数据融合技术在中央空调监控系统中的应用研究
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摘要
当今世界随着科学技术的发展步入了一个信息时代,智能建筑也随之迅猛发展,人们正竭力创造一个安全、健康、舒适宜人并且节能的办公和生活环境。因此高品质的HVAC(Heating Ventilation and Air Conditioning)系统也越来越受到人们的重视,人们已不仅仅满足于常规的冷暖调节功能,而是更加关注室内的热舒适度、健康程度和节能绿色环保,从而对空调系统的设计和控制提出了更高的要求。而对于现有的测控系统而言,其中的传感器/执行器数以万计,并且传感器的数据处理和数据传输能力受到极大地限制。本课题以满足现代人对HVAC系统舒适、健康和节能的要求为出发点,将多传感器数据融合技术引入HVAC监控系统中,并且利用图形化编程语言(G语言)LabVIEW来开发监控系统。
本文分析了中央空调自控系统的结构及其各个部分的基本功能和工作原理,研究了多传感器信息融合系统的结构、功能及方法。将各种信息融合方法从融合层次、运行环境、信息类型、信息表示和不确定性等几个方面进行了比较,进而选取合适的融合算法应用在HVAC监控系统中。在分析的系统结构和功能要求的基础上,分模块对监控系统进行设计,包括信息采集模块、信息显示模块、数据存储模块及报警模块等,并采用图形化编程语言语言LabVIEW来对各模块功能的实现进行了应用软件开发。最后对监控系统进行了人机交互界面的开发。
在对各种融合方法进行比较分析后,本文选择了自适应加权平均算法对初始数据进行一级融合,剔除无效和不可靠的数据,为二级融合提供更加可靠、稳定的数据。二级融合选择模糊优化决策算法,对目标参数进行优化决策,然后再基于PMV准则进行最后参数的决策。有了决策结果以后,将各决策结果作为模糊控制器的输入,进行模糊逻辑推理。本文将执行器的状态信息反馈回来与其它输入一起进行模糊推理融合,使系统更稳定、更准确。
本文在分析HVAC监控系统的特点、需求以及各种数据融合原理的基础上,设计了自适应加权算法、模糊最优化决策及模糊逻辑推理的三级数据融合方案,最后在LabVIEW软件操作平台及Matlab上对各融合算法进行了仿真实现。结果表明,将数据融合技术应用在HVAC监控系统中是有效的、可行的,能够满足舒适、节能的要求。
Current world has entered an information age with developing of science and technology of modern society, and rapid and drastic development is taking place in the field of intelligent building. People are trying to create a safe, healthy, comfortable and pleasant, and energy-efficient office and living environment. Therefore, high-quality HVAC systems are also being taken more and more attention. There has been not only satisfied with the conventional heating and cooling regulatory function, but pay more attention to the indoor thermal comfort, health, high efficiency and energy saving. So the design and control of air-conditioning system is put forward higher requirements. As for the existing supervision and contro systems, in which include tens of thousands of sensors/actuators, and data processing and data transmission capabilities of sensors are extremely limited. To meet comfort, health and energy conservation requirements of modern people on HVAC system, the subject introduces the multi-sensor data fusion technology into the HVAC control system, and graphical programming language (G language) LabVIEW is used to develop the supervision and contro system.
This paper analyzes the structure, basic functions and working principle of various parts in the central air conditioning system, and studies structure, functions and methods of multi-sensor data fusion. A variety of information fusion methods are compared from several aspects such as the level of fusion, operating environments, information types, presentation of information and uncertainty etc., then select appropriate fusion algorithms and apply to the HVAC system. Several modules of the supervision and contro system are designed based on prior analysis of the system structure, functional requirements, such as information acquisition module, signal display module, data storage module, and alarm module. Then graphical programming language LabVIEW is applied to realize functions of each module. Finally, develop human-computer interface of the supervision and contro system.
After a comparative analysis of the fusion methods, the paper selects self-adaptive weighed algorithm for primary data fusion to remove invalid data and provide more reliable and stable data for secondary data fusion. And select fuzzy optimal decision-making algorithm as secondary data fusion method to optimize target parameters, then do the last decision-making based on PMV criteria. After the decision-making, decision results as the input of fuzzy controller are conducted fuzzy logic reasoning with actuator status information feedback to make the system more stable and more accurate.
In this paper, a three-step data fusion scheme that includes self-adaptive weighed algorithm, fuzzy optimal decision-making algorithm and fuzzy logic reasoning is designed based on analysis of the characteristics and demand of HVAC supervision and controsystem as well as the principles of data fusion. Then various fusion algorithms are simulated on the LabVIEW and Matlab software platform. The results show that data fusion technology is applied in the HVAC supervision and contro system is effective and feasible, as well as can meet the comfort, energy-saving requirements.
本文分析了中央空调自控系统的结构及其各个部分的基本功能和工作原理,研究了多传感器信息融合系统的结构、功能及方法。将各种信息融合方法从融合层次、运行环境、信息类型、信息表示和不确定性等几个方面进行了比较,进而选取合适的融合算法应用在HVAC监控系统中。在分析的系统结构和功能要求的基础上,分模块对监控系统进行设计,包括信息采集模块、信息显示模块、数据存储模块及报警模块等,并采用图形化编程语言语言LabVIEW来对各模块功能的实现进行了应用软件开发。最后对监控系统进行了人机交互界面的开发。
在对各种融合方法进行比较分析后,本文选择了自适应加权平均算法对初始数据进行一级融合,剔除无效和不可靠的数据,为二级融合提供更加可靠、稳定的数据。二级融合选择模糊优化决策算法,对目标参数进行优化决策,然后再基于PMV准则进行最后参数的决策。有了决策结果以后,将各决策结果作为模糊控制器的输入,进行模糊逻辑推理。本文将执行器的状态信息反馈回来与其它输入一起进行模糊推理融合,使系统更稳定、更准确。
本文在分析HVAC监控系统的特点、需求以及各种数据融合原理的基础上,设计了自适应加权算法、模糊最优化决策及模糊逻辑推理的三级数据融合方案,最后在LabVIEW软件操作平台及Matlab上对各融合算法进行了仿真实现。结果表明,将数据融合技术应用在HVAC监控系统中是有效的、可行的,能够满足舒适、节能的要求。
Current world has entered an information age with developing of science and technology of modern society, and rapid and drastic development is taking place in the field of intelligent building. People are trying to create a safe, healthy, comfortable and pleasant, and energy-efficient office and living environment. Therefore, high-quality HVAC systems are also being taken more and more attention. There has been not only satisfied with the conventional heating and cooling regulatory function, but pay more attention to the indoor thermal comfort, health, high efficiency and energy saving. So the design and control of air-conditioning system is put forward higher requirements. As for the existing supervision and contro systems, in which include tens of thousands of sensors/actuators, and data processing and data transmission capabilities of sensors are extremely limited. To meet comfort, health and energy conservation requirements of modern people on HVAC system, the subject introduces the multi-sensor data fusion technology into the HVAC control system, and graphical programming language (G language) LabVIEW is used to develop the supervision and contro system.
This paper analyzes the structure, basic functions and working principle of various parts in the central air conditioning system, and studies structure, functions and methods of multi-sensor data fusion. A variety of information fusion methods are compared from several aspects such as the level of fusion, operating environments, information types, presentation of information and uncertainty etc., then select appropriate fusion algorithms and apply to the HVAC system. Several modules of the supervision and contro system are designed based on prior analysis of the system structure, functional requirements, such as information acquisition module, signal display module, data storage module, and alarm module. Then graphical programming language LabVIEW is applied to realize functions of each module. Finally, develop human-computer interface of the supervision and contro system.
After a comparative analysis of the fusion methods, the paper selects self-adaptive weighed algorithm for primary data fusion to remove invalid data and provide more reliable and stable data for secondary data fusion. And select fuzzy optimal decision-making algorithm as secondary data fusion method to optimize target parameters, then do the last decision-making based on PMV criteria. After the decision-making, decision results as the input of fuzzy controller are conducted fuzzy logic reasoning with actuator status information feedback to make the system more stable and more accurate.
In this paper, a three-step data fusion scheme that includes self-adaptive weighed algorithm, fuzzy optimal decision-making algorithm and fuzzy logic reasoning is designed based on analysis of the characteristics and demand of HVAC supervision and controsystem as well as the principles of data fusion. Then various fusion algorithms are simulated on the LabVIEW and Matlab software platform. The results show that data fusion technology is applied in the HVAC supervision and contro system is effective and feasible, as well as can meet the comfort, energy-saving requirements.
引文
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[2]余志强,胡汉章,刘光平.智能建筑环境设备自动化[M].北京:清华大学出版社, 2008, 1.
[3] William J.Fisk, P.E. How IEQ Affects Health, Productivity. ASHRAE Journal, 2002, 5.
[4]诸静.模糊控制原理与应用[M].北京:机械工业出版社, 1995.
[5]李人厚.智能控制理论和方法[M].西安:西安电子科技大学出版社, 1999, 10.
[6]陆振光.浅论智能化建筑[J].经济师, 2001 (6): 206.
[7] Waltz E, Lilnas J. Multi-sensor Data Fusion[M]. Boston: Artech House, 2000.
[8]王崇湘,曹光祖.智能建筑概述—兼议一些热点和误区[J].低压电器, 1999 (1): 58-62.
[9] David L.Hall, James Llinas. Handbook of Multisensor Data Fusion[M]. USA: CRC Press LLC, 2001.
[10]李树江,秦军,刘畅.暖通空调系统优化控制与能量管理的现状及发展趋势[J].暖通空调, 2007, 37 (4): 31-34.
[11]王娜.智能建筑概论[M].北京:人民交通出版社, 2002, 6.
[12]中华人民共和国国家标准.智能建筑设计标准GB/T 50314-2006[S].北京:中国人民共和国建设部, 2006 (12).
[13]何志议.智能建筑中的楼宇自动化系统理论浅析[J].电工技术杂志, 2004 (8): 57-61.
[14]韩宝琦,李树林.制冷空调原理及应用[M].北京:机械工业出版社, 2002.
[15] Gassmann O., Meixner H.. Sensors in Intelligent Buildings[M].北京:化学工业出版社, 2001, 1.
[16] Chen, S.Y.S., Demster. Variable Air Volume Systems for Enviromental Quality[J]. McGraw-Hill, 1995, 71-72.
[17]曾庆茂,丁正生.多传感器信息融合技术综述[J].赣南师范学院学报, 2004 (6): 20-23.
[18]杨万海.多传感器数据融合及其应用[M].西安:西安电子科技大学出版社, 2004, 4.
[19] Luo R C, Kay M G, Lee W G. Future Trends in Multisensor Integration and Fusion[A]. I Industrial Electronics, Symposium Proceedings, ISIE'94[C]. 1994 IEEE International Symposium on Published 1994, 7-12.
[20] Luo R C, Su K L. A Review of High-level Multis-ensor Fusion: Approaches and Applications [A]. Proceedings of the 1999 IEEE International Conference on Multi-sensor Fusion and Integration for Intelligent Systems [C]. Taipei, Taiwan, 1999, 25-31.
[21] Akita R M. User Based Data Fusion Approaches[J]. Information Fusion, 2002 (2), 1457-1462.
[22] ChungA C S, ShenH C. Entropy- based Markov Chains for Multisensor Fusion[J]. Journal of Intelligent and Robotic Systems, 2000, 29 (2): 161-189.
[23]李凡.人工智能中的不确定性[M].万象出版社, 1998.
[24]严怀成,黄心汉,王敏.多传感器数据融合技术及其应用[J].传感器技术, 2005, 24 (6): 1-4.
[25]王凤朝,黄树采,韩朝超.多传感器信息融合及其新技术研究[J].航空计算技术, 2009, 39 (1): 102-106.
[26] Hall D L, Llinas J. An Introduction to Multi-sensor Data Fusion[J]. Proceedings of the IEEE, 1997, 85 (1), 6-23.
[27] S.Sumathi , P.Surekha. LabVIEW Based Advanced Instrumentation Systems[M]. Berlin: Springer, 2007.
[28]岂兴明,周建兴,娇津毅. LabVIEW 8.2中文版入门与典型实例[M].北京:人民邮电出版社,2008, 7.
[29]金维香.图形化程序设计G语言—LabVIEW与虚拟仪器[J].长沙电力学院学报, 2002, 17 (6), 14-17.
[30] Robert H., Bishop. LabVIEW 8实用教程[M].北京:电子工业出版社, 2008.
[31]中华人民共和国国家标准.智能建筑设计标准GB/T 50314-2000[S].北京:中国人民共和国建设部, 2000 (10).
[32]安大伟.暖通空调系统自动化[M].北京:中国建筑工业出版社,2009.
[33]龙华伟,顾永刚. LabVIEW 8.2.1与DAQ数据采集[M].北京:清华大学出版社, 2008.
[34]陈锡辉,张银鸿. LabVIEW 8.20程序设计从入门到精通[M].北京:清华大学出版社,2007.
[35]中华人民共和国国家标准.室内空气质量标准GB/T 18883-2002[S].北京:中国人民共和国环保总局与卫生部, 2002 (11).
[36] ISO standard 7730. Moderate Thermal Environment - Determination of the PMV and PPD Indices and Specification of the Condition for Thermal Comfort[S]. Geneva: International Standard Organization, 1984.
[37]孙皓莹,蒋静坪.基于参数估计的多传感器数据融合[J].传感器技术, 1995 (6): 32-36.
[38]滕召胜,罗隆福,童调生.智能检测系统与数据融合[M].北京:机械工业出版社, 2000.
[39]严怀成,黄心汉,王敏.多传感器数据融合技术及其应用[J].传感器技术, 2005, 24 (10): 1-4.
[40]李荣钧.模糊多准则决策理论与应用[M].北京:科学出版社, 2000, 2.
[41]郭富军,杨昌智.热舒适指标在空调系统控制中的应用研究[J].湖南大学学报, 2000 (9): 57-60.
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