332 lines
12 KiB
Python
332 lines
12 KiB
Python
import requests
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import json
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import re
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import time
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import os
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import pandas as pd
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from requests.adapters import HTTPAdapter
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import threading
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# ================= 配置区域 =================
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BASE_URL = "http://111.198.24.44:88/index.php"
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USERNAME = "TEST"
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PASSWORD = "test" # <--- 请在此填入真实密码
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# --- 调试配置 ---
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# True: 开启调试模式，只处理前 200 条
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# False: 关闭调试模式，跑全量
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DEBUG_MODE = False
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DEBUG_LIMIT = 200
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# --- 并发配置 ---
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MAX_WORKERS = 10
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# --- 文件配置 ---
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TEMPLATE_FILE = "产品-导入模板.csv"
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OUTPUT_FILE = "最终导出数据_含供应商厂家.xlsx"
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# ===========================================
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# 统计计数器
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STATS = {
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"total_processed": 0,
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"skipped_no_id": 0,
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"skipped_has_sales": 0, # 销量不为0
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"skipped_has_relations": 0, # 关联 Key (36/37/325/523/561) 任意一个不为0
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"skipped_has_history": 0, # 【恢复】有仓库历史记录
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"skipped_api_error": 0,
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"success": 0
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}
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STATS_LOCK = threading.Lock()
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class CRMFetcher:
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def __init__(self):
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self.session = requests.Session()
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# 优化连接池，防止高并发报错
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adapter = HTTPAdapter(pool_connections=MAX_WORKERS, pool_maxsize=MAX_WORKERS)
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self.session.mount('http://', adapter)
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self.headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36",
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"X-Requested-With": "XMLHttpRequest"
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}
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def login(self):
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print("[*] 正在登录系统...")
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try:
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payload = {
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"module": "Users", "action": "Authenticate", "return_module": "Users",
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"return_action": "Login", "user_name": USERNAME, "user_password": PASSWORD,
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"login_theme": "newskin"
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}
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resp = self.session.post(BASE_URL, data=payload, headers=self.headers)
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if "logout" in resp.text.lower() or "退出" in resp.text:
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print("[+] 登录成功！")
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return True
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else:
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print(f"[-] 登录失败: {resp.status_code}")
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return False
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except Exception as e:
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print(f"[-] 登录异常: {e}")
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return False
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def fetch_all_products(self):
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"""自动翻页获取产品列表"""
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all_products = []
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page = 1
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page_size = 100
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last_page_ids = []
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print(f"\n[*] 第一阶段：开始获取产品列表 (viewname=397)...")
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if DEBUG_MODE:
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print(f" [提示] 调试模式开启，仅获取前 {DEBUG_LIMIT} 条。")
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while True:
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# 调试限制
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if DEBUG_MODE and len(all_products) >= DEBUG_LIMIT:
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print(f" [调试] 已达到 {DEBUG_LIMIT} 条限制，停止获取。")
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all_products = all_products[:DEBUG_LIMIT]
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break
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payload = {
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"module": "Products", "action": "ProductsAjax", "file": "ListViewData",
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"sorder": "", "start": str(page), "order_by": "", "pagesize": str(page_size),
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"actionId": "1769042712624", "isFilter": "true", "search[viewname]": "397"
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}
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try:
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resp = self.session.post(BASE_URL, data=payload, headers=self.headers)
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data = resp.json()
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page_items = data.get("data", []) if isinstance(data, dict) else data
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if not page_items:
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print(f" 第 {page} 页为空，结束。")
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break
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# 死循环检测
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current_page_ids = [item.get('crmid') for item in page_items]
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if current_page_ids == last_page_ids:
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print(f" 第 {page} 页重复，停止。")
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break
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last_page_ids = current_page_ids
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all_products.extend(page_items)
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print(f" 已获取第 {page} 页 (本页{len(page_items)}条) - 总计: {len(all_products)}条")
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page += 1
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time.sleep(0.2)
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except Exception as e:
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print(f"[-] 获取第 {page} 页出错: {e}")
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break
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return all_products
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def check_single_product(self, item):
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"""
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核心筛选逻辑：
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1. 检查销量 (SalesNum) -> 必须为0
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2. 检查关联 (Key 36, 37, 325, 523, 561) -> 必须全为0
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3. 【恢复】检查历史 (CangkuHistory) -> 必须为空
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"""
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with STATS_LOCK:
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STATS["total_processed"] += 1
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# 1. 获取基础信息
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crm_id = item.get("crmid") or item.get("productid")
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raw_name = item.get("productname", "")
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product_code = item.get("productcode", "")
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if not crm_id:
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with STATS_LOCK: STATS["skipped_no_id"] += 1
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return None
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# 2. 筛选第一步：检查销量 (必须为0)
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sales_str = str(item.get("salesnum", "0")).replace(",", "")
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try:
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sales_num = float(sales_str)
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except ValueError:
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sales_num = 0.0
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if sales_num != 0:
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with STATS_LOCK: STATS["skipped_has_sales"] += 1
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return None
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try:
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# 3. 筛选第二步：检查关联列表
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# 获取所有关联模块的计数值
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check1_params = {
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"module": "Users", "action": "UsersAjax", "file": "setRelatedListCount",
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"modulename": "Products", "record": crm_id
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}
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resp1 = self.session.post(BASE_URL, data=check1_params, headers=self.headers, timeout=10)
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if not resp1.text:
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with STATS_LOCK: STATS["skipped_api_error"] += 1
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return None
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data1 = resp1.json() # 拿到完整的 JSON 字典
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# 定义需要检查的 Key 列表
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target_keys = ["36", "37", "325", "523", "561"]
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# 只要这些 Key 中有一个值不为 "0"，就直接判定为“有关联”，立即跳过
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for key in target_keys:
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val = data1.get(key)
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if val is None:
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try:
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val = data1.get(int(key))
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except:
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pass
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val_str = str(val) if val is not None else "0"
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if val_str != "0":
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with STATS_LOCK: STATS["skipped_has_relations"] += 1
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return None
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# 4. 【恢复】筛选第三步：检查仓库历史 (必须为空)
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check2_params = {
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"module": "Products", "action": "ProductsAjax", "file": "getCangkuHistoryInfo",
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"productid": crm_id, "currpage": "1"
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}
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resp2 = self.session.post(BASE_URL, data=check2_params, headers=self.headers, timeout=10)
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data2 = resp2.json()
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# 获取 entity -> value 列表
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entity_value = data2.get("entity", {}).get("value")
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# 如果列表存在且长度大于0，说明有历史记录，跳过
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if entity_value and len(entity_value) > 0:
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with STATS_LOCK: STATS["skipped_has_history"] += 1
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return None
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# === 全部通过，提取详细数据 ===
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with STATS_LOCK:
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STATS["success"] += 1
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# --- 数据清洗与提取 ---
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# 1. 产品名称 (去除 HTML)
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clean_name = re.sub(r'<[^>]+>', '', str(raw_name)).strip()
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# 2. 厂家 (cf_2128) - 直接获取文本
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manufacturer = str(item.get("cf_2128", "")).strip()
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# 3. 供应商名称 (vendorid) - 去除 HTML 标签，提取文本
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raw_vendor = item.get("vendorid", "")
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clean_vendor = re.sub(r'<[^>]+>', '', str(raw_vendor)).strip()
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# 4. 产品类别 (catalogid) - 根据要求，不含HTML，直接获取文本
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clean_catalog = str(item.get("catalogid", "")).strip()
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return {
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"产品名称": clean_name,
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"产品编码": product_code,
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"厂家": manufacturer,
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"供应商名称": clean_vendor,
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"产品类别": clean_catalog
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}
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except Exception as e:
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with STATS_LOCK:
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STATS["skipped_api_error"] += 1
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return None
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def get_template_columns(filename):
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if not os.path.exists(filename):
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print(f"[-] 错误：找不到模板文件 '{filename}'")
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return None
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try:
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try:
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df = pd.read_csv(filename, encoding='utf-8-sig', nrows=0)
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except UnicodeDecodeError:
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df = pd.read_csv(filename, encoding='gbk', nrows=0)
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return df.columns.tolist()
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except Exception as e:
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print(f"[-] 读取模板表头失败: {e}")
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return None
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def main():
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columns = get_template_columns(TEMPLATE_FILE)
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if not columns:
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return
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fetcher = CRMFetcher()
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if not fetcher.login():
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return
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all_data = fetcher.fetch_all_products()
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total_count = len(all_data)
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if total_count == 0:
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print("[-] 未获取到数据。")
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return
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print(f"\n[*] 第二阶段：并发筛选 {total_count} 条数据 (含多重关联与历史记录验证)...")
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valid_rows = []
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processed_count = 0
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start_time = time.time()
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with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
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future_to_item = {executor.submit(fetcher.check_single_product, item): item for item in all_data}
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for future in as_completed(future_to_item):
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processed_count += 1
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result_dict = future.result()
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if result_dict:
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# 动态映射：只有模板里有的列，才会被写入
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row_data = {col: None for col in columns}
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# 映射关系配置 (Excel列名 : 数据字典Key)
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# 请确保您的CSV模板中包含 "厂家", "供应商名称", "产品类别" 这几列，否则不会写入
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mapping = {
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"产品名称": "产品名称",
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"产品编码": "产品编码",
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"厂家": "厂家",
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"供应商名称": "供应商名称",
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"产品类别": "产品类别"
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}
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for col_name in columns:
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if col_name in mapping:
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row_data[col_name] = result_dict.get(mapping[col_name])
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valid_rows.append(row_data)
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if processed_count % 20 == 0 or processed_count == total_count:
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percent = (processed_count / total_count) * 100
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speed = processed_count / (time.time() - start_time + 0.01)
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print(
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f"\r进度: {processed_count}/{total_count} ({percent:.1f}%) - 选中: {len(valid_rows)} - 速度: {speed:.1f}条/秒",
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end="")
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print("\n\n" + "=" * 40)
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print(" 筛选结果统计")
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print("=" * 40)
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print(f"总处理条数 : {STATS['total_processed']}")
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print(f"[-] 因缺失ID跳过 : {STATS['skipped_no_id']}")
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print(f"[-] 因有销量跳过 : {STATS['skipped_has_sales']}")
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print(f"[-] 因有关联跳过 : {STATS['skipped_has_relations']} (Key 36/37/325/523/561 != 0)")
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print(f"[-] 因有历史跳过 : {STATS['skipped_has_history']} (Has History)")
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print(f"[-] 因API错误跳过 : {STATS['skipped_api_error']}")
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print(f"[+] 最终成功保留 : {STATS['success']}")
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print("=" * 40)
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if valid_rows:
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try:
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df_output = pd.DataFrame(valid_rows, columns=columns)
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print(f"[*] 正在写入 Excel '{OUTPUT_FILE}'...")
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df_output.to_excel(OUTPUT_FILE, index=False)
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print(f"[+] 成功！")
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except Exception as e:
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print(f"[-] 写入失败: {e}")
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else:
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print("[-] 没有数据被选中。")
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if __name__ == "__main__":
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main() |