融合时空特征的离散车间生产-物流协同状态预测  

Prediction of production-logistics collaboration state in discrete manufacturing workshop based on spatio-temporal feature

作　　者：刘从颖 张朝阳 何家威 LIU Congying;ZHANG Chaoyang;HE Jiawei(School of Mechanical Engineering,Jiangnan University,Wuxi 214122,China;Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology,Wuxi 214122,China)

机构地区：[1]江南大学机械工程学院,无锡214122 [2]江苏省食品先进制造装备技术重点实验室,无锡214122

出　　处：《现代制造工程》2025年第3期52-59,149,共9页Modern Manufacturing Engineering

基　　金：国家自然科学基金青年科学基金项目(51805213)。

摘　　要：针对离散车间生产过程复杂、异常扰动频发等因素导致的状态预测难执行问题,提出了一种融合时空特征的离散车间生产-物流协同状态预测方法。首先,基于生产-物流运行逻辑与实时制造数据,分析生产-物流协同关系,确定了生产-物流协同状态的预测指标;其次,根据生产-物流时空特征关系,建立生产-物流时序图模型,进而采用基于图注意力网络-门控循环单元(GAT-GRU)的时空融合网络,对协同状态的预测指标进行预测;最后,对典型的混流生产车间进行案例分析,实验结果表明,所提预测方法在准确性和效率上均优于深度神经网络、去噪自动编码器和门控循环单元等模型,能更加有效地实现生产-物流协同状态预测。In order to solve the problem that the state prediction is difficult to perform due to the complex production process and frequent abnormal disturbances in the discrete workshop,a collaborative state prediction method of workshop production-logistics based on spatio-temporal feature was proposed.Firstly,based on the production-logistics operation logic and real-time manufacturing data,the production-logistics collaboration relationship was analyzed,and the prediction index of the production-logistics collaboration state was determined.Secondly,according to the spatio-temporal feature relationship between production and logistics,a production-logistics time sequence graph model was established,and then the spatio-temporal fusion network based on Graph Attention network-Gated Recurrent Unit(GAT-GRU)was used to predict the prediction indicators of the cooperative state.Finally,a typical mixed-flow production workshop was used as a case study,and the experimental results show that the proposed prediction method was better than the deep neural network,denoising autoencoder,gated recurrent unit and other models in terms of accuracy and efficiency,and can more effectively realize the production-logistics collaborative state prediction.

关 键 词：生产-物流协同 时空特征 图注意力网络 门控循环单元 

分 类 号：TH166[机械工程—机械制造及自动化]