面向畜禽养殖温度监测的数据融合技术研究

Environmental temperature monitoring of Dairy Cattle based on multi-sensor data fusion

针对畜禽养殖过程中环境参数监测不准确导致的畜禽类流行性疾病爆发和传播的问题,文中以多传感器网络为基础,提出一种改进卡尔曼滤波算法来解决温度传感器在采集数据过程中产生噪声干扰的问题;通过改进自适应加权融合算法将去噪后的数据进行融合。结果表明:改进卡尔曼滤波算法具有良好的准确性和抗干扰性,可为后续的数据融合提供可靠的基础;通过改进自适应加权融合算法对去噪后的数据进行融合,可以实时、快速地得到更加接近实际养殖的环境温度。

In allusion to the problem of the outbreak and spread of livestock epidemic diseases caused by the inaccurate monitoring of environmental parameters in the process of livestock breeding,an improved Kalman filter algorithm based on multi-sensor network is proposed to solve the problem of noise interference caused by temperature sensor in the process of data collection.The denoised data is fused by means of the improved adaptive weighted fusion algorithm.The results show that the improved Kalman filter algorithm has good accuracy and anti-interference,which can provide a reliable basis for the subsequent data fusion.By means of the improved adaptive weighted fusion algorithm,the denoised data can be fused in real time and quickly to get more close to the actual breeding environment temperature.