电商发展背景下基于BP神经网络的杭州市鲜活农产品物流需求预测研究

Research on Logistics Demand Forecast of Fresh Agricultural Products in Hangzhou Based on BP Neural Network in the Background of E-Commerce Development

摘要: 本研究聚焦电商高速发展背景下鲜活农产品物流需求的精准预测问题，针对传统预测方法时效性弱、精准度不足的痛点，将BP神经网络模型引入物流需求预测领域。研究通过梳理电商交易数据、物流时效数据、鲜活农产品特性数据等核心指标，构建适配鲜活农产品物流需求的BP神经网络预测模型，优化模型的输入层、隐藏层、输出层参数设置，结合电商订单的季节性、区域性特征完成模型训练与验证。针对年度数据样本量不足的问题，本研究尝试提升数据频度至季度级，同时优化鲜活农产品冷链物流需求量计算公式，纳入城市居民消费量、外来调入量及电商上行发货量等关键指标。通过灰色关联分析筛选核心影响因素后，结合经济学原理深入阐释指标与物流需求的关联机制。研究结果表明，优化后的模型可有效捕捉电商驱动下鲜活农产品物流需求的动态变化规律，预测精准度较传统统计方法提升显著。本研究突破了传统预测方法的局限，为电商与鲜活农产品物流产业的协同规划提供了量化参考，也为农产品物流需求预测提供了可落地的技术方案。研究兼具理论价值与实践意义，创新点突出，可支撑物流企业的后续动态安排与相关决策。

Abstract: This study focuses on the precise prediction of fresh agricultural product logistics demand under the rapid development of e-commerce. Addressing the limitations of traditional prediction methods in timeliness and accuracy, we introduce the BP neural network model into logistics demand forecasting. By analyzing core indicators such as e-commerce transaction data, logistics timeliness data, and characteristics of fresh agricultural products, we construct a BP neural network prediction model tailored for fresh agricultural product logistics demand. The model’s input layer, hidden layer, and output layer parameters are optimized, and training and validation are conducted by incorporating seasonal and regional characteristics of e-commerce orders. To address the issue of insufficient annual data samples, we attempt to increase data frequency to quarterly levels while optimizing the cold chain logistics demand calculation formula for fresh agricultural products. Key indicators such as urban resident consumption volume, external transfer volume, and e-commerce outbound shipment volume are incorporated. Through gray relational analysis to screen core influencing factors, we further explain the correlation mechanism between indicators and logistics demand based on economic principles. The results demonstrate that the optimized model effectively captures the dynamic changes in fresh agricultural product logistics demand driven by e-commerce, achieving significantly improved prediction accuracy compared to traditional statistical methods. This study overcomes the limitations of traditional prediction methods, providing quantitative references for collaborative planning between e-commerce and fresh agricultural product logistics industries, as well as practical technical solutions for agricultural product logistics demand prediction. The research combines theoretical value with practical significance, featuring prominent innovations that support subsequent dynamic arrangements and related decision-making for logistics enterprises.