基于权值时变模型的矿井突水最优逃生路径的动态选择
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TD77+3

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国家自然科学基金资助项目(51509149,51379119);国家重点研发计划资助项目(2018YFC0604702);山东省重点研发计划资助项目(2018GSF120009)


Dynamic selection of optimal escape path of mine water inrush based on weight time-varying model
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National Natural Science Foundation of China(51509149,51379119) ;Supported by National Key R&D Program of China(2018YFC0604702);Key R&D Program of Shandong Province(2018GSF120009)

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    摘要:

    为了减少矿井突水发生时造成的人员伤亡,为井下人员动态选择出合理的逃生路径。综合考虑随时间变化的水位高度和水的流速对矿工逃生路径选择的影响,将其量化为对人体稳定性的影响,并将所研究的时间进行分段,根据不同时段下巷道内平均水流速与临界流速之比求得动态危险系数,并实时加权到邻接矩阵上,建立以巷道初始当量长度与危险性程度为衡量最优路径的权值时变数学模型,运用改进的Dijkstra算法进行求解,并结合W煤矿矿井进行实例分析。结果表明,优化后的模型更能反映突水时巷道内的实际情景,同时,改进的Dijkstra算法能够有效应用于突水时期最优逃生路径的动态选择中,使选择的逃生路径更加安全可靠。

    Abstract:

    In order to reduce casualties caused by mine water inrush, a reasonable escape path is selected for underground personnel. Considering the influence of time-varying water level and water flow rate on the selection of miners ’ escape path, it is quantified as the influence on human stability, and the time studied is segmented. According to the ratio of the average water flow rate to the critical flow rate in the roadway in different periods, the dynamic dangerous coefficient is obtained, and it is weighted to the adjacency matrix in real time. The weight time-varying mathematical model is established to measure the optimal path with the initial equivalent length of the roadway and the degree of risk. The improved-Dijkstra algorithm is used to solve the model, and an example analysis is carried out in W coal mine. The results show that the optimized model can better reflect the actual situation in the roadway during water inrush. At the same time, the improved-Dijkstra algorithm can be effectively used in the dynamic selection of the optimal escape path during water inrush period, so that the selected escape path is more safe and reliable.

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引用本文

于丹,颜伟,李劭昱. 基于权值时变模型的矿井突水最优逃生路径的动态选择[J]. 科学技术与工程, 2022, 22(12): 4762-4771.
Yu Dan, Yan Wei, Li Shaoyu. Dynamic selection of optimal escape path of mine water inrush based on weight time-varying model[J]. Science Technology and Engineering,2022,22(12):4762-4771.

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  • 收稿日期:2021-11-08
  • 最后修改日期:2022-03-01
  • 录用日期:2022-03-29
  • 在线发布日期: 2022-05-07
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