本文選題：網(wǎng)絡(luò)日志 + 故障診斷　； 參考：《北京郵電大學(xué)》2014年碩士論文

【摘要】：隨著現(xiàn)代信息技術(shù)的高速發(fā)展,企業(yè)和個(gè)人對(duì)通信網(wǎng)絡(luò)的安全性和可靠性要求越來(lái)越高,因此快速的網(wǎng)絡(luò)故障診斷和定位顯得越來(lái)越重要。而利用通信網(wǎng)絡(luò)運(yùn)行中產(chǎn)生的日志信息來(lái)幫助進(jìn)行網(wǎng)絡(luò)診斷,成為近年來(lái)網(wǎng)絡(luò)故障診斷研究的一個(gè)熱點(diǎn)。 本文基于與企業(yè)合作方的科研項(xiàng)目,通過(guò)對(duì)日志信息設(shè)置主動(dòng)監(jiān)測(cè)項(xiàng),完成信息抽取。對(duì)抽取出的信息,從時(shí)間維度進(jìn)行關(guān)聯(lián)規(guī)則挖掘,生成頻繁告警序列；從空間維度利用故障樹(shù)分析,定位根源網(wǎng)元故障節(jié)點(diǎn),從而完成網(wǎng)絡(luò)故障診斷。具體來(lái)說(shuō),有以下三方面的工作內(nèi)容： 第一,通過(guò)研究Apriori和WINEPI算法,改進(jìn)其在掃描次數(shù)和存儲(chǔ)結(jié)構(gòu)上的不足,結(jié)合日志中告警信息特點(diǎn),提出了一種基于前綴樹(shù)結(jié)構(gòu)的Prefix-WINEPI頻繁序列挖掘算法。通過(guò)對(duì)比實(shí)驗(yàn),驗(yàn)證了該算法在執(zhí)行時(shí)間和處理增量問(wèn)題方面比WINEPI算法更加高效。 第二,針對(duì)日志信息的字段特征,設(shè)置了主動(dòng)監(jiān)測(cè)項(xiàng),利用公式計(jì)算的方法,快速抽取日志中有用信息,減少無(wú)關(guān)信息干擾；同時(shí),提出了一種將時(shí)間維度的告警關(guān)聯(lián)分析和空間拓?fù)涞墓收蠘?shù)分析相結(jié)合的診斷方法,應(yīng)用于網(wǎng)絡(luò)故障診斷。 第三,將理論工作落實(shí)到實(shí)踐。通過(guò)書(shū)寫(xiě)診斷工具設(shè)計(jì)文檔,完成算法設(shè)計(jì)和數(shù)據(jù)存儲(chǔ)設(shè)計(jì)工作；利用Java Web編程和Web前端的相關(guān)技術(shù),實(shí)現(xiàn)了一個(gè)具體的日志診斷工具。解決了故障診斷問(wèn)題。 本文將理論研究與具體的日志診斷工作相結(jié)合,驗(yàn)證了上述研究所提供方法的有效性和高效性,較好地解決了科研項(xiàng)目中通信網(wǎng)絡(luò)日志故障診斷的實(shí)際問(wèn)題。
[Abstract]:With the rapid development of modern information technology, enterprises and individuals demand more and more security and reliability of communication network, so the rapid network fault diagnosis and location are becoming more and more important. And using the log information generated in the communication network to help the network diagnosis has become a research on network fault diagnosis in recent years. A hot spot.
Based on the scientific research projects with the partners of the enterprise, this paper sets the active monitoring items for the log information and completes the information extraction. The extraction information, the association rules mining from the time dimension, the frequent alarm sequence, the fault tree analysis from the spatial dimension, the location of the root source network fault node, thus the network fault diagnosis is completed. In body, there are three aspects of the following work:
First, by studying the Apriori and WINEPI algorithms and improving the shortage of the number of scanning times and storage structure, a Prefix-WINEPI frequent sequence mining algorithm based on the prefix tree structure is proposed in combination with the characteristics of the alarm information in the log. The comparison experiment shows that the algorithm is compared with the WINEPI algorithm in the execution time and the processing increment. More efficient.
Second, in view of the field characteristics of log information, the active monitoring item is set up, and the useful information in log is extracted quickly by the method of formula calculation. At the same time, a diagnosis method which combines the time dimension alarm correlation analysis with the fault tree analysis of the spatial topology is proposed, which is applied to the network fault diagnosis.
Third, carry out the theoretical work to practice. Through the writing diagnosis tool design document, complete the algorithm design and the data storage design work; use the Java Web programming and the Web front-end related technology, realized a specific log diagnosis tool, solved the fault diagnosis problem.
This paper combines the theoretical research with the specific log diagnosis to verify the effectiveness and efficiency of the method provided by the above research institute, and better solve the practical problem of the fault diagnosis of the communication network log in the scientific research project.

【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP311.13;TP393.06

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