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1、基于多数据融合传感器的分布式温度控制系统摘要： 在过去的几十年，温度控制系统已经被广泛的应用。对于温度控制提出了一种基于多传感器数据融合和CAN总线控制的一般结构。一种新方法是基于多传感器数据融合估计算法参数分布式温控系统。该系统的重要特点是其共性，其适用于很多具体领域的大型的温度控制。实验结果表明该系统具有较高的准确性、可靠性，良好的实时性和广泛的应用前景。关键词： 分布式控制系统；CAN总线控制；智能CAN节点；多数据融合传感器。1介绍 分布式温度控制系统已经被广泛的应用在我们日常生活和生产,包括智能建筑、温室、恒温车间、大中型粮仓、仓库等。这种控制保证环境温度能被保持在两个预先设定的温度
2、间。在传统的温度测量系统中，我们用一个基于温度传感器的单片机系统建立一个RS-485局域网控制器网络。借助网络,我们能实行集中监控和控制.然而,当监测区域分布更广泛和传输距离更远,RS-485总线控制系统的劣势更加突出。在这种情况下，传输和响应速度变得更低，抗干扰能力更差。因此，我们应当寻找新的通信的方法来解决用RS-485总线控制系统而产生的问题。在所有的通讯方式中，适用于工业控制系统的总线控制技术，我们可以突破传统点对点通信方式的限制、建立一个真正的分布式控制与集中管理系统，CAN总线控制比RS-485总线控制系统更有优势。比如更好的纠错能力、改善实时的能力，低成本等。目前，它正被广泛的应
3、用于实现分布式测量和范围控制。 随着传感器技术的发展，越来越多的系统开始采用多传感器数据融合技术来提高他们的实现效果。多传感器数据融合是一种范式对多种来源整合数据,以综合成新的信息，比其他部分的总和更加强大。无论在当代和未来，系统的低成本，节省资源都是传感器中的一项重要指标。2分布式架构的温度控制系统 分布式架构温度控制系统如图中所示的图1。可以看出，这系统由两个模块两个智能CAN节点和一个主要的控制器组成。每个模块部分执行进入分布式架构。下面的是简短的描述下各模块。3.1主要控制器 作为系统的主要控制器，这主pc能和智能CAN节点通信。它致力于监督和控制整个系统，系统配置、显示运行状况、参数
4、初始化和协调各部分间的关系。更重要的是，我们能打印或储存系统的历史温度的数据，这对分析系统性能是非常有用的。3.2智能CAN节点 每一个温度控制系统的智能CAN节点有五个部分：MCU一个单片机，A/D转换单元，温度监测单元传感器群，数字显示器，激发器一个冷却单元和供暖单元。接下来介绍智能CAN节点的工作原理。 在实际操作中，我们划分控制的目标进入一些单元，储存智能CAN节点在一些典型的单元。在每个节点，单片机借助A / D转换单位从温度测量传感器收集温度数据。同时，它执行基本的数据融合运算获得运算的结果,更接近实际。数字显示器及时显示融合节点的结果，所以我们能及时了解在每个控制单元所处的环境温
5、度。 通过比较融合值用主控制器构建一个，这样智能CAN节点可以通过相应的加热或冷却装置实现反馈控制各单元。如果在特别的智能CAN节点融合结果大于设定值，冷却单位将开始工作。相反,如果在节点融合的结果低于设定值加热单位将开始工作。用这种方法,我们不仅能监控环境温度,还能做相应的触发器来实现温度的自动调节。与此同时，每个CAN节点发送数据帧到CAN总线，CAN总线将告知在着单元中的主控制器这温度值，那么这控制器能便利的作出是否修改这参数的决定。自从这CAN节点有调节温度的单元在，整个房间的温度将保持均匀。更重要的是,我们也可以通过在主pc上修改温度的设定值来控制这智能节点。 一般来说,处理器不擅长
6、即时的复杂的数据处理和数据融合,所以如何选择合适的数据融合算法对系统变得至关重要。后一节中,我们将介绍适合于智能CAN节点的数据融合方法。4.多传感器数据融合 旨在利用数据融合在分布式温度控制系统中来消除不确定性,获得更精确、可靠是比从限定的传感器的测量数据的算数平均值更重要。当一些传感器的温度传感器变为无效的，这智能CAN节点还可以通过熔断这些信息而从有用的传感器获得精确温度。4.1实测数据的一致性核实 在我们设计的分布式温度控制系统的温度测量的过程中,突发性干扰或设备故障的影响不可避免的产生测量误差。所以在数据融合前我们应该消除错误的误差。 我们可以利用系统中配备的少量传感器用散点图发消除
7、这个测量误差。用参数来代表数据分布结构包括中值TM,上四位数 Fv,下四位数FL和分散四位数dF. 人们认为每个传感器在温度控制系统的温度测量所得独立。在系统中，有八个传感器在各智能CAN节点的温度传感器群。所以我们在每个CAN节点同一时刻能获得8个温度值。我们安排收集到的温度数据序列由小到大:T1, T2, , T8 在序列中，T1是最低位而T8是最高位。我们定义TM为： 上四位数Fv是区间TM, T8的中值，低四位数 Fl是区间T1, TM的中值，这四位数的离散是：。 该公式,一个是常数,取决于系统测量误差, 通常值是0.5,1.0,2.0等等。在数列中其余的测量值都被看作是于有效值一致的
8、。在智能CAN节点的单片机智将把一致的测量值融合。5. 温度测量的数据融合的举例 分布式温度控制系统运用于一间温室, 我们从8个温度传感器获得一组8个温度值如下八个温度测量值的结果把在这温室中的八个温度的平均值和真实的温度值做比较，我们可以知道测量误差是+ 0.5。之后在介绍这个方法前我们消除从这第五个传感器的测量误差，我们能得到的剩余的七个数据的平均值(7)T = 29.6, 测量误差是-0.4.这剩下的七个传感器被分成两个传感器组，S1, S3, S7 是第一组，S2, S4, S6, S8 是第二组。两组测量数据的算术平均和标准偏差分别如下： 根据公式(13), 我们可以用七个测量温度确
9、定温度融合值。融合温度的结果的误差是-0.3。 很明显,数据融合测量结果比算术的平均值更接近于实际值。在实际操作中,测量温度可能是很分散的变得更大的监测区域,数据融合将更加明显提高了测量精度。6.总结 这基于多数据融合传感器的分布式温度控制系统是通过CAN总线构建。它充分利用了FDCS即时总线控制系统的特点。数据采集，数据融合，系统控制用智能CAN节点得到实现，而系统管理通过主控制器（host PC）被实现。通过使用CAN总线与数据融合技术系统的可靠性和实时的能力被大大提高了。我们确定它在将来会得到广泛的应用。DISTRIBUTED TEMPERATURE CONTROL SYSTEM BAS
10、ED ON MULTI-SENSOR DATA FUSIONAbstract: Temperature control system has been widely used over the past decades. In this paper, a general architecture of distributed temperature control system is put forward based on multi-sensor data fusion and CAN bus. A new method of multi-sensor data fusion based
11、on parameter estimation is proposed for the distributed temperature control system. The major feature of the system is its generality, which is suitable for many fields of large scale temperature control. Experiment shows that this system possesses higher accuracy, reliability, good realtime charact
12、eristic and wide application prospectKeywords: Distributed control system; CAN bus; intelligent CAN node; multi-sensor data fusion.1. Introduction Distributed temperature control system has been widely used in our daily life and production, including intelligent building, greenhouse, constant temper
13、ature workshop, large and medium granary, depot, and so on1. This kind of system should ensure that the environment temperature can be kept between two predefined limits. In the conventional temperature measurement systems we build a network through RS-485 Bus using a single-chip metering system bas
14、ed on temperature sensors. With the aid of the network, we can carry out centralized monitoring and controlling. However, when the monitoring area is much more widespread and transmission distance becomes farther, the disadvantages of RS-485 Bus become more obvious. In this situation, the transmissi
15、on and response speed becomes lower, the anti-interference ability becomes worse. Therefore, we should seek out a new communication method to solve the problems produced by RS-485 Bus.During all the communication manners, the industrial control-oriented field bus technology can ensure that we can br
16、eak through the limitation of traditional point to point communication mode and build up a real distributed control and centralized management system. As a serial communication protocol supporting distributed real-time control, CAN bus has much more merits than RS-485 Bus, such as better error corre
17、ction ability, better real-time ability, lower cost and so on. Presently, it has been extensively used in the implementation of distributed measurement and control domains. With the development of sensory technology, more and more systems begin to adopt multi-sensor data fusion technology to improve
18、 their performances. Multi-sensor data fusion is a kind of paradigm for integrating the data from multiple sources to synthesize the new information so that the whole is greater than the sum of its parts 345. And it is a critical task both in the contemporary and future systems which have distribute
19、d networks of low-cost, resource-constrained sensors2. Distributed architecture of the temperature control system The distributed architecture of the temperature control system is depicted in the Figure 1. As can be seen, the system consists of two modulesseveral intelligent CAN nodes and a main con
20、troller. They are interconnected with each other through CAN bus. Each module performs its part into the distributed architecture. The following is a brief description of each module in the architecture. 31main controllerAs the systems main controller, the host PC can communicate with the intelligen
21、t CAN nodes. It is devoted to supervise and control the whole system, such as system configuration, displaying running condition, parameter initialization and harmonizing the relationships between each part. Whats more, we can print or store the systems history temperature data, which is very useful
22、 for the analysis of the system performance3.2. Intelligent CAN node Each intelligent CAN node of the temperature control system includes five units: MCUa single chip, A/D conversion unit, temperature monitoring unitsensor group, digital display unit and actuatorsa cooling unit and a heating unit. T
23、he operating principle of the intelligent CAN node is described as follows. In the practical application, we divide the region of the control objective into many cells, and lay the intelligent CAN nodes in some of the typical cells. In each node, MCU collects temperature data from the temperature me
24、asurement sensor groups with the aid of the A/D conversion unit. Simultaneously, it performs basic data fusion algorithms to obtain a fusion value which is more close to the real one. And the digital display unit displays the fusing result of the node timely, so we can understand the environment tem
25、perature in every control cell separately. By comparing the fusion value with the set one by the main controller, the intelligent CAN node can implement the degenerative feedback control of each cell through enabling the corresponding heating or cooling devices. If the fusion result is bigger than t
26、he set value in the special intelligent CAN node, the cooling unit will begin to work. On the contrary, if the fusion result is less than the set value in the node the heating unit will begin to work. By this means we can not only monitor the environment temperature, but also can make the correspond
27、ing actuator work so as to regulate the temperature automatically. At the same time every CAN node is able to send data frame to the CAN bus which will notify the main controller the temperature value in the cell so that controller can conveniently make decisions to modify the parameter or not. Sinc
28、e the CAN nodes can regulate the temperature of the cell where they are, the temperature in the whole room will be kept homogeneous. Whats more, we can also control the intelligent node by modifying the temperatures setting value on the host PC.Generally, the processors on the spot are not good at c
29、omplex data processing and data fusing, so it becomes very critical how to choose a suitable data fusion algorithm for the system. In the posterior section, we will introduce a data fusion method which is suitable for the intelligent CAN nodes。4. Multi-sensor data fusion The aim to use data fusion i
30、n the distributed temperature control system is to eliminate the uncertainty, gain a more precise and reliable value than the arithmetical mean of the measured data from finite sensors. Furthermore, when some of the sensors become invalid in the temperature sensor groups, the intelligent CAN node ca
31、n still obtain the accurate temperature value by fusing the information from the other valid sensors. 4.1. Consistency verification of the measured data During the process of temperature measurement in our designed distributed temperature control system, measurement error comes into being inevitably
32、 because of the influence of the paroxysmal disturb or the equipment fault. So we should eliminate the careless mistake before data fusion. We can eliminate the measurement errors by using scatter diagram method in the system equipped with little amount of sensors. Parameters to represent the data d
33、istribution structure include medianTM, upper quartile numberFv, lower quartile numberFL and quartile dispersiondF. It is supposed that each sensor in the temperature control system proceeds temperature measurement independently. In the system, there are eight sensors in each temperature sensor grou
34、p of the intelligent CAN node. So we can obtain eight temperature values in each CAN node at the same time. We arrange the collected temperature data in a sequence from small to large: T1, T2, , T8 In the sequence, T1 is the limit inferior and T8 is the limit superior. We define the medianTM as: (1)
35、 The upper quartileFv is the median of the interval TM, T8.The lower quartile numberFL is the median of the interval T1, TM.The dispersion of the quartile is: （2）We suppose that the data is an aberration one if the distance from the median is greater than adF, that is, the estimation interval of inv
36、alid data is: (3) In the formula, a is a constant, which is dependent on the system measurement error, commonly its value is to be 0.5, 1.0, 2.0 and so on. The rest values in the measurement column are considered as to be the valid ones with consistency. And the Single-Chip in the intelligent CAN no
37、de will fuse the consistent measurement value to obtain a fusion result 5. Temperature measurement data fusion experiment By applying the distributed temperature control system to a greenhouse, we obtain an array of eight temperature values from eight sensors as followsThe mean value of the eight me
38、asurement temperature result isComparing the mean value (8)T with the true temperature value in the cell of the greenhouse, we can know that the measurement error is +0.5. After we eliminate the careless error from the fifth sensor using the method introduced before, we can obtain the mean value of
39、the rest seven data (7)T=29.6, the measurement error is -0.4. The seven rest consistent sensor can be divided into two groups with sensor S1, S3, S7 in the first group and sensor S2, S4, S6, S8 in the second one. The arithmetical mean of the two groups of measured data and the standard deviation are
40、 as follows respectively:According to formula (13), we can educe the temperature fusion value with the seven measured temperature value. The error of the fusion temperature result is -0.3. It is obvious that the measurement result from data fusion is more close to the true value than that from arith
41、metical mean. In the practical application, the measured temperature value may be very dispersive as the monitoring area becomes bigger, data fusion will improve the measuring precision much more obviously.6. Conclusions The distributed temperature control system based on multi-sensor data fusion is
42、 constructed through CAN bus. It takes full advantage of the characteristics of field bus control system-FDCS. Data acquisition, data fusion and system controlling is carried out in the intelligent CAN node, and system management is implemented in the main controller (host PC). By using CAN bus and
43、data fusion technology the reliability and real-time ability of the system is greatly improved. We are sure that it will be widely used in the future.References 1 Waltz E. Liinas J, Multi-sensor Data Fusion, Artech House, New York, 1990. 2 Philips Semiconductors, (1995b). “P82C150: CAN serial linked
44、 I/O device (SLIO) with digital and analog port functions”, preliminary Data Sheet, October 1995. 3 Aslam, J., Li, Q., Rus, D., Three power-aware routing algorithms for sensor networks, Wireless Communications and Mobile Computing, pp.187208, 2003. 4 R.C.Luo, M.G.Kay, Multisensor Integration and Fus
45、ion in Intelligent Systems, IEEE Trans. on Systems, Man, and Cybernetics, Vol. 19, No. 5, pp.901-931 September/October, 1989. 5 Pau LF, Sensors data fusion, Journal of Intelligent and Robotic System, pp. 103-106, 1998. 6 Thomopoulos S C., Sensor integration and data fusion, Journal of Robotic System
46、s, pp.337-372, 1990. 7 Rao B S Y, Durrant-Whyte H F, Sheen J A, A fully decentralized multi-sensor system for tracking and surveillance, The International Journal of Robotics Research, Massachusetts Institute of Technology, Vol 12, No. 1, pp. 20-44, Feb 1993. 8 Tenney R R, Jr sandell N R, Detection
47、with distributed sensors, AES, Vol 17, pp.501-510, 1981 五分钟搞定5000字毕业论文外文翻译，你想要的工具都在这里!在科研过程中阅读翻译外文文献是一个非常重要的环节，许多领域高水平的文献都是外文文献，借鉴一些外文文献翻译的经验是非常必要的。由于特殊原因我翻译外文文献的机会比较多，慢慢地就发现了外文文献翻译过程中的三大利器：Google“翻译”频道、金山词霸（完整版本）和CNKI“翻译助手。具体操作过程如下： 1.先打开金山词霸自动取词功能，然后阅读文献； 2.遇到无法理解的长句时，可以交给Google处理，处理后的结果猛一看，不堪入目，可
48、是经过大脑的再处理后句子的意思基本就明了了； 3.如果通过Google仍然无法理解，感觉就是不同，那肯定是对其中某个“常用单词”理解有误，因为某些单词看似很简单，但是在文献中有特殊的意思，这时就可以通过CNKI的“翻译助手”来查询相关单词的意思，由于CNKI的单词意思都是来源与大量的文献，所以它的吻合率很高。 另外，在翻译过程中最好以“段落”或者“长句”作为翻译的基本单位，这样才不会造成“只见树木，不见森林”的误导。四大工具： 1、Google翻译： google，众所周知，谷歌里面的英文文献和资料还算是比较详实的。我利用它是这样的。一方面可以用它查询英文论文，当然这方面的帖子很多，大家可以搜索，在此不赘述。回到我自己说的翻译上来。下面给大家举个例子来说明如何用吧比如说“电磁感应透明效应”这个词汇你不知道他怎么翻译，首先你可以在CNKI里查中文的，根据它们的关键词中英文对照来做，一般比较准确。 在此主