基于数字地图技术的移动用户数据特征研究与应用

发布时间：2018-05-02 03:02

本文选题：稀疏特征 + 密集特征　；参考：《北方工业大学》2017年硕士论文

【摘要】：随着计算机技术和通信技术的不断进步,移动设备多种多样,功能更加完善,用户的移动数据可以随时随地发送到服务器上,进行有效保存。日积月累,移动数据无论是数量还是种类都变得相当巨大。这对于研究人员挖掘移动数据中所蕴藏的价值,提取移动用户数据特征,向移动用户提供个性化服务,带来了巨大的挑战。本课题依托于网络后台服务器以及Android端滑屏App。用户使用安装了该滑屏App的智能设备时,移动数据便传送并保存到后台服务器上。本文通过分析移动用户数据,根据用户的地理位置信息的稀疏密集程度,提取出了移动数据稀疏特征和密集特征,建立了移动数据稀疏特征模型和密集特征模型。在服务器端实现了移动用户数据特征模型,并将个性化服务器信息推送至客户端。在客户端实现了移动用户数据特征展示,以便用户查看个人的数据特征。首先,结合百度地图API,由地理位置信息转换得到位置语义信息,并对位置语义信息进行类别划分。移动用户刚进入系统时,其移动用户的地理位置数据是稀疏的。为了分析移动用户的访问兴趣点的行为偏好,提取出移动数据稀疏特征—年龄特征、性别特征和类别相似特征。根据提取的移动数据稀疏特征,设计了移动数据稀疏特征模型。其次,随着时间或地点的不断变化,移动数据无论是数量还是种类变得越来越密集。根据当前移动数据的特点,提取出移动数据密集特征—地理位置特征、时间特征和类别特征。利用移动数据密集特征,设计了移动数据密集特征模型。最后,为了验证不同模型的正确性,分析了依据不同模型的得到的推荐结果,并结合Android百度地图API,开发了 Android端移动用户个人数据特征展示模块。此模块包括:移动用户轨迹追踪、移动用户兴趣点展示、移动用户活动区域分布。移动用户个人轨迹追踪包括移动用户实时轨迹追踪和历史轨迹查看。移动用户兴趣点展示是根据设计的移动数据稀疏特征模型和密集特征模型得到的结果推荐到客户端并展示。移动用户个人活动区域分布功能可以选择不同条件来查看自己的活动区域。
[Abstract]:With the development of computer technology and communication technology, mobile devices have a variety of functions, and users' mobile data can be sent to the server at any time and anywhere for effective storage. Over time, mobile data, both in terms of quantity and variety, has become quite large. This is a great challenge for researchers to mine the value of mobile data, extract the features of mobile user data, and provide personalized services to mobile users. This topic depends on the network background server and the Android terminal slide screen App. When a user uses a smart device that installs the slider App, the mobile data is transferred and saved to the background server. Based on the analysis of mobile user data, the sparse feature and dense feature of mobile data are extracted according to the sparse density of user location information, and the sparse feature model and dense feature model of mobile data are established. The mobile user data feature model is implemented on the server side, and the personalized server information is pushed to the client. The mobile user data feature display is implemented on the client side so that the user can view the personal data feature. Firstly, with the help of Baidu map API, the location semantic information is obtained by the transformation of geographical location information, and the location semantic information is classified into categories. When the mobile user enters the system, the geographic location data of the mobile user is sparse. In order to analyze the behavioral preference of mobile users' access points of interest, the sparse features of mobile data, such as age, gender and category similarity, were extracted. According to the extracted sparse features of mobile data, a sparse feature model of mobile data is designed. Secondly, as time or place changes, mobile data become more and more dense in terms of quantity and type. According to the characteristics of current mobile data, the features of mobile data density, such as geographical location feature, time feature and category feature, are extracted. A mobile data dense feature model is designed using mobile data dense features. Finally, in order to verify the correctness of different models, this paper analyzes the recommended results obtained from different models, and develops the Android mobile user personal data feature display module combined with Android Baidu map API. This module includes: mobile user trajectory tracking, mobile user interest point display, mobile user activity area distribution. Personal trajectory tracking of mobile users includes real-time tracking of mobile users and viewing of historical tracks. Mobile user points of interest display is based on the design of mobile data sparse feature model and dense feature model results are recommended to the client and display. Mobile user's individual activity area distribution function can select different conditions to view their own active area.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：P289;TP391.3

【参考文献】