To address current challenges, such as the low visualization of workshops and difficulty in the remote control of equipment, which are supported by the theoretical system of the digital twin, a method of real-time monitoring and dynamic bidirectional interaction in workshops based on the digital twin is proposed. This method aims to realize high-fidelity mapping from the virtual workshop to the physical workshop, laying the groundwork for predicting the status of various production elements in the physical workshop. First, based on research on the theoretical knowledge of the digital twin workshop, a seven-dimensional model and maturity framework for the digital twin workshop are proposed. Building on this, the theoretical knowledge is integrated with practical applications to construct a comprehensive digital twin workshop system (DTWS) architecture. The key technologies involved in the real-time monitoring of virtual and physical workshops are described in detail, including the construction of digital twin workshop models, intelligent perception of multi-source heterogeneous data, virtual–physical interaction, and synchronous operation methods for virtual–physical workshops. Finally, a prototype system for the digital twin workshop is designed and developed to realize the real-time monitoring and remote control of the physical workshop. The feasibility and effectiveness of the proposed method are validated through enterprise applications.

With the increasing demand for personalized services in the market, manufacturing enterprises are facing frequent emergency order insertion and equipment resource shortages, and traditional scheduling methods lack flexibility. This article focuses on the workshop scheduling problem under emergency insertion disturbance, and constructs a dynamic scheduling optimization method considering equipment occupancy status. Firstly, a dynamic scheduling framework is proposed, and a real-time status model is established to monitor emergency insertion and equipment occupancy status in real time. An event-driven dynamic scheduling mechanism is also constructed. Secondly, with the optimization objective of minimizing the maximum completion time, a mixed integer programming model is established, and an improved genetic simulated annealing algorithm is proposed to solve the proposed model. Finally, the proposed method was validated using a standard case set and real production scenarios. The experimental results showed that the solution of the proposed method was better than similar algorithms under three different problem scales. In three emergency insertion scenarios, the proposed method can reduce the disturbance of insertion on the original plan while ensuring equipment utilization, verifying the practicality and effectiveness of the proposed dynamic scheduling method.