Digital twin (DT) technology provides a novel, feasible, and clear implementation path for the realization of smart manufacturing and cyber-physical systems (CPS). Currently, DT is applied to all stages of the product lifecycle, including design, production, and service, although its application in the production stage is not yet extensive. Shop-floor digital twin (SDT) is a digital mapping model of the corresponding physical shop-floor. How to build and apply SDT has always been challenging when applying DT technology in the production phase. To address the existing problems, this paper first reviews the origin and evolution of DT, including its application status in the production stage. Then, an implementation framework for the construction and application of SDT is proposed. Three key implementation techniques are explained in detail: the five-dimensional modeling of SDT; DT-based 3D visual and real-time monitoring of shop-floor operating status; and prediction of shop-floor operating status based on SDT using Markov chain. A DT-based visual monitoring and prediction system (DT-VMPS) for shop-floor operating status is developed, and the feasibility and effectiveness of the proposed method are demonstrated through the use of an engineering case study. Finally, a summary of the contributions of the paper is given, and future research issues are discussed.

数字孪生（DT）技术为智能制造和网络物理系统（CPS）的实现提供了一种新颖，可行且清晰的实施路径。目前，DT已应用于产品生命周期的所有阶段，包括设计，生产和服务，尽管在生产阶段的应用还不广泛。车间数字孪生（SDT）是相应物理车间的数字映射模型。在生产阶段应用DT技术时，如何构建和应用SDT一直是一个挑战。为了解决现有问题，本文首先回顾了DT的起源和发展，包括其在生产阶段的应用状况。然后，提出了SDT的构建和应用的实现框架。详细解释了三种关键的实现技术：SDT的五维建模；基于DT的3D视觉和实时监控车间操作状态；马尔可夫链的基于SDT的车间运行状态预测和预测。开发了基于DT的车间运行状态视觉监控和预测系统（DT-VMPS），并通过工程案例研究证明了该方法的可行性和有效性。最后，总结了论文的贡献，并讨论了未来的研究问题。通过工程案例研究证明了该方法的可行性和有效性。最后，总结了论文的贡献，并讨论了未来的研究问题。通过工程案例研究证明了该方法的可行性和有效性。最后，总结了论文的贡献，并讨论了未来的研究问题。