Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Huang Y (2014) Research on the safety processing and verification method of CBTC system data in urban rail transit. Beijing Jiaotong University, Beijing
Liang H, Zhu L, Yu FR et al (2022) A cross-layer defense method for blockchain empowered CBTC systems against data tampering attacks. IEEE Trans Intell Transp Syst 24(1):501–515
Zhou G (2016) SCBM based safety analysis method and its application in train control systems. Beijing Jiaotong University, Beijing
Li Y, Zhu L, Wang H et al (2023) Joint security and resources allocation scheme design in edge intelligence enabled CBTCs: a two-level game theoretic approach. IEEE Trans Intell Transp Syst 24:13948–13961
Zhu L, Shen C, Wang X et al (2022) A learning based intelligent train regulation method with dynamic prediction for the metro passenger flow. IEEE Trans Intell Transp Syst 24(4):3935–3948
Ozturk Z, Topcuoglu HR, Kandemir MT (2022) Studying error propagation on application data structure and hardware. J Supercomput 78:18691–18724
Safety Critical System Club (2023) Data safety by the SCSC safety initiative working group
Inge JR (2008) Improving the analysis of data in safety-related systems. University of York, Heslington
Safety Management Requirements for Defence (2007) Def Stan 00-56, part 1, requirements and part 2 guidance, issue 4
Falampin J, LeDang H, Leuschel M (2013) Improving railway data verification with ProB. Springer, Berlin
Wang D, Chen X, Huang H (2013) A graph theory-based approach to route location in railway interlocking. Comput Ind Eng 66(4):791–799
Shayan N, Sandidzadeh MA (2022) Standard formulation of data in interlocking system by RailML method: case study Zavaraeh station in Tehran-Bafgh corridor. Road 30(110):141–150
Bai L, Cui Z, Duan X et al (2022) Keyword coupling query of spatiotemporal data based on XML. J Intell Fuzzy Syst 42(3):2219–2228
Sargolzaei Javan M, Akbari MK (2019) SmartData 40: a formal description framework for big data. J Supercomput 75:3585–3620
Huang X, Liu Z (2019) Formal modeling and verification of compilation rules of balise telegram. J China Railw Soc 41(06):100–106
Abo R, Voisin L (2013) Formal implementation of data verification for railway safety-related systems with OVADO. In: International conference on software engineering and formal methods, pp 221–236
Iliasov A, Taylor D, Laibinis L, et al (2022) Formal verification of railway interlocking and its safety case. In: Safety-critical systems symposium 2022. Newcastle University
Feng J, Liu L, Hou X, et al (2023) QoE fairness resource allocation in digital twin-enabled wireless virtual reality systems. IEEE J Sel Areas Commun
Hu H, He J, He X et al (2019) Emergency material scheduling optimization model and algorithms: a review. J Traffic Transp Eng (Engl Ed) 6(5):441–454
Zhang Z, Song H, Wang H et al (2024) A novel brain-inspired approach based on spiking neural network for cooperative control and protection of multiple trains. Eng Appl Artif Intell 127:107252
Faria RR, Capron BDO, Secchi AR et al (2024) A data-driven tracking control framework using physics-informed neural networks and deep reinforcement learning for dynamical systems. Eng Appl Artif Intell 127:107256
Funding
Funding was provided by the National Science Foundation of China (Grant No. 61972211).
Author information
Authors and Affiliations
Contributions
Wang wrote the main manuscript text. Xu provided the experiment data and prepared Section 4.2. All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wang, T., Xu, Q. Research on safety verification methods of static data of train control systems based on deep association rules. J Supercomput 80, 13124–13140 (2024). https://doi.org/10.1007/s11227-024-05948-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11227-024-05948-7