Research on improvement of data fusion algorithm for temperature monitoring in livestock house
In large-scale modern intelligent livestock and poultry breeding,due to the uneven distribution of temperature in livestock and poultry houses and the low efficiency of sensor data collection,it is impossible to comprehensively,accurately and timely reflect the change of temperature in livestock and poultry houses.To improve the performance of temperature monitoring system in livestock breeding,a real-time fusion strategy of layered wireless sensor network(WSN)was proposed in this paper.The WSN designed by this strategy is divided into two layers.Firstly,the temperature data collected by the bottom sensor is preprocessed by an improved unscented Kalman filter(IUKF).Then,the fusion center uses the improved dung beetle algorithm to optimize the nuclear Extreme Learning machine(IDBO-KELM)for real-time fusion of the preprocessed temperature data.The experimental results show that the improved unscented Kalman filter in data preprocessing can effectively suppress noise interference in livestock and poultry houses,overcome abnormal and divergent phenomena in collected data.In terms of multi-sensor data fusion,the IDBO-KELM algorithm established in this article has an accuracy of 99.15%in the training set and 98.12%in the test set,respectively.Compared to the original algorithm,the accuracy is improved by 6.98%,and the data fusion time is 3.36 s,ensuring the efficiency and accuracy of temperature monitoring in poultry houses while reducing computational time.
livestock premisesmulti-sensor data fusionenvironmental monitoringIUKF
万方数据
维普
