基于粗糙集和D-S证据理论的井下风险评估方法  

Underground Risk Assessing Method Based on Rough Set and D-S Evidence Theory

作　　者：李博[1] 黄圆月[1] 

机构地区：[1]中煤科工集团煤炭科学研究总院,北京100013

出　　处：《工矿自动化》2011年第11期38-40,共3页Journal Of Mine Automation

摘　　要：针对以单传感器监测数据作为井下安全性评估依据存在难以获得丰富、全面、准确的环境信息的问题,提出一种基于粗糙集和D-S证据理论的井下风险评估方法,并进行了具体的实例分析。该方法对多传感器采集的参数进行基于粗糙集的一级数据融合及基于D-S证据理论的二级数据融合,实现了对井下多种信息的整合,优化了现有的风险评估方法。测试表明,该方法可保证预测信息的完备性和独立性,实现了对矿井安全性变化趋势的动态预测。For problem of difficult obtainment of abundance,comprehensive and accurate environmental information when data from a single sensor is as basis of underground safety assessment,an underground risk assessing method based on rough sets and D-S evidence theory was proposed,and analysis of specific example was given.The method makes first data fusion based on rough set and secondary data fusion based on D-S evidence theory to fuse data collected by multi-sensor,achieves integration of a variety of underground information,and optimizes existing risk assessment methods.Tests showed that the method can guarantee completeness and independence of forecasting information and realize dynamic prediction of mine safety trend.

关 键 词：矿井 风险评估 多传感器信息融合 粗糙集 D-S证据理论 

分 类 号：TD67[矿业工程—矿山机电]