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CRM-chanpin/导出数据.py

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import requests
import json
import re
import time
import os
import pandas as pd
from concurrent.futures import ThreadPoolExecutor, as_completed
from requests.adapters import HTTPAdapter
import threading
# ================= 配置区域 =================
BASE_URL = "http://111.198.24.44:88/index.php"
USERNAME = "TEST"
PASSWORD = "test" # <--- 请在此填入真实密码
# --- 调试配置 ---
# True: 开启调试模式,只处理前 200 条
# False: 关闭调试模式,跑全量
DEBUG_MODE = False
DEBUG_LIMIT = 200
# --- 并发配置 ---
MAX_WORKERS = 10
# --- 文件配置 ---
TEMPLATE_FILE = "产品-导入模板.csv"
OUTPUT_FILE = "最终导出数据.xlsx"
# ===========================================
# 统计计数器
STATS = {
"total_processed": 0,
"skipped_no_id": 0,
"skipped_has_sales": 0, # 销量不为0
"skipped_has_relations": 0, # 关联 Key (36/37/325/523/561) 任意一个不为0
"skipped_has_history": 0, # 【恢复】有仓库历史记录
"skipped_api_error": 0,
"success": 0
}
STATS_LOCK = threading.Lock()
class CRMFetcher:
def __init__(self):
self.session = requests.Session()
# 优化连接池,防止高并发报错
adapter = HTTPAdapter(pool_connections=MAX_WORKERS, pool_maxsize=MAX_WORKERS)
self.session.mount('http://', adapter)
self.headers = {
"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",
"X-Requested-With": "XMLHttpRequest"
}
def login(self):
print("[*] 正在登录系统...")
try:
payload = {
"module": "Users", "action": "Authenticate", "return_module": "Users",
"return_action": "Login", "user_name": USERNAME, "user_password": PASSWORD,
"login_theme": "newskin"
}
resp = self.session.post(BASE_URL, data=payload, headers=self.headers)
if "logout" in resp.text.lower() or "退出" in resp.text:
print("[+] 登录成功!")
return True
else:
print(f"[-] 登录失败: {resp.status_code}")
return False
except Exception as e:
print(f"[-] 登录异常: {e}")
return False
def fetch_all_products(self):
"""自动翻页获取产品列表"""
all_products = []
page = 1
page_size = 100
last_page_ids = []
print(f"\n[*] 第一阶段:开始获取产品列表 (viewname=397)...")
if DEBUG_MODE:
print(f" [提示] 调试模式开启,仅获取前 {DEBUG_LIMIT} 条。")
while True:
# 调试限制
if DEBUG_MODE and len(all_products) >= DEBUG_LIMIT:
print(f" [调试] 已达到 {DEBUG_LIMIT} 条限制,停止获取。")
all_products = all_products[:DEBUG_LIMIT]
break
payload = {
"module": "Products", "action": "ProductsAjax", "file": "ListViewData",
"sorder": "", "start": str(page), "order_by": "", "pagesize": str(page_size),
"actionId": "1769042712624", "isFilter": "true", "search[viewname]": "397"
}
try:
resp = self.session.post(BASE_URL, data=payload, headers=self.headers)
data = resp.json()
page_items = data.get("data", []) if isinstance(data, dict) else data
if not page_items:
print(f"{page} 页为空,结束。")
break
# 死循环检测
current_page_ids = [item.get('crmid') for item in page_items]
if current_page_ids == last_page_ids:
print(f"{page} 页重复,停止。")
break
last_page_ids = current_page_ids
all_products.extend(page_items)
print(f" 已获取第 {page} 页 (本页{len(page_items)}条) - 总计: {len(all_products)}")
page += 1
time.sleep(0.2)
except Exception as e:
print(f"[-] 获取第 {page} 页出错: {e}")
break
return all_products
def check_single_product(self, item):
"""
核心筛选逻辑
1. 检查销量 (SalesNum) -> 必须为0
2. 检查关联 (Key 36, 37, 325, 523, 561) -> 必须全为0
3. 恢复检查历史 (CangkuHistory) -> 必须为空
"""
with STATS_LOCK:
STATS["total_processed"] += 1
# 1. 获取基础信息
crm_id = item.get("crmid") or item.get("productid")
raw_name = item.get("productname", "")
product_code = item.get("productcode", "")
if not crm_id:
with STATS_LOCK: STATS["skipped_no_id"] += 1
return None
# 2. 筛选第一步:检查销量 (必须为0)
sales_str = str(item.get("salesnum", "0")).replace(",", "")
try:
sales_num = float(sales_str)
except ValueError:
sales_num = 0.0
if sales_num != 0:
with STATS_LOCK: STATS["skipped_has_sales"] += 1
return None
try:
# 3. 筛选第二步:检查关联列表
# 获取所有关联模块的计数值
check1_params = {
"module": "Users", "action": "UsersAjax", "file": "setRelatedListCount",
"modulename": "Products", "record": crm_id
}
resp1 = self.session.post(BASE_URL, data=check1_params, headers=self.headers, timeout=10)
if not resp1.text:
with STATS_LOCK: STATS["skipped_api_error"] += 1
return None
data1 = resp1.json() # 拿到完整的 JSON 字典,例如 {'36': '0', '37': '5', ...}
# === 修改核心逻辑 ===
# 定义需要检查的 Key 列表
target_keys = ["36", "37", "325", "523", "561"]
# 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
for key in target_keys:
# 获取值(兼容 key 可能是 int 或 str 的情况)
val = data1.get(key)
if val is None:
# 尝试用 int 获取(防止 json 解析自动转 int
try:
val = data1.get(int(key))
except:
pass
# 如果获取不到,默认为 0 (即 None 视为 0)
# 否则转字符串进行比较
val_str = str(val) if val is not None else "0"
if val_str != "0":
# 只要有一个不为0命中规则直接跳过无需检查后续 Key
with STATS_LOCK: STATS["skipped_has_relations"] += 1
return None
# === 逻辑结束 ===
# 4. 【恢复】筛选第三步:检查仓库历史 (必须为空)
check2_params = {
"module": "Products", "action": "ProductsAjax", "file": "getCangkuHistoryInfo",
"productid": crm_id, "currpage": "1"
}
resp2 = self.session.post(BASE_URL, data=check2_params, headers=self.headers, timeout=10)
data2 = resp2.json()
# 获取 entity -> value 列表
entity_value = data2.get("entity", {}).get("value")
# 如果列表存在且长度大于0说明有历史记录跳过
if entity_value and len(entity_value) > 0:
with STATS_LOCK: STATS["skipped_has_history"] += 1
return None
# === 全部通过 ===
with STATS_LOCK:
STATS["success"] += 1
clean_name = re.sub(r'<[^>]+>', '', raw_name).strip()
return {
"产品名称": clean_name,
"产品编码": product_code
}
except Exception as e:
with STATS_LOCK:
STATS["skipped_api_error"] += 1
return None
def get_template_columns(filename):
if not os.path.exists(filename):
print(f"[-] 错误:找不到模板文件 '{filename}'")
return None
try:
try:
df = pd.read_csv(filename, encoding='utf-8-sig', nrows=0)
except UnicodeDecodeError:
df = pd.read_csv(filename, encoding='gbk', nrows=0)
return df.columns.tolist()
except Exception as e:
print(f"[-] 读取模板表头失败: {e}")
return None
def main():
columns = get_template_columns(TEMPLATE_FILE)
if not columns:
return
fetcher = CRMFetcher()
if not fetcher.login():
return
all_data = fetcher.fetch_all_products()
total_count = len(all_data)
if total_count == 0:
print("[-] 未获取到数据。")
return
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)...")
valid_rows = []
processed_count = 0
start_time = time.time()
with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor:
future_to_item = {executor.submit(fetcher.check_single_product, item): item for item in all_data}
for future in as_completed(future_to_item):
processed_count += 1
result_dict = future.result()
if result_dict:
row_data = {col: None for col in columns}
if "产品名称" in columns: row_data["产品名称"] = result_dict["产品名称"]
if "产品编码" in columns: row_data["产品编码"] = result_dict["产品编码"]
valid_rows.append(row_data)
if processed_count % 20 == 0 or processed_count == total_count:
percent = (processed_count / total_count) * 100
speed = processed_count / (time.time() - start_time + 0.01)
print(
f"\r进度: {processed_count}/{total_count} ({percent:.1f}%) - 选中: {len(valid_rows)} - 速度: {speed:.1f}条/秒",
end="")
print("\n\n" + "=" * 40)
print(" 筛选结果统计")
print("=" * 40)
print(f"总处理条数 : {STATS['total_processed']}")
print(f"[-] 因缺失ID跳过 : {STATS['skipped_no_id']}")
print(f"[-] 因有销量跳过 : {STATS['skipped_has_sales']}")
print(f"[-] 因有关联跳过 : {STATS['skipped_has_relations']} (Key 36/37/325/523/561 != 0)")
print(f"[-] 因有历史跳过 : {STATS['skipped_has_history']} (Has History)")
print(f"[-] 因API错误跳过 : {STATS['skipped_api_error']}")
print(f"[+] 最终成功保留 : {STATS['success']}")
print("=" * 40)
if valid_rows:
try:
df_output = pd.DataFrame(valid_rows, columns=columns)
print(f"[*] 正在写入 Excel '{OUTPUT_FILE}'...")
df_output.to_excel(OUTPUT_FILE, index=False)
print(f"[+] 成功!")
except Exception as e:
print(f"[-] 写入失败: {e}")
else:
print("[-] 没有数据被选中。")
if __name__ == "__main__":
main()