# 一个简单的AI内容检测评分模型示例
def assess_content_quality(content):
    """
    评估内容的原创性和质量指标
    """
    metrics = {
        "originality_score": 0,       # 原创性评分
        "factual_density": 0,         # 事实密度
        "unique_insights": 0,         # 独特见解数
        "personal_experience": 0,     # 个人经验指标
        "data_citations": 0,          # 数据引用数
    }

    # 检查是否包含个人经验描述
    experience_markers = ["我", "我们", "实测", "亲身体验", "实践中发现"]
    for marker in experience_markers:
        if marker in content:
            metrics["personal_experience"] += 1

    # 检查数据引用
    import re
    data_refs = re.findall(r'\d+\.?\d*%', content)
    metrics["data_citations"] = len(data_refs)

    # 检查独特见解（非模板化内容）
    generic_phrases = [
        "在当今时代", "随着科技的发展",
        "众所周知", "总而言之"
    ]
    generic_count = sum(1 for p in generic_phrases if p in content)
    metrics["originality_score"] = max(0, 100 - generic_count * 15)

    return metrics

# 使用示例
sample_content = "我们在实际项目中测试了AI内容检测..."
result = assess_content_quality(sample_content)
print(f"内容质量评估: {result}")

// AI内容检测的基本原理示意
class AIDetector {
    constructor() {
        this.perplexityThreshold = 30;  // 困惑度阈值
        this.burstinessThreshold = 0.5; // 突发性阈值
    }

    detect(text) {
        // 计算文本困惑度（Perplexity）
        // AI文本通常困惑度较低，因为模型倾向于选择概率最高的词
        const perplexity = this.calculatePerplexity(text);

        // 计算文本突发性（Burstiness）
        // 人类写作的句子长度变化更大
        const burstiness = this.calculateBurstiness(text);

        // 综合评分
        const aiProbability = this.combineScores(perplexity, burstiness);

        return {
            isAI: aiProbability > 0.5,
            confidence: aiProbability,
            perplexity: perplexity,
            burstiness: burstiness
        };
    }

    calculatePerplexity(text) {
        // 使用语言模型计算困惑度
        // 实际实现需要加载预训练模型
        return 0;
    }

    calculateBurstiness(text) {
        // 计算句子长度的方差
        const sentences = text.split(/[。！？.!?]/);
        const lengths = sentences.map(s => s.trim().length).filter(l => l > 0);
        if (lengths.length < 2) return 0;

        const mean = lengths.reduce((a, b) => a + b) / lengths.length;
        const variance = lengths.reduce((a, b) => a + (b - mean) ** 2, 0) / lengths.length;
        return Math.sqrt(variance) / mean;
    }

    combineScores(perplexity, burstiness) {
        // 简化的综合评分
        const perplexityScore = Math.max(0, 1 - perplexity / this.perplexityThreshold);
        const burstinessScore = Math.max(0, 1 - burstiness / this.burstinessThreshold);
        return (perplexityScore + burstinessScore) / 2;
    }
}

import hashlib
from datetime import datetime

class ContentOriginalityChecker:
    '''内容原创性检查工具'''

    def __init__(self):
        self.fingerprints = set()

    def add_reference(self, content):
        '''添加参考内容指纹'''
        fp = hashlib.md5(content.encode()).hexdigest()
        self.fingerprints.add(fp)

    def check_originality(self, content):
        '''
        检查内容原创性
        返回原创性评分和建议
        '''
        # 基础指纹检查
        fp = hashlib.md5(content.encode()).hexdigest()
        is_duplicate = fp in self.fingerprints

        # 结构化特征检查
        features = {
            "word_count": len(content),
            "has_code_blocks": "