Energies, Vol. 19, Pages 546: Hierarchical Topology Knowledge Extraction for Five-Prevention Wiring Diagrams in Substations
Energies doi: 10.3390/en19020546
Authors:
Hui You
Dong Yang
Tian Wu
Qing He
Wenyu Zhu
Xiang Ren
Jia Liu
Five prevention is an important technical means to prevent maloperations in substations, and knowledge extraction from wiring diagrams is the key to intelligent “five prevention logic verification”. To address the error accumulation caused by multimodal object matching in traditional methods, this paper proposes a hierarchical recognition-based approach for topological knowledge extraction. This method establishes a multi-level recognition framework utilizing image tiling, decomposing the wiring diagram recognition task into three hierarchical levels from top to bottom: connection modes, bay types, and switching devices. A depth-first strategy is employed to establish parent–child node relationships, forming an initial topological structure. Based on the recognition results, the proposed approach performs regularized parsing and leverages a bay topology knowledge base to achieve automated matching of inter-device topological relationships. To enhance recognition accuracy, the model incorporates a Swin Transformer block to strengthen global feature perception and adds an ultra-small target detection layer to improve small-object recognition. The experimental results demonstrate that all recognition layers achieve mAP@0.5 exceeding 90%, with an overall precision of 93.9% and a recall rate of 91.7%, outperforming traditional matching algorithms and meeting the requirements for wiring diagram topology knowledge extraction.