信息流优化指南：为什么说卡片式信息聚合软件是知识工作的“操作系统”？

/**
 * 从原始内容提取信息并生成标准卡片
 * @param {Object} rawContent 原始内容对象（含文本、元数据等）
 * @returns {Object} 标准化卡片对象
 */
function createInfoCard(rawContent) {
    if (!rawContent || !rawContent.text) return null;
    
    // 关键信息提取（示例为简单规则，实际可集成NLP模型）
    const text = rawContent.text;
    const title = extractTitle(text, rawContent.metadata?.title);
    const summary = generateSummary(text, 150); // 自动摘要至150字内
    const tags = autoTagging(text); // 基于关键词自动打标
    
    // 构建标准卡片结构
    const card = {
        id: generateCardId(),
        title: title,
        summary: summary,
        source: rawContent.metadata?.url || rawContent.source,
        tags: tags,
        createdTime: new Date().toISOString(),
        connections: [] // 初始关联卡片为空
    };
    
    return card;
}

// 调用示例
const newsContent = {
    text: "某科技公司今日发布新一代AI芯片，性能提升200%...",
    metadata: { url: "https://example.com/news/123", title: "科技快讯：AI芯片突破" }
};
const newsCard = createInfoCard(newsContent);

class CardConnector:
    def __init__(self):
        # 初始化模型（示例简化，实际可使用BERT等嵌入模型）
        self.similarity_threshold = 0.7
    
    def calculate_similarity(self, card1, card2):
        """计算两张卡片内容的语义相似度"""
        # 简化版：基于标签重叠的相似度计算
        tags1 = set(card1['tags'])
        tags2 = set(card2['tags'])
        
        if not tags1 or not tags2:
            return 0.0
            
        intersection = len(tags1.intersection(tags2))
        union = len(tags1.union(tags2))
        return intersection / union if union > 0 else 0.0
    
    def recommend_connections(self, new_card, existing_cards, top_k=5):
        """为新卡片推荐关联的已有卡片"""
        similarities = []
        for card in existing_cards:
            score = self.calculate_similarity(new_card, card)
            if score > self.similarity_threshold:
                similarities.append((card['id'], score))
        
        # 按相似度排序并返回前top_k个
        similarities.sort(key=lambda x: x[1], reverse=True)
        return similarities[:top_k]

# 调用示例
connector = CardConnector()
new_card = {"id": "card_789", "tags": ["AI", "芯片", "性能提升"]}
existing_cards = [
    {"id": "card_123", "tags": ["AI", "机器学习", "算法"]},
    {"id": "card_456", "tags": ["半导体", "芯片",制造"]}
]
recommendations = connector.recommend_connections(new_card, existing_cards)

-- 分析卡片使用效能：识别高价值信息模式
SELECT 
    card_id,
    card_title,
    COUNT(DISTINCT viewer_id) as unique_viewers,
    COUNT(*) as total_views,
    AVG(time_spent_seconds) as avg_read_time,
    -- 计算价值密度得分（基于阅读深度与分享次数）
    (COUNT(DISTINCT viewer_id) * 0.3 + 
     COUNT(*) * 0.2 + 
     AVG(time_spent_seconds) * 0.5) as value_score,
    -- 识别被多次引用且深度阅读的高价值卡片
    COUNT(DISTINCT connection_id) as reference_count
FROM card_usage_logs 
WHERE log_date >= DATE_SUB(NOW(), INTERVAL 30 DAY)
GROUP BY card_id, card_title
HAVING value_score > 0.7 AND reference_count >= 3
ORDER BY value_score DESC;