煤炭是我国赖以生存的能源，在我国具有不可替代的作用。煤炭生产是危险性极高的行业，生产过程中时刻伴随着危险发生，尤其是煤与瓦斯突出事故的发生，严重威胁着人们的生命和财产安全。因此，研究煤与瓦斯突出机理，对煤与瓦斯突出事故进行预警对于煤矿的安全生产具有重要的意义。 煤矿井下环境复杂，煤与瓦斯突出是受多种因素综合作用的复杂非线性问题。本文在研究煤与瓦斯突出机理的基础上，分析了煤与瓦斯突出事故的影响因素，并利用灰色关联分析法选取了预测指标，包括瓦斯放散初速度、瓦斯压力、开采深度、煤体破坏类型和坚固性系数。然后构建了以支持向量机（SVM）算法为核心的煤与瓦斯突出预测模型，并针对参数选择时耗时长、可能错过全局最优解等问题，使用灰狼优化算法（GWO）对SVM进行了优化。最后，利用Libsvm对GWO-SVM进行了仿真，结果表明，基于GWO-SVM的煤与瓦斯突出预测模型具有较好的预测性能，并且与遗传算法优化SVM（GA-SVM）相比，寻优过程中的迭代性能更好。 另外，随着矿山安全监测技术和自动化采集系统的发展和应用，煤矿上的数据采集设备每天都会产生大量的实时数据，如何快速高效地利用这些数据对煤矿安全进行实时的预警是煤矿安全管理所面临的一个难题。本文将Spark并行计算框架引入到煤与瓦斯突出预警中，利用Spark强大的并行计算能力对煤矿实时数据进行快速的计算，并在Spark Streaming中加载训练好的SVM预测模型对煤与瓦斯突出进行实时的预测，提高了数据处理的效率，降低了预警的时延。 本文将Spark与GWO-SVM算法相结合，建立了煤与瓦斯突出预警模型，实现了对煤与瓦斯突出快速而准确的预警，可为煤矿生产的安全管理提供一定的参考意义。

Coal is the energy of our country's survival, it plays an irreplaceable role in our country. Coal production is a very dangerous industry, the production process is always accompanied by danger, especially the occurrence of coal and gas outburst accidents which is a serious threat to people's lives and property safety. Therefore, studying the mechanism of coal and gas outburst for pre-warning the coal and gas outburst accidents is of great significance to the safe production of coal mines. The underground environment of coal mine is complex, and the outburst of coal and gas is a complex nonlinear problem which is influenced by many factors. On the basis of studying the mechanism of coal and gas outburst, this thesis analyzes the main factors leading to the outburst accident of coal and gas, and selects the predictive metrics by using Grey Relation Analysis method, including the initial velocity of gas release, gas pressure, mining depth, coal body failure type and robustness coefficient. Then the prediction model of coal and gas outburst with support vector machine (SVM) algorithm as the core is constructed, and the SVM is optimized by Grey Wolf Optimizer algorithm (GWO) for the problems such as time-consuming and possible missed global optimal solution when selecting parameters. Finally, the GWO-SVM is simulated in Libsvm, and the result shows that the prediction model of coal and gas outburst based on GWO-SVM has a good predictive performance, and the iterative performance in the optimization process is better than that of SVM optimized by genetic algorithm (GA-SVM). In addition, with the development and application of mine safety monitoring technology and automatic acquisition system, the data acquisition equipments in coal mine produce a lot of real-time data every day, how to use these data quickly and efficiently to warn coal mine safety is a difficult problem in coal mine safety management. In this thesis, Spark parallel computing framework is introduced into coal and gas outburst warning, using Spark's powerful parallel computing ability to calculate the real-time coal mine data quickly, loading trained SVM prediction model in Spark Streaming to predict coal and gas outburst in real time, the speed of data processing is improved and the delay of early warning reduces. Combining Spark with GWO-SVM, this thesis establishes a prediction model of coal and gas outburst, and realizes the fast and accurate early warning of coal and gas outburst, which can provide some reference significance for the safety management of coal mine production.