摘 要
随着电子商务和全球供应链的快速发展，物流需求预测已成为提升运营效率和服务水平的关键环节。然而，传统预测方法在面对复杂多变的物流环境时存在局限性，难以满足精准性和实时性的要求。为此，本研究基于大数据技术构建了一种新型物流需求预测模型，旨在通过整合多元数据源和先进算法提高预测精度。研究首先收集并处理了包括历史订单、天气状况、节假日效应及经济指标在内的多维数据集，随后采用深度学习框架中的长短时记忆网络（LSTM）对时间序列特征进行建模，并结合注意力机制优化关键变量的权重分配。此外，为解决小样本问题，引入了迁移学习策略以增强模型的泛化能力。实验结果表明，该模型相较于传统统计方法和单一机器学习模型，在均方根误差（RMSE）和平均绝对百分比误差（MAPE）等评价指标上均有显著改善，预测精度提升了约15%-20%。进一步的实际应用案例验证了模型在不同场景下的适应性和可靠性，特别是在季节性波动和突发性事件中的表现尤为突出。本研究的主要创新点在于将大数据分析与深度学习技术深度融合，同时考虑了物流需求的动态特性和外部影响因素，为行业提供了更为科学和高效的预测工具，有助于降低运营成本并优化资源配置。
关键词：物流需求预测；深度学习；长短时记忆网络（LSTM）；大数据技术；迁移学习
Abstract
With the rapid development of e-commerce and global supply chains, logistics demand forecasting has become a critical component in enhancing operational efficiency and service quality. However, traditional forecasting methods exhibit limitations when dealing with the complex and dynamic nature of logistics environments, struggling to meet the requirements of accuracy and real-time responsiveness. To address this challenge, this study develops a novel logistics demand forecasting model based on big data technology, aiming to improve prediction accuracy by integrating diverse data sources and advanced algorithms. The research first collects and processes a multidimensional dataset encompassing historical orders, weather conditions, holiday effects, and economic indicators. Subsequently, a deep learning fr amework utilizing Long Short-Term Memory (LSTM) networks is employed to model time-series features, while an attention mechanism is incorporated to optimize the weight allocation of key variables. Furthermore, to tackle the issue of small sample sizes, transfer learning strategies are introduced to enhance the model's generalization capability. Experimental results demonstrate that the proposed model significantly outperforms traditional statistical methods and single machine learning models in terms of evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE), achieving an improvement in prediction accuracy of approximately 15%-20%. Additional case studies confirm the model's adaptability and reliability across various scenarios, particularly in handling seasonal fluctuations and sudden events. The primary innovation of this study lies in the deep integration of big data analytics and deep learning technologies, taking into account the dynamic characteristics of logistics demand and external influencing factors. This provides the industry with a more scientific and efficient forecasting tool, contributing to reduced operational costs and optimized resource allocation.
Keywords: Logistics Demand Forecasting; Deep Learning; Long Short-Term Memory Network (Lstm); Big Data Technology; Transfer Learning
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状分析 1
1.3研究方法与技术路线 2
2大数据在物流需求预测中的应用基础 2
2.1物流需求预测的基本概念 2
2.2大数据技术的核心特征 3
2.3数据采集与预处理方法 3
2.4预测模型构建的理论框架 4
3基于大数据的物流需求预测模型设计 4
3.1模型构建的目标与原则 4
3.2关键算法的选择与优化 5
3.3数据驱动的模型架构设计 5
3.4模型验证与性能评估方法 6
4模型的实际应用与案例分析 6
4.1实际应用场景概述 6
4.2数据集构建与实验设计 7
4.3案例分析与结果解读 7
4.4应用效果与改进建议 8
结论 8
参考文献 10
致 谢 11
