摘要
卷烟通风率的稳定性受激光打孔方式、设备参数等多种因素影响,导致传统方法难以全面捕捉这些因素的复杂交互作用,无法有效适配长程依赖关系建模需求,进而难以准确控制烟支通风率精度。为此,提出基于自适应多尺度时序注意力网络(AMTA-Net)的卷制过程激光打孔通风率预测模型。首先,针对激光打孔工艺参数与烟支品类维度不匹配的问题,设计了激光打孔类别特征嵌入模块,通过独热编码与可学习嵌入矩阵的结合,实现了烟支类别与打孔数据的特征融合;其次,为捕捉孔形态与烟支通风率间的复杂非线性关系,设计了多尺度特征融合模块ELAN-1D,该模块采用双层卷积结构,通过不同膨胀率的卷积核和残差结构实现时序特征的深度提取与局部-全局信息的有效捕捉;最后,为刻画孔形态特征的空间异质性与序列关联性,构建了序列注意力特征融合模块,并结合多尺度序列卷积与交叉注意力机制,实现对打孔形态-通风率映射关系的“通道-序列”双维度交叉逻辑建模。实验结果表明,AMTA-Net对滤棒通风率和总通风率预测的均方误差(MSE)低至6.799×10^(-4)和6.874×10^(-4)。相比基线模型,AMTA-Net对滤棒通风率和总通风率的预测MSE分别降低了28.96%和20.61%,平均绝对误差(MAE)分别降低了18.16%和11.51%;相比传统模型,AMTA-Net对2项指标的预测MSE和MAE分别降低了15.64%和8.05%以上。AMTA-Net为激光打孔工艺的精准调控、实现卷烟降焦减害提供了有力的模型支持。
The stability of cigarette ventilation rate is affected by multiple factors,such as laser drilling methods and equipment parameters.This makes it difficult for traditional methods to fully capture the complex interactions among these factors and fail to effectively adapt to the needs of modeling long-range dependencies,thus struggling to accurately control the precision of cigarette ventilation rate.To address this challenge,a laser drilling ventilation rate prediction model for the cigarette manufacturing process based on the adaptive multi-scale temporal attention network(AMTA-Net)was proposed.Firstly,to resolve the dimension mismatch between laser drilling process parameters and cigarette categories,a category feature embedding module for laser drilling was designed.By integrating one-hot encoding with a learnable embedding matrix,feature fusion of cigarette category information and drilling process data is achieved.Secondly,to capture the complex nonlinear relationship between hole morphology and cigarette ventilation rate,a multi-scale feature fusion module named ELAN-1D was proposed.This module adopts a two-layer convolutional architecture,realizing deep extraction of temporal features and effective capture of local-global information through convolution kernels with different dilation rates.Finally,to characterize the spatial heterogeneity and sequential correlation of hole morphology features,a sequential attention feature fusion module was constructed.Combining multi-scale sequential convolution with a cross-attention mechanism,this module implemented a“channel-sequence”dual-dimensional cross-logic modeling for the mapping relationship between drilling morphology and ventilation rate.Experimental results demonstrated that the mean squared error(MSE)of the proposed AMTA-Net for filter ventilation rate and total ventilation rate prediction reached as low as 6.799×10^(-4)and 6.874×10^(-4),respectively.Compared with baseline models,the proposed method reduced the MSE of filter ventilation rate and total ventilation rate by 28.96%and 20.61%,respectively,with the mean absolute error(MAE)decreased by 18.16%and 11.51%.In contrast to traditional models,the MSE and MAE of AMTA-Net were reduced by more than 15.64%and 8.05%,respectively.The method proposed provided robust model support for the precise regulation of laser drilling processes and for achieving cigarette tar reduction and harm mitigation.
作者
易斌
张立斌
刘丹楹
唐军
方俊俊
李雯琦
YI Bin;ZHANG Libin;LIU Danying;TANG Jun;FANG Junjun;LI Wenqi(Technology Center,China Tobacco Yunnan Industrial Co.,Ltd.,Kunming Yunnan 650231,China;Yuxi Cigarette Factory,Hongta Tobacco(Group)Co.,Ltd.,Yuxi Yunnan 653100,China)
出处
《图学学报》
北大核心
2025年第6期1224-1232,共9页
Journal of Graphics
