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Risk assessment of the mine environment information based on multi-sensor information fusion

机译:基于多传感器信息融合的矿山环境信息风险评估

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摘要

In recent years China's frequent mine incidents, the scene of the accident left a lot of information and the remnants of environmental information, with mine difficult after coal mine accident dangerous environment of many characteristics, based on multi-sensor information fusion (MSF) of the coal mine environment Risk assessment of the new algorithm . Which adopts BP neural network algorithm and establishes model of coal mine environmental information risk of neural network model predicted the three-layer back propagation neural network, the neural network to connect the entire network structure is 5 x 12x5. The five input variables are the H2S (%). Temperature (X2), wind speed (m / s), methane (%), CO (%). The five output value is the level of security. The simulation experiments show that the model can accurately assess environmental risk coal mine the extent of the model and can verify the effectiveness and feasibility. The application result shows that the prediction with this method can achieve higher better utility and expensive value.
机译:近年来,我国矿难频发,事故现场留下了大量的信息和残留的环境信息,具有煤矿事故多特征的危险环境后,基于多传感器信息融合(MSF)的矿难。新算法对煤矿环境的风险评估。其中采用BP神经网络算法并建立煤矿环境信息风险模型的神经网络模型预测了三层反向传播神经网络,连接整个网络结构的神经网络为5 x 12x5。五个输入变量是H2S(%)。温度(X2),风速(m / s),甲烷(%),CO(%)。这五个输出值是安全级别。仿真实验表明,该模型可以准确评估煤矿环境风险的程度,并可以验证其有效性和可行性。应用结果表明,该方法的预测效果更好,实用价值更高。

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