To improve the ability to predict the evolution trend of public opinion, a public opinion evolution trend prediction model based on an improved wolf pack algorithm and optimized long-short term memory neural network is proposed. Use Halton Sequence to initialization to improve population diversity. Design step factor to perform Gauss-Sine perturbation transformation to improve wolf group exploration and development capabilities. Combine with the spiral in the whale optimization algorithm to improve the siege mechanism to enhance the local search ability of wolves. The bidirectional memory population is used to increase the cooperative ability of the wolf pack. The improved wolf pack algorithm (IWPA) is applied to the hyperparameter prediction of the LSTM neural network. Using keywords such as “COVID-19” and “Food Safety”, the experiment proves that the IWPA-LSTM neural network public opinion evolution prediction model has good accuracy and generality. The model is suitable for the prediction of various public opinion evolution trends.

In order to better reveal the transmission mechanism of COVID-19, this paper proposes the SEAIHR dynamic model by analyzing the transmission characteristics of COVID-19, considering the self-healing of the hidden lurks and the early isolation of the lurks, introducing “h hospitalization isolation”, “recessive cure”, considering the change of prevention and control intensity, and introducing “morbidity status”. Using the real epidemic data and considering the changes of parameters in different stages, a multi model comparative test was conducted. The experimental results showed that the fitting and prediction accuracy of the SEAIHR model was significantly improved, and the fitting error was 34.4%~72.8% lower than that of the classical model in the early and middle stages of the epidemic, providing reference and guidance for epidemic prevention and control.

In order to promote the development of autonomous driving technology, this paper focuses on the lane-changing decision-making behavior of autonomous vehicles. First, the lane-changing intention is quantified objectively, and then the lane-changing collision probability and the lane-changing dynamic risky coefficient are introduced. Based on the game theory, the decision-making behavior model of the lane-changing game for autonomous vehicles is established. Besides, speed gains are considered as the objective of game gains. Therefore, autonomous vehicles can change lanes in a coordinated, safe and reasonable manner. Finally, with SUMO software, the traditional LC2013 lane-changing model and the decision-making behavior model of the lane-changing game are used for simulation experiments and comparative analysis. The results show that the decision-making behavior model of the lane-changing game has higher stability, reliability, safety and lane utilization.

To explore the behavioral characteristics of massive crowds under the active interaction of millions of streamers and viewers in the field of live streaming, this paper summarized the temporal patterns of live streaming workload and user behavior characteristics of the live streaming platform, taking Douyu and Huya live streaming platforms as examples, a statistical analysis of 123 consecutive days, involving more than 2.4 million anchors, and more than 726 million live streaming data. The live streaming workload has obvious intra-day and intra-week effect. Different live streaming modes have significant differences in live streaming characteristics such as the average number of viewers and followers. The lifetime of streamers and the number of viewers conform to a power law distribution. With the development of the platform, there is a strong linear correlation between the number of streamers and viewers, but its volatility is gradually increasing, reflecting the increasingly strong heterogeneity and non-uniformity of the system. It is of great significance for understanding user behavior patterns in complex systems of live streaming, mining user distribution laws and changing trends, and designing business models such as personalized recommendations.

Hidden Naive Bayesian Model (HNB) and Tree Augmented Naive Bayesian Model (TAN) alleviate the strong independence assumption of Local Naive Bayesian Model (LNB) by mining the intrinsic associations between co-neighboring nodes, but ignore that there are both closely correlated nodes and relatively independent nodes in the real network. On this basis, a package criterion is designed, which divides the co-neighboring nodes into correlated co-neighboring nodes and independent co-neighboring nodes according to the degree of association. Then, packaging HNB and TAN respectively, so that the packaged-based hybrid naive Bayesian models are obtained. On FWFW networks with high average number of co-neighbors, the AUC values of the HNB and TAN models after packaging are increased by 12% and 11.6%, respectively. The experimental results show that the proposed method can effectively improve the link prediction performance and has good robustness.

The intergenerational τ has important theoretical and practical value to explore the law of virus transmission, but the probability distribution function is unknown. Therefore, this paper tries to infer the most probable form of the intergenerational interval distribution function by using the maximum entropy method, and analyzes the information of 4 986 cases of novel coronavirus from 20 countries in the world in detail, and estimates the mean and variance of the intergenerational interval of the virus on the basis of fully considering the diversity of samples. Then, based on this method, the global intergenerational interval distribution function of novel coronavirus was deduced and the basic reproduction number of China is calculated. The conclusion of this study is helpful for further objective analysis of the transmission characteristics of the virus, and provides important reference value for the formulation of regular epidemic prevention and control countermeasures and related research.

The image encryption algorithm based on low dimensional chaotic system has the problems of the key space small and the security not high. In this paper, an improved four-dimensional Lorenz dissipative hyperchaotic system is constructed by introducing a cubic smooth memristor model. The dynamic characteristics of the improved system such as phase portraits, dissipation, equilibrium stability, Lyapunov exponent, and bifurcation are investigated, it is proved that the system has good dynamic characteristics and chaotic attractor. Therefore, a chaotic sequence encryption algorithm based on Hash algorithm is proposed. By histogram, key space analysis,robust analysis and other verifications show that the image encryption algorithm has higher key space and can resist brute-force attack.

In order to effectively alleviate the problem of parking chaos and difficult parking during massive parking, this paper builds a convex optimization model that takes into account the interests of multiple parties, focusing on parking costs, queuing time and balancing the parking demand among multiple parking lots. First, a stable bilateral matching that minimizes parking fees is derived by using a matching game; then, the research is extended considering the interests of multiple parties at the same time, and the mathematical model constructed is a convex optimization problem, which is solved by the alternating direction multiplier method (ADMM) distributed solution. Finally, the simulation results show that the distributed optimization model based on ADMM can meet the interests of multiple parties compared with the matching game algorithm and the greedy algorithm. It is verified that the ADMM distributed optimization model is more efficient than the centralized optimization model from the aspects of privacy protection, computing time and data transfer volume. It is suitable for massive shared parking matching, and the necessity of considering the interests of multiple parties and the influence of weight coefficients on the calculation results are analyzed and verified.

Noise is playing an important role in the regulation of gene transcription in biological systems. This paper studies the dynamics of gene transcription regulation system under the effects of multiplicative Gaussian white noise and additive Lévy noise. Firstly, the Lévy noise is simulated with the Janicki-Weron algorithm, and then the fourth-order Runge-Kutta algorithm is used to calculate the steady-state probability density(SPD) function of the protein concentration, then the steady state analysis of the gene transcriptional regulation system is carried out by the SPD figures. The study found that Gaussian noise intensity, Lévy noise intensity, stability index, and skewness parameter all can induce phase transitions in the gene transcription regulation system; At the same time, the increase of these parameters will cause the gene transcription regulation system to gradually the " on" state changes to the "off" state.

In order to study the impact of traffic accidents on expressway traffic flow, a forced lane change rule was introduced based on the two-lane safe distance cellular automaton model, and a highway vehicle congestion model considering the heterogeneity of vehicles was established.The heterogeneous traffic flow on expressways was used by MATLAB numerical simulation, the impact of the duration of traffic accidents and the proportion of heavy trucks under different traffic densities on expressway congestion traffic flow was analyzed. Research shows that the duration of traffic accidents and the proportion of heavy trucks have a significant impact on the traffic flow of highway congestion; when the vehicle density is greater than 4veh/km and less than 80veh/km, the duration of the traffic accident has a significant impact on traffic flow. And the longer the duration, the greater the impact influences on the traffic flow; when the vehicle density is less than 30veh/km, the proportion of heavy trucks has a significant impact on the traffic flow, and the larger the proportion, the greater the impact influences on the traffic flow.