本文關鍵詞： 射頻識別 相位 改進的K鄰近算法 層次聚類算法　出處：《太原理工大學》2017年碩士論文　論文類型：學位論文

【摘要】：近幾年,非接觸的射頻識別技術(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、遠距離通信、無線識別、具有一定存儲能力且無需經常人工維護等諸多特點,RFID成為信息數(shù)據(jù)收集領域的重要部分。隨著我國物聯(lián)網(wǎng)(Internet of Things,IoT)產業(yè)的飛速發(fā)展,RFID已廣泛地應用于包括,管理供應鏈、跟蹤牲畜、防止假冒、門禁系統(tǒng)、自動結帳以及圖書館書籍跟蹤等諸多領域。無源RFID系統(tǒng)具有無需內置電池、無線識別、成本低等優(yōu)勢,使其成為商場購物數(shù)據(jù)分析的重要技術。本文將RFID系統(tǒng)應用于商場購物數(shù)據(jù)的深度分析,通過對商品各個狀態(tài)的實時信息采集和分析,挖掘顧客的感興趣商品和相關的商品,以及商場的熱點區(qū)域。為商場針對性地進貨、促銷、以及商店布局提供了科學的理論依據(jù),進而能夠根據(jù)客戶的喜好推薦相關產品,為顧客提供更高質量的服務。但如何在海量標簽同時存在,同時移動的情況下,準確、高效完成購物數(shù)據(jù)的收集和分析是難點問題�，F(xiàn)有的多數(shù)數(shù)據(jù)分析算法時延大、能耗大且算法復雜不易實現(xiàn),都很難高效、可靠且具有針對性地解決商場購物數(shù)據(jù)準確、深入分析的問題。本文提出的購物數(shù)據(jù)分析算法,針對性地解決了超市購物數(shù)據(jù)深入分析問題。首先使用閱讀器收集無源RFID標簽的相位信息,將收集的相位信息轉換為商品的相對移動速度。其次,考慮到密集放置的RFID標簽間的相互干擾,針對性地找出了在大型場所中密集放置RFID時的變化規(guī)律,并在此基礎上對k最鄰近算法(k-Nearest Neighbor,kNN)做出改進,提出了改進的k NN算法(Improved k-Nearest Neighbor,I-kNN),利用I-kNN對收集到的相對移動速度序列進行分析,對不同狀態(tài)商品進行分類。之后,利用層次聚類(Hierarchical Agglomerative Clustering,HAC)算法將訓練樣本集中的每個數(shù)據(jù)點都當做一個聚類,通過計算兩個聚類之間的距離,不斷地將速度相近的商品進行合并,識別出各類別商品的相關性。最后,利用現(xiàn)有的商用設備,對所提出的系統(tǒng)建立原型,并進行了算法的實現(xiàn)和性能評估。結果表明,我們的方法在購物數(shù)據(jù)分析算法在實際中是可行的,在計算量和時間延遲方面明顯優(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.
【學位授予單位】：太原理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP391.44

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本文編號：1494441