As a new type of network structure, the Software Defined Network (hereinafter referred to as SDN) provides a new solution for network flow management and optimization, which has made the accurate detection of anomaly SDN flows a hot research topic. This paper presents an SDN-based flow detection method, builds structures for detecting anomaly SDN flows and performs classification detection on the flows using the DPTCM-KNN (Double P-value of Transductive Confidence Machines for K-Nearest Neighbors) algorithm. The experimental results show that the algorithm proposed achieves lower a false positive rate, higher precision and better adaptation to the SDN environment than do other algorithms of the same type.

In order to trace the logistics information and make use of the characteristics of the Consortium Blockchain, a logistics information traceability model based on the Consortium chain is proposed. The research focuses on two key technologies in the model, namely Multi-center Practical Byzantine Fault Tolerance (MCPBFT) consensus algorithm and Information Matching mechanism. In MCPBFT algorithm, many nodes involved in logistics information are divided into multiple consensus sets, and the consistency protocol is improved into two phases based on PBFT algorithm. In the information matching mechanism, the authenticity of the recorded on-chain information is ensured by matching the updated logistics information with the information stored on the chain in advance. By analyzing the two key technologies, the feasibility and superiority of these two technologies are explained. Among them, MCPBFT algorithm can effectively improve the efficiency of consensus and ensure that the logistics information can be updated in time; the information matching mechanism can be safely applied to the traceability model and improve the practicability of the traceability model.

Overhead crane is regarded as a kind of complex nonlinear dynamic system in which the underactuated characteristic makes the controller hardly control the space of load swing effectively. In addition, the system internal and external uncertain factors also give rise to the adverse effect for control performance. Therefore, aiming at these problems, a type-2 fuzzy theory-based sliding mode controller (type-2 FSMC) is designed for damping the oscillation of the payload in which the proposed type-2 FSMC inherits the benefits of type-2 fuzzy logic theory and sliding mode control theory for improving anti-swing control performance. Furthermore, in order to configure the type-2 FSMC parameter efficiently, an adaptive differential evolutionary algorithm-based parameter tuning mechanism is presented for acquiring the optimal control performance. Through computer simulation and comparisons under different operation conditions, the optimal type-2 FSMC demonstrates the effectiveness in eliminating the payload residual vibration.

The use of UAVs for logistics delivery has become a hot topic in current research, and how to plan a reasonable delivery route is the key to the problem. Therefore, this paper proposes a multi-environment logistics delivery route planning model that is based on UAVs, is characterized by a 3D environment model, and aims at the shortest delivery route with minimum flight undulation. In order to find the optimal route in various environments, a multi-strategy improved artificial lemming algorithm, which integrates the Cubic chaotic map initialization, double adaptive t-distribution perturbation, and population dynamic optimization, is proposed. The symmetric nature of the t-distribution ensures that the lemmings conduct extensive searches in both directions within the solution space, thus improving the convergence speed and preventing them from falling into local optimal solutions. Through data experiments and simulation analysis, the improved algorithm can be successfully applied to the 3D route planning model, and the route quality is superior.