Cloud computing technology meets the visual monitoring of logistics supply chain and realizes the whole process control of logistics management. When the location information of logistics vehicles and objects is known, in order to plan the most suitable vehicle scheduling route and realize the optimization objective of logistics transportation, we allocate resources to the goods in the logistics by using genetic algorithm, and calculates the individual fitness through the fitness function, so as to judge the individual's advantages and disadvantages. In this paper, the genetic algorithm model is mainly used to find the optimal solution in the process of data analysis. Also, in order to avoid the increase of operation time caused by too many iterations in the process of operation, this paper also analyzes the data by combining the related theories of quantum computing. The parallelism of the combined algorithm model is improved, the complexity of operation is reduced, and the operation speed can be improved. In order to further improve the accuracy of system identification, this paper also selects the hidden Markov model, predict and correct the data of logistics supply chain, which solves the problems of complicated operation caused by large data volume. After analyzing the basic requirements of logistics transportation, this paper designs a real-time logistics supply chain tracking model based on cloud computing technology, including order management, vehicle scheduling management, sorting management and other modules. After testing, the availability of the system under large operating pressure is determined, and the requirements for basic hardware are low.