Visual Query Method for Large Blackouts Based on Knowledge Graph

Large blackouts are inevitable and happen from time to time. At present, a large amount of data about large blackouts is published on the Internet. Though many outstanding works have achieved great success for analysis of the reasons. How to process large scale data in Chinese on the internet for reason analysis is still a great challenge. To tackle this problem, we propose a visual query method for large blackouts based on the knowledge graph. The web crawler and knowledge graph have been employed in this paper. Firstly, a great amount of data in Chinese is crawled from the internet. Secondly, information about the event entity, relation and attribute can be extracted with a deep learning method. Thirdly, a large blackouts knowledge graph can be built from the information related to those events. Finally, visual queries of nodes, relations, and paths for large blackouts based on the knowledge graph are implemented. In this paper, a visual query method for large blackouts information, relations between different accidents, the path among entity nodes is present. it can help to analyze a large amount of data about large blackouts to improve the safety of the power system.