Research Article

Intelligent perception and collaborative optimization decision of port logistics information based on RFID and IoT integration

Hefei BOE Technology Group Co., Ltd., Hefei 230000, PR China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 6 March 2026
Accepted: 8 June 2026

Abstract

To address the problems of inaccurate integration of multi-source information, insufficient real-time state perception, and susceptibility of dynamic scheduling decisions to local optima in port logistics operations, this paper proposes an information perception and collaborative optimization decision-making method for smart port logistics. Based on radio frequency identification technology and ultra-wideband positioning technology, the proposed method constructs a multi-source perception system integrating the identity, status, and spatial position of logistics entities. On this basis, an additive attention mechanism is introduced to enhance the ability of the long short-term memory network to capture key operational events and temporal variation features, thereby improving the accuracy of port logistics state recognition and position prediction. Furthermore, a tabu search mechanism is incorporated into the ant colony optimization algorithm to alleviate the premature convergence problem in traditional path planning and resource scheduling, enabling perception-data-driven collaborative optimization decision-making. The experimental results show that the root mean square error and mean absolute error of the proposed perception model are 1.84 m and 1.21 m, respectively, and the comprehensive classification performance index reaches 0.955. The final solution cost of the proposed collaborative optimization system is reduced to 303.28 km, with a deviation of only 1.07% from the known optimal solution. Under high-load conditions, the average vessel turnaround time is 70.32 h, while the equipment utilization rate and scheduling completion rate reach 88.90% and 85.74%, respectively. The results indicate that the proposed method can simultaneously improve information perception accuracy, path optimization quality, and resource scheduling stability in complex port operation environments, providing effective support for real-time collaborative decision-making in smart port logistics systems.