本文關(guān)鍵詞： 射頻識(shí)別 相位 改進(jìn)的K鄰近算法 層次聚類(lèi)算法　出處：《太原理工大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：近幾年,非接觸的射頻識(shí)別技術(shù)(Radio Frequency Identification,RFID)已成為人們生活中不可或缺的一部分,憑借著自身體積小、遠(yuǎn)距離通信、無(wú)線識(shí)別、具有一定存儲(chǔ)能力且無(wú)需經(jīng)常人工維護(hù)等諸多特點(diǎn),RFID成為信息數(shù)據(jù)收集領(lǐng)域的重要部分。隨著我國(guó)物聯(lián)網(wǎng)(Internet of Things,IoT)產(chǎn)業(yè)的飛速發(fā)展,RFID已廣泛地應(yīng)用于包括,管理供應(yīng)鏈、跟蹤牲畜、防止假冒、門(mén)禁系統(tǒng)、自動(dòng)結(jié)帳以及圖書(shū)館書(shū)籍跟蹤等諸多領(lǐng)域。無(wú)源RFID系統(tǒng)具有無(wú)需內(nèi)置電池、無(wú)線識(shí)別、成本低等優(yōu)勢(shì),使其成為商場(chǎng)購(gòu)物數(shù)據(jù)分析的重要技術(shù)。本文將RFID系統(tǒng)應(yīng)用于商場(chǎng)購(gòu)物數(shù)據(jù)的深度分析,通過(guò)對(duì)商品各個(gè)狀態(tài)的實(shí)時(shí)信息采集和分析,挖掘顧客的感興趣商品和相關(guān)的商品,以及商場(chǎng)的熱點(diǎn)區(qū)域。為商場(chǎng)針對(duì)性地進(jìn)貨、促銷(xiāo)、以及商店布局提供了科學(xué)的理論依據(jù),進(jìn)而能夠根據(jù)客戶的喜好推薦相關(guān)產(chǎn)品,為顧客提供更高質(zhì)量的服務(wù)。但如何在海量標(biāo)簽同時(shí)存在,同時(shí)移動(dòng)的情況下,準(zhǔn)確、高效完成購(gòu)物數(shù)據(jù)的收集和分析是難點(diǎn)問(wèn)題�，F(xiàn)有的多數(shù)數(shù)據(jù)分析算法時(shí)延大、能耗大且算法復(fù)雜不易實(shí)現(xiàn),都很難高效、可靠且具有針對(duì)性地解決商場(chǎng)購(gòu)物數(shù)據(jù)準(zhǔn)確、深入分析的問(wèn)題。本文提出的購(gòu)物數(shù)據(jù)分析算法,針對(duì)性地解決了超市購(gòu)物數(shù)據(jù)深入分析問(wèn)題。首先使用閱讀器收集無(wú)源RFID標(biāo)簽的相位信息,將收集的相位信息轉(zhuǎn)換為商品的相對(duì)移動(dòng)速度。其次,考慮到密集放置的RFID標(biāo)簽間的相互干擾,針對(duì)性地找出了在大型場(chǎng)所中密集放置RFID時(shí)的變化規(guī)律,并在此基礎(chǔ)上對(duì)k最鄰近算法(k-Nearest Neighbor,kNN)做出改進(jìn),提出了改進(jìn)的k NN算法(Improved k-Nearest Neighbor,I-kNN),利用I-kNN對(duì)收集到的相對(duì)移動(dòng)速度序列進(jìn)行分析,對(duì)不同狀態(tài)商品進(jìn)行分類(lèi)。之后,利用層次聚類(lèi)(Hierarchical Agglomerative Clustering,HAC)算法將訓(xùn)練樣本集中的每個(gè)數(shù)據(jù)點(diǎn)都當(dāng)做一個(gè)聚類(lèi),通過(guò)計(jì)算兩個(gè)聚類(lèi)之間的距離,不斷地將速度相近的商品進(jìn)行合并,識(shí)別出各類(lèi)別商品的相關(guān)性。最后,利用現(xiàn)有的商用設(shè)備,對(duì)所提出的系統(tǒng)建立原型,并進(jìn)行了算法的實(shí)現(xiàn)和性能評(píng)估。結(jié)果表明,我們的方法在購(gòu)物數(shù)據(jù)分析算法在實(shí)際中是可行的,在計(jì)算量和時(shí)間延遲方面明顯優(yōu)于其他算法。
[Abstract]:In recent years, the contactless RFID technology, Radio Frequency Identification (RFID), has become an indispensable part of people's lives, relying on their small size, long-distance communication, wireless identification. With the rapid development of Internet of things of IoT industry in China, RFID has been widely used in including, managing supply chain, and so on. Tracking livestock, preventing counterfeiting, access control systems, automatic checkout and library book tracking. Passive RFID systems have the advantages of no built-in batteries, wireless identification, low cost, etc. In this paper, the RFID system is applied to the in-depth analysis of shopping data in shopping malls. Through the real-time information collection and analysis of the various states of goods, the paper excavates the goods of interest to customers and related commodities. And the hot spot area of the mall. It provides the scientific theoretical basis for the shopping mall to purchase, promote, and store layout, and then can recommend the relevant products according to the customer's preference. But how to complete the collection and analysis of shopping data accurately and efficiently is a difficult problem in the case of simultaneous existence of mass tags and simultaneous movement. Most existing data analysis algorithms have a long time delay. It is difficult to solve the problem of accurate and in-depth analysis of shopping data reliably and pertinently. This paper solves the problem of in-depth analysis of supermarket shopping data. First, we use readers to collect the phase information of passive RFID tags, and convert the collected phase information into the relative moving speed of goods. Taking into account the interference between densely placed RFID tags, this paper finds out the variation law of RFID in large places, and improves the k-nearest neighbor algorithm (k nearest neighbor NN). In this paper, an improved kNN algorithm is proposed to improve k-nearest neighbor I-kNNNs. By using I-kNN, the collected relative moving velocity series are analyzed, and the goods in different states are classified. The hierarchical Agglomerative clustering algorithm is used to treat every data point in the training sample set as a cluster. By calculating the distance between the two clusters, the items with similar speed are continuously merged. Finally, using the existing commercial equipment, the prototype of the proposed system is established, and the algorithm implementation and performance evaluation are carried out. The results show that, Our method is feasible in the analysis of shopping data and is superior to other algorithms in computation and time delay.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP391.44

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