A domain knowledge graph-driven joint recommendation system for wiring mode and automation configuration in differentiated power supply zones
DOI:
https://doi.org/10.4108/ew.14666Keywords:
domain knowledge graph, graph neural network, multi-task joint learning, distribution network planning, knowledge reasoningAbstract
INTRODUCTION: Distribution network planning requires jointly determining wiring modes and automation configurations, yet existing methods treat them independently without regulatory constraints.
OBJECTIVES: To develop a knowledge graph-driven framework for joint wiring mode and automation configuration recommendation across differentiated supply zones.
METHODS: A BERT-CRF pipeline constructs a knowledge graph from regulatory texts. TransR-initialized graph attention networks and CRF decoders with SWRL constraints are coupled via multi-task learning, evaluated on 1,443 records and 12 field tasks.
RESULTS: Acc@3 of 0.817, F1 of 0.757, and CCR of 0.733, surpassing the best baseline by 4.3% and 10.4% respectively. Expert validation scores 4.50/5.00 with 73.3% planning time reduction.
CONCLUSION: Integrating knowledge graph embedding, GNN aggregation, and constraint-aware joint optimization yields accurate, compliant, and traceable distribution planning recommendations.
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Copyright (c) 2026 Min Li, Junhua Gong, Jing Tan, Feipeng Huang, Xiaohong Tan
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