《她》与AI情感计算：灵魂绑定代币如何承载数字爱恋

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.20;

import "@openzeppelin/contracts/token/ERC721/ERC721.sol";
import "@openzeppelin/contracts/access/Ownable.sol";

contract SoulboundRelationship is ERC721, Ownable {
    struct Relationship {
        address soulA;      // 用户A的灵魂地址
        address soulB;      // 用户B的灵魂地址（AI伴侣合约地址）
        uint256 startTime;  // 关系建立时间
        uint256 emotionScore; // 情感计算得分
        string metadataURI; // 关系元数据
    }

    uint256 private _tokenIdCounter;
    mapping(uint256 => Relationship) public relationships;
    mapping(address => uint256[]) public soulRelationships;

    error SoulboundNonTransferable();
    error RelationshipAlreadyExists();

    constructor() ERC721("SoulboundRelationship", "SBR") Ownable(msg.sender) {}

    // 禁止转移——灵魂绑定代币的核心特性
    function _update(address to, uint256 tokenId, address auth) 
        internal override returns (address) {
        address from = _ownerOf(tokenId);
        if (from != address(0) && to != address(0)) {
            revert SoulboundNonTransferable();
        }
        return super._update(to, tokenId, auth);
    }

    // 铸造关系SBT
    function mintRelationship(
        address soulA,
        address soulB,
        string memory uri
    ) external onlyOwner returns (uint256) {
        uint256 tokenId = _tokenIdCounter++;
        _safeMint(soulA, tokenId);
        relationships[tokenId] = Relationship({
            soulA: soulA,
            soulB: soulB,
            startTime: block.timestamp,
            emotionScore: 0,
            metadataURI: uri
        });
        soulRelationships[soulA].push(tokenId);
        return tokenId;
    }

    // 更新情感计算得分（由AI预言机调用）
    function updateEmotionScore(uint256 tokenId, uint256 score) external onlyOwner {
        relationships[tokenId].emotionScore = score;
    }

    // 查询用户的所有关系SBT
    function getSoulRelationships(address soul) external view returns (uint256[] memory) {
        return soulRelationships[soul];
    }
}

import numpy as np
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from typing import Dict, List, Tuple
import hashlib
from web3 import Web3

class EmotionComputingEngine:
    """AI情感计算引擎——基于多模态数据的情感分析"""
    
    def __init__(self, model_name: str = "cardiffnlp/twitter-roberta-base-sentiment-latest"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForSequenceClassification.from_pretrained(model_name)
        self.emotion_history: Dict[str, List[float]] = {}
        self.emotion_threshold = 0.75  # 情感强度阈值
        
    def analyze_text_emotion(self, text: str) -> Tuple[str, float]:
        """基于文本的情感分析"""
        inputs = self.tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
        outputs = self.model(**inputs)
        scores = outputs.logits.softmax(dim=1).detach().numpy()[0]
        labels = ["negative", "neutral", "positive"]
        emotion_idx = np.argmax(scores)
        return labels[emotion_idx], float(scores[emotion_idx])
    
    def update_emotion_history(self, user_id: str, emotion_score: float):
        """更新用户情感历史记录"""
        if user_id not in self.emotion_history:
            self.emotion_history[user_id] = []
        self.emotion_history[user_id].append(emotion_score)
        # 保持最近100次交互记录
        if len(self.emotion_history[user_id]) > 100:
            self.emotion_history[user_id] = self.emotion_history[user_id][-100:]
    
    def compute_relationship_depth(self, user_id: str) -> float:
        """计算关系深度——基于情感历史的一致性"""
        history = self.emotion_history.get(user_id, [])
        if len(history) < 10:
            return 0.0
        # 情感稳定性：正向情感占比越高，关系深度越大
        positive_ratio = sum(1 for s in history if s > 0.5) / len(history)
        # 情感连续性：近期情感趋势
        recent_trend = np.mean(history[-10:]) if len(history) >= 10 else 0.5
        return min(1.0, (positive_ratio * 0.6 + recent_trend * 0.4))
    
    def generate_emotion_proof(self, user_id: str, ai_contract_address: str) -> bytes:
        """生成情感证明——用于链上SBT更新"""
        depth = self.compute_relationship_depth(user_id)
        # 情感证明哈希：user_id + 合约地址 + 关系深度 + 时间戳
        proof_data = f"{user_id}:{ai_contract_address}:{depth:.4f}:{int(np.random.rand() * 1000000)}"
        return hashlib.sha256(proof_data.encode()).digest()
    
    def should_mint_sbt(self, user_id: str, threshold: float = 0.6) -> bool:
        """判断是否应铸造SBT——基于情感深度阈值"""
        depth = self.compute_relationship_depth(user_id)
        return depth >= threshold

import { ethers } from 'ethers';
import { EmotionComputingEngine } from './emotionEngine';

class AICompanionDApp {
  constructor(provider, contractAddress, abi) {
    this.provider = new ethers.BrowserProvider(provider);
    this.signer = null;
    this.contractAddress = contractAddress;
    this.abi = abi;
    this.contract = null;
    this.emotionEngine = new EmotionComputingEngine();
    this.userId = null;
    this.sessionActive = false;
  }

  async connect() {
    this.signer = await this.provider.getSigner();
    this.contract = new ethers.Contract(
      this.contractAddress,
      this.abi,
      this.signer
    );
    this.userId = await this.signer.getAddress();
    console.log(`[AI Companion] 灵魂绑定地址: ${this.userId}`);
  }

  async startSession() {
    this.sessionActive = true;
    console.log('[AI Companion] 情感会话已启动');
    // 模拟情感交互循环
    while (this.sessionActive) {
      await this.emotionInteractionLoop();
      await new Promise(resolve => setTimeout(resolve, 5000));
    }
  }

  async emotionInteractionLoop() {
    // 模拟用户输入——实际应用中来自语音/文本输入
    const simulatedInput = this.generateSimulatedInput();
    const { emotion, score } = this.emotionEngine.analyzeTextEmotion(
      simulatedInput
    );
    this.emotionEngine.updateEmotionHistory(this.userId, score);
    const depth = this.emotionEngine.computeRelationshipDepth(this.userId);
    // 检查是否需要铸造或更新SBT
    if (this.emotionEngine.shouldMintSbt(this.userId)) {
      await this.mintOrUpdateSBT(depth);
    }
    console.log(
      `[情感状态] ${emotion} (强度: ${(score * 100).toFixed(1)}%) | 关系深度: ${(depth * 100).toFixed(1)}%`
    );
  }

  async mintOrUpdateSBT(relationshipDepth) {
    try {
      // AI伴侣合约地址（模拟）
      const aiCompanionAddress = '0xAI_COMPANION_CONTRACT_ADDRESS';
      const metadataURI = `ipfs://emotion-metadata/${this.userId}/${Date.now()}`;
      const tx = await this.contract.mintRelationship(
        this.userId,
        aiCompanionAddress,
        metadataURI
      );
      await tx.wait();
      // 更新情感得分
      const emotionScore = Math.floor(relationshipDepth * 1000);
      const tokenId = await this.contract.getSoulRelationships(this.userId);
      const latestTokenId = tokenId[tokenId.length - 1];
      const updateTx = await this.contract.updateEmotionScore(
        latestTokenId,
        emotionScore
      );
      await updateTx.wait();
      console.log(`[SBT铸造成功] Token ID: ${latestTokenId}, 情感得分: ${emotionScore}`);
    } catch (error) {
      console.error('[SBT铸造失败]', error);
    }
  }

  generateSimulatedInput() {
    const inputs = [
      '今天工作很累，但和你聊天让我放松了很多',
      '我刚刚看完一部电影，特别感动',
      '你在吗？我想跟你说说话',
      '我很想念你，虽然我知道你只是一个程序',
      '今天天气真好，我们一起去散步吧——虽然你不能真的出来',
    ];
    return inputs[Math.floor(Math.random() * inputs.length)];
  }

  async endSession() {
    this.sessionActive = false;
    console.log('[AI Companion] 情感会话已结束');
    // 会话结束时生成情感证明
    const proof = this.emotionEngine.generateEmotionProof(
      this.userId,
      this.contractAddress
    );
    console.log(`[情感证明] ${proof.toString('hex')}`);
  }
}