E9=80=92=E5=AE=A1=E6=A0=B8=E4=BB=A3=E7=A0=81?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- logisticsClass/logisticsTail_US.py | 27 ++++++++++--------- sell/logistic_price/us_price.py | 20 +++++++------- 物流t投递审核.py | 42 +++++++++++++++++------------- 3 files changed, 48 insertions(+), 41 deletions(-) diff --git a/logisticsClass/logisticsTail_US.py b/logisticsClass/logisticsTail_US.py index 88d5726..0b07bef 100644 --- a/logisticsClass/logisticsTail_US.py +++ b/logisticsClass/logisticsTail_US.py @@ -649,8 +649,14 @@ class XmilesLogistics_US(TailLogistics): detail_amount['tail_amount'] = 99999 return detail_amount postcode = int(postcode[:5]) + # 判断邮编是美东还是美西 + zone = self.postcode_table[self.postcode_table['POSTCODE'] == postcode]['地区'] + if zone.empty: + detail_amount['tail_amount'] = 99999 + return detail_amount + - base_fee_list = set() # 将每个包裹费用都记录在列表中 + base_fee_list = [] # 将每个包裹费用都记录在列表中 volume_weight = 0 # 计算抛重，用于计算美西转运美东价格 for package in packages: volume_weight +=package.get_volume_weight(6000) @@ -658,35 +664,30 @@ class XmilesLogistics_US(TailLogistics): detail_amount['tail_amount'] = 99999 return detail_amount if package.lbs_weight <=90 and package.fst_inch <=96 and package.girth_inch <=130: - base_fee_list.add(20) + base_fee_list.append(20) elif package.lbs_weight <=150 and package.fst_inch <=96 and package.girth_inch <=130: - base_fee_list.add(38) + base_fee_list.append(38) elif package.lbs_weight <=150 and package.fst_inch <=108 and package.girth_inch <=165: - base_fee_list.add(50) + base_fee_list.append(50) elif package.lbs_weight <=200 and package.fst_inch <=144 and package.girth_inch <=225: - base_fee_list.add(75) + base_fee_list.append(75) else: - base_fee_list.add(75+0.55*math.ceil(package.lbs_weight)) + base_fee_list.append(75+0.55*math.ceil(package.lbs_weight)) # 取所有费用,最大的费用取全部费用，其他取半价然后求和 base_fee_list = sorted(base_fee_list,reverse=True) # 计算base_fee_list最大值+其他费用的一半 if len(base_fee_list) == 0: + detail_amount['tail_amount'] = 99999 return detail_amount elif len(base_fee_list) == 1: detail_amount["base"] = base_fee_list[0] else: detail_amount["base"] = base_fee_list[0] + sum(base_fee_list[1:])/2 - - # 判断邮编是美东还是美西 - zone = self.postcode_table[self.postcode_table['POSTCODE'] == postcode]['地区'] - if zone.empty: - detail_amount['tail_amount'] = 99999 - return detail_amount zone = zone.iloc[0] if zone =="NJ": detail_amount["transfer"] = 0.5*volume_weight - + for key in detail_amount: if key!= 'tail_amount': detail_amount['tail_amount'] += detail_amount[key] diff --git a/sell/logistic_price/us_price.py b/sell/logistic_price/us_price.py index a1aab6c..8f1036f 100644 --- a/sell/logistic_price/us_price.py +++ b/sell/logistic_price/us_price.py @@ -1,14 +1,14 @@ import pandas as pd import math -express_price = pd.read_excel(r'D:\test\logistics\data\售价尾端价格.xlsx', sheet_name='Sheet1') -key_column = express_price.iloc[:, 8] # 第 I 列 -value_column = express_price.iloc[:, 9] # 第 J 列 -small_column = express_price.iloc[:, 10] # 第 K 列 -big_column = express_price.iloc[:, 11] # 第 L 列 -air_small_dict = dict(zip(key_column, small_column)) -air_big_dict = dict(zip(key_column, big_column)) -# 转换成字典 -ocean_price_dict = dict(zip(key_column, value_column)) +from utils.gtools import MySQLconnect,DBconnect +# 取计算订单物流费的参数 +with DBconnect() as db: + express_dict= pd.read_sql("SELECT * FROM us_shop_logistics",db.eng) + +# 构建字典 +air_small_dict = dict(zip(express_dict['g'], express_dict["空运usps_v2"])) +air_big_dict = dict(zip(express_dict['g'], express_dict["空运fedex_v2"])) +ocean_price_dict = dict(zip(express_dict['g'], express_dict["海运物流配置_v2"])) def ocean_order_price(packages): express_fee = 0 # 快递基础费 long_fee = 0 # 超长费 @@ -19,7 +19,7 @@ def ocean_order_price(packages): express_type_length = '' for package in packages: for key, value in ocean_price_dict.items(): - if max(package.get_volume_weight(8.5), package.weight) <=key: + if max(package.get_volume_weight(8500)*1000, package.weight) <=key: express_fee+=value break if package.fst_size>=116 or package.sed_size>=71 or package.girth>=251: diff --git a/物流t投递审核.py b/物流t投递审核.py index c59f225..d756742 100644 --- a/物流t投递审核.py +++ b/物流t投递审核.py @@ -23,7 +23,7 @@ def fetch_order_data(): oe.单号 AS order_id, oe.运输方式, oe.`目的国`, - oe.快递公司, + 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 @@ -34,7 +34,8 @@ def fetch_order_data(): 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, - ol.postcode AS postcode + + 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 @@ -47,7 +48,7 @@ def fetch_order_data(): 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 20 DAY) + 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' """ @@ -58,6 +59,7 @@ def fetch_order_data(): 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 @@ -81,10 +83,13 @@ def cal_min_fee(raw_data: pd.DataFrame): # 计算一票多件 package_group.add_package(package) # 计算一票多件 - 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) + 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" @@ -141,7 +146,7 @@ def analyze_orders(raw_data: pd.DataFrame): '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(), - '快递跟踪号数量': grouped['快递跟踪号'].unique() + '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） @@ -168,15 +173,15 @@ def analyze_orders(raw_data: pd.DataFrame): status = '正常' comments = [] - if row['订单类型'] == '卡派': - tracking_nos = [list(p.values())[0] for p in row['包裹数据'].values()] - if len(set(tracking_nos)) > 1: - status = '异常' - if len(row['快递跟踪号数量']) > 1 : - comments.append('卡派订单包含多个不同快递单号') + 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('出现混合类型订单，需要核查') + 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: @@ -197,7 +202,7 @@ def analyze_orders(raw_data: pd.DataFrame): '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','快递跟踪号数量'# 使用新列名 + '包裹数据' ,'状态', '备注','快递跟踪号'# 使用新列名 ] return aggregated[final_columns] @@ -234,8 +239,9 @@ def analyze_logistics(df: pd.DataFrame): return all_estimate - df['费用一致'] = df.apply(lambda row: 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: row['最优总物流费用']-all_estimate(row), axis=1) + 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():