Research on the Optimization of Cloud Intelligent Logistics Control System based on GBDT and LSTM Neural Network

In order to achieve the goal of “carbon neutrality and carbon peak”, this paper investigates the optimization method of cloud-based intelligent logistics regulation system based on GBDT and LSTM neural network. We developed a new logistics information transportation optimization and sharing platform - “the most ‘optimal’ transportation”, which integrates a real-time distribution route optimization system and a special travel strategy speed distribution system. The platform adopts GBDT algorithm, simulated annealing algorithm, Logistic’s SUE model, entropy weight TOPSIS method, LSTM neural network, ray tracing method and blockchain technology to realize real-time tracking of logistics transportation, optimization of distribution routes, lowering of logistics costs, and reduction of carbon emissions. Through the analysis of the current situation of the logistics industry at home and abroad and the in-depth study of the development plan of modern logistics, we have explored the path of green transformation of the logistics industry, and provided a scientific and practical solution to achieve the goal of “double carbon”. The research results show that the platform can effectively improve the efficiency and quality of logistics services and contribute to the construction of ecological civilization and sustainable development.