diff --git a/logisticsClass/logisticsTail_US.py b/logisticsClass/logisticsTail_US.py
index 382860a..88d5726 100644
--- a/logisticsClass/logisticsTail_US.py
+++ b/logisticsClass/logisticsTail_US.py
@@ -27,8 +27,7 @@ class WestLogistics_US(TailLogistics):
             cls._load_postcodes()  # 第一次实例化时加载文件
             cls._is_loaded = True  # 标记文件已加载
         return super().__new__(cls)
-    # Path_current_directory = Path(__file__).parent
-    # current_directory = os.path.dirname(__file__)
+
     @classmethod
     def _load_postcodes(cls):
         """加载邮编文件"""
diff --git a/utils/logistics_name_config.py b/utils/logistics_name_config.py
new file mode 100644
index 0000000..d90723d
--- /dev/null
+++ b/utils/logistics_name_config.py
@@ -0,0 +1,18 @@
+
+logistics_name = {
+    "空LAX-FEDEX-SAIR-H": "Fedex-HOME",
+    "空LAX-FEDEX-SAIR-G": "Fedex-GROUD",
+    "海MS-FEDEX-SAIR-H": "Fedex-HOME",
+    "海MS-FEDEX-SAIR-G": "Fedex-GROUD",
+    "海MS-METRO-SAIR": "Metro-SAIR",
+    "海NY-METRO-SAIR": "Metro-SAIR", # 暂时没写
+    "海NY-XMILES-SAIR": "XMILES-SAIR",
+    "海MS-XMILES": "XMILES-SAIR",
+    "海NY-AMT-SAIR": "AM-美东",
+    "海MS-AMT-SAIR": "AM-美西",
+    
+    "海MS-FEDEX02":"Fedex-彩虹小马",
+    "空LAX-FEDEX02":"Fedex-彩虹小马"
+}
+
+#海NY-WWEX-SAIR，海MS-WWEX-SAIR没报价表，也停用了
\ No newline at end of file
diff --git a/物流t投递审核.py b/物流t投递审核.py
new file mode 100644
index 0000000..c59f225
--- /dev/null
+++ b/物流t投递审核.py
@@ -0,0 +1,257 @@
+"""
+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`,
+            oe.快递公司,
+                        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,
+            ol.postcode AS postcode
+        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 20 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
+    """    
+    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
+        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 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(),
+        '快递跟踪号数量': 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':
+            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')
+        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')
+
+        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','快递跟踪号数量'# 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: 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)
+    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()
+