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update K-Nearest Neighbors details

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update K-Nearest Neighbors details
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shenjianZ
2025-01-14 17:22:31 +08:00
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@@ -1,5 +1,14 @@
<?xml version="1.0" encoding="utf-8"?>
<search>
<entry>
<title>script</title>
<url>/posts/34849.html</url>
<content><![CDATA[<h3 id="查看CPU、内存使用率"><a href="#查看CPU、内存使用率" class="headerlink" title="查看CPU、内存使用率"></a>查看CPU、内存使用率</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#!/bin/bash</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 定义颜色</span></span><br><span class="line">RED=<span class="string">&#x27;\033[0;31m&#x27;</span></span><br><span class="line">GREEN=<span class="string">&#x27;\033[0;32m&#x27;</span></span><br><span class="line">YELLOW=<span class="string">&#x27;\033[0;33m&#x27;</span></span><br><span class="line">BLUE=<span class="string">&#x27;\033[0;34m&#x27;</span></span><br><span class="line">NC=<span class="string">&#x27;\033[0m&#x27;</span> <span class="comment"># 无颜色</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">while</span> <span class="literal">true</span>; <span class="keyword">do</span></span><br><span class="line"> <span class="comment"># 获取所有进程的CPU使用率和内存使用率</span></span><br><span class="line"> cpu_usage=$(ps aux | awk <span class="string">&#x27;&#123;sum_cpu += $3&#125; END &#123;print sum_cpu&#125;&#x27;</span>)</span><br><span class="line"> mem_usage=$(ps aux | awk <span class="string">&#x27;&#123;sum_mem += $4&#125; END &#123;print sum_mem&#125;&#x27;</span>)</span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 打印结果，带有时间戳、分隔线和颜色高亮</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;BLUE&#125;</span>==============================<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;YELLOW&#125;</span>Timestamp: <span class="subst">$(date)</span><span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;BLUE&#125;</span>==============================<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;GREEN&#125;</span>Total CPU usage: <span class="variable">$&#123;RED&#125;</span><span class="variable">$cpu_usage</span>%<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;GREEN&#125;</span>Total Memory usage: <span class="variable">$&#123;RED&#125;</span><span class="variable">$mem_usage</span>%<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;BLUE&#125;</span>==============================<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 等待5秒后再次执行</span></span><br><span class="line"> <span class="built_in">sleep</span> 5</span><br><span class="line"><span class="keyword">done</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>
<p><strong>保存脚本到&#x2F;usr&#x2F;local&#x2F;bin目录下</strong></p>
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">mv</span> usage.sh /usr/local/bin/usage</span><br></pre></td></tr></table></figure>
<h3 id="Shell脚本编写的基本信息"><a href="#Shell脚本编写的基本信息" class="headerlink" title="Shell脚本编写的基本信息"></a>Shell脚本编写的基本信息</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#! /bin/bash</span></span><br><span class="line"><span class="comment"># -------------------------------------------------</span></span><br><span class="line"><span class="comment"># Filename: test.sh</span></span><br><span class="line"><span class="comment"># Version: 1.0</span></span><br><span class="line"><span class="comment"># Date: 2024/05/02</span></span><br><span class="line"><span class="comment"># Author: shenjianZ</span></span><br><span class="line"><span class="comment"># Email: shenjianZLT@gmail.com</span></span><br><span class="line"><span class="comment"># Website: https://blog.shenjianl.cn</span></span><br><span class="line"><span class="comment"># Description: this is a test shell</span></span><br><span class="line"><span class="comment"># CopyRight: 2024 All rights reserved shenjianZ</span></span><br><span class="line"><span class="comment"># License GPL</span></span><br><span class="line"><span class="comment"># ------------------------------------------------</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># Your script logic goes here</span></span><br></pre></td></tr></table></figure>]]></content>
</entry>
<entry>
<title>Hello World</title>
<url>/posts/16107.html</url>
@@ -19,16 +28,7 @@
]]></content>
</entry>
<entry>
<title>page</title>
<url>/posts/1441.html</url>
<content><![CDATA[<ul>
<li><a href="./deploy">deploy</a></li>
<li></li>
</ul>
]]></content>
</entry>
<entry>
<title>机器学习</title>
<title>k近邻算法（K-Nearest Neighbors）KNN</title>
<url>/posts/29139.html</url>
<content><![CDATA[<h2 id="k近邻算法（K-Nearest-Neighbors）KNN"><a href="#k近邻算法（K-Nearest-Neighbors）KNN" class="headerlink" title="k近邻算法（K-Nearest Neighbors）KNN"></a><strong>k近邻算法（K-Nearest Neighbors）KNN</strong></h2><p>将当前样本的类别归类于距离最近的<strong>k</strong>个样本的类别</p>
<h4 id="距离公式-2维"><a href="#距离公式-2维" class="headerlink" title="距离公式(2维)"></a><strong>距离公式(2维)</strong></h4><ul>
@@ -61,19 +61,90 @@
<li><p>标准化<br>将数据调整为均值为 0，标准差为 1 的标准正态分布<br>$$ z &#x3D; \frac{x - \mu}{\sigma} $$<br>( z )：标准化后的值 ( x )：原始数据值 ( $\mu$ )：数据的均值 ( $\sigma$)：数据的标准差</p>
<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> StandardScaler <span class="comment"># 标准化</span></span><br></pre></td></tr></table></figure></li>
</ul>
<h3 id="KNN代码实现"><a href="#KNN代码实现" class="headerlink" title="KNN代码实现"></a>KNN代码实现</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt </span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> load_iris</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> MinMaxScaler,StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.neighbors <span class="keyword">import</span> KNeighborsClassifier</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> accuracy_score</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1 数据集获取</span></span><br><span class="line">iris = load_iris()</span><br><span class="line"><span class="comment"># print(iris.feature_names)</span></span><br><span class="line">iris_data = pd.DataFrame(iris.data,columns=[<span class="string">&#x27;Sepal_Length&#x27;</span>, <span class="string">&#x27;Sepal_Width&#x27;</span>, <span class="string">&#x27;Petal_Length&#x27;</span>, <span class="string">&#x27;Petal_Width&#x27;</span>])</span><br><span class="line">iris_data[<span class="string">&#x27;target&#x27;</span>] = iris.target</span><br><span class="line"></span><br><span class="line"><span class="keyword">def</span> <span class="title function_">iris_plot</span>(<span class="params">data,col1,col2</span>):</span><br><span class="line"> sns.lmplot(x=col1,y=col2,data=data,hue=<span class="string">&quot;target&quot;</span>,fit_reg=<span class="literal">False</span>)</span><br><span class="line"> plt.show()</span><br><span class="line"><span class="comment"># 2 数据集可视化</span></span><br><span class="line"><span class="comment"># iris_plot(iris_data, &#x27;Sepal_Width&#x27;, &#x27;Petal_Length&#x27;)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 3 数据集的划分</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=<span class="number">0.2</span>,random_state=<span class="number">44</span>)</span><br><span class="line"><span class="comment"># print(&quot;训练集的特征值：\n&quot;,X_train)</span></span><br><span class="line"><span class="comment"># print(&quot;训练集的目标值：\n&quot;,y_train)</span></span><br><span class="line"><span class="comment"># print(&quot;测试集的特征值：\n&quot;,X_test)</span></span><br><span class="line"><span class="comment"># print(&quot;测试集的特征值：\n&quot;,y_test)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4 归一化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"><span class="comment"># print(&quot;归一化的,X_train：\n&quot;,X_train)</span></span><br><span class="line"><span class="comment"># print(&quot;归一化的X_test：\n&quot;,X_test)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 5 机器学习 KNN</span></span><br><span class="line"><span class="comment"># 5.1 实例化估计器</span></span><br><span class="line">estimator = KNeighborsClassifier(n_neighbors=<span class="number">9</span>)</span><br><span class="line"><span class="comment"># 5.2 进行训练</span></span><br><span class="line">estimator.fit(X_train,y_train)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 6 模型评估</span></span><br><span class="line">y_pred = estimator.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">&quot;预测值：\n&quot;</span>,y_pre)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">&quot;预测值与真实值是否相等：\n&quot;</span>,y_pred==y_test)</span><br><span class="line">accuracy = accuracy_score(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;\nKNN 模型的准确率: <span class="subst">&#123;accuracy:<span class="number">.4</span>f&#125;</span>&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="/img/machinelearning/knn-01.png"></p>
<h3 id="交叉验证与网格搜索"><a href="#交叉验证与网格搜索" class="headerlink" title="交叉验证与网格搜索"></a>交叉验证与网格搜索</h3><figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line"><span class="keyword">import</span> matplotlib.pyplot <span class="keyword">as</span> plt </span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split,GridSearchCV</span><br><span class="line"><span class="keyword">from</span> sklearn.datasets <span class="keyword">import</span> load_iris</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> MinMaxScaler,StandardScaler</span><br><span class="line"><span class="keyword">from</span> sklearn.neighbors <span class="keyword">import</span> KNeighborsClassifier</span><br><span class="line"><span class="keyword">from</span> sklearn.metrics <span class="keyword">import</span> accuracy_score</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1 数据集获取</span></span><br><span class="line">iris = load_iris()</span><br><span class="line">iris_data = pd.DataFrame(iris.data,columns=[<span class="string">&#x27;Sepal_Length&#x27;</span>, <span class="string">&#x27;Sepal_Width&#x27;</span>, <span class="string">&#x27;Petal_Length&#x27;</span>, <span class="string">&#x27;Petal_Width&#x27;</span>])</span><br><span class="line">iris_data[<span class="string">&#x27;target&#x27;</span>] = iris.target</span><br><span class="line"></span><br><span class="line"><span class="comment"># 3 数据集的划分</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=<span class="number">0.2</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4 归一化</span></span><br><span class="line">transfer = StandardScaler()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 5 机器学习 KNN</span></span><br><span class="line"><span class="comment"># 5.1 实例化估计器</span></span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="comment">#不指定 &lt;code&gt; n_neighbors &lt;/code&gt; ,使用网格搜索进行循环训练</span></span><br><span class="line">estimator = KNeighborsClassifier()</span><br><span class="line"><span class="comment"># 5.2 模型调优 -- 交叉验证，网格搜素</span></span><br><span class="line">estimator = GridSearchCV(estimator,param_grid=&#123;<span class="string">&quot;n_neighbors&quot;</span>:[<span class="number">1</span>,<span class="number">3</span>,<span class="number">5</span>,<span class="number">7</span>]&#125;,cv=<span class="number">5</span>) <span class="comment"># 5 折</span></span><br><span class="line"><span class="comment"># 5.2 进行训练</span></span><br><span class="line">estimator.fit(X_train,y_train)</span><br><span class="line"> </span><br><span class="line"><span class="comment"># 6 模型评估</span></span><br><span class="line">y_pred = estimator.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">&quot;预测值：\n&quot;</span>,y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">&quot;预测值与真实值是否相等：\n&quot;</span>,y_pred==y_test)</span><br><span class="line">accuracy = accuracy_score(y_test, y_pred)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;\nKNN 模型的准确率: <span class="subst">&#123;accuracy:<span class="number">.4</span>f&#125;</span>&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 交叉验证的相关参数</span></span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;最好结果：<span class="subst">&#123;estimator.best_score_&#125;</span>&quot;</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;最好模型：<span class="subst">&#123;estimator.best_estimator_&#125;</span>&quot;</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;最好模型结果：<span class="subst">&#123;estimator.cv_results_&#125;</span>&quot;</span>)</span><br></pre></td></tr></table></figure>
<p><img src="/img/machinelearning/cros-valid.png"></p>
<h3 id="机器学习的基本步骤"><a href="#机器学习的基本步骤" class="headerlink" title="机器学习的基本步骤"></a>机器学习的基本步骤</h3><ul>
<li>获取数据集</li>
<li>数据集基本处理<ul>
<li>去重去空、填充等操作 </li>
<li>确定特征值和目标值</li>
<li>分割数据集</li>
</ul>
</li>
<li>特征工程（特征预处理 标准化等）</li>
<li>机器学习</li>
<li>模型评估</li>
</ul>
<h3 id="数据分割的方法"><a href="#数据分割的方法" class="headerlink" title="数据分割的方法"></a>数据分割的方法</h3><ul>
<li>留出法<br>训练&#x2F;测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。<br>单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。<figure class="highlight python"><table><tr><td class="code"><pre><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> KFold,StratifiedKFold</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line">X = np.array([</span><br><span class="line">[<span class="number">1</span>,<span class="number">2</span>,<span class="number">3</span>,<span class="number">4</span>],</span><br><span class="line">[<span class="number">11</span>,<span class="number">12</span>,<span class="number">13</span>,<span class="number">14</span>],</span><br><span class="line">[<span class="number">21</span>,<span class="number">22</span>,<span class="number">23</span>,<span class="number">24</span>],</span><br><span class="line">[<span class="number">31</span>,<span class="number">32</span>,<span class="number">33</span>,<span class="number">34</span>],</span><br><span class="line">[<span class="number">41</span>,<span class="number">42</span>,<span class="number">43</span>,<span class="number">44</span>],</span><br><span class="line">[<span class="number">51</span>,<span class="number">52</span>,<span class="number">53</span>,<span class="number">54</span>],</span><br><span class="line">[<span class="number">61</span>,<span class="number">62</span>,<span class="number">63</span>,<span class="number">64</span>],</span><br><span class="line">[<span class="number">71</span>,<span class="number">72</span>,<span class="number">73</span>,<span class="number">74</span>]</span><br><span class="line">])</span><br><span class="line">y=np.array([<span class="number">1</span>,<span class="number">1</span>,<span class="number">0</span>,<span class="number">0</span>,<span class="number">1</span>,<span class="number">1</span>,<span class="number">0</span>,<span class="number">0</span>])</span><br><span class="line">folder = KFold(n_splits=<span class="number">4</span>)</span><br><span class="line">sfloder = StratifiedKFold(n_splits=<span class="number">4</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">&quot;KFOLD:&quot;</span>)</span><br><span class="line"><span class="keyword">for</span> train,test <span class="keyword">in</span> folder.split(X,y):</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f&quot;train:<span class="subst">&#123;train&#125;</span>,test:<span class="subst">&#123;test&#125;</span>&quot;</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">&quot;SKFOLD:&quot;</span>)</span><br><span class="line"><span class="keyword">for</span> train,test <span class="keyword">in</span> sfloder.split(X,y):</span><br><span class="line"> <span class="built_in">print</span>(<span class="string">f&quot;train:<span class="subst">&#123;train&#125;</span>,test:<span class="subst">&#123;test&#125;</span>&quot;</span>)</span><br></pre></td></tr></table></figure>
<img src="/img/machinelearning/kfold-skfold.png"></li>
<li>自助法</li>
<li>交叉验证法</li>
</ul>
]]></content>
<tags>
<tag>machinelearning</tag>
</tags>
</entry>
<entry>
<title>script</title>
<url>/posts/34849.html</url>
<content><![CDATA[<h3 id="查看CPU、内存使用率"><a href="#查看CPU、内存使用率" class="headerlink" title="查看CPU、内存使用率"></a>查看CPU、内存使用率</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#!/bin/bash</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 定义颜色</span></span><br><span class="line">RED=<span class="string">&#x27;\033[0;31m&#x27;</span></span><br><span class="line">GREEN=<span class="string">&#x27;\033[0;32m&#x27;</span></span><br><span class="line">YELLOW=<span class="string">&#x27;\033[0;33m&#x27;</span></span><br><span class="line">BLUE=<span class="string">&#x27;\033[0;34m&#x27;</span></span><br><span class="line">NC=<span class="string">&#x27;\033[0m&#x27;</span> <span class="comment"># 无颜色</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">while</span> <span class="literal">true</span>; <span class="keyword">do</span></span><br><span class="line"> <span class="comment"># 获取所有进程的CPU使用率和内存使用率</span></span><br><span class="line"> cpu_usage=$(ps aux | awk <span class="string">&#x27;&#123;sum_cpu += $3&#125; END &#123;print sum_cpu&#125;&#x27;</span>)</span><br><span class="line"> mem_usage=$(ps aux | awk <span class="string">&#x27;&#123;sum_mem += $4&#125; END &#123;print sum_mem&#125;&#x27;</span>)</span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 打印结果，带有时间戳、分隔线和颜色高亮</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;BLUE&#125;</span>==============================<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;YELLOW&#125;</span>Timestamp: <span class="subst">$(date)</span><span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;BLUE&#125;</span>==============================<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;GREEN&#125;</span>Total CPU usage: <span class="variable">$&#123;RED&#125;</span><span class="variable">$cpu_usage</span>%<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;GREEN&#125;</span>Total Memory usage: <span class="variable">$&#123;RED&#125;</span><span class="variable">$mem_usage</span>%<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> <span class="built_in">echo</span> -e <span class="string">&quot;<span class="variable">$&#123;BLUE&#125;</span>==============================<span class="variable">$&#123;NC&#125;</span>&quot;</span></span><br><span class="line"> </span><br><span class="line"> <span class="comment"># 等待5秒后再次执行</span></span><br><span class="line"> <span class="built_in">sleep</span> 5</span><br><span class="line"><span class="keyword">done</span></span><br><span class="line"></span><br></pre></td></tr></table></figure>
<p><strong>保存脚本到&#x2F;usr&#x2F;local&#x2F;bin目录下</strong></p>
<figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="built_in">mv</span> usage.sh /usr/local/bin/usage</span><br></pre></td></tr></table></figure>
<title>page</title>
<url>/posts/1441.html</url>
<content><![CDATA[<ul>
<li><a href="./deploy">deploy</a></li>
<li></li>
</ul>
]]></content>
</entry>
<entry>
<title>网络相关</title>
<url>/posts/41168.html</url>
<content><![CDATA[]]></content>
</entry>
<entry>
<title>uniapp 开发</title>
<url>/posts/58817.html</url>
<content><![CDATA[<ul>
<li><a href="../component1">uniapp component</a></li>
</ul>
]]></content>
<tags>
<tag>uniapp</tag>
</tags>
</entry>
<entry>
<title>郑伯克段于鄢</title>
<url>/posts/58638.html</url>
<content><![CDATA[<p>原文如下：</p>
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。</p>
]]></content>
<categories>
<category>古文观止</category>
</categories>
<tags>
<tag>古文观止</tag>
</tags>
</entry>
<entry>
<title>组件使用</title>
<url>/posts/33957.html</url>
<content><![CDATA[<h3 id="组件自动导入"><a href="#组件自动导入" class="headerlink" title="组件自动导入"></a>组件自动导入</h3><figure class="highlight json"><table><tr><td class="code"><pre><span class="line"><span class="attr">&quot;easycom&quot;</span><span class="punctuation">:</span><span class="punctuation">&#123;</span></span><br><span class="line"> <span class="attr">&quot;autoscan&quot;</span><span class="punctuation">:</span> <span class="literal"><span class="keyword">true</span></span><span class="punctuation">,</span></span><br><span class="line"> <span class="attr">&quot;custom&quot;</span><span class="punctuation">:</span> <span class="punctuation">&#123;</span></span><br><span class="line"> <span class="attr">&quot;^tui-(.*)&quot;</span><span class="punctuation">:</span> <span class="string">&quot;@/components/thorui/tui-$1/tui-$1.vue&quot;</span> <span class="comment">// 匹配components目录内的vue文件</span></span><br><span class="line"> <span class="punctuation">&#125;</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br></pre></td></tr></table></figure>
<h3 id="Shell脚本编写的基本信息"><a href="#Shell脚本编写的基本信息" class="headerlink" title="Shell脚本编写的基本信息"></a>Shell脚本编写的基本信息</h3><figure class="highlight bash"><table><tr><td class="code"><pre><span class="line"><span class="meta">#! /bin/bash</span></span><br><span class="line"><span class="comment"># -------------------------------------------------</span></span><br><span class="line"><span class="comment"># Filename: test.sh</span></span><br><span class="line"><span class="comment"># Version: 1.0</span></span><br><span class="line"><span class="comment"># Date: 2024/05/02</span></span><br><span class="line"><span class="comment"># Author: shenjianZ</span></span><br><span class="line"><span class="comment"># Email: shenjianZLT@gmail.com</span></span><br><span class="line"><span class="comment"># Website: https://blog.shenjianl.cn</span></span><br><span class="line"><span class="comment"># Description: this is a test shell</span></span><br><span class="line"><span class="comment"># CopyRight: 2024 All rights reserved shenjianZ</span></span><br><span class="line"><span class="comment"># License GPL</span></span><br><span class="line"><span class="comment"># ------------------------------------------------</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># Your script logic goes here</span></span><br></pre></td></tr></table></figure>]]></content>
<h3 id="tui-sticky-吸顶容器"><a href="#tui-sticky-吸顶容器" class="headerlink" title="tui-sticky 吸顶容器"></a><code>tui-sticky 吸顶容器</code></h3><blockquote>
<p>包含 以下 <code>tui</code> 组件 :</p>
<ul>
<li>tui-sticky</li>
<li>tui-list-view</li>
<li>tui-list-cell</li>
</ul>
</blockquote>
<figure class="highlight html"><table><tr><td class="code"><pre><span class="line"><span class="tag">&lt;<span class="name">tui-sticky</span> <span class="attr">:scrollTop</span>=<span class="string">&quot;scrollTop&quot;</span> <span class="attr">stickyHeight</span>=<span class="string">&quot;104rpx&quot;</span> <span class="attr">container</span>&gt;</span></span><br><span class="line"> <span class="comment">&lt;!-- header start --&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">template</span> <span class="attr">v-slot:header</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">view</span> <span class="attr">class</span>=<span class="string">&quot;sticky-item&quot;</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">view</span> <span class="attr">class</span>=<span class="string">&quot;setting&quot;</span>&gt;</span>设置<span class="tag">&lt;/<span class="name">view</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">view</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">template</span>&gt;</span></span><br><span class="line"> <span class="comment">&lt;!-- header end --&gt;</span></span><br><span class="line"> <span class="comment">&lt;!--内容 start--&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">template</span> <span class="attr">v-slot:content</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">tui-list-view</span> <span class="attr">class</span>=<span class="string">&quot;content&quot;</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">tui-list-cell</span> <span class="attr">:arrow</span>=<span class="string">&quot;false&quot;</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">switch</span> <span class="attr">class</span>=<span class="string">&#x27;switch&#x27;</span> <span class="attr">checked</span> <span class="attr">color</span>=<span class="string">&quot;#FFCC33&quot;</span> /&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">tui-list-cell</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">tui-list-view</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">template</span>&gt;</span></span><br><span class="line"> <span class="comment">&lt;!--内容 end--&gt;</span></span><br><span class="line"><span class="tag">&lt;/<span class="name">tui-sticky</span>&gt;</span></span><br><span class="line"></span><br><span class="line"><span class="tag">&lt;<span class="name">script</span> <span class="attr">setup</span>&gt;</span><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> &#123; ref &#125; <span class="keyword">from</span> <span class="string">&#x27;vue&#x27;</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> &#123; onPageScroll &#125; <span class="keyword">from</span> <span class="string">&#x27;@dcloudio/uni-app&#x27;</span></span></span><br><span class="line"><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 定义 scrollTop 响应式变量</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">const</span> scrollTop = <span class="title function_">ref</span>(<span class="number">0</span>)</span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 监听页面滚动事件</span></span></span><br><span class="line"><span class="language-javascript"> <span class="title function_">onPageScroll</span>(<span class="function">(<span class="params">e</span>) =&gt;</span> &#123;</span></span><br><span class="line"><span class="language-javascript"> scrollTop.<span class="property">value</span> = e.<span class="property">scrollTop</span></span></span><br><span class="line"><span class="language-javascript"> &#125;)</span></span><br><span class="line"><span class="language-javascript"></span><span class="tag">&lt;/<span class="name">script</span>&gt;</span></span><br></pre></td></tr></table></figure>
]]></content>
<tags>
<tag>uniapp</tag>
</tags>
</entry>
<entry>
<title>Docker被墙，如何继续使用？</title>
@@ -119,54 +190,6 @@
</ol>
]]></content>
</entry>
<entry>
<title>网络相关</title>
<url>/posts/41168.html</url>
<content><![CDATA[]]></content>
</entry>
<entry>
<title>uniapp 开发</title>
<url>/posts/58817.html</url>
<content><![CDATA[<ul>
<li><a href="../component1">uniapp component</a></li>
</ul>
]]></content>
<tags>
<tag>uniapp</tag>
</tags>
</entry>
<entry>
<title>组件使用</title>
<url>/posts/33957.html</url>
<content><![CDATA[<h3 id="组件自动导入"><a href="#组件自动导入" class="headerlink" title="组件自动导入"></a>组件自动导入</h3><figure class="highlight json"><table><tr><td class="code"><pre><span class="line"><span class="attr">&quot;easycom&quot;</span><span class="punctuation">:</span><span class="punctuation">&#123;</span></span><br><span class="line"> <span class="attr">&quot;autoscan&quot;</span><span class="punctuation">:</span> <span class="literal"><span class="keyword">true</span></span><span class="punctuation">,</span></span><br><span class="line"> <span class="attr">&quot;custom&quot;</span><span class="punctuation">:</span> <span class="punctuation">&#123;</span></span><br><span class="line"> <span class="attr">&quot;^tui-(.*)&quot;</span><span class="punctuation">:</span> <span class="string">&quot;@/components/thorui/tui-$1/tui-$1.vue&quot;</span> <span class="comment">// 匹配components目录内的vue文件</span></span><br><span class="line"> <span class="punctuation">&#125;</span></span><br><span class="line"><span class="punctuation">&#125;</span></span><br></pre></td></tr></table></figure>
<h3 id="tui-sticky-吸顶容器"><a href="#tui-sticky-吸顶容器" class="headerlink" title="tui-sticky 吸顶容器"></a><code>tui-sticky 吸顶容器</code></h3><blockquote>
<p>包含 以下 <code>tui</code> 组件 :</p>
<ul>
<li>tui-sticky</li>
<li>tui-list-view</li>
<li>tui-list-cell</li>
</ul>
</blockquote>
<figure class="highlight html"><table><tr><td class="code"><pre><span class="line"><span class="tag">&lt;<span class="name">tui-sticky</span> <span class="attr">:scrollTop</span>=<span class="string">&quot;scrollTop&quot;</span> <span class="attr">stickyHeight</span>=<span class="string">&quot;104rpx&quot;</span> <span class="attr">container</span>&gt;</span></span><br><span class="line"> <span class="comment">&lt;!-- header start --&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">template</span> <span class="attr">v-slot:header</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">view</span> <span class="attr">class</span>=<span class="string">&quot;sticky-item&quot;</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">view</span> <span class="attr">class</span>=<span class="string">&quot;setting&quot;</span>&gt;</span>设置<span class="tag">&lt;/<span class="name">view</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">view</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">template</span>&gt;</span></span><br><span class="line"> <span class="comment">&lt;!-- header end --&gt;</span></span><br><span class="line"> <span class="comment">&lt;!--内容 start--&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">template</span> <span class="attr">v-slot:content</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">tui-list-view</span> <span class="attr">class</span>=<span class="string">&quot;content&quot;</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">tui-list-cell</span> <span class="attr">:arrow</span>=<span class="string">&quot;false&quot;</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;<span class="name">switch</span> <span class="attr">class</span>=<span class="string">&#x27;switch&#x27;</span> <span class="attr">checked</span> <span class="attr">color</span>=<span class="string">&quot;#FFCC33&quot;</span> /&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">tui-list-cell</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">tui-list-view</span>&gt;</span></span><br><span class="line"> <span class="tag">&lt;/<span class="name">template</span>&gt;</span></span><br><span class="line"> <span class="comment">&lt;!--内容 end--&gt;</span></span><br><span class="line"><span class="tag">&lt;/<span class="name">tui-sticky</span>&gt;</span></span><br><span class="line"></span><br><span class="line"><span class="tag">&lt;<span class="name">script</span> <span class="attr">setup</span>&gt;</span><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> &#123; ref &#125; <span class="keyword">from</span> <span class="string">&#x27;vue&#x27;</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">import</span> &#123; onPageScroll &#125; <span class="keyword">from</span> <span class="string">&#x27;@dcloudio/uni-app&#x27;</span></span></span><br><span class="line"><span class="language-javascript"></span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 定义 scrollTop 响应式变量</span></span></span><br><span class="line"><span class="language-javascript"> <span class="keyword">const</span> scrollTop = <span class="title function_">ref</span>(<span class="number">0</span>)</span></span><br><span class="line"><span class="language-javascript"> <span class="comment">// 监听页面滚动事件</span></span></span><br><span class="line"><span class="language-javascript"> <span class="title function_">onPageScroll</span>(<span class="function">(<span class="params">e</span>) =&gt;</span> &#123;</span></span><br><span class="line"><span class="language-javascript"> scrollTop.<span class="property">value</span> = e.<span class="property">scrollTop</span></span></span><br><span class="line"><span class="language-javascript"> &#125;)</span></span><br><span class="line"><span class="language-javascript"></span><span class="tag">&lt;/<span class="name">script</span>&gt;</span></span><br></pre></td></tr></table></figure>
]]></content>
<tags>
<tag>uniapp</tag>
</tags>
</entry>
<entry>
<title>郑伯克段于鄢</title>
<url>/posts/58638.html</url>
<content><![CDATA[<p>原文如下：</p>
<p>&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。</p>
]]></content>
<categories>
<category>古文观止</category>
</categories>
<tags>
<tag>古文观止</tag>
</tags>
</entry>
<entry>
<title>Hadoop集群搭建基础环境</title>
<url>/posts/61253.html</url>
@@ -227,56 +250,6 @@
</ul>
]]></content>
</entry>
<entry>
<title>无法访问外网？需要订阅代理服务？</title>
<url>/posts/14011.html</url>
<content><![CDATA[<div class="note info flat"><p><strong>由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的<br>的网站将被屏蔽拦截，接下来我将给出一种使用<code>VPN</code>服务的可行的方案来保证服务的可靠性。</strong></p>
</div>
<h3 id="介绍"><a href="#介绍" class="headerlink" title="介绍"></a>介绍</h3><blockquote>
<p>根据测试，许多提供服务的提供商所在的网站需要使用<code>外部网络</code>才能打开，仅有少部分的网站(<strong>比较贵</strong>)可以直接使用<br>国内网络环境打开直接购买订阅服务。</p>
</blockquote>
<p>那么你现在可以有两个选择：</p>
<ol>
<li><strong>方案一</strong>：使用无需<code>外部网络</code>便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。</li>
<li><strong>方案二</strong>：如果使用此方案，详见下方。</li>
</ol>
<h3 id="解决方案"><a href="#解决方案" class="headerlink" title="解决方案"></a>解决方案</h3><blockquote>
<p>采用<strong>方案二</strong>方式</p>
<p>这是一些订阅服务推广的链接： <a href="https://9.234456.xyz/abc.html?t=638586217737356738">https://9.234456.xyz/abc.html?t=638586217737356738</a> (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)</p>
<p>此教程中我们使用的机场是 </p>
<ol>
<li><code>一元机场</code>: <a href="https://一元机场.com/">https://xn--4gq62f52gdss.com/</a></li>
<li><code>赔钱机场</code>：<a href="https://赔钱机场.com/">https://xn--mes358aby2apfg.com/</a></li>
</ol>
</blockquote>
<h3 id="机场选择的建议："><a href="#机场选择的建议：" class="headerlink" title="机场选择的建议："></a>机场选择的建议：</h3><ul>
<li><p><code>一元机场</code><br><img src="/img/yiyuan.png"><br>可以看到</p>
<ul>
<li><code>12元/年</code>,每月50GB的可用额度，下个月重置流量额度</li>
<li><code>15元/季</code>，即为<code>60元/年</code>，每月有4000GB的可用额度，下个月重置流量额度</li>
<li><code>7元/月</code>，即为<code>84元/年</code>，每个月8000GB的可用额度，下个月重置流量额度<br>根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，<br>建议选择<code>12元/年</code>，否则可以选择<code>15元/季</code>，这意味着每月将有4000GB的可用额度</li>
</ul>
</li>
<li><p><code>赔钱机场</code></p>
<p><img src="/img/peiqian.png"><br><code>赔钱机场</code>的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：</p>
<ul>
<li><code>34.99元/年</code>,每月500GB的可用额度，根据我观察和使用，这个订阅方案比<code>一元机场</code>的性价比更高，且流量使用额度也不用担心</li>
</ul>
</li>
</ul>
<h3 id="如何订阅？"><a href="#如何订阅？" class="headerlink" title="如何订阅？"></a>如何订阅？</h3><div class="note success flat"><p>由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱<code>15202078626@163.com</code>发送你的订阅计划方案，<br>扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）</p>
</div>
<img src="/img/dingyue.png" width='150px'>
<h3 id="完成订阅后如何使用？"><a href="#完成订阅后如何使用？" class="headerlink" title="完成订阅后如何使用？"></a>完成订阅后如何使用？</h3><blockquote>
<p>你可以在 <code>Windows</code>、<code>Mac</code>、<code>Android</code>等平台使用此服务<br>使用订阅的对应链接： <a href="https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60">https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60</a></p>
</blockquote>
]]></content>
<tags>
<tag>网络代理</tag>
</tags>
</entry>
<entry>
<title>Hadoop集群HDFS配置</title>
<url>/posts/61252.html</url>
@@ -364,4 +337,54 @@
]]></content>
</entry>
<entry>
<title>无法访问外网？需要订阅代理服务？</title>
<url>/posts/14011.html</url>
<content><![CDATA[<div class="note info flat"><p><strong>由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的<br>的网站将被屏蔽拦截，接下来我将给出一种使用<code>VPN</code>服务的可行的方案来保证服务的可靠性。</strong></p>
</div>
<h3 id="介绍"><a href="#介绍" class="headerlink" title="介绍"></a>介绍</h3><blockquote>
<p>根据测试，许多提供服务的提供商所在的网站需要使用<code>外部网络</code>才能打开，仅有少部分的网站(<strong>比较贵</strong>)可以直接使用<br>国内网络环境打开直接购买订阅服务。</p>
</blockquote>
<p>那么你现在可以有两个选择：</p>
<ol>
<li><strong>方案一</strong>：使用无需<code>外部网络</code>便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。</li>
<li><strong>方案二</strong>：如果使用此方案，详见下方。</li>
</ol>
<h3 id="解决方案"><a href="#解决方案" class="headerlink" title="解决方案"></a>解决方案</h3><blockquote>
<p>采用<strong>方案二</strong>方式</p>
<p>这是一些订阅服务推广的链接： <a href="https://9.234456.xyz/abc.html?t=638586217737356738">https://9.234456.xyz/abc.html?t=638586217737356738</a> (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)</p>
<p>此教程中我们使用的机场是 </p>
<ol>
<li><code>一元机场</code>: <a href="https://一元机场.com/">https://xn--4gq62f52gdss.com/</a></li>
<li><code>赔钱机场</code>：<a href="https://赔钱机场.com/">https://xn--mes358aby2apfg.com/</a></li>
</ol>
</blockquote>
<h3 id="机场选择的建议："><a href="#机场选择的建议：" class="headerlink" title="机场选择的建议："></a>机场选择的建议：</h3><ul>
<li><p><code>一元机场</code><br><img src="/img/yiyuan.png"><br>可以看到</p>
<ul>
<li><code>12元/年</code>,每月50GB的可用额度，下个月重置流量额度</li>
<li><code>15元/季</code>，即为<code>60元/年</code>，每月有4000GB的可用额度，下个月重置流量额度</li>
<li><code>7元/月</code>，即为<code>84元/年</code>，每个月8000GB的可用额度，下个月重置流量额度<br>根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，<br>建议选择<code>12元/年</code>，否则可以选择<code>15元/季</code>，这意味着每月将有4000GB的可用额度</li>
</ul>
</li>
<li><p><code>赔钱机场</code></p>
<p><img src="/img/peiqian.png"><br><code>赔钱机场</code>的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：</p>
<ul>
<li><code>34.99元/年</code>,每月500GB的可用额度，根据我观察和使用，这个订阅方案比<code>一元机场</code>的性价比更高，且流量使用额度也不用担心</li>
</ul>
</li>
</ul>
<h3 id="如何订阅？"><a href="#如何订阅？" class="headerlink" title="如何订阅？"></a>如何订阅？</h3><div class="note success flat"><p>由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱<code>15202078626@163.com</code>发送你的订阅计划方案，<br>扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）</p>
</div>
<img src="/img/dingyue.png" width='150px'>
<h3 id="完成订阅后如何使用？"><a href="#完成订阅后如何使用？" class="headerlink" title="完成订阅后如何使用？"></a>完成订阅后如何使用？</h3><blockquote>
<p>你可以在 <code>Windows</code>、<code>Mac</code>、<code>Android</code>等平台使用此服务<br>使用订阅的对应链接： <a href="https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60">https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60</a></p>
</blockquote>
]]></content>
<tags>
<tag>网络代理</tag>
</tags>
</entry>
</search>