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The following commit(s) were added to refs/heads/main by this push:
new f33fb7a8 Application scenarios (#757)
f33fb7a8 is described below
commit f33fb7a8b2b5e28be4cd29a09617794745c07bdc
Author: W1y1r <[email protected]>
AuthorDate: Wed May 21 14:15:19 2025 +0800
Application scenarios (#757)
---
src/.vuepress/public/img/Application_scenarios1.png | Bin 0 -> 120163 bytes
src/.vuepress/public/img/Application_scenarios2.png | Bin 0 -> 86490 bytes
src/.vuepress/public/img/Application_scenarios3.png | Bin 0 -> 297566 bytes
src/.vuepress/public/img/Application_scenarios4.png | Bin 0 -> 205427 bytes
.../Master/Tree/IoTDB-Introduction/Scenario.md | 8 ++++----
src/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md | 8 ++++----
src/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md | 8 ++++----
src/UserGuide/latest/IoTDB-Introduction/Scenario.md | 8 ++++----
.../Master/Table/IoTDB-Introduction/Scenario.md | 10 +++++-----
.../Master/Tree/IoTDB-Introduction/Scenario.md | 10 +++++-----
src/zh/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md | 8 ++++----
.../UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md | 9 +++++----
.../latest-Table/IoTDB-Introduction/Scenario.md | 10 +++++-----
src/zh/UserGuide/latest/IoTDB-Introduction/Scenario.md | 10 +++++-----
14 files changed, 45 insertions(+), 44 deletions(-)
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new file mode 100644
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diff --git a/src/.vuepress/public/img/Application_scenarios4.png
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diff --git a/src/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
b/src/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
index 6a84569a..fdf8ab15 100644
--- a/src/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
+++ b/src/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
@@ -36,7 +36,7 @@ In the original architecture, the HBase cluster was used as
the storage database
The data management architecture of the car company using IoTDB as the
time-series data storage engine is shown in the figure below.
-
+
The vehicle data is encoded based on TCP and industrial protocols and sent to
the edge gateway, and the gateway sends the data to the message queue Kafka
cluster, decoupling the two ends of production and consumption. Kafka sends
data to Flink for real-time processing, and the processed data is written into
IoTDB. Both historical data and latest data are queried in IoTDB, and finally
the data flows into the visualization platform through API for application.
@@ -59,7 +59,7 @@ After selecting IoTDB as the storage database of the
intelligent operation and m
The figure below shows the architecture design of the intelligent operation
and maintenance platform of the steel plant.
-
+
## 3. Smart Factory
@@ -73,7 +73,7 @@ A cigarette factory hopes to upgrade from a "traditional
factory" to a "high-end
The figure below shows the factory's IoT system architecture. IoTDB runs
through the three-level IoT platform of the company, factory, and workshop to
realize unified joint debugging and joint control of equipment. The data at the
workshop level is collected, processed and stored in real time through the
IoTDB at the edge layer, and a series of analysis tasks are realized. The
preprocessed data is sent to the IoTDB at the platform layer for data
governance at the business level, such as [...]
-
+
## 4. Condition monitoring
@@ -90,5 +90,5 @@ After using IoTDB as the storage and analysis engine,
combined with meteorologic
The figure below shows the data management architecture of the power plant in
the heating scene.
-
+
diff --git a/src/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
b/src/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
index b295af56..d710750f 100644
--- a/src/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
+++ b/src/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
@@ -36,7 +36,7 @@ In the original architecture, the HBase cluster was used as
the storage database
The data management architecture of the car company using IoTDB as the
time-series data storage engine is shown in the figure below.
-
+
The vehicle data is encoded based on TCP and industrial protocols and sent to
the edge gateway, and the gateway sends the data to the message queue Kafka
cluster, decoupling the two ends of production and consumption. Kafka sends
data to Flink for real-time processing, and the processed data is written into
IoTDB. Both historical data and latest data are queried in IoTDB, and finally
the data flows into the visualization platform through API for application.
@@ -59,7 +59,7 @@ After selecting IoTDB as the storage database of the
intelligent operation and m
The figure below shows the architecture design of the intelligent operation
and maintenance platform of the steel plant.
-
+
## Application 3: Smart Factory
@@ -73,7 +73,7 @@ A cigarette factory hopes to upgrade from a "traditional
factory" to a "high-end
The figure below shows the factory's IoT system architecture. IoTDB runs
through the three-level IoT platform of the company, factory, and workshop to
realize unified joint debugging and joint control of equipment. The data at the
workshop level is collected, processed and stored in real time through the
IoTDB at the edge layer, and a series of analysis tasks are realized. The
preprocessed data is sent to the IoTDB at the platform layer for data
governance at the business level, such as [...]
-
+
## Application 4: Condition monitoring
@@ -90,5 +90,5 @@ After using IoTDB as the storage and analysis engine,
combined with meteorologic
The figure below shows the data management architecture of the power plant in
the heating scene.
-
+
diff --git a/src/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
b/src/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
index b295af56..d710750f 100644
--- a/src/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
+++ b/src/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
@@ -36,7 +36,7 @@ In the original architecture, the HBase cluster was used as
the storage database
The data management architecture of the car company using IoTDB as the
time-series data storage engine is shown in the figure below.
-
+
The vehicle data is encoded based on TCP and industrial protocols and sent to
the edge gateway, and the gateway sends the data to the message queue Kafka
cluster, decoupling the two ends of production and consumption. Kafka sends
data to Flink for real-time processing, and the processed data is written into
IoTDB. Both historical data and latest data are queried in IoTDB, and finally
the data flows into the visualization platform through API for application.
@@ -59,7 +59,7 @@ After selecting IoTDB as the storage database of the
intelligent operation and m
The figure below shows the architecture design of the intelligent operation
and maintenance platform of the steel plant.
-
+
## Application 3: Smart Factory
@@ -73,7 +73,7 @@ A cigarette factory hopes to upgrade from a "traditional
factory" to a "high-end
The figure below shows the factory's IoT system architecture. IoTDB runs
through the three-level IoT platform of the company, factory, and workshop to
realize unified joint debugging and joint control of equipment. The data at the
workshop level is collected, processed and stored in real time through the
IoTDB at the edge layer, and a series of analysis tasks are realized. The
preprocessed data is sent to the IoTDB at the platform layer for data
governance at the business level, such as [...]
-
+
## Application 4: Condition monitoring
@@ -90,5 +90,5 @@ After using IoTDB as the storage and analysis engine,
combined with meteorologic
The figure below shows the data management architecture of the power plant in
the heating scene.
-
+
diff --git a/src/UserGuide/latest/IoTDB-Introduction/Scenario.md
b/src/UserGuide/latest/IoTDB-Introduction/Scenario.md
index 6a84569a..fdf8ab15 100644
--- a/src/UserGuide/latest/IoTDB-Introduction/Scenario.md
+++ b/src/UserGuide/latest/IoTDB-Introduction/Scenario.md
@@ -36,7 +36,7 @@ In the original architecture, the HBase cluster was used as
the storage database
The data management architecture of the car company using IoTDB as the
time-series data storage engine is shown in the figure below.
-
+
The vehicle data is encoded based on TCP and industrial protocols and sent to
the edge gateway, and the gateway sends the data to the message queue Kafka
cluster, decoupling the two ends of production and consumption. Kafka sends
data to Flink for real-time processing, and the processed data is written into
IoTDB. Both historical data and latest data are queried in IoTDB, and finally
the data flows into the visualization platform through API for application.
@@ -59,7 +59,7 @@ After selecting IoTDB as the storage database of the
intelligent operation and m
The figure below shows the architecture design of the intelligent operation
and maintenance platform of the steel plant.
-
+
## 3. Smart Factory
@@ -73,7 +73,7 @@ A cigarette factory hopes to upgrade from a "traditional
factory" to a "high-end
The figure below shows the factory's IoT system architecture. IoTDB runs
through the three-level IoT platform of the company, factory, and workshop to
realize unified joint debugging and joint control of equipment. The data at the
workshop level is collected, processed and stored in real time through the
IoTDB at the edge layer, and a series of analysis tasks are realized. The
preprocessed data is sent to the IoTDB at the platform layer for data
governance at the business level, such as [...]
-
+
## 4. Condition monitoring
@@ -90,5 +90,5 @@ After using IoTDB as the storage and analysis engine,
combined with meteorologic
The figure below shows the data management architecture of the power plant in
the heating scene.
-
+
diff --git a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/Scenario.md
b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/Scenario.md
index 57211542..17faf417 100644
--- a/src/zh/UserGuide/Master/Table/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/Master/Table/IoTDB-Introduction/Scenario.md
@@ -31,7 +31,7 @@
IoTDB
凭借集群高可用、低流量数据同步、跨网闸支持和优异的性能为企业提供国产化自主可控的时序数据管理解决方案，支撑企业应对大规模时序数据管理挑战，推动传统能源和可再生能源的高效管理与整合。
-
+
## 2. 航空航天
@@ -44,7 +44,7 @@ IoTDB 凭借集群高可用、低流量数据同步、跨网闸支持和优异
IoTDB
凭借其国产自研的高效低流量数据同步、离线数据迁移、丰富的部署选择和低资源占用等特点，为行业的数据管理和业务扩展提供了数据基础，为航空航天领域的技术创新和持续发展提供有力支撑。
-
+
## 3. 交通运输
@@ -57,7 +57,7 @@ IoTDB 凭借其国产自研的高效低流量数据同步、离线数据迁移
IoTDB
凭借其高效的时序数据管理和低延迟查询能力，有效应对交通运输行业中的数据爆发式增长，实现多源异构数据高效流转和管理，为铁路、船舶等构建起稳定可靠的智能交通系统管理基础，为行业向智能化和自动化方向发展提供了重要支撑。
-
+
## 4. 钢铁冶炼
@@ -70,7 +70,7 @@ IoTDB 凭借其高效的时序数据管理和低延迟查询能力，有效应
IoTDB
通过其强大的数据存储与计算能力，为钢铁冶炼场景提供跨平台支持、低资源占用的灵活部署方案，丰富的外部接口也使其可以与其他系统高效集成，助力钢铁冶炼行业构建智慧工厂，进一步支撑传统工业加快形成新质生产力。
-
+
## 5. 物联网
@@ -83,4 +83,4 @@ IoTDB 通过其强大的数据存储与计算能力，为钢铁冶炼场景提
作为物联网原生的高性能时序数据库，IoTDB
支持从边缘设备到云端的全链路数据同步和存储分析，具备高并发处理能力，能够满足大规模设备接入的需求。IoTDB为企业提供灵活的数据解决方案，助力发掘设备运行数据中的深层次价值，提升运营效率，推动企业物联网业务的全面发展。
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
index ab59adaf..0a4e4bbb 100644
--- a/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/Master/Tree/IoTDB-Introduction/Scenario.md
@@ -31,7 +31,7 @@
IoTDB
凭借集群高可用、低流量数据同步、跨网闸支持和优异的性能为企业提供国产化自主可控的时序数据管理解决方案，支撑企业应对大规模时序数据管理挑战，推动传统能源和可再生能源的高效管理与整合。
-
+
## 2. 航空航天
@@ -44,7 +44,7 @@ IoTDB 凭借集群高可用、低流量数据同步、跨网闸支持和优异
IoTDB
凭借其国产自研的高效低流量数据同步、离线数据迁移、丰富的部署选择和低资源占用等特点，为行业的数据管理和业务扩展提供了数据基础，为航空航天领域的技术创新和持续发展提供有力支撑。
-
+
## 3. 交通运输
@@ -57,7 +57,7 @@ IoTDB 凭借其国产自研的高效低流量数据同步、离线数据迁移
IoTDB
凭借其高效的时序数据管理和低延迟查询能力，有效应对交通运输行业中的数据爆发式增长，实现多源异构数据高效流转和管理，为铁路、船舶等构建起稳定可靠的智能交通系统管理基础，为行业向智能化和自动化方向发展提供了重要支撑。
-
+
## 4. 钢铁冶炼
@@ -70,7 +70,7 @@ IoTDB 凭借其高效的时序数据管理和低延迟查询能力，有效应
IoTDB
通过其强大的数据存储与计算能力，为钢铁冶炼场景提供跨平台支持、低资源占用的灵活部署方案，丰富的外部接口也使其可以与其他系统高效集成，助力钢铁冶炼行业构建智慧工厂，进一步支撑传统工业加快形成新质生产力。
-
+
## 5. 物联网
@@ -83,4 +83,4 @@ IoTDB 通过其强大的数据存储与计算能力，为钢铁冶炼场景提
作为物联网原生的高性能时序数据库，IoTDB
支持从边缘设备到云端的全链路数据同步和存储分析，具备高并发处理能力，能够满足大规模设备接入的需求。IoTDB为企业提供灵活的数据解决方案，助力发掘设备运行数据中的深层次价值，提升运营效率，推动企业物联网业务的全面发展。
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
b/src/zh/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
index 6a9e186c..3a3ff35c 100644
--- a/src/zh/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/V1.3.x/IoTDB-Introduction/Scenario.md
@@ -38,7 +38,7 @@
该车企以IoTDB为时序数据存储引擎的数据管理架构如下图所示。
-
+
车辆数据基于TCP和工业协议编码后发送至边缘网关，网关将数据发往消息队列Kafka集群，解耦生产和消费两端。Kafka将数据发送至Flink进行实时处理，处理后的数据写入IoTDB中，历史数据和最新数据均在IoTDB中进行查询，最后数据通过API流入可视化平台等进行应用。
@@ -61,7 +61,7 @@
下图为该钢厂的智能运维平台架构设计。
-.png)
+
## 应用3——智能工厂
@@ -75,7 +75,7 @@
下图为该工厂的物联网系统架构，IoTDB贯穿公司、工厂、车间三级物联网平台，实现设备统一联调联控。车间层面的数据通过边缘层的IoTDB进行实时采集、处理和存储，并实现了一系列的分析任务。经过预处理的数据被发送至平台层的IoTDB，进行业务层面的数据治理，如设备管理、连接管理、服务支持等。最终，数据会被集成到集团层面的IoTDB中，供整个组织进行综合分析和决策。
-.png)
+
## 应用4——工况监控
@@ -92,4 +92,4 @@
下图为该电厂的供热场景数据管理架构。
-
+
diff --git a/src/zh/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
b/src/zh/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
index 6a9e186c..a564d286 100644
--- a/src/zh/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/dev-1.3/IoTDB-Introduction/Scenario.md
@@ -38,7 +38,7 @@
该车企以IoTDB为时序数据存储引擎的数据管理架构如下图所示。
-
+
车辆数据基于TCP和工业协议编码后发送至边缘网关，网关将数据发往消息队列Kafka集群，解耦生产和消费两端。Kafka将数据发送至Flink进行实时处理，处理后的数据写入IoTDB中，历史数据和最新数据均在IoTDB中进行查询，最后数据通过API流入可视化平台等进行应用。
@@ -61,7 +61,8 @@
下图为该钢厂的智能运维平台架构设计。
-.png)
+
+
## 应用3——智能工厂
@@ -75,7 +76,7 @@
下图为该工厂的物联网系统架构，IoTDB贯穿公司、工厂、车间三级物联网平台，实现设备统一联调联控。车间层面的数据通过边缘层的IoTDB进行实时采集、处理和存储，并实现了一系列的分析任务。经过预处理的数据被发送至平台层的IoTDB，进行业务层面的数据治理，如设备管理、连接管理、服务支持等。最终，数据会被集成到集团层面的IoTDB中，供整个组织进行综合分析和决策。
-.png)
+
## 应用4——工况监控
@@ -92,4 +93,4 @@
下图为该电厂的供热场景数据管理架构。
-
+
diff --git a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/Scenario.md
b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/Scenario.md
index 57211542..17faf417 100644
--- a/src/zh/UserGuide/latest-Table/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/latest-Table/IoTDB-Introduction/Scenario.md
@@ -31,7 +31,7 @@
IoTDB
凭借集群高可用、低流量数据同步、跨网闸支持和优异的性能为企业提供国产化自主可控的时序数据管理解决方案，支撑企业应对大规模时序数据管理挑战，推动传统能源和可再生能源的高效管理与整合。
-
+
## 2. 航空航天
@@ -44,7 +44,7 @@ IoTDB 凭借集群高可用、低流量数据同步、跨网闸支持和优异
IoTDB
凭借其国产自研的高效低流量数据同步、离线数据迁移、丰富的部署选择和低资源占用等特点，为行业的数据管理和业务扩展提供了数据基础，为航空航天领域的技术创新和持续发展提供有力支撑。
-
+
## 3. 交通运输
@@ -57,7 +57,7 @@ IoTDB 凭借其国产自研的高效低流量数据同步、离线数据迁移
IoTDB
凭借其高效的时序数据管理和低延迟查询能力，有效应对交通运输行业中的数据爆发式增长，实现多源异构数据高效流转和管理，为铁路、船舶等构建起稳定可靠的智能交通系统管理基础，为行业向智能化和自动化方向发展提供了重要支撑。
-
+
## 4. 钢铁冶炼
@@ -70,7 +70,7 @@ IoTDB 凭借其高效的时序数据管理和低延迟查询能力，有效应
IoTDB
通过其强大的数据存储与计算能力，为钢铁冶炼场景提供跨平台支持、低资源占用的灵活部署方案，丰富的外部接口也使其可以与其他系统高效集成，助力钢铁冶炼行业构建智慧工厂，进一步支撑传统工业加快形成新质生产力。
-
+
## 5. 物联网
@@ -83,4 +83,4 @@ IoTDB 通过其强大的数据存储与计算能力，为钢铁冶炼场景提
作为物联网原生的高性能时序数据库，IoTDB
支持从边缘设备到云端的全链路数据同步和存储分析，具备高并发处理能力，能够满足大规模设备接入的需求。IoTDB为企业提供灵活的数据解决方案，助力发掘设备运行数据中的深层次价值，提升运营效率，推动企业物联网业务的全面发展。
-
\ No newline at end of file
+
\ No newline at end of file
diff --git a/src/zh/UserGuide/latest/IoTDB-Introduction/Scenario.md
b/src/zh/UserGuide/latest/IoTDB-Introduction/Scenario.md
index ab59adaf..0a4e4bbb 100644
--- a/src/zh/UserGuide/latest/IoTDB-Introduction/Scenario.md
+++ b/src/zh/UserGuide/latest/IoTDB-Introduction/Scenario.md
@@ -31,7 +31,7 @@
IoTDB
凭借集群高可用、低流量数据同步、跨网闸支持和优异的性能为企业提供国产化自主可控的时序数据管理解决方案，支撑企业应对大规模时序数据管理挑战，推动传统能源和可再生能源的高效管理与整合。
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## 2. 航空航天
@@ -44,7 +44,7 @@ IoTDB 凭借集群高可用、低流量数据同步、跨网闸支持和优异
IoTDB
凭借其国产自研的高效低流量数据同步、离线数据迁移、丰富的部署选择和低资源占用等特点，为行业的数据管理和业务扩展提供了数据基础，为航空航天领域的技术创新和持续发展提供有力支撑。
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## 3. 交通运输
@@ -57,7 +57,7 @@ IoTDB 凭借其国产自研的高效低流量数据同步、离线数据迁移
IoTDB
凭借其高效的时序数据管理和低延迟查询能力，有效应对交通运输行业中的数据爆发式增长，实现多源异构数据高效流转和管理，为铁路、船舶等构建起稳定可靠的智能交通系统管理基础，为行业向智能化和自动化方向发展提供了重要支撑。
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## 4. 钢铁冶炼
@@ -70,7 +70,7 @@ IoTDB 凭借其高效的时序数据管理和低延迟查询能力，有效应
IoTDB
通过其强大的数据存储与计算能力，为钢铁冶炼场景提供跨平台支持、低资源占用的灵活部署方案，丰富的外部接口也使其可以与其他系统高效集成，助力钢铁冶炼行业构建智慧工厂，进一步支撑传统工业加快形成新质生产力。
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## 5. 物联网
@@ -83,4 +83,4 @@ IoTDB 通过其强大的数据存储与计算能力，为钢铁冶炼场景提
作为物联网原生的高性能时序数据库，IoTDB
支持从边缘设备到云端的全链路数据同步和存储分析，具备高并发处理能力，能够满足大规模设备接入的需求。IoTDB为企业提供灵活的数据解决方案，助力发掘设备运行数据中的深层次价值，提升运营效率，推动企业物联网业务的全面发展。
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