A Gated Neighbor Fusion-Based Knowledge Graph for Relay Protection Operation and Maintenance

Relay protection operation and maintenance (O&M) involves multi-source documents such as device manuals, O&M records, and industry regulations, with dense professional terminology and dispersed knowledge, making traditional retrieval methods inadequate for precise association. This paper proposes a knowledge graph construction and application method for relay protection O&M that incorporates a neighborhood gating mechanism. First, a domain schema is established covering five core entity types, primary equipment, protection functions, functional components, setting parameters, and secondary circuit signals, along with the semantic relationships between entities. Second, a MacBERT-GNF-CRF entity recognition model is proposed, which introduces a gated neighborhood feature fusion (GNF) mechanism on the basis of pre-trained semantic encoding to enhance local semantic representations near entity boundaries; experimental results show that this model achieves an F1 score of 0.946. A MacBERT-GNF-RC relation extraction model is constructed to realize automatic discrimination of semantic associations between entity pairs. After entity disambiguation and knowledge fusion, knowledge graph storage and visualization are completed based on Neo4j. Furthermore, a knowledge graph-enhanced alarm question-answering prototype is developed and evaluated using a representative 220 kV composite alarm case and 50 historical alarm cases. Expert evaluation shows that KG augmentation significantly improves alarm interpretation quality and evidence traceability compared to direct LLM answering.