Research on safety verification methods of static data of train control systems based on deep association rules

Currently, the static configuration data checking of the safety critical system is realized by the existing constraint rules which generated from the professional norms. Most of these constraint rules are for single-category data and relatively simple, and only some basic data errors can be identified with which. Therefore, it is necessary to excavate more complete and comprehensive data constraint rules to further improve the performance of the data verification. Data association rules describe the correlation and dependence between data items and reflect the rules and patterns of some attributes appearing simultaneously. Based on which, the in-depth research on the methods of static data verification in the train control system is carried out. Firstly, clustering and dimension reduction methods are used to divide data items into multiple sub-range intervals, which can solve the problem that association rules of the static data are difficult to extract due to floating-point data measurement. Then, a DSRJ algorithm is proposed to judge the existence of association relations among sub-range data items, and a neural network model will be constructed in which a large number of data samples are trained to obtain the data relation function that is transformed further into an association rule. The new subset of data samples can be conducted safety verification according to this rule.