采用单回合优化器学习的牛育肥舍生物感知气候与能源控制的跨域数字孪生
A Trans-Domain Digital Twin for Bio-Aware Control of Climate and Energy in Cattle Fattening Barns Using Single-Episode Optimizer Learning
AI总结：
该研究针对封闭式牛育肥舍气候与能源控制问题，提出带单回合学习的跨域数字孪生框架，结合多速率时间循环架构实现生物感知控制，验证了多系统关联的可行性。
AI中文摘要：
在封闭式牛育肥舍中，室内气候与牛群生长相互依存。温度、相对湿度、气流和通风影响热舒适度、采食量、代谢产热、日增重、饲料效率及能源消耗，而体重增加会改变舍内未来的热湿负荷，进而影响通风、供暖及能源需求。本文提出一种具备单回合学习能力的跨域数字孪生框架，专为封闭式牛育肥舍的生物感知气候与能源控制定制。该框架在多速率时间循环架构中整合了机理气候模拟器、牲畜生长模拟器、模型预测控制、轻量型强化学习及结构化知识记忆。快速时间循环每5分钟运行一次，用于评估执行器决策并维持短期热舒适度、安全性及能源效率；慢速时间循环则基于每日气候条件、饲料效率、产热及生长限制因素提供生物指导。结果表明，气候、生长、能源、饲料、生物指导及记忆可在单个可执行控制周期内关联。剩余局限性包括需进行现场验证、改进饲料压力管理及减少执行器指令的突变。
英文摘要：
In closed cattle-fattening barns, the indoor climate and herd growth are mutually interdependent. Temperature, relative humidity, airflow, and ventilation affect thermal comfort, feed intake, metabolic heat production, daily growth, feed efficiency, and energy consumption, while body-weight gain alters the future heat and moisture loads of the barn and, consequently, its ventilation, heating, and energy requirements. This article proposes a trans-domain digital twin framework with single-episode learning capability, customized for bio-aware climate and energy control in a closed cattle-fattening barn. The framework integrates a mechanistic climate simulator, a livestock growth simulator, model predictive control, lightweight reinforcement learning, and structured knowledge memory within a multi-rate temporal-loop architecture. The fast temporal loop operates every five minutes to evaluate actuator decisions and maintain short-term thermal comfort, safety, and energy efficiency, whereas the slow temporal loop provides biological guidance based on daily climatic conditions, feed efficiency, heat production, and growth-limiting factors. The results show that climate, growth, energy, feed, biological guidance, and memory can be linked within a single executable control cycle. Remaining limitations include the need for field validation, improved management of feed pressure, and reduction of abrupt actuator-command variations.
