Abstract: According to the shortcomings of the structure of "local-cloud" of logistics asset management system, an edge computing offloading decision-making and resource distribution service joint optimization algorithm is designed to address the shortcomings of the "local-cloud" structure of the logistics asset management system. By shifting the initial data computation job to the edge, a better unloading choice and resource distribution strategy are made possible. The time-delay energy consumption system cost model is developed in accordance with the tasks, computing capability, and other data. Dichotomy, the Lagrange multiplier method, and an improved particle swarm optimization algorithm are used to solve the problem, and realize multi-user and multi-node joint iterative optimization with cloud participation. The experimental results demonstrate the effectiveness of the method in lowering system costs overall and reduce the costs of the Random, Greedy, and STPSO algorithms by a combined total of 59.34%, 45.74%, and 24.07%.