Application of the multi-objective model under the fuzzy differential equation to logistics operation of internet of things

摘要

This paper aims to explore the application of the Multi-Objective Model (MOM) in the logistics operation of an e-commerce platform. E-commerce is a new business model, and people are studying the use of various technologies to optimise its operation mode. On this basis, the logistics operation on the e-commerce platform is studied by using the method of moments based on deep learning, Fuzzy Differential Equations (FDEs) and the Internet of Things. On the ImageNet data set, the parameter initialisation of the network is trained and detected. The initial learning rate of the network is 0.0003, and 45,000 iterations are needed to reduce the learning rate. The convergence and stability of FDEs are of great significance for the accuracy of data on e-commerce platforms. The results show that the correlation between web page accessibility and revenue on the e-commerce platform is 0.011, and this data acquisition is based on traditional methods.