鸿蒙AI智能体本地部署新突破:华为小艺OpenClaw模式深度解析

┌─────────────────────────────────────────────┐│             应用接口层 (Application Layer)    ││  ┌─────────┐ ┌─────────┐ ┌─────────┐       ││  │  微信   │ │  淘宝   │ │  银行   │       ││  │  集成   │ │  助手   │ │  客服   │       ││  └─────────┘ └─────────┘ └─────────┘       │├─────────────────────────────────────────────┤│           智能体框架层 (Agent Framework)     ││  ┌───────────────────────────────────────┐  ││  │        OpenClaw核心引擎               │  ││  │  ┌─────┐ ┌─────┐ ┌─────┐ ┌─────┐    │  ││  │  │记忆 │ │工具 │ │规划 │ │执行 │    │  ││  │  │模块 │ │调用 │ │器   │ │器   │    │  ││  │  └─────┘ └─────┘ └─────┘ └─────┘    │  ││  └───────────────────────────────────────┘  │├─────────────────────────────────────────────┤│           运行时引擎层 (Runtime Engine)      ││  ┌─────────┐ ┌─────────┐ ┌─────────┐       ││  │模型推理 │ │任务调度 │ │资源管理 │       ││  │引擎     │ │器       │ │器       │       ││  └─────────┘ └─────────┘ └─────────┘       │├─────────────────────────────────────────────┤│           硬件抽象层 (Hardware Abstraction)  ││  ┌─────────┐ ┌─────────┐ ┌─────────┐       ││  │NPU加速  │ │GPU加速  │ │CPU计算  │       ││  │驱动     │ │驱动     │ │优化     │       ││  └─────────┘ └─────────┘ └─────────┘       │└─────────────────────────────────────────────┘

┌─────────────────────────────────────┐│        长期记忆 (Long-term Memory)    ││  ┌───────────────────────────────┐  ││  │用户画像、偏好、习惯、知识库    │  ││  │更新周期：天/周级别            │  ││  └───────────────────────────────┘  │├─────────────────────────────────────┤│        工作记忆 (Working Memory)     ││  ┌───────────────────────────────┐  ││  │当前会话上下文、任务状态、工具  │  ││  │调用结果、临时变量             │  ││  │生命周期：会话期间             │  ││  └───────────────────────────────┘  │├─────────────────────────────────────┤│        瞬时记忆 (Short-term Memory)  ││  ┌───────────────────────────────┐  ││  │对话轮次、token缓存、临时感知  │  ││  │数据                           │  ││  │生命周期：分钟级别             │  ││  └───────────────────────────────┘  │└─────────────────────────────────────┘

# 代码片段1：OpenClaw记忆系统的Python实现import chromadbimport numpy as npfrom sentence_transformers import SentenceTransformerfrom datetime import datetimeimport jsonclassOpenClawMemorySystem:def__init__(self, device_id):"""初始化记忆系统"""self.device_id = device_idself.embedding_model = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')self.client = chromadb.PersistentClient(path=f"/data/local/ai/memory/{device_id}")self.collection = self.client.get_or_create_collection(            name="user_memory",            metadata={"description": "用户长期记忆存储"}        )defstore_memory(self, content, memory_type="conversation", metadata=None):"""存储记忆片段"""# 生成向量嵌入        embedding = self.embedding_model.encode(content).tolist()# 构建记忆ID        memory_id = f"memory_{datetime.now().strftime('%Y%m%d_%H%M%S_%f')}"# 构建完整元数据        full_metadata = {"type": memory_type,"timestamp": datetime.now().isoformat(),"device_id": self.device_id        }if metadata:            full_metadata.update(metadata)# 存储到向量数据库self.collection.add(            ids=[memory_id],            embeddings=[embedding],            metadatas=[full_metadata],            documents=[content]        )return memory_iddefretrieve_memories(self, query, n_results=5, memory_types=None):"""检索相关记忆"""# 生成查询向量        query_embedding = self.embedding_model.encode(query).tolist()# 构建过滤条件        where_filter = {}if memory_types:ifisinstance(memory_types, str):                memory_types = [memory_types]            where_filter = {"type": {"$in": memory_types}}# 执行查询        results = self.collection.query(            query_embeddings=[query_embedding],            n_results=n_results,            where=where_filter        )# 格式化返回结果        memories = []if results['ids'][0]:for i inrange(len(results['ids'][0])):                memory = {"id": results['ids'][0][i],"content": results['documents'][0][i],"metadata": results['metadatas'][0][i],"similarity": results['distances'][0][i] if results['distances'] elseNone                }                memories.append(memory)return memoriesdefbuild_user_profile(self):"""基于记忆构建用户画像"""# 获取所有记忆        all_memories = self.collection.get()ifnot all_memories['ids']:return {"status": "no_memories"}# 分析记忆内容构建画像        profile = {"interests": self._extract_interests(all_memories['documents']),"habits": self._extract_habits(all_memories['metadatas']),"knowledge_base": self._build_knowledge_base(all_memories['documents']),"last_updated": datetime.now().isoformat()        }return profiledef_extract_interests(self, documents):"""从文档中提取兴趣关键词"""# 简化实现，实际应使用NLP技术        interest_keywords = ["技术", "编程", "AI", "科技", "开发", "代码"]        interests = {}for doc in documents:for keyword in interest_keywords:if keyword in doc:                    interests[keyword] = interests.get(keyword, 0) + 1# 按频率排序        sorted_interests = sorted(interests.items(), key=lambda x: x[1], reverse=True)return [item[0] for item in sorted_interests[:5]]def_extract_habits(self, metadatas):"""从元数据中提取用户习惯"""        habits = {}for meta in metadatas:if"action_type"in meta:                action = meta["action_type"]                timestamp = datetime.fromisoformat(meta["timestamp"])                hour = timestamp.hourif action notin habits:                    habits[action] = {"hours": [], "count": 0}                habits[action]["hours"].append(hour)                habits[action]["count"] += 1# 分析习惯模式        result = {}for action, data in habits.items():if data["count"] > 10:  # 足够多的样本                avg_hour = sum(data["hours"]) / len(data["hours"])                result[action] = {"frequency": data["count"],"preferred_hour": round(avg_hour, 1)                }return resultdef_build_knowledge_base(self, documents):"""构建用户知识库"""# 提取实体和关系（简化版）        knowledge_base = {"technical_terms": set(),"projects_mentioned": set(),"tools_used": set()        }        tech_terms = ["API", "SDK", "框架", "库", "算法", "模型", "部署"]        project_indicators = ["项目", "开发", "实现", "搭建", "构建"]        tools = ["Android Studio", "HarmonyOS", "VS Code", "Git", "Docker"]for doc in documents:# 检查技术术语for term in tech_terms:if term in doc:                    knowledge_base["technical_terms"].add(term)# 检查项目提及for indicator in project_indicators:if indicator in doc:# 提取可能的项目名（简化）                    words = doc.split()for i, word inenumerate(words):if word == indicator and i > 0:                            knowledge_base["projects_mentioned"].add(words[i-1])# 检查工具使用for tool in tools:if tool in doc:                    knowledge_base["tools_used"].add(tool)# 转换为可序列化格式return {"technical_terms": list(knowledge_base["technical_terms"]),"projects_mentioned": list(knowledge_base["projects_mentioned"]),"tools_used": list(knowledge_base["tools_used"])        }# 使用示例if __name__ == "__main__":# 初始化记忆系统    memory_system = OpenClawMemorySystem(device_id="user_001")# 存储一些记忆    memory_system.store_memory(        content="用户询问如何将Qwen3.5模型部署到HarmonyOS设备上",        memory_type="technical_query",        metadata={"topic": "模型部署", "urgency": "high"}    )    memory_system.store_memory(        content="用户经常在晚上9点后使用AI编程助手",        memory_type="user_habit",        metadata={"action_type": "coding_assistance", "time_range": "night"}    )# 检索相关记忆    memories = memory_system.retrieve_memories(        query="模型部署到鸿蒙设备",        n_results=3,        memory_types=["technical_query"]    )print("检索到的记忆:")for memory in memories:print(f"- {memory['content']} (相似度: {memory['similarity']})")# 构建用户画像    profile = memory_system.build_user_profile()print("\n用户画像:")print(json.dumps(profile, ensure_ascii=False, indent=2))

# 代码片段2：OpenClaw任务规划器实现from typing importList, Dict, Any, Optionalfrom enum import Enumimport asynciofrom datetime import datetimeclassTaskStatus(Enum):    PENDING = "pending"    PLANNING = "planning"    EXECUTING = "executing"    COMPLETED = "completed"    FAILED = "failed"classToolType(Enum):    SYSTEM_API = "system_api"    APP_INTEGRATION = "app_integration"    CUSTOM_FUNCTION = "custom_function"    WEB_SEARCH = "web_search"classTaskStep:def__init__(self, step_id: int, description: str, tool_type: ToolType,                  tool_config: Dict[str, Any], dependencies: List[int] = None):self.step_id = step_idself.description = descriptionself.tool_type = tool_typeself.tool_config = tool_configself.dependencies = dependencies or []self.status = TaskStatus.PENDINGself.result = Noneself.error = Noneself.start_time = Noneself.end_time = Nonedefto_dict(self):return {"step_id": self.step_id,"description": self.description,"tool_type": self.tool_type.value,"dependencies": self.dependencies,"status": self.status.value,"result": self.result,"error": self.error        }classOpenClawTaskPlanner:def__init__(self, available_tools: Dict[str, Any]):self.available_tools = available_toolsself.task_graph = {}defplan_task(self, user_request: str, context: Dict[str, Any] = None) -> List[TaskStep]:"""基于用户请求规划任务步骤"""# 使用思维链（CoT）进行任务分解        reasoning_steps = self._chain_of_thought_reasoning(user_request, context)# 将推理步骤转换为可执行的任务步骤        task_steps = self._convert_to_task_steps(reasoning_steps)# 构建依赖关系图self._build_dependency_graph(task_steps)return task_stepsdef_chain_of_thought_reasoning(self, user_request: str, context: Dict[str, Any]) -> List[str]:"""思维链推理过程"""# 实际实现应使用本地大模型进行推理# 这里提供简化示例        reasoning_steps = []# 示例：用户请求"帮我预约明天下午3点的会议室"if"预约"in user_request and"会议室"in user_request:            reasoning_steps = ["1. 理解用户需求：预约会议室","2. 提取关键信息：时间（明天下午3点），资源类型（会议室）","3. 检查日历权限：需要访问系统日历应用","4. 查询会议室可用性：调用会议室管理系统API","5. 验证时间冲突：检查用户日历中该时间段是否有其他安排","6. 执行预约：调用日历API创建会议","7. 发送确认：生成预约确认信息并发送给用户"            ]return reasoning_stepsdef_convert_to_task_steps(self, reasoning_steps: List[str]) -> List[TaskStep]:"""将推理步骤转换为任务步骤"""        task_steps = []for i, step inenumerate(reasoning_steps):            tool_type = self._determine_tool_type(step)            tool_config = self._generate_tool_config(step)            task_step = TaskStep(                step_id=i + 1,                description=step,                tool_type=tool_type,                tool_config=tool_config            )            task_steps.append(task_step)return task_stepsdef_determine_tool_type(self, step_description: str) -> ToolType:"""根据步骤描述确定工具类型"""        step_lower = step_description.lower()ifany(keyword in step_lower for keyword in ["系统", "日历", "权限"]):return ToolType.SYSTEM_APIelifany(keyword in step_lower for keyword in ["应用", "app", "集成"]):return ToolType.APP_INTEGRATIONelifany(keyword in step_lower for keyword in ["api", "查询", "调用"]):return ToolType.CUSTOM_FUNCTIONelse:return ToolType.CUSTOM_FUNCTIONdef_generate_tool_config(self, step_description: str) -> Dict[str, Any]:"""生成工具配置"""        config = {"description": step_description,"timeout": 30,  # 默认超时30秒"retry_count": 2# 默认重试2次        }# 根据步骤类型添加特定配置if"日历"in step_description:            config["api_name"] = "calendar_service"            config["action"] = "create_event"return configdef_build_dependency_graph(self, task_steps: List[TaskStep]):"""构建任务依赖关系图"""# 简化的依赖关系构建# 实际实现应根据步骤语义分析依赖for i, step inenumerate(task_steps):# 简单规则：每个步骤依赖前一个步骤if i > 0:                step.dependencies = [task_steps[i-1].step_id]# 存储任务图self.task_graph = {step.step_id: step for step in task_steps}classOpenClawTaskExecutor:def__init__(self, task_planner: OpenClawTaskPlanner):self.task_planner = task_plannerself.execution_history = []asyncdefexecute_task(self, user_request: str, context: Dict[str, Any] = None) -> Dict[str, Any]:"""执行完整任务"""# 规划任务        task_steps = self.task_planner.plan_task(user_request, context)# 执行任务步骤        results = []for step in task_steps:            step_result = awaitself._execute_step(step)            results.append(step_result)# 如果步骤失败，根据策略决定是否继续if step.status == TaskStatus.FAILED andnotself._should_continue(step):break# 汇总结果        final_result = self._aggregate_results(results)# 记录执行历史self.execution_history.append({"timestamp": datetime.now().isoformat(),"user_request": user_request,"task_steps": [step.to_dict() for step in task_steps],"final_result": final_result        })return final_resultasyncdef_execute_step(self, step: TaskStep) -> Dict[str, Any]:"""执行单个任务步骤"""        step.status = TaskStatus.EXECUTING        step.start_time = datetime.now()try:# 根据工具类型选择执行器if step.tool_type == ToolType.SYSTEM_API:                result = awaitself._execute_system_api(step.tool_config)elif step.tool_type == ToolType.APP_INTEGRATION:                result = awaitself._execute_app_integration(step.tool_config)elif step.tool_type == ToolType.CUSTOM_FUNCTION:                result = awaitself._execute_custom_function(step.tool_config)else:                result = {"error": f"未知工具类型: {step.tool_type}"}            step.result = result            step.status = TaskStatus.COMPLETEDexcept Exception as e:            step.status = TaskStatus.FAILED            step.error = str(e)            step.result = {"error": str(e)}        step.end_time = datetime.now()return step.to_dict()asyncdef_execute_system_api(self, config: Dict[str, Any]) -> Dict[str, Any]:"""执行系统API调用"""# 模拟系统API调用await asyncio.sleep(0.5)  # 模拟网络延迟if config.get("api_name") == "calendar_service":return {"success": True,"event_id": f"event_{datetime.now().strftime('%Y%m%d_%H%M%S')}","message": "日历事件创建成功"            }return {"success": False, "error": "API配置错误"}asyncdef_execute_app_integration(self, config: Dict[str, Any]) -> Dict[str, Any]:"""执行应用集成调用"""# 模拟应用集成调用await asyncio.sleep(0.3)return {"success": True, "message": "应用集成调用成功"}asyncdef_execute_custom_function(self, config: Dict[str, Any]) -> Dict[str, Any]:"""执行自定义函数"""# 模拟自定义函数执行await asyncio.sleep(0.2)return {"success": True, "result": "自定义函数执行完成"}def_should_continue(self, step: TaskStep) -> bool:"""判断步骤失败后是否继续执行"""# 简单策略：关键步骤失败则停止        critical_keywords = ["权限", "验证", "安全"]        step_desc = step.description.lower()ifany(keyword in step_desc for keyword in critical_keywords):returnFalsereturnTruedef_aggregate_results(self, step_results: List[Dict[str, Any]]) -> Dict[str, Any]:"""汇总步骤结果"""        success_steps = [r for r in step_results if r["status"] == TaskStatus.COMPLETED.value]        failed_steps = [r for r in step_results if r["status"] == TaskStatus.FAILED.value]return {"total_steps": len(step_results),"completed_steps": len(success_steps),"failed_steps": len(failed_steps),"overall_success": len(failed_steps) == 0,"step_details": step_results        }# 使用示例asyncdefmain():# 可用工具配置    available_tools = {"calendar_service": {"type": "system_api", "description": "日历服务"},"meeting_room_api": {"type": "custom_function", "description": "会议室管理API"},"notification_service": {"type": "system_api", "description": "通知服务"}    }# 初始化规划器和执行器    planner = OpenClawTaskPlanner(available_tools)    executor = OpenClawTaskExecutor(planner)# 执行任务    user_request = "帮我预约明天下午3点的会议室，并通知团队成员"    context = {"user_id": "user_001","team_members": ["张三", "李四", "王五"]    }    result = await executor.execute_task(user_request, context)print("任务执行结果:")print(f"总体成功: {result['overall_success']}")print(f"完成步骤: {result['completed_steps']}/{result['total_steps']}")ifnot result['overall_success']:print("失败的步骤:")for step in result['step_details']:if step['status'] == TaskStatus.FAILED.value:print(f"  步骤{step['step_id']}: {step['error']}")if __name__ == "__main__":    asyncio.run(main())

// 代码片段3：鸿蒙AI服务集成的ArkTS实现import ai from'@ohos.ai.engine';import common from'@ohos.app.ability.common';// AI服务管理器类classHarmonyOSAIServiceManager {privatecontext: common.Context;// AI服务实例privatedeviceAiService: ai.DeviceAiService | null = null;privatecloudAiService: ai.CloudAiService | null = null;privatepluginManager: ai.PluginManager | null = null;// AI事件监听器privatedeviceAiEventListener: ai.AiEventListener = {onAiEvent: (event: ai.AiEvent): void => {switch (event.eventType) {case ai.AiEventType.MODEL_LOADED:console.log(`AI模型加载成功: ${event.data?.getString('model_name')}`);break;case ai.AiEventType.INFERENCE_COMPLETED:console.log(`推理完成，耗时: ${event.data?.getLong('inference_time_ms')}ms`);break;case ai.AiEventType.RESOURCE_LOW:console.log('AI资源紧张，自动降级处理');this.adjustAiPerformance(true);break;default:console.log(`AI事件: ${event.eventType}`);      }    }  };constructor(context: common.Context) {this.context = context;  }// 初始化AI服务initializeAIServices(): boolean {try {// 1. 获取AI引擎管理器const aiEngineManager = ai.AiEngineManager.getInstance(this.context);// 2. 初始化设备端AI服务（本地推理）this.deviceAiService = aiEngineManager.getDeviceAiService();// 配置本地模型constmodelConfig: ai.AiModelConfig = {modelName: 'qwen3.5-4b-int8',modelPath: '/data/local/ai/models/qwen3.5',computeUnit: ai.AiComputeUnit.NPU, // 优先使用NPUprecision: ai.AiPrecision.INT8      };this.deviceAiService.configureLocalModel(modelConfig);// 设置性能模式this.deviceAiService.performanceMode = ai.AiPerformanceMode.BALANCE;// 注册AI事件监听器this.deviceAiService.registerAiEventListener(this.deviceAiEventListener);// 3. 初始化云端AI服务（备用）this.cloudAiService = aiEngineManager.getCloudAiService();constcloudConfig: ai.AiCloudConfig = {serviceType: ai.AiServiceType.TEXT_GENERATION,apiKey: 'your_api_key_here',endpoint: 'https://api.huawei.com/ai'      };this.cloudAiService.configureCloudService(cloudConfig);// 4. 初始化插件管理器this.pluginManager = aiEngineManager.pluginManager;// 加载OpenClaw插件this.pluginManager.loadPlugin('openclaw_agent', '/data/local/ai/plugins/openclaw');// 注册自定义工具this.pluginManager.registerCustomTool('meeting_scheduler', newMeetingSchedulerTool());this.pluginManager.registerCustomTool('email_composer', newEmailComposerTool());returntrue;    } catch (e) {console.error('AI服务初始化失败:', e);returnfalse;    }  }// 调整AI性能privateadjustAiPerformance(isLowResource: boolean): void {if (!this.deviceAiService) return;if (isLowResource) {// 资源紧张时降低精度this.deviceAiService.performanceMode = ai.AiPerformanceMode.LOW_POWER;this.deviceAiService.precision = ai.AiPrecision.INT4;    } else {// 恢复正常模式this.deviceAiService.performanceMode = ai.AiPerformanceMode.BALANCE;this.deviceAiService.precision = ai.AiPrecision.INT8;    }  }// 执行文本生成任务generateText(prompt: string, useLocal: boolean = true): string {try {const aiService = useLocal ? this.deviceAiService : this.cloudAiService;if (!aiService) return'AI服务未初始化';constrequest: ai.AiTextRequest = {prompt: prompt,maxTokens: 500,temperature: 0.7,topP: 0.9      };const response = aiService.generateText(request);if (response.isSuccessful) {return response.text;      } else {return`生成失败: ${response.errorMessage}`;      }    } catch (e) {return`AI服务异常: ${e.message}`;    }  }// 调用自定义工具executeTool(toolName: string, parameters: Record<string, any>): Record<string, any> {try {if (!this.pluginManager) return { error: '插件管理器未初始化' };const result = this.pluginManager.executeTool(toolName, JSON.stringify(parameters));returnJSON.parse(result);    } catch (e) {return { error: e.message || '未知错误' };    }  }// 获取AI服务状态getAiServiceStatus(): Record<string, any> {const isDeviceAiAvailable = !!this.deviceAiService;const isCloudAiAvailable = !!this.cloudAiService;const isModelLoaded = this.deviceAiService?.isModelLoaded?.('qwen3.5-4b-int8') || false;const performanceMode = this.deviceAiService?.performanceMode || ai.AiPerformanceMode.BALANCE;const activePlugins = this.pluginManager?.loadedPlugins?.keys() || [];return {device_ai_available: isDeviceAiAvailable,cloud_ai_available: isCloudAiAvailable,local_model_loaded: isModelLoaded,current_performance_mode: performanceMode,active_plugins: Array.from(activePlugins)    };  }// 清理资源cleanup(): void {if (this.deviceAiService) {this.deviceAiService.unregisterAiEventListener(this.deviceAiEventListener);    }  }}// 自定义工具示例：会议调度器classMeetingSchedulerToolimplements ai.AiTool {execute(parameters: string): string {const params = JSON.parse(parameters);const title = params['title'] || '团队会议';const startTime = params['start_time'] || '';const participants = params['participants'] || [];// 在实际应用中，这里会调用系统日历APIconst meetingId = `meeting_${Date.now()}`;const result = {success: true,meeting_id: meetingId,title: title,start_time: startTime,participants: participants,message: '会议预约成功'    };returnJSON.stringify(result);  }getToolInfo(): ai.AiToolInfo {return {toolName: 'meeting_scheduler',description: '预约会议并通知参与者',version: '1.0.0'    };  }}// 自定义工具示例：邮件撰写器classEmailComposerToolimplements ai.AiTool {execute(parameters: string): string {const params = JSON.parse(parameters);const recipient = params['recipient'] || '';const subject = params['subject'] || '';const body = params['body'] || '';// 在实际应用中，这里会调用邮件客户端APIconst emailId = `email_${Date.now()}`;const result = {success: true,email_id: emailId,recipient: recipient,subject: subject,message: '邮件草稿已保存'    };returnJSON.stringify(result);  }getToolInfo(): ai.AiToolInfo {return {toolName: 'email_composer',description: '撰写并发送邮件',version: '1.0.0'    };  }}// 使用示例// 注意：实际使用需要真实的HarmonyOS Contextconsole.log('鸿蒙AI服务集成示例（ArkTS）');// 创建服务管理器（在实际应用中从Ability获取Context）// const context = getContext();// const aiManager = new HarmonyOSAIServiceManager(context);// 初始化AI服务// const initialized = aiManager.initializeAIServices();// console.log('AI服务初始化:', initialized);// 获取服务状态// const status = aiManager.getAiServiceStatus();// console.log('AI服务状态:', status);// 执行文本生成// const text = aiManager.generateText('鸿蒙系统的优势有哪些？');// console.log('生成结果:', text);// 调用工具// const toolResult = aiManager.executeTool('meeting_scheduler', {//   title: '项目评审会',//   start_time: '2026-03-13 14:00',//   participants: ['张三', '李四']// });// console.log('工具执行结果:', toolResult);// 清理资源// aiManager.cleanup();

# 代码片段4：OpenClaw安全防护系统的Python实现import hashlibimport hmacimport base64import jsonfrom datetime import datetimefrom typing importDict, Any, Optionalfrom enum import Enumimport cryptographyfrom cryptography.fernet import Fernetfrom cryptography.hazmat.primitives import hashesfrom cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modesfrom cryptography.hazmat.backends import default_backendimport osclassSecurityLevel(Enum):    LOW = "low"# 非敏感数据    MEDIUM = "medium"# 一般隐私数据    HIGH = "high"# 敏感隐私数据    CRITICAL = "critical"# 金融/医疗数据classOpenClawSecuritySystem:def__init__(self, device_id: str):self.device_id = device_idself.security_config = self._load_security_config()self.encryption_keys = self._initialize_encryption_keys()self.access_log = []def_load_security_config(self) -> Dict[str, Any]:"""加载安全配置"""return {"encryption_algorithms": {"low": "AES-128-CBC","medium": "AES-256-CBC","high": "AES-256-GCM","critical": "AES-256-GCM + RSA-2048"            },"access_control": {"max_failed_attempts": 5,"lockout_duration": 300,  # 5分钟"require_biometric": True            },"audit_logging": {"enabled": True,"retention_days": 90,"encrypt_logs": True            }        }def_initialize_encryption_keys(self) -> Dict[str, bytes]:"""初始化加密密钥"""        keys = {}# 从安全硬件获取根密钥（模拟）        root_key = self._get_hardware_root_key()# 派生不同安全级别的密钥for level in SecurityLevel:            salt = f"openclaw_{level.value}_{self.device_id}".encode()            kdf = PBKDF2(                algorithm=hashes.SHA256(),                length=32,                salt=salt,                iterations=100000,                backend=default_backend()            )            keys[level.value] = kdf.derive(root_key)return keysdef_get_hardware_root_key(self) -> bytes:"""从安全硬件获取根密钥（模拟）"""# 在实际设备中，这里会调用TEE/SE的API# 返回模拟的根密钥return os.urandom(32)defencrypt_data(self, data: Any, security_level: SecurityLevel) -> Dict[str, Any]:"""加密数据"""# 序列化数据ifisinstance(data, (dict, list)):            serialized_data = json.dumps(data, ensure_ascii=False).encode('utf-8')elifisinstance(data, str):            serialized_data = data.encode('utf-8')else:            serialized_data = str(data).encode('utf-8')# 根据安全级别选择加密算法        encryption_result = self._encrypt_with_level(serialized_data, security_level)# 记录访问日志self._log_access("encrypt", security_level, len(serialized_data))return encryption_resultdef_encrypt_with_level(self, data: bytes, level: SecurityLevel) -> Dict[str, Any]:"""根据安全级别加密"""        key = self.encryption_keys[level.value]if level == SecurityLevel.LOW:returnself._encrypt_aes_128_cbc(data, key)elif level == SecurityLevel.MEDIUM:returnself._encrypt_aes_256_cbc(data, key)elif level == SecurityLevel.HIGH:returnself._encrypt_aes_256_gcm(data, key)elif level == SecurityLevel.CRITICAL:returnself._encrypt_critical(data, key)else:raise ValueError(f"未知的安全级别: {level}")def_encrypt_aes_256_gcm(self, data: bytes, key: bytes) -> Dict[str, Any]:"""AES-256-GCM加密"""# 生成随机nonce        nonce = os.urandom(12)# 创建加密器        cipher = Cipher(            algorithms.AES(key),            modes.GCM(nonce),            backend=default_backend()        )        encryptor = cipher.encryptor()# 加密数据        ciphertext = encryptor.update(data) + encryptor.finalize()return {"algorithm": "AES-256-GCM","ciphertext": base64.b64encode(ciphertext).decode('ascii'),"nonce": base64.b64encode(nonce).decode('ascii'),"tag": base64.b64encode(encryptor.tag).decode('ascii'),"timestamp": datetime.now().isoformat()        }def_encrypt_critical(self, data: bytes, key: bytes) -> Dict[str, Any]:"""关键数据加密（AES + RSA）"""# 首先生成随机会话密钥        session_key = os.urandom(32)# 使用会话密钥加密数据        aes_result = self._encrypt_aes_256_gcm(data, session_key)# 使用RSA加密会话密钥（模拟）# 在实际应用中，这里会使用设备的RSA公钥        encrypted_session_key = base64.b64encode(            hmac.new(key, session_key, hashlib.sha256).digest()        ).decode('ascii')        aes_result.update({"algorithm": "AES-256-GCM + RSA-2048","encrypted_session_key": encrypted_session_key,"key_wrapping": "RSA-OAEP"        })return aes_resultdefdecrypt_data(self, encrypted_data: Dict[str, Any], security_level: SecurityLevel) -> Any:"""解密数据"""# 验证安全级别        expected_level = security_level.valueif encrypted_data.get("security_level") and encrypted_data["security_level"] != expected_level:raise SecurityError(f"安全级别不匹配: 期望{expected_level}, 实际{encrypted_data['security_level']}")# 根据算法解密        algorithm = encrypted_data.get("algorithm", "")        key = self.encryption_keys[security_level.value]if"AES-256-GCM"in algorithm:            decrypted_bytes = self._decrypt_aes_256_gcm(encrypted_data, key)elif"AES-256-CBC"in algorithm:            decrypted_bytes = self._decrypt_aes_256_cbc(encrypted_data, key)elif"AES-128-CBC"in algorithm:            decrypted_bytes = self._decrypt_aes_128_cbc(encrypted_data, key)else:raise SecurityError(f"不支持的加密算法: {algorithm}")# 尝试解析为JSON，否则返回字符串try:            decrypted_str = decrypted_bytes.decode('utf-8')return json.loads(decrypted_str)except (json.JSONDecodeError, UnicodeDecodeError):return decrypted_bytes.decode('utf-8', errors='replace')def_decrypt_aes_256_gcm(self, encrypted_data: Dict[str, Any], key: bytes) -> bytes:"""AES-256-GCM解密"""        ciphertext = base64.b64decode(encrypted_data["ciphertext"])        nonce = base64.b64decode(encrypted_data["nonce"])        tag = base64.b64decode(encrypted_data["tag"])        cipher = Cipher(            algorithms.AES(key),            modes.GCM(nonce, tag),            backend=default_backend()        )        decryptor = cipher.decryptor()return decryptor.update(ciphertext) + decryptor.finalize()defvalidate_access(self, user_id: str, resource: str, action: str) -> bool:"""验证访问权限"""# 检查失败尝试次数        failed_attempts = self._get_failed_attempts(user_id)if failed_attempts >= self.security_config["access_control"]["max_failed_attempts"]:print(f"用户{user_id}因多次失败尝试被锁定")returnFalse# 检查资源访问权限        has_permission = self._check_permission(user_id, resource, action)ifnot has_permission:self._record_failed_attempt(user_id, resource, action)# 记录访问日志self._log_access(action, SecurityLevel.MEDIUM, 0, {"user_id": user_id,"resource": resource,"authorized": has_permission        })return has_permissiondef_check_permission(self, user_id: str, resource: str, action: str) -> bool:"""检查具体权限"""# 简化的权限检查逻辑# 在实际应用中，这里会有完整的RBAC或ABAC系统# 公开资源的只读权限if resource.startswith("public_") and action == "read":returnTrue# 用户自己的数据if resource.startswith(f"user_{user_id}_") and action in ["read", "write"]:returnTrue# AI服务的基础权限if resource.startswith("ai_service_") and user_id in ["ai_assistant", "system"]:returnTruereturnFalsedef_log_access(self, action: str, security_level: SecurityLevel,                    data_size: int, metadata: Dict[str, Any] = None):"""记录访问日志"""        log_entry = {"timestamp": datetime.now().isoformat(),"device_id": self.device_id,"action": action,"security_level": security_level.value,"data_size": data_size,"metadata": metadata or {}        }self.access_log.append(log_entry)# 如果配置要求加密日志，则加密存储ifself.security_config["audit_logging"]["encrypt_logs"]:            encrypted_log = self.encrypt_data(log_entry, SecurityLevel.MEDIUM)# 在实际应用中，这里会写入加密的日志文件# self._write_encrypted_log(encrypted_log)def_record_failed_attempt(self, user_id: str, resource: str, action: str):"""记录失败尝试"""# 在实际应用中，这里会持久化存储失败尝试print(f"失败尝试记录: 用户{user_id}尝试{action}资源{resource}")def_get_failed_attempts(self, user_id: str) -> int:"""获取失败尝试次数"""# 模拟实现return0defget_security_report(self) -> Dict[str, Any]:"""生成安全报告"""        recent_logs = self.access_log[-100:] iflen(self.access_log) > 100elseself.access_log# 统计不同安全级别的操作        level_counts = {}for log in recent_logs:            level = log.get("security_level", "unknown")            level_counts[level] = level_counts.get(level, 0) + 1return {"device_id": self.device_id,"total_access_logs": len(self.access_log),"recent_operations": len(recent_logs),"security_level_distribution": level_counts,"encryption_keys_initialized": len(self.encryption_keys) > 0,"last_updated": datetime.now().isoformat()        }classSecurityError(Exception):"""安全相关异常"""pass# 使用示例defsecurity_system_demo():print("=== OpenClaw安全系统演示 ===\n")# 初始化安全系统    security_system = OpenClawSecuritySystem(device_id="huawei_mate_60_pro")# 1. 加密不同安全级别的数据    test_data = {"user_id": "test_user","preferences": {"theme": "dark", "language": "zh-CN"},"last_login": datetime.now().isoformat()    }print("1. 数据加密演示")# 中等安全级别加密    encrypted_medium = security_system.encrypt_data(test_data, SecurityLevel.MEDIUM)print(f"  中等安全级别加密结果:")print(f"  算法: {encrypted_medium['algorithm']}")print(f"  密文长度: {len(encrypted_medium['ciphertext'])} 字符")print(f"  时间戳: {encrypted_medium['timestamp'][:19]}")# 高级安全级别加密    encrypted_high = security_system.encrypt_data(test_data, SecurityLevel.HIGH)print(f"\n  高级安全级别加密结果:")print(f"  算法: {encrypted_high['algorithm']}")print(f"  密文长度: {len(encrypted_high['ciphertext'])} 字符")print(f"  包含Nonce和Tag: 是")# 2. 解密数据print("\n2. 数据解密演示")    decrypted_medium = security_system.decrypt_data(encrypted_medium, SecurityLevel.MEDIUM)print(f"  中等安全级别解密结果:")print(f"  用户ID: {decrypted_medium['user_id']}")print(f"  主题偏好: {decrypted_medium['preferences']['theme']}")print(f"  语言设置: {decrypted_medium['preferences']['language']}")# 3. 权限验证print("\n3. 权限验证演示")    test_cases = [        ("user_001", "user_001_profile", "read"),        ("user_001", "user_002_profile", "read"),        ("ai_assistant", "ai_service_chat", "execute"),        ("guest", "public_news", "read")    ]for user_id, resource, action in test_cases:        authorized = security_system.validate_access(user_id, resource, action)print(f"  用户 {user_id} 尝试 {action}{resource}: {'允许'if authorized else'拒绝'}")# 4. 安全报告print("\n4. 安全系统报告")    report = security_system.get_security_report()for key, value in report.items():if key != "device_id":print(f"  {key}: {value}")if __name__ == "__main__":    security_system_demo()