基于云计算的煤矿瓦斯浓度短期预测

Coal Mine Gas Concentration Short-term Prediction Based on Cloud Computing

随着瓦斯浓度数据规模的不断扩增,目前已有的传统算法无法满足海量数据处理的要求。运用云计算在处理大数据集时的较强优势,首先在云平台下搭建了煤矿瓦斯浓度架构,提出了一种基于云计算的遗传优化Elman神经网络模型,并以唐山市某煤矿海量数据为基础进行实验。经验证,此算法在煤矿瓦斯浓度短期预测方面兼具高效性与可行性。

With the increase of gas concentration data size, the existing traditional algorithm can not meet the requirements of mass data processing, using the advantage of cloud computing in huge amounts of data processing, this article first has carried on the study of coal mine gas concentration based on cloud computing architecture, this paper proposes a cloud-based genetic algorithm optimization Elman neural network model, taking a coal mine gas concentration data somewhere in Tangshan as the foundation. The experiment shows that the algorithm is efficient and feasible in terms of short-term forecasting of coal mine gas concentration.