Methods Of Static Data Verification Of CBTC Systems For Urban Rail Transit

Communication Based Train control(CBTC)system is a typical data-driven control system.In order to ensure the safe and efficient system operation,CBTC system will use static data,dynamic data and movement authority(MA)to calculate the control mode curve to monitor the train running speed.Static data of CBTC system include line parameters,trackside equipment parameters and train configuration parameters.Due to data engineers' negligence and lack of experience,numerical deviations will emerge in the various stages of the data supply chain,and will be transferred and amplified step by step in the system application.Finally,CBTC system dangers occur.Therefore,the method of static data verification of CBTC system is necessary.Static data characteristics of CBTC system and the essence of data verification is explored in this thesis.The advantages and disadvantages of the existing data verification methods are deeply studied and summarized.On this basis,the concept of Data Joint Deviation Threshold(DJDT)is proposed innovatively,and thus,the core of data verification is focused on the calculation of multidimensional data joint deviation threshold.A data verification method based on the Multidimensional Data Space Model(MDSM)is proposed in this thesis.Firstly,the data is should be verified from the perspective of the contractual relation between data and its host system with the purpose of calculating the reasonable deviation threshold of each data in MDSM based on the proposed Probability Safety Margin(PSM)method,and so as to construct the reasonable deviation threshold space.Then,the data is should be verified from the perspective of data association constraints.The Data Subdomain Relevance Judgement(DSRJ)method is proposed to extract implicit association rules whose rationality will be verified,to construct the data association space.Finally,the data joint deviation threshold of CBTC system is obtained by finding the intersection of the reasonable deviation threshold space and data association space.Taking the electronic map data of Tianjin Metro Line 6 as an example,the data verification method based on DJDT in this thesis is compared with the traditional data verification methods.The feasibility and advantage are demonstrated.The innovations of this thesis are as follows:(1)By analyzing and summarizing the characteristics of static data of CBTC system,the research aspects of data verification are innovatively proposed in this thesis.That is,the contractual relation between data and its host system and the quantitative or logical relations between data items.There are some constraints between data and its application objects and associated objects,so the occurrence of data errors means that these constraints are violated.(2)A static data verification method based on DJDT is proposed innovatively.Firstly,the multidimensional data space model is constructed,and on this basis,the data reasonable deviation threshold space and data association space is defined respectively.The constraint standard of data verification is strengthened by solving the intersection of each definition space.Thus,the static data verification problem is transformed into the cutting problem of multidimensional data space model.This breaks through the limitation and incompleteness of traditional data verification methods purely based on association rules,and realizes the theoretical innovation of static data verification.(3)In order to solve the problem that the existing simulation methods can not accurately and completely evaluate the reasonable data deviation threshold with finite simulation times,an innovative method Probability Safety Margin(PSM),used to calculate the optimal simulation times,is proposed.The uncertainties of simulation results,emerging in the simulation modelling process,is eliminated with the method of quantile intercepting and confidence processing.On this basis,the Order Statistics(OS)method is used to calculate the optimal simulation times,so that the complete reasonable data deviation threshold is achieved within the limited simulation times.(4)In order to solve the problem that it is difficult to identify the implicit association rules between static data items of CBTC system,a data subdomain relevance judgement method is innovatively proposed.The traditional extraction method for data values is extended for the data subdomains.Based on the previous association judgement methods,the dimension reduction processing model against data intensive values is added,which improves the efficiency of data association judgment,and fills in the blank of extraction method for data implicit association rules.(5)In order to solve the problem that the confidence level of implicit data association rules extracted based on probability and statistics method is low,Satisfiability(SAT)method is proposed to verify the rationality.Meantime,a innovate transforming method from the SAT solving problem to the search problem of Ordered Binary Decision Diagram(OBDD)is proposed,which greatly improves the efficiency of data rationality verification of data implicit association rules.