煤矿安全生产综合监察监管平台设计与实现

基于收集的西南地区40多对矿井瓦斯监测数据，分析矿井瓦斯浓度数理变化特征，建立了数据驱动的矿井瓦斯异常涌出识别方法与模型。该模型从当前瓦斯浓度、瓦斯突变指数、偏离均值指数、长期不变指数、波动幅度指数、中期上升趋势指数等方面对监测点瓦斯风险进行量化评价；使用单一预警指标对趋势偏离、瓦斯突变等异常进行识别；采用各监测点调和平均数，以采掘工作面为单位，量化区域瓦斯灾害风险趋势。研发了煤矿瓦斯灾害大数据风险分析平台，并应用于现场监管监察，应用结果表明该模型能及时准确判识各矿井瓦斯灾害风险，有效提高监管能效。

Research on early warning model of coal mine gas anomaly based on mathematical statistics method

On the basis of the gas monitoring data of more than 40 pairs of coal mines collected in southwest China, the mathematical variation characteristics of gas concentration were analyzed, and a data-driven method and model for identifying abnormal coal mine gas emission were established. The model quantitatively evaluated the gas risk at monitoring points from different dimensions, including current gas concentration, gas mutation index, deviation from the mean value index, long-term constant index, fluctuation amplitude index and medium-term upward trend index. A single early warning index was used to identify anomalies such as trend deviation, gas mutation and so on. Based on the unit of mining face, the risk trend of regional gas disaster was quantified by means of the average of each monitoring point. Finally, a big data risk analysis platform for coal mine gas disasters was developed and applied to supervision. The application result shows that the model can accurately identify the risk of gas disasters and improves the supervision efficiency.