Traffic Safety Situation Evaluation Expert System Driven By Big Data Study

In recent years,with the rapid development of social economy and the continuous improvement of people's requirements for the quality of life,the number of car ownership has been "blowout" growth,which brings convenience and comfort to people's travel,but also brings a series of traffic safety problems.In the face of the trend of serious loss of life and property caused by traffic accidents,following the pace of big data era,an expert system for road traffic safety situation study and judgment is constructed,which enables it to establish an indicator system reflecting the current road traffic safety situation comprehensively and select appropriate evaluation methods based on the full mining of massive data,objectively evaluate the existing problems of the current road,and propose It is imperative to improve measures to reduce the number of traffic accidents and the severity of traffic accidents.Based on the analysis of the research and application status of data mining technology,traffic safety engineering theory and artificial intelligence at home and abroad,this paper studies the traffic safety situation research expert system based on traffic management big data by using interdisciplinary knowledge fusion in the background of Traffic Management Bureau of Shandong public security department.First of all,collect traffic accident data,traffic violation data,traffic flow data,vehicle management data,vehicle driver data,meteorological data and other multi-source data,mining specific factors affecting traffic safety.Secondly,on the basis of big data analysis and mining of public security traffic management,the knowledge base of expert system is established.The knowledge base involves the analysis of many factors of "human vehicle road environment" and the establishment of a multi-level and multi index traffic safety evaluation index system according to the principle of index selection.The weight of different indexes is determined by the method of chromatography entropy.The extension theory and cloud model are combined to transform the qualitative evaluation into the quantitative evaluation,so as to complete the uncertainty and indeterminacy of some boundaries The quantitative treatment of quantitative factors can achieve the purpose of dynamic evaluation of road safety level.By building a grey Markov prediction model to predict the future value of each index according to the data of each index over the years,and taking each prediction value as the matter element to be evaluated of the extension cloud model,the purpose of road safety level prediction can be achieved.The calculation process of evaluation method and prediction index value is the reasoning process of expert system.The whole expert system is developed based on Python language.Through the requirement analysis,overall design,detailed design and the realization of each functional module of the whole expert system,the expert system is developed to make the system reach the level of human experts in a certain range,and realize the transformation from "old kinetic energy" of "brain expert labor" to "new kinetic energy" of "artificial intelligence".