发表机构
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 中文总结
该研究提出HenTwin多模态数字孪生框架，通过五层物联网架构整合多模态数据构建蛋鸡生物状态模型，验证了其稳定性与跨房间适用性，为精准畜牧养殖提供了状态感知的数字孪生解决方案。
AI 中文摘要
蛋鸡的早期生命监测仍受限于碎片化的单模态传感以及缺乏正式的系统级状态表征。HenTwin是一个采用五层物联网架构实现的多模态数字孪生框架，它对从孵化到25周龄的鸡群级多模态生物状态动态进行了形式化表征。研究定义了一个四维生物状态向量，整合了体表温度、声能熵、频带能量比以及基于光流的运动数据，同时将温湿度指数（THI）作为外源性环境输入以保留干预能力。研究从达尔豪斯大学大西洋家禽研究中心的5个受控房间中采集的150只罗曼褐壳蛋鸡（Lohmann LSL-Lite）的纵向多模态数据中，估计了离散时间状态转移模型。估计的转移矩阵表现出模态特异性持续性，同时保持渐近稳定性。扰动分析表明，持续+2.0的THI升高会产生稳定的长期声能熵升高，幅度为0.54纳特，约为研究期间观察到的1.87纳特发育下降幅度的四分之一。Pettitt变点检测识别出12-14周龄时协调的多模态发育状态转移。跨房间验证表明，结构转移参数可在房间间部分转移，而环境输入敏感性需要特定房间校准，这支持了两层物联网部署架构。留一法交叉验证证明了模型一致的样本外性能。HenTwin为精准畜牧养殖中正式的、状态感知的数字孪生推理迈出了第一步。
英文摘要
Early-life monitoring in laying hens remains constrained by fragmented single-modality sensing and the absence of formal system-level state representations. HenTwin, a multimodal digital twin framework implemented as a five-layer IoT architecture, formalizes flock-level multimodal biological state dynamics from hatch through 25 weeks of age. A four-dimensional biological state vector integrating body surface temperature, acoustic energy entropy, band energy ratio, and optical-flow-based motion is defined, with the temperature-humidity index treated as an exogenous environmental input to preserve intervention capability. A discrete-time state transition model is estimated from 25 weeks of longitudinal multimodal data collected from 150 Lohmann LSL-Lite hens across five controlled rooms at the Atlantic Poultry Research Centre, Dalhousie University. The estimated transition matrix exhibits modality-specific persistence while remaining asymptotically stable. Perturbation analysis demonstrates that a sustained +2.0 THI increase produces a stable long-run acoustic entropy elevation of 0.54 nats, approximately one-quarter of the entire 1.87-nat developmental decline observed across the study period. Pettitt change-point detection identifies coordinated multimodal developmental state transitions at Weeks 12-14. Cross-room validation suggests that structural transition parameters are partially transferable across rooms, whereas environmental input sensitivity requires room-specific calibration, supporting a two-tier IoT deployment architecture. Leave-one-out cross-validation demonstrates consistent out-of-sample model performance. HenTwin takes a first step toward formal, state-aware digital twin inference in precision livestock farming.
Comments24 pages, 18 figures, 7 tables
