3D reconstruction and dynamic updating of power grid equipment via fusion of NeRF and lightweight BIM

This study addresses the critical challenges in the 3D reconstruction of power grid equipment, namely, geometric-semantic fragmentation, low dynamic updating efficiency, and the trade-off between high precision and lightweight representation. We propose a novel method that integrates Neural Radiance Fields (NeRF) with lightweight Building Information Modeling (BIM). Our core contributions are as follows. First, a cross-modal alignment mechanism featuring an Attribute-Aware Attention (AAA) Module dynamically embeds BIM semantics into the feature space of NeRF, achieving a unified representation. Second, an incremental updating algorithm, triggered by Structural Similarity Index (SSIM) difference analysis and BIM change logs, employs the Adaptive Boundary (AdaBound) optimizer for local fine-tuning and a Git-style storage strategy, reducing single-device update latency to 3.2 min and storage overhead by 98%. Third, a cloud-edge-end collaborative architecture leverages knowledge distillation to compress the model to 0.3 M parameters and a static-dynamic hierarchical rendering strategy to enable real-time mobile applications. Experiments in a 220 kV substation demonstrated the superiority of our method over traditional BIM and standard NeRF, with a Chamfer Distance of 2.03 cm, semantic recall of 98.7%, and a 98% transmission success rate under weak network conditions. This study provides key technical support for smart grid inspection and dynamic operational analysis.