Manegment And Analysis Of The Database Of Vehicle’s Safety Technology Inspection

Vehicles are a main cause of the traffic accidents on roads,the key to avoiding the traffic accidents is through strengthening the management of the vehicles,discovering and eliminating the hidden dangers of the them.The safety technology inspection on them contributes a lot to preventing the accidents’ occurrence in which vehicles involved.However,the number of the vehicles’ safety inspection stations is far from enough.To relieve the pressure of these stations,the government is promoting the socialization of the inspections on the vehicles which can only technology be performed by it before,which means these stations owned by individuals or companies are authorized to take safety inspections on vehicles.Nevertheless,with this socialization some problems show up such as the inspections are off the standard procedure,faking the results of the inspections,ect.To find out the abnormal data which is caused by the non-standard procedure of the inspections,promoting the standardization on the inspections performed by individuals,providing guidance and help to the supervision on these inspections,in this paper,a method based on the k-means clustering and LOF algorithms is employed to detect these abnormal data.Based on the inspection data whose abnormal data is rejected,the data mining(DM)method is used to find some important knowledge which is useful to the management and maintenance of the vehicles.The regression analysis method is employed to analyze the relationship of the performance of vehicle’s breaking system with ages.Meanwhile,the comparison of change of the breaking system’s performance with ages between different type of vehicles is performed.Finally,the association rule method is used to find the association and connections between the vehicle’s attribute such as its age,weight and its breaking system’s parameters such as its breaking forces.