The study conduct an in-depth exploration of the multifractal characteristics of dairy cows behavioral data, aiming to reveal their complexity and representation in behavioral patterns. By means of Multifractal Detrended Fluctuation Analysis (MFDFA) in conjunction with deep wavelet transform, we extract multifractal indices that precisely depict the differences and dynamic changes of cows behavior. Further, we delves into the potential correlations between these multifractal features and the physiological states of dairy cattle, particularly focusing on their potential applications in predicting different physiological conditions. And an assessment model is developed based on these fractal indices and utilize advanced machine learning techniques to evaluate their effectiveness and accuracy in predicting the physiological states of dairy cattle. This study not only unveils the multifractal properties of cattle behavior but also demonstrates how these features can be utilized to predict significant physiological states, providing new scientific bases and methods for dairy cattle health management.

中文翻译：

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基于深度小波变换的动物行为多重分形表征与识别

该研究对奶牛行为数据的多重分形特征进行了深入探索，旨在揭示其行为模式的复杂性和表征。通过多重分形去趋势波动分析（MFDFA）结合深度小波变换，我们提取了精确描述奶牛行为差异和动态变化的多重分形指数。此外，我们深入研究了这些多重分形特征与奶牛生理状态之间的潜在相关性，特别关注它们在预测不同生理条件方面的潜在应用。并基于这些分形指数开发了评估模型，并利用先进的机器学习技术来评估其预测奶牛生理状态的有效性和准确性。该研究不仅揭示了牛行为的多重分形特性，而且展示了如何利用这些特性来预测重要的生理状态，为奶牛健康管理提供新的科学依据和方法。