基于多源信息融合的煤与瓦斯突出动态预警模型

针对传统的煤与瓦斯突出预警模型融合度不高、自分析与优化能力不足、预警原因难追溯等问题,采用关联规则算法和证据理论算法相结合的方法,建立了基于多源信息融合的煤与瓦斯突出动态预警模型。分析了定性和定量预警指标对应的关联规则项目设置方法,定义了用于煤与瓦斯突出预警分析的关联规则,得到强关联规则确定方法和预警指标优选方法;建立了用于煤与瓦斯突出预警分析的证据理论识别框架,确定了基于关联分析结果的基本置信度分配规则,并给出证据合成方法和基于类概率函数的融合决策方法;研究得出采用基本置信度分配函数进行预警原因追溯的方法,并给出了模型动态更新方法。测试结果表明,利用该模型进行煤与瓦斯突出预警分析,可实现预警指标自动筛选、多指标自动融合分析与决策、预警原因自动追溯及模型的动态更新优化。采用该方法进行煤与瓦斯突出预警是合理可行的。

Dynamic early warning model of coal and gas outburst based on multi-source information fusion

In view of the problems of low fusion degree,insufficient self-analysis and optimization ability,and difficulty in tracing the causes of early warning of traditional coal and gas outburst early warning model,a dynamic early warning model of coal and gas outburst based on multi-source information fusion was established by combining association rule algorithm and evidence theory algorithm.In this paper,the method of setting association rule corresponding to qualitative and quantitative warning indicators was analyzed,and the optimization method of warning indicators and the basic confidence distribution rule were obtained through correlation analysis;the evidence theory identification framework for the early warning analysis of coal and gas outburst was established,the basic confidence distribution rules based on the correlation analysis results were determined,and the evidence synthesis method and the fusion decision method based on the probabilistic function were given;the basic confidence distribution function was used to trace the cause of early warning,and the dynamic updating method of the model was given.The test results show that the model can be used for outburst warning analysis to realize automatic screening of warning indicators,automatic fusion analysis and decision-making of multiple indicators,automatic tracing of warning causes and dynamic updating and optimization of the model,and it is feasible to use this method for coal and gas outburst early warning.