Dynamic clustering for assessing the states of natural technical systems

A.V. Skatkov, A.A. Bryukhovetsky, D.V. Moiseev, V.I. Shevchenko

Sevastopol State University, RF, Sevastopol, Universitetskaya St., 33

Email: dmitriymoiseev@mail.ru

DOI: 10.33075/2220-5861-2020-1-135-144

UDC 681.3


     The article considers the main features related to the development and study of methods of intelligent technology for assessing the state of natural-technical systems. The approach proposed by the authors will allow creating a basis for modeling processes occurring in natural-technical systems (NTS),  carrying out an intelligent analysis of poorly structured data during the monitoring of key NTS indicators, presented in the form of heterogeneous information flows containing, for example, hydrometeorological data on pollution level and composition air, soil, environmental control, maximum permissible emissions of harmful substances, as well as data on the status of resources of a computer system, Kana communications used in the processing of monitoring data. The development of intelligent technology based on the assessment of the state of natural-technical systems using the automatic model of dynamic clustering will lead to an increase in the validity, reliability and efficiency of decision support processes in the study of anthropogenic transformation of the natural environment, as well as in solving problems of ensuring the safety of critical information infrastructure facilities “smart city” The method of dynamic clustering of monitored objects in the class of automaton models is presented, which made it possible to describe processes of changing the state of NTS objects using automatic grammar. The practical significance of the results of the work will lead to a decrease in the level of negative impact of natural and anthropogenic factors, in particular, on the state of ecosystems in the water area of the city of Sevastopol.

Keywords: monitoring systems, dynamic clustering, intelligent technology, big data, modeling of complex systems, data mining.

To quote, follow the DOI link and use the Actions-Cite option or copy:

[IEEE] A. V. Skatkov, A. A. Bryukhovetsky, D. V. Moiseev, and V. I. Shevchenko, “Dynamic cluster-ing for assessing the states of natural technical systems,” Monitoring systems of environment, no. 1, pp. 135–144, Mar. 2020.

Full text in PDF(RUS)


  1. On the approval of the Concept of construction and development of the agro-industrial complex «Safe City», approved. By order of the Government of the Russian Federation 03.12.2014, No. 2446-p. URL: http://14.mchs.gov.ru/document/2632303 (12/23/2019).
  2. Bondur V.G. Aerospace monitoring of oil and gas facilities / V.G. Bondura et al. M.: Scientific World, 2012. 555 p.
  3. Gaysky V.A. Multidimensional harmonic Fourier analysis in measurements of the fields of the marine environment / V.A. Gaysky, P.V. Gaysky // Environmental Monitoring Systems. 2019. Issue. 4 (38). P. 33–42.
  4. Zegzhda P.D. Systematization of cyberphysical systems and assessment of their safety / P.D. Zegzhda, M.A. Poltavtseva, D.S. Lavrova // Problems of information security. Computer systems. 2017. No. 2. P. 127–138.
  5. Dobrynin D.A. Unmanned vehicles, current status and prospects / D.A.Dobrynin // Fourteenth National Conference on Artificial Intelligence with international participation KII-2014 (September 24–27, 2014, Kazan): tr. Conf .: in 3 vols. T. 3. M .: Fizmatlitgiz, 2014. P. 265–274.
  6. Shiryaev A.N. Probabilistic and statistical methods in decision theory. 2-ed., New. / A.N. Shiryaev. M.: MCCMO, 2014. 144 p.
  7. Skatkov A.V. Information technologies for critical infrastructures: monograph / A.V. Skatkov et al. Sevastopol, SevNTU, 2012. 306 p.
  8. Skatkov A.V. Intelligent monitoring system for solving large-scale scientific problems in cloud computing environments / Skatkov A.V., Bryukhovetsky A., A., Moiseev D.V. // Information and control systems of St. Petersburg: Publishing House “GUAP”, No. 2 (87), 2017. P. 19–25.
  9. Skatkov A. Detecting changes simulation of the technological objects’ information states / A. Skatkov, A. Brykhovetskiy, D. Moiseev // MATEC Web of Conferences v. 224, 02072 (2018) ICMTMTE 2018, https: // doi. org / 10.1051 / matecconf / 201822402072
  10. Bryukhovetsky A.A. Modeling of anomaly detection processes in complex structured monitoring data / A.A. Bryukhovetsky, A.V. Skatkov, Yu.E. Shishkin // Environmental Monitoring Systems. Sevastopol: IPTS, 2017. Issue. 3 (29). P. 45–49.
  11. Kulbak S. Information Theory and Statistics / S.Kulbak. M .: Nauka, 1967. 408 p.
  12. Pospelov D.A. Thinking and Automata / D.A. Pospelov, V.N. Pushkin. M.: Sov. Radio, 1972. 24 p.