[Doc]Update README_CN.md (PaddlePaddle#1192)

* Update README_CN.md

* Update README_EN.md

* Update README_EN.md

README_CN.md

@@ -59,6 +59,9 @@
     - [**Paddle YOLOv8**](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) 可以部署的硬件：[**Intel CPU**](./examples/vision/detection/paddledetection/python/infer_yolov8.py)、[**NVIDIA GPU**](./examples/vision/detection/paddledetection/python/infer_yolov8.py)、[**Jetson**](./examples/vision/detection/paddledetection/python/infer_yolov8.py)、[**飞腾**](./examples/vision/detection/paddledetection/python/infer_yolov8.py)、[**昆仑芯**](./examples/vision/detection/paddledetection/python/infer_yolov8.py)、[**昇腾**](./examples/vision/detection/paddledetection/python/infer_yolov8.py)、[**ARM CPU**](./examples/vision/detection/paddledetection/cpp/infer_yolov8.cc)、[**RK3588**](./examples/vision/detection/paddledetection/rknpu2/yolov8.md) 和 [**Sophgo TPU**](./examples/vision/detection/paddledetection/sophgo), 部分硬件包含 **Python** 部署和 **C++** 部署；
     -  [**社区 ultralytics YOLOv8**](https://github.com/ultralytics/ultralytics) 可以部署的硬件：[**Intel CPU**](./examples/vision/detection/yolov8)、[**NVIDIA GPU**](./examples/vision/detection/yolov8)、[**Jetson**](./examples/vision/detection/yolov8)，均包含 **Python** 部署和 **C++** 部署；
     -  FastDeploy 一行模型API切换，可以实现**YOLOv8**、 **PP-YOLOE+**、**YOLOv5** 等模型性能对比。
+ - 服务化部署结合VisualDL新增支持可视化部署。在FastDeploy容器中启动VDL服务后，即可在VDL界面修改模型配置、启动/管理模型服务、查看性能数据、发送请求等，详细操作可参考相关文档
+    - [Serving可视化部署](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/vdl_management.md)
+    - [Serving可视化请求](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/zh_CN/client.md#%E4%BD%BF%E7%94%A8fastdeploy-client%E8%BF%9B%E8%A1%8C%E5%8F%AF%E8%A7%86%E5%8C%96%E8%AF%B7%E6%B1%82)
 
 - **✨👥✨ 社区交流**
 

README_EN.md

@@ -57,6 +57,9 @@ Including [image classification](examples/vision/classification), [object detect
     - You can deploy [**Paddle YOLOv8**](https://github.com/PaddlePaddle/PaddleYOLO/tree/release/2.5/configs/yolov8) on [**Intel CPU**](./examples/vision/detection/paddledetection/python/infer_yolov8.py), [**NVIDIA GPU**](./examples/vision/detection/paddledetection/python/infer_yolov8.py), [**Jetson**](./examples/vision/detection/paddledetection/python/infer_yolov8.py), [**Phytium**](./examples/vision/detection/paddledetection/python/infer_yolov8.py), [**Kunlunxin**](./examples/vision/detection/paddledetection/python/infer_yolov8.py), [**HUAWEI Ascend**](./examples/vision/detection/paddledetection/python/infer_yolov8.py) ,[**ARM CPU**](./examples/vision/detection/paddledetection/cpp/infer_yolov8.cc)  [**RK3588**](./examples/vision/detection/paddledetection/rknpu2) and [**Sophgo TPU**](./examples/vision/detection/paddledetection/sophgo). Both **Python** deployments and **C++** deployments are included. 
     - You can deploy [**ultralytics YOLOv8**](https://github.com/ultralytics/ultralytics) on [**Intel CPU**](./examples/vision/detection/yolov8), [**NVIDIA GPU**](./examples/vision/detection/yolov8), [**Jetson**](./examples/vision/detection/yolov8). Both **Python** deployments and **C++** deployments are included
     -  Fastdeploy supports quick deployment of multiple models, including **YOLOv8**, **PP-YOLOE+**, **YOLOv5** and other models
+-  Serving deployment combined with VisualDL supports visual deployment. After the VDL service is started in the FastDeploy container, you can modify the model configuration, start/manage the model service, view performance data, and send requests on the VDL interface. For details, see related documents
+    - [Serving deployment visualization](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/EN/vdl_management-en.md) 
+    - [Serving request visualization](https://github.com/PaddlePaddle/FastDeploy/blob/develop/serving/docs/EN/client-en.md#use-visualdl-as-fastdeploy-client-for-request-visualization)
 
 - **✨👥✨ Community**