BeiDou‐ ASEAN Cross‐Border Logistics Industrial Internet of Things Dynamic Path Optimization Algorithm Based on Particle Swarm Algorithm

ABSTRACT Aiming at the high‐dimensional dynamic path optimization problem of BeiDou‐ASEAN cross‐border logistics, this paper proposes an industrial Internet of Things optimization method based on improved particle swarm algorithm. By introducing the Close distance function to reduce high‐dimensional extreme dimensional interference, a dynamic inertial weight adjustment strategy and an adaptive variation operator are designed, and the path planning is optimized by combining BeiDou real‐time data. Experiments show that the average relative error of the algorithm in the static road network is only 0.18%, and the dynamic event preoptimization efficiency is more than 85%, which is 41.9% higher than the convergence speed of the traditional algorithm, and the response level of 200 ms is achieved in the 1000‐node road network. The study provides a high‐precision real‐time optimization scheme for cross‐border logistics, which significantly improves regional logistics efficiency.