基于改进型支持度函数的畜禽养殖物联网数据融合方法

Data fusion method of livestock and poultry breeding internet of things based on improved support function

物联网技术已广泛应用在畜禽养殖中,针对畜禽养殖物联网中数据异常实时检测以及多源感知数据融合的需求,该文提出了一种畜禽养殖物联网数据融合模型。首先对传感器采集到的原始数据进行一致性检测,确保数据准确性;其次针对来自同类型传感器的多源同构数据,采用基于改进型支持度函数的加权算法进行数据融合处理,提高融合数据准确度;最后根据畜禽养殖物联网编码规则和数据组织格式,对畜禽养殖过程中的异构感知数据进行统一描述并转换为标准数据格式,为数据分析和应用提供数据基础。该文采用实际生产中的生猪养殖物联网数据进行试验,结果表明:在数据一致性检测阶段,异常数据检测率为96.67%,保证了数据质量;在多源同构数据融合计算中,该文提出的改进型支持度函数与高斯型、新型2种支持度函数相比融合方差最小,为0.192 5,能够有效提高数据融合准确度,满足畜禽养殖物联网数据分析要求。

Currently, IoT(Internet of Things) technology has been widely applied in livestock breeding. For the characteristics of perception data collected from livestock breeding Io T(such as multi-source, heterogeneous, real-time, and so on), we propose a livestock breeding Io T data fusion model. Firstly, it's easy to produce abnormal data in livestock breeding Io T due to the harsh work environment, transmission network noise and other factors. In order to guarantee the accuracy of livestock breeding IoT data, we check the consistency of original data collected by livestock breeding Io T sensors through the regression prediction method based on sliding window. We determine the sliding window size, then estimate the sensor measurement value at a certain moment through the regression forecast method and calculate the prediction interval, next determine whether the actual measurement value is abnormal compared with prediction interval, and finally replace abnormal data with predictive value. Secondly, there is the problem of uneven distribution of environmental monitoring values in breeding room. In order to comprehensively analyze and evaluate the breeding environment and provide accurate basis for automatic control equipment, we propose a homogeneous data weighted fusion algorithm which fuses homogeneous data that are collected from the same type of sensors based on support function. The support function has been proposed to describe a certain support degree or proximity between 2 numbers, and used to calculate the weights of a set of data. The weighted algorithm in this paper improves support functions so as to improve the accuracy of data fusion. We calculate the support degree of a set of pig breeding environment perception data to get corresponding optimal weights through the improved support function and then the weighted fusion for the same type of livestock breeding Io T sensor set data. Finally, in view of the characteristics different with livestock breeding multi-source perception data storage format and sensor devices encoding, the encoding rules format and data organization for livestock breeding IoT are formulated, which can uniformly describe heterogeneous data in the process of livestock and poultry breeding. Such a standard can be converted into a standard data format, which can work as the basis of data analysis and data applications. Areal production data from pig breeding IoT in Tianjin Huikang breeding pig farm show that in the data consistency test phase, the abnormal sensory data detection rate is 96.67%, which ensures the accuracy of the data, and in homogeneous data fusion phase, the improved support function in this paper has the minimum fusion variance of 0.192 5 compared with other 2 kinds of support functions in the multi-source data fusion calculation, which improves the accuracy of homogeneous data fusion and provides the accurate and reasonable basis for the automatic control of equipment. And coding standard of livestock Io T environment factors and sensor equipment makes unified expression to the sensors, which are from different manufacturers and environmental data with varied representations. Livestock breeding Io T data fusion model proposed in this paper can effectively improve the accuracy of data fusion, and meet the requirements of livestock breeding Io T data analysis.