事件驱动架构一致性：消息Schema的AI生成与版本检查

def semantic_compatibility_check(old_schema, new_schema) -> CompatibilityReport:
    """
    超越语法层面的兼容性检查
    """
    report = CompatibilityReport()
    
    # 1. 字段语义变化检测
    for field_name in intersect(old_schema.fields, new_schema.fields):
        old_field = old_schema.fields[field_name]
        new_field = new_schema.fields[field_name]
        
        # 检查文档描述是否变化
        if old_field.doc != new_field.doc:
            semantic_shift = llm.analyze_semantic_shift(
                old_field.doc, new_field.doc
            )
            if semantic_shift.significant:
                report.add_warning(
                    f"字段'{field_name}'的语义可能发生变化",
                    details=semantic_shift.explanation
                )
    
    # 2. 业务规则变化检测
    old_rules = llm.extract_business_rules(old_schema)
    new_rules = llm.extract_business_rules(new_schema)
    
    rule_changes = compare_rules(old_rules, new_rules)
    for change in rule_changes:
        report.add_issue(
            f"业务规则变化: {change.description}",
            impact=change.consumer_impact,
            mitigation=change.migration_guide
        )
    
    # 3. 消费者影响分析
    consumers = discover_consumers(old_schema.name)
    for consumer in consumers:
        impact = llm.assess_consumer_impact(
            consumer.code, new_schema
        )
        if impact.breaking:
            report.add_breaking_change(
                consumer=consumer.name,
                reason=impact.reason,
                action_required=impact.fix_suggestion
            )
    
    return report

class SchemaDiscovery:
    def scan_codebase(self, repo: Repository) -> List[SchemaCandidate]:
        """
        自动发现代码中的事件定义
        """
        candidates = []
        
        # 扫描注解/装饰器
        for file in repo.find_files(pattern='*.ts'):
            ast = parse(file)
            events = extract_event_decorators(ast)
            
            for event in events:
                candidate = SchemaCandidate(
                    name=event.name,
                    source_file=file,
                    schema=ai.generate_schema(event)
                )
                candidates.append(candidate)
        
        return candidates
    
    def register_schema(self, candidate: SchemaCandidate):
        """
        注册到Schema Registry
        """
        # 检查是否已存在
        existing = registry.get(candidate.name)
        
        if existing:
            # 比较差异
            diff = ai.compare_schemas(existing, candidate.schema)
            
            if diff.breaking:
                raise BreakingChangeError(diff.details)
            
            # 建议版本号
            suggested_version = ai.suggest_version(existing, diff)
        
        registry.register(candidate.schema, version=suggested_version)

def detect_schema_conflicts() -> List[Conflict]:
    """
    检测全局Schema冲突
    """
    conflicts = []
    schemas = registry.get_all_schemas()
    
    # 检查命名冲突
    name_groups = group_by_similar_names(schemas)
    for group in name_groups:
        if len(group) > 1:
            conflicts.append(NamingConflict(
                schemas=group,
                suggestion=llm.suggest_naming_convention(group)
            ))
    
    # 检查语义冲突（同一概念不同定义）
    semantic_groups = group_by_semantic_similarity(schemas)
    for group in semantic_groups:
        if has_semantic_divergence(group):
            conflicts.append(SemanticConflict(
                schemas=group,
                analysis=llm.analyze_semantic_divergence(group),
                recommendation=llm.suggest_unification(group)
            ))
    
    # 检查依赖冲突
    dependency_graph = build_dependency_graph(schemas)
    conflicts.extend(find_circular_dependencies(dependency_graph))
    
    return conflicts

def analyze_consumer_impact(schema_change: SchemaChange) -> ImpactReport:
    """
    分析Schema变更对所有消费者的影响
    """
    consumers = registry.find_consumers(schema_change.schema_name)
    
    impact_report = ImpactReport()
    
    for consumer in consumers:
        # 分析消费者代码
        usage_patterns = llm.analyze_schema_usage(
            consumer.code,
            schema_change.schema_name
        )
        
        # 评估影响
        impact = llm.assess_impact(usage_patterns, schema_change)
        
        impact_report.add_consumer_impact(
            consumer=consumer.name,
            team=consumer.team,
            impact_level=impact.level,
            affected_code=impact.locations,
            migration_effort=impact.effort,
            auto_migratable=llm.can_auto_migrate(consumer.code, schema_change)
        )
    
    return impact_report