面向家禽生产中福利约束控制的混合边缘-云数字孪生
A Hybrid Edge Cloud Digital Twin for Welfare-Constrained Control in Poultry Production
- Dalhousie University（达尔豪斯大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对家禽生产环境控制的福利保障与效率问题，提出混合边缘-云数字孪生框架，经测试可显著降低预测误差、氨气违规及通信需求，具备强鲁棒性，实现生物生产系统的可扩展福利管理。
AI中文摘要：
家禽生产受紧密耦合的环境与生物动态特性支配，但商业气候控制在很大程度上仍依赖经验方法，限制了福利保障与运营效率。本文提出一种面向家禽设施实时福利约束环境控制的边缘-云数字孪生框架，该框架整合分布式传感、设备端状态估计、混合物理-数据模型及模型预测控制，以在实际农场约束下实现前瞻性与适应性管理。灰箱热力学与质量平衡公式经补充学习残差后，可捕捉未建模的生物变异性，包括活动依赖型代谢热。该混合模型嵌入状态空间表示，用于边缘端实时估计与控制，而云协同则支持跨农场学习与长时程优化。带宽感知处理与异步同步技术使其可部署于连通性受限环境。在高保真肉鸡生产测试平台上的评估显示，与基于规则的控制及纯物理建模相比，该框架取得显著提升：温度预测误差从1.8摄氏度降至0.4摄氏度，氨气约束违反次数减少90%，通过边缘优先处理使通信需求降低约30倍，域迁移评分为0.92，进一步表明其在不同设施条件下具有强鲁棒性。这些结果表明，基于物理的数字孪生结合实时控制，可实现生物生产系统的可扩展且福利导向的管理。
英文摘要：
Poultry production operates under tightly coupled environmental and biological dynamics, yet commercial climate control remains largely heuristic, limiting welfare assurance and operational efficiency. We introduce an edge-cloud digital twin framework for real-time, welfare-constrained environmental control in poultry facilities. The framework integrates distributed sensing, on-device state estimation, a hybrid physics-data model, and model predictive control to enable anticipatory and adaptive management under practical farm constraints. A grey-box thermodynamic and mass-balance formulation is augmented with a learned residual that captures unmodeled biological variability, including activity-dependent metabolic heat. This hybrid model is embedded within a state-space representation for real-time estimation and control at the edge, while cloud coordination supports cross-farm learning and long-horizon optimization. Bandwidth-aware processing and asynchronous synchronization enable deployment in connectivity-limited environments. Evaluation in a high-fidelity broiler production testbed demonstrates substantial gains over rule-based control and physics-only modeling. Temperature prediction error is reduced from 1.8 degrees Celsius to 0.4 degrees Celsius, ammonia constraint violations decrease by 90 percent, and communication requirements are lowered approximately 30-fold through edge-first processing. A Domain Transfer Score of 0.92 further indicates strong robustness across facility conditions. These results show that physically grounded digital twins, coupled with real-time control, enable scalable and welfare-aware management of biological production systems.
