大数据驱动的车间数字孪生模型构建方法

A Big Data-driven Digital Twin Model Method for Building a Shop Floor

数字孪生技术为车间生产全生命周期的监控和自主决策预测提供理论结构框架,推进车间数字化、智能化转型。从数字孪生架构体系的物理实体融合、数据交互、虚拟实体搭建、模型自主更新以及决策预测五个方面,针对实际车间普遍存在的数据交互延迟和物理空间动态扰动问题,搭建大数据环境下基于状态转移的数字孪生车间模型体系架构,提出基于状态的孪生模型自适应更新策略方法和考虑工件优先级、返修和插单的自决策调度算法。结合加工过程产生的阶段数据和历史大数据,更新数字孪生模型的数据结构和运行规则,同步实际生产状态,提供合理的再调度策略,获得可信的生产预测结果。以某航空非标产品机加为例,搭建所提出的数字孪生模型,利用状态间隔产生的数据更新模型,生成和验证返修件再调度方案,证明了数字孪生模型的有效性。

Digital twin technology provides a theoretical structural framework for monitoring and autonomous decision-making and forecasting throughout the production lifecycle of the workshop,promoting the digital and intelligent transformation of the workshop.The digital twin architecture system is built from five aspects:physical entity fusion,data interaction,virtual entity construction,autonomous model update,decision,and prediction in a big data environment.The architecture solves the problems of data interaction delays and dynamic perturbations in physical space that are common in real workshops.The architecture of a digital twin workshop model based on state transfer in a big data environment is built.State-based twin model adaptive update strategy approach and self-decision scheduling algorithms considering artefact priority,rework,and insertion orders were proposed.The data structure and operating rules of the digital twin model are updated by combining the stage data generated by the process with historical big data.Synchronize the actual production status to provide a rational re-scheduling strategy and obtain credible production forecasts.Taking the processing of an aerospace non-standard product as an example,the proposed digital twin model is built,and the data generated by the state interval is used to update the model,generate and validate a re-scheduling solution for reworked parts,and prove the effectiveness of the digital twin model.