## 7 Conclusions and Future Work  
This study proposes a five-dimensional architecture of the digital twin system for the ocean engineering intelligent workshop and concentrates on introducing a graph neural network in the service-driven layer to transform the workshop disjunctive graph model into multi-dimensional data, and relying on the attention mechanism, analyze the operation and equipment information to complete node embedding and realize feature extraction. The PPO algorithm training is performed to establish an end-to-end learning framework for solving FJSP, and the GAT network parameters are optimized so that the planning model has excellent generalization after the training of small-sized data, which resolves the problems of long computation time and poor optimization effect in the current workshop planning management practice. With the support of the DT system, a large amount of data input ensures that the model is adequately trained and systematically robust in the face of complex conditions in the shop floor scheduling environment. The numerical results show that the model proposed in this paper can complete the output of workshop planning, and the output results and computation time are significantly better than the traditional heuristic methods.  
In future research work, the DT system must fulfill the functional iterations following the continuous upgrading of the intelligent workshop and transform itself from the current \"real-time status feedback and decision optimization in parallel\" mode to the \"prediction data-based virtual-real co-drive\" mode to materialize the workshop production process that enables pre-awareness, advance decision-making, and rapid optimization. In addition, the GNN-based planning model is capable of handling production conflicts quickly, i.e., optimizing the model response speed in the dynamic scheduling procedure, further exploring offline reinforcement learning methods, reducing training costs, and improving the generalization ability of the model for online applications.  
Conceptualization, J.L. and W.Y.; methodology, W.Y.; software, B.Y.; validation, W.Y., B.Y., L.C. and H.Y.; formal analysis, W.Y.; investigation, Y.C. and H.Y.; resources, J.L. and L.C.; data curation, B.Y.; writing--original draft preparation, W.Y.; writing--review and editing, W.Y. and B.Y.; visualization, R.D. and H.Y.; supervision, J.L.; project administration, J.L., B.Y. and L.C.; funding acquisition, J.L. and L.C. All authors have read and agreed to the published version of the manuscript.  
This research program is funded by the projects of Research on Intelligent Manufacturing Solutions and Key Technologies for the Upper Module of Offshore Oil and Gas Production Platforms [Grant number 2018473] and Research on Collaborative Design Technology of High-Tech Ocean-going Passenger Ships [Grant number 2019331].  
Not applicable.  
The authors are responsible for the contents of this publication. In addition, the authors would like to thank lab classmates for their contribution to the writing quality.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multicolumn{2}{c}{**Total Project Duration (Days)**} & \multicolumn{2}{c}{**Calculation Time (s)**} \\ \cline{2-5}  & **DT** & **MES** & **DT** & **MES** \\ \hline A & 246 & 261 & 783.28 & 905.45 \\ B & 242 & 254 & 780.10 & 903.89 \\ C & 239 & 251 & 757.61 & 967.77 \\ \hline \hline \end{tabular} Data collection in the system export results. Commercially sensitive information is not shown, and only system-exported test data are provided.  
\end{table}
Table 4: Comparison of the DT system performance with the existing MES.