Yr 02 0212 tsfle 2.x data model page reconstruction

src/UserGuide/develop/QuickStart/Data-Model.md

@@ -65,4 +65,4 @@ A schema describes is a collection of devices with the same pattern. As shown in
 
 At this point, a physical device can be uniquely identified through 3 Tags [Region] - [Factory] - [Equipment] (orange column in the figure below, also known as device identification information). The Fields collected by the device are [Temperature], [Humidity], [Status], and [Arrival Time] (blue column in the figure below).
 
-![](https://alioss.timecho.com/docs/img/data_model_example_image.png)
+![](/img/data_model_example_image.png)

src/UserGuide/latest/QuickStart/Data-Model.md

@@ -69,4 +69,4 @@ A schema describes is a collection of devices with the same pattern. As shown in
 
 At this point, a physical device can be uniquely identified through 3 Tags [Region] - [Factory] - [Equipment] (orange column in the figure below, also known as device identification information). The Fields collected by the device are [Temperature], [Humidity], [Status], and [Arrival Time] (blue column in the figure below).
 
-![](https://alioss.timecho.com/docs/img/data_model_example_image.png)
+![](/img/data_model_example_image.png)

src/UserGuide/latest/QuickStart/Navigating_Time_Series_Data.md

@@ -24,25 +24,25 @@
 
 In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
 
-![](https://alioss.timecho.com/docs/img/20240505154735.png)
+![](/img/20240505154735.png)
 
 Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
 
-![](https://alioss.timecho.com/docs/img/20240505154843.png)
+![](/img/20240505154843.png)
 
 The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
 
 ## Key Concepts of Time Series Data
 The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
 
-![](https://alioss.timecho.com/docs/img/20240505154513.png)
+![](/img/20240505154513.png)
 
 ### Data Point
 
 - Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
 - Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
 
-![](https://alioss.timecho.com/docs/img/20240505154432.png)
+![](/img/20240505154432.png)
 
 ### Measurement Points
 

src/UserGuide/v1.x/QuickStart/Data-Model.md

@@ -34,7 +34,7 @@ For the above detailed introduction, please refer to：[Entering Time Series Dat
 
 ## Example
 
-![](https://alioss.timecho.com/docs/img/20240502164237-dkcm.png)
+![](/img/20240502164237-dkcm.png)
 
 In the above example, the metadata (Scheme) of TsFile contains 2 devices and 5 time series, and is established as a table structure as shown in the following figure:
 

src/UserGuide/v1.x/QuickStart/Navigating_Time_Series_Data.md

@@ -24,25 +24,25 @@
 
 In today's era of the Internet of Things, various scenarios such as the Internet of Things and industrial scenarios are undergoing digital transformation. People collect various states of devices by installing sensors on them. If the motor collects voltage and current, the blade speed, angular velocity, and power generation of the fan; Vehicle collection of latitude and longitude, speed, and fuel consumption; The vibration frequency, deflection, displacement, etc. of the bridge. The data collection of sensors has penetrated into various industries.
 
-![](https://alioss.timecho.com/docs/img/20240505154735.png)
+![](/img/20240505154735.png)
 
 Generally speaking, we refer to each collection point as a measurement point (also known as a physical quantity, time series, timeline, signal quantity, indicator, measurement value, etc.). Each measurement point continuously collects new data information over time, forming a time series. In the form of a table, each time series is a table formed by two columns: time and value; In a graphical way, each time series is a trend chart formed over time, which can also be vividly referred to as the device's electrocardiogram.
 
-![](https://alioss.timecho.com/docs/img/20240505154843.png)
+![](/img/20240505154843.png)
 
 The massive time series data generated by sensors is the foundation of digital transformation in various industries, so our modeling of time series data mainly focuses on equipment and sensors.
 
 ## Key Concepts of Time Series Data
 The main concepts involved in time-series data can be divided from bottom to top: data points, measurement points, and equipment.
 
-![](https://alioss.timecho.com/docs/img/20240505154513.png)
+![](/img/20240505154513.png)
 
 ### Data Point
 
 - Definition: Consists of a timestamp and a value, where the timestamp is of type long and the value can be of various types such as BOOLEAN, FLOAT, INT32, etc.
 - Example: A row of a time series in the form of a table in the above figure, or a point of a time series in the form of a graph, is a data point.
 
-![](https://alioss.timecho.com/docs/img/20240505154432.png)
+![](/img/20240505154432.png)
 
 ### Measurement Points
 

src/UserGuide/v1.x/QuickStart/QuickStart.md

@@ -22,7 +22,7 @@
 
 ## Sample Data
 
-![](https://alioss.timecho.com/docs/img/2024050517481.png)
+![](/img/2024050517481.png)
 
 ## Installation Method
 

src/zh/UserGuide/develop/QuickStart/Data-Model.md

@@ -37,4 +37,6 @@
 
 此时，物理设备可以通过3个标签【地区】-【工厂】-【设备】（下图橙色列，又称设备标签）进行唯一标识。设备最终采集的指标为【温度】、【湿度】、【状态】、【到达时间】（下图中的蓝色列）。
 
-![](https://alioss.timecho.com/docs/img/Data-model01.png)
+
+![](/img/Data-model01.png)
+

src/zh/UserGuide/latest/QuickStart/Data-Model.md

@@ -37,4 +37,4 @@
 
 此时，物理设备可以通过3个标签【地区】-【工厂】-【设备】（下图橙色列，又称设备标签）进行唯一标识。设备最终采集的指标为【温度】、【湿度】、【状态】、【到达时间】（下图中的蓝色列）。
 
-![](https://alioss.timecho.com/docs/img/Data-model01.png)
+![](/img/Data-model01.png)

src/zh/UserGuide/latest/QuickStart/Navigating_Time_Series_Data.md

@@ -24,28 +24,28 @@
 
 万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。
 
-![](https://alioss.timecho.com/docs/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png)
+![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png)
 
 
 
 通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。
 
-![](https://alioss.timecho.com/docs/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png)
+![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png)
 
 传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。
 
 ## 时序数据中的关键概念有哪些？
 
 时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。
 
-![](https://alioss.timecho.com/docs/img/%E7%99%BD%E6%9D%BF.png)
+![](/img/%E7%99%BD%E6%9D%BF.png)
 
 ### 数据点
 
 - 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。
 - 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
 
-![](https://alioss.timecho.com/docs/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png)
+![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png)
 
 ### 测点
 

src/zh/UserGuide/v1.x/QuickStart/Data-Model.md

@@ -33,7 +33,7 @@
 
 ## 示例
 
-![](https://alioss.timecho.com/docs/img/tsfile%E6%95%B0%E6%8D%AE%E6%A8%A1%E5%9E%8B.png)
+![](/img/tsfile%E6%95%B0%E6%8D%AE%E6%A8%A1%E5%9E%8B.png)
 
 在上述示例中，TsFile 的元数据（Schema）共包含 2 个设备，5条时间序列，建立为表结构如下图：
 

src/zh/UserGuide/v1.x/QuickStart/Navigating_Time_Series_Data.md

@@ -24,28 +24,28 @@
 
 万物互联的今天，物联网场景、工业场景等各类场景都在进行数字化转型，人们通过在各类设备上安装传感器对设备的各类状态进行采集。如电机采集电压、电流，风机的叶片转速、角速度、发电功率；车辆采集经纬度、速度、油耗；桥梁的振动频率、挠度、位移量等。传感器的数据采集，已经渗透在各个行业中。
 
-![](https://alioss.timecho.com/docs/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png)
+![](/img/%E6%97%B6%E5%BA%8F%E6%95%B0%E6%8D%AE%E4%BB%8B%E7%BB%8D.png)
 
 
 
 通常来说，我们把每个采集点位叫做一个**测点（ 也叫物理量、时间序列、时间线、信号量、指标、测量值等）**，每个测点都在随时间的推移不断收集到新的数据信息，从而构成了一条**时间序列**。用表格的方式，每个时间序列就是一个由时间、值两列形成的表格；用图形化的方式，每个时间序列就是一个随时间推移形成的走势图，也可以形象的称之为设备的“心电图”。
 
-![](https://alioss.timecho.com/docs/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png)
+![](/img/%E5%BF%83%E7%94%B5%E5%9B%BE1.png)
 
 传感器产生的海量时序数据是各行各业数字化转型的基础，因此我们对时序数据的模型梳理主要围绕设备、传感器展开。
 
 ## 时序数据中的关键概念有哪些？
 
 时序数据中主要涉及的概念由下至上可分为：数据点、测点、设备。
 
-![](https://alioss.timecho.com/docs/img/%E7%99%BD%E6%9D%BF.png)
+![](/img/%E7%99%BD%E6%9D%BF.png)
 
 ### 数据点
 
 - 定义：由一个时间戳和一个数值组成，其中时间戳为 long 类型，数值可以为 BOOLEAN、FLOAT、INT32 等各种类型。
 - 示例：如上图中表格形式的时间序列的一行，或图形形式的时间序列的一个点，就是一个数据点。
 
-![](https://alioss.timecho.com/docs/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png)
+![](/img/%E6%95%B0%E6%8D%AE%E7%82%B9.png)
 
 ### 测点
 

src/zh/UserGuide/v1.x/QuickStart/QuickStart.md

@@ -22,7 +22,7 @@
 
 ## 数据示例
 
-![](https://alioss.timecho.com/docs/img/WX20240628-173452@2x.png)
+![](/img/WX20240628-173452@2x.png)
 
 ## 安装方式