Abstract
Aiming at the geography and manpower inconvenience during the monitoring and inspection of the gas station, a remote online monitoring system for the gas station based on the sensor network is proposed in this paper, and the early warning analysis of the abnormal state of the gas station is proposed based on the data mining technology.
Firstly, a gas station senor dataset is built based on the sensor network of the gas station. Then, based on the B/S architecture, a gas station online monitoring system is built. Finally, based on the sensor dataset data mining, an abnormal state of the gas station analysis method is proposed.
Experiments show that the classifier method proposed in this paper has the generalization ability, it can analysis and alarm the abnormal state of the gas station which improve the intelligence and convenience of the gas station monitoring.
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Acknowledgments
This paper is supported by the Project for the Key Project of Beijing Municipal Education Commission under Grant No. KZ201610005007, Beijing Postdoctoral Research Foundation under Grant No. 2015ZZ-23, China Postdoctoral Research Foundation under Grant Nos. 2016T90022, 2015M580029, Computational Intelligence and Intelligent System of Beijing Key Laboratory Research Foundation under Grant No. 002000546615004, and The National Natural Science Foundation of China under Grant No. 61672064.
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Wei, Z., Jia, K., Sun, Z. (2018). Sensor Data Mining for Gas Station Online Monitoring. In: Krömer, P., Alba, E., Pan, JS., Snášel, V. (eds) Proceedings of the Fourth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2017. Advances in Intelligent Systems and Computing, vol 682. Springer, Cham. https://doi.org/10.1007/978-3-319-68527-4_28
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DOI: https://doi.org/10.1007/978-3-319-68527-4_28
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