本文關(guān)鍵詞： 電力綜合數(shù)據(jù)網(wǎng) 態(tài)勢評估 態(tài)勢預(yù)測 SNMP 支持向量機(jī)　出處：《電子科技大學(xué)》2016年碩士論文　論文類型：學(xué)位論文

【摘要】：態(tài)勢感知可以細(xì)分為態(tài)勢評估及態(tài)勢預(yù)測,是網(wǎng)絡(luò)的結(jié)構(gòu)配置情況,網(wǎng)絡(luò)資源的使用情況,網(wǎng)絡(luò)運(yùn)行情況以及業(yè)務(wù)因素綜合呈現(xiàn)出的網(wǎng)絡(luò)整體狀態(tài)以及可能的發(fā)展趨勢。通過評估當(dāng)前網(wǎng)絡(luò)態(tài)勢以及預(yù)測網(wǎng)絡(luò)未來運(yùn)行趨勢,可以為網(wǎng)絡(luò)管理員提供深入理解網(wǎng)絡(luò)及用戶行為的途徑。電力綜合數(shù)據(jù)網(wǎng),是電網(wǎng)信息化的支撐網(wǎng)絡(luò),承擔(dān)電網(wǎng)企業(yè)內(nèi)部高速數(shù)據(jù)、話音以及多媒體等業(yè)務(wù)傳輸,呈現(xiàn)網(wǎng)絡(luò)規(guī)模龐大,網(wǎng)絡(luò)拓?fù)鋸?fù)雜、業(yè)務(wù)種類繁多、網(wǎng)絡(luò)協(xié)議豐富的特點(diǎn)。為了保證電力綜合數(shù)據(jù)網(wǎng)的正常高效運(yùn)行,準(zhǔn)確把握網(wǎng)絡(luò)運(yùn)行態(tài)勢的現(xiàn)狀及發(fā)展趨勢是網(wǎng)絡(luò)管理員關(guān)心的重中之重,因此建立針對電力綜合數(shù)據(jù)網(wǎng)的運(yùn)行態(tài)勢評估與預(yù)測系統(tǒng)是一種行之有效的手段。本文針對電力綜合數(shù)據(jù)網(wǎng),通過采集骨干網(wǎng)絡(luò)的SNMP協(xié)議信息,提取網(wǎng)絡(luò)運(yùn)行態(tài)勢因子,對態(tài)勢因子進(jìn)行分類評估及時間序列預(yù)測,以達(dá)到評估整個網(wǎng)絡(luò)當(dāng)前運(yùn)行態(tài)勢及預(yù)測未來運(yùn)行態(tài)勢的目的。本文的具體工作如下:1.研究網(wǎng)絡(luò)態(tài)勢感知系統(tǒng)模型,提出一種基于骨干路由器運(yùn)行狀態(tài)的態(tài)勢評估與預(yù)測模型,以骨干路由器SNMP協(xié)議信息為數(shù)據(jù)來源,建立態(tài)勢因子指標(biāo)體系,并實(shí)現(xiàn)了電力綜合數(shù)據(jù)網(wǎng)運(yùn)行態(tài)勢評估與預(yù)測系統(tǒng)功能模塊。2.研究態(tài)勢評估方法,針對電力綜合數(shù)據(jù)網(wǎng)骨干網(wǎng)提出一種基于K-means聚類預(yù)標(biāo)簽及支持向量機(jī)的態(tài)勢評估方法,利用結(jié)合人工經(jīng)驗(yàn)的K-means聚類為態(tài)勢因子加上狀態(tài)標(biāo)簽,解決了利用有監(jiān)督分類評估時,大量態(tài)勢因子樣本無標(biāo)簽的問題。3.研究態(tài)勢預(yù)測方法,針對時間序列預(yù)測的缺點(diǎn),提出一種基于累加誤差修正及支持向量機(jī)的態(tài)勢預(yù)測方法,同步對態(tài)勢因子及預(yù)測誤差值通過累加處理后進(jìn)行預(yù)測,利用誤差預(yù)測值對態(tài)勢因子初步預(yù)測值進(jìn)行誤差修正,減少了支持向量機(jī)回歸預(yù)測滯后性及樣本突變波動產(chǎn)生的誤差,提高了預(yù)測準(zhǔn)確度。本文在現(xiàn)有態(tài)勢感知研究基礎(chǔ)上,針對電力綜合數(shù)據(jù)網(wǎng)提出了一套運(yùn)行態(tài)勢評估與預(yù)測模型,在評估與預(yù)測的過程中分別使用本文提出的基于K-means聚類預(yù)標(biāo)簽及支持向量機(jī)的態(tài)勢評估方法及基于累加誤差修正及支持向量機(jī)的態(tài)勢預(yù)測方法,經(jīng)過實(shí)驗(yàn)分析和系統(tǒng)實(shí)現(xiàn),本文的模型和方法具有一定實(shí)用價值。
[Abstract]:Situational awareness can be subdivided into situation assessment and situation prediction, which is the configuration of the network structure, the use of network resources, The overall state of the network and the possible development trend presented by the network operation and service factors. By evaluating the current network situation and predicting the future network running trend, It can provide a way for network administrators to understand the network and the behavior of users in depth. Power integrated data network is the supporting network of power network informatization, which undertakes the transmission of high-speed data, voice, multimedia and other services within power grid enterprises. In order to ensure the normal and efficient operation of the power integrated data network, the network has the characteristics of large scale, complex network topology, various types of services and abundant network protocols. Accurately grasping the current situation and developing trend of network operation situation is the most important concern of network administrator. Therefore, it is an effective means to set up an operational situation assessment and prediction system for power integrated data network. In this paper, the SNMP protocol information of backbone network is collected to extract the running situation factor of the network. In order to evaluate the current situation of the whole network and predict the future situation of the whole network, the classification evaluation and time series prediction of the situation factor are carried out. The specific work of this paper is as follows: 1. The model of network situation awareness system is studied. This paper presents a situation assessment and prediction model based on the running state of backbone routers. Based on the SNMP protocol information of backbone routers, a situation factor index system is established. The function module of the power integrated data network running situation assessment and forecasting system is realized. 2. The situation assessment method is studied, and a situation assessment method based on K-means clustering pre-label and support vector machine is proposed for the power integrated data network backbone network. K-means clustering combined with artificial experience is used to add state label to situation factor, which solves the problem that a large number of situation factor samples are not tagged when using supervised classification and evaluation. 3. Study the method of situation prediction, aiming at the shortcomings of time series prediction. A situation prediction method based on accumulative error correction and support vector machine is proposed. The situation factor and prediction error are predicted by accumulative processing synchronously, and the initial prediction value of situation factor is corrected by error prediction value. It reduces the error of prediction lag and sample mutation fluctuation by using support vector machine regression, and improves the accuracy of prediction. A set of operational situation assessment and prediction model for power integrated data network is proposed. In the process of evaluation and prediction, the situation assessment method based on K-means clustering prelabel and support vector machine and the situation prediction method based on accumulative error correction and support vector machine are used in this paper. The model and method in this paper have certain practical value.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM73

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本文編號：1501888