###### Abstract
Independent design for various degrees of digitization in the production process. For example, in the pre-processing, cutting, and part of the welding stages of full automation, the DT system is arranged to participate directly in the production process through the inter-system output instructions to organize the production, and the production status is presented with direct access to the system. In  
Figure 1: Ocean engineering platform manufacturing process. the manual phase, the DT system is responsible for issuing tasks, and each station receives the request to complete the work via a variety of terminals. The implementation of DT has had a revolutionary impact on the optimization of process workflows in OE assembly and construction. The core of DT is full-factor data-driven production process prediction and optimization to guide the operation execution and collect actual data for operation analysis to fulfill the timely adjustment in the execution process, not just a simple visualization display. The work in this paper is geared towards developing companies with large-scale engineering, where all-around compatibility and utilization of existing resources are the crucial roles of the proposed DT architecture. Job planning, as the lifeblood of production control, determines production efficiency and capacity. The primary emphasis of establishing a DT workshop is to implement a twin data-driven planning methodology. In this paper, we propose an algorithmic model for job planning based on Graph Attention Networks (GATs), an improved version of GNN. Different from the traditional Priority Dispatch Rules (PDRs) and heuristic algorithms, a GAT extracts features from the disjunctive graph model in the scheduling problem, and outputs constraint-compliant \"operation-machine\" combinations from the set of operations and the set of machines based on the Markov Decision Process (MDP) to form the decision sequence. Among them, the weight parameters in a GAT are obtained by deep reinforcement learning (DRL) performing small-sample training. This is one of the latest mainstream algorithms in recent years, and most studies [10; 11; 12; 13; 14; 15; 16] have developed solutions for standard arithmetic cases, on the basis of which this paper completes customized development for the characteristics of the OE workshop and case validation based on actual production data. Its high performance, excellent computational effect, and ability to adapt to maintain rapidity computation under a large amount of data input from the DT system are the main reasons why it is selected instead of PDRs and heuristic algorithms in this paper, as compared with the traditional algorithms. Moreover, the GAT model enables effective interfacing between knowledge graphs and expert systems in the future, making it a superior optional method. The DT workshop planning model is trialed under the scenario in the standardized test datasets and the OE manufacturing workshop. The scenario, as the critical node in the production chain of an OE smart factory, is capable of covering the production elements of the whole factory. The case validation is centered around ensuring the high availability of both the DT system and its associated model. There are prerequisites for this industry in the scenario, and the case validation can give a part of the reference of executable solutions to the peers. In general, this paper makes the following contributions:  
* We design the DT workshop system architecture in detail, form a targeted five-dimensional model for OE intelligent manufacturing workshops, and create an integrated discrete operation workshop management model with \"virtual-real co-drive\". * We present a novel model for the DT workshop planning that utilizes GNN. Our method realizes generating the workshop's master plan that exhibits good generalization capabilities. This is achieved through node embedding, graph attention network structure building, and a deep reinforcement learning-based algorithm for model training.