A Study of Automated Calibration Problems of Lightweight 3D Digital Models for Power Grid Engineering

In order to solve the problems of large data volume and low automation level of power grid engineering model, an automated verification method of lightweight 3D digital model of power grid engineering is proposed. The LiDAR data of the power grid project are collected by LiDAR sensor, and the point cloud features are extracted from the collected data by kernel convolution method. Using the instant positioning and map construction methods, the extracted point cloud features are stitched together and reconstructed. Based on the 3D reconstruction results of the grid information model, a lightweight 3D digital model of grid engineering is constructed by selecting the grid processing method based on the building information model and the grid information model. Through the residual correction module, a block point cloud semantic segmentation network is constructed, and the semantic segmentation comparison is implemented on the constructed 3D digital model to automate the verification of the 3D digital model of power grid engineering. The experimental results show that this method can automate the verification of the 3D digital model of power grid project by adjusting the position of the equipment and modifying the size of the structure, which shortens the construction time of the power grid project and improves the efficiency of the management of the power grid project.