| 基于SW-SVR的畜禽养殖物联网异常数据实时检测方法 |
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| 引用本文： | 段青玲,肖晓琰,刘怡然,张璐.基于SW-SVR的畜禽养殖物联网异常数据实时检测方法[J].农业机械学报,2017,48(8):159-165. |
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| 作者姓名： | 段青玲 肖晓琰 刘怡然 张璐 |
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| 作者单位： | 中国农业大学,中国农业大学,中国农业大学,中国农业大学 |
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| 基金项目： | 国家高技术研究发展计划(863计划)项目(2013AA102306)和山东省自主创新项目(2014XGA13054) |
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| 摘 要： | 畜禽养殖物联网由于工作环境恶劣、网络传输故障等因素容易产生异常感知数据,为保证数据质量,根据畜禽养殖物联网数据流周期性、时序性等特点,提出了一种基于滑动窗口与支持向量回归(Sliding window and support vector machines for regression,SW-SVR)的异常数据实时检测方法。首先根据畜禽物联网数据流特征周期以及采样频率确定滑动窗口尺寸;然后通过SVR模型预测畜禽养殖物联网数据流中某一时刻传感器测量值;最后计算预测区间,根据实际测量值是否落入该区间判断是否异常并对异常数据进行置换处理。采用畜禽养殖物联网环境数据进行试验,结果表明:所提滑动窗口计算方法得到的窗口尺寸预测的MAPE为0.188 4,畜禽养殖物联网异常数据检测率达98%,能够有效检测和处理畜禽养殖物联网数据流中的异常数据。
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| 关 键 词： | 异常数据检测 畜禽养殖物联网 滑动窗口 支持向量回归 |
| 收稿时间： | 2016/12/14 0:00:00 |
Anomaly Data Real-time Detection Method of Livestock Breeding Internet of Things Based on SW-SVR |
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| DUAN Qingling,XIAO Xiaoyan,LIU Yiran and ZHANG Lu.Anomaly Data Real-time Detection Method of Livestock Breeding Internet of Things Based on SW-SVR[J].Transactions of the Chinese Society of Agricultural Machinery,2017,48(8):159-165. |
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| Authors: | DUAN Qingling XIAO Xiaoyan LIU Yiran and ZHANG Lu |
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| Institution: | China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University |
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| Abstract: | Due to bad work environment and network transmission failure, it is easy to generate abnormal sensory data in livestock breeding Internet of things system. In order to ensure the quality of sensory data, according to the characteristics of sensory data flow such as periodicity, temporality, infinity, etc., a method was proposed based on sliding window and support vector machines regression (SW-SVR) for livestock breeding Internet of things abnormal sensory data detection in real time. Firstly, the sliding window size was decided according to the characteristic period and sampling frequency of data flow from livestock breeding Internet of things system, and the history data within sliding window was selected as the input value of prediction model. Then, the sensor estimated measurement value at certain moment in livestock breeding Internet of things system was predicted by using SVR model. Finally, the prediction interval (PI) was calculated, and the abnormal sensory data was identified if the sensor actual measurement data fell out of the PI. The abnormal data would be replaced by the predictive data. The abnormal sensory data detection method was tested by data flow from real livestock breeding Internet of things system. Experiment results showed that the mean absolute percent error value of prediction with window size calculated by the sliding window method was 0.1884. The correct detection rate of abnormal data based on SVR model with radial basis function kernel (RBF kernel) achieved 98%, which had higher accuracy compared with BP neural network (BPNN) method. Abnormal data can be effectively detected and treated in livestock breeding Internet of things system. |
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| Keywords: | anomaly data detection livestock breeding internet of things sliding window support vector machines for regression |
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