## 1 Introduction
During this stage, the system can realize production and processing resource constraints, production process allocation, production equipment monitoring, etc. However, due to the lack of real-time interaction and information fusion capabilities, production capacity prediction and delivery dates rely more on historical data for model evaluation [6]. Before building new factories and processing new products, problems such as a lack of historical data are exposed, making it impossible to predict workshop production capacity accurately. Combining the current requirements of intelligent manufacturing systems, the introduction of digital workshops signifies a change in the elements of traditional manufacturing systems. Moreover, the concept of digital twin workshops has also emerged as the times require [7]. In the digital twin manufacturing workshop, the data of production factors such as personnel, equipment, materials, and environment in the physical workshop are fully connected to the information world through the workshop Internet of Things (IoT), RFID, and other information means, realizing mutual interconnection and data sharing. Driven by comprehensive production factor data, the simulation, evaluation, and analysis functions of the virtual workshop and workshop service system can optimize the behavior of each factor while considering the status of other factors. This approach supports the linkage and optimal combination of factors necessary to ensure smooth production operations [8]. The data in the physical space mainly refer to the real data generated by the physical entities of the workshop. Conversely, the data in the information space mainly refer to the data related to the virtual workshop and the workshop service system. These data are not directly collected from the physical space but from virtual workshop data which is based on digital twin data model, algorithm deduction, and system derivation [9]. With the introduction of digital twin technology, a multi-physical, multi-scale, and multi-probability twin of a discrete manufacturing workshop is established through digital means to achieve deep integration and real-time interaction between physical space and information space, offering a basis for data-driven workshop capacity prediction. Considering the characteristics of discrete manufacturing workshops, such as the production of many types of products, small batches, and frequent production changes, this paper discusses the problems of inaccurate production capacity forecasts and unfulfilled delivery dates during the factory production process. Based on predictive data, it is possible to optimize production scheduling methods in digital twin systems and provide follow-up suggestions. The modeling method of discrete manufacturing workshops driven by twin data and the establishment process of factor conditions are considered based on the problems faced by discrete manufacturing workshops. In addition, the neural network algorithm to realize accurate predictions of factory production capacity and delivery management is studied based on the interactive fusion of twin space and twin data. Finally, the feasibility of this method is experimentally verified. The main contributions of this paper are in the following three aspects:  
1. Discussing the current production capacity forecasting methods and problems faced by discrete manufacturing workshops and proposing a digital twin-driven discrete manufacturing workshop capacity forecasting system framework;
2. Innovating the modeling process of discrete manufacturing workshops driven by twin data and proposing a time series prediction algorithm for workshop production capacity based on Long Short-Term Memory (LSTM);
3. Taking the lead in building a digital twin platform for capacity prediction in discrete manufacturing workshops and conducting digital deployment in actual manufacturing workshops to verify the accuracy of the prediction model. The subsequent chapters of this paper are arranged as follows: The related work is described in the second section.