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new f08013cb fix product introduction in apache (#870)
f08013cb is described below
commit f08013cb10a225ff33634b4c28ca0fa919e0ef27
Author: leto-b <[email protected]>
AuthorDate: Fri Sep 12 15:07:24 2025 +0800
fix product introduction in apache (#870)
---
.../IoTDB-Introduction_apache.md | 2 +-
.../IoTDB-Introduction_apache.md | 78 ++++++++++++++--------
.../IoTDB-Introduction_apache.md | 2 +-
.../IoTDB-Introduction_apache.md | 78 ++++++++++++++--------
.../IoTDB-Introduction_apache.md | 2 +-
.../IoTDB-Introduction_apache.md | 2 +-
.../IoTDB-Introduction_apache.md | 2 +-
.../IoTDB-Introduction_apache.md | 2 +-
8 files changed, 108 insertions(+), 60 deletions(-)
diff --git
a/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
index 180121ff..e225263f 100644
--- a/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++ b/src/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -44,7 +44,7 @@ Key components include:
3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified
engine for intelligent analysis, supporting model training, data management,
and integration with machine/deep learning frameworks.
-## 2. TimechoDB Architecture
+## 2. IoTDB Architecture
The diagram below illustrates a typical IoTDB cluster deployment (3
ConfigNodes and 3 DataNodes):
diff --git
a/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
index 5684a8de..e225263f 100644
--- a/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++ b/src/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -21,57 +21,81 @@
# IoTDB Introduction
-Apache IoTDB is a low-cost, high-performance native temporal database for the
Internet of Things. It can solve various problems encountered by enterprises
when building IoT big data platforms to manage time-series data, such as
complex application scenarios, large data volumes, high sampling frequencies,
high amount of unaligned data, long data processing time, diverse analysis
requirements, and high storage and operation costs.
+Apache IoTDB is a low-cost, high-performance IoT-native time-series database.
It addresses challenges faced by enterprises in managing time-series data for
IoT big data platforms, including complex application scenarios, massive data
volumes, high sampling frequencies, frequent out-of-order data, time-consuming
data processing, diverse analytical requirements, and high storage and
maintenance costs.
-- Github repository link: https://github.com/apache/iotdb
+- GitHub Repository:
[https://github.com/apache/iotdb](https://github.com/apache/iotdb)
+- Open-Source Installation Packages:
[https://iotdb.apache.org/Download/](https://iotdb.apache.org/Download/)
+- Installation, Deployment, and Usage Documentation: [Quick
Start](../QuickStart/QuickStart_apache.md)
-- Open source installation package download:
https://iotdb.apache.org/zh/Download/
-- Installation, deployment, and usage documentation:
[QuickStart](../QuickStart/QuickStart_apache.md)
+## 1. Product Ecosystem
+The IoTDB ecosystem consists of multiple components designed to efficiently
manage and analyze massive IoT-generated time-series data.
-## 1. Product Components
+<div style="text-align: center;">
+ <img src="/img/Introduction-en-apache.png"
alt="Introduction-en-apache.png" style="width: 90%;"/>
+</div>
-IoTDB products consist of several components that help users efficiently
manage and analyze the massive amount of time-series data generated by the IoT.
-<div style="text-align: center;">
- <img src="/img/Introduction-en-apache.png"
alt="Introduction-en-timecho.png" style="width: 90%;"/>
+Key components include:
-</div>
+1. **Time-Series Database (Apache IoTDB)**: The core component for time-series
data storage, offering high compression, rich query capabilities, real-time
stream processing, high availability, and scalability. It provides security
guarantees, configuration tools, multi-language APIs, and integration with
external systems for building business applications.
+2. **Time-Series File Format (Apache TsFile)**: A specialized storage format
for time-series data, enabling efficient storage and querying. TsFile underpins
IoTDB and AINode, unifying data management across collection, storage, and
analysis phases.
+3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified
engine for intelligent analysis, supporting model training, data management,
and integration with machine/deep learning frameworks.
-1. Time-series Database (Apache IoTDB): The core component for time-series
data storage, it provides users with high-compression storage capabilities,
rich time-series querying capabilities, real-time stream processing
capabilities, and ensures high availability of data and high scalability of
clusters. It also offers comprehensive security protection. Additionally, IoTDB
provides users with a variety of application tools for easy configuration and
management of the system; multi-languag [...]
-2. Time-series Data Standard File Format (Apache TsFile): This file format is
specifically designed for time-series data and can efficiently store and query
massive amounts of time-series data. Currently, the underlying storage files
for modules such as IoTDB and AINode are supported by Apache TsFile. With
TsFile, users can uniformly use the same file format for data management during
the collection, management, application, and analysis phases, greatly
simplifying the entire process fro [...]
+## 2. IoTDB Architecture
-3. Time-series Model Training and Inference Integrated Engine (IoTDB AINode):
For intelligent analysis scenarios, IoTDB provides the AINode time-series model
training and inference integrated engine, which offers a complete set of
time-series data analysis tools. The underlying engine supports model training
tasks and data management, including machine learning and deep learning. With
these tools, users can conduct in-depth analysis of the data stored in IoTDB
and extract its value.
+The diagram below illustrates a typical IoTDB cluster deployment (3
ConfigNodes and 3 DataNodes):
+<img src="/img/Cluster-Concept03.png" alt="" style="width: 60%;"/>
-## 2. Product Features
-TimechoDB has the following advantages and characteristics:
+## 3. Key Features
-- Flexible deployment methods: Support for one-click cloud deployment,
out-of-the-box use after unzipping at the terminal, and seamless connection
between terminal and cloud (data cloud synchronization tool).
+Apache IoTDB offers the following advantages:
-- Low hardware cost storage solution: Supports high compression ratio disk
storage, no need to distinguish between historical and real-time databases,
unified data management.
+- **Flexible Deployment**:
+ - One-click cloud deployment
+ - Out-of-the-box terminal usage
+ - Seamless terminal-cloud synchronization
-- Hierarchical sensor organization and management: Supports modeling in the
system according to the actual hierarchical relationship of devices to achieve
alignment with the industrial sensor management structure, and supports
directory viewing, search, and other capabilities for hierarchical structures.
+- **Cost-Effective Storage**:
+ - High-compression disk storage
+ - Unified management of historical and real-time data
-- High throughput data reading and writing: supports access to millions of
devices, high-speed data reading and writing, out of unaligned/multi frequency
acquisition, and other complex industrial reading and writing scenarios.
+- **Hierarchical Measurement Point Management**:
+ - Aligns with industrial device hierarchies
+ - Supports directory browsing and search
-- Rich time series query semantics: Supports a native computation engine for
time series data, supports timestamp alignment during queries, provides nearly
a hundred built-in aggregation and time series calculation functions, and
supports time series feature analysis and AI capabilities.
+- **High Throughput Read/Write**:
+ - Supports millions of devices
+ - Handles high-speed, out-of-order, and multi-frequency data ingestion
-- Highly available distributed system: Supports HA distributed architecture,
the system provides 7*24 hours uninterrupted real-time database services, the
failure of a physical node or network fault will not affect the normal
operation of the system; supports the addition, deletion, or overheating of
physical nodes, the system will automatically perform load balancing of
computing/storage resources; supports heterogeneous environments, servers of
different types and different performance [...]
+- **Rich Query Capabilities**:
+ - Native time-series computation engine
+ - Timestamp alignment during queries
+ - Over 100 built-in aggregation and time-series functions
+ - AI-ready time-series feature analysis
-- Extremely low usage and operation threshold: supports SQL like language,
provides multi language native secondary development interface, and has a
complete tool system such as console.
+- **High Availability & Scalability**:
+ - HA distributed architecture with 24/7 uptime
+ - Automatic load balancing for node scaling
+ - Heterogeneous cluster support
-- Rich ecological environment docking: Supports docking with big data
ecosystem components such as Hadoop, Spark, and supports equipment management
and visualization tools such as Grafana, Thingsboard, DataEase.
+- **Low Learning Curve**:
+ - SQL-like query language
+ - Multi-language SDKs
+ - Comprehensive toolchain (e.g., console)
-## 3. Commercial version
+- **Ecosystem Integration**:
+ - Hadoop, Spark, Grafana, ThingsBoard, DataEase, etc.
-Timecho provides the original commercial product TimechoDB based on the open
source version of Apache IoTDB, providing enterprise level products and
services for enterprises and commercial customers. It can solve various
problems encountered by enterprises when building IoT big data platforms to
manage time-series data, such as complex application scenarios, large data
volumes, high sampling frequencies, high amount of unaligned data, long data
processing time, diverse analysis requireme [...]
-Timecho provides a more diverse range of product features, stronger
performance and stability, and a richer set of utility tools based on
TimechoDB. It also offers comprehensive enterprise services to users, thereby
providing commercial customers with more powerful product capabilities and a
higher quality of development, operations, and usage experience.
+## 4. TimechoDB
-- Timecho Official website：https://www.timecho.com/
+Timecho Technology has developed **TimechoDB**, a commercial product based on
Apache IoTDB, to provide enterprise-grade solutions and services for businesses
and commercial clients. TimechoDB addresses the multifaceted challenges
enterprises face when building IoT big data platforms for managing time-series
data, including complex application scenarios, massive data volumes, high
sampling frequencies, frequent out-of-order data, time-consuming data
processing, diverse analytical require [...]
-- TimechoDB installation, deployment and usage
documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html)
\ No newline at end of file
+Leveraging **TimechoDB**, Timecho Technology offers a broader range of product
features, enhanced performance and stability, and a richer suite of efficiency
tools. Additionally, it provides comprehensive enterprise services, delivering
commercial clients with superior product capabilities and an optimized
experience in development, operation, and usage.
+- **Timecho Technology Official Website**:
[https://www.timecho.com/](https://www.timecho.com/)
+- **TimechoDB Documentation**: [Quick
Start](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html)
diff --git
a/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
index 180121ff..e225263f 100644
--- a/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++ b/src/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -44,7 +44,7 @@ Key components include:
3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified
engine for intelligent analysis, supporting model training, data management,
and integration with machine/deep learning frameworks.
-## 2. TimechoDB Architecture
+## 2. IoTDB Architecture
The diagram below illustrates a typical IoTDB cluster deployment (3
ConfigNodes and 3 DataNodes):
diff --git
a/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
index 5684a8de..e225263f 100644
--- a/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++ b/src/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -21,57 +21,81 @@
# IoTDB Introduction
-Apache IoTDB is a low-cost, high-performance native temporal database for the
Internet of Things. It can solve various problems encountered by enterprises
when building IoT big data platforms to manage time-series data, such as
complex application scenarios, large data volumes, high sampling frequencies,
high amount of unaligned data, long data processing time, diverse analysis
requirements, and high storage and operation costs.
+Apache IoTDB is a low-cost, high-performance IoT-native time-series database.
It addresses challenges faced by enterprises in managing time-series data for
IoT big data platforms, including complex application scenarios, massive data
volumes, high sampling frequencies, frequent out-of-order data, time-consuming
data processing, diverse analytical requirements, and high storage and
maintenance costs.
-- Github repository link: https://github.com/apache/iotdb
+- GitHub Repository:
[https://github.com/apache/iotdb](https://github.com/apache/iotdb)
+- Open-Source Installation Packages:
[https://iotdb.apache.org/Download/](https://iotdb.apache.org/Download/)
+- Installation, Deployment, and Usage Documentation: [Quick
Start](../QuickStart/QuickStart_apache.md)
-- Open source installation package download:
https://iotdb.apache.org/zh/Download/
-- Installation, deployment, and usage documentation:
[QuickStart](../QuickStart/QuickStart_apache.md)
+## 1. Product Ecosystem
+The IoTDB ecosystem consists of multiple components designed to efficiently
manage and analyze massive IoT-generated time-series data.
-## 1. Product Components
+<div style="text-align: center;">
+ <img src="/img/Introduction-en-apache.png"
alt="Introduction-en-apache.png" style="width: 90%;"/>
+</div>
-IoTDB products consist of several components that help users efficiently
manage and analyze the massive amount of time-series data generated by the IoT.
-<div style="text-align: center;">
- <img src="/img/Introduction-en-apache.png"
alt="Introduction-en-timecho.png" style="width: 90%;"/>
+Key components include:
-</div>
+1. **Time-Series Database (Apache IoTDB)**: The core component for time-series
data storage, offering high compression, rich query capabilities, real-time
stream processing, high availability, and scalability. It provides security
guarantees, configuration tools, multi-language APIs, and integration with
external systems for building business applications.
+2. **Time-Series File Format (Apache TsFile)**: A specialized storage format
for time-series data, enabling efficient storage and querying. TsFile underpins
IoTDB and AINode, unifying data management across collection, storage, and
analysis phases.
+3. **Time-Series Model Training-Inference Engine (IoTDB AINode)**: A unified
engine for intelligent analysis, supporting model training, data management,
and integration with machine/deep learning frameworks.
-1. Time-series Database (Apache IoTDB): The core component for time-series
data storage, it provides users with high-compression storage capabilities,
rich time-series querying capabilities, real-time stream processing
capabilities, and ensures high availability of data and high scalability of
clusters. It also offers comprehensive security protection. Additionally, IoTDB
provides users with a variety of application tools for easy configuration and
management of the system; multi-languag [...]
-2. Time-series Data Standard File Format (Apache TsFile): This file format is
specifically designed for time-series data and can efficiently store and query
massive amounts of time-series data. Currently, the underlying storage files
for modules such as IoTDB and AINode are supported by Apache TsFile. With
TsFile, users can uniformly use the same file format for data management during
the collection, management, application, and analysis phases, greatly
simplifying the entire process fro [...]
+## 2. IoTDB Architecture
-3. Time-series Model Training and Inference Integrated Engine (IoTDB AINode):
For intelligent analysis scenarios, IoTDB provides the AINode time-series model
training and inference integrated engine, which offers a complete set of
time-series data analysis tools. The underlying engine supports model training
tasks and data management, including machine learning and deep learning. With
these tools, users can conduct in-depth analysis of the data stored in IoTDB
and extract its value.
+The diagram below illustrates a typical IoTDB cluster deployment (3
ConfigNodes and 3 DataNodes):
+<img src="/img/Cluster-Concept03.png" alt="" style="width: 60%;"/>
-## 2. Product Features
-TimechoDB has the following advantages and characteristics:
+## 3. Key Features
-- Flexible deployment methods: Support for one-click cloud deployment,
out-of-the-box use after unzipping at the terminal, and seamless connection
between terminal and cloud (data cloud synchronization tool).
+Apache IoTDB offers the following advantages:
-- Low hardware cost storage solution: Supports high compression ratio disk
storage, no need to distinguish between historical and real-time databases,
unified data management.
+- **Flexible Deployment**:
+ - One-click cloud deployment
+ - Out-of-the-box terminal usage
+ - Seamless terminal-cloud synchronization
-- Hierarchical sensor organization and management: Supports modeling in the
system according to the actual hierarchical relationship of devices to achieve
alignment with the industrial sensor management structure, and supports
directory viewing, search, and other capabilities for hierarchical structures.
+- **Cost-Effective Storage**:
+ - High-compression disk storage
+ - Unified management of historical and real-time data
-- High throughput data reading and writing: supports access to millions of
devices, high-speed data reading and writing, out of unaligned/multi frequency
acquisition, and other complex industrial reading and writing scenarios.
+- **Hierarchical Measurement Point Management**:
+ - Aligns with industrial device hierarchies
+ - Supports directory browsing and search
-- Rich time series query semantics: Supports a native computation engine for
time series data, supports timestamp alignment during queries, provides nearly
a hundred built-in aggregation and time series calculation functions, and
supports time series feature analysis and AI capabilities.
+- **High Throughput Read/Write**:
+ - Supports millions of devices
+ - Handles high-speed, out-of-order, and multi-frequency data ingestion
-- Highly available distributed system: Supports HA distributed architecture,
the system provides 7*24 hours uninterrupted real-time database services, the
failure of a physical node or network fault will not affect the normal
operation of the system; supports the addition, deletion, or overheating of
physical nodes, the system will automatically perform load balancing of
computing/storage resources; supports heterogeneous environments, servers of
different types and different performance [...]
+- **Rich Query Capabilities**:
+ - Native time-series computation engine
+ - Timestamp alignment during queries
+ - Over 100 built-in aggregation and time-series functions
+ - AI-ready time-series feature analysis
-- Extremely low usage and operation threshold: supports SQL like language,
provides multi language native secondary development interface, and has a
complete tool system such as console.
+- **High Availability & Scalability**:
+ - HA distributed architecture with 24/7 uptime
+ - Automatic load balancing for node scaling
+ - Heterogeneous cluster support
-- Rich ecological environment docking: Supports docking with big data
ecosystem components such as Hadoop, Spark, and supports equipment management
and visualization tools such as Grafana, Thingsboard, DataEase.
+- **Low Learning Curve**:
+ - SQL-like query language
+ - Multi-language SDKs
+ - Comprehensive toolchain (e.g., console)
-## 3. Commercial version
+- **Ecosystem Integration**:
+ - Hadoop, Spark, Grafana, ThingsBoard, DataEase, etc.
-Timecho provides the original commercial product TimechoDB based on the open
source version of Apache IoTDB, providing enterprise level products and
services for enterprises and commercial customers. It can solve various
problems encountered by enterprises when building IoT big data platforms to
manage time-series data, such as complex application scenarios, large data
volumes, high sampling frequencies, high amount of unaligned data, long data
processing time, diverse analysis requireme [...]
-Timecho provides a more diverse range of product features, stronger
performance and stability, and a richer set of utility tools based on
TimechoDB. It also offers comprehensive enterprise services to users, thereby
providing commercial customers with more powerful product capabilities and a
higher quality of development, operations, and usage experience.
+## 4. TimechoDB
-- Timecho Official website：https://www.timecho.com/
+Timecho Technology has developed **TimechoDB**, a commercial product based on
Apache IoTDB, to provide enterprise-grade solutions and services for businesses
and commercial clients. TimechoDB addresses the multifaceted challenges
enterprises face when building IoT big data platforms for managing time-series
data, including complex application scenarios, massive data volumes, high
sampling frequencies, frequent out-of-order data, time-consuming data
processing, diverse analytical require [...]
-- TimechoDB installation, deployment and usage
documentation：[QuickStart](https://www.timecho.com/docs/UserGuide/latest/QuickStart/QuickStart_timecho.html)
\ No newline at end of file
+Leveraging **TimechoDB**, Timecho Technology offers a broader range of product
features, enhanced performance and stability, and a richer suite of efficiency
tools. Additionally, it provides comprehensive enterprise services, delivering
commercial clients with superior product capabilities and an optimized
experience in development, operation, and usage.
+- **Timecho Technology Official Website**:
[https://www.timecho.com/](https://www.timecho.com/)
+- **TimechoDB Documentation**: [Quick
Start](https://www.timecho.com/docs/zh/UserGuide/latest/QuickStart/QuickStart_timecho.html)
diff --git
a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
index add91edb..5e619833 100644
---
a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++
b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物
2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前
IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过
TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。
3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode
时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在
IoTDB 中的数据进行深入分析，挖掘出其中的价值。
-## 2. TimechoDB 整体架构
+## 2. IoTDB 整体架构
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
diff --git
a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
index b19cf2c4..8a42a7f7 100644
---
a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++
b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物
2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前
IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过
TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。
3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode
时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在
IoTDB 中的数据进行深入分析，挖掘出其中的价值。
-## 2. TimechoDB 整体架构
+## 2. IoTDB 整体架构
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
diff --git
a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
index add91edb..5e619833 100644
---
a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++
b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物
2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前
IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过
TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。
3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode
时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在
IoTDB 中的数据进行深入分析，挖掘出其中的价值。
-## 2. TimechoDB 整体架构
+## 2. IoTDB 整体架构
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
diff --git
a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
index b19cf2c4..8a42a7f7 100644
--- a/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
+++ b/src/zh/UserGuide/latest/IoTDB-Introduction/IoTDB-Introduction_apache.md
@@ -44,7 +44,7 @@ IoTDB 体系由若干个组件构成，帮助用户高效地管理和分析物
2. **时序数据标准文件格式（Apache TsFile）**：该文件格式是一种专为时序数据设计的存储格式，可以高效地存储和查询海量时序数据。目前
IoTDB、AINode 等模块的底层存储文件均由 Apache TsFile 进行支撑。通过
TsFile，用户可以在采集、管理、应用&分析阶段统一使用相同的文件格式进行数据管理，极大简化了数据采集到分析的整个流程，提高时序数据管理的效率和便捷度。
3. **时序模型训推一体化引擎（IoTDB AINode）**：针对智能分析场景，IoTDB 提供 AINode
时序模型训推一体化引擎，它提供了一套完整的时序数据分析工具，底层为模型训练引擎，支持训练任务与数据管理，与包括机器学习、深度学习等。通过这些工具，用户可以对存储在
IoTDB 中的数据进行深入分析，挖掘出其中的价值。
-## 2. TimechoDB 整体架构
+## 2. IoTDB 整体架构
下图展示了一个常见的 IoTDB 3C3D（3 个 ConfigNode、3 个 DataNode）的集群部署模式：
