Research on Coal Seam Floor Water Inrush Monitoring Based on Perception of IoT Coupled with GIS

Affiliation(s)

School of Energy and Safety, Anhui University of Science and Technology, Huainan, China.

School of Energy and Safety, Anhui University of Science and Technology, Huainan, China.

Abstract

To the complication and uncertainty in coal seam floor water-inrush monitoring, Internet of Things (IoT) perception is applied to the monitoring and controlling of coal seam floor water inrush with major impacting factors analyzed, and an open distribution information processing platform is constructed based on IoT-GIS coupling perception. Then using the platform to comprehensively perceive various floor water inrush impacting parameters, an AHP model is established. At this stage, by means of weight reasoning algorithm based on dynamic Bayesian network, the AHP weight can be worked out using the two-way probability transfer and chain rules. Then the multiple factors are spatially fused by GIS to form a non-linear mathematical model for the calculation of the water inrush relative probability index. After that, the discrimination threshold of the comb graph for the floor water inrush relative probability index is used to further identify the floor water inrush mode. The experiments in 10# Coal Seam of Suntuan Mine show that, the accuracy perceived the floor water inrush is above 92%, and the platform of IoT-GIS coupling perception has the obvious technical advantage than traditional monitoring technology. Therefore, it has has demonstrated strong systematic robustness, important theoretical and application significance.

To the complication and uncertainty in coal seam floor water-inrush monitoring, Internet of Things (IoT) perception is applied to the monitoring and controlling of coal seam floor water inrush with major impacting factors analyzed, and an open distribution information processing platform is constructed based on IoT-GIS coupling perception. Then using the platform to comprehensively perceive various floor water inrush impacting parameters, an AHP model is established. At this stage, by means of weight reasoning algorithm based on dynamic Bayesian network, the AHP weight can be worked out using the two-way probability transfer and chain rules. Then the multiple factors are spatially fused by GIS to form a non-linear mathematical model for the calculation of the water inrush relative probability index. After that, the discrimination threshold of the comb graph for the floor water inrush relative probability index is used to further identify the floor water inrush mode. The experiments in 10# Coal Seam of Suntuan Mine show that, the accuracy perceived the floor water inrush is above 92%, and the platform of IoT-GIS coupling perception has the obvious technical advantage than traditional monitoring technology. Therefore, it has has demonstrated strong systematic robustness, important theoretical and application significance.

Cite this paper

X. Meng, J. Wang and Z. Gao, "Research on Coal Seam Floor Water Inrush Monitoring Based on Perception of IoT Coupled with GIS,"*Engineering*, Vol. 4 No. 8, 2012, pp. 467-476. doi: 10.4236/eng.2012.48061.

X. Meng, J. Wang and Z. Gao, "Research on Coal Seam Floor Water Inrush Monitoring Based on Perception of IoT Coupled with GIS,"

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