import requests
import pandas as pd
import time
from tqdm import tqdm

cookies = {
    'memberSESSIONID': '485a36b9-eb05-4f25-806c-ddea074cb6a2',
    'erpLanguage': 'zh-CN',
    'tmall': 'false',
    'Hm_lvt_25f5e7a3a5dbb293d7dd35d5f1be8d0a': '1775553704,1775799422,1776043595,1776153876',
    'HMACCOUNT': 'A6A0585E8C70051D',
    'prodOrg': '11240984669918128522',
    'unp': '15851362811188895763',
    'un': '15851362811188895763',
    '_up': '-NillNN-qyBEJ--t3vnSknvoOF53yPCOts8N0no-Uu9VX_TGoZrQjaZJ9Q3d-WrAAGgt60MgQHajHWBHMKKxj0CuWypi1JgKCFP1EPEk-HbqE_UQqY4i0wEO9vFRv-ZNHu3M-GTc1p2wFnGqpuxUiOMSmF_GORVsrEj9nR66kMm3MhA.',
    'sensorsdata2015jssdkcross': '%7B%22distinct_id%22%3A%2215851362811188895763%22%2C%22first_id%22%3A%2219b6df76a22f46-04a98afdd2a11d8-4c657b58-1327104-19b6df76a2312c7%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22%24device_id%22%3A%2219b6df76a22f46-04a98afdd2a11d8-4c657b58-1327104-19b6df76a2312c7%22%7D',
    'Hm_lpvt_25f5e7a3a5dbb293d7dd35d5f1be8d0a': '1776405066',
}

headers = {
    'accept': 'application/json, text/plain, */*',
    'accept-language': 'zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6',
    'content-type': 'application/json;charset=UTF-8',
    'origin': 'https://yunxiu.f6car.cn',
    'priority': 'u=1, i',
    'referer': 'https://yunxiu.f6car.cn/erp/view/index.html',
    'sec-ch-ua': '"Microsoft Edge";v="147", "Not.A/Brand";v="8", "Chromium";v="147"',
    'sec-ch-ua-mobile': '?0',
    'sec-ch-ua-platform': '"Windows"',
    'sec-fetch-dest': 'empty',
    'sec-fetch-mode': 'cors',
    'sec-fetch-site': 'same-origin',
    'traceparent': '00-83110f5326614e2db80da5c48ab36d5d-ce4bdbb226a3286a-01',
    'tracestate': 'rum=v2&browser&dz2uw0c5ay@e5930ea8eb782ae&9134c88bc6224ae4a4167c07dc0ad82e&uid_nli6u6uor4pqvx0f',
    'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/147.0.0.0 Safari/537.36 Edg/147.0.0.0',
    'x-requested-with': 'XMLHttpRequest',
    # 'cookie': 'memberSESSIONID=485a36b9-eb05-4f25-806c-ddea074cb6a2; erpLanguage=zh-CN; tmall=false; Hm_lvt_25f5e7a3a5dbb293d7dd35d5f1be8d0a=1775553704,1775799422,1776043595,1776153876; HMACCOUNT=A6A0585E8C70051D; prodOrg=11240984669918128522; unp=15851362811188895763; un=15851362811188895763; _up=-NillNN-qyBEJ--t3vnSknvoOF53yPCOts8N0no-Uu9VX_TGoZrQjaZJ9Q3d-WrAAGgt60MgQHajHWBHMKKxj0CuWypi1JgKCFP1EPEk-HbqE_UQqY4i0wEO9vFRv-ZNHu3M-GTc1p2wFnGqpuxUiOMSmF_GORVsrEj9nR66kMm3MhA.; sensorsdata2015jssdkcross=%7B%22distinct_id%22%3A%2215851362811188895763%22%2C%22first_id%22%3A%2219b6df76a22f46-04a98afdd2a11d8-4c657b58-1327104-19b6df76a2312c7%22%2C%22props%22%3A%7B%22%24latest_traffic_source_type%22%3A%22%E7%9B%B4%E6%8E%A5%E6%B5%81%E9%87%8F%22%2C%22%24latest_search_keyword%22%3A%22%E6%9C%AA%E5%8F%96%E5%88%B0%E5%80%BC_%E7%9B%B4%E6%8E%A5%E6%89%93%E5%BC%80%22%2C%22%24latest_referrer%22%3A%22%22%7D%2C%22%24device_id%22%3A%2219b6df76a22f46-04a98afdd2a11d8-4c657b58-1327104-19b6df76a2312c7%22%7D; Hm_lpvt_25f5e7a3a5dbb293d7dd35d5f1be8d0a=1776405066',
}

url = 'https://yunxiu.f6car.cn/member/car/carListForPermission'

source_excel_path = r"D:\Idea Project\F6+宜搭+其它(1)\张阳脚本\文件输出\车辆信息.xlsx"
source_sheet_name = "Sheet3"
output_path = r"D:\Idea Project\F6+宜搭+其它(1)\张阳脚本\文件输出\车辆信息_按车牌导出.xlsx"

df = pd.read_excel(source_excel_path, sheet_name=source_sheet_name)
all_data = []

for _, row in tqdm(df.iterrows(), total=len(df)):
    car_number = str(row.get('车牌号', '')).strip()
    if not car_number or car_number.lower() == 'nan':
        continue

    page_no = 1
    matched = False
    while True:
        json_data = {
            'keyWord': car_number,
            'pageSize': 100,
            'pageNo': page_no,
        }

        response = requests.post(
            url,
            cookies=cookies,
            headers=headers,
            json=json_data,
            timeout=30,
        )
        data = response.json()
        data_block = (data.get('data') or {})
        items = (data_block.get('data') or [])

        for item in items:
            tm_car = item.get('tmCarInfo') or {}
            tm_customer = item.get('tmCustomerInfo') or {}
            current_car_number = f"{tm_car.get('carPrefix', '')}{tm_car.get('carNo', '')}"
            if current_car_number == car_number:
                car_id = tm_car.get('pkId')
                customer_id = tm_customer.get('pkId')
                all_data.append([car_id, customer_id, current_car_number])
                matched = True
                break

        if matched:
            break

        total = data_block.get('total') or 0
        if page_no * 100 >= int(total):
            break
        page_no += 1
        time.sleep(0.3)

    if not matched:
        all_data.append([None, None, car_number])

    time.sleep(2)

result_df = pd.DataFrame(all_data, columns=['carId', 'customerId', 'car_number'])
result_df.to_excel(output_path, index=False)
print("已保存：", str(output_path))