目的 提出一种数字孪生技术在烟叶包装箱中的应用方法，改进目前烟草产业中烟叶复烤生产环节存在缺乏自动化落料检验以及工业开箱机器人难以判断烟叶包装箱内是否有黄卡纸夹层材料的问题。方法 通过构建复杂烟叶包装箱夹芯材料的数字孪生模型，采用图像处理结合卷积神经网络得到了基于机器视觉的复杂烟叶包装箱夹芯材料检测方法，系统可对麦秆板夹层形状进行标定并确定其中心位置，实现工业机器人精确定位烟叶包装箱夹层材料以及装箱时自动增减物料，并进行有效的数字化物料信息追踪。结果 实验表明，机器人判断包装箱夹层材料的准确率高达94.88%，且效率优于人工检查。结论 可在一定程度上节省人力资源，提高工厂的生产效率。

The work aims to propose a method of applying digital twin technology in tobacco packaging to solve the problem that at the current tobacco industry, there is a lack of automation in the production of tobacco roasting drop inspection and industrial opening robots, so that it is difficult to determine whether there is a yellow cardboard sandwich material in the tobacco packaging box. Through the construction of a complex tobacco packaging sandwich material digital twin model, image processing was used in combination with the convolutional neural network to get the complex tobacco packaging sandwich material detection method based on machine vision. The system could calibrate the shape of the straw board sandwich and determine its center, and enable the industrial robot to accurately locate the sandwich material of the tobacco packaging box, automatically increase or decrease materials when packing, and achieve effective digital material information tracking. Experiments showed that the accuracy of the robot in determining the sandwich material of the box was as high as 94.88%, and the efficiency was better than that of manual checking. It can save human resources to a certain extent and improve the production efficiency of the factory.