add point number count method in timecho

src/.vuepress/sidebar/V2.0.x/en-Table.ts

@@ -38,7 +38,7 @@ export const enSidebar = {
       prefix: 'Background-knowledge/',
       children: [
         { text: 'Common Concepts', link: 'Cluster-Concept_apache' },
-        { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data' },
+        { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data_apache' },
         { text: 'Modeling Scheme Design', link: 'Data-Model-and-Terminology_apache' },
         { text: 'Data Type', link: 'Data-Type_apache' },
       ],

src/.vuepress/sidebar/V2.0.x/en-Tree.ts

@@ -40,7 +40,7 @@ export const enSidebar = {
         { text: 'Common Concepts', link: 'Cluster-Concept_apache' },
         {
           text: 'Timeseries Data Model',
-          link: 'Navigating_Time_Series_Data',
+          link: 'Navigating_Time_Series_Data_apache',
         },
         {
           text: 'Modeling Scheme Design',

src/.vuepress/sidebar/V2.0.x/zh-Table.ts

@@ -38,7 +38,7 @@ export const zhSidebar = {
       prefix: 'Background-knowledge/',
       children: [
         { text: '常见概念', link: 'Cluster-Concept_apache' },
-        { text: '时序数据模型', link: 'Navigating_Time_Series_Data' },
+        { text: '时序数据模型', link: 'Navigating_Time_Series_Data_apache' },
         { text: '建模方案设计', link: 'Data-Model-and-Terminology_apache' },
         { text: '数据类型', link: 'Data-Type_apache' },
       ],

src/.vuepress/sidebar/V2.0.x/zh-Tree.ts

@@ -38,7 +38,7 @@ export const zhSidebar = {
       prefix: 'Background-knowledge/',
       children: [
         { text: '常见概念', link: 'Cluster-Concept_apache' },
-        { text: '时序数据模型', link: 'Navigating_Time_Series_Data' },
+        { text: '时序数据模型', link: 'Navigating_Time_Series_Data_apache' },
         { text: '建模方案设计', link: 'Data-Model-and-Terminology_apache' },
         { text: '数据类型', link: 'Data-Type' },
       ],

src/.vuepress/sidebar_timecho/V2.0.x/en-Table.ts

@@ -38,7 +38,7 @@ export const enSidebar = {
       prefix: 'Background-knowledge/',
       children: [
         { text: 'Common Concepts', link: 'Cluster-Concept_timecho' },
-        { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data' },
+        { text: 'Timeseries Data Model', link: 'Navigating_Time_Series_Data_timecho' },
         { text: 'Modeling Scheme Design', link: 'Data-Model-and-Terminology_timecho' },
         { text: 'Data Type', link: 'Data-Type_timecho' },
       ],

src/.vuepress/sidebar_timecho/V2.0.x/en-Tree.ts

@@ -40,7 +40,7 @@ export const enSidebar = {
         { text: 'Common Concepts', link: 'Cluster-Concept_timecho' },
         {
           text: 'Timeseries Data Model',
-          link: 'Navigating_Time_Series_Data',
+          link: 'Navigating_Time_Series_Data_timehco',
         },
         {
           text: 'Modeling Scheme Design',

src/.vuepress/sidebar_timecho/V2.0.x/zh-Table.ts

@@ -38,7 +38,7 @@ export const zhSidebar = {
       prefix: 'Background-knowledge/',
       children: [
         { text: '常见概念', link: 'Cluster-Concept_timecho' },
-        { text: '时序数据模型', link: 'Navigating_Time_Series_Data' },
+        { text: '时序数据模型', link: 'Navigating_Time_Series_Data_timecho' },
         { text: '建模方案设计', link: 'Data-Model-and-Terminology_timecho' },
         { text: '数据类型', link: 'Data-Type_timecho' },
       ],

src/.vuepress/sidebar_timecho/V2.0.x/zh-Tree.ts

@@ -38,7 +38,7 @@ export const zhSidebar = {
       prefix: 'Background-knowledge/',
       children: [
         { text: '常见概念', link: 'Cluster-Concept_timecho' },
-        { text: '时序数据模型', link: 'Navigating_Time_Series_Data' },
+        { text: '时序数据模型', link: 'Navigating_Time_Series_Data_timehco' },
         { text: '建模方案设计', link: 'Data-Model-and-Terminology_timecho' },
         { text: '数据类型', link: 'Data-Type' },
       ],

src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_apache.md

@@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios
 
 ## 1. Time Series Data Mode
 
-Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md)
+Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_apache.md)
 
 ## 2. Tree-Table Twin Mode in IoTDB
 

src/UserGuide/Master/Table/Background-knowledge/Data-Model-and-Terminology_timecho.md

@@ -25,7 +25,7 @@ This section introduces how to transform time series data application scenarios
 
 ## 1. Time Series Data Mode
 
-Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data.md)
+Before designing an IoTDB data mode, it's essential to understand time series data and its underlying structure. For more details, refer to: [Time Series Data Mode](../Background-knowledge/Navigating_Time_Series_Data_timecho.md)
 
 ## 2. Tree-Table Twin Mode in IoTDB
 

src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data.md

@@ -1,3 +1,6 @@
+---
+redirectTo: Navigating_Time_Series_Data_apache.html
+---
 <!--
 
     Licensed to the Apache Software Foundation (ASF) under one
@@ -17,35 +20,4 @@
     specific language governing permissions and limitations
     under the License.
 
--->
-# Timeseries Data Model
-
-## 1. What is Time Series Data?
-
-In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example:
-
-- **Motors** record voltage and current.
-- **Wind Turbines** track blade speed, angular velocity, and power output.
-- **Vehicles** capture GPS coordinates, speed, and fuel consumption.
-- **Bridges** monitor vibration frequency, deflection, and displacement.
-
-Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**.
-
-![](/img/time-series-data-en-01.png)
-
-Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device.
-
-![](/img/time-series-data-en-02.png)
-
-Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**.
-
-## 2. Key Concepts in Time Series Data
-
-Several fundamental concepts define time-series data:
-
-| **Device**              | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:<br>- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.<br>- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.<br>- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).<br>- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. |
-| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
-| **FIELD**   | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:<br>- Energy and power: Current, voltage, wind speed, rotational speed.<br>- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.<br>- Manufacturing: Temperature, humidity.                                                                                                                                                                                                                                                            |
-| **Data Point**        | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32. <br>In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.<br> <img src="/img/time-series-data-en-03.png" alt="" style="width: 70%;"/>                                                                                                                                                                                                                                                                            |
-| **Frequency**       | The sampling frequency determines how often a sensor records data within a given timeframe.<br>For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second).                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
-| **TTL**  | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.<br>IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:<br>- Manage disk space efficiently, preventing storage overflow.<br>- Maintain high query performance.<br>- Reduce memory resource consumption.                                                                                                                                                                                                                                                                                                      |
+-->

src/UserGuide/Master/Table/Background-knowledge/Navigating_Time_Series_Data_apache.md

@@ -0,0 +1,51 @@
+<!--
+
+    Licensed to the Apache Software Foundation (ASF) under one
+    or more contributor license agreements.  See the NOTICE file
+    distributed with this work for additional information
+    regarding copyright ownership.  The ASF licenses this file
+    to you under the Apache License, Version 2.0 (the
+    "License"); you may not use this file except in compliance
+    with the License.  You may obtain a copy of the License at
+
+        http://www.apache.org/licenses/LICENSE-2.0
+
+    Unless required by applicable law or agreed to in writing,
+    software distributed under the License is distributed on an
+    "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, either express or implied.  See the License for the
+    specific language governing permissions and limitations
+    under the License.
+
+-->
+# Timeseries Data Model
+
+## 1. What is Time Series Data?
+
+In today's interconnected world, industries such as the Internet of Things (IoT) and manufacturing are undergoing rapid digital transformation. Sensors are widely deployed on various devices to collect real-time operational data. For example:
+
+- **Motors** record voltage and current.
+- **Wind Turbines** track blade speed, angular velocity, and power output.
+- **Vehicles** capture GPS coordinates, speed, and fuel consumption.
+- **Bridges** monitor vibration frequency, deflection, and displacement.
+
+Sensor data collection has permeated almost every industry, generating vast amounts of **time series data**.
+
+![](/img/time-series-data-en-01.png)
+
+Each data collection point is referred to as a **measurement point** (also known as a physical quantity, time series, signal, metric, or measurement value). As time progresses, new data is continuously recorded for each measurement point, forming a **time series**. In tabular form, a time series consists of two columns: **timestamp** and **value**. When visualized, a time series appears as a trend chart over time, resembling an "electrocardiogram" of a device.
+
+![](/img/time-series-data-en-02.png)
+
+Given the vast amount of time-series data generated by sensors, structuring this data effectively is essential for digital transformation across industries. Therefore, time-series data modeling is primarily centered around **devices** and **sensors**.
+
+## 2. Key Concepts in Time Series Data
+
+Several fundamental concepts define time-series data:
+
+| **Device**              | Also known as an entity or equipment, a device is a real-world object that generates time-series data. In IoTDB, a device serves as a logical grouping of multiple time series. A device could be a physical machine, a measuring instrument, or a collection of sensors. Examples include:<br>- Energy sector: A wind turbine, identified by parameters such as region, power station, line, model, and instance.<br>- Manufacturing sector: A robotic arm, uniquely identified by an IoT platform-assigned ID.<br>- Connected vehicles: A car, identified by its Vehicle Identification Number (VIN).<br>- Monitoring systems: A CPU, identified by attributes such as data center, rack, hostname, and device type. |
+| ------------------------------- |------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **FIELD**   | Also referred to as a physical quantity, signal, metric, or status point, a field represents a specific measurable property recorded by a sensor. Each field corresponds to a measurement point that periodically captures environmental data. Examples include:<br>- Energy and power: Current, voltage, wind speed, rotational speed.<br>- Connected vehicles: Fuel level, vehicle speed, latitude, longitude.<br>- Manufacturing: Temperature, humidity.                                                                                                                                                                                                                                                            |
+| **Data Point**        | A data point consists of a timestamp and a value. The timestamp is typically stored as a long integer, while the value can be of various data types such as BOOLEAN, FLOAT, or INT32. <br>In tabular format, a data point corresponds to a single row in a time-series dataset, while in graphical representation, it is a single point on a time-series chart.<br> <img src="/img/time-series-data-en-03.png" alt="" style="width: 70%;"/>                                                                                                                                                                                                                                                                            |
+| **Frequency**       | The sampling frequency determines how often a sensor records data within a given timeframe.<br>For example, if a temperature sensor records data once per second, its sampling frequency is 1Hz (1 sample per second).                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 |
+| **TTL**  | TTL (Time-to-Live) defines the retention period of stored data. Once the TTL expires, the data is automatically deleted.<br>IoTDB allows different TTL values for different datasets, enabling automated, periodic data deletion. Proper TTL configuration helps:<br>- Manage disk space efficiently, preventing storage overflow.<br>- Maintain high query performance.<br>- Reduce memory resource consumption.                                                                                                                                                                                                                                                                                                      |