Application and Communication Optimization Technology of Unmanned Distribution Car under Deep Learning in Logistics Express of COVID-19

This work aims to solve the problem that the daily necessities of urban residents cannot be delivered during coronavirus disease 2019 (COVID-19), thereby reducing the possibility of the delivery personnel contracting COVID-19 due to the need to transport medicines to the hospital during the epidemic. Firstly, this work studies the application and communication optimization technology of unmanned delivery cars based on deep learning (DL) under COVID-19. Secondly, a route planning method for unmanned delivery cars based on the DL method is proposed under the influence of factors such as maximum flight time, load, and road conditions. This work analyzes and introduces unmanned delivery cars from four aspects combined with the actual operation of unmanned delivery cars and related literature: the characteristics, delivery mode, economy, and limitations of unmanned delivery cars. The unmanned delivery car is in the promotion stage. A basic AVRPTW model is established that minimizes the total delivery cost without considering the charging behavior under the restriction of some routes, delivery time, load, and other factors. The path optimization problem of unmanned delivery cars in various situations is considered. A multiobjective optimization model of the unmanned delivery car in the charging/swap mode is established with the goal of minimizing the total delivery cost and maximizing customer satisfaction under the premise of meeting the car driving requirements. An improved genetic algorithm is designed to solve the established model. Finally, the model is tested, and its results are analyzed. The effectiveness of this route planning method is proved through case analysis. Customer satisfaction, delivery time, cost input, and other aspects have been greatly improved through the improvement and optimization of the unmanned delivery car line, which has been well applied in practice. In addition, unmanned delivery cars are affected by many factors such as load, and the service time required for delivery is longer. Therefore, this work chooses an unmanned distribution car with strong endurance to improve distribution efficiency. The new hospital contactless distribution mode discussed here will play an important role in promoting future development.