摘要
为了实现矿井火灾的早期预警,采用了多源信息融合技术,集成了煤矿安全监控系统、火灾束管监测系统、无线自组网温度监测系统和分布式光纤温度监测系统的指标数据,基于实验研究、现场观测、专家经验等数据,建立了矿井火灾多源信息融合预警系统,结合人工神经网络、多指标预警逻辑推理、运用灰色关联分析法建立实验数据拟合函数的计算方法,实现了矿井火灾多源信息融合算法,处理来自不同位置、具有不同物理意义的多源火灾信息,得出了火灾预警指标体系和火灾发展规律,以及煤自燃程度6个阶段的预警指标气体判定和对应的温度范围.
To realize early warning of coal mine fire, we utilize the technology of multi-source information fusion, integrate the index data of safety monitoring system, beam tube monitoring system of fire, wireless ad-hoc network monitoring system of temperature, and distributed fiber optics monitoring system of temperature in coal mine. Based on the data of experimental test, in-site observation and expert experiences, et al, the early warning system of multi-source information fusion for coal mine fire is set up. Combined with the algorithmic method of artificial neural network, logical reasoning of multi-criteria early warning, and fitting functions of experiment data by means of gray relative analysis method, the algorithm of multi-source information fusion of coal mine fire is developed, which can dispose multi-source information of fire from different locations with different physical significance, hence establish the early warning index system and derive law of development for coal mine fire, and finally determine the gas judges of early warning index and their corresponding temperature ranges for the 6 stages of coal spontaneous combustion degree.
出处
《采矿与安全工程学报》
EI
北大核心
2011年第4期638-643,共6页
Journal of Mining & Safety Engineering
基金
国家"十一五"科技支撑计划项目(2007BAK29B03)
"长江学者和创新团队发展计划"创新团队项目(IRT0856)
陕西省教育厅专项科研计划项目(09JK590)
关键词
矿井火灾
多源信息融合
算法
预警系统
coal mine fire
multi-source information fusion
algorithm
early warning system
