基于温度场云图的储粮数量监控方法研究
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摘要
为保障储备粮按照计划进出仓库,同时减少储粮监管与稽核的工作量,该文提出了基于温度场云图RGB颜色特征的储粮监管方法。调用历史粮温数据并进行预处理,生成粮堆各平面温度场云图,利用温度场云图的RGB颜色特征分布计算云图的相似度,据此设定异常判定阈值,计算相邻时间粮堆各平面云图的相似度,依据阈值进行异常检测,从而实现储粮监管。同时该文通过模拟5种粮堆异常情况,进行了模拟检测试验,并与基于温度场云图LBP纹理特征的检测算法进行对比,结果显示:基于温度场云图RGB颜色特征的算法平均查全率、平均查准率、运算速率均优于基于云图LBP纹理特征的算法,分别为98.6%、97.3%、320 ms/次。进行了储粮监管检测试验,结果表明,该方法不仅能够应用于储粮数量的监管,也能够检测出粮堆局部发热。该研究结果为储粮数量监控方法的提出奠定基础。
A reliable method of grain storage supervision can effectively guarantee the quantity of grain storage in and out of warehouses according to plan and reduce the loss of unplanned entry and exit. In recent years, there has been a method of monitoring grain storage by video equipment, but the security of storage and management of video surveillance is poor and inconvenient to use. In this paper, we proposed a method for grain storage supervision based on the similarity of RGB color features of temperature field cloud map. Firstly, the historical grain data of the grain storage was called and pre-processed to remove the random code, error and other data. According to the correlation of the temperature at the adjacent temperature measurement points, the grain temperature data of each plane in the grain bulk was interpolated and the temperature field cloud map was generated. Then the similarity of the temperature field cloud map at the adjacent days was calculated by the similarity algorithm based on the RGB color feature distribution, similarity threshold was set according to the similarity of cloud maps during normal storage. Finally, the abnormal movement in the grain bulk was judged according to the similarity threshold. In order to verify the feasibility of grain storage regulation based on similarity of temperature field cloud map, five kinds of abnormal movement in grain bulk were simulated. The five kinds of abnormal movement respectively were: the half part of the grain bulk at right side and latter side, the quarter part of the grain bulk at right side and the latter side, and overall of the grain bulk. Similarity algorithm based on the RGB color feature distribution was used to detect abnormal movement of grain bulk. Meanwhile, the method based on the similarity of LBP texture feature was also used to compare with the method, the results showed that the mean of recall rate of the method based on the RGB color feature distribution was 98.6%, the mean of precision rate was 97.3%, and the operation speed was about 320 ms/time. The mean of recall rate of the similarity detection algorithm based on the LBP texture feature was 97.3%, the mean of precision rate was 96.2% and the operation speed was about 540 ms/time. The data were analyzed by analysis of variance, the results showed that the influence of anomaly types and temperature plane on recall rate was very significant and the influence of abnormal type on precision rate was very significant, and the influence of temperature measurement plane on precision rate was not significant. Taking into precision rate, recall rate and algorithm speed consideration, similarity detection algorithm of cloud map based on RGB color feature distribution was more suitable for the detection of grain storage supervision. The test of grain storage supervision was carried out, and the results showed that the algorithm can not only regulate the grain storage, but also detect the local heat in the grain bulk. The purpose of this study was to lay the foundation for a reliable and simple regulatory approach to grain storage regulation.
A reliable method of grain storage supervision can effectively guarantee the quantity of grain storage in and out of warehouses according to plan and reduce the loss of unplanned entry and exit. In recent years, there has been a method of monitoring grain storage by video equipment, but the security of storage and management of video surveillance is poor and inconvenient to use. In this paper, we proposed a method for grain storage supervision based on the similarity of RGB color features of temperature field cloud map. Firstly, the historical grain data of the grain storage was called and pre-processed to remove the random code, error and other data. According to the correlation of the temperature at the adjacent temperature measurement points, the grain temperature data of each plane in the grain bulk was interpolated and the temperature field cloud map was generated. Then the similarity of the temperature field cloud map at the adjacent days was calculated by the similarity algorithm based on the RGB color feature distribution, similarity threshold was set according to the similarity of cloud maps during normal storage. Finally, the abnormal movement in the grain bulk was judged according to the similarity threshold. In order to verify the feasibility of grain storage regulation based on similarity of temperature field cloud map, five kinds of abnormal movement in grain bulk were simulated. The five kinds of abnormal movement respectively were: the half part of the grain bulk at right side and latter side, the quarter part of the grain bulk at right side and the latter side, and overall of the grain bulk. Similarity algorithm based on the RGB color feature distribution was used to detect abnormal movement of grain bulk. Meanwhile, the method based on the similarity of LBP texture feature was also used to compare with the method, the results showed that the mean of recall rate of the method based on the RGB color feature distribution was 98.6%, the mean of precision rate was 97.3%, and the operation speed was about 320 ms/time. The mean of recall rate of the similarity detection algorithm based on the LBP texture feature was 97.3%, the mean of precision rate was 96.2% and the operation speed was about 540 ms/time. The data were analyzed by analysis of variance, the results showed that the influence of anomaly types and temperature plane on recall rate was very significant and the influence of abnormal type on precision rate was very significant, and the influence of temperature measurement plane on precision rate was not significant. Taking into precision rate, recall rate and algorithm speed consideration, similarity detection algorithm of cloud map based on RGB color feature distribution was more suitable for the detection of grain storage supervision. The test of grain storage supervision was carried out, and the results showed that the algorithm can not only regulate the grain storage, but also detect the local heat in the grain bulk. The purpose of this study was to lay the foundation for a reliable and simple regulatory approach to grain storage regulation.
引文
[1]Gonzales H B,Armstrong P R,Maghirang R G.Simultaneous monitoring of stored grain with relative humidity,temperature,and carbon dioxide sensors[J]Applied Engineering in Agriculture,2009,25(4):595-604.
[2]Neethirajan S,Freund M S,Jayas D S,et al.Development of carbon dioxide(CO2)sensor for grain quality monitoring[J].Biosystems Engineering,2010,106(4):395-404.
[3]Zhou H,Liu J,Jayas D S,et al.Distributed parameter model predictive control method for forced air ventilation through stored grain[J].Applied Engineering in Agriculture,2014,30(4):593-600.
[4]Hammami F,Mabrouk S B,Mami A.Modelling and simulation of heat exchange and moisture content in a cereal storage silo[J].Mathematical&Computer Modelling of Dynamical Systems,2016,22(3):1-14.
[5]陈思羽.基于绝对水势的储粮湿热迁移及通风管理的研究[D].长春:吉林大学,2016.
[6]陈龙,吴文福,秦骁,等.基于绝对水势图的粮仓远程智能通风测控系统[J].农业工程学报,2015,31(4):299-305.Chen Long,Wu Wenfu,Qin Xiao,et al.Remote intelligent measurement and control system for granary ventilation based on absolute water potential diagram[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(4):299-305.(in Chinese with English abstract)
[7]Lawrence J,Maier D E,Hardin J,et al.Development and validation of a headspace model for a stored grain silo filled to its eave[J].Journal of Stored Products Research,2012,49:176-183.
[8]Abalone R,Gastón A,Bartosik R,et al.Gas concentration in the interstitial atmosphere of a wheat silo-bag.Part I:Model development and validation[J].Journal of Stored Products Research,2011,47(4):268-275.
[9]赵新蕖,刘亚卓,张丽伟.基于改进的GM(2,1)模型的粮仓温湿度预测[J].中国粮油学报,2012,27(8):85-87,97.Zhao Xinqu,Liu Yazhuo,Zhang Liwei.The forecasts of temperature and humidity of the barn based on the improved GM(2,1)[J].Journal of the Chinese Cereals and Oils Association,2012,27(8):85-87,97.(in Chinese with English abstract)
[10]梁琨,沈明霞,路顺涛,等.基于平衡水分模型的稻谷含水率实时监测系统[J].农业机械学报,2013,44(1):125-130.Liang Kun,Shen Mingxia,Lu Shuntao,et al.Real-time monitoring system for grain moisture content based on equilibrium moisture model[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(1):125-130.(in Chinese with English abstract)
[11]林鹰.储备粮智能监管与稽核系统关键技术研究[D].天津:天津大学,2008.Lin Ying.Rearch on the Key Technologies of Reserved Grain Intelligent Supervision and Audit System[D].Tianjian:Tianjin University,2008.(in Chinese with English abstract)
[12]张昌彩.国外粮食储备管理及其对我国的启示[J].经济研究参考,2004,24:33-43.Zhang Changcai.The management of foreign grain reserve and its enlightenment to China[J].Review of Economic Research,2004,24:33-43.(in Chinese with English abstract)
[13]唐为明.加拿大的粮食流通技术[J].粮油科技,2003(3):2-7.Tang Weiming.Food circulation technology in Canada[J].Science and Technology of Grain and Oil,2003(3):2-7.(in Chinese with English abstract)
[14]王铁滨,苏建民,白玲,等.数字林业智能地衡管理系统设计[J].林业机械与木工设备,2007,35(1):34-36.Wang Tiebin,Su Jianmin,Bai Ling,et al.Design of digital forestry intelligent land balance management system[J].Forestry Machinery&Woodworking Equipment,2007,35(1):34-36.(in Chinese with English abstract)
[15]汪志明,徐亚明,汪志良,等.GPS RTK技术在武钢堆料场矿料体积测量中的应用[J].测绘信息与工程,2003,28(1):13-14.Wang Zhiming,Xu Yaming,Wang Zhiliang,et al.Software of plumb line monitoring system and its application[J].Journal of Geomatics,2003,28(1):13-14.(in Chinese with English abstract)
[16]李集田,贾欣志,檀慧明.一种大面积煤堆体积自动检测系统[J].光学机械,1991(5):39-45.Li Jitian,Jia Xinzhi,Tan Huiming.An automatic system used to measure volumes of large heaps of coal[J].Optics and Precision Engineering,1991(5):39-45.(in Chinese with English abstract)
[17]陈得民,张元.基于压力传感器网络的粮仓储粮数量监测系统[J].微计算机信息,2009,25(5):142-143.Chen Demin,Zhang Yuan.The system of monitoring the number of grain storage based on the pressure sensor network[J].Microcomputer Information,2009,25(5):142-143.(in Chinese with English abstract)
[18]Chaudhury A R,Pantazis A K,Chronis N.An image contrast-based pressure sensor[J].Sensors and Actuators A:Physical,2016,245:63-67.
[19]秦瑶,陈洁,方广有,等.雷达层析成像技术对粮仓储量信息的探测[J].电波科学学报,2010,25(1):66-72,201.Qing Yao,Chen Jie,Fang Guangyou,et al.Radar tomography technology to detect reserves of barn[J].Chinese Journal of Radio Science,2010,25(1):66-72,201.(in Chinese with English abstract)
[20]Wang Y T,Feng H Y.Effects of scanning orientation on outlier formation in 3D laser scanning of reflective surfaces[J].Optics and Lasers in Engineering,2016,81:31-45.
[21]祝玉华,陈军涛,甄彤,等.储粮数量检测技术研究进展[J].河南工业大学学报:自然科学版,2016,37(6):116-122.Zhu Yuhua,Chen Juntao,Zhen Tong,et al.Present situation and prospect of grain storage quantity detection technology[J].Journal of Henan University of Technology,2016,37(6):116-122.(in Chinese with English abstract)
[22]尹君,吴子丹,张忠杰,等.不同仓型的粮堆温度场重现及对比分析[J].农业工程学报,2015,31(1):281-287.Yin Jun,Wu Zidan,Zhang Zhongjie,et al.Comparison and analysis of temperature field reappearance in stored grain of different warehouses[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(1):281-287.(in Chinese with English abstract)
[23]Murala S,Wu Q M J.Spherical symmetric 3D local ternary patterns for natural,texture and biomedical image indexing and retrieval[J].Neurocomputing,2015,149:1502-1514.
[24]Huang M,Huazhong S,Yaqiong M,et al.Content-based image retrieval technology using multi-feature fusion[J].Optik-International Journal for Light and Electron Optics,2016,126(19):2144-2148.
[25]Ghosh S,Ghosh A.Content based retrieval of malaria positive images from a clinical database via recognition in RGB colour space[J].Advances in Intelligent Systems and Computing,2014,249(2):1-8.
[26]Yan Z,Fanzhi Z,Zhao H M.An image retrieval algorithm based on hierarchical compressive sensing in HSV space[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2016,3(55):77-82.
[27]吴瑞林,王建中,袁克海.多分格相关与皮尔逊相关的蒙特卡罗仿真[J].北京航空航天大学学报,2009,35(12):1507-1510,1515.Wu Ruilin,Wang Jianzhong,Yuan Kehai.Monte Carlo simulation of polychoric correlation and Pearson correlation coefficient[J].Journal of Beijing University of Aeronautics and Astronautics,2009,35(12):1507-1510,1515.(in Chinese with English abstract)
[28]Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[29]Ojala T,Pietikainen M,Maenpaa T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.(in Chinese with English abstract)
[30]崔宏伟,吴文福,吴子丹,等.基于粮温时空相关性的储粮数量监管方法研究[J].农业机械学报,2019,50(1):321-330.Cui Hongwei,Wu Wenfu,Wu Zidan,et al.Reserves monitoring method for grain storage based on temporal and spatial correlation of crain temperature[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(1):321-330.(in Chinese with English abstract)
[31]陈桂香,王海涛,张虎.通风过程中粮堆内热湿传递及霉变预测CFD研究[J].中国粮油学报,2015,30(7):85-90.Chen Guixiang,Wang Haitao,Zhang Hu.CFD study on heat and moisture transfer and mildew prediction of stored grains in the ventilation process[J].Journal of the Chinese Cereals and Oils Association,2015,30(7):85-90.(in Chinese with English abstract)
[32]尹君,吴子丹,张忠杰,等.基于多场耦合理论浅析浅圆仓局部结露机理[J].中国粮油学报,2015,30(5):90-95.Yin Jun,Wu Zidan,Zhang Zhongjie,et al.Analysis of the formation mechanism of partial condensation for the stored grain in steel squat silo based on multi-fields coupling theory[J].Journal of the Chinese Cereals and Oils Association,2015,30(5):90-95.(in Chinese with English abstract)
[2]Neethirajan S,Freund M S,Jayas D S,et al.Development of carbon dioxide(CO2)sensor for grain quality monitoring[J].Biosystems Engineering,2010,106(4):395-404.
[3]Zhou H,Liu J,Jayas D S,et al.Distributed parameter model predictive control method for forced air ventilation through stored grain[J].Applied Engineering in Agriculture,2014,30(4):593-600.
[4]Hammami F,Mabrouk S B,Mami A.Modelling and simulation of heat exchange and moisture content in a cereal storage silo[J].Mathematical&Computer Modelling of Dynamical Systems,2016,22(3):1-14.
[5]陈思羽.基于绝对水势的储粮湿热迁移及通风管理的研究[D].长春:吉林大学,2016.
[6]陈龙,吴文福,秦骁,等.基于绝对水势图的粮仓远程智能通风测控系统[J].农业工程学报,2015,31(4):299-305.Chen Long,Wu Wenfu,Qin Xiao,et al.Remote intelligent measurement and control system for granary ventilation based on absolute water potential diagram[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(4):299-305.(in Chinese with English abstract)
[7]Lawrence J,Maier D E,Hardin J,et al.Development and validation of a headspace model for a stored grain silo filled to its eave[J].Journal of Stored Products Research,2012,49:176-183.
[8]Abalone R,Gastón A,Bartosik R,et al.Gas concentration in the interstitial atmosphere of a wheat silo-bag.Part I:Model development and validation[J].Journal of Stored Products Research,2011,47(4):268-275.
[9]赵新蕖,刘亚卓,张丽伟.基于改进的GM(2,1)模型的粮仓温湿度预测[J].中国粮油学报,2012,27(8):85-87,97.Zhao Xinqu,Liu Yazhuo,Zhang Liwei.The forecasts of temperature and humidity of the barn based on the improved GM(2,1)[J].Journal of the Chinese Cereals and Oils Association,2012,27(8):85-87,97.(in Chinese with English abstract)
[10]梁琨,沈明霞,路顺涛,等.基于平衡水分模型的稻谷含水率实时监测系统[J].农业机械学报,2013,44(1):125-130.Liang Kun,Shen Mingxia,Lu Shuntao,et al.Real-time monitoring system for grain moisture content based on equilibrium moisture model[J].Transactions of the Chinese Society for Agricultural Machinery,2013,44(1):125-130.(in Chinese with English abstract)
[11]林鹰.储备粮智能监管与稽核系统关键技术研究[D].天津:天津大学,2008.Lin Ying.Rearch on the Key Technologies of Reserved Grain Intelligent Supervision and Audit System[D].Tianjian:Tianjin University,2008.(in Chinese with English abstract)
[12]张昌彩.国外粮食储备管理及其对我国的启示[J].经济研究参考,2004,24:33-43.Zhang Changcai.The management of foreign grain reserve and its enlightenment to China[J].Review of Economic Research,2004,24:33-43.(in Chinese with English abstract)
[13]唐为明.加拿大的粮食流通技术[J].粮油科技,2003(3):2-7.Tang Weiming.Food circulation technology in Canada[J].Science and Technology of Grain and Oil,2003(3):2-7.(in Chinese with English abstract)
[14]王铁滨,苏建民,白玲,等.数字林业智能地衡管理系统设计[J].林业机械与木工设备,2007,35(1):34-36.Wang Tiebin,Su Jianmin,Bai Ling,et al.Design of digital forestry intelligent land balance management system[J].Forestry Machinery&Woodworking Equipment,2007,35(1):34-36.(in Chinese with English abstract)
[15]汪志明,徐亚明,汪志良,等.GPS RTK技术在武钢堆料场矿料体积测量中的应用[J].测绘信息与工程,2003,28(1):13-14.Wang Zhiming,Xu Yaming,Wang Zhiliang,et al.Software of plumb line monitoring system and its application[J].Journal of Geomatics,2003,28(1):13-14.(in Chinese with English abstract)
[16]李集田,贾欣志,檀慧明.一种大面积煤堆体积自动检测系统[J].光学机械,1991(5):39-45.Li Jitian,Jia Xinzhi,Tan Huiming.An automatic system used to measure volumes of large heaps of coal[J].Optics and Precision Engineering,1991(5):39-45.(in Chinese with English abstract)
[17]陈得民,张元.基于压力传感器网络的粮仓储粮数量监测系统[J].微计算机信息,2009,25(5):142-143.Chen Demin,Zhang Yuan.The system of monitoring the number of grain storage based on the pressure sensor network[J].Microcomputer Information,2009,25(5):142-143.(in Chinese with English abstract)
[18]Chaudhury A R,Pantazis A K,Chronis N.An image contrast-based pressure sensor[J].Sensors and Actuators A:Physical,2016,245:63-67.
[19]秦瑶,陈洁,方广有,等.雷达层析成像技术对粮仓储量信息的探测[J].电波科学学报,2010,25(1):66-72,201.Qing Yao,Chen Jie,Fang Guangyou,et al.Radar tomography technology to detect reserves of barn[J].Chinese Journal of Radio Science,2010,25(1):66-72,201.(in Chinese with English abstract)
[20]Wang Y T,Feng H Y.Effects of scanning orientation on outlier formation in 3D laser scanning of reflective surfaces[J].Optics and Lasers in Engineering,2016,81:31-45.
[21]祝玉华,陈军涛,甄彤,等.储粮数量检测技术研究进展[J].河南工业大学学报:自然科学版,2016,37(6):116-122.Zhu Yuhua,Chen Juntao,Zhen Tong,et al.Present situation and prospect of grain storage quantity detection technology[J].Journal of Henan University of Technology,2016,37(6):116-122.(in Chinese with English abstract)
[22]尹君,吴子丹,张忠杰,等.不同仓型的粮堆温度场重现及对比分析[J].农业工程学报,2015,31(1):281-287.Yin Jun,Wu Zidan,Zhang Zhongjie,et al.Comparison and analysis of temperature field reappearance in stored grain of different warehouses[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2015,31(1):281-287.(in Chinese with English abstract)
[23]Murala S,Wu Q M J.Spherical symmetric 3D local ternary patterns for natural,texture and biomedical image indexing and retrieval[J].Neurocomputing,2015,149:1502-1514.
[24]Huang M,Huazhong S,Yaqiong M,et al.Content-based image retrieval technology using multi-feature fusion[J].Optik-International Journal for Light and Electron Optics,2016,126(19):2144-2148.
[25]Ghosh S,Ghosh A.Content based retrieval of malaria positive images from a clinical database via recognition in RGB colour space[J].Advances in Intelligent Systems and Computing,2014,249(2):1-8.
[26]Yan Z,Fanzhi Z,Zhao H M.An image retrieval algorithm based on hierarchical compressive sensing in HSV space[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2016,3(55):77-82.
[27]吴瑞林,王建中,袁克海.多分格相关与皮尔逊相关的蒙特卡罗仿真[J].北京航空航天大学学报,2009,35(12):1507-1510,1515.Wu Ruilin,Wang Jianzhong,Yuan Kehai.Monte Carlo simulation of polychoric correlation and Pearson correlation coefficient[J].Journal of Beijing University of Aeronautics and Astronautics,2009,35(12):1507-1510,1515.(in Chinese with English abstract)
[28]Ojala T,Pietikainen M,Harwood D.A comparative study of texture measures with classification based on featured distributions[J].Pattern Recognition,1996,29(1):51-59.
[29]Ojala T,Pietikainen M,Maenpaa T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEE Trans.on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.(in Chinese with English abstract)
[30]崔宏伟,吴文福,吴子丹,等.基于粮温时空相关性的储粮数量监管方法研究[J].农业机械学报,2019,50(1):321-330.Cui Hongwei,Wu Wenfu,Wu Zidan,et al.Reserves monitoring method for grain storage based on temporal and spatial correlation of crain temperature[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(1):321-330.(in Chinese with English abstract)
[31]陈桂香,王海涛,张虎.通风过程中粮堆内热湿传递及霉变预测CFD研究[J].中国粮油学报,2015,30(7):85-90.Chen Guixiang,Wang Haitao,Zhang Hu.CFD study on heat and moisture transfer and mildew prediction of stored grains in the ventilation process[J].Journal of the Chinese Cereals and Oils Association,2015,30(7):85-90.(in Chinese with English abstract)
[32]尹君,吴子丹,张忠杰,等.基于多场耦合理论浅析浅圆仓局部结露机理[J].中国粮油学报,2015,30(5):90-95.Yin Jun,Wu Zidan,Zhang Zhongjie,et al.Analysis of the formation mechanism of partial condensation for the stored grain in steel squat silo based on multi-fields coupling theory[J].Journal of the Chinese Cereals and Oils Association,2015,30(5):90-95.(in Chinese with English abstract)