【摘要】：隨著能源問(wèn)題與環(huán)境問(wèn)題的逐漸顯現(xiàn),電動(dòng)汽車(chē)因?yàn)槲廴拘〉膬?yōu)點(diǎn)成為各國(guó)研究熱點(diǎn),為了保證電動(dòng)汽車(chē)的可靠運(yùn)行,需要對(duì)電動(dòng)汽車(chē)運(yùn)行進(jìn)行監(jiān)控,所以電動(dòng)汽車(chē)監(jiān)控很有必要。電動(dòng)汽車(chē)監(jiān)控主要分為電池監(jiān)控與GPS監(jiān)控,本文開(kāi)發(fā)的GIS系統(tǒng)是對(duì)GPS數(shù)據(jù)的監(jiān)控。 對(duì)GIS系統(tǒng)的總體功能與子系統(tǒng)進(jìn)行設(shè)計(jì),在設(shè)計(jì)階段采用物理結(jié)構(gòu)圖對(duì)系統(tǒng)物理結(jié)構(gòu)進(jìn)行分析,并在此基礎(chǔ)上對(duì)系統(tǒng)功能模塊進(jìn)行劃分,之后通過(guò)活動(dòng)圖、數(shù)據(jù)流圖等分析方法確定各個(gè)子系統(tǒng)的設(shè)計(jì)方案,并確定各個(gè)子系統(tǒng)的開(kāi)發(fā)技術(shù),在此基礎(chǔ)上,對(duì)系統(tǒng)進(jìn)行開(kāi)發(fā)實(shí)現(xiàn)。 在對(duì)系統(tǒng)數(shù)據(jù)分析過(guò)程中,發(fā)現(xiàn)GPS數(shù)據(jù)有數(shù)據(jù)缺失的情況,因此文中對(duì)缺失數(shù)據(jù)插補(bǔ)進(jìn)行研究。首先將缺失數(shù)據(jù)進(jìn)行分類(lèi),分為單個(gè)點(diǎn)缺失與連續(xù)點(diǎn)缺失,在此基礎(chǔ)上針對(duì)兩種類(lèi)型提出不同的插補(bǔ)方法,單點(diǎn)缺失中重點(diǎn)研究了組均值插補(bǔ),連續(xù)點(diǎn)缺失重點(diǎn)研究多點(diǎn)三次樣條插值方法,得出插補(bǔ)結(jié)果后通過(guò)均方差比較各種方法的插值效果。均方差定量分析表明,上述方法可以較好的達(dá)到數(shù)據(jù)插補(bǔ)的需要。 在系統(tǒng)開(kāi)發(fā)中會(huì)出現(xiàn)GPS位置點(diǎn)發(fā)生偏移的情況,針對(duì)這種情況,文中研究了地圖匹配方法,引入道路匹配權(quán)值的概念,將相似性系數(shù)、靠近性系數(shù)、交叉點(diǎn)相似性系數(shù)作為匹配權(quán)值的計(jì)算依據(jù)。在此基礎(chǔ)上,研究車(chē)輛從啟動(dòng)到運(yùn)行過(guò)程中的地圖匹配方法,最終將匹配方法在系統(tǒng)中模擬實(shí)驗(yàn),實(shí)驗(yàn)表明文中提出的地圖匹配方法可以準(zhǔn)確判斷不同路段情況下的道路匹配。
[Abstract]:With the gradual emergence of energy and environmental problems, electric vehicles, because of the advantages of small pollution, have become the focus of research all over the world. In order to ensure the reliable operation of electric vehicles, it is necessary to monitor the operation of electric vehicles. So electric vehicle monitoring is necessary. Electric vehicle monitoring is mainly divided into battery monitoring and GPS monitoring. The GIS system developed in this paper is the monitoring of GPS data. The overall function and subsystem of GIS system are designed, the physical structure of the system is analyzed by physical structure diagram in the design stage, and then the functional modules of the system are divided, and then the activity diagram is adopted. Data flow diagram and other analysis methods determine the design scheme of each subsystem, and determine the development technology of each subsystem. On this basis, the system is developed and implemented. In the process of analyzing the system data, the missing data is found in the GPS data, so the missing data interpolation is studied in this paper. Firstly, the missing data are classified into single point deletion and continuous point deletion. On this basis, different interpolation methods are proposed for the two types. In the single point deletion, the group mean interpolation is mainly studied. The multi-point cubic spline interpolation method is studied in the absence of continuous points. The interpolation results are obtained and the interpolation results are compared by the mean square error (RMS). The quantitative analysis of mean square error shows that the above method can meet the need of data interpolation. In this paper, the map matching method is studied, the concept of road matching weight value is introduced, the similarity coefficient, the proximity coefficient, the similarity coefficient and the similarity coefficient are introduced. The similarity coefficient of intersection points is used as the basis for the calculation of matching weights. On this basis, the paper studies the map matching method in the process of vehicle starting to running, and finally simulates the matching method in the system. The experiment shows that the map matching method proposed in this paper can accurately judge the road matching in different road sections.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類(lèi)號(hào)】：U469.72;P208

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本文編號(hào)：2180416