【摘要】：煤礦井下人員定位系統(tǒng)是煤礦企業(yè)實(shí)現(xiàn)安全生產(chǎn)的重要保證之一。隨著采煤企業(yè)規(guī)模的不斷擴(kuò)大化,由定位設(shè)備采集的井下人員位置數(shù)據(jù)也在快速增長。為了實(shí)現(xiàn)對礦井人員更加規(guī)范與合理的安全管理能力,煤礦井下人員定位系統(tǒng)勢必要存儲(chǔ)和傳輸更加海量的復(fù)雜數(shù)據(jù)。通過對海量人員位置數(shù)據(jù)的壓縮優(yōu)化,不僅可以有效減少系統(tǒng)存儲(chǔ)和傳輸海量數(shù)據(jù)的負(fù)擔(dān),降低帶寬資源的消耗,還可以大量縮減人員位置數(shù)據(jù)的冗余度,對井下各類工種的行為軌跡分析和挖掘提供便利。由于傳統(tǒng)的壓縮算法沒有結(jié)合定位數(shù)據(jù)的時(shí)空特性,只是從文本的角度來進(jìn)行壓縮,因此,基于通用型的數(shù)據(jù)壓縮算法并無法達(dá)到較高的壓縮比。通過分析定位數(shù)據(jù)的時(shí)空特性,論文將定位數(shù)據(jù)的壓縮劃分為對軌跡類數(shù)據(jù)壓縮問題。目前,軌跡壓縮算法多是針對于車輛定位數(shù)據(jù),通過對部分特征點(diǎn)的保留而實(shí)現(xiàn)對軌跡數(shù)據(jù)的粗粒度壓縮。該算法雖然能夠?qū)崿F(xiàn)極高的壓縮比,但是無法保證壓縮質(zhì)量。本文通過對現(xiàn)有軌跡壓縮算法的研究與對比,并結(jié)合煤礦井下人員定位系統(tǒng)的定位原理和人員位置數(shù)據(jù)的特點(diǎn),從而實(shí)現(xiàn)對人員定位數(shù)據(jù)的壓縮。首先,綜合分析了相關(guān)軌跡算法的優(yōu)勢和缺陷,進(jìn)而選取基于路網(wǎng)特征的算法思想實(shí)現(xiàn)了以巷道信息來描述人員軌跡信息的初步壓縮方案。其次,選取人員在巷道中的折返、停留信息作為人員運(yùn)動(dòng)特征的關(guān)鍵信息。通過對井下人員在巷道中折返點(diǎn)以及長時(shí)間停留點(diǎn)的分析和判定,實(shí)現(xiàn)了對井下人員在巷道內(nèi)的具體運(yùn)動(dòng)情況的記錄,從而完成人員位置數(shù)據(jù)的壓縮。最后,論文采用時(shí)間比例等距劃分的方式,逐步對相鄰關(guān)鍵點(diǎn)之間信息進(jìn)行重構(gòu),實(shí)現(xiàn)了數(shù)據(jù)的解壓縮。相較于簡單的基于路網(wǎng)特征的軌跡壓縮算法,應(yīng)用改進(jìn)算法對井下人員位置數(shù)據(jù)壓縮,不但可以獲得較高的壓縮率,而且可以顯著降低重構(gòu)數(shù)據(jù)的誤差,提高壓縮數(shù)據(jù)的質(zhì)量和應(yīng)用價(jià)值。結(jié)合井下人員的運(yùn)動(dòng)受巷道分布情況限制的特性,人員的運(yùn)動(dòng)軌跡會(huì)遵循特定的規(guī)律。對此,提出基于樣本軌跡的軌跡壓縮算法。在試驗(yàn)中,利用若干樣本軌跡對海量新軌跡進(jìn)行重構(gòu),實(shí)現(xiàn)了用樣本軌跡對海量軌跡數(shù)據(jù)的壓縮,獲得較高的壓縮率。
[Abstract]:The personnel positioning system in coal mine is one of the important guarantee for coal mine enterprises to realize safe production. With the expansion of the scale of coal mining enterprises, the location data of underground personnel collected by positioning equipment are also growing rapidly. In order to achieve a more standardized and reasonable safety management ability for mine personnel, the mine personnel positioning system is bound to store and transmit more massive complex data. By optimizing the compression of massive human location data, it can not only effectively reduce the burden of storing and transmitting massive data, reduce the consumption of bandwidth resources, but also greatly reduce the redundancy of personnel location data. It is convenient to analyze and excavate the behavior track of all kinds of underground work. Because the traditional compression algorithm does not combine the spatio-temporal characteristics of the location data, but only from the point of view of the text, the data compression algorithm based on general purpose can not achieve a higher compression ratio. By analyzing the spatial and temporal characteristics of the location data, the compression of the location data is divided into the compression of the locus data. At present, trajectory compression algorithms are mostly aimed at vehicle location data, and the coarse granularity compression of track data is realized by preserving some feature points. Although the algorithm can achieve extremely high compression ratio, it can not guarantee the compression quality. Based on the research and comparison of the existing algorithms of trajectory compression, combined with the positioning principle of the personnel positioning system in coal mine and the characteristics of the personnel position data, the compression of the personnel location data can be realized in this paper. Firstly, the advantages and disadvantages of the related trajectory algorithm are analyzed synthetically, and then the algorithm based on the road network features is selected to realize the preliminary compression scheme of describing the human trajectory information by the laneway information. Secondly, the key information of the movement of the personnel is to select the reentry and stay information in the laneway. Through the analysis and judgment of the entry point and the long stay point in the roadway, the record of the specific movement of the downhole personnel in the tunnel is realized, and the data of the personnel position is compressed. Finally, the paper reconstructs the information between the adjacent key points step by using the method of time proportional isometric partition, and realizes the decompression of the data. Compared with the simple path compression algorithm based on road network features, the improved algorithm can not only obtain a higher compression ratio, but also reduce the error of reconstruction data significantly. Improve the quality and application value of compressed data. Combined with the characteristic that the movement of underground personnel is restricted by the distribution of roadway, the movement track of personnel will follow a specific law. A trajectory compression algorithm based on sample trajectory is proposed. In the experiment, several sample trajectories are used to reconstruct the massive new trajectories, and the compression ratio of the massive trajectory data is obtained by using the sample trajectories.
【學(xué)位授予單位】：太原科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TD76

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 程倩;丁云峰;;基于路網(wǎng)的GPS軌跡在線壓縮方法[J];計(jì)算機(jī)系統(tǒng)應(yīng)用;2016年06期

2 張鑫宇;宋瑾鈺;;LBS系統(tǒng)研究現(xiàn)狀綜述[J];工業(yè)控制計(jì)算機(jī);2016年04期

3 樊慶富;張磊;劉磊軍;鮑蘇寧;房晨;;基于偏移量計(jì)算的在線GPS軌跡數(shù)據(jù)壓縮[J];計(jì)算機(jī)工程與應(yīng)用;2017年08期

4 劉磊軍;房晨;張磊;鮑蘇寧;;基于運(yùn)動(dòng)狀態(tài)改變的在線全球定位系統(tǒng)軌跡數(shù)據(jù)壓縮[J];計(jì)算機(jī)應(yīng)用;2016年01期

5 江俊文;張凱;王曉玲;金澈清;;基于行駛特征的軌跡壓縮技術(shù)[J];計(jì)算機(jī)科學(xué)與探索;2016年09期

6 江俊文;王曉玲;;軌跡數(shù)據(jù)壓縮綜述[J];華東師范大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年05期

7 李川;張彪;李艷梅;楊寧;王勇;;路網(wǎng)空間中GPS軌跡壓縮的新方法[J];北京郵電大學(xué)學(xué)報(bào);2015年02期

8 李亞兵;;煤礦安全監(jiān)測監(jiān)控系統(tǒng)的發(fā)展現(xiàn)狀及發(fā)展趨勢分析[J];科技資訊;2014年21期

9 張達(dá)夫;張昕明;;基于時(shí)空特性的GPS軌跡數(shù)據(jù)壓縮算法[J];交通信息與安全;2013年03期

10 夏圣凱;王常法;江有福;;船載AIS數(shù)據(jù)分段實(shí)時(shí)壓縮改進(jìn)方法[J];科技視界;2013年09期

相關(guān)博士學(xué)位論文 前2條

1 呂振華;移動(dòng)對象軌跡數(shù)據(jù)挖掘研究[D];華東師范大學(xué);2016年

2 吳建軍;城市交通網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu)復(fù)雜性研究[D];北京交通大學(xué);2008年

相關(guān)碩士學(xué)位論文 前10條

1 潘偉;定位數(shù)據(jù)壓縮算法研究[D];大連工業(yè)大學(xué);2015年

2 曹靜;基于GPS數(shù)據(jù)的用戶軌跡相似性分析[D];中國海洋大學(xué);2015年

3 李文靜;基于Hadoop的道路匹配算法與車輛行駛軌跡還原系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];西北大學(xué);2014年

4 趙俊杰;煤礦井下人員定位系統(tǒng)的設(shè)計(jì)與實(shí)現(xiàn)[D];河南師范大學(xué);2014年

5 馮神柱;路網(wǎng)軌跡數(shù)據(jù)的壓縮存儲(chǔ)技術(shù)研究[D];杭州電子科技大學(xué);2014年

6 趙迪華;車輛導(dǎo)航系統(tǒng)中的地圖匹配算法研究[D];云南大學(xué);2012年

7 宋岸峰;實(shí)時(shí)數(shù)據(jù)并行壓縮技術(shù)的研究[D];華中科技大學(xué);2012年

8 張楠;可持續(xù)發(fā)展視角下大型煤炭企業(yè)發(fā)展戰(zhàn)略研究[D];首都經(jīng)濟(jì)貿(mào)易大學(xué);2011年

9 石楠;基于RFID的井下人員定位系統(tǒng)的軟件設(shè)計(jì)與實(shí)現(xiàn)[D];西安科技大學(xué);2010年

10 文水英;實(shí)時(shí)數(shù)據(jù)庫中歷史數(shù)據(jù)壓縮算法的研究[D];中南大學(xué);2008年

，

本文編號：2122612