###### Abstract  
In the era of Industry 4.0 to 5.0, the manufacturing industry is dedicated to improving its production efficiency, control capability and competitiveness with intelligent enhancement. As a typical discrete manufacturing industry, it is difficult for ocean engineering (OE) manufacturers to accurately control the entire production process, and the establishment of an integrated system supported by digital twin (DT) technology is a better solution. This paper proposes a comprehensive set of system architectures for the DT workshop. It focuses on planning, which is the main line of control, to establish a model based on graph neural networks (GNNs) and suggests five decision-support approaches associated with the model from a practical application perspective. The utilization of complete twin data for prediction and visual simulation effectively eliminates the problem of unexpected factors interfering with scheduling in enterprise production planning and achieves the goals of rapid processing and just-in-time completion. The planning model is based on the attention mechanism, which characterizes the disjunctive graph, extracts the input GNN, and outputs the scheduling decision by constructing the multi-attention network of operations and machines to deal with the complicated \"operation-machine\" combination relationship. The proposed method has been verified in the case of structural assembly and welding workshops, has validity and reliability, and is superior to the traditional priority scheduling rules and heuristics in terms of precision rate and rapidity. Furthermore, the DT system completes the production line application, and its proven reliability supports its full-scale application in future smart factories. digital twin; graph neural network; intelligent workshop modeling; ocean engineering manufacturing; decision support +
Footnote †: journal: Applied Sciences  
Siemens Tecnomatix [3] and Dassault's 3D Experience [4], piloted in civil engineering, robotics manufacturing, aerospace, and other fields. Prior to full commercial application, it is necessary to establish a reliable data processing model for the interaction of simulated and actual measurement data to achieve iterative optimization of the DT system. Ocean Engineering (OE) manufacturing, a giant system engineering in discrete manufacturing mode, is developing late in the field of digitalization and has completed the networking and upgrading work of crucial equipment in recent years, lacking the ability to process and apply a large amount of production data to guide the optimization of production processes, and is in urgent need of a systematic, intelligent control framework for the whole production process. The OE manufacturing process is complicated and involves the intersection of multiple disciplinary fields, specifically material pretreatment, cutting, plate assembly, welding, and assembly, as shown in Figure 1. Considering two modes of the production process, human-machine collaboration and fully automated operation of equipment, the architecture must be designed according to the actual conditions and process routes when establishing the DT workshop. In this paper, based on the five-dimensional model proposed by [PERSON], [PERSON], et al. [5], we refine the operational behavior state criterion and real-time mapping under the finite state mechanism according to the integrated operation mode of structural assembly and welding workshop, and combine the feasible approaches in practice from several research papers [6; 7; 8; 9] to design the architecture of OE intelligent workshop implementation, covering equipment layer, acquisition layer, data layer, application layer and presentation layer. Compared with the DT system architecture utilized in the conventional manufacturing industry, this paper considers the data transformation process among multiple systems and the compatibility of multiple types of equipment due to the lack of production line flexibility in large-scale construction projects.