Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8–10, 2023, Guangzhou, China

Research Article

Research on Extension Method of Knowledge Graph of Power Equipment Health Management based on Blockchain

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  • @INPROCEEDINGS{10.4108/eai.8-12-2023.2344475,
        author={Xingdong  Bai and Shiwen  Ling and Xueliang  Zhang and Kang  Liu},
        title={Research on Extension Method of Knowledge Graph of Power Equipment Health Management based on Blockchain},
        proceedings={Proceedings of the 5th Management Science Informatization and Economic Innovation Development Conference, MSIEID 2023, December 8--10, 2023, Guangzhou, China},
        publisher={EAI},
        proceedings_a={MSIEID},
        year={2024},
        month={4},
        keywords={knowledge graph; electrical equipment; blockchain; health management; data storage; neo4j},
        doi={10.4108/eai.8-12-2023.2344475}
    }
    
  • Xingdong Bai
    Shiwen Ling
    Xueliang Zhang
    Kang Liu
    Year: 2024
    Research on Extension Method of Knowledge Graph of Power Equipment Health Management based on Blockchain
    MSIEID
    EAI
    DOI: 10.4108/eai.8-12-2023.2344475
Xingdong Bai1, Shiwen Ling2,*, Xueliang Zhang1, Kang Liu3
  • 1: Ultra-High Voltage Company,State Grid Gansu Electric Power Company
  • 2: Lanzhou Jiaotong University
  • 3: State Grid Gansu Electric Power Company
*Contact email: 1602936663@qq.com

Abstract

To effectively integrate electrical equipment data and achieve collaborative knowledge building and sharing, this paper proposed a blockchain-based method for expanding the knowledge graph in power equipment health management.Firstly, considering the characteristics of power equipment health management, the overall framework of the knowledge graph was presented, and entity concepts and entity relationships were extracted using rule-based methods and deep learning techniques. Secondly, a blockchain architecture model is designed based on power equipment health management methods, utilizing blockchain technology to enhance the content and functionality of the knowledge graph through data storage, data chaining, and knowledge authentication.Finally, the neo4j diagram database is used for visual display.Experimental results show that this method effectively improves the comprehensiveness of the power equipment health management knowledge graph, achieves knowledge graph expansion, and further enhances the efficiency of power equipment health management.