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一种基于微服务的红外点云数字孪生系统

A Microservices-based Infrared Point Cloud Digital Twin System

  • 摘要: 红外相机已被广泛应用于智能电网的布局优化、设备监测和故障诊断1。红外图像可以直观地显示设备表面的温度变化,但无法展示设备的三维结构和全局视角。在本项目中,部分设备同时配备了可见光和红外相机,但原系统对温度监测业务仅采集了一张红外图像。如何使用一张红外图像与点云进行融合,同时解耦现有系统功能、实现灵活部署和动态扩展是本文面临的主要挑战。因此,本文提出了一种基于微服务的红外点云数字孪生系统,用于将一张红外图像与点云进行融合。首先,使红外相机拍摄一张红外图像,并将图像及位姿信息通过文件微服务传输到红外微服务。接下来,红外微服务利用点云数据和位姿信息,通过光栅化方法生成点云图像,并与红外图像进行配准。然后,使用PnP算法重新计算相机位姿,并用矫正位姿生成点云信息图,再从屏幕空间中筛选出距离相机最近处的点。最后,解析红外图像中的温度数据,并赋值给信息图中的相应点。结果将保存为文件,并生成新的版本信息,通过文件微服务通知客户端进行更新和显示。通过对真实场景红外图像和点云数据的测试,本文验证了该方法能够准确还原二维红外图像中的温度信息。

     

    Abstract: Thermal cameras have widespread applications in smart grid layout optimization, equipment monitoring, and fault diagnosis1. While these cameras visually represent temperature changes on device surfaces, they fail to provide a comprehensive view of the three-dimensional structure and heating state of the devices. In this project, some devices are equipped with both visible light and infrared cameras, but the original system only captures single infrared image for temperature monitoring. Therefore, this paper proposes a microservices-based infrared point cloud digital twin system for the fusion of single infrared image with point cloud. Firstly, an infrared image is captured by the infrared camera, the image and pose information are transmitted to infrared microservice through file microservice. Next, infrared microservice utilizes point cloud data and pose information to generate a point cloud image through rasterization method and match it's features with the infrared image. Then, the camera pose is recalculated using the PnP algorithm, and a point cloud information map is generated using the corrected pose. The results are saved as files and new version information is generated, notifying the client for updates and display through the file microservice. Through testing with real-world infrared images and point cloud data, this paper validates that the proposed method accurately restores temperature information from the two-dimensional infrared image.

     

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