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研究冶金控制系统的故障数据智能挖掘方法,提高挖掘的准确性。在冶金控制系统故障诊断的过程中,需要对故障数据进行有效的挖掘,针对故障部件及时进行处理。但由于冶金控制系统中的不同硬件设备数据之间的关联性比较复杂,容易给挖掘结果带来干扰,降低挖掘的准确性,为此,提出基于粒子群算法的冶金控制系统故障数据智能挖掘方法。对粒子群进行更新处理,实现故障数据挖掘。实验结果表明,利用改进算法进行冶金控制系统的故障数据智能挖掘,能够极大提高挖掘的准确性,满足冶金生产的实际需求。
Research the fault data intelligent mining method of metallurgical control system to improve the accuracy of mining. In the process of metallurgical control system fault diagnosis, it is necessary to excavate the fault data effectively and deal with the faulty components in time. However, due to the complexity of the metallurgical control system, the correlation between the data of different hardware devices is relatively complex, which is likely to cause interference to the excavation results and reduce the accuracy of mining. To this end, the fault data intelligent mining method of metallurgical control system based on Particle Swarm Optimization . Particle swarm update processing to achieve fault data mining. The experimental results show that using the improved algorithm to mine the fault data of the metallurgical control system can greatly improve the accuracy of mining and meet the actual needs of metallurgical production.