本文選題：民航旅客信息系統(tǒng) + 軟件可靠性預(yù)測(cè)　； 參考：《中國(guó)民航大學(xué)》2017年碩士論文

【摘要】：民航旅客信息系統(tǒng)的7*24小時(shí)安全無故障運(yùn)行,關(guān)系到航空公司的切身利益和航空旅客的出行體驗(yàn),然而由于一系列的不可靠因素,國(guó)內(nèi)外的民航旅客信息系統(tǒng)故障可謂是時(shí)有發(fā)生。因此,如何準(zhǔn)確地評(píng)測(cè)系統(tǒng)的可靠性,維持系統(tǒng)的正常運(yùn)行,是一個(gè)非常迫切的任務(wù)。本文嘗試對(duì)大量的歷史民航旅客信息系統(tǒng)運(yùn)維監(jiān)控?cái)?shù)據(jù)進(jìn)行分析,采用關(guān)鍵字提取以及正則表達(dá)式語義解析的方法,提取出數(shù)據(jù)中的可靠性失效數(shù)據(jù),用于神經(jīng)網(wǎng)絡(luò)軟件可靠性建模,準(zhǔn)確預(yù)測(cè)失效數(shù)據(jù),旨在減少可靠性預(yù)測(cè)中的潛在不可知因素,提高預(yù)測(cè)的精度。其主要工作如下:首先,分析和研究了可靠性數(shù)據(jù)提取方法,并設(shè)計(jì)了一種基于正則表達(dá)式的民航旅客信息系統(tǒng)可靠性數(shù)據(jù)提取方法,實(shí)現(xiàn)了程序化提取。其次,建立了民航旅客信息系統(tǒng)的BP神經(jīng)網(wǎng)絡(luò)和Elman神經(jīng)網(wǎng)絡(luò)兩種可靠性模型,并利用建立的可靠性模型對(duì)民航旅客信息系統(tǒng)可靠性失效數(shù)據(jù)進(jìn)行預(yù)測(cè),驗(yàn)證了神經(jīng)網(wǎng)絡(luò)可靠性模型在民航旅客信息系統(tǒng)中的可行性,較好的解決了前期模型的系統(tǒng)適用性問題。再次,根據(jù)前兩種模型的實(shí)驗(yàn)驗(yàn)證,發(fā)現(xiàn)了多用戶量的系統(tǒng)軟件中存在的可靠性數(shù)據(jù)的隨機(jī)性和動(dòng)態(tài)性比較強(qiáng)的問題,并針對(duì)此類情況提出一種將灰色模型和Elman神經(jīng)網(wǎng)絡(luò)相結(jié)合的灰色神經(jīng)網(wǎng)絡(luò)軟件可靠性預(yù)測(cè)模型。最后,將三種模型的預(yù)測(cè)結(jié)果依據(jù)軟件可靠性模型評(píng)價(jià)準(zhǔn)則做了詳細(xì)對(duì)比,結(jié)果表明基于灰色Elman神經(jīng)網(wǎng)絡(luò)的軟件可靠性模型具有更好的預(yù)測(cè)精度和適用性,并在較大程度上提高了軟件可靠性預(yù)測(cè)的準(zhǔn)確性。
[Abstract]:The 7 / 24 hour safe and trouble-free operation of the civil aviation passenger information system is related to the vital interests of the airline and the travel experience of the airline passengers. However, due to a series of unreliable factors, At home and abroad, civil aviation passenger information system failures can be said to occur from time to time. Therefore, how to accurately evaluate the reliability of the system and maintain the normal operation of the system, is a very urgent task. This paper attempts to analyze a large number of operation and maintenance monitoring data of historical civil aviation passenger information system, and extracts the reliability failure data from the data by using the methods of keyword extraction and regular expression semantic analysis. In order to reduce the potential unknowable factors in reliability prediction and improve the accuracy of prediction, it is used for the reliability modeling of neural network software and accurate prediction of failure data. The main work is as follows: firstly, the reliability data extraction method is analyzed and studied, and a method based on regular expression is designed to extract the reliability data of civil aviation passenger information system. Secondly, two reliability models of civil aviation passenger information system, BP neural network and Elman neural network, are established, and the reliability failure data of civil aviation passenger information system are predicted by the established reliability model. The feasibility of the neural network reliability model in civil aviation passenger information system is verified, and the applicability of the previous model is well solved. Thirdly, according to the experimental verification of the first two models, the problem of randomness and dynamics of reliability data in multi-user system software is found. A software reliability prediction model based on grey neural network and Elman neural network is proposed. Finally, the prediction results of the three models are compared in detail according to the evaluation criteria of software reliability model. The results show that the software reliability model based on grey Elman neural network has better prediction accuracy and applicability. The accuracy of software reliability prediction is improved to a large extent.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：V354;TP183

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