本文選題：高速鐵路 + 云計(jì)算　； 參考：《西南交通大學(xué)》2013年碩士論文

【摘要】：科技的不斷進(jìn)步推動(dòng)了高速鐵路的快速發(fā)展.而其作為一個(gè)節(jié)能、環(huán)保、快速和準(zhǔn)點(diǎn)的交通工具日漸成為了人們出行的首選。隨著高鐵裝備水平的不斷改進(jìn)和提高,安全性和舒適性也成為人們關(guān)注的焦點(diǎn)。為了保證列車(chē)的安全運(yùn)行,我們需要實(shí)時(shí)地對(duì)列車(chē)運(yùn)行數(shù)據(jù)進(jìn)行分析處理,掌握列車(chē)的運(yùn)營(yíng)狀態(tài),做出決策判斷。 為了能夠獲得足夠的高鐵列車(chē)運(yùn)行信息,工程人員利用大量的傳感器來(lái)采集噪聲和振動(dòng)等多種類(lèi)型的信號(hào)數(shù)據(jù)。然而,分析處理這種大規(guī)模增長(zhǎng)的數(shù)據(jù)給傳統(tǒng)的信號(hào)分析方法帶來(lái)了前所未有的嚴(yán)峻挑戰(zhàn),而云計(jì)算作為一個(gè)新興的并行處理技術(shù),融合了網(wǎng)格計(jì)算、分布式計(jì)算和并行計(jì)算等特點(diǎn),在大數(shù)據(jù)計(jì)算和網(wǎng)絡(luò)存儲(chǔ)方面具有卓越的性能表現(xiàn)。 Hadoop作為一種云計(jì)算框架,包含了文件系統(tǒng)HDFS和MapReduce編程模型,其具有高可靠性和高容錯(cuò)性等特點(diǎn),尤其是MapReduce模型,采用分而治之的設(shè)計(jì)思想,有效地化解了大數(shù)據(jù)對(duì)現(xiàn)代程序設(shè)計(jì)過(guò)程帶來(lái)的挑戰(zhàn)。本文利用云計(jì)算技術(shù)對(duì)高鐵數(shù)據(jù)處理領(lǐng)域十分重要的數(shù)據(jù)預(yù)處理和信號(hào)分析算法進(jìn)行并行化,包括高鐵原始數(shù)據(jù)解包算法和數(shù)字信號(hào)分析中廣為應(yīng)用的快速傅里葉變換算法FFT。高鐵原始數(shù)據(jù)解包作為高鐵數(shù)據(jù)預(yù)處理的第一步,為后期的數(shù)據(jù)預(yù)處理過(guò)程如數(shù)據(jù)平滑、去除異常點(diǎn)和去除線性趨勢(shì)項(xiàng)等奠定了數(shù)據(jù)基礎(chǔ),對(duì)其并行化解決了傳統(tǒng)解包算法處理測(cè)試數(shù)據(jù)集的瓶頸。實(shí)驗(yàn)證實(shí),該算法在并行性方而表現(xiàn)良好。 為了給工程人員提供一個(gè)系統(tǒng)化的數(shù)據(jù)預(yù)處理環(huán)境,本文設(shè)計(jì)了一個(gè)基于云計(jì)算的高鐵數(shù)據(jù)預(yù)處理系統(tǒng),其將多種并行數(shù)據(jù)預(yù)處理算法整合到一起,并且提供了對(duì)Hadoop集群的配置功能,工程人員只需要在系統(tǒng)中按需求提交處理任務(wù),系統(tǒng)通過(guò)分析將任務(wù)操作步驟轉(zhuǎn)交給Hadoop系統(tǒng),待處理完畢之后,工程人員便可將處理結(jié)果下載到本地,極大地方便了數(shù)據(jù)預(yù)處理過(guò)程。 FFT作為離散傅里葉變換的一種快速算法,成為了數(shù)字信號(hào)分析領(lǐng)域中重要的工具,廣泛應(yīng)用于圖像處理和通信技術(shù)等領(lǐng)域。高鐵信號(hào)數(shù)據(jù)處理中也同樣需要用到FFT算法,然而傳統(tǒng)的串行FFT算法并不能適應(yīng)大規(guī)模的高鐵運(yùn)行數(shù)據(jù)。于是,本文基于云計(jì)算技術(shù)設(shè)計(jì)了一個(gè)并行FFT算法,實(shí)驗(yàn)證明,該算法在準(zhǔn)確率方而與串行算法結(jié)果保持一致,且節(jié)點(diǎn)間的并行性提升了運(yùn)算效率,可以適應(yīng)大規(guī)模的高鐵數(shù)據(jù)集處理需求。
[Abstract]:The continuous progress of science and technology has promoted the rapid development of high-speed railway. As an energy-saving, environmental-friendly, fast and punctual transportation, it has become the first choice for people to travel. With the continuous improvement and improvement of high-speed equipment, safety and comfort have become the focus of attention. In order to ensure the safe operation of the train, we need to analyze and process the train operation data in real time, master the operation state of the train, and make the decision judgment. In order to obtain enough information of high-speed train operation, engineers use a large number of sensors to collect various kinds of signal data, such as noise and vibration. However, analyzing and processing this kind of large-scale growth data brings unprecedented challenges to traditional signal analysis methods. Cloud computing, as a new parallel processing technology, integrates grid computing. Distributed computing and parallel computing have excellent performance in big data computing and network storage. As a cloud computing framework, Hadoop includes file system HDFS and MapReduce programming model. It has the characteristics of high reliability and high fault tolerance, especially the MapReduce model, which adopts the design idea of divide-and-conquer. It effectively resolves the challenge brought by big data to the modern programming process. This paper uses cloud computing technology to parallelize the data preprocessing and signal analysis algorithms which are very important in the field of high-speed rail data processing, including the fast Fourier transform algorithm (FFTFT), which is widely used in high-speed rail raw data unpacking algorithm and digital signal analysis. As the first step of high-speed railway data preprocessing, the unpacking of raw data of high speed rail lays the data foundation for the later data preprocessing process such as data smoothing, removing abnormal points and removing linear trend items, etc. Parallelization solves the bottleneck of traditional unpacking algorithm in processing test data sets. Experiments show that the algorithm performs well in parallelism. In order to provide a systematic data preprocessing environment for engineers, this paper designs a high-speed railway data preprocessing system based on cloud computing, which integrates a variety of parallel data preprocessing algorithms. And the configuration function of Hadoop cluster is provided. Engineers only need to submit the processing task according to the requirement in the system. The system passes the task operation steps to the Hadoop system through the analysis, and after the processing is finished, Engineers can download the processing results to the local, which greatly facilitates the data preprocessing process. As a fast algorithm of discrete Fourier transform (DFT), FFT has become an important tool in the field of digital signal analysis, and has been widely used in image processing and communication technology. The FFT algorithm is also used in high-speed rail signal data processing, but the traditional serial FFT algorithm can not adapt to the large-scale high-speed rail operation data. Therefore, this paper designs a parallel FFT algorithm based on cloud computing technology. Experimental results show that the algorithm is consistent with the result of serial algorithm in accuracy, and the parallelism between nodes improves the computing efficiency. It can meet the needs of large-scale high-speed railway data set processing.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：TP338.6

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 孫苗鐘;;基于MATLAB的振動(dòng)信號(hào)平滑處理方法[J];電子測(cè)量技術(shù);2007年06期

2 孫志堅(jiān) ,劉學(xué)梅 ,黃建喜 ,肖欣;用FPGA實(shí)現(xiàn)SAR實(shí)時(shí)信號(hào)處理的進(jìn)一步研究[J];電子技術(shù)應(yīng)用;2005年02期

3 朱曉航;劉亞男;;OFDM中的FFT模塊設(shè)計(jì)及其FPGA實(shí)現(xiàn)[J];電子科技;2007年02期

4 郭進(jìn);張亞?wèn)|;;中國(guó)高速鐵路信號(hào)系統(tǒng)分析與思考[J];北京交通大學(xué)學(xué)報(bào);2012年05期

5 林偉偉;張志立;齊德昱;;基于網(wǎng)格的分布并行計(jì)算策略[J];計(jì)算機(jī)工程;2006年09期

6 趙成兵;李天瑞;王仲剛;高子U，

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