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中文摘要：
传统灰色模型GM（1,1）对于随机波动性较大的数据序列拟合较差,预测精度较低,为了弥补这一缺陷,更准确预测煤层自然发火的趋势与危险性,将GM（1,1）模型和马尔科夫模型有机结合,构建了灰色马尔科夫模型。用灰色马尔科夫模型对柴里煤矿实测CO发生量进行预测,与传统GM（1,1）模型的预测结果比较,灰色马尔科夫模型的拟合精度更好,平均相对误差更小,简便、实用,能够为矿井煤自燃火灾的防治工作提供科学的理论依据。
英文摘要：
The traditional grey model GM （ 1, 1 ） for random volatile data sequence fitting is poorer and predic- tion accuracy is low. In order to compensate for this defect and the more accurately predict coal spontaneous combustion tendency and danger, combining GM （1, 1） model with Markov model, a grey Markov model is developed. Using the grey Markov model to forecast CO volume of Chaili coal, the fitting precision of the grey Markov model is better, average relative error is smaller,and the grey Markov model is simple, practical, and able to provide scientific theory basis for coal fire prevention and control work of mine spontaneous combustion compared with the prediction results of traditional GM （1, 1 ） model.
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