<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>供应链 on 标准答案</title><link>https://www.xin800.com/tags/%E4%BE%9B%E5%BA%94%E9%93%BE/</link><description>Recent content in 供应链 on 标准答案</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Fri, 31 Jul 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.xin800.com/tags/%E4%BE%9B%E5%BA%94%E9%93%BE/index.xml" rel="self" type="application/rss+xml"/><item><title>Transformer 注意力整套完整流程梳理</title><link>https://www.xin800.com/post/transformer-attention-pipeline/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/transformer-attention-pipeline/</guid><description>从向量到QKV到点积到缩放到Softmax到加权融合，完整的Transformer注意力流程，全程业务语言，供应链案例贯穿。</description></item><item><title>什么是交叉注意力机制（Cross-Attention）</title><link>https://www.xin800.com/post/what-is-cross-attention/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/what-is-cross-attention/</guid><description>交叉注意力机制实现两组不同业务信息之间的关联匹配，Q来自A数据集，K/V来自B数据集，是机器翻译和知识库匹配的核心技术。</description></item><item><title>什么是自注意力机制（Self-Attention）</title><link>https://www.xin800.com/post/what-is-self-attention/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/what-is-self-attention/</guid><description>自注意力机制实现同一组数据内部所有条目的两两关联计算，依靠QKV向量与相似度打分自动识别远距离业务关联，是Transformer的核心基础。</description></item><item><title>向量接近的判断逻辑是什么</title><link>https://www.xin800.com/post/how-to-judge-vector-proximity/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/how-to-judge-vector-proximity/</guid><description>详解向量相似度判断的底层逻辑：向量不是逐字编码，而是整体语义的打包。通过余弦相似度衡量方向重合度。</description></item><item><title>欧式距离在物流优化场景的使用</title><link>https://www.xin800.com/post/euclidean-distance-logistics/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/euclidean-distance-logistics/</guid><description>物流网点片区划分、配送路线聚合场景中，欧氏距离不只判断地理远近，还能兼顾货量、时效等量化指标。</description></item><item><title>欧式距离适用场景</title><link>https://www.xin800.com/post/euclidean-distance-use-cases/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/euclidean-distance-use-cases/</guid><description>欧氏距离同时考量特征方向和数值大小，适合结构化数值聚类、备件指标分类、坐标点位匹配等场景。</description></item><item><title>点积业务场景与计算逻辑</title><link>https://www.xin800.com/post/dot-product-business/</link><pubDate>Fri, 31 Jul 2026 00:00:00 +0000</pubDate><guid>https://www.xin800.com/post/dot-product-business/</guid><description>点积是一套快速计算公式，用来衡量两组向量大方向是否一致，输出一个数字代表匹配程度，是注意力机制计算关联程度的基础运算。</description></item></channel></rss>