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在钢铁生产过程中,针对不同的产品原料,找出连铸环节的最佳温度曲线对生产出质量优良的产品有着重要的意义。钢铁加工厂引入MES系统后,MES的实时数据库记录了生产过程的数据,对历史数据进行分析可以挖掘出有效、可靠的温度曲线信息。数据挖掘技术中的关联规则算法Apriori针对的是离散型、线性的事务型数据,本文针对Apriori算法的缺陷,对其进行了改进,提出可以处理连续、非线性的生产数据的算法—基于粗糙集的关联规则挖掘算法Apriori_MES。并将其应用在钢铁厂MES的实时数据库中,挖掘出不同原料在连铸环节中的最佳温度控制曲线,为生产过程提供了辅助。
In the process of steel production, finding out the best temperature curve of continuous casting for different product raw materials is of great significance to producing good quality products. After the MES system was introduced into the steel processing plant, the MES real-time database recorded the data of the production process. Analyzing the historical data can find out the effective and reliable temperature curve information. Apriori, an algorithm for association rules in data mining, aims at discrete and linear transactional data. In this paper, we improve Apriori algorithm and propose an algorithm that can process continuous and nonlinear production data - based on rough set Apriori_MES algorithm for mining association rules. And applied it to the real-time database of MES of steel mills to find out the best temperature control curve of different raw materials in the continuous casting process, which provided the auxiliary for the production process.