Abstract: An electrode sheet thermal-lamination adjustment mechanism includes a feeding assembly, a deviation correction assembly, and a visual recognition assembly. The feeding assembly includes a feeding platform. The incoming material strip includes a separator strip and a first electrode sheet disposed within the separator strip. The deviation correction assembly includes a first deviation correction member, a second deviation correction member, and a third deviation correction member. The second deviation correction member and the third deviation correction member are sequentially disposed along the X direction. The X direction is parallel with the conveying direction of the second electrode sheet. The Y direction is perpendicular to the X direction. The X direction and the Y direction are in the same plane. The visual recognition assembly faces the feeding platform. The visual recognition assembly is configured to perform visual recognition on the second electrode sheet on the feeding platform.

Abstract: Disclosed is a data sharing method, including: building a trust alliance block chain, the trust alliance block chain comprising a main chain and at least one slave chain; each slave chain corresponding to a domain; and each domain comprising a leader node; establishing a virtual slave chain on the trust alliance block chain; designating, by the leader node in each domain, a node as a federated learning node; assigning, by the leader node in each domain, a virtual identity to the federated learning node to join the federated learning node to the virtual slave chain; and performing, by each federated learning node, a joint training on a local federated learning model using data generated in its own domain to establish a public federated learning model, through which data are shared among the domains. The present disclosure further provides a data sharing system, an electronic device and a storage medium.

Abstract: Disclosed is a data sharing method, including: building a trust alliance block chain, the trust alliance block chain comprising a main chain and at least one slave chain; each slave chain corresponding to a domain; and each domain comprising a leader node; establishing a virtual slave chain on the trust alliance block chain; designating, by the leader node in each domain, a node as a federated learning node; assigning, by the leader node in each domain, a virtual identity to the federated learning node to join the federated learning node to the virtual slave chain; and performing, by each federated learning node, a joint training on a local federated learning model using data generated in its own domain to establish a public federated learning model, through which data are shared among the domains. The present disclosure further provides a data sharing system, an electronic device and a storage medium.

Abstract: A system of traffic prediction for IoT nodes includes at least one access node, a transmission network and a cloud platform. The access node is configured to collect traffic data, cluster the traffic data into access traffic data and network traffic data, input the access traffic data into an access traffic prediction model to obtain a prediction result of the access traffic at a next moment, and upload the network traffic data and the prediction result of the access traffic to the cloud platform; the cloud platform is configured to input the network traffic data into a network traffic prediction model to obtain a prediction result of the network traffic for each access node at the next moment, and obtain a prediction result of traffic for each node according to the prediction result of the network traffic and the prediction result of the access traffic.