Affiliations: [a] School of Information Science and Technology, Nantong University, Nantong, Jiangsu, China | [b] Department of Information Science, Nantong University Xinglin College, Qidong, Jiangsu, China | [c] Suzhou Bailian Energy Conservation Technology Co., Ltd, Shanghai, China
Correspondence:
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Corresponding authors: Xiaoyong Chen, School of Information Science and Technology, Nantong University, Nantong, Jiangsu 226019, China. E-mail: [email protected]. Haohao Yue, Suzhou Bailian Energy Conservation Technology Co., Ltd, Shanghai 215000, China. E-mail: [email protected].
Abstract: This work aims to address the evolving demands of logistics development by proposing an innovative solution: the Intelligent Cloud-based Logistics Service Platform (LSP), which seamlessly integrates Cloud Computing (CC) and the Internet of Things (IoT). The primary objective is to enhance the efficiency and effectiveness of logistics operations through advanced technology integration. Then, short-term logistics Demand Forecasting Model (DFM) and real-time Information Tracking System (ITS) are designed based on the proposed Cloud-based LSP. Specifically, based on Deep Learning, Ensemble Empirical Mode Decomposition (EEMD), and Local Mean Decomposition (LMD), the EEMD-LMD is employed for the logistics DFM. Simultaneously, the proposed real-time logistics ITS is optimized by updating its hardware equipment through the wireless sensor. Then, the Kalman filter is employed for data processing. This work contributes to the ongoing transformation of logistics management, offering practical solutions to meet the dynamic challenges of modern supply chain management.
Keywords: Cloud-based logistics, local mean decomposition (LMD), information tracking, deep learning, Internet of Things
