【摘要】：當(dāng)網(wǎng)絡(luò)節(jié)點(diǎn)因?yàn)楫惓；蛘吖收闲纬删W(wǎng)絡(luò)告警時(shí),往往其周邊的網(wǎng)絡(luò)節(jié)點(diǎn)也會(huì)有相當(dāng)數(shù)量的網(wǎng)絡(luò)告警出現(xiàn),這些告警信息之間往往存在著某種相關(guān)性。如何找到這些告警的相關(guān)性,從而準(zhǔn)確地定位根源告警是網(wǎng)絡(luò)故障診斷的核心重點(diǎn),也是難點(diǎn)。最初,專(zhuān)家系統(tǒng)在網(wǎng)絡(luò)故障診斷中得到了廣泛的研究與應(yīng)用,但其在知識(shí)庫(kù)建立和自學(xué)習(xí)上存在不足。隨著數(shù)據(jù)挖掘技術(shù)在各個(gè)研究領(lǐng)域的廣泛應(yīng)用,并且取得了大量的研究成果,于是相關(guān)研究人員嘗試著在網(wǎng)絡(luò)故障診斷領(lǐng)域內(nèi)探索其應(yīng)用,大量研究了基于關(guān)聯(lián)規(guī)則挖掘的網(wǎng)絡(luò)故障診斷技術(shù),將專(zhuān)家系統(tǒng)和數(shù)據(jù)挖掘技術(shù)結(jié)合起來(lái),從而解決知識(shí)和自學(xué)習(xí)問(wèn)題,最終獲得了較大的成功。雖然在網(wǎng)絡(luò)故障診斷中引入關(guān)聯(lián)規(guī)則挖掘,取得了較大的成功,但是仍然存在一些不足之處:一方面,由于網(wǎng)絡(luò)告警和網(wǎng)絡(luò)告警根源之間存在著一種模糊關(guān)系,并非簡(jiǎn)單的確定性映射關(guān)系,而在此之前的處理方法忽略了這一點(diǎn),僅僅是強(qiáng)硬劃分網(wǎng)絡(luò)告警和網(wǎng)絡(luò)告警根源之間的對(duì)應(yīng)關(guān)系,這勢(shì)必會(huì)對(duì)后期的網(wǎng)絡(luò)根源告警定位診斷的準(zhǔn)確性產(chǎn)生一定的影響。另一方面,因?yàn)榫W(wǎng)絡(luò)具有分層的特點(diǎn),所以網(wǎng)絡(luò)告警在進(jìn)行傳播的過(guò)程中受到網(wǎng)絡(luò)分層的影響,先前的方法未曾考慮到網(wǎng)絡(luò)告警和網(wǎng)絡(luò)各層次間的關(guān)系。與此同時(shí),由于網(wǎng)絡(luò)設(shè)備供應(yīng)商的不同,網(wǎng)絡(luò)設(shè)備產(chǎn)生的網(wǎng)絡(luò)告警在內(nèi)容和格式上存在一定的差異,在一定程度上影響了網(wǎng)絡(luò)告警相關(guān)性分析。此外,因?yàn)榛陉P(guān)聯(lián)規(guī)則挖掘算法所處理的數(shù)據(jù)對(duì)象必須是事務(wù)化的數(shù)據(jù),所以如果需要對(duì)網(wǎng)絡(luò)告警信息進(jìn)行關(guān)聯(lián)規(guī)則挖掘,就需要事先對(duì)相關(guān)的數(shù)據(jù)進(jìn)行處理。針對(duì)上述問(wèn)題,本文在關(guān)聯(lián)規(guī)則挖掘技術(shù)的基礎(chǔ)之上,結(jié)合相關(guān)的模糊理論和模糊推理控制技術(shù),研究了基于模糊關(guān)聯(lián)規(guī)則挖掘的網(wǎng)絡(luò)告警根源診斷,論文的主要內(nèi)容有如下幾點(diǎn):1.針對(duì)網(wǎng)絡(luò)告警信息之間的不確定性以及信息的不統(tǒng)一,需要建立統(tǒng)一的全局網(wǎng)絡(luò)告警信息模型:分析網(wǎng)絡(luò)告警中各個(gè)屬性字段的含義,以及網(wǎng)絡(luò)告警中存在的不確定性,依據(jù)網(wǎng)絡(luò)告警的特征和相關(guān)的規(guī)則提取并量化關(guān)鍵屬性,建立網(wǎng)絡(luò)告警信息模型。同時(shí)為了體現(xiàn)告警受網(wǎng)絡(luò)層次的影響,引入告警類(lèi)型屬性Alarm Type,并對(duì)各個(gè)層次的告警進(jìn)行細(xì)分羅列編號(hào),本文將網(wǎng)絡(luò)分為三層。2.針對(duì)傳統(tǒng)的模糊聚類(lèi)算法FCM在進(jìn)行網(wǎng)絡(luò)告警信息模糊化處理時(shí),由于聚類(lèi)中心是通過(guò)隨機(jī)初始化生成,使得聚類(lèi)中心取值不合理,從而容易導(dǎo)致算法陷入局部最優(yōu)以及模糊網(wǎng)絡(luò)告警的模糊評(píng)價(jià)區(qū)間不一致的問(wèn)題。為此,通過(guò)對(duì)初始化聚類(lèi)中心矩陣的生成策略進(jìn)行改進(jìn),從而優(yōu)化FCM算法。利用改進(jìn)的FCM,對(duì)網(wǎng)絡(luò)告警進(jìn)行模糊化處理,最終形成模糊化告警模型。通過(guò)引入的模糊隸屬度來(lái)描述網(wǎng)絡(luò)告警之間的模糊性關(guān)系,從而有別于傳統(tǒng)的布爾型邏輯表示。3.由于本文是基于模糊關(guān)聯(lián)規(guī)則進(jìn)行規(guī)則挖掘分析網(wǎng)絡(luò)告警,但是關(guān)聯(lián)規(guī)則挖掘算法處理的數(shù)據(jù)對(duì)象要求是事務(wù)化的數(shù)據(jù),所以需要事先對(duì)前面獲得的模糊化網(wǎng)絡(luò)告警進(jìn)行事務(wù)化處理。本文擬通過(guò)滑動(dòng)窗口機(jī)制進(jìn)行事務(wù)化處理,以滿(mǎn)足規(guī)則挖掘分析的需要,形成模糊告警事務(wù)庫(kù)。4.針對(duì)在模糊關(guān)聯(lián)規(guī)則挖掘過(guò)程中,隨著向高次項(xiàng)頻繁集進(jìn)行挖掘,會(huì)出現(xiàn)模糊支持度計(jì)數(shù)驟減的現(xiàn)象,如果仍然采用靜態(tài)的最小支持度F_MIN_SUP,就會(huì)使得部分頻繁項(xiàng)被遺漏,從而丟失部分強(qiáng)關(guān)聯(lián)規(guī)則。為此,本文引入動(dòng)態(tài)更新最小支持度的思想,實(shí)現(xiàn)DFARM(動(dòng)態(tài)最小支持度模糊關(guān)聯(lián)規(guī)則挖掘)算法。最后,結(jié)合布爾型關(guān)聯(lián)規(guī)則挖掘算法BARM,通過(guò)模糊化和非模糊化兩種告警事務(wù)庫(kù)進(jìn)行實(shí)驗(yàn)仿真,進(jìn)行性能對(duì)比分析,突出硬劃分問(wèn)題。5.詳細(xì)研究分析模糊推理模塊的各個(gè)重要組成部分,著重分析了正向推理驅(qū)動(dòng)策略和反向推理驅(qū)動(dòng)策略,以及反模糊化對(duì)推理結(jié)果的解釋。最終通過(guò)相關(guān)的實(shí)驗(yàn),進(jìn)行各種推理組合的性能測(cè)試,最后獲得最優(yōu)的推理組合模糊匹配算子Hamming和合成方法Trip-I,配合正向推理驅(qū)動(dòng)策略。最終通過(guò)測(cè)試,可以準(zhǔn)確對(duì)網(wǎng)絡(luò)故障告警的根源節(jié)點(diǎn)進(jìn)行定位。
[Abstract]:When a network alarm is formed by abnormal or faulty network nodes, a considerable number of network alarms will appear in the network nodes around the network nodes, and there is often some correlation between these alarm information. Initially, expert system has been widely studied and applied in network fault diagnosis, but it has some shortcomings in knowledge base building and self-learning. With the wide application of data mining technology in various research fields, and has made a lot of research results, so related researchers try to network fault diagnosis field. In order to solve the problem of knowledge and self-learning, a great deal of network fault diagnosis technology based on association rule mining is studied. The expert system and data mining technology are combined to solve the problem of knowledge and self-learning. Finally, the application of association rule mining in network fault diagnosis is successful, but it still exists. There are some shortcomings: on the one hand, because there is a fuzzy relationship between network alarm and the root of network alarm, it is not a simple deterministic mapping relationship, but the previous processing methods neglected this point, only a hard division of the corresponding relationship between network alarm and the root of network alarm, which is bound to later network. On the other hand, network alarms are affected by network layering because of the layered nature of the network. Previous methods have not considered the relationship between network alarms and network layers. Similarly, there are some differences in content and format of network alarms produced by network devices, which affect the correlation analysis of network alarms to a certain extent. In view of the above problems, this paper, on the basis of association rules mining technology, combines fuzzy theory and fuzzy inference control technology, studies the diagnosis of network alarm roots based on fuzzy association rules mining. The main contents of this paper are as follows: 1. It is necessary to establish a unified global network alarm information model because of the uncertainty and inconsistency of the information between the two alarms. It analyzes the meaning of each attribute field in the network alarm and the uncertainty in the network alarm. It extracts and quantifies the key attributes according to the characteristics of the network alarm and relevant rules, and establishes the network alarm information model. In order to show that alarms are affected by network hierarchy, the Alarm Type is introduced and the alarms of each hierarchy are subdivided into three layers. 2. For the traditional fuzzy clustering algorithm FCM, the clustering center is generated by random initialization, which makes the network alarm information fuzzy. The unreasonable value of clustering center leads to the problem that the algorithm falls into local optimum and the fuzzy evaluation interval of fuzzy network alarm is inconsistent. To this end, the FCM algorithm is optimized by improving the generation strategy of the initial clustering center matrix. The network alarm is fuzzified by the improved FCM, and the model is finally formed. Fuzzy membership is introduced to describe the fuzzy relationship between network alarms, which is different from the traditional Boolean logic representation. 3. Because this paper is based on fuzzy association rules for rule mining and analysis of network alarms, but association rules mining algorithm processing data objects require transactional data. In order to satisfy the requirement of rule mining and analysis, a fuzzy alarm transaction database is formed. 4. In the process of mining fuzzy association rules, the fuzzy alarm transaction database will appear when mining frequent sets of high-order items. If the static minimum support F_MIN_SUP is still used, some frequent items will be omitted and some strong association rules will be lost. Therefore, the idea of dynamic updating minimum support is introduced to realize DFARM (dynamic minimum support fuzzy association rules mining). Finally, the Boolean algorithm is combined. Association rules mining algorithm BARM, through fuzzy and non-fuzzy alarm transaction database for experimental simulation, performance comparison analysis, highlighting the hard partitioning problem. 5. Detailed study and analysis of the important components of fuzzy reasoning module, focusing on the analysis of forward reasoning driving strategy and backward reasoning driving strategy, as well as anti-fuzzy reasoning strategy. Finally, through the relevant experiments, the performance of various combinations of reasoning is tested. Finally, the optimal combinations of reasoning fuzzy matching operator Hamming and synthesis method Trip-I are obtained, which are combined with the forward reasoning driving strategy. Finally, through the test, the root node of network fault alarm can be accurately located.
【學(xué)位授予單位】：江西農(nóng)業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP311.13;TP393.06

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