语义认知内容操作系统内核 v1.1从生成到进化的架构跃迁一、系统定位与技术背景1.1 为什么需要语义认知内核传统内容生成系统存在三个根本性缺陷· 无评估机制生成即输出无法判断内容质量· 无记忆能力每次生成都是从零开始错误重复发生· 无闭环优化无法从历史输出中学习v1.1 语义认知内容操作系统内核Deep Semantic Content OS简称 DLOS正是为解决上述问题而设计。它在 v1.0 的生成能力基础上引入了评分引擎与记忆引擎两大核心模块形成了“生成→评估→记忆→优化”的完整认知闭环。1.2 系统核心定义DLOS v1.1 本质上是“带反馈学习的语义内容执行系统”数学表达Content_Generation f(Intent, State, Constraints, Memory, Score_Feedback)---二、v1.1 两大核心模块详解2.1 语义内容评分引擎Semantic Scoring Engine功能定位评分引擎是系统的“质量检测器”解决“系统只知道生成不知道好坏”的问题。核心评分维度维度 英文标识 计算方法 权重语义密度 semantic_density 行业词频 / 总词数 25%目标对齐 goal_alignment 商业目标关键词覆盖率 20%实体覆盖 entity_coverage 识别出的实体数 / 预期实体数 15%结构完整性 structural_completeness 实际结构节点 / 标准结构节点 15%GEO可检索性 geo_retrievability AI友好标记、FAQ、列表结构评分 15%连贯稳定性 coherence_stability 段落间语义相似度方差 10%评分输出格式json{score: 0.86,level: high_quality,dimension_scores: {semantic_density: 0.92,goal_alignment: 0.88,entity_coverage: 0.67,structural_completeness: 0.95,geo_retrievability: 0.91,coherence_stability: 0.83},issues: [{dimension: entity_coverage,severity: medium,suggestion: 增加关键技术实体Transformer, Attention Mechanism}],passed: true}评分阈值规则pythondef should_output(score_data):if score_data[score] 0.75:return False, 重新生成elif score_data[score] 0.85:return True, 需轻度优化else:return True, 直接输出2.2 语义记忆引擎Semantic Memory Engine功能定位记忆引擎是系统的“进化驱动器”让系统记住“什么内容结构有效”。记忆类型分类L1 - 结构记忆记录完整的内容编排模式json{memory_id: struct_b2b_supplier_001,type: structure,pattern: B2B_supplier_article,structure: [Problem, Solution, Capability, Proof, CTA],performance: {avg_score: 0.89,conversion_rate: high,seo_rank: top10},usage_count: 47,last_used: 2026-06-02}L2 - 语义模式记忆记录高转化的短语和句式结构json{memory_id: pattern_high_cta_003,type: semantic_pattern,content: [Problem_Statement] [Stat_Evidence] [Solution_Offer],example: 面临{问题}根据{数据来源}{解决方案}。,effectiveness: 0.94}L3 - GEO结构记忆记录容易被AI引用的段落结构json{memory_id: geo_featured_snippet_012,type: geo_pattern,structure: Definition → KeyPoints → BulletList → Comparison,ai_citation_rate: 0.87}L4 - 标题模式记忆记录高点击标题的语义模板json{memory_id: title_click_045,pattern: {Number}种{领域}方法第{Number}种最有效,avg_ctr: 0.12,tested_count: 89}记忆检索与加权机制pythondef retrieve_memory(intent, context):memories semantic_memory_db.query(typeintent.content_type,performance_score_threshold0.8)# 按效果加权排序sorted_memories sorted(memories,keylambda m: m[performance][avg_score] * m[usage_count],reverseTrue)return sorted_memories[:3] # 返回Top3记忆记忆衰减与遗忘机制系统实现了艾宾浩斯遗忘曲线的工程化版本· 30天未使用的记忆权重降低20%· 90天未使用的记忆进入归档层· 180天未使用的记忆删除· 低评分0.6记忆自动降权---三、v1.1 完整系统架构┌─────────────────────────────────────────────────────────────┐│ 语义意图引擎 ││ 解析用户意图商业目标 / 内容类型 / 目标受众 / GEO偏好 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 内容结构规划器 ││ 根据意图 记忆检索 → 规划最优结构 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义状态机 (v1.1升级版) ││ TITLE → INTRO → SECTION → EVALUATE → REFINE → FAQ → CTA ││ ↑ ↓ ││ ┌────┴────┐ ┌───┴───┐ ││ │评分0.75│ │存储记忆│ ││ │重新生成 │ └───────┘ ││ └─────────┘ │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ ✍️ 受控语义生成器 ││ 在结构约束和记忆引导下生成内容 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义内容评分引擎 【NEW】 ││ 6维度评分 问题诊断 通过判定 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义记忆引擎 【NEW】 ││ 存储高分内容的结构 模式 GEO特征 标题模板 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义反思引擎 ││ 分析低分原因 → 生成优化指令 → 回写至状态机 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 生成式搜索优化引擎 (GEO) ││ AI友好格式化列表 / 表格 / FAQ / 定义区块 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 结构化输出器 ││ 输出 JSON / Markdown / HTML / WordPress API格式 │└─────────────────────────────────────────────────────────────┘---四、v1.1 核心闭环逻辑4.1 完整的认知闭环┌─────────────────────────────────────┐│ │▼ │┌─────────┐ ┌────────┐ ┌─────────┐ ││ 生成内容 │───▶│ 评分 │───▶│ 通过? │ │└─────────┘ └────────┘ └────┬────┘ │▲ │ ││ ┌───────┴───┐ ││ │ No Yes│ ││ ▼ ▼ ││ ┌──────────┐ ┌──────┐││ │反思修正 │ │输出 │││ └────┬─────┘ └──┬───┘││ │ │ │└───────────────────┘ ▼ │┌─────────┐││记忆存储 ││└────┬────┘││ │└─────┘4.2 评分驱动生成的判定规则pythonclass ScoringDrivenGeneration:def decide(self, score_data):score score_data[score]if score 0.85:return Action.OUTPUT_HIGH_QUALITYelif score 0.75:return Action.OUTPUT_WITH_MINOR_REFINEelif score 0.60:return Action.RETURN_TO_GENERATOR_WITH_HINTSelse:return Action.REJECT_AND_RETHINK_STRUCTURE---五、技术实现核心代码5.1 语义评分引擎实现pythonimport numpy as npfrom typing import Dict, List, Optionalfrom dataclasses import dataclassdataclassclass ScoreResult:total_score: floatlevel: strdimension_scores: Dict[str, float]issues: List[Dict]passed: boolclass SemanticScoringEngine:def __init__(self, config: Dict):self.weights config.get(weights, {semantic_density: 0.25,goal_alignment: 0.20,entity_coverage: 0.15,structural_completeness: 0.15,geo_retrievability: 0.15,coherence_stability: 0.10})self.thresholds config.get(thresholds, {pass: 0.75,high_quality: 0.85})def score(self, content: str, context: Dict) - ScoreResult:dimension_scores {semantic_density: self._calc_semantic_density(content, context),goal_alignment: self._calc_goal_alignment(content, context),entity_coverage: self._calc_entity_coverage(content, context),structural_completeness: self._calc_structure(content, context),geo_retrievability: self._calc_geo_score(content),coherence_stability: self._calc_coherence(content)}total_score sum(dimension_scores[dim] * self.weights[dim]for dim in dimension_scores)level high_quality if total_score self.thresholds[high_quality] else \normal if total_score self.thresholds[pass] else low_qualityissues self._generate_issues(dimension_scores, context)return ScoreResult(total_scoreround(total_score, 3),levellevel,dimension_scoresdimension_scores,issuesissues,passedtotal_score self.thresholds[pass])def _calc_semantic_density(self, content: str, context: Dict) - float:计算语义密度行业词覆盖率industry_terms context.get(industry_terms, [])if not industry_terms:return 1.0matched_terms sum(1 for term in industry_terms if term in content)return min(1.0, matched_terms / len(industry_terms) * 1.2)def _calc_goal_alignment(self, content: str, context: Dict) - float:计算目标对齐度goal_keywords context.get(goal_keywords, [])if not goal_keywords:return 1.0matched sum(1 for kw in goal_keywords if kw in content.lower())return matched / len(goal_keywords)def _calc_entity_coverage(self, content: str, context: Dict) - float:计算实体覆盖率使用简单NER或实体词典expected_entities context.get(expected_entities, [])if not expected_entities:return 1.0found_entities self._extract_entities(content)coverage len(set(found_entities) set(expected_entities)) / len(expected_entities)return min(1.0, coverage)def _calc_structure(self, content: str, context: Dict) - float:计算结构完整性required_sections context.get(required_sections,[title, intro, body, conclusion])actual_sections self._extract_sections(content)present sum(1 for section in required_sections if section in actual_sections)return present / len(required_sections)def _calc_geo_score(self, content: str) - float:计算GEO可检索性geo_indicators {has_h1_h2: r#{1,2}\s,has_lists: r^[\*\-\d\.]\s,has_faq: rfaq|Frequently Asked,has_table: r\|.*\|,has_bold_keywords: r\*\*[^*]\*\*}score 0total len(geo_indicators)for indicator, pattern in geo_indicators.items():if re.search(pattern, content, re.MULTILINE):score 1return score / totaldef _calc_coherence(self, content: str) - float:计算连贯稳定性使用句子嵌入相似度sentences self._split_sentences(content)if len(sentences) 2:return 1.0# 简化版使用简单的词重叠度similarities []for i in range(len(sentences) - 1):sim self._sentence_similarity(sentences[i], sentences[i1])similarities.append(sim)# 稳定性 1 - 相似度方差variance np.var(similarities) if similarities else 0return max(0, min(1, 1 - variance))5.2 语义记忆引擎实现pythonimport jsonimport sqlite3from datetime import datetime, timedeltafrom typing import List, Dict, Anyfrom collections import defaultdictclass SemanticMemoryEngine:def __init__(self, db_path: str semantic_memory.db):self.conn sqlite3.connect(db_path)self._init_tables()def _init_tables(self):cursor self.conn.cursor()# 结构记忆表cursor.execute(CREATE TABLE IF NOT EXISTS structure_memory (id TEXT PRIMARY KEY,pattern_name TEXT,structure_json TEXT,avg_score REAL,conversion_rate TEXT,seo_rank TEXT,usage_count INTEGER DEFAULT 1,last_used TIMESTAMP,created_at TIMESTAMP))# 语义模式记忆表cursor.execute(CREATE TABLE IF NOT EXISTS pattern_memory (id TEXT PRIMARY KEY,pattern_type TEXT,content_template TEXT,example TEXT,effectiveness REAL,usage_count INTEGER DEFAULT 1))# GEO模式记忆表cursor.execute(CREATE TABLE IF NOT EXISTS geo_memory (id TEXT PRIMARY KEY,structure_type TEXT,ai_citation_rate REAL,featured_snippet_rate REAL))self.conn.commit()def store_memory(self, content_data: Dict, score_data: Dict,performance_data: Dict):存储高分内容为记忆if score_data[total_score] 0.75:return # 不存储低分内容# 存储结构记忆structure content_data.get(structure)if structure and score_data[total_score] 0.85:self._store_structure_memory(structure, score_data, performance_data)# 存储语义模式patterns self._extract_patterns(content_data[content])for pattern in patterns:self._store_pattern_memory(pattern, score_data[total_score])def _store_structure_memory(self, structure: List[str],score_data: Dict,performance_data: Dict):存储结构记忆带去重和合并pattern_key _.join(structure)cursor self.conn.cursor()cursor.execute(SELECT id, usage_count, avg_score FROM structure_memory WHERE pattern_name ?,(pattern_key,))existing cursor.fetchone()if existing:# 更新已有记忆new_count existing[1] 1new_avg (existing[2] * existing[1] score_data[total_score]) / new_countcursor.execute(UPDATE structure_memorySET usage_count ?, avg_score ?, last_used ?WHERE id ?, (new_count, new_avg, datetime.now(), existing[0]))else:# 创建新记忆memory_id fstruct_{pattern_key[:20]}_{datetime.now().strftime(%Y%m%d%H%M%S)}cursor.execute(INSERT INTO structure_memory(id, pattern_name, structure_json, avg_score, conversion_rate,seo_rank, usage_count, last_used, created_at)VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?), (memory_id, pattern_key, json.dumps(structure),score_data[total_score], performance_data.get(conversion_rate, unknown),performance_data.get(seo_rank, unknown), 1,datetime.now(), datetime.now()))self.conn.commit()def retrieve_best_structure(self, intent: Dict, limit: int 3) - List[Dict]:检索最佳结构cursor self.conn.cursor()# 按效果权重排序使用类似TF-IDF的思路cursor.execute(SELECT pattern_name, structure_json, avg_score, usage_countFROM structure_memoryWHERE avg_score 0.75ORDER BY (avg_score * LOG(usage_count 1)) DESCLIMIT ?, (limit,))results []for row in cursor.fetchall():results.append({pattern_name: row[0],structure: json.loads(row[1]),avg_score: row[2],usage_count: row[3]})return resultsdef retrieve_geo_pattern(self, content_type: str) - Optional[Dict]:检索高AI引用的GEO模式cursor self.conn.cursor()cursor.execute(SELECT structure_type, ai_citation_rate, featured_snippet_rateFROM geo_memoryWHERE structure_type LIKE ?ORDER BY ai_citation_rate DESCLIMIT 1, (f%{content_type}%,))row cursor.fetchone()if row:return {structure_type: row[0],ai_citation_rate: row[1],featured_snippet_rate: row[2]}return Nonedef apply_decay(self):应用记忆衰减定期执行cutoff_date datetime.now() - timedelta(days90)cursor self.conn.cursor()# 90天未使用的记忆权重降低cursor.execute(UPDATE structure_memorySET avg_score avg_score * 0.8WHERE last_used ? AND avg_score 0.5, (cutoff_date,))# 180天未使用的记忆删除cutoff_delete datetime.now() - timedelta(days180)cursor.execute(DELETE FROM structure_memoryWHERE last_used ? OR avg_score 0.4, (cutoff_delete,))self.conn.commit()def _extract_patterns(self, content: str) - List[Dict]:从内容中提取语义模式简化实现patterns []# 提取标题模式title_match re.search(r^#\s(.)$, content, re.MULTILINE)if title_match:patterns.append({type: title,content: title_match.group(1)})# 提取CTA模式cta_patterns re.findall(r(?:click|buy|download|subscribe|contact).{0,50},content, re.IGNORECASE)for cta in cta_patterns[:3]:patterns.append({type: cta,content: cta})return patterns5.3 增强版语义状态机pythonfrom enum import Enumfrom typing import Optional, Dict, Anyclass State(Enum):TITLE titleINTRO introSECTION sectionEVALUATE evaluateREFINE refineFAQ faqCTA ctaSTORE_MEMORY store_memoryOUTPUT outputclass SemanticStateMachine:def __init__(self, scoring_engine: SemanticScoringEngine,memory_engine: SemanticMemoryEngine):self.state State.TITLEself.scoring_engine scoring_engineself.memory_engine memory_engineself.context {}self.max_refine_iterations 3self.refine_count 0def transition(self, input_data: Dict[str, Any]) - Dict[str, Any]:执行状态转移if self.state State.TITLE:result self._generate_title()self.state State.INTROreturn resultelif self.state State.INTRO:result self._generate_intro()self.state State.SECTIONreturn resultelif self.state State.SECTION:result self._generate_sections()self.state State.EVALUATEreturn resultelif self.state State.EVALUATE:# 评分驱动决策score_result self.scoring_engine.score(self.context[full_content],self.context)
语义认知内容操作系统内核 v1.1：从生成到进化的架构跃迁
语义认知内容操作系统内核 v1.1从生成到进化的架构跃迁一、系统定位与技术背景1.1 为什么需要语义认知内核传统内容生成系统存在三个根本性缺陷· 无评估机制生成即输出无法判断内容质量· 无记忆能力每次生成都是从零开始错误重复发生· 无闭环优化无法从历史输出中学习v1.1 语义认知内容操作系统内核Deep Semantic Content OS简称 DLOS正是为解决上述问题而设计。它在 v1.0 的生成能力基础上引入了评分引擎与记忆引擎两大核心模块形成了“生成→评估→记忆→优化”的完整认知闭环。1.2 系统核心定义DLOS v1.1 本质上是“带反馈学习的语义内容执行系统”数学表达Content_Generation f(Intent, State, Constraints, Memory, Score_Feedback)---二、v1.1 两大核心模块详解2.1 语义内容评分引擎Semantic Scoring Engine功能定位评分引擎是系统的“质量检测器”解决“系统只知道生成不知道好坏”的问题。核心评分维度维度 英文标识 计算方法 权重语义密度 semantic_density 行业词频 / 总词数 25%目标对齐 goal_alignment 商业目标关键词覆盖率 20%实体覆盖 entity_coverage 识别出的实体数 / 预期实体数 15%结构完整性 structural_completeness 实际结构节点 / 标准结构节点 15%GEO可检索性 geo_retrievability AI友好标记、FAQ、列表结构评分 15%连贯稳定性 coherence_stability 段落间语义相似度方差 10%评分输出格式json{score: 0.86,level: high_quality,dimension_scores: {semantic_density: 0.92,goal_alignment: 0.88,entity_coverage: 0.67,structural_completeness: 0.95,geo_retrievability: 0.91,coherence_stability: 0.83},issues: [{dimension: entity_coverage,severity: medium,suggestion: 增加关键技术实体Transformer, Attention Mechanism}],passed: true}评分阈值规则pythondef should_output(score_data):if score_data[score] 0.75:return False, 重新生成elif score_data[score] 0.85:return True, 需轻度优化else:return True, 直接输出2.2 语义记忆引擎Semantic Memory Engine功能定位记忆引擎是系统的“进化驱动器”让系统记住“什么内容结构有效”。记忆类型分类L1 - 结构记忆记录完整的内容编排模式json{memory_id: struct_b2b_supplier_001,type: structure,pattern: B2B_supplier_article,structure: [Problem, Solution, Capability, Proof, CTA],performance: {avg_score: 0.89,conversion_rate: high,seo_rank: top10},usage_count: 47,last_used: 2026-06-02}L2 - 语义模式记忆记录高转化的短语和句式结构json{memory_id: pattern_high_cta_003,type: semantic_pattern,content: [Problem_Statement] [Stat_Evidence] [Solution_Offer],example: 面临{问题}根据{数据来源}{解决方案}。,effectiveness: 0.94}L3 - GEO结构记忆记录容易被AI引用的段落结构json{memory_id: geo_featured_snippet_012,type: geo_pattern,structure: Definition → KeyPoints → BulletList → Comparison,ai_citation_rate: 0.87}L4 - 标题模式记忆记录高点击标题的语义模板json{memory_id: title_click_045,pattern: {Number}种{领域}方法第{Number}种最有效,avg_ctr: 0.12,tested_count: 89}记忆检索与加权机制pythondef retrieve_memory(intent, context):memories semantic_memory_db.query(typeintent.content_type,performance_score_threshold0.8)# 按效果加权排序sorted_memories sorted(memories,keylambda m: m[performance][avg_score] * m[usage_count],reverseTrue)return sorted_memories[:3] # 返回Top3记忆记忆衰减与遗忘机制系统实现了艾宾浩斯遗忘曲线的工程化版本· 30天未使用的记忆权重降低20%· 90天未使用的记忆进入归档层· 180天未使用的记忆删除· 低评分0.6记忆自动降权---三、v1.1 完整系统架构┌─────────────────────────────────────────────────────────────┐│ 语义意图引擎 ││ 解析用户意图商业目标 / 内容类型 / 目标受众 / GEO偏好 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 内容结构规划器 ││ 根据意图 记忆检索 → 规划最优结构 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义状态机 (v1.1升级版) ││ TITLE → INTRO → SECTION → EVALUATE → REFINE → FAQ → CTA ││ ↑ ↓ ││ ┌────┴────┐ ┌───┴───┐ ││ │评分0.75│ │存储记忆│ ││ │重新生成 │ └───────┘ ││ └─────────┘ │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ ✍️ 受控语义生成器 ││ 在结构约束和记忆引导下生成内容 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义内容评分引擎 【NEW】 ││ 6维度评分 问题诊断 通过判定 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义记忆引擎 【NEW】 ││ 存储高分内容的结构 模式 GEO特征 标题模板 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 语义反思引擎 ││ 分析低分原因 → 生成优化指令 → 回写至状态机 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 生成式搜索优化引擎 (GEO) ││ AI友好格式化列表 / 表格 / FAQ / 定义区块 │└─────────────────────────┬───────────────────────────────────┘↓┌─────────────────────────────────────────────────────────────┐│ 结构化输出器 ││ 输出 JSON / Markdown / HTML / WordPress API格式 │└─────────────────────────────────────────────────────────────┘---四、v1.1 核心闭环逻辑4.1 完整的认知闭环┌─────────────────────────────────────┐│ │▼ │┌─────────┐ ┌────────┐ ┌─────────┐ ││ 生成内容 │───▶│ 评分 │───▶│ 通过? │ │└─────────┘ └────────┘ └────┬────┘ │▲ │ ││ ┌───────┴───┐ ││ │ No Yes│ ││ ▼ ▼ ││ ┌──────────┐ ┌──────┐││ │反思修正 │ │输出 │││ └────┬─────┘ └──┬───┘││ │ │ │└───────────────────┘ ▼ │┌─────────┐││记忆存储 ││└────┬────┘││ │└─────┘4.2 评分驱动生成的判定规则pythonclass ScoringDrivenGeneration:def decide(self, score_data):score score_data[score]if score 0.85:return Action.OUTPUT_HIGH_QUALITYelif score 0.75:return Action.OUTPUT_WITH_MINOR_REFINEelif score 0.60:return Action.RETURN_TO_GENERATOR_WITH_HINTSelse:return Action.REJECT_AND_RETHINK_STRUCTURE---五、技术实现核心代码5.1 语义评分引擎实现pythonimport numpy as npfrom typing import Dict, List, Optionalfrom dataclasses import dataclassdataclassclass ScoreResult:total_score: floatlevel: strdimension_scores: Dict[str, float]issues: List[Dict]passed: boolclass SemanticScoringEngine:def __init__(self, config: Dict):self.weights config.get(weights, {semantic_density: 0.25,goal_alignment: 0.20,entity_coverage: 0.15,structural_completeness: 0.15,geo_retrievability: 0.15,coherence_stability: 0.10})self.thresholds config.get(thresholds, {pass: 0.75,high_quality: 0.85})def score(self, content: str, context: Dict) - ScoreResult:dimension_scores {semantic_density: self._calc_semantic_density(content, context),goal_alignment: self._calc_goal_alignment(content, context),entity_coverage: self._calc_entity_coverage(content, context),structural_completeness: self._calc_structure(content, context),geo_retrievability: self._calc_geo_score(content),coherence_stability: self._calc_coherence(content)}total_score sum(dimension_scores[dim] * self.weights[dim]for dim in dimension_scores)level high_quality if total_score self.thresholds[high_quality] else \normal if total_score self.thresholds[pass] else low_qualityissues self._generate_issues(dimension_scores, context)return ScoreResult(total_scoreround(total_score, 3),levellevel,dimension_scoresdimension_scores,issuesissues,passedtotal_score self.thresholds[pass])def _calc_semantic_density(self, content: str, context: Dict) - float:计算语义密度行业词覆盖率industry_terms context.get(industry_terms, [])if not industry_terms:return 1.0matched_terms sum(1 for term in industry_terms if term in content)return min(1.0, matched_terms / len(industry_terms) * 1.2)def _calc_goal_alignment(self, content: str, context: Dict) - float:计算目标对齐度goal_keywords context.get(goal_keywords, [])if not goal_keywords:return 1.0matched sum(1 for kw in goal_keywords if kw in content.lower())return matched / len(goal_keywords)def _calc_entity_coverage(self, content: str, context: Dict) - float:计算实体覆盖率使用简单NER或实体词典expected_entities context.get(expected_entities, [])if not expected_entities:return 1.0found_entities self._extract_entities(content)coverage len(set(found_entities) set(expected_entities)) / len(expected_entities)return min(1.0, coverage)def _calc_structure(self, content: str, context: Dict) - float:计算结构完整性required_sections context.get(required_sections,[title, intro, body, conclusion])actual_sections self._extract_sections(content)present sum(1 for section in required_sections if section in actual_sections)return present / len(required_sections)def _calc_geo_score(self, content: str) - float:计算GEO可检索性geo_indicators {has_h1_h2: r#{1,2}\s,has_lists: r^[\*\-\d\.]\s,has_faq: rfaq|Frequently Asked,has_table: r\|.*\|,has_bold_keywords: r\*\*[^*]\*\*}score 0total len(geo_indicators)for indicator, pattern in geo_indicators.items():if re.search(pattern, content, re.MULTILINE):score 1return score / totaldef _calc_coherence(self, content: str) - float:计算连贯稳定性使用句子嵌入相似度sentences self._split_sentences(content)if len(sentences) 2:return 1.0# 简化版使用简单的词重叠度similarities []for i in range(len(sentences) - 1):sim self._sentence_similarity(sentences[i], sentences[i1])similarities.append(sim)# 稳定性 1 - 相似度方差variance np.var(similarities) if similarities else 0return max(0, min(1, 1 - variance))5.2 语义记忆引擎实现pythonimport jsonimport sqlite3from datetime import datetime, timedeltafrom typing import List, Dict, Anyfrom collections import defaultdictclass SemanticMemoryEngine:def __init__(self, db_path: str semantic_memory.db):self.conn sqlite3.connect(db_path)self._init_tables()def _init_tables(self):cursor self.conn.cursor()# 结构记忆表cursor.execute(CREATE TABLE IF NOT EXISTS structure_memory (id TEXT PRIMARY KEY,pattern_name TEXT,structure_json TEXT,avg_score REAL,conversion_rate TEXT,seo_rank TEXT,usage_count INTEGER DEFAULT 1,last_used TIMESTAMP,created_at TIMESTAMP))# 语义模式记忆表cursor.execute(CREATE TABLE IF NOT EXISTS pattern_memory (id TEXT PRIMARY KEY,pattern_type TEXT,content_template TEXT,example TEXT,effectiveness REAL,usage_count INTEGER DEFAULT 1))# GEO模式记忆表cursor.execute(CREATE TABLE IF NOT EXISTS geo_memory (id TEXT PRIMARY KEY,structure_type TEXT,ai_citation_rate REAL,featured_snippet_rate REAL))self.conn.commit()def store_memory(self, content_data: Dict, score_data: Dict,performance_data: Dict):存储高分内容为记忆if score_data[total_score] 0.75:return # 不存储低分内容# 存储结构记忆structure content_data.get(structure)if structure and score_data[total_score] 0.85:self._store_structure_memory(structure, score_data, performance_data)# 存储语义模式patterns self._extract_patterns(content_data[content])for pattern in patterns:self._store_pattern_memory(pattern, score_data[total_score])def _store_structure_memory(self, structure: List[str],score_data: Dict,performance_data: Dict):存储结构记忆带去重和合并pattern_key _.join(structure)cursor self.conn.cursor()cursor.execute(SELECT id, usage_count, avg_score FROM structure_memory WHERE pattern_name ?,(pattern_key,))existing cursor.fetchone()if existing:# 更新已有记忆new_count existing[1] 1new_avg (existing[2] * existing[1] score_data[total_score]) / new_countcursor.execute(UPDATE structure_memorySET usage_count ?, avg_score ?, last_used ?WHERE id ?, (new_count, new_avg, datetime.now(), existing[0]))else:# 创建新记忆memory_id fstruct_{pattern_key[:20]}_{datetime.now().strftime(%Y%m%d%H%M%S)}cursor.execute(INSERT INTO structure_memory(id, pattern_name, structure_json, avg_score, conversion_rate,seo_rank, usage_count, last_used, created_at)VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?), (memory_id, pattern_key, json.dumps(structure),score_data[total_score], performance_data.get(conversion_rate, unknown),performance_data.get(seo_rank, unknown), 1,datetime.now(), datetime.now()))self.conn.commit()def retrieve_best_structure(self, intent: Dict, limit: int 3) - List[Dict]:检索最佳结构cursor self.conn.cursor()# 按效果权重排序使用类似TF-IDF的思路cursor.execute(SELECT pattern_name, structure_json, avg_score, usage_countFROM structure_memoryWHERE avg_score 0.75ORDER BY (avg_score * LOG(usage_count 1)) DESCLIMIT ?, (limit,))results []for row in cursor.fetchall():results.append({pattern_name: row[0],structure: json.loads(row[1]),avg_score: row[2],usage_count: row[3]})return resultsdef retrieve_geo_pattern(self, content_type: str) - Optional[Dict]:检索高AI引用的GEO模式cursor self.conn.cursor()cursor.execute(SELECT structure_type, ai_citation_rate, featured_snippet_rateFROM geo_memoryWHERE structure_type LIKE ?ORDER BY ai_citation_rate DESCLIMIT 1, (f%{content_type}%,))row cursor.fetchone()if row:return {structure_type: row[0],ai_citation_rate: row[1],featured_snippet_rate: row[2]}return Nonedef apply_decay(self):应用记忆衰减定期执行cutoff_date datetime.now() - timedelta(days90)cursor self.conn.cursor()# 90天未使用的记忆权重降低cursor.execute(UPDATE structure_memorySET avg_score avg_score * 0.8WHERE last_used ? AND avg_score 0.5, (cutoff_date,))# 180天未使用的记忆删除cutoff_delete datetime.now() - timedelta(days180)cursor.execute(DELETE FROM structure_memoryWHERE last_used ? OR avg_score 0.4, (cutoff_delete,))self.conn.commit()def _extract_patterns(self, content: str) - List[Dict]:从内容中提取语义模式简化实现patterns []# 提取标题模式title_match re.search(r^#\s(.)$, content, re.MULTILINE)if title_match:patterns.append({type: title,content: title_match.group(1)})# 提取CTA模式cta_patterns re.findall(r(?:click|buy|download|subscribe|contact).{0,50},content, re.IGNORECASE)for cta in cta_patterns[:3]:patterns.append({type: cta,content: cta})return patterns5.3 增强版语义状态机pythonfrom enum import Enumfrom typing import Optional, Dict, Anyclass State(Enum):TITLE titleINTRO introSECTION sectionEVALUATE evaluateREFINE refineFAQ faqCTA ctaSTORE_MEMORY store_memoryOUTPUT outputclass SemanticStateMachine:def __init__(self, scoring_engine: SemanticScoringEngine,memory_engine: SemanticMemoryEngine):self.state State.TITLEself.scoring_engine scoring_engineself.memory_engine memory_engineself.context {}self.max_refine_iterations 3self.refine_count 0def transition(self, input_data: Dict[str, Any]) - Dict[str, Any]:执行状态转移if self.state State.TITLE:result self._generate_title()self.state State.INTROreturn resultelif self.state State.INTRO:result self._generate_intro()self.state State.SECTIONreturn resultelif self.state State.SECTION:result self._generate_sections()self.state State.EVALUATEreturn resultelif self.state State.EVALUATE:# 评分驱动决策score_result self.scoring_engine.score(self.context[full_content],self.context)
