docs(openclaw): 更新对接指南并重构蓉城易购处理逻辑
- 将蓉城易购处理逻辑从启动器迁移至专用服务类，提升代码模块化 - 更新 OpenClaw 指南，详细说明新增的多种处理模式（Excel、特殊供应商、条码映射） - 统一 headless_api 入口，通过参数化支持不同处理流程
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import re
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import time
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import pandas as pd
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import logging
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from typing import Optional, Callable
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from app.services.order_service import OrderService
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logger = logging.getLogger(__name__)
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class SpecialSuppliersService:
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"""
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处理特殊供应商逻辑的服务类，如蓉城易购等
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"""
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def __init__(self, config_manager=None):
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self.config_manager = config_manager
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self.order_service = OrderService(config_manager)
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def process_rongcheng_yigou(self, src_path: str, progress_cb: Optional[Callable[[int, str], None]] = None) -> Optional[str]:
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"""
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处理蓉城易购订单
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"""
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try:
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if progress_cb: progress_cb(10, "正在处理蓉城易购...")
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def _pick_col(df, exact_list=None, contains_list=None):
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cols = list(df.columns)
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if exact_list:
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for name in exact_list:
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for c in cols:
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if str(c).strip() == str(name).strip():
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return c
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if contains_list:
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for kw in contains_list:
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for c in cols:
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if kw in str(c):
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return c
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return None
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try:
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df_raw = pd.read_excel(src_path, header=2)
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except Exception:
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df_raw = pd.read_excel(src_path)
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df_raw = df_raw.iloc[2:].reset_index(drop=True)
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# 去除全空列与行
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df_raw = df_raw.dropna(how='all', axis=1).dropna(how='all', axis=0)
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# 选择关键列
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col_no = _pick_col(df_raw, contains_list=['序号'])
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col_name = _pick_col(df_raw, contains_list=['商品名称','品名','名称'])
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col_bc = _pick_col(df_raw, contains_list=['商品条码','条码'])
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col_unit = _pick_col(df_raw, exact_list=['单位（订购单位）'], contains_list=['订购单位','小单位','单位'])
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col_qty = _pick_col(df_raw, contains_list=['订购数量','订货数量','数量'])
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col_price= _pick_col(df_raw, exact_list=['优惠后金额（小单位）'], contains_list=['单价','销售价','进货价','优惠后金额'])
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col_amt = _pick_col(df_raw, exact_list=['出库小计(元)'], contains_list=['金额','优惠后金额','小计','合计','出库小计'])
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selected = [c for c in [col_no,col_name,col_bc,col_unit,col_qty,col_price,col_amt] if c]
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if not selected or len(selected) < 4:
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df = pd.read_excel(src_path)
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df = df.iloc[2:].reset_index(drop=True)
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keep_idx = [0, 2, 3, 9, 12, 15, 17]
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keep_idx = [i for i in keep_idx if i < df.shape[1]]
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df2 = df.iloc[:, keep_idx].copy()
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target_cols = ['序号','商品名称','商品条码','单位','数量','单价','金额']
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df2.columns = target_cols[:len(df2.columns)]
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else:
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df2 = df_raw[selected].copy()
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rename_map = {}
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if col_no: rename_map[col_no] = '序号'
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if col_name: rename_map[col_name] = '商品名称'
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if col_bc: rename_map[col_bc] = '商品条码（小条码）'
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if col_unit: rename_map[col_unit] = '单位'
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if col_qty: rename_map[col_qty] = '订购数量（小单位）'
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if col_price: rename_map[col_price] = '单价（小单位)'
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if col_amt: rename_map[col_amt] = '优惠后金额（小单位）'
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df2 = df2.rename(columns=rename_map)
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if '单位' in df2.columns:
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df2['单位'] = df2['单位'].astype(str).str.strip().replace({'件':'份'})
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# 分裂多条码行并均分数量
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bc_col = '商品条码（小条码）' if '商品条码（小条码）' in df2.columns else ('商品条码' if '商品条码' in df2.columns else ('条码' if '条码' in df2.columns else None))
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qty_col = '订购数量（小单位）' if '订购数量（小单位）' in df2.columns else ('订购数量' if '订购数量' in df2.columns else ('数量' if '数量' in df2.columns else None))
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up_col = '单价（小单位)' if '单价（小单位)' in df2.columns else ('单价' if '单价' in df2.columns else ('销售价' if '销售价' in df2.columns else None))
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amt_col = '优惠后金额（小单位）' if '优惠后金额（小单位）' in df2.columns else ('金额' if '金额' in df2.columns else ('小计' if '小计' in df2.columns else None))
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if bc_col and qty_col:
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rows = []
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for _, row in df2.iterrows():
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bc_val = str(row.get(bc_col, '')).strip()
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if bc_val and any(sep in bc_val for sep in [',','，','、','/',' ']):
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parts = []
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temp_bc = bc_val
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for sep in [',','，','、','/',' ']:
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temp_bc = temp_bc.replace(sep, ' ')
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for token in temp_bc.split():
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tok = ''.join([ch for ch in token if ch.isdigit()])
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if tok: parts.append(tok)
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parts = [p for p in parts if p]
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if len(parts) >= 2:
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try:
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q_total = float(row.get(qty_col, 0) or 0)
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except Exception:
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q_total = 0
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if q_total > 0:
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n = len(parts)
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base = int(q_total) // n if q_total.is_integer() else q_total / n
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remainder = int(q_total) % n if q_total.is_integer() else 0
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for i, bc in enumerate(parts):
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new_row = row.copy()
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new_row[bc_col] = bc
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q_each = base + (1 if remainder > 0 and i < remainder else 0)
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new_row[qty_col] = q_each
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if up_col and amt_col:
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try:
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upv = float(new_row.get(up_col, 0) or 0)
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new_row[amt_col] = upv * float(q_each)
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except Exception: pass
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rows.append(new_row)
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else: rows.append(row)
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else: rows.append(row)
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else: rows.append(row)
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df2 = pd.DataFrame(rows)
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out_dir = os.path.dirname(src_path)
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base = os.path.basename(src_path)
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final_name = f"蓉城易购预处理-{base}"
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final_path = os.path.join(out_dir, final_name)
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df2.to_excel(final_path, index=False)
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if progress_cb: progress_cb(60, "预处理完成，开始标准流程...")
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result = self.order_service.process_excel(final_path, progress_cb=lambda p: progress_cb(60 + int(p*0.4), "Excel处理中...") if progress_cb else None)
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return result
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except Exception as e:
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logger.error(f"处理蓉城易购订单出错: {e}")
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return None
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