Abstract:  In order to monitor the behavioral status and physical information of cattle， a multi‑sensor fusion cattle collar is designed in this paper. The collar selects a control chip based on Bluetooth 5.0， and three kinds of sensor devices are connected to realize the collection of the four basic sequence data of cattle behavior posture （acceleration and angular velocity values）， body surface temperature， indoor position， and three audio data of cow calls （normal condition and estrus condition） and swallowing sounds. SVM， KNN and RFC are used to classify the walking， standing， eating and lying behaviors of cattle. Among them， the average accuracy of RFC is the highest， reaching 99.59%， followed by KNN and SVM， and the accuracy rates are respectively 99.01% and 85.23%. The GRU‑based deep learning algorithm is used to classify cattle calls and swallowing sounds， and the overall accuracy rate reaches 90.72%. The fitting correction of the collected body surface temperature and rectal temperature shows that the fitting degree [R2] is higher than 0.9. The results show that the cow collar based on multi‑sensor fusion can not only effectively collect traditional sequence data， but also collect audio data synchronously. It can provide multi‑dimensional data support for cattle behavior analysis.

Key words:  , collar； multi?sensor fusion； behavior classification； voice recognition

摘要： 为监测牛只行为状态和体征信息，设计一种多传感器融合的牛项圈。选择基于蓝牙5.0的控制芯片，外接3种传感器设备，实现牛只行为姿态（加速度和角速度值）、体表温度、室内位置4项基本序列数据以及叫声（正常情况、发情情况）和吞咽声3项音频数据的采集。采用SVM、KNN、RFC对牛只行走、站立、进食和躺卧行为进行分类，其中RFC的准确率最高，达到99.59%，KNN和SVM次之，准确率分别为99.01%、85.23%。使用基于GRU的深度学习算法对牛只叫声与吞咽声进行分类，整体准确率达到90.72%。对采集的体表温度与直肠温度进行拟合校正，拟合度R2均高于0.9。结果表明，基于多传感器融合的牛项圈不仅可以有效采集传统序列数据，还可以同步采集音频数据，为牛只行为学分析提供多维度的数据支持。

关键词: 牛项圈, 多传感器融合, 行为分类, 声音识别