基于数据融合的日光温室传感器布设
|
摘要
针对北方日光温室内环境监测传感器的布设问题,设计了基于神经网络、分批估计理论与自适应加权平均融合算法的日光温室传感器布设方案.利用11个监测点采集到的温室内西红柿生长环境温度和湿度数据,在运用BP神经网络进行缺失值补全的基础上,结合分批估计理论和自适应加权平均融合算法进行多传感器数据融合.通过对比融合值与原始数据的相对误差,选择最佳传感器数据,以此为基础确定最优传感器布设区域.结果表明,相对于算数平均融合与自适应加权平均融合,基于分批估计的自适应加权平均融合方法可以更合理地反应多传感器数据特征.
Aiming at the problem of environmental monitoring sensor layout in north sunlight greenhouse,a layout scheme for sensors based on the neural network,batch estimation theory and adaptive weighted average fusion algorithm was designed. With the temperature and humidity data for the tomato growing environment collected from 11 monitoring points in the greenhouse and based on the complement of missing values with the BP neural network,the multi-sensor data fusion was performed in combination with the patch estimation theory and adaptive weighted average fusion algorithm. Through comparing the relative error between the fusion value and original data,the optimal sensor data were selected,and thus the optimal sensor layout area was determined. The results show that compared with the arithmetic mean fusion and adaptive weighted average fusion, the adaptive weighted average fusion method based on the batch estimation can reflect the characteristics of multi-sensor data more reasonably.
Aiming at the problem of environmental monitoring sensor layout in north sunlight greenhouse,a layout scheme for sensors based on the neural network,batch estimation theory and adaptive weighted average fusion algorithm was designed. With the temperature and humidity data for the tomato growing environment collected from 11 monitoring points in the greenhouse and based on the complement of missing values with the BP neural network,the multi-sensor data fusion was performed in combination with the patch estimation theory and adaptive weighted average fusion algorithm. Through comparing the relative error between the fusion value and original data,the optimal sensor data were selected,and thus the optimal sensor layout area was determined. The results show that compared with the arithmetic mean fusion and adaptive weighted average fusion, the adaptive weighted average fusion method based on the batch estimation can reflect the characteristics of multi-sensor data more reasonably.
引文
[1]崔琳.北方日光温室无线传感器多数据融合技术的研究[D].沈阳:沈阳农业大学,2016.(CUI Lin.Study on data analysis platform of northern sunlight greenhouse’s monitor and control system[D].Shenyang:Shenyang Agricultural University,2016.)
[2]He L,Lian J,Ma B,et al.Optimal multiaxial sensor placement for modal identification of large structures[J].Structural Control and Health Monitoring,2014,21:61-79.
[3]刘寒冰,吴春利,程永春.不同适应度函数的遗传算法在桥梁结构传感器布设中的应用[J].吉林大学学报(工学版),2012,42(1):51-56.(LIU Han-bing,WU Chun-li,CHENG Yong-chun.Sensor placement on bridge structure based on genetic algorithms with different fitness function[J].Journal of Jilin University(Engineering and Technology Edition),2012,42(1):51-56.)
[4]温凯方.基于鸽群算法的传感器优化布置方法研究[D].大连:大连理工大学,2016.(WEN Kai-fang.Optimal sensor placement based on pigeon colony algorithm(PCA)[D].Dalian:Dalian University of Technology,2016.)
[5]张楚旋,李夕兵,董陇军,等.微震监测传感器布设方案评价模型及应用[J].东北大学学报(自然科学版),2016,37(4):594-608.(ZHANG Chu-xuan,LI Xi-bing,DONG Long-jun,et al.Evaluation model of microseismic monitoring sensor layout scheme and its application[J].Journal of Northeastern University(Natural Science),2016,37(4):594-608.)
[6]孙玉文.基于无线传感器网络的农田环境监测系统研究与实现[D].南京:南京农业大学,2013.(SUN Yu-wen.Research and implementation of field environment monitoring system based on wireless sensor networks[D].Nanjing:Nanjing Agricultural University,2013.)
[7]王凡,黄磊,吴素萍,等.多路数据采集与处理模型的设计及水分传感器埋设位置优化[J].农业工程学报,2015,31(21):148-153.(WANG Fan,HUANG Lei,WU Su-ping,et al.Design of multi-channel data acquisition and processing model and optimization of moisture sensor buried position[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(21):148-153.)
[8]彭秀媛,白冰,王枫,等.日光温室内环境科学数据监测传感器的布设[J].江苏农业科学,2017,45(10):167-170.(PENG Xiu-yuan,BAI Bing,WANG Feng,et al.Layout of environmental science data monitoring sensors in solar greenhouses[J].Jiangsu Agricultural Sciences,2017,45(10):167-170.)
[2]He L,Lian J,Ma B,et al.Optimal multiaxial sensor placement for modal identification of large structures[J].Structural Control and Health Monitoring,2014,21:61-79.
[3]刘寒冰,吴春利,程永春.不同适应度函数的遗传算法在桥梁结构传感器布设中的应用[J].吉林大学学报(工学版),2012,42(1):51-56.(LIU Han-bing,WU Chun-li,CHENG Yong-chun.Sensor placement on bridge structure based on genetic algorithms with different fitness function[J].Journal of Jilin University(Engineering and Technology Edition),2012,42(1):51-56.)
[4]温凯方.基于鸽群算法的传感器优化布置方法研究[D].大连:大连理工大学,2016.(WEN Kai-fang.Optimal sensor placement based on pigeon colony algorithm(PCA)[D].Dalian:Dalian University of Technology,2016.)
[5]张楚旋,李夕兵,董陇军,等.微震监测传感器布设方案评价模型及应用[J].东北大学学报(自然科学版),2016,37(4):594-608.(ZHANG Chu-xuan,LI Xi-bing,DONG Long-jun,et al.Evaluation model of microseismic monitoring sensor layout scheme and its application[J].Journal of Northeastern University(Natural Science),2016,37(4):594-608.)
[6]孙玉文.基于无线传感器网络的农田环境监测系统研究与实现[D].南京:南京农业大学,2013.(SUN Yu-wen.Research and implementation of field environment monitoring system based on wireless sensor networks[D].Nanjing:Nanjing Agricultural University,2013.)
[7]王凡,黄磊,吴素萍,等.多路数据采集与处理模型的设计及水分传感器埋设位置优化[J].农业工程学报,2015,31(21):148-153.(WANG Fan,HUANG Lei,WU Su-ping,et al.Design of multi-channel data acquisition and processing model and optimization of moisture sensor buried position[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(21):148-153.)
[8]彭秀媛,白冰,王枫,等.日光温室内环境科学数据监测传感器的布设[J].江苏农业科学,2017,45(10):167-170.(PENG Xiu-yuan,BAI Bing,WANG Feng,et al.Layout of environmental science data monitoring sensors in solar greenhouses[J].Jiangsu Agricultural Sciences,2017,45(10):167-170.)