Abstract
The mutual penetration and deep integration of the Internet of Things and the power grid make the modern power grid more intelligent. The big data of the smart grid is distributed among different levels of multiple business systems of each unit. There are different data structures, inconsistent patterns, and inconsistent standards. It is difficult for Chinese smart grid to manage multi-source heterogeneous data uniformly, This paper studies how to combine data fusion technology with enterprise management requirements, and converts distributed data in different business systems into a unified, accurate, and decision-oriented format. Accordingly, we can eliminate information barriers, share enterprise data resources, and promote the company’s management level as well. Firstly, data cleaning technology is adopted in this paper to preprocess multi-source heterogeneous data of the smart grid. The aim is to make a unified structure and facilitate the data fusion. Then a multi-source heterogeneous data fusion model is proposed to achieve data fusion in different levels according to the layered strategies of the Internet of Things. The data fusion and Markov logic network are used to focus on the data conflict problem in the process of fusion.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Yuanbin, X.: Research and application of multi-source heterogeneous parameter fusion method based on power big data. Electron. Des. Eng. 24(14), 14–16 (2016)
Jie, Y., Wang, Q.: A survey of multi-source data fusion algorithms. Aerosp. Electron. Warf. 33(06), 37–41 (2017)
Richardson, M., Domingos, P.: Markov logic networks. Mach. Learn. 62(1–2), 107–136 (2006)
Yang, L.: Research and Application of Smart Grid Big Data Fusion Method. North China Electric Power University, Beijing (2016)
Zhang, Y., Li, Q., Peng, Z.: Two-phase data conflict resolution method based on Markov logic network. J. Comput. 35(01), 101–111 (2012)
Yuan, T.: Research and Implementation of Multiple Source Heterogeneous Data Fusion Method for EMUs. Beijing Jiaotong University, Beijing (2017)
Kapoor, A., Biswas, K.K., Hanmandlu, M.: Unusual human activity detection using Markov Logic Networks. In: IEEE International Conference on Identity 2017, Security and Behavior Analysis (ISBA), New Delhi, India, pp. 1–6 (2017)
Yang, K., Zhao, S., Hua, Q.: Fast dense stereo matching based on recursive adaptive weights. J. Beihang Univ. 39(07), 963–967 (2013)
Huang, J., Huang, X.: Design and implementation of correlation weight algorithm based on hadoop platform. Comput. Eng. 3(20), 1–6 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Jia, K., Ju, X., Zhang, H. (2018). Research on Big Data Fusion Method of Smart Grid in the Environment of Internet of Things. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11067. Springer, Cham. https://doi.org/10.1007/978-3-030-00018-9_56
Download citation
DOI: https://doi.org/10.1007/978-3-030-00018-9_56
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-00017-2
Online ISBN: 978-3-030-00018-9
eBook Packages: Computer ScienceComputer Science (R0)