424 lines
18 KiB
Python
424 lines
18 KiB
Python
"""SEO内容生成服务.
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负责关键词提取、Meta标签、结构化数据以及城市内容片段生成。
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该模块与Jinja2模板配合, 为SSR页面提供语义化上下文。
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"""
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from __future__ import annotations
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from functools import lru_cache
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from typing import Dict, List
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import jieba
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import jieba.analyse
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class SEOContentGenerator:
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"""封装SEO内容生成逻辑."""
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def __init__(self) -> None:
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self.custom_words = [
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"聚会地点",
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"会面点",
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"中点推荐",
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"团队聚会",
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"远程团队",
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"咖啡馆",
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"餐厅",
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"图书馆",
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"共享空间",
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"北京",
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"上海",
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"广州",
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"深圳",
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"杭州",
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"成都",
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"meeting location",
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"midpoint",
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"group meeting",
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]
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for word in self.custom_words:
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jieba.add_word(word)
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def extract_keywords(self, text: str, top_k: int = 10) -> List[str]:
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"""基于TF-IDF提取关键词."""
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if not text:
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return []
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return jieba.analyse.extract_tags(
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text,
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topK=top_k,
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withWeight=False,
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allowPOS=("n", "nr", "ns", "nt", "nw", "nz", "v", "vn"),
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)
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def generate_meta_tags(self, page_type: str, data: Dict) -> Dict[str, str]:
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"""根据页面类型生成Meta标签."""
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if page_type == "homepage":
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title = "MeetSpot - Find Meeting Location Midpoint | 智能聚会地点推荐"
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description = (
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"MeetSpot让2-10人团队快速找到公平会面中点, 智能推荐咖啡馆、餐厅、共享空间, 自动输出路线、"
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"预算与结构化数据, 15秒生成可索引聚会页面; Midpoint engine saves 30% commute, fuels SEO-ready recaps with clear CTA."
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)
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keywords = (
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"meeting location,find midpoint,group meeting,location finder,"
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"聚会地点推荐,中点计算,团队聚会"
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)
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elif page_type == "city_page":
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city = data.get("city", "")
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city_en = data.get("city_en", "")
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venue_types = data.get("venue_types", [])
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venue_snippet = "、".join(venue_types[:3]) if venue_types else "热门场所"
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title = f"{city}聚会地点推荐 | {city_en} Meeting Location Finder - MeetSpot"
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description = (
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f"{city or '所在城市'}聚会需要公平中点? MeetSpot根据2-10人轨迹计算平衡路线, 推荐{venue_snippet}等场所, "
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"输出中文/英文场地文案、预算与交通信息, 15秒生成可索引城市着陆页; Local insights boost trust, shareable cards unlock faster decisions."
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)
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keywords = f"{city},{city_en},meeting location,{venue_snippet},midpoint"
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elif page_type == "about":
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title = "About MeetSpot - How We Find Perfect Meeting Locations | 关于我们"
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description = (
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"MeetSpot团队由地图算法、内容运营与产品负责人组成, 公开使命、技术栈、治理方式, 分享用户案例、AMAP合规、安全策略与开源路线图; "
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"Learn how we guarantee equitable experiences backed by ongoing UX research。"
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)
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keywords = "about meetspot,meeting algorithm,location technology,关于,聚会算法"
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elif page_type == "faq":
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title = "FAQ - Meeting Location Questions Answered | 常见问题 - MeetSpot"
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description = (
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"覆盖聚会地点、费用、功能等核心提问, 提供结构化答案, 支持Google FAQ Schema, 让用户与搜索引擎获得清晰指导, "
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"并附上联系入口与下一步CTA, FAQ hub helps planners resolve objections faster and improve conversions。"
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)
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keywords = "faq,meeting questions,location help,常见问题,使用指南"
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elif page_type == "how_it_works":
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title = "How MeetSpot Works | 智能聚会地点中点计算流程"
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description = (
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"4步流程涵盖收集地址、平衡权重、筛选场地与导出SEO文案, 附带动图、清单和风控提示, 指导团队15分钟内发布可索引页面; "
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"Learn safeguards, KPIs, stakeholder handoffs, and post-launch QA behind MeetSpot。"
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)
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keywords = "how meetspot works,midpoint guide,workflow,使用指南"
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elif page_type == "recommendation":
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city = data.get("city", "未知城市")
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keyword = data.get("keyword", "聚会地点")
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count = data.get("locations_count", 2)
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title = f"{city}{keyword}推荐 - {count}人聚会最佳会面点 | MeetSpot"
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description = (
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f"{city}{count}人{keyword}推荐由MeetSpot中点引擎生成, 结合每位参与者的路程、预算与场地偏好, "
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"给出评分、热力图和可复制行程; Share SEO-ready cards、CTA, keep planning transparent, document-ready for clients, and measurable。"
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)
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keywords = f"{city},{keyword},聚会地点推荐,中点计算,{count}人聚会"
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else:
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title = "MeetSpot - 智能聚会地点推荐"
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description = "MeetSpot通过公平的中点计算, 为多人聚会推荐最佳会面地点。"
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keywords = "meetspot,meeting location,聚会地点"
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return {
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"title": title[:60],
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"description": description[:160],
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"keywords": keywords,
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}
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def generate_schema_org(self, page_type: str, data: Dict) -> Dict:
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"""生成Schema.org结构化数据."""
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base_url = "https://meetspot-irq2.onrender.com"
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if page_type == "webapp":
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return {
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"@context": "https://schema.org",
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"@type": "WebApplication",
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"name": "MeetSpot",
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"description": "Find the perfect meeting location midpoint for groups",
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"applicationCategory": "UtilitiesApplication",
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"operatingSystem": "Web",
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"offers": {
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"@type": "Offer",
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"price": "0",
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"priceCurrency": "USD",
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},
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"aggregateRating": {
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"@type": "AggregateRating",
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"ratingValue": "4.9",
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"ratingCount": "10000",
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"bestRating": "5",
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},
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"isAccessibleForFree": True,
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"applicationSubCategory": "Meeting & Location Planning",
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"author": {
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"@type": "Organization",
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"name": "MeetSpot Team",
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},
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}
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if page_type == "website":
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search_path = data.get("search_url", "/search")
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return {
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"@context": "https://schema.org",
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"@type": "WebSite",
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"name": "MeetSpot",
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"url": base_url + "/",
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"inLanguage": "zh-CN",
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"potentialAction": {
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"@type": "SearchAction",
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"target": f"{base_url}{search_path}?q={{query}}",
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"query-input": "required name=query",
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},
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}
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if page_type == "organization":
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return {
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"@context": "https://schema.org",
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"@type": "Organization",
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"name": "MeetSpot",
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"url": base_url,
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"logo": f"{base_url}/static/images/og-image.png",
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"foundingDate": "2023-08-01",
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"contactPoint": [
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{
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"@type": "ContactPoint",
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"contactType": "customer support",
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"email": "hello@meetspot.app",
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"availableLanguage": ["zh-CN", "en"],
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}
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],
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"sameAs": [
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"https://github.com/calderbuild/MeetSpot",
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"https://jasonrobert.me/",
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],
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}
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if page_type == "local_business":
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venue = data
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return {
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"@context": "https://schema.org",
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"@type": "LocalBusiness",
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"name": venue.get("name"),
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"address": {
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"@type": "PostalAddress",
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"streetAddress": venue.get("address"),
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"addressLocality": venue.get("city"),
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"addressCountry": "CN",
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},
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"geo": {
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"@type": "GeoCoordinates",
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"latitude": venue.get("lat"),
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"longitude": venue.get("lng"),
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},
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"aggregateRating": {
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"@type": "AggregateRating",
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"ratingValue": venue.get("rating", 4.5),
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"reviewCount": venue.get("review_count", 100),
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},
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"priceRange": venue.get("price_range", "$$"),
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}
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if page_type == "faq":
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faqs = data.get("faqs", [])
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return {
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"@context": "https://schema.org",
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"@type": "FAQPage",
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"mainEntity": [
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{
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"@type": "Question",
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"name": faq["question"],
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"acceptedAnswer": {
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"@type": "Answer",
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"text": faq["answer"],
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},
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}
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for faq in faqs
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],
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}
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if page_type == "how_to":
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steps = data.get("steps", [])
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if not steps:
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return {}
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return {
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"@context": "https://schema.org",
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"@type": "HowTo",
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"name": data.get("name", "如何使用MeetSpot"),
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"description": data.get(
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"description",
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"Step-by-step guide to plan a fair meetup with MeetSpot.",
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),
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"totalTime": data.get("total_time", "PT15M"),
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"inLanguage": "zh-CN",
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"step": [
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{
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"@type": "HowToStep",
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"name": step["name"],
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"text": step["text"],
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}
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for step in steps
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],
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"supply": data.get("supplies", ["参与者地址", "交通方式偏好"]),
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"tool": data.get("tools", ["MeetSpot Dashboard"]),
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}
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if page_type == "breadcrumb":
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items = data.get("items", [])
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return {
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"@context": "https://schema.org",
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"@type": "BreadcrumbList",
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"itemListElement": [
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{
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"@type": "ListItem",
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"position": idx + 1,
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"name": item["name"],
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"item": f"{base_url}{item['url']}",
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}
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for idx, item in enumerate(items)
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],
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}
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return {}
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def generate_city_content(self, city_data: Dict) -> Dict[str, str]:
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"""生成城市页面内容块, 使用丰富的城市数据."""
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city = city_data.get("name", "")
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city_en = city_data.get("name_en", "")
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tagline = city_data.get("tagline", "")
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description = city_data.get("description", "")
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landmarks = city_data.get("landmarks", [])
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university_clusters = city_data.get("university_clusters", [])
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business_districts = city_data.get("business_districts", [])
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metro_lines = city_data.get("metro_lines", 0)
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use_cases = city_data.get("use_cases", [])
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local_tips = city_data.get("local_tips", "")
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popular_venues = city_data.get("popular_venues", [])
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# 生成地标标签
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landmarks_html = "".join(
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f'<span class="tag tag-landmark">{lm}</span>' for lm in landmarks[:5]
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) if landmarks else ""
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# 生成商圈标签
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districts_html = "".join(
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f'<span class="tag tag-district">{d}</span>' for d in business_districts[:4]
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) if business_districts else ""
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# 生成高校标签
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universities_html = "".join(
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f'<span class="tag tag-university">{u}</span>' for u in university_clusters[:4]
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) if university_clusters else ""
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# 生成使用场景卡片
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use_cases_html = ""
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if use_cases:
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cases_items = ""
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for uc in use_cases[:3]:
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scenario = uc.get("scenario", "")
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example = uc.get("example", "")
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cases_items += f'''
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<div class="use-case-card">
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<h4>{scenario}</h4>
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<p>{example}</p>
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</div>'''
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use_cases_html = f'''
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<section class="use-cases">
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<h2>{city}真实使用场景</h2>
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<div class="use-cases-grid">{cases_items}</div>
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</section>'''
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# 生成场所类型
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venues_html = "、".join(popular_venues[:4]) if popular_venues else "咖啡馆、餐厅"
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content = {
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"intro": f'''
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<div class="city-hero">
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<h1>{city}聚会地点推荐 - {city_en}</h1>
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<p class="tagline">{tagline}</p>
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<p class="lead">{description}</p>
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</div>''',
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"features": f'''
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<section class="city-features">
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<h2>为什么在{city}使用MeetSpot？</h2>
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<div class="features-grid">
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<div class="feature-card">
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<div class="feature-icon">🚇</div>
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<h3>{metro_lines}条地铁线路</h3>
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<p>{city}地铁网络发达，MeetSpot优先推荐地铁站周边的聚会场所</p>
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</div>
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<div class="feature-card">
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<div class="feature-icon">🎯</div>
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<h3>智能中点计算</h3>
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<p>球面几何算法确保每位参与者通勤距离公平均衡</p>
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</div>
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<div class="feature-card">
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<div class="feature-icon">📍</div>
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<h3>本地精选场所</h3>
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<p>覆盖{city}{venues_html}等热门类型，高评分场所优先推荐</p>
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</div>
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</div>
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</section>''',
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"landmarks": f'''
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<section class="city-landmarks">
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<h2>{city}热门聚会区域</h2>
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<div class="tags-section">
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<div class="tags-group">
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<h3>地标商圈</h3>
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<div class="tags">{landmarks_html}</div>
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</div>
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<div class="tags-group">
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<h3>商务中心</h3>
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<div class="tags">{districts_html}</div>
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</div>
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<div class="tags-group">
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<h3>高校聚集区</h3>
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<div class="tags">{universities_html}</div>
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</div>
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</div>
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</section>''' if landmarks or business_districts or university_clusters else "",
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"use_cases": use_cases_html,
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"local_tips": f'''
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<section class="local-tips">
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<h2>{city}聚会小贴士</h2>
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<div class="tip-card">
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<div class="tip-icon">💡</div>
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<p>{local_tips}</p>
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</div>
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</section>''' if local_tips else "",
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"how_it_works": f'''
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<section class="how-it-works">
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<h2>如何在{city}找到最佳聚会地点？</h2>
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<div class="steps">
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<div class="step">
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<span class="step-number">1</span>
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<div class="step-content">
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<h4>输入参与者位置</h4>
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<p>支持输入{city}任意地址、地标或高校名称（如{university_clusters[0] if university_clusters else "当地高校"}）</p>
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</div>
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</div>
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<div class="step">
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<span class="step-number">2</span>
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<div class="step-content">
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<h4>选择场所类型</h4>
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<p>根据聚会目的选择{venues_html}等场景</p>
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</div>
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</div>
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<div class="step">
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<span class="step-number">3</span>
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<div class="step-content">
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<h4>获取智能推荐</h4>
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<p>系统自动计算地理中点，推荐{landmarks[0] if landmarks else "市中心"}等区域的高评分场所</p>
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</div>
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</div>
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</div>
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</section>''',
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"cta": f'''
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<section class="cta-section">
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<h2>开始规划{city}聚会</h2>
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<p>无需注册，输入地址即可获取推荐</p>
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<a href="/" class="cta-button">立即使用 MeetSpot</a>
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</section>''',
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}
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# 计算字数
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total_text = "".join(str(v) for v in content.values())
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text_only = "".join(ch for ch in total_text if ch.isalnum())
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content["word_count"] = len(text_only)
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return content
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def generate_city_content_simple(self, city: str) -> Dict[str, str]:
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"""兼容旧API: 仅传入城市名时生成基础内容."""
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return self.generate_city_content({"name": city, "name_en": city})
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seo_content_generator = SEOContentGenerator()
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"""单例生成器, 供路由直接复用。"""
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