首页 > 应用开发 > IT综合 > Python实战:跨平台电商数据聚合系统的技术实现
mermaid
graph TD
A[API请求层] --> B(分布式代理池)
B --> C{多平台协议适配}
C -->|淘宝| D[数据签名+反爬策略]
C -->|京东| E[动态Cookie管理]
C -->|拼多多| F[流量伪装机制]
D/E/F --> G[数据清洗中间件]
G --> H[(MySQL/ES存储)]
python
import hashlib
import requests
def fetch_product_data(platform, item_id): # 平台协议适配器
adapters = {
"taobao": {
"endpoint": "https://api.example.com/item/get",
"sign": lambda s: hashlib.md5(s.encode()).hexdigest()[:8]
},
"jd": {
"endpoint": "https://gw.example.com/jd/item",
"headers": {"X-Client-Type": "open_api"}
}
}
# 动态生成签名参数
config = adapters[platform]
params = {"item_id": item_id}
if platform == "taobao":
params["sign"] = config["sign"](f"{item_id}_{int(time.time())}")
# 通过代理池发起请求
response = requests.get(
config["endpoint"],
params=params,
headers=config.get("headers", {}),
proxies={"https": get_proxy(platform)} # 代理IP池
)
# 数据清洗与标准化
return {
"title": response.json()["data"]["name"],
"price": float(response.json()["data"]["price"]),
"platform": platform
}# 调用示例product = fetch_product_data("taobao", "610947572360")
print(f"获取商品成功: {product['title']} | 当前价: {product['price']}元")
python
from apscheduler.schedulers.background import BackgroundScheduler
def price_monitor():
products = [
("taobao", "610947572360"),
("jd", "100043467842")
]
for platform, item_id in products:
data = fetch_product_data(platform, item_id)
if data["price"] < get_alert_price(item_id):
send_alert_email(item_id, data["price"])# 创建定时任务scheduler = BackgroundScheduler()
scheduler.add_job(price_monitor, 'interval', hours=1)
scheduler.start()
来自 “ ITPUB博客 ” ,链接:https://blog.itpub.net/70026917/viewspace-3073675/ ,如需转载,请注明出处,否则将追究法律责任。