Deep Semantic Fusion and Overlap-Aware Transformer Registration for Intelligent Recognition and 3D Modeling of Power Grid Equipment

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Artificial intelligence, computer vision, and digital twin technologies have increased the demand for intelligent recognition and dynamic modeling of power grid equipment. Existing methods remain limited by noisy multi-source documents, weak semantic-to-geometric mapping, and unstable registration under low overlap and occlusion. To address these issues, this paper proposes a multi-source semantic fusion framework following the chain of semantic information extraction, parameter-to-model mapping, and dynamic point cloud registration. First, PCNN-ATTRA-RL is designed as a distantly supervised relation extraction model. It uses PCNN encoding, relation-aware sentence-level attention, and reinforcement-learning-based noise filtering to extract equipment objects, attributes, and relations from heterogeneous texts. Second, a semantic-driven parametric modeling method maps equipment type, dimensions, ports, and topology to reusable geometric templates, so 3D models can be generated and assembled automatically. Third, an overlap-aware Transformer registration network combines engineering-rule weights and sliding-window optimization to update the generated model with field point clouds. Experiments show that the proposed framework improves knowledge acquisition, semantic consistency of generated models, and point cloud alignment accuracy.