Site updated: 2024-05-15 14:40:12

2024/05/15/UE/网络/UE4网络中UObject创建和销毁/index.html

@@ -28,7 +28,7 @@
 <meta property="og:description" content="本文中我们专注ContentBlockPayloadHeader和DeleteObject信息.">
 <meta property="og:locale" content="zh_CN">
 <meta property="article:published_time" content="2024-05-15T02:54:53.916Z">
-<meta property="article:modified_time" content="2023-11-09T10:04:11.351Z">
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 <meta property="article:tag" content="UE网络">
 <meta property="article:tag" content="UObject创建和销毁">
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       <span class="post-meta-item-text">发表于</span>
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-      <time title="创建时间：2024-05-15 10:54:53" itemprop="dateCreated datePublished" datetime="2024-05-15T10:54:53+08:00">2024-05-15</time>
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-      <span class="post-meta-item-text">更新于</span>
-      <time title="修改时间：2023-11-09 18:04:11" itemprop="dateModified" datetime="2023-11-09T18:04:11+08:00">2023-11-09</time>
+      <time title="创建时间：2024-05-15 10:54:53 / 修改时间：14:26:02" itemprop="dateCreated datePublished" datetime="2024-05-15T10:54:53+08:00">2024-05-15</time>
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2024/05/15/UE/网络/UE4网络优化-FastSharedPath/index.html

@@ -31,8 +31,8 @@
 <meta property="article:published_time" content="2024-05-15T02:54:53.937Z">
 <meta property="article:modified_time" content="2024-05-15T02:35:37.950Z">
 <meta property="article:author" content="NoBodyNoOne">
-<meta property="article:tag" content="UE">
 <meta property="article:tag" content="UE网络">
+<meta property="article:tag" content="UE">
 <meta property="article:tag" content="FastSharedPath">
 <meta name="twitter:card" content="summary">
 <meta name="twitter:image" content="https://fdcumt.github.io/2024/05/15/UE/%E7%BD%91%E7%BB%9C/UE4%E7%BD%91%E7%BB%9C%E4%BC%98%E5%8C%96-FastSharedPath/assets/image-20240515091236899.png">
@@ -495,8 +495,8 @@ <h1 id="延展">延展</h1>
 </div>
 
           <div class="post-tags">
-              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/UE%E7%BD%91%E7%BB%9C/" rel="tag"># UE网络</a>
+              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/FastSharedPath/" rel="tag"># FastSharedPath</a>
           </div>
 

2024/05/15/UE/网络/UE网络-断点指定Actor/index.html

@@ -31,8 +31,8 @@
 <meta property="article:published_time" content="2024-05-15T02:54:54.032Z">
 <meta property="article:modified_time" content="2024-04-30T07:20:22.477Z">
 <meta property="article:author" content="NoBodyNoOne">
-<meta property="article:tag" content="UE">
 <meta property="article:tag" content="UE网络">
+<meta property="article:tag" content="UE">
 <meta property="article:tag" content="断点指定Actor">
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 <meta name="twitter:image" content="https://fdcumt.github.io/2024/05/15/UE/%E7%BD%91%E7%BB%9C/UE%E7%BD%91%E7%BB%9C-%E6%96%AD%E7%82%B9%E6%8C%87%E5%AE%9AActor/assets/image-20240418102637952.png">
@@ -328,8 +328,8 @@ <h1 class="post-title" itemprop="name headline">
 </div>
 
           <div class="post-tags">
-              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/UE%E7%BD%91%E7%BB%9C/" rel="tag"># UE网络</a>
+              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/%E6%96%AD%E7%82%B9%E6%8C%87%E5%AE%9AActor/" rel="tag"># 断点指定Actor</a>
           </div>
 

2024/05/15/UE/网络/UE网络-核心关键图/index.html

@@ -31,8 +31,8 @@
 <meta property="article:modified_time" content="2024-05-15T03:15:37.414Z">
 <meta property="article:author" content="NoBodyNoOne">
 <meta property="article:tag" content="hidden">
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 <meta property="article:tag" content="UE网络">
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 <meta property="article:tag" content="核心关键图">
 <meta name="twitter:card" content="summary">
 
@@ -330,8 +330,8 @@ <h1 class="post-title" itemprop="name headline">
 
           <div class="post-tags">
               <a href="/tags/hidden/" rel="tag"># hidden</a>
-              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/UE%E7%BD%91%E7%BB%9C/" rel="tag"># UE网络</a>
+              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/%E6%A0%B8%E5%BF%83%E5%85%B3%E9%94%AE%E5%9B%BE/" rel="tag"># 核心关键图</a>
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2024/05/15/UE/网络/UE网络-零散的积累/index.html

@@ -20,18 +20,18 @@
 
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-    <meta name="description" content="动态压缩Vector 根据FVector三个分量的最大值, 压缩Vector, 节省带宽. 这里还要注意一点, 当数值比较大时, 可能压缩后比压缩前占用的bit数量还要大, 即更占用带宽. 1234567891011121314151617181920212223242526272829&#x2F;&#x2F; FRepMovement.SerializeQuantizedVector bool Seria">
+    <meta name="description" content="动态压缩Vector 根据FVector三个分量的最大值, 压缩Vector, 节省带宽. 这里还要注意一点, 当数值比较大时, 可能压缩后比压缩前占用的bit数量还要大, 即更占用带宽.">
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 <meta property="og:title" content="UE网络-零散的积累">
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 <meta property="og:site_name" content="NoBodyNoOne">
-<meta property="og:description" content="动态压缩Vector 根据FVector三个分量的最大值, 压缩Vector, 节省带宽. 这里还要注意一点, 当数值比较大时, 可能压缩后比压缩前占用的bit数量还要大, 即更占用带宽. 1234567891011121314151617181920212223242526272829&#x2F;&#x2F; FRepMovement.SerializeQuantizedVector bool Seria">
+<meta property="og:description" content="动态压缩Vector 根据FVector三个分量的最大值, 压缩Vector, 节省带宽. 这里还要注意一点, 当数值比较大时, 可能压缩后比压缩前占用的bit数量还要大, 即更占用带宽.">
 <meta property="og:locale" content="zh_CN">
 <meta property="article:published_time" content="2024-05-15T02:54:54.064Z">
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+<meta property="article:modified_time" content="2024-05-15T06:38:49.885Z">
 <meta property="article:author" content="NoBodyNoOne">
-<meta property="article:tag" content="UE">
 <meta property="article:tag" content="UE网络">
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 <meta property="article:tag" content="UE网络-零散的积累">
 <meta name="twitter:card" content="summary">
 
@@ -236,7 +236,7 @@ <h1 class="post-title" itemprop="name headline">
       <span class="post-meta-item-text">发表于</span>
 
 
-      <time title="创建时间：2024-05-15 10:54:54 / 修改时间：11:19:04" itemprop="dateCreated datePublished" datetime="2024-05-15T10:54:54+08:00">2024-05-15</time>
+      <time title="创建时间：2024-05-15 10:54:54 / 修改时间：14:38:49" itemprop="dateCreated datePublished" datetime="2024-05-15T10:54:54+08:00">2024-05-15</time>
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@@ -287,10 +287,10 @@ <h1 class="post-title" itemprop="name headline">
 
 
 
-    <div class="post-body" itemprop="articleBody"><span id="more"></span>
-<h1 id="动态压缩vector">动态压缩Vector</h1>
+    <div class="post-body" itemprop="articleBody"><h1 id="动态压缩vector">动态压缩Vector</h1>
 <p>根据FVector三个分量的最大值, 压缩Vector, 节省带宽. 这里还要注意一点,
 当数值比较大时, 可能压缩后比压缩前占用的bit数量还要大, 即更占用带宽.</p>
+<span id="more"></span>
 <figure class="highlight c++"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment">// FRepMovement.SerializeQuantizedVector </span></span><br><span class="line"><span class="function"><span class="type">bool</span> <span class="title">SerializeQuantizedVector</span><span class="params">(FArchive&amp; Ar, FVector&amp; Vector, EVectorQuantization QuantizationLevel)</span></span></span><br><span class="line"><span class="function"></span>&#123;</span><br><span class="line">    <span class="comment">// Since FRepMovement used to use FVector_NetQuantize100, we&#x27;re allowing enough bits per component</span></span><br><span class="line">    <span class="comment">// regardless of the quantization level so that we can still support at least the same maximum magnitude</span></span><br><span class="line">    <span class="comment">// (2^30 / 100, or ~10 million).</span></span><br><span class="line">    <span class="comment">// This uses no inherent extra bandwidth since we&#x27;re still using the same number of bits to store the</span></span><br><span class="line">    <span class="comment">// bits-per-component value. Of course, larger magnitudes will still use more bandwidth,</span></span><br><span class="line">    <span class="comment">// as has always been the case.</span></span><br><span class="line">    <span class="comment">// 因为FrepMovement使用过FVector_NetQuantize100, 允许每个分量有足够多的bit来忽略量化登记, 所以仍然支持至少同等量级.</span></span><br><span class="line">    <span class="comment">// 他不会使用额外带宽, 因为我们已经使用同等数量的bit来存储每个分量的bit值. 当然, 更大量级将会使用更多带宽.</span></span><br><span class="line">    <span class="keyword">switch</span>(QuantizationLevel)</span><br><span class="line">    &#123;</span><br><span class="line">        <span class="keyword">case</span> EVectorQuantization::RoundTwoDecimals:</span><br><span class="line">        &#123; <span class="comment">// 保留两位小数</span></span><br><span class="line">            <span class="keyword">return</span> <span class="built_in">SerializePackedVector</span>&lt;<span class="number">100</span>, <span class="number">30</span>&gt;(Vector, Ar);</span><br><span class="line">        &#125;</span><br><span class="line"></span><br><span class="line">        <span class="keyword">case</span> EVectorQuantization::RoundOneDecimal:</span><br><span class="line">        &#123; <span class="comment">// 保留一位小数</span></span><br><span class="line">            <span class="keyword">return</span> <span class="built_in">SerializePackedVector</span>&lt;<span class="number">10</span>, <span class="number">27</span>&gt;(Vector, Ar);</span><br><span class="line">        &#125;</span><br><span class="line"></span><br><span class="line">        <span class="keyword">default</span>:</span><br><span class="line">        &#123; <span class="comment">// 默认不保留小数</span></span><br><span class="line">            <span class="keyword">return</span> <span class="built_in">SerializePackedVector</span>&lt;<span class="number">1</span>, <span class="number">24</span>&gt;(Vector, Ar);</span><br><span class="line">        &#125;</span><br><span class="line">    &#125;</span><br><span class="line">&#125;</span><br></pre></td></tr></table></figure>
 <p>详细解析<code>WritePackedVector</code>函数.</p>
 <p><img
@@ -322,8 +322,8 @@ <h1 id="动态压缩vector">动态压缩Vector</h1>
 </div>
 
           <div class="post-tags">
-              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/UE%E7%BD%91%E7%BB%9C/" rel="tag"># UE网络</a>
+              <a href="/tags/UE/" rel="tag"># UE</a>
               <a href="/tags/UE%E7%BD%91%E7%BB%9C-%E9%9B%B6%E6%95%A3%E7%9A%84%E7%A7%AF%E7%B4%AF/" rel="tag"># UE网络-零散的积累</a>
           </div>