## 3 System Framework for Capacity Prediction in Discrete Manufacturing Workshop  
Discrete manufacturing workshops often allocate and schedule production based on the specific process and equipment status of products, resulting in many constraints on production scheduling and abnormally complex production process control. During the production process, there are many non-standard products due to the large variety of product types, and most production enterprises often have on-site workers for flexible adaptation [30]. Consequently, data such as production evaluation, scheduling, production capacity prediction, and delivery time prediction rely on the abilities and experience of employees, leading to inaccuracies in predicting the actual production capacity of the workshop. In the production process of discrete manufacturing workshops, a variety of heterogeneous data sources, such as customer data, material data, production activity planning data, equipment status, and time-varying production process control, are involved. Currently, data analysis and processing are only performed manually or through ERP, MES, and other application systems. However, these approaches lack real-time interaction and information fusion, thereby limiting effective control over current production factors, production activity plans, and production process control within the workshop. The digital twin of discrete manufacturing workshops represents a digital mapping of discrete workshops, as shown in Figure 1, providing a system framework for predicting workshop capacity driven by digital twins. Based on the digital twin five-dimensional model theory, the discrete manufacturing workshop twin system involves physical workshops, virtual workshops, service systems, twin data, and connected services. The construction process of the digital twin system includes the construction of production factors, such as the human-machine material environment in the workshop, a production activity plan and process construction that organizes and distributes production factors reasonably, and system modeling processes, i.e., production system monitoring, analysis, and optimization. The digital twin system depicts all elements, processes, and business of the physical workshop, along with real-time interaction and system integration based on twin data. Through a full-factor modeling process based on the twin system, precise characterization of production factors in discrete manufacturing workshops can be achieved, leading to the establishment of a twin model that exhibits virtual and real consistencies. Additionally, a dynamic modeling process based on models and twin data can effectively solve the problem of twin data-driven production process state identification and dynamic iterative modeling. This process enables equipment fault diagnosis and predictive maintenance through iterative analysis based on the real-time status of the production line. Furthermore, the simulation analysis and decision-making of the production process are achieved based on the twin model and data fusion-driven modeling process by establishing constraints and coupling relationships among production factors, models, and data. The precise prediction of production capacity for discrete manufacturing workshops is shown in Figure 1. Firstly, a digital twin of discrete workshops is constructed using the software of Process Simulate 16.0.0. This architecture enables real-time interaction between information space and physical space through the utilization of digital chain technology. At this level, virtual and physical space production lines achieve physical fusion based on the same 3D and behavioral models. Based on mutual control between virtual space and physical space, bidirectional data transmission and service integration are achieved. Secondly, the data related to workshop production (cluster analysis, cleaning, and packaging) are preprocessed, which mainly includes workshop process data, workshop material data, and workshop equipment status.