目的 为了实时监测农产品始终处于低温、适宜湿度等条件下，实现农产品质量信息的及时反馈和预警。方法 针对农产品在冷链物流中的质量安全问题，首先分析影响其质量安全的因素，整合供应链上的追溯信息和监测信息，构建农产品的质量安全预警指标体系。然后设计结合交叉变异算子的自适应混合粒子群算法（AHPSO）优化支持向量机（SVM）参数，以此建立基于AHPSO-SVM的农产品冷链物流质量安全预警模型。结果 以苹果的预警指标体系为例，经过模型训练和预测后，预测输出曲线与期望输出曲线均能较好拟合，误差值小。结论 该方法较传统的BP神经网络与支持向量机方法，在解决实际问题中预测结果精度更高，可以有效提高农产品冷链物流中质量安全预警的准确性。

The work aims to monitor the agricultural products always under such conditions as low temperature and appropriate humidity in real time to achieve the timely feedback and early warning of the information on the agricultural product quality. With respect to the problem of quality and safety of agricultural products during the cold-chain logistics, first, the factors that affected the quality and safety were analyzed, and the retroactive and monitoring information on the supply chain was integrated to construct the early-warning indicator system for the quality and safety of the agricultural products. Then, an adaptive hybrid particle swarm optimization (AHPSO) algorithm combined with the crossover and mutation operator was designed to optimize support vector machine (SVM) parameters so that the AHPSO-SVM early-warning model of agricultural products' quality and safety during cold-chain logistics was built. With the early-warning indicator system of apples as an example, after the model training and prediction, the predicted output curve and the expected output curve could be better fitted, and the error value was small. Compared with the traditional BP neural network and SVM, in solving practical problems by the proposed method, the prediction accuracy is higher and the quality and safety early-warning accuracy during the cold-chain logistics of agricultural products can be effectively improved.