Research on Optimization of Fresh Cold Chain Based on PCM Passive Temperature Control and Intelligent Algorithms

Abstract

Against the background of rapid development in the fresh food industry and increasing demand for green and low-carbon logistics, traditional cold chain systems face prominent pain points such as unstable temperature control, high energy consumption, and inefficient scheduling. Taking Muyangsu Cold Chain Co., Ltd. as the research object, this paper proposes a comprehensive optimization scheme integrating PCM passive temperature control, intelligent algorithms, and 5G Internet of Things. By constructing a multi-objective optimization model, introducing improved Sparrow Search Algorithm and Reinforcement Learning-based Whale Optimization Algorithm, and building a combined prediction model, the study systematically optimizes temperature stability, scheduling efficiency, and risk management. The results show that the proposed scheme significantly reduces energy consumption and loss rate while improving logistics efficiency and service reliability. This research provides a replicable technical framework and practical reference for the high-quality development of the fresh cold chain. In addition, this study conducts in-depth investigation and analysis of the company’s current operation status and existing problems. The research systematically constructs a multi-dimensional risk assessment and prevention system for cold chain transportation. The proposed technical solution is verified through simulation and real-scenario testing. The research results offer clear guidance for the practical transformation and upgrading of cold chain enterprises. It also provides valuable insights for promoting standardized and intelligent development in the fresh cold chain industry.