基于物联网的系统用于实时监测和人工智能驱动的能源消耗预测,用于新鲜水果和蔬菜运输
Chayapol Kamyod1,2, Sujitra Arwatchananukul1,3, Nattapol Aunsri1,3
1School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai 57100, Thailand.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了一种物联网 (IoT) 平台,用于在运输过程中实时监控新鲜产品. 人工智能模型预测能源消耗,优化电池寿命,以延长可靠的冷链物流.
科学领域:
- 农业工程 农业工程
- 计算机科学 计算机科学
- 物流管理物流管理
背景情况:
- 运输过程中温度和湿度的波动会降低新鲜产品的质量.
- 有效的监控系统对于维护易腐货物的完整性至关重要.
- 现有的解决方案可能缺乏自主性或实时数据分析.
研究的目的:
- 开发和验证一个独立的物联网 (IoT) 平台,用于在新鲜产品的运输过程中进行监控.
- 为了实现节能运行,延长电池寿命.
- 利用人工智能预测能源消耗和优化数据收集策略.
主要方法:
- 一个电池驱动的物联网设备被设计成独立记录温度,湿度,GPS和耗电量.
- 在收集的数据上训练机器学习模型 (GBM,RF,LR),以预测能源消耗.
- 一个集成的系统与MQTT,Web应用程序和Node-RED实现了数据管理和可视化.
主要成果:
- 梯度增强机 (GBM) 和线性回归 (LR) 模型在预测能源消耗方面实现了高精度 (R2≈0.88).
- 随机森林 (RF) 确定温度是影响能源使用的主要因素,其次是行程持续时间和湿度.
- 该平台展示了通过人工智能驱动的洞察力在易腐物流领域进行主动决策的潜力.
结论:
- 开发的物联网平台提供了一个强大的解决方案,用于实时,自主在新鲜农产品的过境监控.
- 人工智能驱动的能源消耗预测能够优化数据采样和传输策略,扩大运营自主性.
- 这项技术支持冷链物流的增强质量控制和主动管理.
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