摘要
针对目前奶牛动态称量存在的问题,提出了一种基于经验模态分解(Empirical mode decomposition,EMD)的动态称量算法。首先,对称量设备采集到的非线性、非平稳振荡信号进行数据预处理,得到有效信号;其次,对有效信号进行初判断,若符合预设条件即为走停状态,利用算术平均法求取均值;若不符合预设条件,则利用EMD算法区分动物的慢速行走、快速行走和剧烈运动行进状态,并计算动态称量值。实验中发现,剧烈运动状态下获取的数据波动很大,需进行滤波后再计算质量。实验结果表明,本文提出的动态称量算法可判断奶牛行进状态,计算得到的动态质量与静态质量相比,走停状态下误差率在0. 16%以内,慢速行走、快速行走状态下误差率在1%以内,剧烈运动状态下误差率在1. 35%以内。
The weight of dairy cows is an important data in the process of healthy breeding. Aiming at the current problems of dynamic weighing,a dynamic weighing algorithm was proposed based on empirical mode decomposition( EMD). Firstly,the nonlinear and non-stationary oscillation signals collected by the weighing equipment were preprocessed to obtain the effective part of the signal. Secondly,the effective part was initially judged,and if it met the preset condition,it was the walking-stop state,the arithmetic average method was used to obtain weighing value. Finally,if it did not accord with the walking-stop state,the EMD algorithm can be used in distinguishing the slow walking,fast walking and strenuous moving states of the animal,and calculating the dynamic weighing value. In the algorithm design,it was found that the data acquired under severe motion fluctuated greatly and the weight value needed to be calculated after filtering. The experimental results showed that the dynamic weighing algorithm proposed can judge the motion state of dairy cows. The calculated weight value was less than 0. 16% in the walking-stop state compared with the static weight. The error rate was less than 1% in slow walking and fast walking. The error in the state of motion was within 1. 35% under strenuous motion. The research method can provide technical support for dairy cow dynamic weighing technology.
引文
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