采用混合SE-TCN-变压器模型与卡尔曼波器进行肉房的多阶段表面温度预测
Pengshen Zheng1,2, Wanchao Zhang1,2, Bin Gao1,2
1Key Laboratory of Smart Breeding (Co-Construction by Ministry and Province), Ministry of Agriculture and Rural Affairs, Tianjin 300384, China.
Sensors (Basel, Switzerland)
|October 16, 2025
概括
本研究提出了一种新的混合模型,用于准确预测表面温度 (AT),这对于管理肉生产中的热应激至关重要. 这种先进的模型显著提高了预测准确性,提高了群体福利和生产力.
科学领域:
- 农业工程 农业工程
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 密集的肉生产面临着快速环境变化造成热应激的挑战.
- 表面温度 (AT),一个复合指数,对于家禽舍的预测气候控制至关重要.
- 有效的热应激管理对于肉的福利和经济表现至关重要.
研究的目的:
- 为肉生产环境开发和验证一个多步骤的表面温度预测模型.
- 提高热环境预测的准确性和稳定性,以积极控制气候.
- 为智能通风和减热压力策略提供可靠的工具.
主要方法:
- 开发了一种混合的SE-TCN-变压器架构,将卡尔曼过纳入AT预测中.
- 利用商业肉场的多源时间序列数据进行模型培训.
- 评估模型的性能与基准模型 (LSTM,GRU,Autoformer,Informer) 在各种预测时间范围内 (5,15,30分钟).
主要成果:
- 与基准模型相比,拟议的混合模型显示预测误差明显较低.
- 获得更高的确定系数,表明在AT预测中具有更高的准确性.
- 集成SE注意力,变压器和卡尔曼平滑显著提高了模型的稳定性.
结论:
- 开发的模型提供了一个精确可靠的工具,用于智能通风和热应激管理在肉生产.
- 结果为使用混合深度学习架构的多步热环境预测提供了科学见解.
- 该研究提供了通过先进的气候控制来优化肉福利和生产绩效的实际指导.
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