Cognitive graphical interface of the dispatcher workplace of the situational center in the agro-industrial enterprise

The relevance of the research is determined by the need to improve the efficiency of managing complex agricultural equipment in agricultural enterprises of the Russian Federation. In the context of an increasing number of emergencies, a key role is assigned to the dispatcher of the situation center, whose efficiency of actions directly depends on the quality of the graphical interface of the visualization system Therefore, the aim of the research is to reduce the time for eliminating emergency situations by optimizing the time of the information analysis and decision-making by the dispatcher based on a cognitive-graphical interface.

The article proposes an original formalisation of the total time to eliminate an accident, presented as the sum of four independent components: the time to detect and identify an accident, the time to analyse information and make a decision, the time for logistics and the time to eliminate an accident. It is shown that the most manageable factor is the second component, which depends on the method of decision support and the quality of the graphical interface. To optimize it, the authors provide a cognitive graphical interface based on the Novoselov fractal, which implements a hierarchical self-similar visualisation structure. Moreover, 36 controlled parameters are integrated in a single interactive window, grouped into six semantic blocks (complexity of the situation, danger, weather conditions, team composition, available funds, logistics).

The experimental results show that the proposed interface reduces the time for information analysis and decision-making by 30–40 % compared to classical interfaces without loss of accuracy. The developed principles can be used to modernize the software of situational centers of agro-industrial enterprises.

1. Zhizdyuk A. A., Builov V. N., Chumakova S. V., Akhilbekov M. N. Application of digital twins of technological farm machines during their operation and maintenance. Agricultural Engineering. 2025;27(1):20–25. https://doi.org/10.26897/2687-1149-2025-1-20-25. EDN: CCHMVF. (In Russ.).

2. Chernysh A. P. Konstruktorsko-tekhnologicheskoye obespecheniye nadezhnosti mashin agropromyshlennogo kompleksa pri remonte putem formirovaniya tekhnologicheskikh remontnykh blokov. Kemerovo, 2013. 320 p. ISBN 978-5-90228224-2. EDN: RJLYNP. (In Russ.).

3. Podvesovskii A. G., Titarev D. V., Isaev R. A. Fuzzy cognitive models in software projects analysis and planning. Herald of Computer and Information Technologies. 2019;8: 22–31. https://doi.org/10.14489/vkit.2019.08.pp.022-031. EDN: XBXEXX. (In Russ.).

4. Pazderin A. V. The existing system of emergency control automation of the Unified Energy System of Russia and directions of its development. Rosseti Special Issue. 2025;2(37):30–33. EDN: YHQBUQ. (In Russ.).

5. Listyukhin V. A. Prospects for the implementation of information-measuring systems for monitoring parameters of 0.4–10 kV overhead lines in difficult weather conditions. Rosseti Special Issue. 2023;3(30):26–31. EDN: YHQBUQ. (In Russ.).

6. Podoplelova E. S. Analysis of multi-criteria decision-making methods on the example of the ranking problem. Izvestiya YuFU. Engineering Sciences. 2023;3(233):118–125. https://doi.org/10.18522/2311-3103-2023-3-118-125. EDN: QWZEBE. (In Russ.).

7. Pishchukhina T. A. Designing a scheme of a decision support system based on the ontology of reconfigurable manufacturing. Ontology of Designing. 2022;2(44):231–244. https://doi.org/10.18287/2223-9537-2022-12-2-231-244. EDN: UXPDWX. (In Russ.).

8. Yemel'yanova Yu. G., Fralenko V. P. Methods of cognitivegraphical representation of information for effective monitoring of complex technical systems. Program Systems: Theory and Applications. 2018;4:117–158. https://doi.org/10.25209/2079-3316-2018-9-4-117-158. EDN: HYAUHE. (In Russ.).

9. Yemel'yanova Yu. G. Development of methods for cognitive mapping of states of real-time dynamic systems. Artificial Intelligence and Decision Making. 2016;3:21–30. EDN: WWUTID. (In Russ.).

10. Emelyanova Yu., Fralenko V., Khachumov V. Methods of complex estimation of cognitive graphic images. Program Systems: Theory and Applications. 2018;9(3):49–63. https://doi.org/10.25209/2079-3316-2017-9-3-49-63. EDN: YAJGJF. (In Russ.).

11. Paas F., Tuovinen J., Tabbers H. [et al.]. Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist. 2003;38(1):63–71. https://doi.org/10.1207/S15326985EP3801_8.

12. Novikova S. I. EEG Rhythms and Cognitive Processes. Journal of Modern Foreign Psychology. 2015;4(4):77–86. https://doi.org/10.17759/jmfp.2015040410. (In Russ.).

13. Szafir D. A. Modeling color difference for visualization design. IEEE Transactions on Visualization and Computer Graphics. 2016;22(1):392–401. https://doi.org/10.1109/TVCG.2017.2744359.

14. Prudnikov S. I. Decision support model for intellectual property management. Journal of Computational and Engineering Mathematics. 2025;12(4):61–70. https://doi.org/10.14529/jcem25040.

15. Endert A., Ribarsky W., Turkay C. [et al.]. The state of the art in integrating machine learning into visual analytics. Computer Graphics Forum. 2017;36(8):458–486. https://doi.org/10.1111/cgf.13092.

16. Joye Y., Steg L., Unal A. [et al.]. When complex is easy on the mind: Internal repetition of visual information in complex objects is a source of perceptual fluency. Journal of Experimental Psychology: Human Perception and Performance. 2016;1(42):103–114. https://doi.org/10.1037/xhp0000105.