基于朴素贝叶斯和D-S证据理论的多时空数据融合

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Abstract Aiming at solving the problem of different sampling periods between sensors and various devices in the electric internet of things, this paper proposes a multi-time-space data fusion method based on naive Bayes and D-S evidence theory. The outstanding advantage of this method is that it combines the data of sensors in multiple time periods and multiple different locations. Firstly, the naive Bayes classifier is used to obtain the reliability distribution, which overcomes the shortcomings of the original expert system for reliability distribution. Then D-S evidence theory is used to fuse, and finally get the state evaluation of the system, which effectively integrate multi-time-space data. The experimental results show that the proposed method has obvious improvement compared with other machine learning algorithms, and can effectively evaluate the state of the system.

Key words： electric internet of things naive Bayes D-S evidence theory multi-time-space data fusion

Received: 14 April 2019 Published: 19 November 2019

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	Lu Jun
	Wang Ziyao
	Yu Tao

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Lu Jun,Wang Ziyao,Yu Tao. Multi-time-space data fusion based on naive Bayes and D-S evidence theory[J]. Electrical Engineering, 2019, 20(11): 27-32.

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http://dqjs.cesmedia.cn/EN/Y2019/V20/I11/27

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