结构化数据审计
掌握GEO结构化数据审计的完整方法：Schema标记覆盖率、正确性、完整性检查与修复

# 批量Schema验证脚本
import json, requests, sys
from urllib.parse import urljoin

# Google Rich Results Test API
API_URL = "https://search.google.com/test/rich-results"

def validate_schema(page_url, html_content):
    """验证页面Schema标记"""
    # 提取JSON-LD
    import re
    pattern = r'<script[^>]*type="application/ld\+json"[^>]*>(.*?)</script>'
    matches = re.findall(pattern, html_content, re.DOTALL)
    
    issues = []
    for i, match in enumerate(matches):
        try:
            data = json.loads(match)
            # 检查必填字段
            if isinstance(data, dict):
                schema_type = data.get('@type', 'Unknown')
                check_required_fields(schema_type, data, issues)
        except json.JSONDecodeError as e:
            issues.append(f"JSON-LD #{i+1}: JSON语法错误 - {str(e)}")
    
    return issues

def check_required_fields(schema_type, data, issues):
    """检查必填字段"""
    required = {
        'Article': ['headline', 'datePublished', 'author', 'publisher'],
        'Product': ['name'],
        'FAQPage': ['mainEntity'],
        'Organization': ['name', 'url']
    }
    fields = required.get(schema_type, [])
    for field in fields:
        if field not in data:
            issues.append(f"{schema_type}: 缺少必填字段 '{field}'")

# 批量验证
with open('urls.txt', 'r') as f:
    urls = [line.strip() for line in f if line.strip()]

for url in urls:
    response = requests.get(url)
    issues = validate_schema(url, response.text)
    if issues:
        print(f"\n{url}:")
        for issue in issues:
            print(f"  - {issue}")
    else:
        print(f"{url}: OK")