机制和数据驱动的谷物状况信息感知方法,用于全面监测谷物储存
Yunshandan Wu1,2, Ji Zhang1,2, Xinze Li1,2
1College of Biological and Agricultural Engineering, Jilin University, Changchun 130022, China.
Foods (Basel, Switzerland)
|October 16, 2025
概括
这项研究引入了谷物监测的新框架,提高了准确性并降低了损坏风险. 它为智能谷物存储和智能通风控制提供了全面的见解.
科学领域:
- 农业工程 农业工程
- 数据科学数据科学数据科学
- 物理建模物理建模
背景情况:
- 传统的谷物监测依赖于有限的数据,阻碍了全面的状况评估.
- 现有的方法很难考虑太阳辐射和外部温度等环境因素.
- 整体的谷物状况感知对于减少收获后损失至关重要.
研究的目的:
- 开发和验证一个新的机制和数据驱动 (MDD) 框架,用于全面监测谷物状况.
- 整合物理机制与实时传感器数据进行准确的分析.
- 为智能粮食储存管理提供可操作的见解.
主要方法:
- 开发了一个机制和数据驱动 (MDD) 框架,集成物理机制和实时传感器数据.
- 实施了一种新的插值优化的模型参数初始化技术.
- 使用温度场重建和云图比较进行了多维验证.
主要成果:
- 在低温区预测中获得了高精度 (1.89%的误差).
- 精确重建温度场 (≤0.5°C偏差) 和捕获的时空动态.
- 证明了低温区区域的显著减少 (22.97%) 并确定了温度和湿度之间的相关性.
结论:
- 该MDD框架使整体的谷物监测和准确的状态感知.
- 这种新的初始化技术提高了温度和湿度的预测准确性.
- 这项研究为智能通风,冷凝风险警告和防提供了基础,减少了收获后损失.
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