安徽省生鲜农产品冷链物流需求预测研究  

Research on demand prediction of cold chain logistics for fresh agricultural products in Anhui Province

作　　者：徐超毅[1] 胡望敏 XU Chaoyi;HU Wangmin(School of Economics and Management,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学经济与管理学院,安徽淮南232001

出　　处：《哈尔滨商业大学学报（自然科学版）》2024年第4期485-493,共9页Journal of Harbin University of Commerce：Natural Sciences Edition

基　　金：国家自然科学基金项目(71973001);安徽理工大学研究生创新基金项目(2023CX2171).

摘　　要：生鲜农产品等冷链产品市场需求快速增长,冷链物流的供给无法满足人们的需求给生鲜农产品带来新的挑战.安徽省作为一个农产品丰富的地区,生鲜农产品的供应对于满足市场需求至关重要.收集了2001~2022年生鲜农产品产量数据,采用反向传播神经网络(Back Propagation Neural Network,BP神经网络)、长短时记忆(long short-term memory,LSTM)、粒子群算法优化的长短期记忆神经网络(Particle Swarm Optimization-Long Short-Term Memory,PSO-LSTM)三种模型进行训练和验证,通过三种模型的对比分析,三种模型相对误差分别为0.13%、0.06%、0.02%.结果表明,PSO-LSTM模型预测精度最高,拟合效果最好,能够有效预测未来四年安徽省生鲜农产品冷链物流需求,以应对不断增长的冷链物流需求压力.The demand for fresh agricultural products and other cold chain products in the market is rapidly increasing,but the supply of cold chain logistics cannot meet people's needs,bring new challenges to fresh agricultural products.As a region rich in agricultural products,the supply of fresh agricultural products in Anhui Province is crucial for meeting market demand.Therefore,this paper collected fresh agricultural product yield data from 2001 to 2022 and trained and validated three models:back propagation neural network(BP neural network),long short term memory(LSTM),and particle swarm optimization long short term memory(PSO-LSTM).Through comparative analysis of the three models,the relative errors of the three models were 0.13%,0.06%,and 0.02%,respectively.The results showed that the PSO-LSTM model has the highest prediction accuracy and the best fitting effect,and can effectively predict the cold chain logistics demand for fresh agricultural products in Anhui Province in the next four years to cope with the growing pressure of cold chain logistics demand.

关 键 词：BP神经网络 LSTM模型 PSO-LSTM模型 生鲜农产品冷链物流 需求预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] F326.6[自动化与计算机技术—控制科学与工程]