Bibliographic Details
| Title: |
海上风电数据可视化平台设计与实现. (Chinese) |
| Alternate Title: |
Design and Implementation of an Offshore Wind Power Data Visualization Platform. (English) |
| Authors: |
黄 鑫, 王银丰, 扶艺辉, 叶瑞帆, 刘 瑜 |
| Source: |
Electronic Science & Technology; 2025, Vol. 38 Issue 9, p33-40, 8p |
| Subject Terms: |
DATA visualization, OFFSHORE wind power plants, SOFTWARE frameworks, THREE-dimensional modeling |
| Geographic Terms: |
ZHEJIANG Sheng (China) |
| Abstract (English): |
In view of the problem of difficult management of offshore wind farms, this study takes an offshore wind farm in Zhejiang as an example to design and implement an offshore wind power data visualization platform. The front-end of the platform is based on framework technologies such as Vue and Echarts, while the back-end is based on technologies such as Springboot. The platform utilizes multiple data charts such as maps, pie charts, and line charts from the Echarts framework to visualize wind power data, while using the UMG (Unreal Motion Graphics) system from UE4 (Unreal Engine4) to achieve interactive display of 3D models of offshore wind farms. The experimental results shows that the platform runs smoothly on the PC side, while low frame count on the browser side affects usage. By using this platform, users can quickly understand real-time production information of offshore wind farms, improve the ability to remotely manage wind farms through data visualization, and eliminate safety hazards. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
针对海上风电场难以管理的问题, 文中以浙江某海上风电场为例, 设计并实现了一个海上风电数据可视化 平台。 平台前端基于 Vue 和 Echarts 等框架技术, 后端基于 Springboot 等技术。 平台利用 Echarts 框架的地图、饼图和折 线图等多元数据图表实现风电数据可视化, 同时使用 UE4 (Unreal Engine4) 的 UMG (Unreal Motion Graphics) 系统实现海 上风电场的 3 D 模型交互展示。 实验结果表明, 平台在 PC 端运行流畅, 但在浏览器端帧数较低。 用户利用该平台可以快 速了解海上风电场的实时生产信息, 通过数据可视化提高了远程管理风场能力, 排除了安全隐患。 [ABSTRACT FROM AUTHOR] |
|
Copyright of Electronic Science & Technology is the property of Electronic Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |