Modify the number of clients (#336)
diff --git a/src/UserGuide/Master/Tools-System/Benchmark.md b/src/UserGuide/Master/Tools-System/Benchmark.md
index c765100..86ff88b 100644
--- a/src/UserGuide/Master/Tools-System/Benchmark.md
+++ b/src/UserGuide/Master/Tools-System/Benchmark.md
@@ -305,7 +305,7 @@
| Parameter Name | Example |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/UserGuide/Master/User-Manual/AINode_timecho.md b/src/UserGuide/Master/User-Manual/AINode_timecho.md
index 8d1999de..43fa103 100644
--- a/src/UserGuide/Master/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/Master/User-Manual/AINode_timecho.md
@@ -37,7 +37,7 @@
Compared with building a machine learning service alone, it has the following advantages:
-- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. For example, to create a model, you can use the CREATE MODEL statement, and to reason with a model, you can use the CALL INFERENCE(...) statement. statement to create a model and CALL INFERENCE(...) statement to reason with a model, making it easier and more convenient to use.
+- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. Creating a model can be done using the CREATE MODEL statement, and using a model for inference can be done using the CALL INFERENCE (...) statement, making it simpler and more convenient to use.
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
diff --git a/src/UserGuide/V1.2.x/Tools-System/Benchmark.md b/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
index 43f9b43..0f22a52 100644
--- a/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
+++ b/src/UserGuide/V1.2.x/Tools-System/Benchmark.md
@@ -298,7 +298,7 @@
| Parameter Name | Example |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/UserGuide/latest/Tools-System/Benchmark.md b/src/UserGuide/latest/Tools-System/Benchmark.md
index dd48d84..c529997 100644
--- a/src/UserGuide/latest/Tools-System/Benchmark.md
+++ b/src/UserGuide/latest/Tools-System/Benchmark.md
@@ -297,7 +297,7 @@
| Parameter Name | Example |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/UserGuide/latest/User-Manual/AINode_timecho.md b/src/UserGuide/latest/User-Manual/AINode_timecho.md
index 8d1999de..5b35b9e 100644
--- a/src/UserGuide/latest/User-Manual/AINode_timecho.md
+++ b/src/UserGuide/latest/User-Manual/AINode_timecho.md
@@ -37,7 +37,8 @@
Compared with building a machine learning service alone, it has the following advantages:
-- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. For example, to create a model, you can use the CREATE MODEL statement, and to reason with a model, you can use the CALL INFERENCE(...) statement. statement to create a model and CALL INFERENCE(...) statement to reason with a model, making it easier and more convenient to use.
+- **Simple and easy to use**: no need to use Python or Java programming, the complete process of machine learning model management and inference can be completed using SQL statements. Creating a model can be done using the CREATE MODEL statement, and using a model for inference can be done using the CALL INFERENCE (...) statement, making it simpler and more convenient to use.
+
- **Avoid Data Migration**: With IoTDB native machine learning, data stored in IoTDB can be directly applied to the inference of machine learning models without having to move the data to a separate machine learning service platform, which accelerates data processing, improves security, and reduces costs.
diff --git a/src/zh/UserGuide/Master/Tools-System/Benchmark.md b/src/zh/UserGuide/Master/Tools-System/Benchmark.md
index efbd369..9c89db2 100644
--- a/src/zh/UserGuide/Master/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/Master/Tools-System/Benchmark.md
@@ -313,7 +313,7 @@
| 参数名称 | 示例 |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md b/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
index 8a0b5db..29031f2 100644
--- a/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/Master/User-Manual/AINode_timecho.md
@@ -37,7 +37,7 @@
与单独构建机器学习服务相比，具有以下优势：
-- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更急简单便捷。
+- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更加简单便捷。
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
diff --git a/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md b/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
index 3e19f21..bdffaab 100644
--- a/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/V1.2.x/Tools-System/Benchmark.md
@@ -313,7 +313,7 @@
| 参数名称 | 示例 |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md b/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
index af5678d..899ead5 100644
--- a/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
+++ b/src/zh/UserGuide/latest/Deployment-and-Maintenance/Stand-Alone-Deployment_timecho.md
@@ -65,7 +65,7 @@
#### 系统通用配置
-打开通用配置文件（./conf/iotdb-confignode.properties 文件），设置以下参数：
+打开通用配置文件（./conf/iotdb-common.properties 文件），设置以下参数：
| **配置项** | **说明** | **默认值** | **推荐值** | 备注 |
| :-----------------------: | :------------------------------: | :------------: | :----------------------------------------------: | :-----------------------: |
@@ -75,7 +75,7 @@
#### ConfigNode配置
-打开ConfigNode配置文件（./conf/iotdb-system.properties文件），设置以下参数：
+打开ConfigNode配置文件（./conf/iotdb-confignode.properties文件），设置以下参数：
| **配置项** | **说明** | **默认** | 推荐值 | **备注** |
| :-----------------: | :----------------------------------------------------------: | :-------------: | :----------------------------------------------: | :----------------: |
@@ -86,7 +86,7 @@
#### DataNode 配置
-打开DataNode配置文件 ./conf/iotdb-datanode.properties，设置以下参数：
+打开DataNode配置文件（./conf/iotdb-datanode.properties文件），设置以下参数：
| **配置项** | **说明** | **默认** | 推荐值 | **备注** |
| :------------------------------ | :----------------------------------------------------------- | :-------------- | :----------------------------------------------- | :----------------- |
diff --git a/src/zh/UserGuide/latest/Tools-System/Benchmark.md b/src/zh/UserGuide/latest/Tools-System/Benchmark.md
index 3a6d572..1b8d52f 100644
--- a/src/zh/UserGuide/latest/Tools-System/Benchmark.md
+++ b/src/zh/UserGuide/latest/Tools-System/Benchmark.md
@@ -313,7 +313,7 @@
| 参数名称 | 示例 |
| -------------------- | --------------------- |
-| CLIENT_NUMBER | 100 |
+| CLIENT_NUMBER | 10 |
| QUERY_DEVICE_NUM | 2 |
| QUERY_SENSOR_NUM | 2 |
| QUERY_AGGREGATE_FUN | count |
diff --git a/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md b/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
index 8a0b5db..29031f2 100644
--- a/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
+++ b/src/zh/UserGuide/latest/User-Manual/AINode_timecho.md
@@ -37,7 +37,7 @@
与单独构建机器学习服务相比，具有以下优势：
-- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更急简单便捷。
+- **简单易用**：无需使用 Python 或 Java 编程，使用 SQL 语句即可完成机器学习模型管理与推理的完整流程。如创建模型可使用CREATE MODEL语句、使用模型进行推理可使用CALL INFERENCE(...)语句等，使用更加简单便捷。
- **避免数据迁移**：使用 IoTDB 原生机器学习可以将存储在 IoTDB 中的数据直接应用于机器学习模型的推理，无需将数据移动到单独的机器学习服务平台，从而加速数据处理、提高安全性并降低成本。
