基于软件屏蔽的不同存储区域实时库存统计:时尚零售的可行性分析

Giovanni Esposito, Davide Mezzogori, Mattia Neroni, A. Rizzi, Giovanni Romagnoli
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引用次数: 4

摘要

RFID是一项成熟的技术,在过去的几十年里,它在不同行业的实施一直在稳步增长。一个重要的和相对最近的RFID突破是移动标签的水平,从托盘或箱级,到项目级。这一发展开辟了一系列新的用例和好处,特别是在零售业。其中一个新的用例是通过定位和跟踪附加在物品上的标签来估计物品的位置。这个问题通常被视为分类问题,特别是当在零售商店读取的标签必须位于销售楼层或后台区域时。简化这种分类的典型方法包括通过硬件安装对感兴趣的区域进行物理屏蔽,尽管这种解决方案价格昂贵且缺乏灵活性。在本文中,我们提出了一种不同的解决方案,即基于软件的屏蔽方法,来解决分类问题。我们的解决方案利用了物品级RFID标签,并基于众所周知的逻辑回归。每当通过手持阅读器执行阅读会话时,分类模型实时(即在几秒钟内)估计标签物品与阅读器在同一区域,哪些不在同一区域,无需安装任何屏蔽硬件。根据本文提出的验证初步测试,我们模拟了一家时尚零售店,所提出的方法的总体平均准确率为95.5%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Software-based shielding for real-time inventory count in different store areas: A feasibility analysis in fashion retail
RFID is an established technology and its implementation has been increasing steadily in different industries in the last decades. An important and relatively recent RFID breakthrough has been that of moving the level of tagging from pallet- or case-level, to item-level. This development has opened up a new set of use cases and benefits, especially in retail. One of these new use cases is the estimation of items’ location by positioning and tracking the tags attached to them. This problem is often seen as a classification problem, especially when tags that are read at the retail store must be located either in the sales floor or in the backroom area. The typical approach to ease this classification consists of physically shielding the interested areas via hardware installations, although this solution is expensive and lacks flexibility. In this paper, we present a different solution, namely a software-based shielding approach, to address the classification problem. Our solution makes use of item-level RFID tags and is based on the well-known logistic regression. Whenever a reading session is performed by means of a handheld reader, the classification model estimates in real-time (i.e. within a few seconds) which tagged items are in the same area of the reader and which are not, with no need of any shielding hardware installation. According to the validation preliminary tests presented in this paper, in which we simulated a fashion retail store, the proposed approach has an overall average accuracy of 95.5%.
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