本文選題：智能消防管理系統(tǒng) + ZigBee無線網(wǎng)絡(luò)　； 參考：《電子科技大學(xué)》2014年碩士論文

【摘要】：校園是一個人口密集區(qū),而且宿舍是學(xué)生居住和活動的重要場所之一,人員具有很強的流動性和密集性,如果學(xué)生在宿舍違規(guī)使用大功率電器,就會電路的承載的功率過大,容易引發(fā)火災(zāi),一般情況下,火災(zāi)發(fā)生前,都會產(chǎn)生大量的煙霧,建立監(jiān)控宿舍煙霧系統(tǒng),能夠?qū)馂?zāi)的發(fā)生進行防范。在當(dāng)前階段,傳感技術(shù)、無線通信技術(shù)的飛速發(fā)展為我們的智能消防管理系統(tǒng)的更新和換代提供了堅實的技術(shù)保障,而本文的研究正是在這樣的技術(shù)背景之下,針對如何提高消防管理系統(tǒng)智能水平的問題進行了重點研究,以期望能夠降低誤報率和漏報率。在本文的研究中,首先對當(dāng)前階段國內(nèi)外無線監(jiān)測網(wǎng)絡(luò)的實際應(yīng)用情況進行了系統(tǒng)的分析,并梳理了主要的研究成果,選擇了適用于宿舍智能消防管理系統(tǒng)的ZigBee無線監(jiān)控網(wǎng)絡(luò),根據(jù)高校宿舍樓實際情況設(shè)計并構(gòu)建了無線智能消防管理系統(tǒng)通信網(wǎng)絡(luò),并根據(jù)具體的系統(tǒng)設(shè)計的要求,選擇合適的煙霧與火光控制進行煙霧控制管理。最終以BP神經(jīng)網(wǎng)絡(luò)算法作為火災(zāi)探測算法,該算法在實際的應(yīng)用過程中具有容錯性強、誤差率小、故障率低的特點,能夠?qū)Χ喾N不同類型的火災(zāi)數(shù)據(jù)進行及時而有效的處理,以此為基礎(chǔ)來判定火災(zāi)的實際情況。然后介紹了虛擬儀器的應(yīng)用情況,并對LabVIEW的特點進行了重點分析,本研究中應(yīng)用LabVIEW虛擬儀器開發(fā)了BP神經(jīng)網(wǎng)絡(luò)算法,除此之外也設(shè)計并實現(xiàn)了一套以神經(jīng)網(wǎng)絡(luò)算法為基礎(chǔ)的火災(zāi)自動報警虛擬系統(tǒng),并構(gòu)建了宿舍火災(zāi)識別模型,并且對其進行了仿真實驗和火災(zāi)模擬實驗,在完成整個實驗工作之后,其結(jié)果充分表明,該算法可以有效解決火災(zāi)探測靈敏度與誤報率之間的矛盾,達到了預(yù)期的效果。應(yīng)用LabVIEW開發(fā)的無線智能消防管理系統(tǒng)具有可靠性高、故障率低等特點,但是還是存在一些有待完善的地方,所以文章的最后對該系統(tǒng)提出了一些改善的方法,并對它的發(fā)展作出了展望。
[Abstract]:Campus is a densely populated area, and dormitories are one of the most important places for students to live and activities. The personnel are highly mobile and dense. If students violate the use of high-power electrical appliances in dormitories, they will carry too much power. It is easy to cause fire. Generally, a large amount of smoke will be produced before the fire, and the smoke system of monitoring dormitory can be established to prevent the fire. At the present stage, the rapid development of sensing technology and wireless communication technology has provided a solid technical guarantee for the renewal and replacement of our intelligent fire control management system, and the research of this paper is under such a technical background. This paper focuses on how to improve the intelligence level of fire control management system in order to reduce false alarm rate and false alarm rate. In the research of this paper, firstly, the actual application of wireless monitoring network at home and abroad at present stage is systematically analyzed, and the main research results are combed, and the ZigBee wireless monitoring network suitable for dormitory intelligent fire control management system is selected. According to the actual situation of university dormitory, the communication network of wireless intelligent fire control management system is designed and constructed. According to the requirements of system design, appropriate smoke and fire control are selected for smoke control and management. Finally, the BP neural network algorithm is used as the fire detection algorithm. The algorithm has the characteristics of strong fault tolerance, low error rate and low failure rate in the actual application process. It can deal with many different types of fire data in a timely and effective manner. This is the basis for judging the actual situation of the fire. Then the application of virtual instrument is introduced, and the characteristics of LabVIEW are analyzed. In this study, BP neural network algorithm is developed by using LabVIEW virtual instrument. In addition, a set of fire automatic alarm virtual system based on neural network algorithm is designed and implemented, and the fire identification model of dormitory is constructed, and the simulation experiment and fire simulation experiment are carried out. The experimental results show that the algorithm can effectively solve the contradiction between the sensitivity of fire detection and the false alarm rate and achieve the desired results. The wireless intelligent fire control management system developed by LabVIEW has the characteristics of high reliability and low failure rate, but there are still some problems to be improved. The prospect of its development is also given.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU892;TP311.52

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