Analysis Of Historical Trajectory Data Of Underground Personnel

With the widespread application of positioning equipment in underground coal mines,the location of moving objects in underground mines can be accurately located,and walking trajectories can be effectively tracked.With the widespread application of positioning equipment in underground coal mines,the location of moving objects in underground mines can be accurately located,and walking trajectories can be effectively tracked.With the development of information technology,the speed of generating trajectory data of moving objects in underground mines is getting faster and faster,and massive trajectory data has also been accumulated.At present,these data are only used for track playback,attendance management and other functions in the coal mine personnel positioning system.The trajectory information formed by these trajectory data contains temporal and spatial information and semantic information,and contains the behavioral intentions,activity rules,and working status of underground personnel.It is urgently needed to analyze it effectively to expand the function of underground personnel positioning system.The specific work content is as follows.First of all,this thesis uses the historical trajectory data of underground personnel in Heyi Mine,Yulin City,Shaanxi Province as the research data.Since the data collection process generally uses RFID technology for positioning,these methods may cause positioning errors,data missing,and data coverage.Therefore,this thesis analyzes the original trajectory data table,sorts out and merges these data according to personnel and departments,and obtains reasonable personnel trajectory data.Secondly,in view of the large difference in the density distribution of downhole personnel trajectory data,this thesis proposes a downhole personnel hotspot area discovery framework.The framework includes two parts: key location candidates and hotspot area clustering,which reduces the complexity of data and the amount of calculation,and introduces The self-decay function is used to filter the clustering results multiple times,and finally merge to obtain accurate downhole personnel hot spots.Then,according to the time and space features contained in downhole personnel trajectory data,this thesis proposes a framework for finding abnormal trajectories of downhole personnel.The framework extracts the temporal and spatial features in the trajectory data,combines the distance calculation method based on the trajectory similarity measure,and uses density-based The clustering method detects abnormal trajectories,and detects abnormal types by analyzing the density Exp(t)and "k?" of the field trajectory in the trajectory cluster.Finally,in order to better display the results of data analysis,this thesis uses the Web GIS development platform to build the system,and displays it based on two aspects:hot spots and abnormal trajectories of underground personnel.