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针对煤层底板突水预测的复杂性和不确定性,把物联网(IoT)感知技术应用于底板突水的预测监控,分析影响底板突水的主控因素,构建一种开放的分布式IoT-GIS耦合感知信息处理平台。全方位获取影响底板突水的各类参数。建立层次分析(AHP)模型。采用基于动态贝叶斯网络的权值推理算法,利用概率双向传递及链式规则推导出AHP权值。再由GIS进行多因素空间融合处理,建立用以计算突水相对概率指数的非线性数学模型。依据底板突水相对概率指数梳状分布曲线的分区阈值,进一步辨识底板突水模式。在孙疃煤矿10号煤层完成的试验表明,IoT-GIS耦合感知平台对底板突水的感知准确率大于92%。
In view of the complexity and uncertainty of coal floor water inrush prediction, the Internet of Things (IoT) sensing technology is applied to the forecasting monitoring of floor water inrush, and the main control factors that affect water inrush from the floor are analyzed. An open distributed IoT- GIS coupled sensing information processing platform. All-round access to various parameters affecting the floor water bursting. Establish Analytic Hierarchy Process (AHP) model. Adopting the weight value inference algorithm based on dynamic Bayesian network, the AHP weights are deduced by using the two-way probability transmission and the chain rule. Then GIS multi-factors spatial fusion processing, to establish a mathematical model for calculating the relative probability index of water inrush. According to the partitioning threshold of the relative probability index comb distribution curve of the floor water inrush, the floor water inrush pattern is further identified. Experiments done in No 10 coal seam of Sun 疃 Coal Mine show that the IoT-GIS coupled sensing platform has an accurate sensing accuracy of over 92% on water inrush from the floor.