Internet of Things (IoT) finance extends financial services to the whole physical commodity society with the help of IoT technology to realize financial automation and intelligence. However, the security of IoT finance still needs to be improved. Blockchain has the characteristics of decentralization, immutability, faster settlement, etc., and has been gradually applied to the field of IoT finance. Blockchain is also considered to be an effective way to resolve the problems of the traditional supply chain finance industry, such as the inability to transmit core enterprise credit, the failure of full-chain business information connections and the difficulty of clearing and settlement. Supply chain finance allows the strongest enterprise in the supply chain to apply for credit guarantee from the bank to obtain bank loans, and use the funds for circulation in the supply chain to ensure that each enterprise in the whole supply chain can obtain working capital to realize profits, so as to maximize common interests. In this paper, a financial management platform based on the integration of blockchain and supply chain has been designed and implemented. Blockchain is used to integrate supply chain finance to synchronize the bank account payment system, realize the automatic flow of funds, process supervision and automatically settle account periods based on smart contracts. The four functional modules of the system are designed using unified modeling language (UML), and the model view controller (MVC) architecture is selected as the main architecture of the system. The results of the system test show that the proposed platform can effectively improve the system security, and can use the information in the blockchain to provide multi-level financing services for enterprises in supply chain finance.

In recent years, land–air collaborative logistics delivery is a promising distribution method. It combines the flexibility of unmanned aerial vehicles (UAVs) with the high payload capacity of vehicles, expanding the service range of UAVs while reducing carbon emissions. However, most existing research has focused on path planning and resource allocation for either UAVs or vehicles alone. Therefore, to address the shortcomings of the current research, this paper proposes an intelligent land–air collaboration delivery algorithm for trajectory optimization and resource scheduling. Subsequently, this paper develops a land–air collaboration reliable delivery logistics distribution system, showcasing the driving routes of vehicles and UAVs. Meanwhile, the mode of UAV–vehicle collaboration not only saves operating costs compared to traditional logistics delivery but also achieves energy conservation and emission reduction goals. During the specific design and implementation process of this platform, blockchain technology is integrated into the logistics delivery service to ensure data security and prevent tampering, making the system more efficient and reliable. Finally, through testing and verification of the system’s functionalities, its completeness is demonstrated.

In the era of the information explosion, big data are always around us. Academic big data are defined as a large amount of data generated in the life cycle of all academic activities, which usually contains a large amount of academic information. Academic conferences can effectively promote academic exchanges among scholars. In recent years, academic conferences in various fields have been held around the world. However, with the increase in the number of academic conferences, the quality of conferences and the efficiency of hosting and participating in conferences are uneven. In today’s fast-paced life, high-quality and efficient academic conferences have become the first choice of scholars. In this paper, a conference recommendation method based on a big data analysis of users’ interests and preferences is proposed to help users choose high-quality academic conferences and to help organizers reduce conference costs and improve the conference operation efficiency. The method first divides the research fields of user-related academic conferences into three categories: the fields that users are interested in, the fields that users attend, and the research fields that users follow up. Then, the weights of these three categories are set, and the importance of each category recommendation related to the user is calculated. Finally, the conference recommendation index is calculated and several conferences with a high recommendation value are recommended to users. The experimental results show that the proposed conference recommendation method provides a convenient and fast service to conference participants and conference organizers. The developed big data platform can significantly improve the operation and participation efficiency of academic conferences, reduce the costs, and give full play to the role and value of academic conferences.

Ground-air cooperative package distribution is a promising delivery method, especially during Corona virus Disease 2019. It can extend the coverage of vehicles by exploring the flexibility of unmanned aerial vehicles (UAVs), expand the distribution of vehicles and reduce carbon emissions. Most existing studies focus on their trajectory optimization, while often overlooking their coordination for global information, and the complexity and reliability of collaborative delivery problem. To address the above issues, we first formulate an optimization problem to minimize the service cost of both UAVs and vehicles. To ensure service reliability, the constraints of UAVs during takeoff, service, and landing phases are comprehensively considered. We then propose a lightweight reinforcement learning solution to minimize the flight distance of UAVs and the number of required vehicles. Finally, theoretical analysis and performance evaluations show that compared with other representative algorithms, the designed algorithm has advantages in terms of robustness, effectiveness and stability.