亚马逊绿光3部AI动画：当流媒体开始定制AI原生内容

// SPDX-License-Identifier: MIT
pragma solidity ^0.8.19;

contract AIContentProvenance {
    
    enum ContributionType {
        HUMAN_SCRIPT,
        HUMAN_DIRECTION,
        HUMAN_CHARACTER_DESIGN,
        HUMAN_EDITING,
        AI_GENERATED_VISUALS,
        AI_GENERATED_DIALOGUE,
        AI_GENERATED_MUSIC,
        AI_GENERATED_ANIMATION,
        AI_ASSISTED_EDITING,
        MIXED_COLLABORATION
    }
    
    struct Contribution {
        address contributor;
        ContributionType contributionType;
        string contentHash;
        string modelIdentifier;
        uint256 timestamp;
        uint256 revenueShareBasis;
        bool isVerified;
        string verificationMetadata;
    }
    
    struct ContentWork {
        string title;
        string platform;
        bytes32 ipfsRoot;
        address registrar;
        uint256 registrationTime;
        Contribution[] contributions;
        bool finalized;
        string contentCategory;
        uint256 aiTransparencyScore;
    }
    
    struct DistributionLog {
        string territory;
        uint256 streamCount;
        uint256 revenueWei;
        uint256 distributedAt;
    }
    
    mapping(bytes32 => ContentWork) public registry;
    mapping(address => bool) public certifiedAuditors;
    mapping(bytes32 => DistributionLog[]) public distributions;
    mapping(address => uint256) public pendingPayouts;
    
    event ContentRegistered(
        bytes32 indexed workId,
        string title,
        string platform,
        address registrar,
        uint256 timestamp
    );
    
    event ContributionLogged(
        bytes32 indexed workId,
        address indexed contributor,
        ContributionType contributionType,
        string modelIdentifier,
        uint256 revenueShareBasis
    );
    
    event WorkFinalized(
        bytes32 indexed workId,
        uint256 totalContributions,
        uint256 aiTransparencyScore,
        uint256 timestamp
    );
    
    event RevenueDistributed(
        bytes32 indexed workId,
        address indexed contributor,
        uint256 amount,
        string territory
    );
    
    modifier onlyCertifiedAuditor() {
        require(certifiedAuditors[msg.sender], "Not a certified auditor");
        _;
    }
    
    modifier workNotFinalized(bytes32 workId) {
        require(!registry[workId].finalized, "Work already finalized");
        _;
    }
    
    modifier onlyRegistrar(bytes32 workId) {
        require(
            msg.sender == registry[workId].registrar,
            "Only registrar can perform this action"
        );
        _;
    }
    
    function registerWork(
        string memory title,
        string memory platform,
        string memory ipfsHash,
        string memory category
    ) external returns (bytes32) {
        bytes32 workId = keccak256(
            abi.encodePacked(title, platform, block.timestamp, msg.sender)
        );
        
        ContentWork storage work = registry[workId];
        work.title = title;
        work.platform = platform;
        work.ipfsRoot = bytes32(bytes(ipfsHash));
        work.registrar = msg.sender;
        work.registrationTime = block.timestamp;
        work.finalized = false;
        work.contentCategory = category;
        work.aiTransparencyScore = 0;
        
        emit ContentRegistered(
            workId, title, platform, msg.sender, block.timestamp
        );
        
        return workId;
    }
    
    function logContribution(
        bytes32 workId,
        ContributionType contributionType,
        string memory contentHash,
        string memory modelIdentifier,
        uint256 revenueShareBasis,
        string memory verificationMeta
    ) external workNotFinalized(workId) {
        require(registry[workId].registrar != address(0), "Work not registered");
        require(revenueShareBasis <= 10000, "Invalid revenue share");
        
        Contribution memory newContribution = Contribution({
            contributor: msg.sender,
            contributionType: contributionType,
            contentHash: contentHash,
            modelIdentifier: modelIdentifier,
            timestamp: block.timestamp,
            revenueShareBasis: revenueShareBasis,
            isVerified: false,
            verificationMetadata: verificationMeta
        });
        
        registry[workId].contributions.push(newContribution);
        
        emit ContributionLogged(
            workId, msg.sender, contributionType, modelIdentifier, revenueShareBasis
        );
    }
    
    function getAIVsHumanRatio(bytes32 workId) public view returns (
        uint256 humanShare,
        uint256 aiShare,
        uint256 mixedShare,
        uint256 totalContributions
    ) {
        ContentWork storage work = registry[workId];
        totalContributions = work.contributions.length;
        
        for (uint256 i = 0; i < totalContributions; i++) {
            ContributionType ct = work.contributions[i].contributionType;
            
            if (ct == ContributionType.HUMAN_SCRIPT || 
                ct == ContributionType.HUMAN_DIRECTION || 
                ct == ContributionType.HUMAN_EDITING ||
                ct == ContributionType.HUMAN_CHARACTER_DESIGN) {
                humanShare += work.contributions[i].revenueShareBasis;
            } else if (ct == ContributionType.AI_GENERATED_VISUALS || 
                       ct == ContributionType.AI_GENERATED_DIALOGUE || 
                       ct == ContributionType.AI_GENERATED_MUSIC ||
                       ct == ContributionType.AI_GENERATED_ANIMATION) {
                aiShare += work.contributions[i].revenueShareBasis;
            } else {
                mixedShare += work.contributions[i].revenueShareBasis;
            }
        }
        
        return (humanShare, aiShare, mixedShare, totalContributions);
    }
    
    function verifyAIAttribution(
        bytes32 workId,
        string memory modelId,
        bytes memory proof
    ) external onlyCertifiedAuditor workNotFinalized(workId) {
        bytes32 proofHash = keccak256(abi.encodePacked(workId, modelId, proof));
        require(proofHash != bytes32(0), "Invalid proof");
        
        ContentWork storage work = registry[workId];
        for (uint256 i = 0; i < work.contributions.length; i++) {
            if (keccak256(bytes(work.contributions[i].modelIdentifier)) == 
                keccak256(bytes(modelId))) {
                work.contributions[i].isVerified = true;
            }
        }
    }
    
    function calculateTransparencyScore(bytes32 workId) internal view returns (uint256) {
        ContentWork storage work = registry[workId];
        uint256 verifiedCount = 0;
        
        for (uint256 i = 0; i < work.contributions.length; i++) {
            if (work.contributions[i].isVerified) {
                verifiedCount++;
            }
        }
        
        if (work.contributions.length == 0) return 0;
        return (verifiedCount * 10000) / work.contributions.length;
    }
    
    function finalizeWork(bytes32 workId) external 
        onlyRegistrar(workId) 
        workNotFinalized(workId) 
    {
        ContentWork storage work = registry[workId];
        require(work.contributions.length > 0, "No contributions logged");
        
        uint256 totalShare = 0;
        for (uint256 i = 0; i < work.contributions.length; i++) {
            totalShare += work.contributions[i].revenueShareBasis;
        }
        require(totalShare == 10000, "Revenue shares must total 10000 basis points");
        
        work.aiTransparencyScore = calculateTransparencyScore(workId);
        work.finalized = true;
        
        emit WorkFinalized(
            workId, 
            work.contributions.length, 
            work.aiTransparencyScore, 
            block.timestamp
        );
    }
    
    function distributeRevenue(
        bytes32 workId,
        string memory territory
    ) external payable onlyRegistrar(workId) {
        require(registry[workId].finalized, "Work must be finalized first");
        require(msg.value > 0, "No revenue to distribute");
        
        ContentWork storage work = registry[workId];
        
        distributions[workId].push(DistributionLog({
            territory: territory,
            streamCount: 0,
            revenueWei: msg.value,
            distributedAt: block.timestamp
        }));
        
        for (uint256 i = 0; i < work.contributions.length; i++) {
            uint256 share = (msg.value * work.contributions[i].revenueShareBasis) / 10000;
            pendingPayouts[work.contributions[i].contributor] += share;
            
            emit RevenueDistributed(
                workId, 
                work.contributions[i].contributor, 
                share, 
                territory
            );
        }
    }
    
    function claimPayout() external {
        uint256 amount = pendingPayouts[msg.sender];
        require(amount > 0, "No pending payout");
        
        pendingPayouts[msg.sender] = 0;
        
        (bool success, ) = msg.sender.call{value: amount}("");
        require(success, "Transfer failed");
    }
}

from typing import Tuple

class AnimationPipelineRunner:
    def __init__(self, config: dict):
        self.config = config
        self.pipeline = AIAnimationPipeline(style_guide=config["style_guide"])
        self.provenance_tracker = BlockchainProvenanceTracker(
            contract_address=config["provenance_contract"],
            chain_id=config["chain_id"]
        )
    
    async def run_full_episode(self, episode_spec: EpisodeSpec) -> Tuple[bytes, str]:
        work_id = await self.provenance_tracker.register(
            title=episode_spec.title,
            platform="prime_video",
            category="ai_native_animation"
        )
        
        for scene in episode_spec.scenes:
            await self.provenance_tracker.log_ai_contribution(
                work_id=work_id,
                contribution_type="AI_GENERATED_VISUALS",
                model_id="visual-gen-v4",
                content_hash=hash(scene)
            )
        
        episode_data = await self.pipeline.generate_episode(episode_spec.scenes)
        
        await self.provenance_tracker.finalize(work_id)
        
        return episode_data, work_id

class StreamingRecommendationEngine {
  constructor(userGraph, contentCatalog) {
    this.userGraph = userGraph;
    this.contentCatalog = contentCatalog;
    this.embeddingModel = new ContentEmbeddingModel('streaming-v4');
    this.aiGenerator = new AIVariantGenerator({
      maxConcurrentVariants: 128,
      qualityThreshold: 0.89,
      generationTimeout: 30000
    });
    this.personalizationLayer = new PersonalizationTransformer({
      attentionHeads: 32,
      layers: 24,
      contextWindow: 500,
      moodDetectionInterval: 60000
    });
    this.costOptimizer = new GenerationCostOptimizer({
      maxCostPerRecommendation: 0.05,
      cacheStrategy: "lru_with_variant_dedup",
      prefetchHorizon: 300
    });
  }

  async buildViewerProfile(userId) {
    const watchHistory = await this.userGraph.getWatchHistory(userId);
    const interactionSignals = await this.userGraph.getInteractions(userId);
    const socialGraph = await this.userGraph.getSocialConnections(userId);
    const deviceContext = await this.userGraph.getDeviceContext(userId);
    
    const temporalProfile = this.calculateTemporalDecay(watchHistory);
    const moodProfile = await this.inferMoodState(interactionSignals);
    const socialInfluence = this.calculateSocialInfluence(socialGraph, watchHistory);
    const narrativeDNA = this.extractNarrativeDNA(watchHistory);
    const sessionContext = this.captureSessionContext(deviceContext);
    
    return {
      userId,
      temporalProfile,
      moodProfile,
      socialInfluence,
      narrativeDNA,
      sessionContext,
      genreAffinities: this.extractGenreAffinities(watchHistory),
      narrativePreference: this.classifyNarrativePreference(watchHistory),
      attentionSpanEstimate: this.estimateAttentionSpan(watchHistory),
      aiContentTolerance: this.calculateAIContentTolerance(watchHistory),
      rewatchPatterns: this.analyzeRewatchBehavior(watchHistory),
      skipPatterns: this.analyzeSkipBehavior(watchHistory),
      timeSlotPreference: this.analyzeTimeSlotPatterns(watchHistory)
    };
  }

  async decisionGenerateOrRecommend(userId) {
    const profile = await this.buildViewerProfile(userId);
    const bestExisting = await this.findBestExistingMatch(profile);
    
    if (profile.aiContentTolerance > 0.7 && 
        profile.sessionContext.availableTime > 60 &&
        this.costOptimizer.canAffordGeneration(profile)) {
      
      const generatedVariant = await this.generatePersonalizedVariant(
        bestExisting.content.baseIP,
        profile
      );
      
      if (generatedVariant.predictedScore > bestExisting.score * 1.2) {
        return {
          type: "ai_generated",
          content: generatedVariant,
          baseIP: bestExisting.content.baseIP,
          personalizationFactors: generatedVariant.personalizationLog
        };
      }
    }
    
    return {
      type: "existing_recommendation",
      content: bestExisting.content,
      score: bestExisting.score
    };
  }

  async generatePersonalizedVariant(baseIP, viewerProfile) {
    const cachedVariant = await this.costOptimizer.checkCache(baseIP, viewerProfile);
    if (cachedVariant) return cachedVariant;
    
    const variantParameters = {
      pacing: this.adaptPacing(
        baseIP.defaultPacing,
        viewerProfile.attentionSpanEstimate,
        viewerProfile.skipPatterns
      ),
      narrativeComplexity: this.adaptComplexity(
        baseIP.storyArc,
        viewerProfile.narrativePreference
      ),
      visualStyle: this.adaptVisualStyle(
        baseIP.aestheticProfile,
        viewerProfile.moodProfile
      ),
      audioMix: this.adaptAudio(
        baseIP.soundtrack,
        viewerProfile.moodProfile,
        viewerProfile.sessionContext.deviceType
      ),
      thematicEmphasis: this.adaptThemes(
        baseIP.themes,
        viewerProfile.narrativeDNA.topThemes
      ),
      runtime: this.adaptRuntime(
        baseIP.defaultRuntime,
        viewerProfile.sessionContext.availableTime
      )
    };

    const variants = await Promise.all(
      Array.from({length: 8}, (_, i) =>
        this.aiGenerator.renderVariant(baseIP, {
          ...variantParameters,
          variantSeed: i,
          viewerContext: viewerProfile
        })
      )
    );

    const bestVariant = variants.reduce((best, current) => 
      current.predictedEngagement > best.predictedEngagement ? current : best
    );

    await this.costOptimizer.cache(baseIP, viewerProfile, bestVariant);
    
    return bestVariant;
  }

  async scoreAndRank(userId) {
    const profile = await this.buildViewerProfile(userId);
    const candidates = await this.contentCatalog.getCandidates(profile);
    const aiNativeContent = candidates.filter(c => c.isAINative);

    if (aiNativeContent.length > 0 && profile.aiContentTolerance > 0.6) {
      const variantBatches = await Promise.all(
        aiNativeContent.map(content =>
          this.generatePersonalizedVariant(content, profile)
        )
      );
      candidates.push(...variantBatches);
    }

    const scored = await Promise.all(
      candidates.map(async candidate => {
        const embedding = await this.embeddingModel.encode(candidate);
        const profileEmbedding = await this.embeddingModel.encodeViewerProfile(profile);
        const baseScore = this.cosineSimilarity(embedding, profileEmbedding);
        const recencyBonus = this.calculateRecencyBonus(candidate.publishDate);
        const socialBoost = await this.calculateSocialBoost(candidate, profile.socialInfluence);
        const noveltyFactor = this.calculateNovelty(candidate, profile.genreAffinities);
        const diversityPenalty = this.calculateDiversityPenalty(candidate, profile.temporalProfile);
        const aiAffinityBoost = candidate.isAINative ? profile.aiContentTolerance * 0.15 : 0;
        const timeOfDayAdjust = this.adjustForTimeOfDay(candidate, new Date().getHours());

        return {
          content: candidate,
          score: baseScore * 1.0 + recencyBonus * 0.08 + socialBoost * 0.15 +
                 noveltyFactor * 0.12 + aiAffinityBoost - diversityPenalty * 0.05 +
                 timeOfDayAdjust * 0.06,
          explanation: this.generateExplanation(candidate, profile)
        };
      })
    );

    return scored.sort((a, b) => b.score - a.score).slice(0, 50);
  }

  cosineSimilarity(vecA, vecB) {
    const dotProduct = vecA.reduce((sum, a, i) => sum + a * vecB[i], 0);
    const magA = Math.sqrt(vecA.reduce((sum, a) => sum + a * a, 0));
    const magB = Math.sqrt(vecB.reduce((sum, b) => sum + b * b, 0));
    return dotProduct / (magA * magB);
  }
}

const engine = new StreamingRecommendationEngine(
  new UserGraphService(),
  new ContentCatalogService()
);