数据挖掘算法在实验室信息管理系统中的应用

Application of Data Mining Algorithm in Laboratory Information Management System

煤矿安全实验室现有实验项目30余项,涉及煤层瓦斯涌出预测、瓦斯抽采技术和煤与瓦斯突出防治方向。现有的实验室信息管理系统已积累了海量的实验数据,为了能够更深层次地挖掘数据背后隐藏的有价值的信息,应用数据挖掘算法,将实验室信息管理系统采集到的大量数据作为原始数据进行预处理;通过K-means聚类算法建立原始数据性质的聚类模型,并借助模糊C均值算法进行优化和改进,以聚类模型为基础建立数据分布优化模型来找到数据样本中的特征。结果表明:评价结果与实际相符,数据挖掘方法有效地分析出煤与瓦斯突出危险性,辅助煤与瓦斯灾害防治,在实验室信息管理系统中应用数据挖掘算法,能够为瓦斯灾害防治提供有效技术支撑。

Coal mine safety laboratory has more than 30 existing experimental projects,the experimental project involves prediction of coal seam gas gushing,gas drainage technology,and prevention and control of coal and gas outburst.The existing laboratory information management system has accumulated massive experimental data.In order to explore the valuable information hidden behind the data at a deeper level,the data mining algorithm is applied,the laboratory information management system collectes a large number of data as the original data for preprocessing.The K-means clustering algorithm is used to establish the clustering model of the original data properties,and the fuzzy C-means algorithm is used for optimization and improvement.The results show that the evaluation results are consistent with the reality.The two data mining methods can effectively analyze the coal and gas outburst risk,and assist the coal and gas disaster prevention.The application of data mining algorithm in the laboratory information management system can provide effective technical support for gas disaster prevention and control.