温室智能控制中信息融合算法的研究
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摘要
多传感器信息融合技术是一项前沿技术,初期只是应用于军事领域的战场控制如C4I系统等,二战以后已逐渐被引入到非军事领域,例如：智能楼宇、遥感系统等等。其涉及的领域技术包括概率统计学、建模理论、模式识别、神经网络等。在温室内采用多个传感器对环境进行实时检测,有效地弥补了单一传感器失效无法对目标监测,进而无法进行数据传输的缺点。
实现温室智能控制是根据作物自身的生理特点,部分或全部克服外界气候环境以及其它非自身因素的束缚,从而为作物生长创造最佳条件,达到增产和节能的目的。实现温室智能控制首先需要确定制约环境的各个因素,同时确定他们之间的关系,进而采取相应的管理、调控措施。系统根据农业领域专家的不断探索与实践最终确定了温度、光照度、湿度这三个量为农作物生长的三个主要影响因素。本温室环境控制系统以工控机为核心,采用现代信息处理技术,对以往更多依赖于农业经验知识技术判断各种作物生理状况的温室控制进行了改进。
本文主要研究了多传感器信息融合的原理、结构和常用的算法,分析了多传感器系统的融合模型,在对比各个融合级别的信息融合算法的优缺点的基础上,重点介绍D-S证据理论相关知识和专家系统的内在含义。对于同质传感器输送的同源信息,采用分布图法去除干扰,达到了对信息进行初步优化处理的目的。对D-S组合算法进行了改进,同时对系统的知识构造识别框架进行了模糊化处理,在此基础上为D-S模型进行基本概率分配函数赋值,然后对经过分布图处理的温度、湿度和光照度信息进行分组融合,最终由专家系统做出决策。实验表明,这种方法提高了温室环境参数测控的决策准确性,可显著改善温室环境的控制效果。
The multi-sensor information fusion technique is a cutting edge technology, initially only applied to the Battlefield control of the military, such as the C4I system, etc, this technology has gradually been introduced into the non-military areas after World WarⅡ, for example:intelligent buildings, remote sensing systems and so on. It relates to areas of technology which include probability statistics, modeling theory, pattern recognition and neural networks. Using multiple sensors to real-time detection on the environment in a greenhouse, it effectively compensates the shortcoming that the failure of a single sensor can not transmit data because a volume can not be monitored.
According to the physiological characteristics of crops, wholly or partly to overcome the climatic and environmental factors outside world and other non-self-bondage, so as to create the best conditions for crop growth and achieved the purpose of increasing production and energy efficiency, firstly, to determine the various factors of the constrained environment and the relationship between them, then to take the appropriate management and control measures. Through the continuous exploration and practice of experts in the agricultural field, ultimately to determine the temperature, light intensity and humidity are greater impacts on the environment. Be seen as the main three factors to affect crop. Greenhouse environmental control systems often use IPC as the core. computer technology is more emphasized, for depending on the knowledge technology of agriculture and experience, greenhouse controls of judging the physiological state are clearly shortcomings.
This paper mainly studies the principle of multi-sensor information fusion, structures and the commonly used algorithms, and analysis of multi-sensor system integration model, detailed analysis of the advantages and disadvantages of the different levels of information fusion algorithm, focuses on the D-S evidence theory knowledge and the intrinsic meaning of expert system. For the homology of the information transferred by homogeneous sensor, our purpose is to eliminate interference by compatibility matrix method and achieve the purpose of a preliminary optimization of information. Using of an improved D-S combination algorithm, the identification framework is constructed by knowledge of the expert system, and to be assignment for the basic probability assignment function in the D-S model, and then attempt to integrate for the processed information of temperature, humidity and illumination by compatibility matrix method, the final decision is given by the expert system. Experiments show that, this approach improves the accuracy of decision-making and control in parameters of greenhouse environmental, and to improve the effect of greenhouse environmental control.
实现温室智能控制是根据作物自身的生理特点,部分或全部克服外界气候环境以及其它非自身因素的束缚,从而为作物生长创造最佳条件,达到增产和节能的目的。实现温室智能控制首先需要确定制约环境的各个因素,同时确定他们之间的关系,进而采取相应的管理、调控措施。系统根据农业领域专家的不断探索与实践最终确定了温度、光照度、湿度这三个量为农作物生长的三个主要影响因素。本温室环境控制系统以工控机为核心,采用现代信息处理技术,对以往更多依赖于农业经验知识技术判断各种作物生理状况的温室控制进行了改进。
本文主要研究了多传感器信息融合的原理、结构和常用的算法,分析了多传感器系统的融合模型,在对比各个融合级别的信息融合算法的优缺点的基础上,重点介绍D-S证据理论相关知识和专家系统的内在含义。对于同质传感器输送的同源信息,采用分布图法去除干扰,达到了对信息进行初步优化处理的目的。对D-S组合算法进行了改进,同时对系统的知识构造识别框架进行了模糊化处理,在此基础上为D-S模型进行基本概率分配函数赋值,然后对经过分布图处理的温度、湿度和光照度信息进行分组融合,最终由专家系统做出决策。实验表明,这种方法提高了温室环境参数测控的决策准确性,可显著改善温室环境的控制效果。
The multi-sensor information fusion technique is a cutting edge technology, initially only applied to the Battlefield control of the military, such as the C4I system, etc, this technology has gradually been introduced into the non-military areas after World WarⅡ, for example:intelligent buildings, remote sensing systems and so on. It relates to areas of technology which include probability statistics, modeling theory, pattern recognition and neural networks. Using multiple sensors to real-time detection on the environment in a greenhouse, it effectively compensates the shortcoming that the failure of a single sensor can not transmit data because a volume can not be monitored.
According to the physiological characteristics of crops, wholly or partly to overcome the climatic and environmental factors outside world and other non-self-bondage, so as to create the best conditions for crop growth and achieved the purpose of increasing production and energy efficiency, firstly, to determine the various factors of the constrained environment and the relationship between them, then to take the appropriate management and control measures. Through the continuous exploration and practice of experts in the agricultural field, ultimately to determine the temperature, light intensity and humidity are greater impacts on the environment. Be seen as the main three factors to affect crop. Greenhouse environmental control systems often use IPC as the core. computer technology is more emphasized, for depending on the knowledge technology of agriculture and experience, greenhouse controls of judging the physiological state are clearly shortcomings.
This paper mainly studies the principle of multi-sensor information fusion, structures and the commonly used algorithms, and analysis of multi-sensor system integration model, detailed analysis of the advantages and disadvantages of the different levels of information fusion algorithm, focuses on the D-S evidence theory knowledge and the intrinsic meaning of expert system. For the homology of the information transferred by homogeneous sensor, our purpose is to eliminate interference by compatibility matrix method and achieve the purpose of a preliminary optimization of information. Using of an improved D-S combination algorithm, the identification framework is constructed by knowledge of the expert system, and to be assignment for the basic probability assignment function in the D-S model, and then attempt to integrate for the processed information of temperature, humidity and illumination by compatibility matrix method, the final decision is given by the expert system. Experiments show that, this approach improves the accuracy of decision-making and control in parameters of greenhouse environmental, and to improve the effect of greenhouse environmental control.
引文
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[21]JOY T, SHAFAE M. An Application of Motion Control and Motion Planning[C]. International Conference on Computing, Engineering and Information, Fullerton, 2009:175-181.
[22]丁胜峰.基于模糊推理的多源图像融合研究[D].南京理工大学硕士学位论文,2004：2-4.
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[26]刘学明,李相平,梁春兰.环境可控温室控制系统总体设计[J].刑台职业技术学院学报,2004,21：64-67.
[27]郑秀莲.现代温室气候的专家控制系统[J].机电工程,2003,20(3)：42-45.
[28]黄赫,李宝泽,曹泽阳.多传感器目标数据融合及关键技术[J].控制与制导,2009,(10)：50-52.
[29]陈黎敏.智能传感器的数据处理方法[J].传感器技术,2004,23(5)：19-23.
[30]杨万海.多传感数据融合及其应用.西安：西安电子科技大学出版社,2004：35-39.
[31]潘泉,于听.多传感信息融合与自动化.自动化学报,2002,28(增刊)：117-124.
[32]MIAO Y Z, MA X P, ZHANG H X. An Improved Extension of the D-S Evidence Theory to Fuzzy Set[C]. ICCGI'08. The Third International Multi-Conference on Computing in the Global Information Technology, Nanjing,2008:148-153.
[33]WANG BAOCHENG, LI DANHE, SUN XIAOFENG, WU WEIYANG The studies of Single-phase Inverter Fault Diagnosis Based on D-S Evidential Theory and Fuzzy Logical Theory[C]. CES/IEEE 5th International Power Electronics and Motion Control Conference.2006:1-4.
[34]LIU LILI, LIU YUAN. Research on the Technology of Network Intrusion Detection Based on Modified D-S Evidence Theory[C]. WCSE'09. WRI World Congress on Software Engineering.2009:447-450.
[35]董九英.多传感器数据融合的主成分方法研究[J].计算机工程与应用,2009,45(33)：111-113.
[36]董永贵.传感技术与系统[M].北京：清华大学出版社,2006：12-23.
[37]张洪润.传感器技术大全[M].北京：北京航空航天大学出版社,2007,1496-1499.
[38]葛运建,戈瑜,吴仲城等.浅谈我国传感器技术发展中的若干问题[J].世界产品与技术,2003,(3)：22-24.
[39]杨俊欣.传感器技术的发展及国防应用概括[J].传感器技术,2006,23(5)：182-184.
[40]刘刚.基于神经网络的智能传感器的数据处理[J].传感器技术,2004,23(8)：24-26.
[41]胡向东,刘京诚.传感技术[M].重庆：重庆大学出版社,2006：35-44.
[42]董永贵.微型传感器[M].北京：清华大学出版社,2007：51-55.
[43]DAN STROMBERG, MARIA ANDERSSON, FREDRIK LANTZ. On platform based sensor management[J]. Proceedings of the Fifth International Conference on Information Fusion, Annapolis Maryland,2002, (1):600-607.
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[45]YangPanhong, WtiJianning. The Research and Architecture of Agriculture Expert Decision System, ICEMI'2003,2:1799-1805.
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[2]聂西利. 数据融合算法的初步研究以及在数据采集系统中的应用[D].浙江大学硕士学位论文,2006：1-3.
[3]葛运建,戈瑜,吴仲城等.浅谈我国传感器技术发展中的若干问题[J].世界产品与技术,2003,(3)：22-24.
[4]段汝娇.基于大信息量的异质多传感器数据融合[D].江南大学硕士论文,2008：1-2.
[5]何友,王国宏等.多传感器信息融合及其应用[M].北京：电子工业出版社,2000：14-17.
[6]续春荣.多传感器数据融合技术研究进展[J].地壳构造与地壳应力,2005,(4)：19-22.
[7]刘爱华,满宝元.传感器原理与应用技术.北京,人民邮电出版社,2007：4-11.
[8]贾伯年,俞朴.传感器技术[M].南京：东南大学出版社,1996：14-25
[9]周桃云.数据融合理论及其在组合导航系统中的应用[D].西北工业大学硕士论文,2007：3-7.
[10]郁文贤,雍少为,郭桂荣.多传感器信息融合技术述评[J].国防科技大学学报,1994,16(4)：1-11.
[11]董永贵.传感技术与系统.北京：清华大学出版社,2006：12-23.
[12]赵奎.医疗设备控制系统中融合理论的研究及嵌入使系统实现[D].湖南大学硕士学位论文,2005：1-5.
[13]周静.车载多传感器称重系统的研究[D].哈尔滨工程大学硕士学位论文,2004：7-10.
[14]王耀南,李树涛.多传感器信息融合及其应用综述[J].控制与决策,2001,16(5)：518-522.
[15]贾均.多传感器信息融合及其在鱼雷制导中的应用[D].西北工业大学硕士学位论文,2004：4-8.
[16]刘爱华,满宝元.传感器原理与应用技术[M].北京,人民邮电出版社,2007：4-11.
[17]罗志增,蒋静平.基于D-S理论的多信息融合方法及应用[J].电子学报,1999,27(9)：99-103.
[18]QINGDONG DU, JIN LI, XIAO CHEN. Fault Diagnosis of Generator Based on D-S. Evidence Theory ISDA'08. Eighth International Conference on Intelligent Systems Design and Applications, Beijing,2008:660-663.
[19]GUOPING XU, WEIFENG TIAN, ZHIHUA JIN, LI QIAN Combination of Conflict Evidences Using Grey Association Analysis and D-S Rule[C]. International Conference on Computational Intelligence and Security. Tianjin. 2006:657-661.
[20]WANG PANHONG, WEI JIANNING. Research and Architecture of Agriculture Expert Decision System[C]. ICEMI'2007, Shanghai,2007:1799-1805.
[21]JOY T, SHAFAE M. An Application of Motion Control and Motion Planning[C]. International Conference on Computing, Engineering and Information, Fullerton, 2009:175-181.
[22]丁胜峰.基于模糊推理的多源图像融合研究[D].南京理工大学硕士学位论文,2004：2-4.
[23]VAN STRATEN, G. BENTUM J W, TAP R F. Paradigms in Greenhouse Climate Control:on Hierarchy and Energy Saving[C]. Mathematical and Control Applications in Agricuiture and Horticulture, Hannover,2006:58-60.
[24]高建平,赵龙庆.温室计算机控制与管理技术的发展概况及在我国的应用前景[J].计算机与农业,2003,(2)：12-15.
[25]杜尚丰,李迎霞等.中国温室环境控制硬件系统研究进展[J].农业工程学报,2004,20：7-12.
[26]刘学明,李相平,梁春兰.环境可控温室控制系统总体设计[J].刑台职业技术学院学报,2004,21：64-67.
[27]郑秀莲.现代温室气候的专家控制系统[J].机电工程,2003,20(3)：42-45.
[28]黄赫,李宝泽,曹泽阳.多传感器目标数据融合及关键技术[J].控制与制导,2009,(10)：50-52.
[29]陈黎敏.智能传感器的数据处理方法[J].传感器技术,2004,23(5)：19-23.
[30]杨万海.多传感数据融合及其应用.西安：西安电子科技大学出版社,2004：35-39.
[31]潘泉,于听.多传感信息融合与自动化.自动化学报,2002,28(增刊)：117-124.
[32]MIAO Y Z, MA X P, ZHANG H X. An Improved Extension of the D-S Evidence Theory to Fuzzy Set[C]. ICCGI'08. The Third International Multi-Conference on Computing in the Global Information Technology, Nanjing,2008:148-153.
[33]WANG BAOCHENG, LI DANHE, SUN XIAOFENG, WU WEIYANG The studies of Single-phase Inverter Fault Diagnosis Based on D-S Evidential Theory and Fuzzy Logical Theory[C]. CES/IEEE 5th International Power Electronics and Motion Control Conference.2006:1-4.
[34]LIU LILI, LIU YUAN. Research on the Technology of Network Intrusion Detection Based on Modified D-S Evidence Theory[C]. WCSE'09. WRI World Congress on Software Engineering.2009:447-450.
[35]董九英.多传感器数据融合的主成分方法研究[J].计算机工程与应用,2009,45(33)：111-113.
[36]董永贵.传感技术与系统[M].北京：清华大学出版社,2006：12-23.
[37]张洪润.传感器技术大全[M].北京：北京航空航天大学出版社,2007,1496-1499.
[38]葛运建,戈瑜,吴仲城等.浅谈我国传感器技术发展中的若干问题[J].世界产品与技术,2003,(3)：22-24.
[39]杨俊欣.传感器技术的发展及国防应用概括[J].传感器技术,2006,23(5)：182-184.
[40]刘刚.基于神经网络的智能传感器的数据处理[J].传感器技术,2004,23(8)：24-26.
[41]胡向东,刘京诚.传感技术[M].重庆：重庆大学出版社,2006：35-44.
[42]董永贵.微型传感器[M].北京：清华大学出版社,2007：51-55.
[43]DAN STROMBERG, MARIA ANDERSSON, FREDRIK LANTZ. On platform based sensor management[J]. Proceedings of the Fifth International Conference on Information Fusion, Annapolis Maryland,2002, (1):600-607.
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