The GA Modeling And Feature Analyzing Method Of Motion Trajectory Based On Indoor Sensor Network Data

With the continuous development and progress of geographic information technology, the development of the Internet of things makes it possible to continuously long time observe spatial target. The sensor and the mobile terminals have become an important means of the dynamical observation of geography and the dynamical observation data of geography presents the characteristics of diversification and intensification. GIS research needs to be focused more on the human behavior and the social events that related to the human behavior as the putting forward of the concept of "service oriented" GIS. The trajectory of time and space which is the most intuitive reflect of human behavior and intrinsically including geometric structure characteristics, is an important window of behavioral research.The spatio-temporal trajectory research needs to fully consider the spatio-temporal dynamics of the trajectory when data organized. On the basis of the geometric structure analysis of the spatio-temporal trajectory, it can form specific behavioral mode and implement the transition of geometric analysis to the semantic feature analysis of the behavioral characteristics. The spatio-temporal trajectory analysis method needs to make progress at a certain degree in the space scales and the fine level in response to the emerging research of the indoor positioning, indoor human behavior, including make further progress at the aspects of the indoor scene network modeling, the integrated analysis of the sensor data, the positioning, tracking and semantic features resolution of indoor activities as well as semantic feature analysis.This paper studies the spatio-temporal trajectory based on the background of indoor area and uses the sensor to monitor human activities so as to try to build indoor human behavior trajectory by using the monitoring data. Furthermore, this paper introduces the theory of geometric algebra method, constructs the sensor scene network, implements the expression and path calculation of the dynamic network under the geometric algebra space, analyzes the human movement characteristics and semantic characteristics, sets up the smallest semantic unit, realizes the transformation of spatial data to the semantic characteristics and analyzes the regional space characteristic and topological features of the individual sports and group sports. In this paper, the research work mainly includes the following aspects:(l)The integrated express of the scene network and sensor data based on the geometric algebra. This paper studies the indoor spatial targeted positioning method, targeted on the sensor data to build sensor network based on the graph theory, realizes the consistency expression of the nodes-segment-path of the space network on the basis of the geometrical algebra blade structure and develops the principle of path calculation and node connectivity based on the general product connectivity.(2)The trajectory reconstruction and analysis based on the indoor sensor. This paper puts forward to the target tracking method of the human indoor activities on the basis of the sensor topology structure and data characteristics. Moreover, this paper takes advantage of the network analysis method on the basis of geometric algebra to build a real-time calculation rules of the dynamic network path so as to construct the indoor human behavioral trajectory. Furthermore, it studies the spatial characteristics and trend characteristics of human movement activities by using the human behavioral trajectory researches.(3)The spatial semantic model building and behavioral mining based on the geometric algebra. This paper studies the human movement characteristics and behavioral patterns, constructs the semantic characteristics of human body movement. Moreover, the paper uses the blade structure to make geometric algebra code for the human movement and the semantic. Again, it also develops the transformation algorithm of the node clustering semantic to realize the transformation of the spatial monitoring data to the movement semantic. Lastly, it uses the spatial statistics analysis method according to the result of the semantic transformation to study the spatial distribution characteristics of human behavioral activities.