Knowledge graph construction and Internet of Things optimisation for power grid data knowledge extraction

• Autores: Xiangju Sun, Ting-Bin Cao, Xing Li
• Localización: Applied Mathematics and Nonlinear Sciences, ISSN-e 2444-8656, Vol. 8, Nº. 1, 2023, págs. 2729-2738
• Idioma: inglés
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• Resumen

  • Problems exist in power grid data management that have unclear relationships, weak security and low accuracy. Byanalysing the knowledge graph construction characteristics of smart grid data information and knowledge extraction, thegrid data management platform is reshaped architecturally, and the knowledge graph construction technology is embed-ded in the grid data management framework. For the aforementioned problems, the knowledge graph construction andInternet of Things optimisation framework of power grid data knowledge extraction are proposed in this article. Firstly,the semantic search (KGSS) algorithm based on the knowledge graph is used. The KGSS algorithm extracts knowledgefrom structured, semi-structured and unstructured grid data through the massively parallel processing acquisition modeland Hadoop database, and constructs knowledge entities, attributes and inter-entity relationships. Then, it optimises andpredicts through the knowledge graph construction and Internet of Things optimisation framework extracted from powergrid data knowledge. Finally, the experimental results show that the accuracy rate of the KGSS algorithm is 92%. Theexperimental results also show that it provides new ideas and research directions for power grid data under big data in thefuture