本文選題：移動互聯(lián)網(wǎng) + 用戶感知　； 參考：《北京郵電大學(xué)》2016年碩士論文

【摘要】：隨著移動智能終端和移動網(wǎng)絡(luò)的飛速發(fā)展,移動互聯(lián)網(wǎng)業(yè)務(wù)也已由最初單一的短彩信業(yè)務(wù)發(fā)展到如今的移動游戲、移動社交、移動視頻和移動即時通信等多種多樣的新型業(yè)務(wù)。同時,移動網(wǎng)絡(luò)運(yùn)營商對網(wǎng)絡(luò)質(zhì)量關(guān)注的重點(diǎn)也從單一的語音業(yè)務(wù)轉(zhuǎn)移到了種類繁多的移動互聯(lián)網(wǎng)業(yè)務(wù)上。通常運(yùn)營商對用戶感知的評價是通過將網(wǎng)絡(luò)質(zhì)量KPI指標(biāo)映射為用戶感知指標(biāo)來實(shí)現(xiàn)的,即用KPI指標(biāo)中的接通率、掉話率、用戶投訴等指標(biāo)來衡量用戶感知的好壞。但是,KPI指標(biāo)與實(shí)際用戶感知兩者之間是有本質(zhì)區(qū)別的,也就是說傳統(tǒng)的KPI指標(biāo)不能準(zhǔn)確地反映用戶的真實(shí)感受。用戶感知的評判結(jié)果是否與用戶實(shí)際感受相吻合,關(guān)鍵在于從網(wǎng)絡(luò)側(cè)指標(biāo)中提取出用戶使用業(yè)務(wù)的實(shí)際感知情況。本文從移動運(yùn)營商網(wǎng)絡(luò)側(cè)豐富的數(shù)據(jù)源入手,基于大數(shù)據(jù)挖掘的結(jié)果,針對移動互聯(lián)網(wǎng)主流業(yè)務(wù),建立主流業(yè)務(wù)的用戶感知評價體系,并探索基于用戶感知的網(wǎng)絡(luò)優(yōu)化、流量經(jīng)營、客戶維系等方法,以保障移動運(yùn)營商網(wǎng)絡(luò)的精細(xì)化運(yùn)營。主要內(nèi)容為:首先,本文對移動網(wǎng)絡(luò)的架構(gòu)以及移動網(wǎng)絡(luò)中所產(chǎn)生的數(shù)據(jù)源進(jìn)行了詳細(xì)描述,并結(jié)合數(shù)據(jù)源的特點(diǎn)對移動網(wǎng)絡(luò)多數(shù)據(jù)源分析方法進(jìn)行分析對比。同時,本文綜述了移動互聯(lián)網(wǎng)業(yè)務(wù)用戶感知模型的研究現(xiàn)狀,總結(jié)了目前用戶感知模型研究中存在的問題。通過對不同的研究方法進(jìn)行比對,衡量各種研究方法的優(yōu)缺點(diǎn),以此為基礎(chǔ)加以改進(jìn)產(chǎn)生了本文使用的評價方法。其次,本文重點(diǎn)介紹了基于大數(shù)據(jù)挖掘的移動互聯(lián)網(wǎng)主流業(yè)務(wù)的用戶感知建模方法。先對主流業(yè)務(wù)的評價指標(biāo)進(jìn)行選擇,并應(yīng)用系統(tǒng)聚類法對各類指標(biāo)進(jìn)行篩選、合并,最終確定模型搭建中使用的指標(biāo)范圍。之后,本文運(yùn)用AHP層次分析法確定各類指標(biāo)對各項(xiàng)業(yè)務(wù)的用戶感知權(quán)重,最終形成基于單用戶的主流業(yè)務(wù)用戶感知評價模型。最后基于中國聯(lián)通的現(xiàn)網(wǎng)數(shù)據(jù),完成了某城市移動互聯(lián)網(wǎng)主流業(yè)務(wù)的用戶級及小區(qū)級的用戶感知評價。最后,本文將所建立的用戶感知模型與現(xiàn)網(wǎng)應(yīng)用相結(jié)合,通過建立的用戶感知模型來對現(xiàn)網(wǎng)中存在的問題進(jìn)行分析篩查,并結(jié)合網(wǎng)絡(luò)KPI,對問題進(jìn)行精準(zhǔn)定位,精準(zhǔn)有效地解決現(xiàn)網(wǎng)中存在的網(wǎng)絡(luò)問題,證明了本文所建立的用戶感知模型的有效性及其在運(yùn)營商工作中的重要意義。
[Abstract]:With the rapid development of mobile intelligent terminals and mobile networks, mobile Internet services have also developed from a single short MMS service to a variety of new services such as mobile games, mobile social, mobile video and mobile instant messaging. At the same time, mobile network operators focus on network quality from a single voice service to a wide variety of mobile Internet services. Usually the operators evaluate the user perception by mapping the network quality KPI index to the user perception index, that is, using the KPI index of the connection rate, drop rate, user complaints and other indicators to measure the good or bad of the user perception. However, there is an essential difference between the KPI index and the actual user perception, which means that the traditional KPI index can not accurately reflect the true feelings of the user. Whether the judgment result of user perception is consistent with the user's actual perception is the key to extract the actual perception of the user's using service from the network side index. This paper starts with the abundant data sources on the network side of the mobile operators, and based on the results of big data mining, establishes the user perception evaluation system for the mainstream services of the mobile Internet, and explores the network optimization based on the user perception. Traffic management, customer maintenance and other methods to ensure the fine operation of mobile operators network. The main contents are as follows: firstly, the architecture of mobile network and the data source generated in mobile network are described in detail, and the analysis methods of multiple data sources in mobile network are analyzed and compared according to the characteristics of data sources. At the same time, this paper summarizes the research status of mobile Internet service user perception model, and summarizes the existing problems in the research of user perception model. Through the comparison of different research methods, the advantages and disadvantages of various research methods are measured, and the evaluation methods used in this paper are improved on this basis. Secondly, this paper focuses on user aware modeling of mainstream mobile Internet services based on big data mining. Firstly, the evaluation index of mainstream business is selected, and the system clustering method is applied to screen and merge all kinds of indicators. Finally, the range of indicators used in the model building is determined. After that, this paper uses AHP to determine the user perceived weight of all kinds of indexes, and finally forms a single user based mainstream service user perception evaluation model. Finally, based on the current network data of China Unicom, the user perception evaluation of the mainstream mobile Internet service in a certain city is completed. Finally, this paper combines the established user perception model with the existing network application, through the established user perception model to analyze and screen the existing problems in the network, and combined with the network KPI to accurately locate the problems. It proves the validity of the user perception model established in this paper and its significance in the work of operators.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TN929.5;TP393.0;TP311.13

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