The traditional literatures mainly consider the cooperative relationship while neglecting the competitive relationship between manufacturing and logistics enterprises during interactive development. An improved model, namely Logistic-Volterra model, was proposed based on the traditional Logistic model, which considered the contribution coefficients and competition coefficients at the same time. Firstly, the Logistic-Volterra model was built and the stability solution was sovled, then the mathematical conditions for achieving stability and the interpretation of reality were discussed. Secondly, the affecting factors on the interactive development of manufacturing and logistics enterprises were discovered by using Matlab numerical simulation, and the differences between the improved model and traditional model were also discussed. Finally, the manufacturing enterprise A and logistics enterprise B were taken as an example to analyze the competitive behavior in the process of cooperation; furthermore, the impact of coopetition behavior on the interests was also analyzed. The theoretical analysis and simulation results show that the stability of the system is highly affected by contribution coefficient, competition coefficient and environmental capability, the result is more reasonable when considering the competition relationship in the model. It means that manufacturing and logistics enterprises should fully address the effects of competition on both sides.

Current research on statistical publication of differential privacy data stream only considers one-dimensional data stream. However, many applications require privacy protection publishing two-dimensional data stream, which makes traditional models and methods unusable. To solve the issue, firstly, a differential privacy statistical publication algorithm for fixed-length two-dimensional data stream, call PTDSS, was proposed. The tuple frequency of the two-dimensional data stream under certain condition was calculated by a one-time linear scan to the data stream with low-cost space. Basing on the result of sensitivity analysis, a certain amount of noise was added into the statistical results so as to meet the differential privacy requirement. After that, a differential privacy continuous statistical publication algorithm for any length two-dimensional data stream using sliding window model, called PTDSS-SW, was presented. The theoretical analysis and experimental results show that the proposed algorithms can safely preserve the privacy in the statistical publication of two-dimensional data stream and ensure the relative error of the released data in the range of 10% to 95%.

To solve the problem of optimization learning models in Belief Rule Base (BRB), a new parameter training approach based on the Particle Swarm Optimization (PSO) algorithm was proposed, which is one of the swarm intelligence algorithms. The optimization learning model was converted to nonlinear optimization problem with constraints. During the optimization process, all particles were limited in the search space and the particles with no speed were given velocity in order to maintain the diversity of the population of particles and achieve parameter training. In the practical pipeline leak detection problem, the Mean Absolute Error (MAE) of the trained system was 0.166478. The experimental results show the proposed method has good accuracy and it can be used for parameter training.