第一幕：银幕前的寂静

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

class CurationEngine:
    def __init__(self, corpus_embeddings, diversity_weight=0.3):
        self.corpus = corpus_embeddings
        self.diversity_weight = diversity_weight
        self.selected = []

    def score_film(self, candidate_embedding):
        if not self.selected:
            return 1.0
        selected_matrix = np.array([self.corpus[i] for i in self.selected])
        similarity = cosine_similarity(
            candidate_embedding.reshape(1, -1),
            selected_matrix
        )
        avg_sim = np.mean(similarity)
        raw_score = 1.0 - avg_sim
        return raw_score * (1 + self.diversity_weight)

    def select_program(self, candidates, n_slots):
        scored = []
        for idx, emb in candidates:
            s = self.score_film(emb)
            scored.append((idx, s))
        scored.sort(key=lambda x: x[1], reverse=True)
        self.selected = [idx for idx, _ in scored[:n_slots]]
        return self.selected

    def rebalance(self, tag_matrix):
        selected_tags = tag_matrix[self.selected]
        coverage = np.sum(selected_tags, axis=0)
        deficit_indices = np.where(coverage == 0)[0]
        return deficit_indices

pragma solidity ^0.8.20;

contract FestivalCurationDAO {
    struct Film {
        uint256 id;
        address submitter;
        string metadataURI;
        uint256 aiScore;
        uint256 humanScore;
        bool accepted;
    }

    mapping(uint256 => Film) public films;
    mapping(address => uint256) public judgeStake;
    uint256 public filmCount;
    uint256 public quorum;

    event FilmSubmitted(uint256 indexed filmId, address submitter);
    event ScoreFinalized(uint256 indexed filmId, uint256 finalScore);

    function submitFilm(string calldata metadataURI) external payable returns (uint256) {
        require(msg.value >= 0.01 ether);
        filmCount++;
        films[filmCount] = Film({
            id: filmCount,
            submitter: msg.sender,
            metadataURI: metadataURI,
            aiScore: 0,
            humanScore: 0,
            accepted: false
        });
        emit FilmSubmitted(filmCount, msg.sender);
        return filmCount;
    }

    function recordAIScore(uint256 filmId, uint256 score) external {
        require(score <= 100);
        films[filmId].aiScore = score;
    }

    function vote(uint256 filmId, uint256 score) external {
        require(judgeStake[msg.sender] > 0);
        films[filmId].humanScore = score;
        uint256 composite = (films[filmId].aiScore * 40 + score * 60) / 100;
        if (composite >= 70) {
            films[filmId].accepted = true;
        }
        emit ScoreFinalized(filmId, composite);
    }

    function stakeJudge() external payable {
        judgeStake[msg.sender] += msg.value;
    }

    function getSlot(uint256 filmId) external view returns (bool, uint256) {
        return (films[filmId].accepted, films[filmId].aiScore);
    }
}

const curationPipeline = async (submissions, config) => {
  const embeddings = await Promise.all(
    submissions.map(async (film) => ({
      id: film.id,
      vector: await extractMultimodalEmbedding(film.videoURL, film.transcript),
      tags: await classifyThemes(film.transcript, config.taxonomy),
      emotionArc: await analyzeEmotionArc(film.videoURL)
    }))
  );

  const clusters = runHDBSCAN(embeddings.map(e => e.vector), {
    minClusterSize: config.minClusterSize || 5
  });

  const slotAllocation = allocateSlots(clusters, config.totalSlots, {
    diversity: config.diversityWeight,
    quality: config.qualityThreshold,
    novelty: config.noveltyBonus
  });

  const program = [];
  for (const [clusterId, nSlots] of Object.entries(slotAllocation)) {
    const clusterFilms = embeddings
      .filter(e => clusters[e.id] === parseInt(clusterId))
      .sort((a, b) => b.qualityScore - a.qualityScore)
      .slice(0, nSlots);
    program.push(...clusterFilms);
  }

  const coherenceScore = evaluateProgramCoherence(program, config.themeMatrix);
  return { program, coherenceScore, clusterSummary: summarizeClusters(clusters) };
};

const extractMultimodalEmbedding = async (videoURL, transcript) => {
  const frames = await sampleKeyFrames(videoURL, { interval: 30, method: 'scene_change' });
  const visualEmb = await visionModel.embed(frames);
  const textEmb = await languageModel.embed(transcript);
  const audioEmb = await audioModel.embed(await extractAudio(videoURL));
  return fuseEmbeddings([visualEmb, textEmb, audioEmb], { weights: [0.35, 0.45, 0.20] });
};

const allocateSlots = (clusters, totalSlots, weights) => {
  const allocation = {};
  const clusterSizes = {};
  for (const id of Object.values(clusters)) {
    clusterSizes[id] = (clusterSizes[id] || 0) + 1;
  }
  const totalFilms = Object.values(clusterSizes).reduce((a, b) => a + b, 0);
  for (const [id, size] of Object.entries(clusterSizes)) {
    const proportional = (size / totalFilms) * totalSlots;
    allocation[id] = Math.max(1, Math.round(proportional * weights.diversity));
  }
  return allocation;
};