# Construction of Power Fault Knowledge Graph Based on Deep Learning
## 5 Conclusions  
The main work of this paper is summarized as follows:  
1. Aiming at the problems in the process of constructing a fault knowledge graph, this paper proposed a log anomaly detection model: BiSGRU-GA, which can quickly and efficiently detect the anomaly log in the power system and serve as fault data after further analysis and processing, it enriches the source of fault data and facilitate relationship analysis;
2. Aiming to solve the problems of the low accuracy of traditional entity recognition models and relationship recognition models and being unable to make full use of the context information of sentences in the construction of fault knowledge graph, an entity extraction model based on BBiGRU-GA-CRF and a relationship extraction model based on BiGRU-GA are proposed. On the basis of making full use of the  
Figure 19: Comparison of relation extraction models experimental results in different dimensions.  
context information of sentences, higher accuracy of entity extraction and relationship extraction is achieved.  
Based on knowledge graph technology and system log anomaly detection technology, this paper makes a detailed study on the construction of a power system fault knowledge graph and its application in fault diagnosis. However, due to the limitations of the researchers' knowledge and practical experience in this field, more in-depth research and practice are still needed for entity and relationship extraction methods and fault diagnosis algorithms. Future research work will be implemented from the following aspects:  
1. In terms of fault data, although this paper obtains a large number of fault data based on the power system anomaly log and expands the source of fault data, the scale of the data set is still small. In the future research work, we will continue to collect relevant fault data to expand the scale of the data set. In addition, we should continue to deeply study the ontology construction technology to enhance the preciseness of fault data, so as to better complete the research work of entity and relationship extraction;
2. In the theoretical research of models and methods, the BiSGRU-GA model, BBiGRU-GA-CRF model, and BiGRU-GA model used in this paper improved on the basis of the mainstream deep learning model, and achieved good results. In future study and research work, we will try to apply the above model to other relevant production fields, and improve and optimize the problems and shortcomings exposed by the model according to the subsequent experimental results.  
Conceptualization: P.L. and B.T.; methodology: P.L. and S.G.; resources: X.L., L.Y. and Y.L.; data curation: C.M. and W.Z.; writing--original draft preparation: S.G.; writing--review and editing: P.L. and L.B.; supervision: P.L.; funding acquisition: S.G. All authors have read and agreed to the published version of the manuscript.  
This work is supported by China's national key R&D plan and supported by the project name: research and application demonstration of key technologies of integrated emergency communication based on radio and television system, subject No. 2017 YFC0806200.  
Not applicable.  
We are grateful to the anonymous reviewers for comments on the original manuscript.