remove independent activation

Author: leto-b

src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### 1.2 Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### 1.3 Deployment mode
 <div >

src/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### 1.2 Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### 1.3 Deployment mode
 <div >

src/UserGuide/V1.3.x/Deployment-and-Maintenance/AINode_Deployment_apache.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### Deployment mode
 <div >

src/UserGuide/V1.3.x/Deployment-and-Maintenance/AINode_Deployment_timecho.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### Deployment mode
 <div >

src/UserGuide/dev-1.3/Deployment-and-Maintenance/AINode_Deployment_apache.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### Deployment mode
 <div >

src/UserGuide/dev-1.3/Deployment-and-Maintenance/AINode_Deployment_timecho.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### Deployment mode
 <div >

src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_apache.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### 1.2 Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### 1.3 Deployment mode
 <div >

src/UserGuide/latest/Deployment-and-Maintenance/AINode_Deployment_timecho.md

@@ -27,7 +27,7 @@
  AINode is the third type of endogenous node provided by IoTDB after the Configurable Node and DataNode. This node extends its ability to perform machine learning analysis on time series by interacting with the DataNode and Configurable Node of the IoTDB cluster. It supports the introduction of existing machine learning models from external sources for registration and the use of registered models to complete time series analysis tasks on specified time series data through simple SQL statements. The creation, management, and inference of models are integrated into the database engine. Currently, machine learning algorithms or self-developed models are available for common time series analysis scenarios, such as prediction and anomaly detection.
 
 ### 1.2 Delivery Method
- It is an additional package outside the IoTDB cluster, with independent installation and activation.
+ It is an additional package outside the IoTDB cluster, with independent installation.
 
 ### 1.3 Deployment mode
 <div >

src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_apache.md

@@ -27,7 +27,7 @@
 AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，该节点通过与 IoTDB 集群的 DataNode、ConfigNode 的交互，扩展了对时间序列进行机器学习分析的能力，支持从外部引入已有机器学习模型进行注册，并使用注册的模型在指定时序数据上通过简单 SQL 语句完成时序分析任务的过程，将模型的创建、管理及推理融合在数据库引擎中。目前已提供常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
 
 ### 1.2 交付方式
- 是 IoTDB 集群外的额外套件，独立安装包，独立激活。
+ 是 IoTDB 集群外的额外套件，独立安装包。
 
 ### 1.3 部署模式
 <div >

src/zh/UserGuide/Master/Tree/Deployment-and-Maintenance/AINode_Deployment_timecho.md

@@ -27,7 +27,7 @@
 AINode 是 IoTDB 在 ConfigNode、DataNode 后提供的第三种内生节点，该节点通过与 IoTDB 集群的 DataNode、ConfigNode 的交互，扩展了对时间序列进行机器学习分析的能力，支持从外部引入已有机器学习模型进行注册，并使用注册的模型在指定时序数据上通过简单 SQL 语句完成时序分析任务的过程，将模型的创建、管理及推理融合在数据库引擎中。目前已提供常见时序分析场景（例如预测与异常检测）的机器学习算法或自研模型。
 
 ### 1.2 交付方式
- 是 IoTDB 集群外的额外套件，独立安装包，独立激活。
+ 是 IoTDB 集群外的额外套件，独立安装包。
 
 ### 1.3 部署模式
 <div >