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Hybrid Edge-Cloud Digital Twin for Poultry Control
US & West ·
A hybrid edge-cloud digital twin architecture is introduced for climate control in poultry facilities. It integrates distributed sensing, on-device state estimation, a grey-box physics-data model, and model predictive control to handle biological variability and welfare constraints.
Why it matters
The approach demonstrates digital twins in agriculture for optimizing control and ensuring animal welfare.
A multi-viewpoint modeling framework is proposed for integrating and reusing digital twins. It treats integration as a cross-model consistency problem and uses LLM for compatibility analysis.
The study investigates how structuring persona information affects accuracy of LLM-based digital twins. It argues that structural organization, not information volume, is the key bottleneck. Unstructured summaries are compared with structured persona representations.
A Monte Carlo-based simulator is developed for photon emission, transmission, and detection in the QEYSSat satellite quantum link. It probabilistically accounts for major physical effects, improving accuracy over analytical models.
