基于数据融合理论的煤矿瓦斯动态预测技术

针对煤矿瓦斯预测中存在的问题，提出了一种以数据融合理论为基础，利用贝叶斯估计方法和自适应加权进行信息处理和数据融合，利用Dempster-Shafer证据理论解决瓦斯预测过程中的不确定性和不精确性问题，并综合考虑煤矿井下瓦斯浓度等相关参数的新方法，实现了对瓦斯状态信息的在线整合，优化了预测参数，提高了煤矿瓦斯预测预报的准确性.该方法从整体上确立了同源数据的完备性，实现了煤矿瓦斯的动态预测.

Dynamic prediction technology for gas based on data fusion theory

A new method was presented based on fusion method,used Bayesian analysis and self-adapting weighted data to precess information and fuse data.It used the Dempster-Shafer evidence theory to deal with the indefinability and indefinability produced in gas prediction.It comprehensively considered the gas concentration and other related parameters and realized the optimization and integration of gas measurement and predicted parameters.This method enhances to establish the completeness of the data source and improve the accuracy of gas detection system for the coal mines.