Deep learning-based performance optimization of massive power grid 3D models and viewable loading research

Efficient visualization of 3D models plays a crucial role in various fields such as power grids, but it faces challenges of massive data and rendering performance. This paper proposes a technique for model optimization and visual domain loading based on deep learning to enhance the efficiency of visualizing massive power grid 3D models. We design a 3D model simplification algorithm based on an encoder-decoder network, which significantly compresses model data while preserving geometric details. Additionally, we develop a visual domain prediction network based on attention mechanisms to achieve dynamic visual domain loading. Experimental results demonstrate the outstanding performance of this technique: a 64.2% reduction in memory usage, a 57.9% increase in rendering frame rates, and a 68% frame rate improvement while maintaining 95% visual quality, providing support for the efficient transmission and rendering of massive complex 3D models. Future work will focus on expanding related algorithms, enhancing interpretability, advancing 3D data processing, and visualization technologies to facilitate industrial digital transformation.