## 3 Proposed Blockchain and Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework  
This section provides a layered description of the proposed architecture, explaining the different layers of data processing and their deployment implications for the overall framework. Then, the whole data processing process is introduced from the global point of view. As shown in Figure 1, the heuristic multi-cooperation scheduling framework proposed in this paper is mainly divided into three layers: (1) the IIoT device layer, (2) the edge layer, and (3) the cloud layer.  
* _Device layer_: At this layer, sensors collect industrial data and then forward the data to the upper layer, the edge layer. Many types of data in IIoT come from different industrial plants. SM requires manufacturers to collaborate to complete an industrial task. The interaction between various manufacturing in SM can be risky because the data provided by each plant does not guarantee validity. Therefore, the interaction between them cannot be carried out without any security guarantee, and the interaction between them can only be made after the confirmation of the legitimacy of their identity and data validity. To schedule the collaboration between manufacturers more efficiently, their scheduling decisions should be made after investigating the global status and identifying the validity of the collaboration object.  
* _Edge Layer_: At this layer, we deploy a public Blockchain network where devices authenticate. Before devices at the device layer send data, they need to go through the consensus mechanism of the Blockchain system to authenticate their identity and data validity. After that, the base station makes a cluster according to the geographical distribution of data sources. Each manufacturer has a unique identification symbol so that each cluster can accommodate a specific range of complex manufacturing. Because the cluster's data can be used to identify its source, the headquarters can locate the corresponding manufacturing state more accurately and quickly when making decisions. In addition, all the devices that join the cluster must be approved by other nodes in the network through the consensus mechanism. Since only certified data can be added to the cluster, any necessary data for SM after that will be provided by the manufacturer in the cluster.
* _Cloud Layer_: DT is deployed in this layer, and a device layer mirror environment is made, where data will be scheduled for SM. The identifier judges the data used in digital manufacturing and only received from the corresponding cluster, thus ensuring the accuracy of data sources. When an SM manufacturer needs to interact with other SM manufacturers, the decision is made through the headquarters outside the DT when interacting with the headquarters. The local data processing results are encrypted and sent to the headquarters. The security of sensitive data is guaranteed because the calculation of sensitive data is realized in DT, and processing results only carry out the interaction with the headquarters.  
In our proposed framework, the blockchain network provides authentication for manufacturing. It creates clusters that are easy to locate and trace. DT ensures that sensitive data are not transferred to the public network, thus ensuring the efficiency and security of SM operations. Data are transferred bottom-up from the device layer to the cloud. In the following three sections, we provide a detailed workflow of blockchain deployment in the edge layer and DT deployment in the cloud.  
Figure 1: Proposed blockchain and digital twin-based heuristic multi-cooperation scheduling framework.