CN1963878A - Intelligence inspection prewarning forecasting apparatus for fire of high-rise building - Google Patents

Intelligence inspection prewarning forecasting apparatus for fire of high-rise building Download PDF

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CN1963878A
CN1963878A CN 200610123788 CN200610123788A CN1963878A CN 1963878 A CN1963878 A CN 1963878A CN 200610123788 CN200610123788 CN 200610123788 CN 200610123788 A CN200610123788 A CN 200610123788A CN 1963878 A CN1963878 A CN 1963878A
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张小英
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South China University of Technology SCUT
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Abstract

本发明公开了一种高层建筑火灾智能监测预警预报装置,包括火灾信号探测器、信号传递装置,数据管理器、火灾报警控制器和报警设备,所述数据管理器的一端通过模数转换器与火灾信号探测器连接,另一端通过信号传递装置与火灾报警控制器连接,所述火灾报警控制器通过数模转换器与报警设备连接。本发明装置基于高层建筑火灾的非线性动力学机理开发,根据现场探测温度、气体流速、烟气浓度、压力等信号对火灾的复杂性和不确定性进行定量计算,分析当前火情以及火灾发展趋势,有较高的可靠性和科学性。

The invention discloses a high-rise building fire intelligent monitoring, early warning and forecasting device, which includes a fire signal detector, a signal transmission device, a data manager, a fire alarm controller and an alarm device. One end of the data manager is connected with an analog-to-digital converter. The fire signal detector is connected, and the other end is connected with the fire alarm controller through the signal transmission device, and the fire alarm controller is connected with the alarm equipment through the digital-to-analog converter. The device of the invention is developed based on the nonlinear dynamic mechanism of high-rise building fires, and quantitatively calculates the complexity and uncertainty of fires based on signals such as on-site detection temperature, gas flow velocity, smoke concentration, and pressure, and analyzes the current fire situation and fire development. Trend, high reliability and scientific.

Description

高层建筑火灾智能监测预警预报装置High-rise building fire intelligent monitoring, early warning and forecasting device

技术领域technical field

本发明涉及一种火灾监测预警预报装置,具体是指一种具有自学习功能的高层建筑火灾智能监测预警预报装置。The invention relates to a fire monitoring, early warning and forecasting device, in particular to a high-rise building fire intelligent monitoring, early warning and forecasting device with a self-learning function.

背景技术Background technique

随着城市经济建设的迅速发展,人民生活水平的不断提高以及其它各项事业的兴旺发达,城市用地日益紧张,促进建筑物正朝着高层化、密集化方向发展,建筑物的装修用料和方式也日趋多样化,随着用电负荷及煤气耗量的加大,对火灾自动报警系统设计提出了更高、更严格的要求。高层建筑的火灾有:火势蔓延快、人员疏散困难、扑救难度大、火险隐患多的特点,因此极具危害性。为确保人民生命财产的安全,火灾自动报警系统设计就成为高层建筑设计中最重要的内容之一。使用优质先进的火灾监测预警预报装置,准确判断起火部位,以及准确预测火源是否会发展成火灾,并对火灾级别和发展程度进行预测,便于进行及时的扑救,对确保高层建筑运营安全尤其重要。With the rapid development of urban economic construction, the continuous improvement of people's living standards and the prosperity of other undertakings, the urban land is becoming more and more tense, and the buildings are being developed in the direction of high-rise and densification. The methods are also becoming more and more diversified. With the increase of electricity load and gas consumption, higher and stricter requirements are put forward for the design of automatic fire alarm system. Fires in high-rise buildings have the characteristics of rapid fire spread, difficult evacuation of personnel, difficulty in fighting and rescue, and many fire hazards, so they are extremely harmful. In order to ensure the safety of people's life and property, the design of automatic fire alarm system has become one of the most important contents in the design of high-rise buildings. Use high-quality and advanced fire monitoring, early warning and forecasting devices to accurately judge the location of the fire, and accurately predict whether the fire source will develop into a fire, and predict the level and development of the fire, so as to facilitate timely rescue, which is especially important to ensure the safety of high-rise buildings. .

火灾是一种在时空上失控的燃烧现象,当前要提高高层建筑火灾预警的准确性和及时性,最缺乏的是对火灾这一复杂物体现象的机理研究,和对火灾发生、发展作出准确、及时判断的方法。对于普通可燃物质燃烧的表现形式来看,首先是产生燃烧气体,然后是释放烟雾,在氧气供应充分的条件下,才能达到燃烧,产生火焰,并散发出大量的热,使环境温度升高。因此火情发展在多数情况下,总是在初起和阴燃阶段所占的时间比较长,此时火灾的破坏性未达到最大,若能及早对火灾进行预警和控制,就可有效地避免严重灾情的发生。Fire is a burning phenomenon that is out of control in time and space. At present, to improve the accuracy and timeliness of fire warning in high-rise buildings, what is most lacking is the mechanism research of fire, a complex object phenomenon, and the accurate and accurate prediction of fire occurrence and development. method of judging in time. For the manifestations of the combustion of ordinary combustible substances, firstly, combustion gas is generated, and then smoke is released. Only under the condition of sufficient oxygen supply can combustion be achieved, flame is generated, and a large amount of heat is emitted, which raises the ambient temperature. Therefore, in most cases, the fire development always takes a long time in the initial stage and the smoldering stage. At this time, the destructiveness of the fire has not reached the maximum. occurrence of serious disasters.

在目前的高层建筑火灾监测预警预报装置中,通常采用单一的感烟型传感器、感温光缆或CCD(计算机控制显示摄像机),其缺点是:对高层建筑结构和火灾特点针对性不强,监测手段单一,可靠性差,如感烟型传感器无法探测酒精燃烧产生的火焰,感温传感器则不易发现阴燃火,CCD摄像机无法辨别移动高温物体与火灾的差别,从而可能产生漏报警;同时,现有的感光、感烟、感温型探测技术,只能探测火焰或者是火灾发生在某些探测区域内,而无法确定火灾发生的确切部位,另外由于环境的干扰,还常常出现漏检、误报的情况。In the current high-rise building fire monitoring, early warning and forecasting devices, a single smoke sensor, temperature-sensitive optical cable or CCD (computer-controlled display camera) is usually used. The means are single and the reliability is poor. For example, the smoke sensor cannot detect the flame produced by the burning of alcohol, the temperature sensor is difficult to detect the smoldering fire, and the CCD camera cannot distinguish the difference between the moving high-temperature object and the fire, which may cause a missed alarm; at the same time, the current Some light-sensitive, smoke-sensitive, and temperature-sensitive detection technologies can only detect flames or fires in certain detection areas, but cannot determine the exact location of the fire. In addition, due to environmental interference, missed detections and false detections often occur. reported situation.

发明内容Contents of the invention

本发明的目的是克服现有高层建筑火灾监测预警预报装置存在的缺陷与不足,提供一种基于人工智能和模糊控制技术,具有自学习功能的、准确、高效的高层建筑火灾智能监测预警预报装置。The purpose of the present invention is to overcome the defects and deficiencies existing in the existing high-rise building fire monitoring, early warning and forecasting devices, and provide an accurate and efficient high-rise building fire intelligent monitoring, early warning and forecasting device based on artificial intelligence and fuzzy control technology, with self-learning function .

一种高层建筑火灾智能监测预警预报装置,包括火灾信号探测器、信号传递装置,数据管理器、火灾报警控制器和报警设备,所述数据管理器的一端通过模数转换器与火灾信号探测器连接,另一端通过信号传递装置与火灾报警控制器连接,所述火灾报警控制器通过数模转换器与报警设备连接。A high-rise building fire intelligent monitoring, early warning and forecasting device includes a fire signal detector, a signal transmission device, a data manager, a fire alarm controller and an alarm device, and one end of the data manager communicates with the fire signal detector through an analog-to-digital converter. The other end is connected to the fire alarm controller through a signal transmission device, and the fire alarm controller is connected to the alarm equipment through a digital-to-analog converter.

所述数据管理器为计算机,包括数据库、数据处理器两部分;所述数据管理器与高层建筑系统通风换热测量装置连接;所述火灾信号探测器,为离子感烟、气体传感器和/或温度传感器;所述火灾报警控制器还包括手动报警按钮;所述报警设备包括消防联动、消防广播、火警电话、应急照明和报警记录设备。The data manager is a computer, including two parts of a database and a data processor; the data manager is connected to a high-rise building system ventilation heat transfer measurement device; the fire signal detector is an ion smoke sensor, a gas sensor and/or temperature sensor; the fire alarm controller also includes a manual alarm button; the alarm equipment includes fire linkage, fire broadcast, fire alarm telephone, emergency lighting and alarm recording equipment.

本发明与现有建筑火灾的预警技术相比,具有如下优点和效果:Compared with the early warning technology of existing building fires, the present invention has the following advantages and effects:

(1)数据传递采用多重优先级网络通言技术,任何一个节点机下的新的火灾报警信号总是拥有最高优先权,保证其先于其他事件信号被传送到消防控制系统,结合计算机并行处理技术,保证系统的反应时间短,运行速度快;(1) Data transmission adopts multi-priority network communication technology. The new fire alarm signal under any node machine always has the highest priority to ensure that it is transmitted to the fire control system before other event signals, combined with computer parallel processing technology to ensure short response time and fast running speed of the system;

(2)运用模糊神经网络技术预测高层建筑火灾发生的火警级别,将预测结果返回系统,利用神经网络的自学习能力,不断修正样本集和判别规则,实现系统的高容错性和智能化;(2) Use fuzzy neural network technology to predict the fire alarm level of high-rise building fires, return the prediction results to the system, use the self-learning ability of the neural network, and continuously modify the sample set and discrimination rules to achieve high fault tolerance and intelligence of the system;

(3)引入非线性动力学对高层建筑火灾机理进行基础理论,建立不同结构特点中的高层建筑火灾模型,根据现场探测温度、气体流速、烟气浓度、压力等信号对火灾的复杂性和不确定性进行定量计算,分析当前火情以及火灾发展趋势,为救援和灭火工作提供强有力的理论基础,大大增强预警系统的可靠性和科学性。(3) Introduce nonlinear dynamics to carry out basic theory on the fire mechanism of high-rise buildings, establish fire models of high-rise buildings with different structural characteristics, and analyze the complexity and variability of fires according to the signals such as on-site detection temperature, gas flow rate, smoke concentration, and pressure. Quantitative calculation with determinism, analysis of the current fire situation and fire development trend, provide a strong theoretical basis for rescue and fire fighting work, and greatly enhance the reliability and scientificity of the early warning system.

附图说明Description of drawings

图1是本发明的结构示意图;Fig. 1 is a structural representation of the present invention;

图2是图1所示数据处理器对火灾灾情判断的原理图;Fig. 2 is the schematic diagram of judging the fire disaster situation by the data processor shown in Fig. 1;

图3本发明判断火灾是否发生和定位的原理图;Fig. 3 present invention judges the principle diagram of whether fire takes place and locates;

图4本发明预测灾情发展情况的非线性处理过程示意图。Fig. 4 is a schematic diagram of the non-linear processing process for predicting the development of disaster situation in the present invention.

具体实施方式Detailed ways

下面结合实施例和附图对本发明作进一步详细的描述。The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings.

如图1所示,本发明高层建筑火灾智能监测预警预报装置,包括火灾信号探测器1、火灾信号探测器2、模数转换器3、数据管理器4、监视器5、信号传递装置6、火灾报警控制器7、数模转换器8、报警设备9。As shown in Figure 1, the high-rise building fire intelligent monitoring, early warning and forecasting device of the present invention includes a fire signal detector 1, a fire signal detector 2, an analog-to-digital converter 3, a data manager 4, a monitor 5, a signal transmission device 6, Fire alarm controller 7, digital-to-analog converter 8, and alarm equipment 9.

数据管理器4通过计算机来实现,其由数据处理器4-1、数据库4-2两部分相互连接组成;火灾信号探测器1、2可以是离子感烟、气体传感器、温度传感器或火焰光探测器,也可以是离子感烟、气体传感器或温度传感器和火焰光探测器的结合;数据处理器4-1通过模数转换器3与火灾信号探测器1、2连接,数据处理器4-1与监视器5、高层建筑系统通风换热测量装置10分别连接;数据处理器4-1还通过信号传递装置6与火灾报警控制器7连接,火灾报警控制器7通过数模转换器8与报警设备9连接,火灾报警控制器7设有手动报警按钮11,报警设备9包括高层建筑建筑中已经装备的消防联动9-1、消防广播9-2、火警电话9-3、应急照明9-4、报警记录9-5等设备。火灾信号探测器可以采用HST8110智能光电感烟探测器、HST8120智能感温控测器、HST8130智能感烟感温复合控测器、HST8140智能感烟感温CO复合控测器。The data manager 4 is realized by a computer, and it is composed of a data processor 4-1 and a database 4-2 connected to each other; the fire signal detectors 1 and 2 can be ion smoke detectors, gas sensors, temperature sensors or flame light detectors It can also be a combination of ion smoke sensor, gas sensor or temperature sensor and flame light detector; the data processor 4-1 is connected with the fire signal detectors 1 and 2 through the analog-to-digital converter 3, and the data processor 4-1 It is respectively connected with the monitor 5 and the ventilation and heat exchange measuring device 10 of the high-rise building system; the data processor 4-1 is also connected with the fire alarm controller 7 through the signal transmission device 6, and the fire alarm controller 7 communicates with the alarm through the digital-to-analog converter 8 The equipment 9 is connected, the fire alarm controller 7 is provided with a manual alarm button 11, and the alarm equipment 9 includes the equipped fire-fighting linkage 9-1, fire-fighting broadcast 9-2, fire alarm telephone 9-3, and emergency lighting 9-4 in high-rise buildings. , Alarm record 9-5 and other equipment. The fire signal detector can use HST8110 intelligent photoelectric smoke detector, HST8120 intelligent temperature sensor, HST8130 intelligent smoke and temperature composite controller, HST8140 intelligent smoke and temperature CO composite controller.

本发明采用模糊神经网络,利用探测器、调研、实验、理论计算得到的火灾信号组成训练样本集,用模糊神经网络技术进行训练,形成具有高容错性、复杂模式分类和辩识的火灾智能预警装置。如图2所示,本发明设计的火灾探测神经网络,其构成包括三个输入神经元、五个隐藏神经元和三个输出神经元。左边的三项IN1、IN2、IN3为输入层,在实际应用中火灾探测器发出的烟雾、温度及气体三个信号被转化归一到[0,1],再传递给输入层;右边三项OT1,OT2,OT3为输出层,分别表示火灾概率,火险概率、阴燃火概率,输出值范围也是[0,1];输入和输出层之间的IM1~IM5为隐藏层,输入信号通过隐藏层后再送到输出层。在IN(I)与IM(J)与OT(K)之间各有15条连接弧,其权值分别为Wij,Vjk。从输入层到中间层的总和定义为NET1(j):The present invention adopts the fuzzy neural network, uses the fire signals obtained by detectors, investigations, experiments and theoretical calculations to form a training sample set, and uses fuzzy neural network technology for training to form an intelligent early warning of fire with high fault tolerance, complex pattern classification and identification device. As shown in Figure 2, the fire detection neural network designed by the present invention comprises three input neurons, five hidden neurons and three output neurons. The three items IN1, IN2, and IN3 on the left are the input layer. In practical applications, the three signals of smoke, temperature and gas emitted by the fire detector are converted and normalized to [0, 1], and then passed to the input layer; the three items on the right OT1, OT2, and OT3 are the output layers, which respectively represent the fire probability, fire danger probability, and smoldering fire probability, and the output value range is also [0, 1]; IM1~IM5 between the input and output layers is the hidden layer, and the input signal passes through the hidden layer and then sent to the output layer. There are 15 connecting arcs between IN(I) and IM(J) and OT(K), and their weights are Wij and Vjk respectively. The sum from the input layer to the middle layer is defined as NET1(j):

NETNET 11 (( jj )) == ΣΣ Mm (( INiINi ·&Center Dot; WW ijij ))

NET1(j)时值即隐含层的输出用Sigmoid函数转换到[0,1];The time value of NET1(j), that is, the output of the hidden layer, is converted to [0, 1] with the Sigmoid function;

IMj = 1 1 + exp [ - NET 1 ( j ) · r 1 ] 类似有中间层到输出层的总和定义为NET2(k): IM = 1 1 + exp [ - NET 1 ( j ) &Center Dot; r 1 ] Similarly, the sum from the intermediate layer to the output layer is defined as NET2(k):

NETNET 22 (( kk )) == ΣΣ jj == 11 NN (( IMjIM ·· VV jkjk ))

同样,NET2(k)也被转换到[0,1]:Likewise, NET2(k) is also converted to [0, 1]:

OTKOTK == 11 11 ++ expexp [[ -- NRTNRT 22 (( kk )) ·&Center Dot; rr 22 ]]

r1和r2的作用是修正Sigmoid函数曲线倾斜度的系数,通常分别取为1.0和1.2。在该火灾探测系统中,使用一个12种模式的学习定义表,如下表所示。The role of r1 and r2 is to correct the coefficient of the slope of the Sigmoid function curve, usually taken as 1.0 and 1.2 respectively. In this fire detection system, a 12-mode learning definition table is used, as shown in the table below.

表自学习定义Table self-learning definition

  编号 serial number     输入 input     输出 output     感烟探测器   Smoke detector     感温探测器  Heat detector     气体探测器   Gas Detector     火灾概率 Fire Probability     火险概率 Fire probability     阴燃火概率 Smoldering fire probability     D D   R R     D D   R R     D D     R R     1 1     0.1 0.1     0 0     1 1     0.7 0.7   0.661 0.661     0.6 0.6   0.702 0.702     0.9 0.9     0.802 0.802     2 2     0.3 0.3     0.5 0.5     1 1     0.9 0.9   0.885 0.885     0.9 0.9   0.889 0.889     0.1 0.1     0.037 0.037     3 3     0.1 0.1     0 0     0.2 0.2     0.3 0.3   0.254 0.254     0.2 0.2   0.187 0.187     0.4 0.4     0.289 0.289     4 4     0.5 0.5     0.1 0.1     0.8 0.8     0.8 0.8   0.829 0.829     0.8 0.8   0.786 0.786     0.7 0.7     0.722 0.722     5 5     0 0     0.3 0.3     0.1 0.1     0.1 0.1   0.094 0.094     0.1 0.1   0.098 0.098     0.1 0.1     0 0     6 6     0 0     0 0     1 1     0.4 0.4   0.453 0.453     0.7 0.7   0.588 0.588     0.3 0.3     0.376 0.376     7 7     0 0     1 1     0 0     0.2 0.2   0.190 0.190     0.3 0.3   0.307 0.307     0.05 0.05     0 0     8 8     0.3 0.3     0.2 0.2     0.5 0.5     0.7 0.7   0.781 0.781     0.6 0.6   0.701 0.701     0.3 0.3     0.247 0.247     9 9     0.6 0.6     0.8 0.8     0.8 0.8     0.95 0.95   0.902 0.902     0.95 0.95   0.904 0.904     0.05 0.05     0.073 0.073     10 10     0.2 0.2     0 0     0.3 0.3     0.6 0.6   0.542 0.542     0.4 0.4   0.431 0.431     0.75 0.75     0.756 0.756     11 11     0.1 0.1     0 0     0.1 0.1     0.1 0.1   0.189 0.189     0.05 0.05   0.119 0.119     0.1 0.1     0.205 0.205     12 12     0.4 0.4     0.2 0.2     0 0     0.7 0.7   0.714 0.714     0.65 0.65   0.529 0.529     0.2 0.2     0.260 0.260

这样,第m个输入模式的平方误差Em和12种模式的平方误差总和E可表示为:In this way, the square error E m of the mth input mode and the sum E of the square errors of the 12 modes can be expressed as:

EE. mm == ΣΣ kk == 11 33 11 22 (( Oo TT kk -- TT kk )) 22

E = Σ m = 1 12 ( E m ) and E. = Σ m = 1 12 ( E. m )

调节权值Wij,Vjk使E达到最小,即完成了神经网络学习过程。确定权值后,神经网络输入层开始接收探测器的电位值,按照上述方法对输出值进行计算,将计算出的数值分别与火灾概率、火险概率即阴燃火概率进行比较,最后作出是否发生火灾的判断。如图3所示,火灾信号(如烟、温等)探测数值经数模转换器3转换,进入数据处理器4-1的模糊神经网络计算模块4-1-1,进行火情大小的模糊识别,识别结果输出是否会发生火灾的判断信号B(0或者1)。Adjust the weight Wij, Vjk to make E reach the minimum, that is, the neural network learning process is completed. After determining the weight value, the neural network input layer starts to receive the potential value of the detector, calculates the output value according to the above method, compares the calculated value with the fire probability, the fire danger probability, that is, the smoldering fire probability, and finally decides whether or not it occurs Judgment of fire. As shown in Figure 3, the detected values of fire signals (such as smoke, temperature, etc.) are converted by the digital-to-analog converter 3, and enter the fuzzy neural network calculation module 4-1-1 of the data processor 4-1 to perform fuzzy calculation of the size of the fire. Recognition, the recognition result outputs a judgment signal B (0 or 1) whether a fire will occur.

高层建筑中气候、热流量、自然光等环境因素的变化会干扰监测仪器的判别结果,其中由于神经网络计算误差导致的判别输出信号A和B之间差异,本发明采用神经网络对人工监视判别结果的学习得到改进,实现自学习和自适应功能。Changes in environmental factors such as climate, heat flow, and natural light in high-rise buildings will interfere with the discrimination results of monitoring instruments. Wherein, due to the difference between the discrimination output signals A and B caused by neural network calculation errors, the present invention uses neural networks to analyze the discrimination results of manual monitoring. The learning is improved to realize self-learning and self-adaptive functions.

高层建筑火灾一方面是一个受多种因素影响的,复杂的非线性特征,火灾中热解、另一方面,尽管受到众多热灾害因素的影响,体现出复杂性,但是,在相似的环境和条件下,火灾的发生和发展过程却又能体现出相似的规律。高层建筑建筑通常可分为中庭、房间、楼梯间、电梯井这几类空间,各类空间中建筑材料、构造、空间特征、换热换气系统都按照其标准要求设计,从而同类空间具有相似的燃烧环境。基于对高层建筑建筑特点和燃烧过程的认识,本发明采用非线性模型和模式识别相结合的方法,对高层建筑中火灾的发展进行预测。依据高层建筑不同类型空间建筑特点,建立挥发分热解与燃烧模型、可燃物轰燃模型、火焰传播模型、火灾蔓延模型、烟气羽流的混沌模型以及烟气蔓延的耦合映象格子模型,从而构造出考虑各种相关因素的火灾预测模型。分别针对不同类型空间,对基础研究、实验室实验、现场调研以及数值模拟,积累起相关特点,构成火灾发展模式集,存储于火灾的基础数据,进行归类,存储于火灾数据库4-2中。On the one hand, high-rise building fire is a complex nonlinear feature affected by many factors. On the other hand, although it is affected by many thermal disaster factors, it shows complexity. However, in similar environments and Under the same conditions, the occurrence and development of fire can reflect similar laws. High-rise buildings can usually be divided into spaces such as atriums, rooms, stairwells, and elevator shafts. The building materials, structures, space features, and heat exchange and ventilation systems in various spaces are designed according to their standard requirements, so that similar spaces have similar characteristics. burning environment. Based on the understanding of the characteristics of high-rise buildings and the combustion process, the invention uses a method of combining nonlinear models and pattern recognition to predict the development of fires in high-rise buildings. According to the characteristics of different types of space buildings in high-rise buildings, the volatile pyrolysis and combustion model, the combustibles flashover model, the flame propagation model, the fire spread model, the chaos model of the smoke plume, and the coupled image lattice model of the smoke spread are established. Thus, a fire prediction model considering various related factors is constructed. According to different types of spaces, basic research, laboratory experiments, field investigations and numerical simulations are used to accumulate relevant characteristics to form a fire development model set, which is stored in the basic fire data, classified, and stored in the fire database 4-2 .

如图4所示,火灾信号探测器获得的模拟信号通过模数转换器3传输给数据处理器4-1的火灾发展预测模块4-1-3,高层建筑系统通风换热测量装置10测得的通风换热参数,也通过数据接口传输给火灾发展预测模块4-1-3。当数据处理器4-1探测到火灾发生时,通过对监测信号的寻址,获得火灾发生的具体位置,并将定位信号传输给火灾发展预测模块4-1-3。火灾发展预测模块4-1-3通过该定位信号,表征当前火灾发展程度的探测信号:温度、压力、烟气浓度、气体成份以及通风条件,在数据库4-2中查询该类型高层建筑空间火灾参照模式集,进行模式比较,分析当前火灾是属于阴燃、火灾初期或者是属于大火范畴。然后将这些探测信号传入相应的非线性预测计算模型,通过挥发分热解与燃烧模型、可燃物轰燃模型、火焰传播模型、火灾蔓延模型、烟气羽流的混沌模型以及烟气蔓延的耦合映象格子模型,对火灾的发展进行实时预测。模式比较和火灾发展的预测结果,以及数据库4-2事先存储的相应处理决策都显示在监视器5上。同时,当前火灾特征信号以及火灾的发展程度反馈给数据库4-2,补充参照模式集。As shown in Figure 4, the analog signal obtained by the fire signal detector is transmitted to the fire development prediction module 4-1-3 of the data processor 4-1 through the analog-to-digital converter 3, and the high-rise building system ventilation heat transfer measurement device 10 measures the The ventilation and heat transfer parameters are also transmitted to the fire development prediction module 4-1-3 through the data interface. When the data processor 4-1 detects a fire, it obtains the specific location of the fire by addressing the monitoring signal, and transmits the positioning signal to the fire development prediction module 4-1-3. The fire development prediction module 4-1-3 uses the positioning signal to represent the detection signal of the current fire development degree: temperature, pressure, smoke concentration, gas composition and ventilation conditions, and queries the space fire of this type of high-rise building in the database 4-2. Referring to the model set, carry out model comparison, and analyze whether the current fire belongs to smoldering, initial fire or belongs to the category of large fire. Then these detection signals are sent to the corresponding nonlinear prediction calculation model, through the volatile pyrolysis and combustion model, the combustibles flashover model, the flame propagation model, the fire spread model, the chaos model of the smoke plume, and the smoke spread model. Coupled mapping lattice model for real-time prediction of fire development. The model comparison and prediction results of fire development are displayed on the monitor 5, as well as the corresponding processing decisions stored in advance by the database 4-2. At the same time, the current fire characteristic signal and the development degree of the fire are fed back to the database 4-2 to supplement the reference model set.

由于火灾现象具有多变性,某些物理量受到环境其他因素的影响,瞬时值表现出一定的随机性,从而使实际探测到的信号难以和给出模式完全吻合,因此,本发明中模式比较采用了模糊识别的方法,通过待监测对象与已知模式的贴近度大小进行判断。Due to the variability of the fire phenomenon, some physical quantities are affected by other factors in the environment, and the instantaneous value shows a certain degree of randomness, so that the actual detected signal is difficult to completely match the given model. Therefore, the model comparison in the present invention uses The method of fuzzy recognition is judged by the closeness between the object to be monitored and the known pattern.

综上所述,本发明设计使用先进的火灾探测算法和火灾模式识别方法来判断火灾信息,是具有“较高智能”的智能化火灾探测报警系统,其工作原理为:In summary, the present invention is designed to use advanced fire detection algorithms and fire pattern recognition methods to judge fire information. It is an intelligent fire detection and alarm system with "higher intelligence". Its working principle is as follows:

将探测器现场探测和调查得到的温度、压力、烟气、气体成份等数据经模数转换器把信号输入高层建筑火灾数据管理器4,再将物理量信号变化过程经信号传递装置6传送给火灾报警控制器7,物理量信号变化过程与本系统建立的高层建筑火灾参照模式进行对比,对比之后得到对高层建筑火灾灾情的预测情况,做出火灾是否发生的判断,再根据这种判断决定是否给出火灾报警信号,判断有火警信号之后,将火警信号经数模转换器传递给报警设备9,消防机构收到火警后开展扑救工作。通过手动火灾报警按钮11人为地对火灾报警控制器7做出反应使警报有效地实施,对装置每一次所做出的火灾灾情判断和火警准确率经模数转换器反馈回高层建筑火灾数据管理器4,人工神经网络技术将反馈的数据进行自学习,不断修正样本集和判别规则,进而完善森林火灾参照模式;最后反馈回来的数据存放在火灾数据库4-2中,作为以后高层建筑火灾判别的基础数据。本设计运用火灾非线性机理、模糊理论和人工神经网络技术相结合建立起的高层建筑火灾数据库,具有自学习的能力和知识发现的过程,获得可靠的容错性高的训练样本集和判别规则,能不断自我补充和完善,有助于高层建筑火灾的机理研究和提高火警智能化的程度,大大提高高层建筑火灾火警的准确性、及时性和可靠性。The temperature, pressure, smoke, gas composition and other data obtained by the on-site detection and investigation of the detector are input into the high-rise building fire data manager 4 through the analog-to-digital converter, and then the physical quantity signal change process is transmitted to the fire department through the signal transmission device 6. The alarm controller 7 compares the change process of the physical quantity signal with the high-rise building fire reference model established by this system. A fire alarm signal is sent out, and after judging that there is a fire alarm signal, the fire alarm signal is transmitted to the alarm device 9 through a digital-to-analog converter, and the fire-fighting agency carries out rescue work after receiving the fire alarm. The manual fire alarm button 11 artificially responds to the fire alarm controller 7 to effectively implement the alarm, and the fire disaster judgment and fire alarm accuracy rate made by the device each time are fed back to the fire data management of high-rise buildings through the analog-to-digital converter. Device 4, the artificial neural network technology self-learns the feedback data, continuously revises the sample set and discrimination rules, and then improves the forest fire reference model; the finally fed back data is stored in the fire database 4-2, which will be used as a high-rise building fire discrimination in the future basic data. This design uses the fire nonlinear mechanism, fuzzy theory and artificial neural network technology to establish a high-rise building fire database. It has the ability of self-learning and the process of knowledge discovery, and obtains reliable training sample sets and discrimination rules with high fault tolerance. It can continuously supplement and improve itself, which is helpful to the mechanism research of high-rise building fire and the degree of intelligent fire alarm, and greatly improves the accuracy, timeliness and reliability of high-rise building fire alarm.

Claims (6)

1, a kind of intelligence inspection prewarning forecasting apparatus for fire of high-rise building, it is characterized in that comprising fire signal detector, apparatus for transmitting signal, data management system, fire alarm control unit and panalarm, one end of described data management system is connected with fire signal detector by analog to digital converter, the other end is connected with fire alarm control unit by apparatus for transmitting signal, and described fire alarm control unit is connected with panalarm by digital to analog converter.
2, a kind of intelligence inspection prewarning forecasting apparatus for fire of high-rise building according to claim 1, it is characterized in that: described data management system is a computing machine, comprises database, data processor two parts.
3, a kind of intelligence inspection prewarning forecasting apparatus for fire of high-rise building according to claim 1 and 2 is characterized in that: described data management system is connected with skyscraper system ventilation heat exchange measurement mechanism.
4, a kind of intelligence inspection prewarning forecasting apparatus for fire of high-rise building according to claim 3 is characterized in that: described fire signal detector is ionic smoke sensor, gas sensor and/or temperature sensor.
5, a kind of intelligence inspection prewarning forecasting apparatus for fire of high-rise building according to claim 4, it is characterized in that: described fire alarm control unit also comprises manual pull station.
6, a kind of intelligence inspection prewarning forecasting apparatus for fire of high-rise building according to claim 5 is characterized in that: described panalarm comprises fire-fighting link, fire broadcast, Fire telephone, emergency lighting and alarm logging equipment.
CN 200610123788 2006-11-27 2006-11-27 Intelligence inspection prewarning forecasting apparatus for fire of high-rise building Pending CN1963878A (en)

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