Research on Emergency Logistics Model of Agricultural Products Based on Coupling of Petri Net and Blockchain

In this COVID-19 epidemic, due to insufficient awareness of the impact of sudden public health emergencies on agricultural logistics at this stage, agricultural products were left unsold, stocks were backlogged, and losses were severe. In the process of distribution, we should not only ensure a short time cycle and avoid the contamination of agricultural products by foreign bacteria, but also pay attention to the waste of human, material, and financial resources. Therefore, this study mainly adopts the combination of the petrochemical network and block chain to build an agricultural products emergency logistics model. This paper first shows the operation mechanism of the petri dish network and blockchain coupling in the form of a graph and then uses the culture network modelling and simulation tool PIPE to directly verify the construction model. It is proved that the structure and overall business process of the agricultural products logistics system constructed by combining the Petri net and block chain are reasonable, reliable, and feasible in practical application and development. It is hoped that this study can provide a reference direction for agricultural emergency logistics

A Multi-objective Particle Swarm Optimization Algorithm Based on Reverse Learning

In order to solve the contradiction between population diversity and convergence in particle swarm optimization algorithm, in this paper, a particle swarm optimization algorithm with reverse learning is proposed. On this basis, the values of learning factor and constraint factor parameters are modified, and the linear decreasing weight strategy was adopted. By modifying the learning factor and the constraint factor, the algorithm improves the particle optimization ability. It balances the global search and local search of the particle, and the convergence speed is improved by using the inertia weight. When it is detected that the algorithm falls into the local optimal region, the position information of these poor particles is used to guide some particles to reverse learning at a faster flight speed, and the particles are quickly pulled out of the local optimal region. The reverse learning process can not only improve the diversity of particle population, but also ensure the global detection ability of the algorithm. Experimental results show that, compared with the basic MOPSO algorithm, this algorithm has fast convergence speed and high solution accuracy in function optimization

Traffic Congestion Warning Model Based on GIS \ GPS \ GPRS \ RFID Technology

To get the road network in the vehicle's location, speed, direction and a series of information, it innovative RFID-based dynamic access, GPRS (General Packet Radio Service) two-way communications. GPS real-time positioning and GIS path tracking system of the dynamic traffic conditions can be received from information flow, data flow, timely mutual transmission and accurate identification of different vehicles. Thus, through a summary of the above information and combining GIS geographic information system, we can obtain city status in real-time traffic information. It used BP-GM hybrid model to predict traffic way and changed previous single model for efficient and accurate prediction of future time. In order to achieve travel warning and traffic congestion route, quantitative research results and timely feedbacks were used for coming period of road network conditions to prevent the occurrence of massive congestion