本文關(guān)鍵詞：基于數(shù)據(jù)倉庫的機(jī)場能源信息管理研究　出處：《中國民航大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)據(jù)倉庫 ETL K-means聚類 時間序列 群體細(xì)分

【摘要】：機(jī)場領(lǐng)域在日常運(yùn)營中積累了大量的能源數(shù)據(jù)信息。隨著機(jī)場規(guī)模的不斷發(fā)展壯大,機(jī)場能源消耗的急劇上升,機(jī)場能源信息科學(xué)管理顯得愈加重要,而基于傳統(tǒng)數(shù)據(jù)庫的統(tǒng)計(jì)查詢分析方式越來越不能滿足機(jī)場能源信息管理與分析的需求。為此本文提出將數(shù)據(jù)倉庫技術(shù)運(yùn)用到機(jī)場能源信息管理中,構(gòu)建機(jī)場能源信息數(shù)據(jù)倉庫,并在數(shù)據(jù)倉庫的基礎(chǔ)上進(jìn)行聚類挖掘。首先分析機(jī)場能源管理數(shù)據(jù)倉庫系統(tǒng)需求,設(shè)計(jì)了數(shù)據(jù)倉庫模型;確立了機(jī)場能源管理數(shù)據(jù)倉庫的兩大主題結(jié)構(gòu),并為兩個主題設(shè)計(jì)了邏輯模型結(jié)構(gòu)和物理模型結(jié)構(gòu);根據(jù)機(jī)場能源數(shù)據(jù)特點(diǎn)設(shè)計(jì)兩層ETL架構(gòu),對機(jī)場能源數(shù)據(jù)進(jìn)行處理;實(shí)現(xiàn)遠(yuǎn)程異構(gòu)數(shù)據(jù)庫的訪問和數(shù)據(jù)的抽取裝載,對數(shù)據(jù)進(jìn)行轉(zhuǎn)換,包括數(shù)據(jù)清洗、數(shù)據(jù)集成。其次提出以機(jī)場能源數(shù)據(jù)倉庫為基礎(chǔ)進(jìn)行聚類分析,實(shí)現(xiàn)對歷史數(shù)據(jù)的有效信息挖掘;對K-Means算法在K值選取和初始化聚類中心上的不足進(jìn)行改進(jìn)。構(gòu)建以逐月能耗值為數(shù)據(jù)點(diǎn)的多維能耗時間序列,確定用能單位的能耗模式。最后,基于改進(jìn)K-Means算法對機(jī)場能源數(shù)據(jù)進(jìn)行聚類實(shí)驗(yàn)分析。通過經(jīng)典數(shù)據(jù)集測試改進(jìn)K-means算法的有效性;以機(jī)場月度綜合能耗數(shù)據(jù)進(jìn)行聚類,建立不同類單位群體能耗基準(zhǔn)值;最后通過對能耗水平序列以及能耗變化率序列進(jìn)行聚類,建立群體細(xì)分模型。
[Abstract]:The airport has accumulated energy a lot of data in their daily operations. With the continuous development and expansion of the scale of the airport, the sharp rise in energy consumption of the airport, the airport energy information scientific management becomes more and more important, and the traditional database query based on statistical analysis method could not satisfy the airport's energy information management and analysis in this paper will demand. Data warehouse technology is applied to the airport energy information management, construction of the airport energy information data warehouse, and Clustering Mining Based on data warehouse. Firstly, the airport energy management data warehouse system requirements, design the data warehouse model; established two thematic structure of Airport energy management data warehouse, and the design of logic structure model the physical model and structure for the two theme; design of two layer ETL architecture according to the characteristics of airport energy data, airport source number According to the processing; remote access to heterogeneous databases and data loading, data conversion, data cleaning, data integration. Secondly, to the airport energy data warehouse based on clustering analysis, realize the effective information of historical data mining; K-Means algorithm for value selection and initialize clustering center on K improved. To construct the monthly energy consumption value of multidimensional time series data of energy consumption, energy consumption can be determined by model units. Finally, the improved K-Means algorithm of airport energy data clustering experiment analysis based on improved K-means algorithm. The classic data set test; clustering to airport monthly comprehensive energy consumption data, establish the same unit energy consumption benchmark value; finally, through clustering on energy consumption and energy consumption level sequence change rate sequence, establish group segmentation model.

【學(xué)位授予單位】：中國民航大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：V351.3;TP311.13

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本文編號：1410415