Optimal path planning and data simulation of emergency material distribution based on improved neural network algorithm

Today, data storage technology is also gradually improving. Various industries can store massive amounts of data for analysis. The global climate change and the bad ecology led to frequent occurrence of natural disasters. Therefore, it is necessary to establish an effective emergency materials distribution system. The neural network model is used to calculate and the optimal emergency distribution route is analyzed according to the historical information and the data. Considering backpropagation, this paper further disposing a method to further improve the calculation of neural network algorithm. From the perspective of structural parameters of neural network algorithms, this paper uses genetic algorithms to construct predictions, and combines the actual purpose of material distribution after disasters. Considering the capacity constraints of distribution centers, time constraints, material needs of disaster relief points and different means of transportation, a dual-objective path planning with multiple distribution centers and multiple disaster relief points with the shortest overall delivery time and lowest overall delivery cost is constructed. By establishing an emergency material distribution system, it can maximize the prompt and accurate delivery after a natural disaster occurs, and solves the urgent needs of the people.