热连轧数据采集的多样本处理策略

热连轧带钢生产过程中，实测数据的处理方式影响到模型自学习的精度，最终影响到产品的控制精度.为解决此问题，建立了针对实测数据的多样本处理策略，采用变异系数的方式排除了高离散性的数据，并通过数据映射的方式将采集到的有效数据进行同步，最终获得了高可信度的自学习源数据，大大提高了模型自学习的有效性及预报精度.将该多样本处理策略应用到国内某热连轧生产线的精轧机组，现场实际应用效果表明:带钢头部的轧制力预报精度达到了233%，满足了自动厚度控制系统的控制要求，提高了产品的质量.

Multi sample Processing Strategy of Data Acquisition in Tandem Hot Rolling

In hot strip rolling processes， the methods of processing the actual data influence the self learning precise of the model and further affect the product quality control precise. To solve this problem mentioned above， a multi sample strategy of practical data acquisition was established and the high dispersion data were eliminated with the sample variation coefficient method， while the collected valid data synchronization was realized with the samples mapping method through which the self learning source data with highly reliability were achieved that improved the validity of the self learning of the model and the predict precise. This multi sample strategy was applied to a tandem hot mill in China， the practical application results showed that the rolling force predicted precise was up 233%， which met the requirement of the automation gauge control system， and the product quality was improved.