| Autor: |
Liu, Wei, Gu, Yanghao, Zeng, Zhiqiang, Qi, Donglian, Li, Dezhi, Luo, Yuanyuan, Li, Qi, Wei, Su |
| Zdroj: |
Electronics (2079-9292); Nov2024, Vol. 13 Issue 22, p4430, 15p |
| Abstrakt: |
The normal operation of automated equipment is essential for power grid regulation, making the accurate identification and diagnosis of defects in this equipment highly significant. Constructing a knowledge graph for automated equipment defects offers an effective solution to challenges such as delayed reporting, low efficiency, and data omissions in manually recorded defects. To address this, we developed a framework for constructing an automated equipment defect knowledge graph by designing appropriate patterns and data layers. For knowledge extraction, we introduced two models: RoBERTa-BiLSTM for named entity recognition (NER) and ALBERT-BiGRU for relation extraction (RE), both of which demonstrated improved performance in their respective tasks. Additionally, we applied the KBGAT model for knowledge graph completion. Finally, Neo4j was used for storing, visualizing, and analyzing the knowledge graph, highlighting its significance in the operation of power grids and the advancement of digital power systems. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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