煤与瓦斯突出智能预警方法及系统

Coal and gas outburst intelligent early-warning method and system

针对传统煤与瓦斯突出预警系统自学习能力不足、信息融合度不高、智能化水平和预警准确性有待提升的问题,基于关联规则和证据理论,建立了突出预警指标自主动态优选方法和多指标融合决策模型,开发了突出智能预警系统。介绍了突出危险判识事务集构建模式,提出预警指标项的非对称二元化转换方法,以及预警指标项与突出危险项之间关联规则的构建方法,定义了反映漏报率大小的函数,形成基于关联规则的突出预警指标优选方法;研究提出突出预警证据理论识别框架,构建出突出预警证据体基本概率分配函数,给出突出预警证据合成方法,得到反映突出危险概率大小的函数,形成了基于证据理论的突出预警多指标融合决策方法;在此基础上,采用分层架构设计,开发出煤与瓦斯突出智能预警系统,实现了突出灾害的在线监测、智能分析、融合预警、在线发布与远程查询。现场应用效果显示,突出预警准确率达到91.53%,且无漏报现象,为矿井突出灾害防治提供了良好的技术支撑。

Aiming at the problems of the traditional coal and gas outburst early-warning system, such as insufficient self-learning ability, low information fusion, poor intelligence level and low early warning accuracy, based on correlation analysis and evidence theory algorithm, an independent dynamic optimization method of prominent early warning indicators and a multi indicator fusion decision-making model are established, and a prominent intelligent early warning system is developed. The construction mode of hazard identification affairs set is put forward;the asymmetric binary processing method for early warning indexis established;the construction method of association rules between early warning indicators and outburst itemsis put forward;a function reflecting the size of the false alarm rate is defined;the optimal method of warning index based on association rule analysis is formed. A evidence theory identification framework of coal and gas outburst early warning fusion analysis is put forward. The basic probability distribution function of evidence body is constructed;the synthesis method of evidence is given;a function reflecting the danger probability is constructed;a multi-index fusion decision-making method for coal and gas outburst early warning has been formed. On this basis, the intelligent early warning system of coal and gas outburst based on service architecture model is developed, which has the functions of online monitoring, intelligent analysis, integrated early warning, online release and remote query. The field application results show that the accuracy rate is 91.53%, and no missing alarm. It provides a good technical support for the prevention and control of coal and gas outburst.