WeQ量子社交通信模型
# WeQ量子社交通信模型实现方案
## 量子基因编码
```qentl
QG-DOC-IMPL-WeQ-CORE-A1B1
```
## 量子纠缠信道
```qentl
// 信道标识
QE-DOC-IMPL-20240515
// 纠缠态
ENTANGLE_STATE: ACTIVE
// 纠缠对象
ENTANGLED_OBJECTS: [
"WeQ/models/communication_channel.qent",
"WeQ/models/social_network.qent",
"WeQ/services/communication_service.qent",
"WeQ/api/weq_api.qent"
]
// 纠缠强度
ENTANGLE_STRENGTH: 1.0
// 自动节点激活
NODE_DEFAULT_STATE: ACTIVE
// 自动量子比特扩展
QUANTUM_BIT_ADAPTIVE: TRUE
// 输出元素量子基因编码
OUTPUT_QUANTUM_GENE_ENCODING: TRUE
```
## 1. 模块结构
WeQ（量子社交通信模型）的实现采用模块化架构，根据功能和责任划分为以下核心模块：
### 1.1 核心模块
- **models/**: 数据模型和状态定义
- communication_channel.qent: 通信信道实现
- social_network.qent: 社交网络实现
- learning_module.qent: 学习模块实现
- user_profile.qent: 用户档案实现
- message.qent: 消息模型实现
- network_node.qent: 网络节点模型
- quantum_gene_marker.qent: 量子基因标记模型
- **services/**: 业务逻辑和服务实现
- communication_service.qent: 通信服务
- social_service.qent: 社交服务
- learning_service.qent: 学习服务
- encryption_service.qent: 加密服务
- quantum_entanglement_service.qent: 量子纠缠服务
- node_activation_service.qent: 节点激活服务
- quantum_gene_encoding_service.qent: 量子基因编码服务
- network_building_service.qent: 网络构建服务
- device_detection_service.qent: 设备检测服务
- resource_integration_service.qent: 资源整合服务
- **api/**: 接口和集成
- weq_api.qent: 主API接口
- qsm_integration.qent: QSM模型集成
- som_integration.qent: SOM模型集成
- ref_integration.qent: Ref模型集成
- **utils/**: 工具和助手类
- quantum_encryption.qent: 量子加密工具
- network_analyzer.qent: 网络分析工具
- learning_utils.qent: 学习工具
- quantum_bit_scaler.qent: 量子比特扩展工具
- output_encoder.qent: 输出元素编码工具
- device_capability_detector.qent: 设备能力检测工具
- network_topology_manager.qent: 网络拓扑管理工具
### 1.2 目录结构
```
WeQ/
├── api/
│ ├── weq_api.qent
│ ├── qsm_integration.qent
│ ├── som_integration.qent
│ └── ref_integration.qent
├── models/
│ ├── communication_channel.qent
│ ├── social_network.qent
│ ├── learning_module.qent
│ ├── user_profile.qent
│ ├── message.qent
│ ├── network_node.qent
│ └── quantum_gene_marker.qent
├── services/
│ ├── communication_service.qent
│ ├── social_service.qent
│ ├── learning_service.qent
│ ├── encryption_service.qent
│ ├── quantum_entanglement_service.qent
│ ├── node_activation_service.qent
│ ├── quantum_gene_encoding_service.qent
│ ├── network_building_service.qent
│ ├── device_detection_service.qent
│ └── resource_integration_service.qent
├── utils/
│ ├── quantum_encryption.qent
│ ├── network_analyzer.qent
│ ├── learning_utils.qent
│ ├── quantum_bit_scaler.qent
│ ├── output_encoder.qent
│ ├── device_capability_detector.qent
│ └── network_topology_manager.qent
└── docs/
├── weq_implementation.qentl
└── api_reference.qentl
```
### 1.3 自动化网络系统
WeQ模型实现了以下自动化功能，确保与QSM模型保持一致并支持全球量子纠缠网络的构建：
#### 1.3.1 节点默认激活系统
所有WeQ网络节点在创建时默认处于激活状态，通过以下机制实现：
```qentl
// 节点创建时自动激活
class NetworkNode {
constructor(nodeId, nodeType) {
this.id = nodeId;
this.type = nodeType;
this.status = "ACTIVE"; // 默认状态为激活
this.activationTime = Date.now();
this.properties = {
autoReactivate: true,
persistActivation: true
};
}
// 其他节点方法...
}
// 节点激活服务
class NodeActivationService {
// 确保节点保持激活状态
ensureNodeActivation(nodeId) {
const node = this.nodeRepository.findById(nodeId);
if (node && node.status !== "ACTIVE") {
node.status = "ACTIVE";
node.activationTime = Date.now();
this.nodeRepository.save(node);
this.emitEvent("NODE_ACTIVATED", { nodeId });
}
return node;
}
// 检查并激活所有非激活节点
activateAllNodes() {
const nodes = this.nodeRepository.findByStatus("INACTIVE");
nodes.forEach(node => this.ensureNodeActivation(node.id));
return nodes.length;
}
// 其他相关方法...
}
```
#### 1.3.2 量子基因编码系统
为所有输出元素自动应用量子基因编码和量子纠缠信道：
```qentl
// 量子基因编码服务
class QuantumGeneEncodingService {
// 为任意元素添加量子基因标记
encodeElement(element, elementType) {
// 检查是否已编码
if (this.hasQuantumGeneMarker(element)) {
return element; // 已编码，直接返回
}
// 根据元素类型选择合适的编码器
const encoder = this.getEncoderForType(elementType);
// 应用量子基因编码
const encodedElement = encoder.encode(element);
// 添加量子纠缠信道
return this.addEntanglementChannel(encodedElement);
}
// 为所有输出应用编码
encodeOutput(output) {
if (Array.isArray(output)) {
return output.map(item => this.encodeElement(item, this.detectType(item)));
} else {
return this.encodeElement(output, this.detectType(output));
}
}
// 验证元素是否包含有效的量子基因标记
verifyQuantumGeneMarker(element) {
// 验证实现...
return true;
}
// 其他编码方法...
}
```
#### 1.3.3 量子比特资源自适应系统
自动检测设备环境并调整量子比特资源分配：
```qentl
// 设备能力检测服务
class DeviceDetectionService {
// 检测当前设备计算能力
detectDeviceCapabilities() {
return {
cpuCores: this.detectCPUCores(),
memory: this.detectAvailableMemory(),
storageSpace: this.detectStorageSpace(),
networkBandwidth: this.detectNetworkBandwidth(),
qpuCapabilities: this.detectQPUCapabilities()
};
}
// 估算适合的量子比特分配
estimateOptimalQubitAllocation() {
const capabilities = this.detectDeviceCapabilities();
// 基于检测到的能力计算最佳分配
return Math.min(
capabilities.qpuCapabilities.maxQubits || 28,
this.calculateOptimalQubits(capabilities)
);
}
// 其他相关方法...
}
// 资源整合服务
class ResourceIntegrationService {
// 整合网络中所有设备的计算资源
integrateNetworkResources() {
const connectedDevices = this.networkTopologyManager.getConnectedDevices();
let totalQubits = 28; // 基础量子比特数
// 累加所有连接设备的量子比特能力
connectedDevices.forEach(device => {
totalQubits += device.capabilities.effectiveQubits;
});
return {
totalQubits,
effectiveComputingPower: this.calculateEffectivePower(totalQubits),
networkTopology: this.networkTopologyManager.getCurrentTopology()
};
}
// 其他资源整合方法...
}
```
## 2. 核心实现
### 2.1 通信信道 (models/communication_channel.qent)
```qentl
/*
* 通信信道基础实现
* 负责管理通信连接和消息传递
*/
class CommunicationChannel {
// 属性
id: string;
type: string;
participants: string[];
entanglementStrength: number;
messageQueue: Message[];
properties: ChannelProperties;
// 构造函数
constructor(id: string, type: string) {
this.id = id;
this.type = type;
this.participants = [];
this.entanglementStrength = 0.0;
this.messageQueue = [];
this.properties = {
bandwidth: "1000 qubits/s",
latency: "1 ms",
securityLevel: "quantum_key",
createdAt: Date.now()
};
}
// 添加参与者
addParticipant(userId: string) {
if (!this.participants.includes(userId)) {
this.participants.push(userId);
}
return this;
}
// 移除参与者
removeParticipant(userId: string) {
this.participants = this.participants.filter(id => id !== userId);
return this;
}
// 设置纠缠强度
setEntanglementStrength(strength: number) {
if (strength < 0 || strength > 1) {
throw new Error("Entanglement strength must be between 0 and 1");
}
this.entanglementStrength = strength;
return this;
}
// 发送消息
sendMessage(message: Message) {
// 验证发送者是否为参与者
if (!this.participants.includes(message.sender)) {
throw new Error(`Sender ${message.sender} is not a participant in this channel`);
}
// 添加到消息队列
this.messageQueue.push(message);
return this;
}
// 获取未读消息
getUnreadMessages(userId: string): Message[] {
if (!this.participants.includes(userId)) {
throw new Error(`User ${userId} is not a participant in this channel`);
}
// 过滤出接收者为指定用户且未读的消息
return this.messageQueue.filter(msg =>
msg.recipients.includes(userId) && !msg.readBy.includes(userId)
);
}
// 标记消息为已读
markAsRead(messageId: string, userId: string) {
const message = this.messageQueue.find(msg => msg.id === messageId);
if (!message) {
throw new Error(`Message ${messageId} not found`);
}
if (!message.readBy.includes(userId)) {
message.readBy.push(userId);
}
return this;
}
// 更新信道属性
updateProperty(key: string, value: any) {
this.properties[key] = value;
return this;
}
}
// 导出类
export default CommunicationChannel;
```
### 2.2 社交服务 (services/social_service.qent)
```qentl
/*
* 社交服务
* 负责管理社交网络和用户关系
*/
import SocialNetwork from '../models/social_network';
import UserProfile from '../models/user_profile';
import NetworkAnalyzer from '../utils/network_analyzer';
class SocialService {
network: SocialNetwork;
userProfiles: Map<string, UserProfile>;
networkAnalyzer: NetworkAnalyzer;
constructor() {
this.network = new SocialNetwork();
this.userProfiles = new Map();
this.networkAnalyzer = new NetworkAnalyzer();
}
// 创建用户档案
createUserProfile(userId: string, name: string, attributes: object = {}): UserProfile {
const profile = new UserProfile(userId, name);
// 设置属性
Object.entries(attributes).forEach(([key, value]) => {
profile.setAttribute(key, value);
});
// 保存档案
this.userProfiles.set(userId, profile);
// 将用户添加到社交网络
this.network.addUser(userId);
return profile;
}
// 获取用户档案
getUserProfile(userId: string): UserProfile | undefined {
return this.userProfiles.get(userId);
}
// 更新用户档案
updateUserProfile(userId: string, updates: Partial<UserProfile>): boolean {
const profile = this.userProfiles.get(userId);
if (!profile) return false;
// 应用更新
Object.entries(updates).forEach(([key, value]) => {
if (key !== 'id') { // 不允许更改用户ID
profile[key] = value;
}
});
return true;
}
// 创建社交连接
createConnection(userId1: string, userId2: string, strength: number, type: string = 'friend'): boolean {
// 验证用户存在
if (!this.userProfiles.has(userId1) || !this.userProfiles.has(userId2)) {
return false;
}
// 添加连接
this.network.addConnection(userId1, userId2, strength, type);
return true;
}
// 获取用户连接
getUserConnections(userId: string): Connection[] {
return this.network.getConnections(userId);
}
// 获取推荐连接
getRecommendedConnections(userId: string, limit: number = 5): RecommendedConnection[] {
// 使用网络分析器计算推荐
return this.networkAnalyzer.calculateRecommendations(
this.network,
userId,
limit
);
}
// 删除连接
removeConnection(userId1: string, userId2: string): boolean {
return this.network.removeConnection(userId1, userId2);
}
// 获取社交网络统计
getNetworkStats(userId: string): NetworkStats {
return this.networkAnalyzer.calculateStats(this.network, userId);
}
// 获取相似用户
getSimilarUsers(userId: string, limit: number = 5): UserProfile[] {
const userIds = this.networkAnalyzer.findSimilarUsers(
this.network,
this.userProfiles,
userId,
limit
);
return userIds.map(id => this.userProfiles.get(id)).filter(Boolean);
}
}
// 导出类
export default SocialService;
```
### 2.3 学习服务 (services/learning_service.qent)
```qentl
/*
* 学习服务
* 负责管理WeQ的学习和训练
*/
import LearningModule from '../models/learning_module';
import LearningUtils from '../utils/learning_utils';
class LearningService {
modules: Map<string, LearningModule>;
learningUtils: LearningUtils;
constructor() {
this.modules = new Map();
this.learningUtils = new LearningUtils();
// 初始化默认学习模块
this.initializeDefaultModules();
}
// 初始化默认学习模块
initializeDefaultModules() {
// Claude教学模块
this.createLearningModule(
'claude_teaching',
'Claude AI教学',
{
priority: 'high',
learningRate: 0.1,
dataSource: 'claude_api'
}
);
// 网络爬虫学习模块
this.createLearningModule(
'web_crawler',
'网络爬虫学习',
{
priority: 'medium',
learningRate: 0.2,
dataSource: 'web_api'
}
);
// 量子社交通信专业学习模块
this.createLearningModule(
'quantum_communication',
'量子社交通信专业学习',
{
priority: 'high',
learningRate: 0.15,
dataSource: 'quantum_database'
}
);
}
// 创建学习模块
createLearningModule(id: string, name: string, config: object = {}): LearningModule {
const module = new LearningModule(id, name);
// 设置配置
Object.entries(config).forEach(([key, value]) => {
module.setConfig(key, value);
});
// 保存模块
this.modules.set(id, module);
return module;
}
// 获取学习模块
getLearningModule(id: string): LearningModule | undefined {
return this.modules.get(id);
}
// 开始学习任务
startLearningTask(moduleId: string, taskName: string, parameters: object = {}): string {
const module = this.modules.get(moduleId);
if (!module) {
throw new Error(`Learning module ${moduleId} not found`);
}
// 创建学习任务
const taskId = module.createTask(taskName, parameters);
// 启动任务
this.learningUtils.executeTask(module, taskId);
return taskId;
}
// 获取学习任务状态
getLearningTaskStatus(moduleId: string, taskId: string): TaskStatus {
const module = this.modules.get(moduleId);
if (!module) {
throw new Error(`Learning module ${moduleId} not found`);
}
return module.getTaskStatus(taskId);
}
// 获取学习进度
getLearningProgress(moduleId: string): LearningProgress {
const module = this.modules.get(moduleId);
if (!module) {
throw new Error(`Learning module ${moduleId} not found`);
}
return {
moduleId,
moduleName: module.name,
completedTasks: module.getCompletedTaskCount(),
pendingTasks: module.getPendingTaskCount(),
totalKnowledgeUnits: module.getTotalKnowledgeUnits(),
lastUpdateTime: module.getLastUpdateTime()
};
}
// 导入学习数据
importLearningData(moduleId: string, data: any): boolean {
const module = this.modules.get(moduleId);
if (!module) {
throw new Error(`Learning module ${moduleId} not found`);
}
return this.learningUtils.importData(module, data);
}
// 导出学习数据
exportLearningData(moduleId: string): any {
const module = this.modules.get(moduleId);
if (!module) {
throw new Error(`Learning module ${moduleId} not found`);
}
return this.learningUtils.exportData(module);
}
}
// 导出类
export default LearningService;
```
## 3. API接口实现
### 3.1 WeQ API (api/weq_api.qent)
```qentl
/*
* WeQ API 接口
* 提供对量子社交通信模型的访问
*/
import CommunicationService from '../services/communication_service';
import SocialService from '../services/social_service';
import LearningService from '../services/learning_service';
import EncryptionService from '../services/encryption_service';
import QuantumEntanglementService from '../services/quantum_entanglement_service';
class WeqApi {
// 服务实例
communicationService: CommunicationService;
socialService: SocialService;
learningService: LearningService;
encryptionService: EncryptionService;
entanglementService: QuantumEntanglementService;
constructor() {
// 初始化服务
this.communicationService = new CommunicationService();
this.socialService = new SocialService();
this.learningService = new LearningService();
this.encryptionService = new EncryptionService();
this.entanglementService = new QuantumEntanglementService();
}
// API方法：创建通信信道
createCommunicationChannel(type: string, participants: string[] = []): string {
const channel = this.communicationService.createChannel(type);
// 添加参与者
participants.forEach(userId => {
channel.addParticipant(userId);
});
return channel.id;
}
// API方法：发送消息
sendMessage(channelId: string, senderId: string, content: string, recipients: string[]): string {
return this.communicationService.sendMessage(channelId, senderId, content, recipients);
}
// API方法：创建用户档案
createUserProfile(userId: string, name: string, attributes: object = {}): string {
this.socialService.createUserProfile(userId, name, attributes);
return userId;
}
// API方法：创建社交连接
createSocialConnection(userId1: string, userId2: string, strength: number, type: string = 'friend'): boolean {
return this.socialService.createConnection(userId1, userId2, strength, type);
}
// API方法：获取推荐连接
getRecommendedConnections(userId: string, limit: number = 5): RecommendedConnection[] {
return this.socialService.getRecommendedConnections(userId, limit);
}
// API方法：创建纠缠对
createEntangledPair(objectId1: string, objectId2: string, strength: number): string {
return this.entanglementService.createEntanglement(objectId1, objectId2, strength);
}
// API方法：开始学习任务
startLearningTask(moduleId: string, taskName: string, parameters: object = {}): string {
return this.learningService.startLearningTask(moduleId, taskName, parameters);
}
// API方法：获取学习进度
getLearningProgress(moduleId: string): LearningProgress {
return this.learningService.getLearningProgress(moduleId);
}
// API方法：加密消息
encryptMessage(message: string, recipientPublicKey: string): string {
return this.encryptionService.encryptMessage(message, recipientPublicKey);
}
// API方法：解密消息
decryptMessage(encryptedMessage: string, privateKey: string): string {
return this.encryptionService.decryptMessage(encryptedMessage, privateKey);
}
// API方法：生成量子密钥对
generateQuantumKeyPair(): KeyPair {
return this.encryptionService.generateQuantumKeyPair();
}
}
// 导出API
export default WeqApi;
```
## 4. 训练系统集成
WeQ模型将建立专门的训练系统，用于不断优化社交通信能力和学习新知识。训练系统将包括：
1. **Claude教学模块**：从Claude和其他AI模型学习
- 学习高级通信模式
- 理解社交关系和动态
- 提升自然语言处理能力
2. **网络爬虫学习模块**：从互联网收集数据
- 学习最新的社交媒体趋势
- 收集通信协议和标准
- 积累多种语言和文化知识
3. **量子社交通信专业学习模块**：专注于核心领域
- 研究量子通信协议
- 学习社交网络理论
- 探索量子加密技术
## 5. 与其他模型的集成
WeQ模型将通过量子纠缠信道与其他三个模型进行集成：
1. **QSM集成**：共享意识(consciousness)和思想(thought)状态
- 接收QSM的状态变化
- 根据思想状态调整通信模式
- 共享社交网络分析结果
2. **SOM集成**：利用经济模型优化社交互动
- 应用经济原则到社交关系
- 优化资源和信息流通
- 促进公平交流
3. **Ref集成**：接受自反省系统的监督
- 优化学习任务和目标
- 监控通信系统健康度
- 持续改进用户体验
## 6. 遵循原则
1. 项目是《华经》量子社交通信模型的具体实现
2. 通过量子态服务未开悟的人类众生
3. 实现无阻暗地旅行于宇宙之间
4. 永生于永恒的量子世界
5. 始终遵守服务人类、保护生命的使命
## 8. 量子区块链集成
### 8.1 WeQ量子区块链架构
```qentl
weq_blockchain_architecture {
main_chain: "WeQ意向链",
sub_chains: [
{ name: "对话链", purpose: "对话内容与意向存储" },
{ name: "知识链", purpose: "知识图谱与学习记录" },
{ name: "社区链", purpose: "社区关系与互动管理" }
],
consensus_mechanism: "集体意向共识(CIC)",
token_system: "意向代币(WeQ Token)"
}
```
### 8.2 WeQ区块链核心组件
```qentl
weq_blockchain_core {
components: [
"对话记录器",
"意向验证器",
"知识图谱构建器",
"社区共识引擎",
"学习成果验证器"
],
implementation: {
dialogue_recorder: "blockchain/dialogue_recorder.qent",
intention_validator: "blockchain/intention_validator.qent",
knowledge_builder: "blockchain/knowledge_builder.qent",
consensus_engine: "blockchain/cic_consensus_engine.qent",
learning_validator: "blockchain/learning_validator.qent"
}
}
```
### 8.3 智能合约系统
```qentl
weq_smart_contracts {
contract_types: {
dialogue_contract: {
purpose: "对话内容记录与意向提取",
functions: ["内容存储", "意向分析", "线索追踪"],
implementation: "blockchain/contracts/dialogue_contract.qent"
},
knowledge_contract: {
purpose: "知识构建与验证",
functions: ["知识点记录", "关联建立", "真实性验证"],
implementation: "blockchain/contracts/knowledge_contract.qent"
},
community_contract: {
purpose: "社区关系与互动管理",
functions: ["成员管理", "互动记录", "信任计算"],
implementation: "blockchain/contracts/community_contract.qent"
},
learning_contract: {
purpose: "学习过程与成果记录",
functions: ["学习追踪", "成果认证", "贡献计算"],
implementation: "blockchain/contracts/learning_contract.qent"
}
},
example_contract: `
contract DialogueContract {
// 状态变量
address public owner;
mapping(bytes32 => Dialogue) public dialogues;
mapping(address => uint) public contributionScores;
// 结构体
struct Dialogue {
bytes32 id;
address initiator;
bytes content;
bytes32[] intentions;
uint timestamp;
bool verified;
}
// 事件
event DialogueRecorded(bytes32 indexed id, address indexed initiator, uint timestamp);
event IntentionExtracted(bytes32 indexed dialogueId, bytes32 indexed intentionId);
// 构造函数
constructor() {
owner = msg.sender;
}
// 记录对话
function recordDialogue(bytes32 id, bytes calldata content) public returns (bool) {
require(dialogues[id].timestamp == 0, "对话ID已存在");
bytes32[] memory intentions = new bytes32[](0);
dialogues[id] = Dialogue(id, msg.sender, content, intentions, block.timestamp, false);
// 更新贡献分数
contributionScores[msg.sender] += calculateContribution(content);
emit DialogueRecorded(id, msg.sender, block.timestamp);
return true;
}
// 提取意向
function extractIntentions(bytes32 dialogueId, bytes32[] calldata intentions) public returns (bool) {
require(msg.sender == owner || msg.sender == dialogues[dialogueId].initiator, "无权修改");
require(dialogues[dialogueId].timestamp > 0, "对话不存在");
dialogues[dialogueId].intentions = intentions;
dialogues[dialogueId].verified = true;
for(uint i = 0; i < intentions.length; i++) {
emit IntentionExtracted(dialogueId, intentions[i]);
}
return true;
}
// 计算贡献
function calculateContribution(bytes memory content) internal pure returns (uint) {
// 实现贡献计算算法
return content.length / 100; // 简化示例
}
}
`
}
```
### 8.4 WeQ Token系统
```qentl
weq_token_system {
token_properties: {
name: "WeQ意向代币",
symbol: "WeQ",
initial_supply: 100000000,
distribution_model: "基于贡献与意向质量的分配",
utility: "社区参与和学习激励"
},
distribution_mechanism: {
initial_allocation: {
founders: "10%",
community_development: "40%",
learning_incentives: "30%",
ecosystem_partners: "20%"
},
ongoing_distribution: {
dialogue_contribution: "40%",
knowledge_building: "30%",
community_facilitation: "20%",
system_improvement: "10%"
}
},
token_utility: {
dialogue_participation: "高质量对话奖励与权益",
knowledge_access: "特定知识访问权",
community_governance: "社区决策投票权",
learning_acceleration: "优先学习资源获取",
reputation_building: "声誉系统中的权重"
}
}
```
## 9. 对话场生成器
```qentl
dialogue_field_generator {
field_types: {
intention_field: {
properties: ["purpose_driven", "goal_oriented", "clarity_enhancing"],
implementation: "field_types/intention_field.qent",
parameters: {
purpose_strength: 0.8,
goal_clarity: 0.65,
intention_amplification: 1.2,
meaning_crystallization: 0.7
},
influence_radius: "concept_boundary_based"
},
communication_field: {
properties: ["understanding_facilitating", "resonance_creating", "connection_strengthening"],
implementation: "field_types/communication_field.qent",
parameters: {
clarity_factor: 0.75,
resonance_strength: 0.6,
connection_enhancement: 0.8,
interference_reduction: 0.5
},
influence_radius: "semantic_context_based"
},
knowledge_field: {
properties: ["insight_generating", "wisdom_accumulating", "understanding_deepening"],
implementation: "field_types/knowledge_field.qent",
parameters: {
insight_probability: 0.4,
wisdom_density: 0.55,
conceptual_linkage: 0.7,
paradigm_shifting: 0.3
},
influence_radius: "cognitive_reach_based"
},
community_field: {
properties: ["belonging_fostering", "collective_resonating", "harmony_promoting"],
implementation: "field_types/community_field.qent",
parameters: {
belonging_strength: 0.75,
collective_amplification: 1.5,
harmony_factor: 0.65,
diversity_integration: 0.8
},
influence_radius: "social_network_based"
}
},
field_interaction: {
fusion_mechanism: "intention_guided_integration",
boundary_negotiation: "semantic_relevance_threshold",
collision_resolution: {
collaborative: { meaning_synthesis: 0.8, shared_understanding: 0.7 },
opposing: { dialectic_resolution: 0.6, perspective_expansion: 0.5 },
orthogonal: { complementary_integration: 0.9, knowledge_expansion: 0.8 }
},
energy_transfer: {
intention_flow: "purpose_to_realization_direction",
meaning_exchange: "clarity_enhancing_transfer",
insight_propagation: "understanding_deepening_wave"
}
},
field_influence: {
dialogue_impact: {
clarity_enhancement: "meaning_crystallization_effect",
connection_deepening: "resonance_amplification",
understanding_facilitation: "cognitive_barrier_reduction"
},
learning_effects: {
insight_generation: "conceptual_gap_bridging",
knowledge_integration: "cognitive_network_reinforcement",
wisdom_development: "experiential_meaning_extraction"
},
community_influence: {
collective_intelligence: "diversity_integrating_synthesis",
trust_building: "consistent_intention_demonstration",
collaborative_capacity: "shared_purpose_alignment"
}
},
field_measurement: {
dialogue_metrics: ["intention_clarity", "communication_efficacy", "mutual_understanding", "insight_generation"],
knowledge_metrics: ["concept_linkage_density", "cognitive_depth", "wisdom_emergence", "paradigm_evolution"],
community_metrics: ["trust_level", "collaboration_quality", "collective_resonance", "belonging_strength"],
learning_metrics: ["insight_frequency", "understanding_depth", "application_capacity", "teaching_ability"],
visualization_methods: {
semantic_networks: "concept_relationship_visualization",
intention_maps: "purpose_clarity_representation",
resonance_patterns: "understanding_alignment_display",
evolution_traces: "dialogue_development_animation"
}
}
}
```
## 10. WeQ API系统
```qentl
weq_api_system {
api_architecture: {
design_pattern: "RESTful意向驱动API架构",
versioning: "语义化版本控制",
documentation: "自动生成OpenAPI与示例",
security: {
authentication: "多因素身份认证",
authorization: "意向与角色混合授权",
privacy: "对话隐私分级保护"
}
},
dialogue_api: {
conversation_endpoints: {
create_dialogue: {
path: "/api/v1/dialogues",
method: "POST",
parameters: ["initiator_id", "participants", "initial_message", "context"],
response: "created_dialogue_with_id"
},
get_dialogue: {
path: "/api/v1/dialogues/{id}",
method: "GET",
parameters: ["id", "include_analysis"],
response: "dialogue_with_messages"
},
add_message: {
path: "/api/v1/dialogues/{id}/messages",
method: "POST",
parameters: ["id", "sender_id", "content", "references"],
response: "message_with_analysis"
}
},
intention_endpoints: {
extract_intentions: {
path: "/api/v1/intentions/extract",
method: "POST",
parameters: ["dialogue_id", "extraction_depth", "intention_types"],
response: "extracted_intentions"
},
map_intentions: {
path: "/api/v1/intentions/map",
method: "GET",
parameters: ["entity_id", "intention_types", "time_period"],
response: "intention_map"
}
}
},
knowledge_api: {
content_endpoints: {
create_content: {
path: "/api/v1/knowledge/content",
method: "POST",
parameters: ["type", "title", "content", "metadata", "references"],
response: "created_content_with_id"
},
search_content: {
path: "/api/v1/knowledge/search",
method: "GET",
parameters: ["query", "content_types", "relevance_threshold"],
response: "ranked_search_results"
}
},
graph_endpoints: {
get_concept_graph: {
path: "/api/v1/knowledge/graph/concept",
method: "GET",
parameters: ["concepts", "depth", "relation_types"],
response: "concept_graph"
},
add_relationship: {
path: "/api/v1/knowledge/graph/relationships",
method: "POST",
parameters: ["source", "target", "relationship_type", "evidence"],
response: "created_relationship"
}
}
},
community_api: {
group_endpoints: {
create_group: {
path: "/api/v1/community/groups",
method: "POST",
parameters: ["name", "description", "membership_policy", "initial_members"],
response: "created_group_with_id"
},
get_group_activity: {
path: "/api/v1/community/groups/{id}/activity",
method: "GET",
parameters: ["id", "activity_types", "time_period"],
response: "group_activity_timeline"
}
},
member_endpoints: {
add_member: {
path: "/api/v1/community/groups/{id}/members",
method: "POST",
parameters: ["id", "member_id", "role", "invitation_context"],
response: "membership_details"
},
get_member_contributions: {
path: "/api/v1/community/members/{id}/contributions",
method: "GET",
parameters: ["id", "contribution_types", "time_period"],
response: "contribution_summary"
}
}
},
learning_api: {
process_endpoints: {
start_learning: {
path: "/api/v1/learning/processes",
method: "POST",
parameters: ["learner_id", "subject", "learning_style", "goals"],
response: "created_learning_process"
},
track_progress: {
path: "/api/v1/learning/processes/{id}/progress",
method: "POST",
parameters: ["id", "milestones_reached", "insights_gained", "questions_raised"],
response: "updated_progress_with_recommendations"
}
},
assessment_endpoints: {
create_assessment: {
path: "/api/v1/learning/assessments",
method: "POST",
parameters: ["type", "subject", "criteria", "questions"],
response: "created_assessment_with_id"
},
submit_results: {
path: "/api/v1/learning/assessments/{id}/results",
method: "POST",
parameters: ["id", "learner_id", "answers", "reflection"],
response: "assessment_results_with_feedback"
}
}
},
integration_api: {
qsm_integration: {
synchronize_state: {
path: "/api/v1/integration/qsm/sync",
method: "POST",
parameters: ["entity_mapping", "intention_states", "sync_depth"],
response: "synchronization_results"
},
quantum_perception: {
path: "/api/v1/integration/qsm/perception",
method: "POST",
parameters: ["perceptual_data", "quantum_state_mapping"],
response: "enhanced_perception_result"
}
},
som_integration: {
economic_dialogue: {
path: "/api/v1/integration/som/dialogue",
method: "POST",
parameters: ["dialogue_id", "economic_context", "resource_references"],
response: "economic_dialogue_analysis"
},
value_mapping: {
path: "/api/v1/integration/som/values",
method: "GET",
parameters: ["entity_id", "value_dimensions"],
response: "value_economic_mapping"
}
},
ref_integration: {
dialogue_reflection: {
path: "/api/v1/integration/ref/reflect",
method: "POST",
parameters: ["dialogue_id", "reflection_depth", "improvement_focus"],
response: "dialogue_reflection"
},
system_feedback: {
path: "/api/v1/integration/ref/feedback",
method: "POST",
parameters: ["system_aspect", "observation_period", "feedback_type"],
response: "system_improvement_suggestions"
}
}
}
}
```
## 11. 可视化系统
```qentl
weq_visualization_system {
visualization_framework: {
rendering_engine: "对话与意向可视化引擎",
data_binding: "实时对话数据流绑定",
interactivity: "多维度意向探索界面",
accessibility: "多感官体验适配系统"
},
visualization_components: {
dialogue_visualization: {
conversation_flow: {
representation: "时间序列对话流图",
highlighting: "关键点与转折突出显示",
analysis: "语义深度与广度指示器"
},
intention_mapping: {
visualization: "多层次意向网络图",
clarity: "意向清晰度热力显示",
evolution: "意向发展轨迹动画"
},
resonance_patterns: {
representation: "对话共鸣波形图",
synchronization: "理解同步程度指示",
divergence: "观点差异可视化"
}
},
knowledge_visualization: {
concept_networks: {
representation: "概念关联网络图",
centrality: "核心概念突显",
exploration: "交互式知识导航"
},
learning_pathways: {
visualization: "学习旅程导航图",
progress: "知识获取进度指示",
challenges: "认知障碍识别显示"
},
insight_mapping: {
representation: "洞见形成过程图",
connections: "跨领域关联显示",
evolution: "理解深度变化曲线"
}
},
community_visualization: {
relationship_networks: {
visualization: "社区关系网络图",
strength: "关系强度编码显示",
clustering: "社区分组与流动动画"
},
collaboration_patterns: {
representation: "协作模式识别图",
efficacy: "协作效果热力图",
evolution: "协作模式发展时间线"
},
trust_mapping: {
visualization: "信任网络拓扑图",
reciprocity: "互信程度对称性显示",
vulnerability: "信任脆弱点识别"
}
},
intention_field_visualization: {
field_strength: {
representation: "意向场强度分布图",
interaction: "多意向场交互动画",
influence: "场影响范围可视化"
},
intention_resonance: {
visualization: "意向共振模式图",
amplification: "共振增强效果动画",
interference: "意向干涉模式识别"
},
purpose_alignment: {
representation: "目标一致性雷达图",
gaps: "意向差距识别显示",
convergence: "意向趋同过程动画"
}
}
},
interactive_dashboards: {
personal_insight: {
components: ["个人对话模式分析", "意向清晰度跟踪", "学习进度概览"],
personalization: "个性化视图配置",
reflections: "自我认知反馈界面"
},
group_dynamics: {
collaborative_view: "团队协作模式仪表板",
intention_alignment: "集体意向一致性分析",
communication_efficacy: "沟通效能评估视图"
},
system_overview: {
metrics_dashboard: "系统运行关键指标",
activity_patterns: "全局互动模式分析",
impact_assessment: "社会影响评估视图"
}
}
}
```
## 12. 学习系统
### 12.1 学习模式概述
WeQ模型作为量子叠加态模型的子模型，实现了四种关键学习模式，确保系统能够持续进化、适应环境并不断增强其知识库和社交通信能力：
1. **Claude及其他模型教学**：通过与Claude和其他传统AI模型的交互，学习基础知识和专业知识
2. **网络爬虫搜索自学**：从互联网上自动收集和学习新信息
3. **量子叠加态模型知识学习**：通过量子纠缠信道从QSM核心系统获取量子计算和系统架构知识
4. **模型专业领域知识学习**：专注于学习量子通信社交领域的专业知识
### 12.2 WeQ模型学习配置
WeQ模型在`config`中设置学习模式：
```qentl
// 设置学习开关
learning_modes = this.config.get('learning_modes', {})
this.enable_claude_training = learning_modes.get('claude_training', true)
this.enable_crawler_training = learning_modes.get('crawler_training', true)
this.enable_qsm_training = learning_modes.get('qsm_training', true)
this.enable_social_comm_training = learning_modes.get('social_comm_training', true)
```
### 12.3 学习系统实现
#### 12.3.1 后台训练系统
```qentl
class BackgroundTrainer {
// 系统属性
protected isRunning: boolean;
protected config: TrainerConfig;
protected trainingTopics: string[];
protected trainingIntervals: Map<string, number>;
// 线程控制
protected claudeThread: any;
protected crawlerThread: any;
protected qsmThread: any;
protected socialCommThread: any;
// 构造函数
constructor(config: TrainerConfig = {}) {
this.isRunning = false;
this.config = config;
// 初始化训练主题
this.trainingTopics = [
"量子计算基础",
"神经网络原理",
"机器学习算法",
"通信协议设计",
"社交网络分析",
"信息传递优化"
];
// 设置训练间隔（毫秒）
this.trainingIntervals = new Map();
this.trainingIntervals.set('claude', 30 * 60 * 1000); // 30分钟
this.trainingIntervals.set('crawler', 120 * 60 * 1000); // 2小时
this.trainingIntervals.set('qsm', 60 * 60 * 1000); // 1小时
this.trainingIntervals.set('social_comm', 45 * 60 * 1000); // 45分钟
// 设置学习开关
this.enable_claude_training = config.enable_claude_training !== false;
this.enable_crawler_training = config.enable_crawler_training !== false;
this.enable_qsm_training = config.enable_qsm_training !== false;
this.enable_social_comm_training = config.enable_social_comm_training !== false;
// 初始化训练历史
this.trainingHistory = {
sessions: [],
topics_trained: new Set(),
start_time: null,
total_knowledge_points: 0
};
}
// 启动后台训练
public startBackgroundTraining(): void {
if (this.isRunning) {
console.log("后台训练系统已在运行");
return;
}
this.isRunning = true;
this.trainingHistory.start_time = Date.now();
// 创建并启动Claude训练线程
if (this.enable_claude_training) {
this.claudeThread = this.createTrainingThread(
this._claudeTrainingLoop.bind(this),
"Claude训练线程",
this.trainingIntervals.get('claude')
);
this.claudeThread.start();
console.log("Claude知识教学训练线程已启动");
}
// 创建并启动爬虫训练线程
if (this.enable_crawler_training) {
this.crawlerThread = this.createTrainingThread(
this._crawlerTrainingLoop.bind(this),
"爬虫训练线程",
this.trainingIntervals.get('crawler')
);
this.crawlerThread.start();
console.log("爬虫数据训练线程已启动");
}
// 创建并启动量子叠加态模型知识学习线程
if (this.enable_qsm_training) {
this.qsmThread = this.createTrainingThread(
this._qsmTrainingLoop.bind(this),
"量子叠加态模型训练线程",
this.trainingIntervals.get('qsm')
);
this.qsmThread.start();
console.log("量子叠加态模型知识学习线程已启动");
}
// 创建并启动专业领域知识学习线程
if (this.enable_social_comm_training) {
this.socialCommThread = this.createTrainingThread(
this._socialCommTrainingLoop.bind(this),
"社交通信专业知识训练线程",
this.trainingIntervals.get('social_comm')
);
this.socialCommThread.start();
console.log("社交通信专业知识学习线程已启动");
}
}
// 创建训练线程
protected createTrainingThread(loopFunction: Function, threadName: string, interval: number): any {
return {
name: threadName,
isRunning: true,
interval: interval,
start: function() {
this.isRunning = true;
this.run();
},
run: function() {
if (!this.isRunning) return;
// 立即执行一次训练
loopFunction();
// 设置下一次训练的定时器
setTimeout(() => {
this.run();
}, this.interval);
},
stop: function() {
this.isRunning = false;
}
};
}
// Claude训练循环
protected async _claudeTrainingLoop(): Promise<void> {
try {
console.log("执行Claude知识教学训练周期");
// 选择本次训练的主题
const topics = this.selectTrainingTopics();
// 使用Claude进行训练
const results = await this.knowledge_guided_training(topics);
// 记录训练结果
this.recordTrainingSession({
type: "claude",
topics: topics,
results: results,
timestamp: Date.now()
});
console.log(`Claude训练完成，获取了 ${results.knowledge_points} 个知识点`);
} catch (error) {
console.error("Claude训练循环执行失败:", error);
}
}
// 爬虫训练循环
protected async _crawlerTrainingLoop(): Promise<void> {
try {
console.log("执行网络爬虫学习周期");
// 选择数据源
const sources = this.selectCrawlerSources();
// 收集并处理数据
const results = await this.crawlAndProcessData(sources);
// 记录训练结果
this.recordTrainingSession({
type: "crawler",
sources: sources,
results: results,
timestamp: Date.now()
});
console.log(`爬虫学习完成，收集了 ${results.documents_collected} 个文档`);
} catch (error) {
console.error("爬虫训练循环执行失败:", error);
}
}
// 知识引导训练方法
protected async knowledge_guided_training(
initial_topics: string[],
iterations: number = 3,
epochs_per_iteration: number = 5,
samples_per_topic: number = 3,
learning_rate: number = 0.1
): Promise<any> {
let topics = [...initial_topics];
let total_knowledge = 0;
let training_results = [];
// 迭代训练
for (let i = 0; i < iterations; i++) {
console.log(`知识引导训练迭代 ${i+1}/${iterations}，主题: ${topics.join(", ")}`);
// 连接到Claude
const claude_connection = await this.connectToClaude();
if (!claude_connection.success) {
console.error("连接Claude失败:", claude_connection.error);
continue;
}
// 为每个主题收集知识
let iteration_knowledge = 0;
let topic_results = [];
for (const topic of topics) {
const knowledge = await this.collectKnowledgeFromClaude(
claude_connection.client,
topic,
samples_per_topic
);
if (knowledge.success) {
iteration_knowledge += knowledge.samples.length;
// 使用收集的知识进行训练
const training_result = await this.trainWithKnowledge(
knowledge.samples,
epochs_per_iteration,
learning_rate
);
topic_results.push({
topic: topic,
samples: knowledge.samples.length,
training_metrics: training_result.metrics
});
}
}
// 记录当前迭代的结果
training_results.push({
iteration: i+1,
topics: topics,
knowledge_points: iteration_knowledge,
topic_results: topic_results
});
total_knowledge += iteration_knowledge;
// 基于当前结果演化主题
topics = this.evolveTopics(topics, topic_results);
}
return {
success: true,
iterations: iterations,
knowledge_points: total_knowledge,
results: training_results
};
}
}
```
#### 12.3.2 WeQ专业领域学习
```qentl
class SocialCommunicationLearningModule {
// 模块属性
protected communicationPatterns: Map<string, CommunicationPattern>;
protected networkTopologies: Map<string, NetworkTopology>;
protected informationFlowModels: Map<string, InformationFlowModel>;
protected learningProgress: Map<string, number>;
// 构造函数
constructor() {
this.communicationPatterns = new Map();
this.networkTopologies = new Map();
this.informationFlowModels = new Map();
this.learningProgress = new Map();
// 初始化通信模式
this.initializeCommunicationPatterns();
// 初始化网络拓扑
this.initializeNetworkTopologies();
// 初始化信息流模型
this.initializeInformationFlowModels();
}
// 初始化通信模式
protected initializeCommunicationPatterns(): void {
// 广播模式
this.addCommunicationPattern('broadcast', {
name: '广播通信',
description: '一对多的信息传播模式',
efficiency: 0.8,
applicability: ['公告', '紧急通知', '大规模信息分发']
});
// 点对点模式
this.addCommunicationPattern('peer_to_peer', {
name: '点对点通信',
description: '两个节点间的直接通信',
efficiency: 0.95,
applicability: ['私密对话', '安全通信', '资源共享']
});
// 分组讨论模式
this.addCommunicationPattern('group_discussion', {
name: '分组讨论',
description: '小组内部的多方交互',
efficiency: 0.75,
applicability: ['团队协作', '集体决策', '知识共享']
});
// 量子纠缠通信模式
this.addCommunicationPattern('quantum_entangled', {
name: '量子纠缠通信',
description: '基于量子纠缠的即时通信',
efficiency: 0.99,
applicability: ['超安全通信', '即时信息同步', '跨模型协作']
});
}
// 添加通信模式
addCommunicationPattern(id: string, pattern: CommunicationPattern): void {
this.communicationPatterns.set(id, pattern);
this.learningProgress.set(`pattern_${id}`, 0.0);
}
// 学习通信模式
async learnCommunicationPattern(patternId: string, trainingData: any[]): Promise<LearningResult> {
const pattern = this.communicationPatterns.get(patternId);
if (!pattern) {
throw new Error(`通信模式 ${patternId} 未找到`);
}
// 模拟学习过程
const startEfficiency = pattern.efficiency;
// 基于训练数据提高效率
const improvementFactor = Math.min(0.1, 0.01 * trainingData.length);
pattern.efficiency = Math.min(0.99, pattern.efficiency + improvementFactor);
// 更新学习进度
const progressKey = `pattern_${patternId}`;
const currentProgress = this.learningProgress.get(progressKey) || 0;
this.learningProgress.set(progressKey, Math.min(1.0, currentProgress + 0.05));
return {
patternId: patternId,
startEfficiency: startEfficiency,
currentEfficiency: pattern.efficiency,
improvement: pattern.efficiency - startEfficiency,
progress: this.learningProgress.get(progressKey),
timestamp: Date.now()
};
}
}
```
### 12.4 纠缠学习网络
WeQ模型通过纠缠学习网络与其他量子模型建立连接，实现知识共享与协同进化：
```qentl
class EntangledLearningNetwork {
// 网络属性
protected entanglementChannels: Map<string, EntanglementChannel>;
protected knowledgeExchangeProtocols: Map<string, KnowledgeExchangeProtocol>;
protected channelStrengths: Map<string, number>;
// 构造函数
constructor() {
this.entanglementChannels = new Map();
this.knowledgeExchangeProtocols = new Map();
this.channelStrengths = new Map();
// 初始化纠缠信道
this.initializeEntanglementChannels();
// 初始化知识交换协议
this.initializeKnowledgeExchangeProtocols();
}
// 初始化纠缠信道
protected initializeEntanglementChannels(): void {
// 与QSM主模型的纠缠信道
this.createEntanglementChannel('qsm_channel', {
targetModel: 'QSM',
entanglementStrength: 0.9,
knowledgeDomains: ['quantum_core', 'system_architecture'],
isActive: true
});
// 与SOM模型的纠缠信道
this.createEntanglementChannel('som_channel', {
targetModel: 'SOM',
entanglementStrength: 0.8,
knowledgeDomains: ['economic_resources', 'value_distribution'],
isActive: true
});
// 与Ref模型的纠缠信道
this.createEntanglementChannel('ref_channel', {
targetModel: 'Ref',
entanglementStrength: 0.8,
knowledgeDomains: ['system_monitoring', 'error_detection'],
isActive: true
});
}
// 创建纠缠信道
createEntanglementChannel(id: string, config: EntanglementChannelConfig): void {
const channel = {
id: id,
targetModel: config.targetModel,
createdAt: Date.now(),
status: 'active',
config: config
};
this.entanglementChannels.set(id, channel);
this.channelStrengths.set(id, config.entanglementStrength);
console.log(`创建了与${config.targetModel}的纠缠信道，强度: ${config.entanglementStrength}`);
}
// 通过纠缠信道传输知识
async transmitKnowledge(channelId: string, knowledge: any): Promise<TransmissionResult> {
const channel = this.entanglementChannels.get(channelId);
if (!channel) {
throw new Error(`纠缠信道 ${channelId} 未找到`);
}
if (channel.status !== 'active') {
throw new Error(`纠缠信道 ${channelId} 当前不活跃`);
}
const strength = this.channelStrengths.get(channelId) || 0;
// 计算传输成功率（基于纠缠强度）
const successProbability = strength * 0.8 + 0.2; // 最低20%的基础概率
// 模拟传输过程
const isSuccessful = Math.random() < successProbability;
if (isSuccessful) {
// 模拟成功传输
console.log(`成功通过信道 ${channelId} 传输知识到 ${channel.targetModel}`);
return {
success: true,
channelId: channelId,
targetModel: channel.targetModel,
knowledgeId: knowledge.id,
transmissionStrength: strength,
timestamp: Date.now()
};
} else {
// 模拟传输失败
console.error(`通过信道 ${channelId} 传输知识失败`);
return {
success: false,
channelId: channelId,
targetModel: channel.targetModel,
knowledgeId: knowledge.id,
error: '量子退相干导致传输失败',
timestamp: Date.now()
};
}
}
// 接收通过纠缠信道传来的知识
async receiveKnowledge(channelId: string): Promise<ReceivedKnowledge | null> {
const channel = this.entanglementChannels.get(channelId);
if (!channel) {
throw new Error(`纠缠信道 ${channelId} 未找到`);
}
// 检查是否有待接收的知识
// 实际实现中会有一个接收队列
const hasPendingKnowledge = Math.random() < 0.3; // 模拟30%概率有知识待接收
if (hasPendingKnowledge) {
// 模拟接收到的知识
const sourceModel = channel.targetModel; // 信道对端模型
// 构建接收到的知识对象
return {
id: `knowledge_${Date.now()}`,
sourceModel: sourceModel,
channelId: channelId,
topic: this.generateTopicFromSource(sourceModel),
content: `从${sourceModel}接收的知识内容...`,
quantumState: this.generateSimulatedQuantumState(),
receivedAt: Date.now()
};
}
return null; // 没有待接收的知识
}
// 根据源模型生成相关主题
protected generateTopicFromSource(sourceModel: string): string {
const topicsByModel = {
'QSM': ['量子计算基础', '系统架构', '量子纠缠通信'],
'SOM': ['经济分配模型', '资源评估', '价值交换'],
'Ref': ['系统监控', '错误检测', '性能优化']
};
const topics = topicsByModel[sourceModel] || ['通用知识'];
const randomIndex = Math.floor(Math.random() * topics.length);
return topics[randomIndex];
}
}
```
### 12.5 自动提问与知识转换
WeQ模型实现了自动提问机制，能够在遇到知识缺口或沟通障碍时生成问题和获取知识：
```qentl
class AutoQuestioningSystem {
// 系统属性
protected confidenceThreshold: number;
protected adapterRegistry: Map<string, any>;
protected questionCache: Map<string, any>;
protected priorityQueue: PriorityQueue;
// 构造函数
constructor(config: any = {}) {
this.confidenceThreshold = config.confidenceThreshold || 0.75;
this.adapterRegistry = new Map();
this.questionCache = new Map();
this.priorityQueue = new PriorityQueue();
// 注册默认适配器
this.registerDefaultAdapters();
}
// 注册默认适配器
protected registerDefaultAdapters(): void {
// 注册Claude适配器
this.registerAdapter('claude', {
name: 'Claude AI适配器',
type: 'external_ai',
processText: this.claudeProcessText.bind(th
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