基于预测模型的异常农情数据在线检测方法的研究  

Research on online detection method for the abnormal agricultural data based on prediction model

作　　者：王文 饶元[1] 李绍稳[1] Arthur GENIS WANG Wen;RAO Yuan;LI Shaowen;Arthur GENIS(College of Information and Computer Sciences,Anhui Agricultural University,Hefei China,230036;Katif Research Center for Coastal Deserts Development,Netivot Israel,8771002)

机构地区：[1]安徽农业大学信息与计算机学院,安徽合肥230036 [2]Katif沿海沙漠开发研究中心,以色列内提沃特8771002

出　　处：《湖南农业大学学报（自然科学版）》2020年第4期495-500,共6页Journal of Hunan Agricultural University(Natural Sciences)

基　　金：农业部引进国际先进农业科学技术“948”项目(2015–Z44、2016–X34);安徽省自然科学基金项目(1608085QF126);安徽省重点研究和开发计划面上攻关项目(1804a07020108,201904a06020056);安徽农业大学省级大创项目(201910364263)。

摘　　要：为保证农业物联网传感器的数据感知质量,构建了基于滑动窗口和预测模型(支持向量回归、K近邻、梯度提升回归和随机森林)的异常农情数据在线检测框架,提出了基于数据特征的滑动窗口尺寸计算方法,运用熵权逼近最优排序法评价预测模型适用性。采用羊圈环境数据(空气温度、相对湿度、CO2和H2S体积分数)进行试验,结果表明,滑动窗口尺寸计算方法优于仅基于采样间隔和特征周期的计算方法;模型预测误差与其异常检测性能负相关,且对误检率影响更大;支持向量回归模型对空气温度和相对湿度异常数据检测适用性最好,贴近度达0.8以上,梯度提升回归和K近邻模型分别对CO2和H2S体积分数异常数据检测适用性较优,两者贴近度均在0.6左右。In order to guarantee the quality of perceptional data, an online detection framework for the abnormal agricultural data is constructed based on the sliding window and the prediction models, which including support vector regression, K-nearest neighbor, gradient boosting regression and random forest. The calculation method of the sliding window size is proposed based on data features. The applicability of the prediction models is evaluated by using entropy weight TOPSIS. Through the sheepfold’s monitoring data of the air temperature, the relative humidity, and the CO2 and H2S volume fractions, it is demonstrated that the proposed calculation method of sliding window size is superior to the calculation method simply based on the sampling interval and characteristic period. The prediction errors of these models are negatively correlated with the abnormal detection performance and could impose significant influence on false positive rate. Support vector regression model is the most appropriate candidate for detecting the abnormal data in air temperature and relative humidity with the close degree greater than 0.8, whereas the most appropriate candidates for dealing with CO2 and H2S volume fractions are gradient boosting regression model and K nearest neighbor model, both of them with the close degrees of 0.6.

关 键 词：农业物联网 异常数据 在线检测 预测模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]