【摘要】：隨著多媒體通信技術(shù)的迅猛發(fā)展,越來(lái)越多的用戶能夠在家中享受IPTV(Internet Protocol Television)服務(wù)。為了保證極佳的用戶體驗(yàn),IPTV運(yùn)營(yíng)商們盡力支撐高質(zhì)量視頻節(jié)目并確保視頻傳輸通暢。與此同時(shí),運(yùn)維部門的壓力也越來(lái)越大,通常每日都要解決不同程度、不同層面的困難與故障。因此,如何客觀準(zhǔn)確地評(píng)估IPTV服務(wù)質(zhì)量并及時(shí)維護(hù)IPTV網(wǎng)絡(luò)正常運(yùn)行已成為當(dāng)下的研究熱點(diǎn)。本論文提出了全新的IPTV運(yùn)維質(zhì)量評(píng)估方案,該方案是一套能高效精準(zhǔn)定位并診斷IPTV網(wǎng)絡(luò)故障的綜合系統(tǒng)。該IPTV業(yè)務(wù)服務(wù)質(zhì)量評(píng)估系統(tǒng)在構(gòu)建過(guò)程中,特別融合潛在用戶報(bào)障與潛在設(shè)備預(yù)警這兩方面。在采用故障設(shè)備預(yù)測(cè)模型來(lái)解決潛在設(shè)備預(yù)警過(guò)程中,不僅使用傳統(tǒng)服務(wù)質(zhì)量QoS(Quality of Service)指標(biāo),也充分考慮并采用能反映用戶觀看行為的客觀用戶體驗(yàn)質(zhì)量QoE(Quality of Experience)指標(biāo)。此外,本文針對(duì)IPTV故障定位診斷還進(jìn)行以下工作:一方面,本文提出了一個(gè)綜合故障設(shè)備預(yù)測(cè)模型,該模型首先挖掘機(jī)頂盒指標(biāo)參數(shù)與網(wǎng)絡(luò)狀況之間的關(guān)聯(lián),使IPTV網(wǎng)絡(luò)故障診斷更加高效準(zhǔn)確,特別是針對(duì)卡頓花屏故障現(xiàn)象。該故障設(shè)備預(yù)測(cè)模型包括質(zhì)差指標(biāo)、記錄、用戶三方面。首先,質(zhì)差指標(biāo)模型作為替代傳統(tǒng)決策樹生成中的特征提取部分,負(fù)責(zé)從候選機(jī)頂盒指標(biāo)中篩選出有效有限指標(biāo)。其次,質(zhì)差記錄模型是基于改進(jìn)的決策樹算法,通過(guò)不斷調(diào)整閾值以達(dá)到高準(zhǔn)確率以及低誤判率。最后的質(zhì)差用戶模型將反映用戶觀看體驗(yàn)質(zhì)量的觀看時(shí)長(zhǎng)考慮在內(nèi)。實(shí)驗(yàn)結(jié)果表明,質(zhì)差用戶模型的預(yù)測(cè)準(zhǔn)確率能高達(dá)83.25%。另一方面,針對(duì)傳統(tǒng)算法在非均衡IPTV數(shù)據(jù)集下用戶報(bào)障預(yù)測(cè)效果不理想的問(wèn)題,本文將影響網(wǎng)絡(luò)服務(wù)質(zhì)量的傳統(tǒng)網(wǎng)絡(luò)參數(shù)QoS和主觀反映用戶體驗(yàn)質(zhì)量QoE的MOS(Mean Opinion Score)評(píng)分結(jié)合來(lái)預(yù)測(cè)用戶是否報(bào)障。本文在已有的ODR-BSMOTE-SVM算法基礎(chǔ)上,針對(duì)過(guò)采樣算法產(chǎn)生噪聲以及核參數(shù)沒(méi)有進(jìn)行優(yōu)化的缺陷,提出了一種改進(jìn)型算法。該改進(jìn)算法首先采用欠采樣和過(guò)采樣算法及數(shù)據(jù)清洗算法對(duì)原始非均衡數(shù)據(jù)進(jìn)行處理,然后通過(guò)自適應(yīng)變核參數(shù)尋找近似最優(yōu)值,最終實(shí)現(xiàn)提升分類效果。實(shí)驗(yàn)結(jié)果表明,較傳統(tǒng)標(biāo)準(zhǔn)支持向量機(jī)(SVM)算法和ODR-BSMOTE-SVM算法,本文所提出的算法能獲得更佳的預(yù)測(cè)效果。
[Abstract]:With the rapid development of multimedia communication technology, more and more users can enjoy IPTV (Internet Protocol Television) services at home. IPTV operators try to support high-quality video programs and ensure that video is transmitted smoothly in order to ensure an excellent user experience. At the same time, the pressure of operations and maintenance departments is increasing, usually to solve different levels of difficulties and failures on a daily basis. Therefore, how to evaluate IPTV QoS objectively and accurately and maintain the normal operation of IPTV network in time has become a hot research topic. In this paper, a new IPTV operation and maintenance quality evaluation scheme is proposed, which is a comprehensive system which can locate and diagnose IPTV network faults efficiently and accurately. In the process of constructing the IPTV service quality assessment system, the potential user reporting barrier and potential equipment warning are especially integrated. In the process of using fault equipment prediction model to solve the potential equipment warning process, not only the traditional quality of service (QoS (Quality of Service) index is used, but also the objective user experience quality (QoE (Quality of Experience) index, which can reflect the user's viewing behavior, is fully considered and adopted. In addition, this paper also does the following work for IPTV fault location diagnosis: on the one hand, this paper proposes a comprehensive fault equipment prediction model, which first mining the relationship between set-top box index parameters and network condition. Make the IPTV network fault diagnosis more efficient and accurate, especially for the phenomenon of Carton flower screen fault. The fault equipment prediction model includes three aspects: quality index, record and user. First, the quality difference index model, as the feature extraction part of the traditional decision tree generation, is responsible for screening out the effective limited index from the candidate set-top box index. Secondly, the quality difference record model is based on the improved decision tree algorithm, by constantly adjusting the threshold to achieve high accuracy and low error rate. The final quality difference user model takes into account the length of viewing time that reflects the quality of the user's viewing experience. The experimental results show that the prediction accuracy of the quality difference user model can reach 83.25%. On the other hand, aiming at the problem that the performance of the traditional algorithm is not satisfactory under the unbalanced IPTV data set, In this paper, the traditional network parameter QoS which affects the quality of service of the network is combined with the MOS (Mean Opinion Score) score, which reflects the quality of user experience. In this paper, based on the existing ODR-BSMOTE-SVM algorithm, an improved algorithm is proposed to overcome the defects of over-sampling algorithm which produces noise and the kernel parameters are not optimized. The improved algorithm firstly processes the original unbalanced data by using the under-sampling and over-sampling algorithm and the data cleaning algorithm, and then finds the approximate optimal value through adaptive variable kernel parameters, and finally realizes the improvement of classification effect. The experimental results show that the proposed algorithm can obtain better prediction results than the traditional standard support vector machine (SVM) algorithm and the ODR-BSMOTE-SVM algorithm.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN949.292

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