Abstract
The power grid disaster response system is an important part of the power grid, and the multi-perception data is an important basis for data perception and in-depth decision-making of the power grid disaster situation. Through the establishment of the multi-perception data analysis model, it can meet the needs of the grid emergency business to deal with various emergencies quickly, efficiently, accurately and flexibly.
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
References
Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Comput Vis 233–254
Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Intell (06):713–724. https://doi.org/10.1109/tpami.2003.1201821
He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell (12):2341–2353
Shwartz S, Namer E, Schechner YY (2006) Blind haze separation. Proc IEEE CVPR (01):1984–1991
Omer I, Werman M (2004) Color lines: image specific color representation. In: Proceeding of IEEE computer vision and pattern recognition, vol 06, pp 946–953
Wang F (2006) Quantitative methods and applications in GIS. CRC Press, Boca Raton, Florida, pp 77–96
Yan Z, Parent C, Spaccapietra S et al (2010) A hybrid model and computing platform for spatio-semantic trajectories. In: Proceedings of the 7th extended semantic web conference (ESWC), Heraklion, Greece, Cited:1
Bajwa S, Chung E, Kuwahara M (2005) Performance evaluation of an adaptive travel time prediction model. In: Proceeding of the 8th international IEEE conference on intelligent transportation systems, Vienna, Austria
Aamodt A, Plaza E (1994) Case-based reasoning: foundational issues, methodological variations and system approaches. AI Commun 7(1):39–59
Kwon J, Barkley T, Hranac R et al (2010) Decomposition of travel time reliability into various sources: incidents, weather, work zones, special events, and base capacity. In: Proceedings of 90th transportation research board (TRB) annual meeting, Washington DC
Acknowledgements
First of all, I would like to thank my colleague Min Xu, who has provided me with various helps in the creation of the paper, especially in the verification of data collection, so that I can fully mine various applications of multi-source sensor data. In addition, I also want to thank my company for providing me with the basic conditions for continuous research.
This paper was supported by the science and technology project in State Grid Corporation, which name is ‘Research on the Key Technologies of Power Grid Disaster Intelligent Perception and Emergency Command (5700-202019185A-0-0-00).’
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhu, L., Yang, J., Yang, J., Wang, H. (2021). Research on Multi-perception Data Analysis Model for Power Grid Emergency Services. In: Yu, Z., Patnaik, S., Wang, J., Dey, N. (eds) Advancements in Mechatronics and Intelligent Robotics. Advances in Intelligent Systems and Computing, vol 1220. Springer, Singapore. https://doi.org/10.1007/978-981-16-1843-7_34
Download citation
DOI: https://doi.org/10.1007/978-981-16-1843-7_34
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-1842-0
Online ISBN: 978-981-16-1843-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)