Research And Implementation Of Behavior Analysis Technology For Community Correctors

With the development of electronic monitoring technology and judicial system reform electronic supervision technology has been researched and applied in the field of community correction.With the accumulation of information on the location of prisoners in the community,how to use this data to help community correction work is a very meaningful research direction.In response to this demand,this paper has made some improvements on the behavior tracking of the community correction field based on the DBSCAN algorithm for segmentation and clustering.The method can identify and extract feature trajectories of community corrections.And combined with POI data to analyze its semantic information,it provides a new treatment method for the trajectory processing problem of community correction.The main research contents are as follows:(1)Convert the traditional structured data to the map database neo4j,implement conversion storage of related information.Use spring boot and maven to build the project and use D3 to visualize the graph data.It greatly facilitated the daily management of community correction management personnel.(2)Fitting the trajectory data makes the trajectory of the prisoner appear more realistic.Process the abnormal collection data and then distiguish the display.(3)The historical traj ectory of the prisoners was statistically analyzed by day.Then combine POI data with trajectory data.It makes the combination of travel mode and semantics more practical.Modeling general travel trajectories,Compare the new arrival trajectory data with the model.Identified the abnormal trajectories,Remind the manager whether the trajectory of the prisoner deviates from his general travel trajectory and improve the pertinence of the management personnel to deal with the problem.The research results show that the processing method of this paper has higher accuracy in the travel pattern recognition ofthe community correction service personnel with more regular travel modes,and the identification of irregular travel trajectories needs to be improved.