| import{_ as e,o as n,c as s,e as i}from"./app-Bx8hKGcu.js";const a={},l=i(`<h2 id="数据修复" tabindex="-1"><a class="header-anchor" href="#数据修复"><span>数据修复</span></a></h2><h3 id="timestamprepair" tabindex="-1"><a class="header-anchor" href="#timestamprepair"><span>TimestampRepair</span></a></h3><h4 id="函数简介" tabindex="-1"><a class="header-anchor" href="#函数简介"><span>函数简介</span></a></h4><p>本函数用于时间戳修复。根据给定的标准时间间隔，采用最小化修复代价的方法，通过对数据时间戳的微调，将原本时间戳间隔不稳定的数据修复为严格等间隔的数据。在未给定标准时间间隔的情况下，本函数将使用时间间隔的中位数 (median)、众数 (mode) 或聚类中心 (cluster) 来推算标准时间间隔。</p><p><strong>函数名：</strong> TIMESTAMPREPAIR</p><p><strong>输入序列：</strong> 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE</p><p><strong>参数：</strong></p><ul><li><code>interval</code>: 标准时间间隔（单位是毫秒），是一个正整数。在缺省情况下，将根据指定的方法推算。</li><li><code>method</code>：推算标准时间间隔的方法，取值为 'median', 'mode' 或 'cluster'，仅在<code>interval</code>缺省时有效。在缺省情况下，将使用中位数方法进行推算。</li></ul><p><strong>输出序列：</strong> 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。</p><h4 id="使用示例" tabindex="-1"><a class="header-anchor" href="#使用示例"><span>使用示例</span></a></h4><h5 id="指定标准时间间隔" tabindex="-1"><a class="header-anchor" href="#指定标准时间间隔"><span>指定标准时间间隔</span></a></h5><p>在给定<code>interval</code>参数的情况下，本函数将按照指定的标准时间间隔进行修复。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2021-07-01T12:00:00.000+08:00| 1.0| |
| |2021-07-01T12:00:10.000+08:00| 2.0| |
| |2021-07-01T12:00:19.000+08:00| 3.0| |
| |2021-07-01T12:00:30.000+08:00| 4.0| |
| |2021-07-01T12:00:40.000+08:00| 5.0| |
| |2021-07-01T12:00:50.000+08:00| 6.0| |
| |2021-07-01T12:01:01.000+08:00| 7.0| |
| |2021-07-01T12:01:11.000+08:00| 8.0| |
| |2021-07-01T12:01:21.000+08:00| 9.0| |
| |2021-07-01T12:01:31.000+08:00| 10.0| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的SQL语句：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> timestamprepair<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">'interval'</span><span class="token operator">=</span><span class="token string">'10000'</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+----------------------------------------------------+ |
| | Time|timestamprepair(root.test.d2.s1, "interval"="10000")| |
| +-----------------------------+----------------------------------------------------+ |
| |2021-07-01T12:00:00.000+08:00| 1.0| |
| |2021-07-01T12:00:10.000+08:00| 2.0| |
| |2021-07-01T12:00:20.000+08:00| 3.0| |
| |2021-07-01T12:00:30.000+08:00| 4.0| |
| |2021-07-01T12:00:40.000+08:00| 5.0| |
| |2021-07-01T12:00:50.000+08:00| 6.0| |
| |2021-07-01T12:01:00.000+08:00| 7.0| |
| |2021-07-01T12:01:10.000+08:00| 8.0| |
| |2021-07-01T12:01:20.000+08:00| 9.0| |
| |2021-07-01T12:01:30.000+08:00| 10.0| |
| +-----------------------------+----------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h5 id="自动推算标准时间间隔" tabindex="-1"><a class="header-anchor" href="#自动推算标准时间间隔"><span>自动推算标准时间间隔</span></a></h5><p>如果<code>interval</code>参数没有给定，本函数将按照推算的标准时间间隔进行修复。</p><p>输入序列同上，用于查询的 SQL 语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> timestamprepair<span class="token punctuation">(</span>s1<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+--------------------------------+ |
| | Time|timestamprepair(root.test.d2.s1)| |
| +-----------------------------+--------------------------------+ |
| |2021-07-01T12:00:00.000+08:00| 1.0| |
| |2021-07-01T12:00:10.000+08:00| 2.0| |
| |2021-07-01T12:00:20.000+08:00| 3.0| |
| |2021-07-01T12:00:30.000+08:00| 4.0| |
| |2021-07-01T12:00:40.000+08:00| 5.0| |
| |2021-07-01T12:00:50.000+08:00| 6.0| |
| |2021-07-01T12:01:00.000+08:00| 7.0| |
| |2021-07-01T12:01:10.000+08:00| 8.0| |
| |2021-07-01T12:01:20.000+08:00| 9.0| |
| |2021-07-01T12:01:30.000+08:00| 10.0| |
| +-----------------------------+--------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="valuefill" tabindex="-1"><a class="header-anchor" href="#valuefill"><span>ValueFill</span></a></h3><h4 id="函数简介-1" tabindex="-1"><a class="header-anchor" href="#函数简介-1"><span>函数简介</span></a></h4><p><strong>函数名：</strong> ValueFill</p><p><strong>输入序列：</strong> 单列时序数据，类型为INT32 / INT64 / FLOAT / DOUBLE</p><p><strong>参数：</strong></p><ul><li><code>method</code>: {"mean", "previous", "linear", "likelihood", "AR", "MA", "SCREEN"}, 默认为 "linear"。其中，“mean” 指使用均值填补的方法； “previous" 指使用前值填补方法；“linear" 指使用线性插值填补方法；“likelihood” 为基于速度的正态分布的极大似然估计方法；“AR” 指自回归的填补方法；“MA” 指滑动平均的填补方法；"SCREEN" 指约束填补方法；缺省情况下使用 “linear”。</li></ul><p><strong>输出序列：</strong> 填补后的单维序列。</p><p><strong>备注：</strong> AR 模型采用 AR(1)，时序列需满足自相关条件，否则将输出单个数据点 (0, 0.0).</p><h4 id="使用示例-1" tabindex="-1"><a class="header-anchor" href="#使用示例-1"><span>使用示例</span></a></h4><h5 id="使用-linear-方法进行填补" tabindex="-1"><a class="header-anchor" href="#使用-linear-方法进行填补"><span>使用 linear 方法进行填补</span></a></h5><p>当<code>method</code>缺省或取值为 'linear' 时，本函数将使用线性插值方法进行填补。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| NaN| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| NaN| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| NaN| |
| |2020-01-01T00:00:22.000+08:00| NaN| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuefill<span class="token punctuation">(</span>s1<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-----------------------+ |
| | Time|valuefill(root.test.d2)| |
| +-----------------------------+-----------------------+ |
| |2020-01-01T00:00:02.000+08:00| NaN| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 108.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.7| |
| |2020-01-01T00:00:22.000+08:00| 121.3| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+-----------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h5 id="使用-previous-方法进行填补" tabindex="-1"><a class="header-anchor" href="#使用-previous-方法进行填补"><span>使用 previous 方法进行填补</span></a></h5><p>当<code>method</code>取值为 'previous' 时，本函数将使前值填补方法进行数值填补。</p><p>输入序列同上，用于查询的 SQL 语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuefill<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">"method"</span><span class="token operator">=</span><span class="token string">"previous"</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------------+ |
| | Time|valuefill(root.test.d2,"method"="previous")| |
| +-----------------------------+-------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| NaN| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 110.5| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 116.0| |
| |2020-01-01T00:00:22.000+08:00| 116.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+-------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h3 id="valuerepair" tabindex="-1"><a class="header-anchor" href="#valuerepair"><span>ValueRepair</span></a></h3><h4 id="函数简介-2" tabindex="-1"><a class="header-anchor" href="#函数简介-2"><span>函数简介</span></a></h4><p>本函数用于对时间序列的数值进行修复。目前，本函数支持两种修复方法：<strong>Screen</strong> 是一种基于速度阈值的方法，在最小改动的前提下使得所有的速度符合阈值要求；<strong>LsGreedy</strong> 是一种基于速度变化似然的方法，将速度变化建模为高斯分布，并采用贪心算法极大化似然函数。</p><p><strong>函数名：</strong> VALUEREPAIR</p><p><strong>输入序列：</strong> 仅支持单个输入序列，类型为 INT32 / INT64 / FLOAT / DOUBLE。</p><p><strong>参数：</strong></p><ul><li><code>method</code>：修复时采用的方法，取值为 'Screen' 或 'LsGreedy'. 在缺省情况下，使用 Screen 方法进行修复。</li><li><code>minSpeed</code>：该参数仅在使用 Screen 方法时有效。当速度小于该值时会被视作数值异常点加以修复。在缺省情况下为中位数减去三倍绝对中位差。</li><li><code>maxSpeed</code>：该参数仅在使用 Screen 方法时有效。当速度大于该值时会被视作数值异常点加以修复。在缺省情况下为中位数加上三倍绝对中位差。</li><li><code>center</code>：该参数仅在使用 LsGreedy 方法时有效。对速度变化分布建立的高斯模型的中心。在缺省情况下为 0。</li><li><code>sigma</code> ：该参数仅在使用 LsGreedy 方法时有效。对速度变化分布建立的高斯模型的标准差。在缺省情况下为绝对中位差。</li></ul><p><strong>输出序列：</strong> 输出单个序列，类型与输入序列相同。该序列是修复后的输入序列。</p><p><strong>提示：</strong> 输入序列中的<code>NaN</code>在修复之前会先进行线性插值填补。</p><h4 id="使用示例-2" tabindex="-1"><a class="header-anchor" href="#使用示例-2"><span>使用示例</span></a></h4><h5 id="使用-screen-方法进行修复" tabindex="-1"><a class="header-anchor" href="#使用-screen-方法进行修复"><span>使用 Screen 方法进行修复</span></a></h5><p>当<code>method</code>缺省或取值为 'Screen' 时，本函数将使用 Screen 方法进行数值修复。</p><p>输入序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+---------------+ |
| | Time|root.test.d2.s1| |
| +-----------------------------+---------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 126.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| |
| |2020-01-01T00:00:22.000+08:00| 100.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| NaN| |
| +-----------------------------+---------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><p>用于查询的 SQL 语句：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuerepair<span class="token punctuation">(</span>s1<span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+----------------------------+ |
| | Time|valuerepair(root.test.d2.s1)| |
| +-----------------------------+----------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 106.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| |
| |2020-01-01T00:00:22.000+08:00| 120.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+----------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div><h5 id="使用-lsgreedy-方法进行修复" tabindex="-1"><a class="header-anchor" href="#使用-lsgreedy-方法进行修复"><span>使用 LsGreedy 方法进行修复</span></a></h5><p>当<code>method</code>取值为 'LsGreedy' 时，本函数将使用 LsGreedy 方法进行数值修复。</p><p>输入序列同上，用于查询的 SQL 语句如下：</p><div class="language-sql line-numbers-mode" data-ext="sql" data-title="sql"><pre class="language-sql"><code><span class="token keyword">select</span> valuerepair<span class="token punctuation">(</span>s1<span class="token punctuation">,</span><span class="token string">'method'</span><span class="token operator">=</span><span class="token string">'LsGreedy'</span><span class="token punctuation">)</span> <span class="token keyword">from</span> root<span class="token punctuation">.</span>test<span class="token punctuation">.</span>d2 |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div></div></div><p>输出序列：</p><div class="language-text line-numbers-mode" data-ext="text" data-title="text"><pre class="language-text"><code>+-----------------------------+-------------------------------------------------+ |
| | Time|valuerepair(root.test.d2.s1, "method"="LsGreedy")| |
| +-----------------------------+-------------------------------------------------+ |
| |2020-01-01T00:00:02.000+08:00| 100.0| |
| |2020-01-01T00:00:03.000+08:00| 101.0| |
| |2020-01-01T00:00:04.000+08:00| 102.0| |
| |2020-01-01T00:00:06.000+08:00| 104.0| |
| |2020-01-01T00:00:08.000+08:00| 106.0| |
| |2020-01-01T00:00:10.000+08:00| 108.0| |
| |2020-01-01T00:00:14.000+08:00| 112.0| |
| |2020-01-01T00:00:15.000+08:00| 113.0| |
| |2020-01-01T00:00:16.000+08:00| 114.0| |
| |2020-01-01T00:00:18.000+08:00| 116.0| |
| |2020-01-01T00:00:20.000+08:00| 118.0| |
| |2020-01-01T00:00:22.000+08:00| 120.0| |
| |2020-01-01T00:00:26.000+08:00| 124.0| |
| |2020-01-01T00:00:28.000+08:00| 126.0| |
| |2020-01-01T00:00:30.000+08:00| 128.0| |
| +-----------------------------+-------------------------------------------------+ |
| </code></pre><div class="line-numbers" aria-hidden="true"><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div><div class="line-number"></div></div></div>`,71),d=[l];function t(r,c){return n(),s("div",null,d)}const u=e(a,[["render",t],["__file","Data-Repairing.html.vue"]]),o=JSON.parse('{"path":"/zh/UserGuide/V0.13.x/UDF-Library/Data-Repairing.html","title":"","lang":"zh-CN","frontmatter":{"description":"数据修复 TimestampRepair 函数简介 本函数用于时间戳修复。根据给定的标准时间间隔，采用最小化修复代价的方法，通过对数据时间戳的微调，将原本时间戳间隔不稳定的数据修复为严格等间隔的数据。在未给定标准时间间隔的情况下，本函数将使用时间间隔的中位数 (median)、众数 (mode) 或聚类中心 (cluster) 来推算标准时间间隔。 函...","head":[["link",{"rel":"alternate","hreflang":"en-us","href":"https://iotdb.apache.org/UserGuide/V0.13.x/UDF-Library/Data-Repairing.html"}],["meta",{"property":"og:url","content":"https://iotdb.apache.org/zh/UserGuide/V0.13.x/UDF-Library/Data-Repairing.html"}],["meta",{"property":"og:site_name","content":"IoTDB Website"}],["meta",{"property":"og:description","content":"数据修复 TimestampRepair 函数简介 本函数用于时间戳修复。根据给定的标准时间间隔，采用最小化修复代价的方法，通过对数据时间戳的微调，将原本时间戳间隔不稳定的数据修复为严格等间隔的数据。在未给定标准时间间隔的情况下，本函数将使用时间间隔的中位数 (median)、众数 (mode) 或聚类中心 (cluster) 来推算标准时间间隔。 函..."}],["meta",{"property":"og:type","content":"article"}],["meta",{"property":"og:locale","content":"zh-CN"}],["meta",{"property":"og:locale:alternate","content":"en-US"}],["meta",{"property":"og:updated_time","content":"2023-07-10T03:11:17.000Z"}],["meta",{"property":"article:modified_time","content":"2023-07-10T03:11:17.000Z"}],["script",{"type":"application/ld+json"},"{\\"@context\\":\\"https://schema.org\\",\\"@type\\":\\"Article\\",\\"headline\\":\\"\\",\\"image\\":[\\"\\"],\\"dateModified\\":\\"2023-07-10T03:11:17.000Z\\",\\"author\\":[]}"]]},"headers":[{"level":2,"title":"数据修复","slug":"数据修复","link":"#数据修复","children":[{"level":3,"title":"TimestampRepair","slug":"timestamprepair","link":"#timestamprepair","children":[]},{"level":3,"title":"ValueFill","slug":"valuefill","link":"#valuefill","children":[]},{"level":3,"title":"ValueRepair","slug":"valuerepair","link":"#valuerepair","children":[]}]}],"git":{"createdTime":1688958677000,"updatedTime":1688958677000,"contributors":[{"name":"CritasWang","email":"critas@outlook.com","commits":1}]},"readingTime":{"minutes":7.78,"words":2335},"filePathRelative":"zh/UserGuide/V0.13.x/UDF-Library/Data-Repairing.md","localizedDate":"2023年7月10日","autoDesc":true}');export{u as comp,o as data}; |
