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new 99ed1590 Update the first paragraph of the AINode page (#707)
99ed1590 is described below
commit 99ed15908a0c45db4a35d94f8e5af16fec8c3581
Author: W1y1r <[email protected]>
AuthorDate: Tue Apr 22 12:18:17 2025 +0800
Update the first paragraph of the AINode page (#707)
---
src/UserGuide/Master/Tree/AI-capability/AINode_apache.md | 3 ++-
src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md | 3 ++-
src/UserGuide/V1.3.x/AI-capability/AINode_apache.md | 3 ++-
src/UserGuide/V1.3.x/AI-capability/AINode_timecho.md | 3 ++-
src/UserGuide/dev-1.3/AI-capability/AINode_apache.md | 3 ++-
src/UserGuide/dev-1.3/AI-capability/AINode_timecho.md | 3 ++-
src/UserGuide/latest/AI-capability/AINode_apache.md | 2 +-
src/UserGuide/latest/AI-capability/AINode_timecho.md | 3 ++-
src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md | 2 +-
src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md | 2 +-
src/zh/UserGuide/V1.3.x/AI-capability/AINode_apache.md | 2 +-
src/zh/UserGuide/V1.3.x/AI-capability/AINode_timecho.md | 2 +-
src/zh/UserGuide/dev-1.3/AI-capability/AINode_apache.md | 2 +-
src/zh/UserGuide/dev-1.3/AI-capability/AINode_timecho.md | 2 +-
src/zh/UserGuide/latest/AI-capability/AINode_apache.md | 2 +-
src/zh/UserGuide/latest/AI-capability/AINode_timecho.md | 2 +-
16 files changed, 23 insertions(+), 16 deletions(-)
diff --git a/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md
b/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md
index 95fcccd3..6b17ebb4 100644
--- a/src/UserGuide/Master/Tree/AI-capability/AINode_apache.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md
b/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md
index 204f91cf..eda8713c 100644
--- a/src/UserGuide/Master/Tree/AI-capability/AINode_timecho.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/V1.3.x/AI-capability/AINode_apache.md
b/src/UserGuide/V1.3.x/AI-capability/AINode_apache.md
index 95fcccd3..6b17ebb4 100644
--- a/src/UserGuide/V1.3.x/AI-capability/AINode_apache.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/V1.3.x/AI-capability/AINode_timecho.md
b/src/UserGuide/V1.3.x/AI-capability/AINode_timecho.md
index 204f91cf..eda8713c 100644
--- a/src/UserGuide/V1.3.x/AI-capability/AINode_timecho.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/dev-1.3/AI-capability/AINode_apache.md
b/src/UserGuide/dev-1.3/AI-capability/AINode_apache.md
index 95fcccd3..6b17ebb4 100644
--- a/src/UserGuide/dev-1.3/AI-capability/AINode_apache.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/dev-1.3/AI-capability/AINode_timecho.md
b/src/UserGuide/dev-1.3/AI-capability/AINode_timecho.md
index 204f91cf..eda8713c 100644
--- a/src/UserGuide/dev-1.3/AI-capability/AINode_timecho.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/latest/AI-capability/AINode_apache.md
b/src/UserGuide/latest/AI-capability/AINode_apache.md
index 95fcccd3..44b0d3fe 100644
--- a/src/UserGuide/latest/AI-capability/AINode_apache.md
+++ b/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 [...]
+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
diff --git a/src/UserGuide/latest/AI-capability/AINode_timecho.md
b/src/UserGuide/latest/AI-capability/AINode_timecho.md
index 204f91cf..eda8713c 100644
--- a/src/UserGuide/latest/AI-capability/AINode_timecho.md
+++ b/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 [...]
+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
diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md
b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md
index 33441888..60ee30ff 100644
--- a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_apache.md
+++ b/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：
diff --git a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md
b/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md
index f06cc6e3..3ea24544 100644
--- a/src/zh/UserGuide/Master/Tree/AI-capability/AINode_timecho.md
+++ b/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：
diff --git a/src/zh/UserGuide/V1.3.x/AI-capability/AINode_apache.md
b/src/zh/UserGuide/V1.3.x/AI-capability/AINode_apache.md
index b6620803..e88a3e77 100644
--- a/src/zh/UserGuide/V1.3.x/AI-capability/AINode_apache.md
+++ b/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
diff --git a/src/zh/UserGuide/V1.3.x/AI-capability/AINode_timecho.md
b/src/zh/UserGuide/V1.3.x/AI-capability/AINode_timecho.md
index c67c41de..14d8bbf6 100644
--- a/src/zh/UserGuide/V1.3.x/AI-capability/AINode_timecho.md
+++ b/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
diff --git a/src/zh/UserGuide/dev-1.3/AI-capability/AINode_apache.md
b/src/zh/UserGuide/dev-1.3/AI-capability/AINode_apache.md
index b6620803..e88a3e77 100644
--- a/src/zh/UserGuide/dev-1.3/AI-capability/AINode_apache.md
+++ b/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
diff --git a/src/zh/UserGuide/dev-1.3/AI-capability/AINode_timecho.md
b/src/zh/UserGuide/dev-1.3/AI-capability/AINode_timecho.md
index c67c41de..14d8bbf6 100644
--- a/src/zh/UserGuide/dev-1.3/AI-capability/AINode_timecho.md
+++ b/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
diff --git a/src/zh/UserGuide/latest/AI-capability/AINode_apache.md
b/src/zh/UserGuide/latest/AI-capability/AINode_apache.md
index 33441888..60ee30ff 100644
--- a/src/zh/UserGuide/latest/AI-capability/AINode_apache.md
+++ b/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：
diff --git a/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md
b/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md
index f06cc6e3..3ea24544 100644
--- a/src/zh/UserGuide/latest/AI-capability/AINode_timecho.md
+++ b/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：
