Abstract
With the continuous increase of the scale of the power grid and the rapid development of the smart grid, the coverage of the wireless power private network has gradually expanded. How to make full use of the data information of the intelligent terminal of the power grid to realize unattended network monitoring and automatic operation and maintenance is an urgent problem to be solved at present. The knowledge graph construction model proposed in this article is to combine machine learning algorithms to convert huge and scattered terminal device data information and fault case data into professional domain knowledge graphs and store them in graph databases. In order to use knowledge reasoning to realize the treatment of grid faults in the region and assist decision-making. In this way, network failure prediction and decision guidance to the operation and maintenance personnel are realized. And improve the efficiency of power grid problem handling, save a lot of manpower monitoring, making the overall security control of the power grid better control.
Published Version
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