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我国许多大、中型煤矿都建立了通风安全监测系统、井下突水监控系统、井下煤与瓦斯突出监测系统等煤炭安全决策系统,这些系统中积累了大量的原始数据。如何将数据演变成可以科学决策的信息是煤矿安全生产要考虑的问题。粗糙集理论作为能够定量分析不确定和不完备信息与知识的方法,为数据挖掘提供了一种新的方法。为了更好地解决在决策表不完备和不一致情况下的推理和决策问题,提出了一种基于属性简约的缺省规则挖掘模型。最后设计出了基于粗糙集的数据挖掘系统,将其应用到井下工作面瓦斯涌出量数据挖掘分析中,取得了不错的效果。
Many large and medium-sized coal mines in our country have set up coal safety decision-making systems such as ventilation safety monitoring system, downhole water inrush monitoring system, and underground coal and gas outburst monitoring system. These systems have accumulated a large amount of raw data. How to evolve the data into information that can make scientific decisions is a problem to be considered in coal mine safety production. Rough set theory, as a method that can quantitatively analyze uncertain and incomplete information and knowledge, provides a new method for data mining. In order to solve the reasoning and decision-making problems in the case of incomplete and inconsistent decision table, a default rule mining model based on attribute reduction is proposed. Finally, a data mining system based on rough set is designed and applied to the data mining analysis of gas emission in underground working face, and good results have been achieved.