基于知识图谱的报文故障分析与检索

doi:10.3969/j.issn.0255-8297.2023.03.002

Abstract

Abstract: Aiming at the problems of complex fault analysis mode and increasing difficulty of fault removal caused by the expansion of the power grid, a fault analysis and retrieval of message based on knowledge graph is proposed. Firstly, the construction of power dispatching fault knowledge graph is completed by combining natural language processing technology with expert documents. Then the expert knowledge is stored in the graph in the form of atomic rules (indivisible rules) to achieve intelligent fault analysis and retrieval and assist maintenance personnel in decision-making, thereby improving the efficiency of business process. Finally, combined with the knowledge graph and log information, artificial intelligence is used to analyze the cause of failure, and the optimal solution is obtained from multiple potential solutions. Experimental results on both real and synthetic data sets show that the proposed method can achieve good results on fault analysis and retrieval in power dispatching.

Key words: fault analysis, knowledge graph, natural language processing, dispatching automation

CLC Number:

P751.1

JI Wenlu, DENG Xing, ZHU Hongqin, ZHAO Yang, JIANG Jielin. Fault Analysis and Retrieval of Message Based on Knowledge Graph[J]. Journal of Applied Sciences, 2023, 41(3): 378-390.

References

[1] Jiang Y, Ni Y, Su A, et al. New information interface scheme for substation monitoring information based on e language and IEC104 protocol [C]//2020 Asia Energy and Electrical Engineering Symposium (AEEES), 2020: 678-683.
[2] Wang B, Li F, Chen T, et al. Research on deep analysis technology of real time interaction protocol in power industrial control system [C]//2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), 2021: 75-80.
[3] Paul B B, Orven E L. Design and implementation of real-time mobile-based water temperature monitoring system [J]. Procedia Computer Science, 2017, 124: 698-705.
[4] 毕艳冰, 蒋林, 王新军, 等. 面向服务的智能电网调度控制系统架构方案[J]. 电力系统自动化, 2015, 39(2): 92-99. Bi Y B, Jiang L, Wang X J, et al. Service-oriented architecture scheme for smart grid dispatch control system [J]. Automation of Electric Power Systems, 2015, 39(2): 92-99. (in Chinese)
[5] 程路明, 楼平, 诸骏豪, 等. 基于APRIORI-贝叶斯优化XGBoost的电力通信网根告警预测[J]. 电力建设, 2022, 43(1): 113-121. Cheng L M, Lou P, Zhu J H, et al. Power communication network root alarm prediction based on APRIORI Bayesian optimization XGBoost [J]. Power Construction, 2022, 43(1): 113-121. (in Chinese)
[6] 黄军凯, 张迅, 刘君, 等. 基于IGA-BP混合算法的电力变压器智能故障诊断分析[J]. 信息系统工程, 2021(10): 105-107. Huang J K, Zhang X, Liu J, et al. Intelligent fault diagnosis analysis of power transformer based on IGA-BP hybrid algorithm [J]. Information System Engineering, 2021(10): 105-107. (in Chinese)
[7] 朱燕芳, 闫磊, 常康, 等. 基于深度卷积神经网络的电力系统故障预测[J]. 电源学报, 2022: 1-14. Zhu Y F, Yan L, Chang K, et al. Power system fault prediction based on deep convolution neural network [J]. Journal of Power Supply, 2022: 1-14. (in Chinese)
[8] Yang B, Liao Y. Research on enterprise risk knowledge graph based on multi-source data fusion [J]. Neural Computing and Applications, 2022, 34(4): 2569-2582.
[9] Liu G, Li L. Knowledge fragment cleaning in a genealogy knowledge graph [C]//2020 IEEE International Conference on Knowledge Graph (ICKG), 2020: 521-528.
[10] 曹皓伟, 徐建良, 窦方坤. 基于Neo4j生物医药知识图谱的构建[J]. 计算机时代, 2020, 336(6): 35-38. Cao H W, Xu J L, Dou F K. Construction based on Neo4j biomedical knowledge graph [J]. Computer Age, 2020, 336(6): 35-38. (in Chinese)
[11] Wei T, Jiang Y, Wang Y, et al. Semi-automated construction of a knowledge graph with template [J]. IOP Conference Series Materials Science and Engineering, 2020, 782: 032054.
[12] Zhao Q, Huang H, Ding H. Study on military regulations knowledge construction based on knowledge graph [C]//20217th International Conference on Big Data and Information Analytics (BigDIA), 2021: 180-184.
[13] 阮光册, 樊宇航, 夏磊. 知识图谱在实体检索中的应用研究综述[J]. 图书情报工作, 2020, 64(14): 126-135. Ruan G C, Fan Y H, Xia L. Overview of the application of knowledge atlas in entity retrieval [J]. Library and Information Work, 2020, 64(14): 126-135. (in Chinese)
[14] Wu X, Xiao L, Sun Y, et al. A survey of human-in-the-loop for machine learning [J]. Future Generation Computer Systems, 2022, 135: 364-381.
[15] 唐钰, 唐加山. 一种改进的TF-IDF文本分类算法[J]. 信息技术与信息化, 2022, 3: 13-16. Tang Y, Tang J S. An improved TF-IDF text classification algorithm [J]. Information Technology and Informatization, 2022, 3: 13-16. (in Chinese)
[16] Li X, Deng Q. Chinese position segmentation based on ALBERT-BiGRU-CRF model [C]//2021 International Symposium on Computer Technology and Information Science (ISCTIS), 2021: 116-120.
[17] Sun Q, Jankovic M V, Bally L, et al. Predicting blood glucose with an LSTM and BiLSTM based deep neural network [C]//201814th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018: 1-5.
[18] Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes [C]//AAAI Conference on Artificial Intelligence, 2014: 1112-1119.
[19] Fu Z, Xian Y, Zhu Y, et al. HOOPS: human-in-the-loop graph reasoning for conversational recommendation [C]//Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021: 2415-2421.
[20] Kusnerr M, Sun Y, Kolkinl N, et al. From word embeddings to document distances [C]//Proceedings of the 32nd International conference on machine learning, 2015: 957-966.
[21] Jayakodi K, Bandara M, Perera I, et al. WordNet and cosine similarity based classifier of exam questions using bloom’s taxonomy [J]. International Journal of Emerging Technologies in Learning, 2016, 11: 142-149.
[22] Athrey R G, Bansal S K, Ngomo A C N, et al. Template-based question answering using recursive neural networks [C]//IEEE 15th International Conference on Semantic Computing (ICSC). IEEE, 2021: 195-198.
[23] Liu C, Zhang Q, Feng J, et al. A Chinese word segmentation method based on dictionary and HMM [C]//6th International Conference on Electronic Information Technology and Computer Engineering, 2022: 644-649.
[24] Bourlard H, Morgan N. Hybrid HMM/ANN systems for speech recognition: overview and new research directions [J]. International School on Neural Networks, Initiated by IIASS and EMFCSC, 1997: 389-417.
[25] Roy S, Mariappan R, Dandapat S, et al. QART: a system for real-time holistic quality assurance for contact center dialogues [C]//AAAI Conference on Artificial Intelligence, 2016: 3768-3775.
[26] Shao T, Cai F, Chen H, et al. Length-adaptive neural network for answer selection [C]//International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 869-872.
[27] Sakata W, Shibata T, Tanaka R, et al. FAQ retrieval using query-question similarity and BERT-based query-answer relevance [C]//International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 1113-1116.

Fault Analysis and Retrieval of Message Based on Knowledge Graph

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 4

Recommended Articles

Metrics

Comments

[1]	LIU Minghui, TANG Wangjing, XU Bin, TONG Meihan, WANG Liming, ZHONG Qi, XU Jianjun. Named Entity Recognition Algorithm Enhanced with Entity Category Information [J]. Journal of Applied Sciences, 2023, 41(1): 1-9.
[2]	OUYANG Zhiyou, CHEN Chen, WANG Yuqian, CHEN Jingang, YIN Zhao, ZHOU Qingsong. Protein Small Molecule Affinity Prediction Based on Natural Language Processing [J]. Journal of Applied Sciences, 2019, 37(3): 327-335.
[3]	ZHANG Bo-liang, ZHONG Wei-dong, YANG Xiao-yuan. Differential Fault Analysis on Feistel Block Cipher in Internet of Things [J]. Journal of Applied Sciences, 2016, 34(5): 547-554.
[4]	SHEN Xuan, ZHAO Guang-yao, LI Chao, LI Rui-lin. Differential Fault Analysis on Encryption Mode of MD5 [J]. Journal of Applied Sciences, 2015, 33(5): 481-490.