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Defect knowledge graph construction and application in multi-cloud IoT
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AI
DOI：10.1186/s13677-022-00334-1.png)
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Abstract
En 中文
As the State Grid Multi-cloud IoT platform grows and improves, an increasing number of IoT applications generate massive amounts of data every day. To meet the demands of intelligent management of State Grid equipment, we proposed a scheme for constructing the defect knowledge graph of power equipment based on multi-cloud. The scheme is based on the State Grid Multi-cloud IoT architecture and adheres to the design specifications of the State Grid SG-EA technical architecture. This scheme employs ontology design based on a fusion algorithm and proposes a knowledge graph reasoning method named GRULR based on logic rules to achieve a consistent and shareable model. The model can be deployed on multiple clouds independently, increasing the system's flexibility, robustness, and security. The GRULR method is designed with two independent components, Reasoning Evaluator and Rule Miner, that can be deployed in different clouds to adapt to the State Grid Multi-cloud IoT architecture. By sharing high-quality rules across multiple clouds, this method can avoid vendor locking and perform iterative updates. Finally, the experiment demonstrates that the GRULR method performs well in large-scale knowledge graphs and can complete the reasoning task of the defect knowledge graph efficiently.
Keywords:
Multi-cloud IoT
Defect knowledge graph
Ontology design
Knowledge graph reasoning
Journal
J
IF:
4.3
Papers:
729
Citations:
2.2K