基于大数据挖掘的卷烟设备参数优化与模拟仿真  

作　　者：张金霞 黄玲玲 石炬 马胜军 罗幼喜[2] 

机构地区：[1]湖北中烟工业有限责任公司广水卷烟厂,湖北广水432721 [2]湖北工业大学理学院,武汉430068

出　　处：《科技创新与应用》2024年第25期68-71,75,共5页Technology Innovation and Application

摘　　要：卷烟质量的好坏是衡量消费者满意度的关键度量因素。对于空头这种卷烟过程中出现的A类质量问题,如何在生产过程中实时控制,降低其缺陷率一直是卷烟企业高度关注的焦点问题。该文站在卷烟厂实际生产的角度,利用W市卷烟厂提供的实际生产数据,先后建立基于决策树的最优参数模型,BP神经网络预测模型以及情景分析模拟仿真,对该卷烟厂的空头剔除量过高且生产不稳定的问题进行机台参数控制区间寻优以及多情景计算机模拟仿真,并给出相应的解决方案。The quality of cigarettes is the key factor to measure consumer satisfaction.For the category A quality problems in the process of short cigarettes,how to control them in real time in the production process and reduce their defect rate has always been the focus of cigarette enterprises.From the point of view of the actual production of the cigarette factory,using the actual production data provided by the W city cigarette factory,this paper successively establishes the optimal parameter model based on the decision tree,the BP neural network prediction model and the scenario analysis simulation.The machine parameter control interval optimization and multi-scenario computer simulation are carried out for the problem of excessive short elimination and unstable production in the cigarette factory,and the corresponding solutions are given.

关 键 词：数字化转型 设备最优参数调控 决策树 BP神经网络 情景分析 

分 类 号：F426.8[经济管理—产业经济]