Abstract
In view of the poor teaching function and operation performance of the existing power grid monitoring technology course training system, the optimal design of the power grid monitoring technology course teaching training system is realized by using multi-modal information processing technology from three aspects of hardware, database and software functions. Firstly, adjust the operation mode of the system server, refit the curriculum teaching information collector, system communication network architecture, embedded processor, memory and other hardware equipment, and use the optimization circuit to realize the connection of hardware equipment. Then set the unified storage mode of data, and build the system database combined with the logical relationship between data. Based on this, a virtual training scene of power grid monitoring technology course teaching is constructed, in which the implementation process of power grid monitoring technology is simulated, and the course resources are retrieved by using multimodal information processing technology. Experiments show that compared with the traditional system, the functional operation success rate and throughput of the system in this paper are higher.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Young, K.Y., Cheng, S.L., Ko, C.H., et al.: A novel teaching and training system for industrial applications based on augmented reality. J. Chinese Institute of Engineers 43(8), 796–806 (2020)
Zheng, X.: Analysis of teaching dilemma and path choice of comprehensive accounting training course based on computer-aided technology. J. Physics Conference Series 1648, 032123 (2020)
Ding, Y.: Performance analysis of public management teaching practice training based on artificial intelligence technology. J. Intelligent and Fuzzy Syst. 40(5), 1–14 (2020)
Gao, H.: Engineering training based on virtual simulation technology. Computer Simulation 37(7), 391–393+408 (2020)
Hong, F.: Discussion on the performance evaluation index system of basic course teaching team in colleges and universities. Asian Agricultural Res. 12(03), 76-78 (2020)
Dezhbankhan, F., Baranovich, D.L., Abedalaziz, N., et al.: Impacts of metacognition management system (MMS) training course on metacognitive competencies. Int. Educ. Stud. 14(1), 12 (2020)
Sns, A., Ya, B., Ks, B., et al.: Development of a-txt system compatible introductory teaching materials for electric power engineering using gaming simulation - ScienceDirect. Procedia Computer Science 176, 1557–1566 (2020)
Liu, H., Shen, J., Wang, G., Liu, S., Guo, Q.: Construction of virtual simulation experimental teaching resource platform based on engineering education. Experimental Technology and Manage. 36(12), 19–22+35 (2019)
Zheng, X., Luo, Y., Peng, X., et al.: Construction of training platform for simulation substation based on combination of virtuality and reality. Experimental Technology and Manage. 36(12), 201–205 (2019)
Jiao, R., Zhong, X., Jiao, J., et al.: Research on digital physical hybrid simulation training system for smart substation.Journal of State Grid Technology College 23(04), 77–80 (2020)
Funding
Research topic of Beijing Polytechnic: Research on Operation Control Technology of Multi-terminal Flexible DC System in DC Distribution Network (2020Z107-KXZ).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Zhang, L., Xiao, Y., Qu, M. (2022). Teaching and Training System of Power Grid Monitoring Technology Based on Multimodal Information Processing. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-031-21161-4_34
Download citation
DOI: https://doi.org/10.1007/978-3-031-21161-4_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-21160-7
Online ISBN: 978-3-031-21161-4
eBook Packages: Computer ScienceComputer Science (R0)