adjust the Engilish AINode doc in V1.3 and master (#158)

src/.vuepress/sidebar_timecho/V1.3.x/en.ts

@@ -89,7 +89,7 @@ export const enSidebar = {
         { text: 'Data Sync', link: 'Data-Sync_timecho' },
         { text: 'Tiered Storage', link: 'Tiered-Storage_timecho' },
         { text: 'View', link: 'IoTDB-View_timecho' },
-        { text: 'IoTDB AINode', link: 'IoTDB-AINode_timecho' },
+        { text: 'AINode', link: 'AINode_timecho' },
         { text: 'Database Programming', link: 'Database-Programming' },
         { text: 'Security Management', link: 'Security-Management_timecho' },
         { text: 'Authority Management', link: 'Authority-Management' },

src/.vuepress/sidebar_timecho/V1.3.x/zh.ts

@@ -89,7 +89,7 @@ export const zhSidebar = {
         { text: '数据同步', link: 'Data-Sync_timecho' },
         { text: '多级存储', link: 'Tiered-Storage_timecho' },
         { text: '视图', link: 'IoTDB-View_timecho' },
-        { text: 'AINode机器学习框架', link: 'IoTDB-AINode_timecho' },
+        { text: 'AINode', link: 'AINode_timecho' },
         { text: '数据库编程', link: 'Database-Programming' },
         { text: '安全控制', link: 'Security-Management_timecho' },
         { text: '权限管理', link: 'Authority-Management' },

src/UserGuide/Master/User-Manual/IoTDB-AINode_timecho.md → src/UserGuide/Master/User-Manual/AINode_timecho.md

@@ -19,9 +19,9 @@
 
 -->
 
-# Endogenous Machine Learning Framework (AINode)
+# AINode（Machine Learning Framework）
 
-AINode is the third type of endogenous node provided by IoTDB after ConfigNode and DataNode, which extends the capability of machine learning analysis of time series by interacting with DataNode and ConfigNode of IoTDB cluster, supports the introduction of pre-existing machine learning models from the outside to be registered, and uses the registered models in the It supports the process of introducing existing machine learning models from outside for registration, and using the registered models to complete the time series analysis tasks on the specified time series data through simple SQL statements, which integrates the model creation, management and inference in the database engine. At present, we have provided machine learning algorithms or self-developed models for common timing analysis scenarios (e.g. prediction and anomaly detection).
+AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB, which extends the capability of machine learning analysis of time series by interacting with DataNode and ConfigNode of IoTDB cluster, supports the introduction of pre-existing machine learning models from the outside to be registered, and uses the registered models in the It supports the process of introducing existing machine learning models from outside for registration, and using the registered models to complete the time series analysis tasks on the specified time series data through simple SQL statements, which integrates the model creation, management and inference in the database engine. At present, we have provided machine learning algorithms or self-developed models for common timing analysis scenarios (e.g. prediction and anomaly detection).
 
 The system architecture is shown below:
 ::: center

src/UserGuide/latest/User-Manual/IoTDB-AINode_timecho.md → src/UserGuide/latest/User-Manual/AINode_timecho.md

@@ -19,9 +19,9 @@
 
 -->
 
-# Endogenous Machine Learning Framework (AINode)
+# AINode（Machine Learning Framework）
 
-AINode is the third type of endogenous node provided by IoTDB after ConfigNode and DataNode, which extends the capability of machine learning analysis of time series by interacting with DataNode and ConfigNode of IoTDB cluster, supports the introduction of pre-existing machine learning models from the outside to be registered, and uses the registered models in the It supports the process of introducing existing machine learning models from outside for registration, and using the registered models to complete the time series analysis tasks on the specified time series data through simple SQL statements, which integrates the model creation, management and inference in the database engine. At present, we have provided machine learning algorithms or self-developed models for common timing analysis scenarios (e.g. prediction and anomaly detection).
+AINode is the third internal node after ConfigNode and DataNode in Apache IoTDB, which extends the capability of machine learning analysis of time series by interacting with DataNode and ConfigNode of IoTDB cluster, supports the introduction of pre-existing machine learning models from the outside to be registered, and uses the registered models in the It supports the process of introducing existing machine learning models from outside for registration, and using the registered models to complete the time series analysis tasks on the specified time series data through simple SQL statements, which integrates the model creation, management and inference in the database engine. At present, we have provided machine learning algorithms or self-developed models for common timing analysis scenarios (e.g. prediction and anomaly detection).
 
 The system architecture is shown below:
 ::: center