基于DTW的瓦斯爆炸灾害风险模式识别

Research on patter recognition of gas explosion disaster risk based on dynamic time warping in coal mines

以煤矿瓦斯爆炸灾害防控为目的,构建了瓦斯爆炸灾害风险模式识别指标体系,提出了以指标含义与值域设置风险模式类别分界点的区间线性化数据规范方法,减少了计算过程中的信息量损失.通过区间数方法对指标进行赋权后,应用动态时间弯曲算法(DTW),以指标风险模式类别分界点为基础形成了多个加权参考序列,并将待识别样本转换成待识别加权序列,通过待识别加权序列与加权参考序列的最短动态弯曲距离获得待识别样本的风险模式类别,建立了基于DTW的煤矿瓦斯爆炸灾害风险模式识别模型.算例结果符合实际,该方法对瓦斯爆炸灾害风险模式具有良好的识别能力.

For the prevention and control of gas explosion disaster risk in the coal mine, the risk index system was established to patter recognition of gas explosion. Interval linear data normalization method was put forward to reduce the information loss in the process of computing by using the risk patter cut - off of indexes. The risk index was weighted with interval data of multiple comparison method. According to dynamic time warping （DTW） , the 5 weighted reference sequence was constructed by using the indexes risk patter cut - off which was normalized, and the weighted sequence was constructed to be identified for the unknown risk patter sample. And then, disaster risk pattern recognition model was established based on DTW which obtaining risk pattern by the shortest dynamic time warping distance of the weighted reference sequence and to be identified weighted sequence. The pattern recognition example was gave that based on 14 indicators of gas explosion disaster risk, and the risk identification results were in good agreement with the actual situation. Results show that, DTW model of risk patter recognition has good identification ability for explosion disaster risk model, which will be a new way to study the pre - control of gas explosion disaster risk in coal mines.