基于WIFI定位技术的动线管理系统的研究与实现
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摘要
无线互联网技术的发展,促进了无线定位行业的发展和应用。近年来无线定位技术在医疗、设备管理以及商业智能等领域获得了广泛的应用。在诸多应用中,对大型卖场中顾客动线信息的采集和分析是其重要的一个方面。动线是顾客在超市内购物形成的轨迹,在一定程度上能反映出商场的布局合理性以及顾客的偏好性。动线管理的目标是既满足顾客便捷舒适的购物需求,又能最大程度上促进商家销售任务的完成。动线的通过率、停留率和销售率是动线研究的三项指标,目前对动线研究主要围绕着这些因素展开。动线研究分为数据收集环节和数据分析环节。数据收集分为人工和无线定位技术两种方式,相比前者,无线定位技术具有准确方便和不易侵犯顾客隐私等优点,故而在国内外被广泛应用在有关动线的分析研究领域。
本文对动线研究领域中的偏好动线和热区生成算法进行了研究。针对目前基于用户访问矩阵(UAM)和顾客访问矩阵(CAM)的偏好动线求解算法中出现的回路或死循环问题,提出了基于偏好值和簇的偏好图生成(PCG)算法。针对热区研究中存在的热区定义不清、缺乏明确的度量方法和分类算法问题,提出了基于定位信息次数的区域热度的定义,以及基于半径逐级递增的区域定位信息次数统计规则和一种改进型基于k均值的区域热度聚类方法。
在以上研究基础上,本文设计并实现了基于WIFI定位技术的超市动线管理系统。用户可选择实时查看顾客的位置信息和移动轨迹,以及动线通过率、停留率和购买率等分析结果。
Nowadays the wireless network technology develops rapidly, which promote the development of wireless location service areas. The wireless location technology gains great success in medical measures, instruments managements and business intelligence areas. In business intelligence areas, the research on the shopping path is an important application. The shopping path is defined as the line produced by the customer moving in the shop. The shopping path can show the preferences of the customers towards the different areas. The pass rate, the stay rate and the buy rate is the most three important parameters of the shopping path, and they represent the layout, attraction and sales value of the shop. And these parameters are the center points of the research work. The research of the shopping path can be divided into two paths:data collecting and data analysis. There are two kinds of methods to collect the shopping path data:the manual work and the automatic work. Comparing the former, the latter has nicety, easiness and safe priorities. Considering its low cost and convenience to use, we propose a WIFI deployment model for collecting and analyzing the customers' shopping path data and purchased items data.
In this paper, we do research work of the generation algorithms of preference paths and hot areas. To solve the problems of forever looping paths and break paths which are easily appeared while using the User Access Matrix (UAM) and Customer Access Matrix (CAM) algorithms, we propose a Preference and Cluster based generation Graph (PCG) algorithm. To solve the problems of definition mistiness and absence of a measuring standard and classification of hot areas, we propose a definition of hot degree based on the numbers of location messages, a radius step rising detection algorithm to calculate the location messages, and an improved k-means cluster algorithm for the classification of areas' hot degrees.
On the basis of the above study, we design and implement a Shopping path administration system based on WIFI, which can show the customers' location information and history tracks, the pass rate, the stay rate,the sales rate and so on.
本文对动线研究领域中的偏好动线和热区生成算法进行了研究。针对目前基于用户访问矩阵(UAM)和顾客访问矩阵(CAM)的偏好动线求解算法中出现的回路或死循环问题,提出了基于偏好值和簇的偏好图生成(PCG)算法。针对热区研究中存在的热区定义不清、缺乏明确的度量方法和分类算法问题,提出了基于定位信息次数的区域热度的定义,以及基于半径逐级递增的区域定位信息次数统计规则和一种改进型基于k均值的区域热度聚类方法。
在以上研究基础上,本文设计并实现了基于WIFI定位技术的超市动线管理系统。用户可选择实时查看顾客的位置信息和移动轨迹,以及动线通过率、停留率和购买率等分析结果。
Nowadays the wireless network technology develops rapidly, which promote the development of wireless location service areas. The wireless location technology gains great success in medical measures, instruments managements and business intelligence areas. In business intelligence areas, the research on the shopping path is an important application. The shopping path is defined as the line produced by the customer moving in the shop. The shopping path can show the preferences of the customers towards the different areas. The pass rate, the stay rate and the buy rate is the most three important parameters of the shopping path, and they represent the layout, attraction and sales value of the shop. And these parameters are the center points of the research work. The research of the shopping path can be divided into two paths:data collecting and data analysis. There are two kinds of methods to collect the shopping path data:the manual work and the automatic work. Comparing the former, the latter has nicety, easiness and safe priorities. Considering its low cost and convenience to use, we propose a WIFI deployment model for collecting and analyzing the customers' shopping path data and purchased items data.
In this paper, we do research work of the generation algorithms of preference paths and hot areas. To solve the problems of forever looping paths and break paths which are easily appeared while using the User Access Matrix (UAM) and Customer Access Matrix (CAM) algorithms, we propose a Preference and Cluster based generation Graph (PCG) algorithm. To solve the problems of definition mistiness and absence of a measuring standard and classification of hot areas, we propose a definition of hot degree based on the numbers of location messages, a radius step rising detection algorithm to calculate the location messages, and an improved k-means cluster algorithm for the classification of areas' hot degrees.
On the basis of the above study, we design and implement a Shopping path administration system based on WIFI, which can show the customers' location information and history tracks, the pass rate, the stay rate,the sales rate and so on.
引文
[1]丁丽梅.大型超市客动线研究[J].北京：北方经贸,2010,4：53-57.
[2]Hui. An Integrative Framework and Prospectus for Model Building[J]. Marketing Science.2009,28(2):320-335.
[3]Hui Integrated Model of Grocery Store Shopping Path and Purchase Behavior. Working paper,2007, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=960960.
[4]Tugba Yaman, Selcuk Karabati, Fikri Karaesmen. Clustering grocery customers' in-store shopping paths by using optimization-based models[C],20th EURO Mini Conference, Neringa, LITHUANIA,2008:20-23
[5]Hui, Peter. The Traveling Salesman Goes Shopping:The Systematic Deviations of Grocery Paths from TSP Optimality. Marketing Science.2009,28(3):566-572.
[6]Xing, Shen. Efficient Data Mining for Web Navigation Patterns[J], Information and Software Technology.2004,46(4):55-63
[7]C.H.Yun, M.S.Chen. Using Pattern-Join and Purchase-Combination for Mining Web Transaction Patterns in an Electronic Commerce Environment[C]. IEEE COMPSAC.2000,99-104.
[8]I-En Liao. Shopping Path Analysis and Transaction Mining Based on RFID Technology RFID[C]. Istanbul:RFID Eurasia 2007 1st Annual.2007,1:1-4
[9]Hui. Path Data in Marketing:An Integrative Framework and Prospectus for Model Building. Market Scientce.2009,320-325
[10]Keiji, Takai, Katsutoshi, Yada. Relation between Stay-Time and Purchase Probability Based on RFID Data in a Japanese Supermarket[C]. Knowledge-based and intelligent information and engineering systems.2010,3:254-263.
[11]高勇.欧亚卖场动线规划效果调研与建议[D].吉林：吉林大学.2008
[12]刘乃安.线局域网原理技术与应用[M].西安：西安电子科技大学出版社.2004：452-495.
[13]张利.基于WIFI技术的定位系统的设计与实现[D].北京：北京邮电大学.2009.
[14]张晓静.基于IEEE 802.11的Ad-hoc网络中多媒体业务的服务质量保障技术研究[D].北京：北京邮电大学.2010.
[15]姜莉.基于WIFI室内定位关键技术的研究[D].大连：大连理工大学.2010.
[16]赵娟.无线局域网802.11x技术[J].北京：网络安全技术与应用,2010,8：23-25.
[17]刘元安,翟明岳,吴惠兰等.宽带无线接入和无线局域网[M].北京：北京邮电大学出版社,2001：57-59
[18]李伟.基于无线定位技术的交通信息获取方法研究[J].北京：计算机应用研究,2006,7：80-82
[19]徐盈.室内定位系统设计[D].西安：西安电子科技大学.2009.
[20]杨章林.基于RSSI的WLAN定位系统研究与实现[D].大连：大连理工大学.2009.
[21]Salvatore. WLAN based outdoor localization using pattern matching algorithm[J]. International Journal of Wireless In-formation Networks,2007,4:265-279.
[22]梁尧,徐玉滨,邓志安,周牧.用于WLAN指纹匹配定位的室内接收信号强度特性研究[J].北京：计算机科学,2009,36(4)：39-41.
[23]王雪莉,卢才武,顾清华,刘子实.无线定位技术及其在地下矿山中的应用[J].北京：金属矿山,2009,4：121-125.
[24]陈文周.WiFI技术研究及应用[J].北京：数据通信,2008,2：14-18.
[25]李思齐.基于WIFI定位系统的设计与实现[D].北京：北京交通大学.2009
[26]王凤珍.基于WIFI的移动IP研究.北京：网络安全与应用.2005,11：71-73
[27]Ekahau. Ekahau User Guide. Finland:www.ekahau.com.
[28]施伯乐,丁宝康,汪卫.数据库系统教程[M].北京：高等教育出版社.2008：116-131.
[29]Jiawei Han,Micheline Kamber.数据挖掘概念与技术[M].北京：机械工业出版社.2007：186-189
[30]杜强,贾丽艳.SAS统计分析标准教程.北京：人民邮电出版社.2010：150-172
[31]W.Richard Stevens. TCP/IP详解.北京：人民邮电出版社.2010：263-265.
[2]Hui. An Integrative Framework and Prospectus for Model Building[J]. Marketing Science.2009,28(2):320-335.
[3]Hui Integrated Model of Grocery Store Shopping Path and Purchase Behavior. Working paper,2007, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=960960.
[4]Tugba Yaman, Selcuk Karabati, Fikri Karaesmen. Clustering grocery customers' in-store shopping paths by using optimization-based models[C],20th EURO Mini Conference, Neringa, LITHUANIA,2008:20-23
[5]Hui, Peter. The Traveling Salesman Goes Shopping:The Systematic Deviations of Grocery Paths from TSP Optimality. Marketing Science.2009,28(3):566-572.
[6]Xing, Shen. Efficient Data Mining for Web Navigation Patterns[J], Information and Software Technology.2004,46(4):55-63
[7]C.H.Yun, M.S.Chen. Using Pattern-Join and Purchase-Combination for Mining Web Transaction Patterns in an Electronic Commerce Environment[C]. IEEE COMPSAC.2000,99-104.
[8]I-En Liao. Shopping Path Analysis and Transaction Mining Based on RFID Technology RFID[C]. Istanbul:RFID Eurasia 2007 1st Annual.2007,1:1-4
[9]Hui. Path Data in Marketing:An Integrative Framework and Prospectus for Model Building. Market Scientce.2009,320-325
[10]Keiji, Takai, Katsutoshi, Yada. Relation between Stay-Time and Purchase Probability Based on RFID Data in a Japanese Supermarket[C]. Knowledge-based and intelligent information and engineering systems.2010,3:254-263.
[11]高勇.欧亚卖场动线规划效果调研与建议[D].吉林：吉林大学.2008
[12]刘乃安.线局域网原理技术与应用[M].西安：西安电子科技大学出版社.2004：452-495.
[13]张利.基于WIFI技术的定位系统的设计与实现[D].北京：北京邮电大学.2009.
[14]张晓静.基于IEEE 802.11的Ad-hoc网络中多媒体业务的服务质量保障技术研究[D].北京：北京邮电大学.2010.
[15]姜莉.基于WIFI室内定位关键技术的研究[D].大连：大连理工大学.2010.
[16]赵娟.无线局域网802.11x技术[J].北京：网络安全技术与应用,2010,8：23-25.
[17]刘元安,翟明岳,吴惠兰等.宽带无线接入和无线局域网[M].北京：北京邮电大学出版社,2001：57-59
[18]李伟.基于无线定位技术的交通信息获取方法研究[J].北京：计算机应用研究,2006,7：80-82
[19]徐盈.室内定位系统设计[D].西安：西安电子科技大学.2009.
[20]杨章林.基于RSSI的WLAN定位系统研究与实现[D].大连：大连理工大学.2009.
[21]Salvatore. WLAN based outdoor localization using pattern matching algorithm[J]. International Journal of Wireless In-formation Networks,2007,4:265-279.
[22]梁尧,徐玉滨,邓志安,周牧.用于WLAN指纹匹配定位的室内接收信号强度特性研究[J].北京：计算机科学,2009,36(4)：39-41.
[23]王雪莉,卢才武,顾清华,刘子实.无线定位技术及其在地下矿山中的应用[J].北京：金属矿山,2009,4：121-125.
[24]陈文周.WiFI技术研究及应用[J].北京：数据通信,2008,2：14-18.
[25]李思齐.基于WIFI定位系统的设计与实现[D].北京：北京交通大学.2009
[26]王凤珍.基于WIFI的移动IP研究.北京：网络安全与应用.2005,11：71-73
[27]Ekahau. Ekahau User Guide. Finland:www.ekahau.com.
[28]施伯乐,丁宝康,汪卫.数据库系统教程[M].北京：高等教育出版社.2008：116-131.
[29]Jiawei Han,Micheline Kamber.数据挖掘概念与技术[M].北京：机械工业出版社.2007：186-189
[30]杜强,贾丽艳.SAS统计分析标准教程.北京：人民邮电出版社.2010：150-172
[31]W.Richard Stevens. TCP/IP详解.北京：人民邮电出版社.2010：263-265.