Research And Application Of Spatiotemporal Co-occurrence Pattern Mining Algorithm Under Trajectory Data

With the development of computer technology,the improvement of spatio-temporal data acquisition equipment and the increase of acquisition channels,the amount of data stored in spatio-temporal data has increased significantly,which also leads to the difficulty of obtaining interesting patterns from a large number of spatio-temporal data.Therefore,the importance of efficient and automated pattern mining and analysis is increasing.Supported by the rapid development of target tracking technology,the spatial and temporal data of moving targets such as pedestrians,vehicles and ships can be effectively recorded.These spatial and temporal data contain a large number of potentially valuable models and knowledge,and have very important research significance in urban planning and construction,traffic management,location-based services,etc.In order to discover the moving rules of moving targets,a new model based on co-occurrence pattern analysis method is established,and the mining analysis and application of patterns are carried out.The main research work of this paper is as follows:(1)Aiming at the problems of data repetition,data missing and data sparseness in trajectory data,a trajectory point fitting method based on piecewise quadric Bessel curve and a trajectory reconstruction method based on the combination of spatiotemporal trajectory model and piecewise quadric Bessel curve are proposed.The fitting and reconstruction experiments are carried out based on this method.The experimental results show that this method can fit well.The pedestrian trajectory with complex motion is obtained,and the trajectory point set with large missing degree is interpolated effectively,which improves the availability of data.(2)Aiming at the problem that traditional co-occurrence pattern mining algorithms are inefficient and can not be directly applied in practical application,this paper considers that there are triggering relationships among the elements of patterns based on existing co-occurrence pattern mining algorithms,proposes spatio-temporal triggering co-occurrence pattern,and constructs a coarse-fine-grained hybrid spatio-temporal model(CFSTICOP model)based on moving pedestrian targets.The hybrid model is effective.The sparking relationship between moving objects and the spatio-temporal relationship between corresponding instances are preserved to solve the problem of data persistence.In addition,this paper proposes a hybrid spatio-temporal model mining algorithm(CFSTICOP-Miner)based on the combination of coarse and fine granularity,which can prune redundant patterns in advance,and design and verify the efficiency and feasibility of the mining algorithm.(3)Combining the above two methods,based on the real pedestrian spatio-temporal data set,this paper designs a spatio-temporal association mining system for pedestrian moving objects,displays the mining results visually,and analyses the spatio-temporal trigger co-occurrence pattern's spatio-temporal distribution characteristics.Experiments prove that the algorithm results meet the needs of the actual scene and provide location-based services and intersection.Provides the auxiliary reference through the planning and so on.