Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (6A): 220300141-9.doi: 10.11896/jsjkx.220300141

• Artificial Intelligence • Previous Articles     Next Articles

Building Natural Language Interfaces for Distributed SCADA Systems Using Semantic Parsing

WANG Tao1,4, GUO Wushi3,4, DENG Jian5, CHEN Liang2,4   

  1. 1 School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China;
    2 School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China;
    3 School of Information and Software Engineering,University of Electronic Science and Technology,Chengdu 610054,China;
    4 Robotics Engineering Laboratory for Sichuan Equipment Manufacturing Industry,Deyang,Sichuan 618000,China;
    5 China Resources Power Investment Company Limited,Qingdao,Shandong 266071,China
  • Online:2023-06-10 Published:2023-06-12
  • About author:WANG Tao,born in 1982,master.His main research interests include indus-trial internet and internet of things.
  • Supported by:
    Key Science and Technology Project of Deyang(2018SZY066),Sichuan Engineering Technical College Planning Science and Research Project(KJGH2020G08),Deyang Science and Technology Plan Project(Transformation of Results)(2022KCZ158) and Sichuan Province Science and Technology Plan Project(2022YFG0224).

PDF (PC)

270

Abstract: Due to the traditional program fixed window interface human-computer interaction,large distributed industrial process SCADA systems are mainly operated in the central control room and maintained by professional staff,so the system construction and operation and maintenance costs are very high,and it is significant to explore the natural human-computer interaction interface and guide the system adaptive services.Taking a distributed SCADA system for various professional fields as the background,this paper analyzes the core requirements of natural human-computer interaction from the perspective of actual operation.Different semantic parsing algorithms are recommended according to the complexity of natural language instructions.For basic natural language instructions,TF-IDF keyword extraction algorithm is used and combined with cosine similarity for structured extraction,which is parsed into SCADA manipulation intermediate language and converted into actual manipulation instructions by formalization.For complex natural language instructions,a structured instruction parsing algorithm based on dependency syntax analysis is used to realize the real-time control interface.Experimental results show that the proposed natural language interface can better solve the human-computer natural language interaction problem of SCADA system.The average accuracy,recall and F-value of instruction parsing is 89.27%,89.28% and 89.27%,respectively.The average response time is 1.593s,which provides a more convenient means of interaction,especially for industrial and agricultural information control.

Key words: Natural language interface, Neural network language model, Dependent syntactic analysis, SCADA system, Semantic parsing

CLC Number: 

  • TP311
[1]BELLEGARDA J R,SILVERMAN K E A J I T O S,PROCESSING A.Natural language spoken interface control using data-driven semantic inference[J].IEEE Transactions on Speech and Audio Processing,2003,11(3):267-277.
[2]ZHENG Z,ZHAI M,PENG H,et al.Architecture and key technologies of distributed SCADA system for power dispatching and control[J].Dianli Xitong Zidonghua/Automation of Electric Power Systems,2017,41:71-77.
[3]SU Y,AWADALLAH A H,KHABSA M,et al.Building natural language interfaces to Web APIs[C]//Proceedings of 26th ACM International Conference on Information and Knowledge Management(CIKM 2017).Singapore,2017:177-186.
[4]MIN Q,SHI Y,ZHANG Y.A pilot study for Chinese SQL semantic parsing[C]//Proceedings of 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing,EMNLP(IJCNLP 2019).Hong Kong,2019:3652-3658.
[5]CHU E T H,HUANG Z Z.Dbos:A dialog-based object query system for hospital nurses[J].Sensors(Switzerland),2020,20,1-15.
[6]SETYAWAN M Y H,AWANGGA R M,EFENDI S R.Comparison Of Multinomial Naive Bayes Algorithm And Logistic Regression For Intent Classification In Chatbot[C]//Procee-dings of 2018 International Conference on Applied Engineering(ICAE 2018).Batam,Indonesia:2018 IEEE Indonesia CSS/RAS Joint Chapter.2018.
[7]LE Q,MIKOLOV T.Distributed representations of sentences and documents[C]//Proceedings of 31st International Confe-rence on Machine Learning(ICML 2014).Beijing,2014:2931-2939.
[8]PENNINGTON J,SOCHER R,MANNING C D.GloVe:Global vectors for word representation[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP 2014).Doha,2014:1532-1543.
[9]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies(NAACL HLT 2019).Minneapolis,2019:4171-4186.
[10]HOCHREITER S,SCHMIDHUBER J J N C.Long Short-Term Memory[J].Neural Computation,1997,9(8):1735-1780.
[11]CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of 2014 Conference on Empirical Methods in Natural Language Processing(EMNLP 2014).Doha,2014:1724-1734.
[12]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[C]//Proceedings of the IEEE.1998:2278-2323.
[13]ZHOU P,SHI W,TIAN J,et al.Attention-based bidirectional long short-term memory networks for relation classification[C]//Proceedings of 54th Annual Meeting of the Association for Computational Linguistics(ACL 2016).Berlin,2016:207-212.
[14]KHOMENKO V,SHYSHKOV O,RADYVONENKO O,et al.Accelerating recurrent neural network training using sequence bucketing and multi-GPU data parallelization[C]//Proceedings of 1st IEEE International Conference on Data Stream Mining and Processing(DSMP 2016).Lviv,2016:100-103.
[15]VERLEYSEN M,FRANCOIS D.The curse of dimensionality indata mining and time series prediction.In Proceedings of 8th International Workshop on Artificial Neural Networks[C]//Computational Intelligence and Bioinspired Systems(IWANN 2005).Vilanova i la Geltru,2005:758-770.
[16]GOODMAN B A,GROS Z,et al.Research in knowledge repre-sentation for natural language communication and planning assistance[R].1988.
[17]BENGIO Y,DUCHARME R,VINCENT P,et al.A Neural Probabilistic Language Model[J].Journal of Machine Learning Research,2003.
[18]SOWMYA KAMATH S,ANANTHANARAYANA V S.Discovering composable web services using functional semantics and service dependencies based on natural language requests[J].Information Systems Frontiers 2019,21:175-189.
[19]TIAN C Y,CHEN D H,WANG M,et al.Structured Proces-sing for Pathological Reports Based on Dependency Parsing[J].Journal of Computer Research and Development,2016,53:2669-2680.
[20]XU X,LIU C,SONG D.SQLNet:Generating Structured Queries From Natural Language Without Reinforcement Learning[J].arXiv:1711.04436,2017.
[21]YU T,LI Z,ZHANG Z,et al.TypeSQL:Knowledge-based type-aware neural text-to-SQL generation[C]//Proceedings of 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2018:588-594.
[22]HWANG W,YIM J,PARK S,et al.A Comprehensive Exploration on WikiSQL with Table-Aware Word Contextualization[J].arXiv:1902.01069,2019.
[1] YANG Xiao-ping, ZHANG Zhong-xia, WANG Liang, ZHANG Yong-jun, MA Qi-feng, WU Jia-nan and ZHANG Yue. Automatic Construction and Optimization of Sentiment Lexicon Based on Word2Vec [J]. Computer Science, 2017, 44(1): 42-47.
[2] FENG Yun-tian, ZHANG Hong-jun, HAO Wen-ning and CHEN Gang. Named Entity Recognition Based on Deep Belief Net [J]. Computer Science, 2016, 43(4): 224-230.
[3] SUN Xiao, SUN Chong-yuan and REN Fu-ji. New Word Detection and Emotional Tendency Judgment Based on Deep Structured Model [J]. Computer Science, 2015, 42(9): 208-213.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.