本文選題：物聯(lián)網(wǎng) + 數(shù)據(jù)融合　； 參考：《石家莊鐵道大學(xué)》2014年碩士論文

【摘要】：物聯(lián)網(wǎng)的產(chǎn)生提高了生產(chǎn)的效率、增加了生活的便利,隨著數(shù)據(jù)源的成倍增加、傳感識別層的設(shè)備也隨著增加,傳感端的數(shù)據(jù)量級也不斷增大。利用數(shù)據(jù)融合技術(shù)對傳感器及處理設(shè)備的架構(gòu)進行合理的組織、對數(shù)據(jù)進行二次處理有很大的必要。同時,由于“云”服務(wù)、新一代網(wǎng)絡(luò)等新技術(shù)的引入,物聯(lián)網(wǎng)中數(shù)據(jù)融合的研究已經(jīng)成為一個重要的課題。因此,本文做了以下工作： 介紹了物聯(lián)網(wǎng)中數(shù)據(jù)融合的起源、優(yōu)勢、應(yīng)用及發(fā)展現(xiàn)狀,以數(shù)據(jù)融合模型與數(shù)據(jù)融合算法兩方面的角度對國內(nèi)外研究物聯(lián)網(wǎng)數(shù)據(jù)融合的成果進行總結(jié),并指出數(shù)據(jù)融合在物聯(lián)網(wǎng)中發(fā)展現(xiàn)狀和面臨的挑戰(zhàn)。 對融合算法的概念、背景、級別分類等內(nèi)容進行了具體的介紹,并對數(shù)據(jù)融合模型與數(shù)據(jù)融合算法進行了詳細描述。它們決定了系統(tǒng)的性能,其實現(xiàn)的基礎(chǔ)功能對上層的高級服務(wù)提供支持。 簡要的介紹了物聯(lián)網(wǎng)大數(shù)據(jù)出現(xiàn)的背景,并分析了物聯(lián)網(wǎng)中大數(shù)據(jù)量融合處理的需求,接著對傳統(tǒng)的壓縮融合算法優(yōu)缺點進行了分析和總結(jié)。經(jīng)典的壓縮融合算法實現(xiàn)過程與運行簡單、處理開銷小。但是其中的絕對增量技術(shù)不具有一般的糾錯功能,對于異常的讀數(shù)處理起來比較困難。而對于相對增量技術(shù),原始數(shù)據(jù)的變化范圍受到融合數(shù)據(jù)空間的限制,如果相鄰數(shù)據(jù)差異過大將無法被記錄,將未做任何的判斷就被當成異常點進行丟棄。針對經(jīng)典壓縮算法的不足,提出了嵌套式的增量壓縮融合方法。試驗結(jié)果表明,在數(shù)據(jù)量相同的情況下,嵌套增量壓縮算法的壓縮比優(yōu)于經(jīng)典的增量壓縮算法,四種算法在壓縮時間上性能相差較小,適合實時存儲。而在解壓上,嵌套式的方法時間較長,尤其是時間嵌套的方式,這是因為在壓縮后,為了突出分布式處理的特點,數(shù)據(jù)進行了打包存儲,占用了時間,而數(shù)據(jù)間隔的方法具有簡單的糾錯功能,增加了時間的開銷。 分析了蕪湖大橋的健康監(jiān)測系統(tǒng),對整體架構(gòu)、傳感器布設(shè)等進行了介紹,對預(yù)警系統(tǒng)的需求進行了分析：將監(jiān)控的變量鎖定在縱向位移上,將監(jiān)控時間分為列車通過時斷和無列車上橋時段。對整體融合模型和存儲結(jié)構(gòu)進行了設(shè)計,針對系統(tǒng)的分析,將融合算法分為兩個模塊：預(yù)警模塊與監(jiān)控模塊進行實現(xiàn),通過試驗證明,設(shè)計的系統(tǒng)所模擬得到的縱向位移曲線與現(xiàn)實監(jiān)測的震動曲線非常相似,并且相對誤差與震動位移的大小呈負相關(guān)。 最后對全文的工作做了總結(jié),并對下一步的研究工作進行了展望。
[Abstract]:The production of the Internet of things improves the efficiency of production and increases the convenience of life. With the increase of data sources, the equipment of sensor recognition layer increases, and the data level of sensor end increases. The data fusion technology is used to organize the structure of sensor and processing equipment reasonably, and it is necessary to process the data twice. At the same time, due to the introduction of new technologies such as cloud services and new generation networks, the research of data fusion in the Internet of things has become an important subject. Therefore, this paper does the following work: This paper introduces the origin, advantage, application and development of data fusion in Internet of things, and summarizes the research results of data fusion of Internet of things at home and abroad from two aspects: data fusion model and data fusion algorithm. The status quo and challenges of data fusion in the Internet of things are also pointed out. The concept, background and classification of the fusion algorithm are introduced in detail, and the data fusion model and the data fusion algorithm are described in detail. They determine the performance of the system, its implementation of the basic functions to support the upper level of advanced services. This paper briefly introduces the background of the emergence of big data in the Internet of things, and analyzes the requirements of mass data fusion in the Internet of things, and then analyzes and summarizes the advantages and disadvantages of the traditional compression fusion algorithm. The classic compression fusion algorithm is simple to implement and run, and the processing overhead is small. But the absolute increment technique does not have the general error correction function, so it is difficult to deal with the abnormal reading. For the relative increment technique, the range of the original data is limited by the fusion data space. If the difference between the adjacent data is too large, it will be discarded as the outlier if the difference between the adjacent data is too large to be recorded. A nested incremental compression fusion method is proposed to overcome the shortcomings of classical compression algorithms. The experimental results show that the compression ratio of the nested incremental compression algorithm is better than that of the classical incremental compression algorithm when the amount of data is the same, and the performance of the four algorithms is small in compression time, which is suitable for real-time storage. In decompression, the nested method takes a long time, especially the time nesting method, because after compression, in order to highlight the characteristics of distributed processing, the data is packed and stored, which takes up time. The method of data interval has simple error correction function and increases the time cost. This paper analyzes the health monitoring system of Wuhu Bridge, introduces the whole structure and sensor layout, and analyzes the requirements of the early warning system: locking the monitored variables on the longitudinal displacement, The monitoring time is divided into two periods: the train is broken and the train is not on the bridge. The whole fusion model and storage structure are designed. According to the analysis of the system, the fusion algorithm is divided into two modules: early warning module and monitoring module. The longitudinal displacement curve simulated by the designed system is very similar to the vibration curve monitored in reality, and the relative error is negatively correlated with the magnitude of vibration displacement. Finally, the work of the full text is summarized, and the next research work is prospected.
【學(xué)位授予單位】：石家莊鐵道大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP391.44;TN929.5

【參考文獻】

相關(guān)期刊論文 前2條

1 孫其博;劉杰;黎，

本文編號：1787555