Research On The Application Of Knowledge Discovery Algorithm Based On Traffic Data Of Public Security

With the public security organs' continuous construction of traffic video surveillance and vehicle bayonet system over the years,the main urban areas and main streets are basically gridding,and the key road bayonets are basically full-covered.With the continuous growth of the bayonets and monitoring equipments over the years,the data collected by the equipments is also constantly accumulated.The relevant departments of the public security organs have kept a large amount of traffic behavior information and data information of the bayonets.However,because the public security organs lacked the ability and mode to analyze the data in-depth in the past few years,the application of vehicle monitoring information by relevant departments is still at a simple query and statistics level,which makes these historical data cannot bring the greatest management benefits.In order to improve the efficiency and effect of traffic related information,this paper uses named entity recognition,clustering analysis,association rule mining and other algorithms to study and discover knowledge of public security traffic system,which can provide better traffic management services and fight crimes.First of all,as for pre-processing the data of the road bayonet system,the dynamic data is processed by equalization to avoid the impact of the short-term behavior of vehicle owners on the classification of vehicle usage habits.In addition,for unstructured text data,this thesis uses CRF algorithm to identify privacy data.It lays the foundation for the safe mining of sensitive text information.Secondly,this thesis makes a research on knowledge discovery model about vehicle monitoring information,and proposes a method of processing data through cluster analysis.The method of k-means clustering analysis is used to analyze and cluster the traffic passing data of road bayonet system,and the continuous numerical value is transformed into discrete variables of state property.According to the clustering results,the similarity between each cluster core with vehicle driving data can be compared and the vehicle driving behavior can be classified.Finally,the paper uses Apriori algorithm to analyze the recorded data of public security bayonet system,and gives the concrete implementation steps and case analysis from the aspects of data preprocessing,Apriori algorithm's executing flow,association rules' interpretation.The results indicate that different types of vehicles in the driving records show different laws,and digging these vehicle driving rules can play an auxiliary role in effective monitoring of vehicles and the handling of cases.Experiments show that named entity recognition,clustering analysis,association rule mining and other algorithms can effectively accomplish knowledge discovery tasks,and have wide application prospects in the field of public security traffic.