## 2 Related Work
### Existing Research
The author shows that DT can simulate and imitate physical resources on the software level and can deal with them instantly through code debugging and product testing, thus increasing production efficiency and manufacturing accuracy and reducing costs. [PERSON] et al. [21] deployed DT for manufacturing scheduling in SM. They deployed the CPS unit as a virtual and physical twin communication medium. Virtual space deployment in physical space monitoring resources is used to collect data and establish the model, through the model, to obtain the corresponding scheduling plan and carry out the simulation, the final feedback to the physical space. This method is much more accurate than the traditional method because the data obtained based on statistics are constant, which cannot fully reflect the changing environment and the influence on parameters in the production process. [PERSON] et al. [16] proposed a DT-based low-carbon SM workshop. The model proposed by the author can train and filter historical data and then predict and evaluate carbon emissions based on DT. Sensor-based network configuration can select appropriate sensors to construct sub-networks for a specific production planning and scheduling task, and the expected efficiency will also be improved. SM is a typical multi-party collaborative system where each component in the production chain has trust issues. Although DT supports SM running more efficiently by simulating the changes in the physical world, if the data of every sensor are copied back to DT, it is bound to have some worthless data that will interfere with the simulation and prediction. There is a probability of malicious data entering DT to mislead SM in the correct flow of events. Therefore, the data source needs to be authenticated and identified, and Blockchain is an excellent solution to this problem [20]. [PERSON] et al. [17] show that Blockchain can solve this problem and realize more transparent and secure transmission and storage. Blockchain can search the source of materials in SM, which improve transaction efficiency by eliminating wrong products. [PERSON] et al. [22] deployed Blockchain and smart contracts based on real-time data collection of IoT sensors to ensure device legitimacy and data security while reducing decision delay. Using big data technology to manage large-scale datasets, SM fault diagnosis is predicted and analyzed. [PERSON] et al. [23] divided SM into five bottom-up layers, and layers 1-3 use blockchain to implement authentication and validity identification of data sources. The collected data are analyzed based on deep learning at the fourth layer, and the analysis results are applied to SM at the fifth layer to meet the requirements of automation and scalability. The methods combining DT and Blockchain have been relatively well explored, and our study can benefit from these prior studies and proposed methods. For example, [PERSON] et al. [19] proposed a DT-BC-based SM service collaboration mechanism for an industrial Internet platform. The proposed mechanism uses DT to enhance the service performance and solve the trust problem between parties and participants in manufacturing services. The author pointed out that there are many challenges in managing SM services in the industrial Internet platform, such as the integration technology of physical and network information, the value-added technology based on manufacturing collaboration, and the trust of stakeholders. [PERSON] et al. [26] proposed a decentralized data twin framework to solve industrial networks' lack of trust and low-efficiency issues. [PERSON]'s work deploys Blockchain to solve data sharing, network security, integrity, invariability, and traceability challenges. This framework also protects information transparency and enhances privacy. The model fitsmany industries and has high practical value in engineering and construction.