本文關(guān)鍵詞： 射頻識(shí)別技術(shù) 暢銷類標(biāo)簽 時(shí)間有效性 能量有效性　出處：《太原理工大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：最早的射頻識(shí)別技術(shù)（Radio Frequency Identification，RFID）源于20世紀(jì)90年代，它是人類在科技發(fā)展道路上的重大進(jìn)展，改變了人類的消費(fèi)方式與習(xí)慣。隨著射頻識(shí)別技術(shù)的逐漸成熟，該技術(shù)越來越多地被人們應(yīng)用于物流業(yè)、智能門禁系統(tǒng)、目標(biāo)定位、追蹤和識(shí)別等關(guān)鍵應(yīng)用，且其所具備的優(yōu)越性已日益受到全國的重視與應(yīng)用，它的發(fā)展?fàn)顩r成為各界矚目的焦點(diǎn)。 所謂暢銷類產(chǎn)品就是指該類別的產(chǎn)品數(shù)量超過了一定的閾值，其具有鮮明的季節(jié)性特點(diǎn)。在商品的銷售過程中，商家往往希望了解顧客的喜好，找出比較暢銷的幾類產(chǎn)品，來決定下一步的營銷策略。由于用戶需求和喜好的不同，再加上其他等等因素的影響，暢銷類產(chǎn)品是在實(shí)時(shí)變化的，而并不是一成不變的。因此，隨時(shí)對產(chǎn)品進(jìn)行不斷調(diào)整并對銷售狀況進(jìn)行實(shí)時(shí)跟蹤變得越來越重要。而RFID技術(shù)的出現(xiàn)，，很好的滿足了這種實(shí)時(shí)性的需求，只需在待識(shí)別的物品上附著RFID電子標(biāo)簽，經(jīng)過相應(yīng)的算法便能及時(shí)查找出暢銷類產(chǎn)品，便于商家日后更好地進(jìn)行銷售。 現(xiàn)有的關(guān)于識(shí)別暢銷類標(biāo)簽的算法都必須掃描范圍內(nèi)的所有標(biāo)簽來進(jìn)行識(shí)別，且這些算法都只關(guān)注于時(shí)間的研究，并沒有對能量進(jìn)行研究。針對這些不足，本文提出了反向閾值（Reserve Threshold-Based Scheme，RTBS）暢銷類標(biāo)簽識(shí)別算法和正向閾值（Positive Threshold-Based Scheme，PTBS）暢銷類標(biāo)簽識(shí)別算法，這兩種算法的主要目的是在眾多標(biāo)簽中以高效、節(jié)能的方式識(shí)別出暢銷類標(biāo)簽。RTBS和PTBS算法不僅能夠?qū)﹂喿x器識(shí)別范圍內(nèi)的一個(gè)標(biāo)簽子集進(jìn)行暢銷類標(biāo)簽的識(shí)別，而且還首次考慮到了識(shí)別暢銷類標(biāo)簽過程中的能量問題。 首先，RTBS和PTBS兩種算法都是從所有標(biāo)簽中找出指定集合中的標(biāo)簽，然后，通過Hash函數(shù)選取樣本標(biāo)簽并進(jìn)行映射，其次，通過閱讀器和標(biāo)簽之間的通信獲取時(shí)隙的狀態(tài)并對其進(jìn)行分析和處理，從而識(shí)別出暢銷類標(biāo)簽。不同的是，RTBS采用的是反向思維方式，而PTBS采用的是正向思維方式。且在搜索階段，RTBS算法采用的是基礎(chǔ)的 點(diǎn)名‖機(jī)制，即廣播標(biāo)簽的類別ID；而PTBS算法采用的是雙布魯姆濾波器取交集的方法。仿真結(jié)果表明，RTBS和PTBS兩種算法都夠識(shí)別出來暢銷類標(biāo)簽，并都具有時(shí)間有效性和能量有效性。
[Abstract]:Radio Frequency Identification (RFID), the earliest RFID technology, originated in 1990s. It is a great progress in the development of science and technology, and has changed human consumption patterns and habits. This technology has been used more and more in the logistics industry, intelligent access control system, target positioning, tracking and identification and other key applications, and its advantages have been paid more and more attention and application. Its development status has become the focus of attention from all walks of life. The so-called best-selling products mean that the number of products in this category exceeds a certain threshold and has distinctive seasonal characteristics. In the process of selling goods, merchants often want to know the preferences of customers and find out some of the best-selling products. To determine the next marketing strategy. Because of different user needs and preferences, and other factors, the best-selling products are changing in real time, not inflexible. It is becoming more and more important to constantly adjust the product and track the sales status in real time. However, the appearance of RFID technology can meet the requirement of real time, just attach RFID tag to the object to be identified. After the corresponding algorithm can find out the best-selling products in time, so that merchants can sell better in the future. Existing algorithms for identifying best-selling class tags must scan all tags in range, and these algorithms focus only on time, not on energy. In this paper, we propose a reverse threshold Threshold-Based Threshold-Based Schema) best-selling class label recognition algorithm and a forward threshold Positive Threshold-Based Schema best-selling class label recognition algorithm. The main purpose of these two algorithms is to achieve high efficiency in a large number of tags. The best selling class tags. RTBS and PTBS algorithm can not only identify a subset of the best selling class tags in the recognition range of readers, but also consider the energy problem in the process of identifying the best selling class labels for the first time. First, the two algorithms of PTBS and RTBS are to find out the tags in the specified collection from all the tags, then, the sample labels are selected and mapped through the Hash function. Secondly, Through the communication between readers and tags, the status of time slots is obtained, analyzed and processed to identify the best-selling class tags. The difference is that RTBS uses reverse thinking. PTBS adopts a positive thinking mode, and in the search phase, it uses the basic naming mechanism. The PTBS algorithm uses a double Bloom filter to select the intersection method. The simulation results show that both the two algorithms can identify the best-selling class labels, and both have time-efficient and energy-efficient.
【學(xué)位授予單位】：太原理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TP391.44

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