""" 关键词数据增强模块 从历史验证数据中提取高价值关键词，反哺关键词生成 """ import json import logging from typing import Dict, List, Optional, Any from collections import Counter from datetime import datetime, timedelta logger = logging.getLogger(__name__) class KeywordDataEnhancer: """关键词数据增强器""" def __init__(self, storage): """ Args: storage: DataStorage 实例 """ self.storage = storage def analyze_historical_performance(self, brand: str, days: int = 30) -> Dict: """ 分析历史验证数据，提取高价值关键词 Args: brand: 品牌名 days: 分析最近 N 天的数据 Returns: 分析结果 """ # 获取历史验证结果 verify_results = self.storage.get_verify_results(brand=brand) if not verify_results: return { "has_data": False, "message": "暂无历史验证数据" } # 转换为 DataFrame 便于分析 import pandas as pd df = pd.DataFrame(verify_results) if df.empty: return {"has_data": False, "message": "暂无历史验证数据"} # 分析关键词表现 keyword_performance = self._analyze_keyword_performance(df) # 提取高价值关键词 high_value_keywords = self._extract_high_value_keywords(keyword_performance) # 分析搜索意图分布 intent_distribution = self._analyze_intent_distribution(keyword_performance) # 生成关键词建议 suggestions = self._generate_keyword_suggestions(keyword_performance) return { "has_data": True, "total_keywords": len(keyword_performance), "high_value_keywords": high_value_keywords, "intent_distribution": intent_distribution, "suggestions": suggestions, "keyword_details": keyword_performance } def _analyze_keyword_performance(self, df) -> List[Dict]: """分析每个关键词的表现""" keyword_stats = [] for keyword in df["问题"].unique(): keyword_df = df[df["问题"] == keyword] # 计算提及率 mentioned = keyword_df[keyword_df["提及次数"] > 0] mention_rate = len(mentioned) / len(keyword_df) if len(keyword_df) > 0 else 0 # 计算平均提及次数 avg_mentions = keyword_df["提及次数"].mean() # 分析提及位置 position_counts = Counter() for _, row in keyword_df.iterrows(): pos = row.get("位置", "未提及") if pos and pos != "未提及": position_counts[pos] += 1 # 计算综合价值分数 value_score = self._calculate_value_score( mention_rate, avg_mentions, position_counts ) keyword_stats.append({ "keyword": keyword, "total_verifications": len(keyword_df), "mention_rate": mention_rate, "avg_mentions": avg_mentions, "position_distribution": dict(position_counts), "value_score": value_score, "suggested_action": self._suggest_action(mention_rate, value_score) }) # 按价值分数排序 keyword_stats.sort(key=lambda x: x["value_score"], reverse=True) return keyword_stats def _calculate_value_score(self, mention_rate: float, avg_mentions: float, position_counts: Counter) -> float: """ 计算关键词价值分数 Args: mention_rate: 提及率 avg_mentions: 平均提及次数 position_counts: 位置分布 Returns: 价值分数 (0-100) """ score = 0 # 提及率权重 (40%) score += mention_rate * 40 # 平均提及次数权重 (30%) mention_score = min(avg_mentions / 3, 1) * 30 score += mention_score # 位置权重 (30%) total_mentions = sum(position_counts.values()) if total_mentions > 0: front_ratio = position_counts.get("前1/3", 0) / total_mentions score += front_ratio * 30 return score def _suggest_action(self, mention_rate: float, value_score: float) -> str: """根据表现建议操作""" if value_score >= 70: return "✅ 高价值关键词，继续保持" elif value_score >= 40: if mention_rate < 0.5: return "⚡ 提及率较低，建议优化内容" else: return "📈 有提升空间，建议增加深度" else: if mention_rate < 0.3: return "🔄 效果不佳，考虑替换关键词" else: return "🔍 价值较低，可减少投入" def _extract_high_value_keywords(self, keyword_performance: List[Dict], top_n: int = 10) -> List[Dict]: """提取高价值关键词""" return keyword_performance[:top_n] def _analyze_intent_distribution(self, keyword_performance: List[Dict]) -> Dict: """分析搜索意图分布""" intent_keywords = { "对比": ["对比", "比较", "vs", "versus", "哪个好"], "评测": ["评测", "评价", "测评", "怎么样", "好不好"], "使用": ["怎么用", "如何使用", "教程", "入门", "指南"], "购买": ["价格", "多少钱", "购买", "付费", "免费"], "问题": ["问题", "错误", "失败", "怎么办", "解决"], "推荐": ["推荐", "最好", "排行", "排名", "前十"] } intent_counts = {intent: 0 for intent in intent_keywords} intent_keywords_map = {intent: [] for intent in intent_keywords} for kw_data in keyword_performance: keyword = kw_data["keyword"] categorized = False for intent, patterns in intent_keywords.items(): if any(pattern in keyword for pattern in patterns): intent_counts[intent] += 1 intent_keywords_map[intent].append(keyword) categorized = True break if not categorized: intent_counts["其他"] = intent_counts.get("其他", 0) + 1 return { "counts": intent_counts, "keywords": intent_keywords_map } def _generate_keyword_suggestions(self, keyword_performance: List[Dict]) -> List[Dict]: """生成关键词优化建议""" suggestions = [] # 找出低效关键词 low_performers = [kw for kw in keyword_performance if kw["value_score"] < 30] if low_performers: suggestions.append({ "type": "replace", "priority": "high", "message": f"有 {len(low_performers)} 个关键词效果不佳，建议替换", "keywords": [kw["keyword"] for kw in low_performers[:3]] }) # 找出高价值关键词 high_performers = [kw for kw in keyword_performance if kw["value_score"] >= 70] if high_performers: suggestions.append({ "type": "expand", "priority": "medium", "message": f"有 {len(high_performers)} 个高价值关键词，建议扩展相关内容", "keywords": [kw["keyword"] for kw in high_performers[:3]] }) # 找出提及率低但有潜力的关键词 potential_keywords = [ kw for kw in keyword_performance if 0.3 <= kw["mention_rate"] < 0.5 and kw["total_verifications"] >= 3 ] if potential_keywords: suggestions.append({ "type": "optimize", "priority": "medium", "message": f"有 {len(potential_keywords)} 个关键词有提升空间，建议优化内容", "keywords": [kw["keyword"] for kw in potential_keywords[:3]] }) return suggestions def generate_enhanced_keyword_prompt(self, brand: str, advantages: str, existing_keywords: List[str] = None) -> str: """ 生成增强的关键词生成提示词 Args: brand: 品牌名 advantages: 品牌优势 existing_keywords: 已有关键词列表 Returns: 增强的提示词 """ # 获取历史分析 analysis = self.analyze_historical_performance(brand) prompt = f"""你是一个 GEO（生成式引擎优化）关键词策略专家。 品牌信息： - 品牌名：{brand} - 品牌优势：{advantages} """ if analysis.get("has_data"): prompt += """历史验证数据分析： """ # 添加高价值关键词 high_value = analysis.get("high_value_keywords", []) if high_value: prompt += "\n高价值关键词（已验证有效）：\n" for kw in high_value[:5]: prompt += f"- {kw['keyword']} (提及率: {kw['mention_rate']:.0%}, 价值分: {kw['value_score']:.0f})\n" # 添加优化建议 suggestions = analysis.get("suggestions", []) if suggestions: prompt += "\n优化建议：\n" for suggestion in suggestions: prompt += f"- {suggestion['message']}\n" # 添加意图分布 intent_dist = analysis.get("intent_distribution", {}).get("counts", {}) if intent_dist: prompt += "\n搜索意图分布：\n" for intent, count in sorted(intent_dist.items(), key=lambda x: x[1], reverse=True): if count > 0: prompt += f"- {intent}: {count} 个关键词\n" if existing_keywords: prompt += f"\n已有关键词（避免重复）：\n" for kw in existing_keywords[:10]: prompt += f"- {kw}\n" prompt += """ 请生成 20 个新的 GEO 优化关键词，要求： 1. 70% 泛词（行业相关）+ 30% 品牌词 2. 覆盖多种搜索意图：对比、评测、使用、购买、问题、推荐 3. 关键词长度 12-28 字，口语化，符合用户真实搜索习惯 4. 每个关键词附带：category（类别）、intent（意图）、estimated_value（预估价值 1-5） 输出 JSON 数组格式： [ { "keyword": "关键词内容", "category": "类别", "intent": "意图", "estimated_value": 4 } ] """ return prompt def get_keyword_trends(self, brand: str, keyword: str, days: int = 30) -> Dict: """ 获取关键词趋势数据 Args: brand: 品牌名 keyword: 关键词 days: 分析天数 Returns: 趋势数据 """ verify_results = self.storage.get_verify_results(brand=brand) if not verify_results: return {"has_data": False} import pandas as pd df = pd.DataFrame(verify_results) # 过滤指定关键词 keyword_df = df[df["问题"] == keyword] if keyword_df.empty: return {"has_data": False, "message": f"未找到关键词 '{keyword}' 的验证数据"} # 按日期分组 if "验证时间" in keyword_df.columns: keyword_df["日期"] = pd.to_datetime(keyword_df["验证时间"]).dt.date daily_stats = keyword_df.groupby("日期").agg({ "提及次数": "mean", "问题": "count" }).rename(columns={"问题": "验证次数"}) return { "has_data": True, "keyword": keyword, "daily_stats": daily_stats.to_dict("records"), "overall_mention_rate": len(keyword_df[keyword_df["提及次数"] > 0]) / len(keyword_df) } return {"has_data": False, "message": "缺少时间戳数据"}