结构相似子序列快速聚类算法及其在奶牛发情检测中的应用

准确高效的奶牛发情检测技术能够提高其受胎率、缩短胎间距,是改善奶牛繁殖效率和提高经济效益的重要手段。规模化、集约化养殖环境下,众多学术与科学研究证实奶牛行为方式和活动量是判断其是否发情的重要指标。目前常用奶牛行为决策方法主要是针对单点数据进行行为分类,而奶牛运动传感数据是按照时间顺序采集的多元时间序列数据,因此该文提出基于结构相似度的子序列段快速聚类算法(SC-SS,subsequence clustering based on structural similarity),首先利用加速度一阶差分值将奶牛运动动态时间序列传感数据划分成若干子序列段,然后计算子序列段加速度值、能量、标准方差等特征结构相似度;最后根据各个子序列的结构相似度进行快速聚类。试验数据分析对比表明,SC-SS较常用K-means算法具有更高的运行效率,可更有效地完成奶牛行为分类,提高奶牛发情检测的准确率。

Subsequence clustering algorithm based on structural similarity and its application in cow estrus detection

The efficient and accurate estrus detection technology can enhance conception rates and shorten birth spacing of the breeding dairy cow, which is an important means for improving economic benefit. For the large-scale, intensive farming environment, the cow's behavior and activity is an important indicator to determine whether estrus which is confirmed by numerous academic and scientific research. Usually the cow's behavior decision-making methods are using the single point of data to classify behavior. However the cow's locomotory sensor data were multivariate time series data which were collected by time sequence. This paper presented a subsequence fast clustering algorithm based on structural similarity (SC-SS). The algorithm first partitioned the sensing time series data into several subsequence segment according to the first-order differential value of acceleration; then calculating the structure similarity of each subsequence segment by comparing their features of acceleration, energy, standard deviation; Finally the subsequence were grouped into three clusters. Experiment results on real data set demonstrate that the SC-SS is more efficient than K-means, has and more effective for classification of cow's behavior, which can increase the accuracy of cow's estrus detection.