Update the first paragraph of the AINode page

src/UserGuide/Master/Tree/AI-capability/AINode_apache.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/UserGuide/V1.3.x/AI-capability/AINode_apache.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/UserGuide/V1.3.x/AI-capability/AINode_timecho.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/UserGuide/dev-1.3/AI-capability/AINode_apache.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/UserGuide/dev-1.3/AI-capability/AINode_timecho.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/UserGuide/latest/AI-capability/AINode_apache.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
 
 The system architecture is shown below:
 ::: center

src/UserGuide/latest/AI-capability/AINode_timecho.md

@@ -21,7 +21,8 @@
 
 # AINode
 
-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).
+AINode is an IoTDB native node designed to support the registration, management, and invocation of large-scale time series models. It comes with industry-leading proprietary time series models such as Timer and Sundial. These models can be invoked through standard SQL statements, enabling real-time inference of time series data at the millisecond level, and supporting application scenarios such as trend forecasting, missing value imputation, and anomaly detection for time series data.
+
 
 The system architecture is shown below:
 ::: center

src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 

src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 

src/zh/UserGuide/V1.3.x/AI-capability/AINode_apache.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 ::: center

src/zh/UserGuide/V1.3.x/AI-capability/AINode_timecho.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 ::: center

src/zh/UserGuide/dev-1.3/AI-capability/AINode_apache.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 ::: center

src/zh/UserGuide/dev-1.3/AI-capability/AINode_timecho.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 ::: center

src/zh/UserGuide/latest/AI-capability/AINode_apache.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：
 

src/zh/UserGuide/latest/AI-capability/AINode_timecho.md

@@ -21,7 +21,7 @@
 
 # AINode
 
-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。
+AINode 是支持时序大模型注册、管理、调用的 IoTDB 原生节点，内置业界领先的自研时序大模型，如 Timer、Sundial 等，可通过标准 SQL 语句进行调用，实现时序数据的毫秒级实时推理，可支持时序趋势预测、缺失值填补、异常值检测等应用场景。
 
 系统架构如下图所示：