地球信息科学学报 ›› 2020, Vol. 22 ›› Issue (1): 21-29.doi: 10.12082/dqxxkx.2020.190552

• 专辑:地理智能 • 上一篇    下一篇

PIR传感网数据的几何代数建模与行为分析

袁林旺1,2,3,*(), 俞肇元1,2,3, 罗文1,2,3, 袁帅1,2,3, 周春烨1,2,3   

  1. 1. 虚拟地理环境教育部重点实验室 南京师范大学,南京210023
    2. 江苏省地理环境演化国家重点实验室培育建设点,南京 210023
    3. 江苏省地理信息资源开发与利用协同创新中心,南京 210023
  • 收稿日期:2019-09-27 修回日期:2019-11-26 出版日期:2020-01-25 发布日期:2020-04-08
  • 通讯作者: 袁林旺 E-mail:yuanlinwang@njnu.edu.cn
  • 作者简介:袁林旺(1973— ),男,江苏海安人,博士,教授,主要从事GIS理论与方法研究。
  • 基金资助:
    国家杰出青年科学基金项目(41625004);国家自然科学基金项目(41571380);国家自然科学基金项目(41601417)

Geometric Algebraic Modeling and Movement Behavior Analysis of the PIR Sensor Network

YUAN Linwang1,2,3,*(), YU Zhaoyuan1,2,3, LUO Wen1,2,3, YUAN Shuai1,2,3, ZHOU Chunye1,2,3   

  1. 1. Key Laboratory of Virtual Geographic Environment, Nanjing Normal University, Ministry of Education, Nanjing 210023, China
    2. Cultivation Base of State Key Laboratory of Geographical Environment Evolution, Jiangsu Province, Nanjing 210023, China
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2019-09-27 Revised:2019-11-26 Online:2020-01-25 Published:2020-04-08
  • Contact: YUAN Linwang E-mail:yuanlinwang@njnu.edu.cn
  • Supported by:
    National Science Fund for Distinguished Young Scholars of China(41625004);National Natural Science Foundation of China(41571380);National Natural Science Foundation of China(41601417)

摘要:

现有基于PIR(Passive InfraRed)传感器数据的人体行为研究主要局限于运动时空分布、聚类等,对行为轨迹的重建和语义特征的解析相对较少,亟需发展新的建模与行为分析方法。本文尝试利用室内区域PIR传感器监测数据进行时空轨迹重构及其所揭示的运动语义特征研究。本文引入几何代数理论方法,构建传感器场景网络,实现了几何代数空间下动态网络的表达与路径计算,分析了人体运动特征及语义特征,建立最小语义单元,实现了空间数据到语义特征的转化,并可对个人和群体运动的空间区域特征和拓扑特征分析提供支撑。论文将传统PIR传感器网络分析从以几何、统计等为主的信号提取,转变为基于几何代数系统中不同类型代数结构的生成与转化问题,为诸如PIR传感网数据分析一类的非确定问题的求解提供了新的思路和数学基础,可为物联网GIS的构建提供借鉴。

关键词: 几何代数, PIR, 传感器网络, 行为轨迹, 轨迹提取, 语义模板, 运动语义, 运动行为分析

Abstract:

Existing research on human behavior based on the PIR (Passive InfraRed) sensor data is limited by the spatial-temporal distribution of motion, clustering, and so on. The reconstruction of behavior trajectory and analysis of semantic features are relatively few, so it is urgent to develop new modeling and behavior analysis methods for the PIR data. This paper attempts to reconstruct the spatial and temporal trajectories by using the PIR (Passive InfraRed) sensor monitoring data. PIR sensors have the characteristics of low price and privacy protection. However, because only Boolean logical response sequence can be obtained by PIR sensors, it is difficult to accurately obtain movement trajectories. Its application has been relatively limited, and it is difficult to conduct movement behavior feature analysis. Traditional PIR sensor network analysis methods are mostly based on the signal extraction idea, which cannot integrate geometric features and semantic information at the same time. By introducing the geometric algebra theory, the sensor scene network can be constructed to realize the path expression and calculation of dynamic network in the geometric algebra space. This paper analyzed the characteristics of human movement features and semantic features, established semantic units, and realized the transformation from spatial data to semantic features. The spatial and topological characteristics of individual and crowd movements were analyzed. We proposed a generation and transformation-based methods of algebraic structures in the geometric algebra system, which provides a new idea and mathematical basis for solving non-deterministic problems such as the PIR sensor network data based analysis, and can provide reference for the construction of internet of things GIS.

Key words: geometric algebra, PIR, sensor network, movement trajectory, trajectory extraction, semantic template, movement semantics, movement behavior analysis