📦 Continuous Learning — 持续学习优化

# 施工自动化的持续学习 该技能支持从施工自动化会话中自动提取有价值的模式、解决方案和最佳实践，以构建机构知识。 ## 使用时机 在以下场景激活本技能： - 完成复杂估算会话后 - 解决非平凡数据处理问题后 - 发现新的集成模式时 - 成功完成文档处理后 - 开发新自动化工作流时 ## 模式提取框架 ### 1. 会话分析 ``python class ConstructionSessionAnalyzer: """Extract learnings from automation sessions""" # Categories of learnable patterns PATTERN_CATEGORIES = [ 'data_processing', # Data transformation patterns 'estimation', # Cost estimation techniques 'scheduling', # Schedule optimization patterns 'integration', # API/system integration patterns 'document_processing', # Document handling patterns 'quality_assurance', # Validation and QA patterns 'error_handling', # Error resolution patterns 'optimization' # Performance optimization patterns ] def analyze_session(self, session_log: list) -> dict: """Extract patterns from session history""" patterns = { 'successful_solutions': [], 'error_resolutions': [], 'optimization_discoveries': [], 'integration_patterns': [], 'reusable_code': [], 'decision_rationales': [] } for entry in session_log: if self._is_solution(entry): patterns['successful_solutions'].append( self._extract_solution_pattern(entry) ) if self._is_error_resolution(entry): patterns['error_resolutions'].append( self._extract_error_pattern(entry) ) if self._is_optimization(entry): patterns['optimization_discoveries'].append( self._extract_optimization(entry) ) return patterns ` ### 2. 施工知识类别 #### 2.1 成本估算模式 `yaml # Example learned pattern pattern: name: "electrical_cost_adjustment_pattern" category: "estimation" context: "When estimating electrical work for high-rise buildings" problem: "Standard rates don't account for vertical transportation costs" solution: | Apply height factor multiplier: - Floors 1-5: 1.0x base rate - Floors 6-15: 1.15x base rate - Floors 16-30: 1.25x base rate - Floors 30+: 1.35x base rate confidence: 0.85 source_sessions: ["session_2026_01_15", "session_2026_01_20"] validations: 3 ` #### 2.2 BIM 数据处理模式 `yaml pattern: name: "revit_level_extraction" category: "data_processing" context: "Extracting elements by level from Revit exports" problem: "Elements sometimes missing level association" solution: | 1. First check 'Level' parameter 2. If missing, check 'Reference Level' parameter 3. If still missing, derive from bounding box Z coordinate 4. Map Z ranges to known level elevations code_snippet: | def get_element_level(element: dict, levels: list) -> str: # Direct level parameter if level := element.get('Level'): return level # Reference level fallback if ref_level := element.get('Reference Level'): return ref_level # Derive from geometry z_coord = element['BoundingBox']['Min']['Z'] return find_nearest_level(z_coord, levels) confidence: 0.92 ` #### 2.3 集成模式 `yaml pattern: name: "procore_rate_limit_handling" category: "integration" context: "Syncing data with Procore API" problem: "API returns 429 Too Many Requests during bulk operations" solution: | Implement exponential backoff with jitter: 1. Initial delay: 1 second 2. Multiply by 2 on each retry 3. Add random jitter (0-500ms) 4. Max retries: 5 5. Max delay: 32 seconds code_snippet: | async def procore_request_with_retry(url, data): delay = 1 for attempt in range(5): try: response = await procore_api.post(url, data) return response except RateLimitError: jitter = random.uniform(0, 0.5) await asyncio.sleep(delay + jitter) delay = 2 raise MaxRetriesExceeded() confidence: 0.95 ` #### 2.4 错误解决模式 `yaml pattern: name: "cwicr_no_match_resolution" category: "error_handling" context: "CWICR semantic search returns no relevant matches" problem: "Query too specific or uses non-standard terminology" solution: | Resolution steps: 1. Simplify query to core concepts 2. Remove brand names and specifications 3. Try alternative terminology (US vs UK terms) 4. Expand search to parent category 5. If still no match, flag for manual mapping examples: - original: "Kohler K-4519 wall-mounted water closet" simplified: "wall mounted toilet" - original: "Lutron Caseta wireless dimmer switch" simplified: "dimmer switch" confidence: 0.88 ` ### 3. 学习流水线 `python class ConstructionLearningPipeline: """Continuous learning pipeline for construction automation""" def __init__(self, knowledge_base_path: str): self.kb_path = knowledge_base_path self.patterns = self._load_patterns() def learn_from_session(self, session: dict) -> list: """Extract and store learnings from session""" # Analyze session analyzer = ConstructionSessionAnalyzer() new_patterns = analyzer.analyze_session(session['log']) # Validate patterns validated = [] for pattern in new_patterns['successful_solutions']: if self._validate_pattern(pattern): # Check if similar pattern exists existing = self._find_similar_pattern(pattern) if existing: # Reinforce existing pattern self._reinforce_pattern(existing, pattern) else: # Add new pattern self._add_pattern(pattern) validated.append(pattern) # Persist to knowledge base self._save_patterns() return validated def apply_learnings(self, context: dict) -> list: """Retrieve relevant patterns for current context""" relevant_patterns = [] for pattern in self.patterns: similarity = self._calculate_similarity(pattern['context'], context) if similarity > 0.7: relevant_patterns.append({ 'pattern': pattern, 'relevance': similarity }) return sorted(relevant_patterns, key=lambda x: x['relevance'], reverse=True) def _validate_pattern(self, pattern: dict) -> bool: """Validate pattern before adding to knowledge base""" # Check minimum confidence if pattern.get('confidence', 0) < 0.6: return False # Check for code quality (if code snippet) if code := pattern.get('code_snippet'): if not self._is_valid_code(code): return False # Check for completeness required_fields = ['name', 'category', 'context', 'solution'] if not all(f in pattern for f in required_fields): return False return True ` ### 4. 知识库结构 ` knowledge_base/ ├── patterns/ │ ├── estimation/ │ │ ├── height_factors.yaml │ │ ├── material_adjustments.yaml │ │ └── labor_productivity.yaml │ ├── data_processing/ │ │ ├── revit_extraction.yaml │ │ ├── ifc_parsing.yaml │ │ └── excel_transformations.yaml │ ├── integration/ │ │ ├── procore_patterns.yaml │ │ ├── plangrid_patterns.yaml │ │ └── webhook_handlers.yaml │ └── error_handling/ │ ├── cwicr_resolutions.yaml │ ├── api_errors.yaml │ └── data_validation.yaml ├── code_snippets/ │ ├── python/ │ ├── javascript/ │ └── sql/ ├── decision_trees/ │ ├── estimate_type_selection.yaml │ ├── schedule_method_selection.yaml │ └── integration_approach.yaml └── metrics/ ├── pattern_usage.json └── success_rates.json ` ### 5. 会话结束学习提示 每次施工自动化会话结束时： `markdown ## 会话学习回顾 ### 行之有效的做法 - [Successful approaches discovered] - [Efficient patterns used] - [Integrations that worked smoothly] ### 已克服的挑战 - [Errors encountered and how resolved] - [Workarounds developed] - [Edge cases handled] ### 发现的新模式 - [Novel approaches to problems] - [Optimization techniques found] - [Reusable code created] ### 需保留的知识 - [Key learnings to remember] - [Context-specific solutions] - [Client/project-specific adaptations] ### 对未来的建议 - [Improvements to suggest] - [Patterns to apply elsewhere] - [Automation opportunities identified] ` ### 6. 模式应用 启动新的施工任务时： `python def suggest_approaches(task_context: dict) -> list: """Suggest learned approaches for new tasks""" pipeline = ConstructionLearningPipeline('knowledge_base/') relevant = pipeline.apply_learnings(task_context) suggestions = [] for item in relevant[:5]: # Top 5 suggestions pattern = item['pattern'] suggestions.append({ 'name': pattern['name'], 'relevance': f"{item['relevance']100:.0f}%", 'summary': pattern['solution'][:200], 'confidence': pattern['confidence'], 'previous_uses': pattern.get('usage_count', 0) }) return suggestions ` ## 与其他技能的集成 本技能可与以下技能协同： - verification-loop-construction：从验证失败中学习 - security-review-construction：捕获安全模式 - estimation skills：构建估算知识库 - integration skills：捕获 API 模式 ## 使用命令 `bash # Extract learnings from current session /learn # View patterns for current context /suggest-patterns # Add manual pattern /add-pattern --category estimation --name "my_pattern" # Export knowledge base /export-kb --format yaml `` --- Every session is an opportunity to learn. Capture knowledge to compound expertise over time.