多源数据融合的牛只运动行为分析与模型优化研究

Research on cattle movement behavior analysis and model optimization based on multi-source data fusion

文章通过融合多源数据,构建深度学习模型,对牛只运动行为进行精准分析。采用多种传感器记录空间坐标和时间戳,提取运动轨迹特征,同时通过深度学习模型对牛只运动的动力学特征提取。为确保高质量的行为数据,进行数据预处理及质量控制,并通过混淆矩阵和均方误差等指标进行了全面评估。为牛只运动行为分析提供了可靠的方法,并在实验结果中展现了模型的鲁棒性和泛化能力。

In this study,a deep learning model was built by integrating multi-source data to accurately analyze the movement behavior of cattle.A variety of sensors were used to record spatial coordinates and time stamps to extract motion track features.Meanwhile,dynamic features of cattle motion were extracted by deep learning models.In order to ensure high quality of behavioral data,data preprocessing and quality control are carried out,and comprehensive evaluation is carried out by confounding matrix and mean square error.This study provides a reliable method for the analysis of cattle motor behavior,and demonstrates the robustness and generalization ability of the model in the experimental results.