Abstract
Development of digital economy requires improving of IT personnel training to ensure a more complete interaction of education and industry. In particular, it is necessary to develop the students’ ability to design industrial intellectual information systems. The article analyzes the experience of Lobachevsky State University of Nizhny Novgorod in organizing a series of educational projects for the information support of Sergach Sugar Plant. These projects were aimed at the development of components of an intellectual and information system for monitoring and controlling sugar production. The purpose of the research was to create educational and methodological support of these projects. Such educational support includes the definition of objectives, plans and schedules, the creation of educational resources. The most important educational objective of the projects was the practical learning of the methodology of intellectual information system creating. The project approach was used as the main method of organizing the educational and research work. The considered training experience is of a great importance not only for solving the partial production problem. It can be useful in a general case when students study the methodology of intellectual information system creating. This approach is the important form in the training of IT personnel for the digital economy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arias-Pérez, J., Velez-Ocampo, J., Cepeda-Cardona, J.: Strategic orientation toward digitalization to improve innovation capability: why knowledge acquisition and exploitation through external embeddedness matter. J. Knowl. Manag. 25(5), 1319–1335 (2021). https://doi.org/10.1108/JKM-03-2020-0231
Li, L., Ye, F., Zhan, Y., et al.: Unraveling the performance puzzle of digitalization: evidence from manufacturing firms. J. Bus. Res. 149, 54–64 (2021). https://doi.org/10.1016/j.jbusres.2022.04.071
Petrikova, E.M.: Digital transformation of the economy and financing of the national project “Digital Economy of the Russian Federation.” Financ. Manag. 2, 94–105 (2021)
Zaikina, L.V.: Introduction and development of digital technologies in Russia. Rossijskij ekonomicheskij vestnik (Russ. Econ. Bull.) 4(6), 100–108 (2021). (In Russian)
Glinkina, O.V., Ganina, S.A., Maslennikova, A.V., et al.: Digital changes in the economy: advanced opportunities for digital innovation. Int. J. Manag. 11(3), 457–466 (2020). https://doi.org/10.34218/IJM.11.3.2020.049
Rastorguev, S.V., Tjan, Y.S.: Digitalization of the Russian economy: trends, personnel, platforms, challenges to the state. Monitoring obshchestvennogo mneniya: ekonomicheskie i social’nye peremeny. (Public opinion monitoring: economic and social changes). 153(5), 136–161 (2019). https://doi.org/10.14515/monitoring.2019.5.08. (In Russia)
Rajkov, A.N., et al.: The concept of an information system to support the interaction of enterprises of the agro-industrial complex, science and education. Cifrovaya ekonomika. (Digit. Econ.) 3(19), 45–51 (2022). (In Russian)
Shen, L., Zhang, X., Liu, H.: Digital technology adoption, digital dynamic capability, and digital transformation performance of textile industry: moderating ole of digital innovation orientation. Manager. Decision Econ. 43(6), 2038–2054 (2021). https://doi.org/10.1002/mde.3507
Kharchenko, S.V.: Formation of an automated information system at the enterprises of sugar companies as the main element of the internal control system. Successes Mod. Sci. 4, 29–33 (2015)
Tishchenko, I.A.: Digital economy as a contour of the study of digital transformation of the economy. Econ. Humanitarian Sci. 3(362), 3–15 (2022). https://doi.org/10.33979/2073-7424-2022-362-3-3-15
Soldatenko, I.S., et al.: Modernization of math-related courses in engineering education in Russia based on best practices in European and Russian universities. In: 44th Annual Conference of the European Society for Engineering Education - Engineering Education on Top of the World: Industry-University Cooperation, SEFI, p. 131 (2016)
Snegurenko, A.P., et al.: Using E-learning tools to enhance students-mathematicians’ competences in the context of international academic mobility programmes. Integraciya obrazovaniya. (Integrat. Educ.) 23(1), 8–22 (2019). https://doi.org/10.15507/1991-9468.094.023.201901.008-022. (In Russian)
Balandin, D.V., Kuzenkov, O.A., Egamov, A.I.: Project-based learning in training IT-personnel for the digital economy. E3S Web Conf. 380, 01035 (2023). https://doi.org/10.1051/e3sconf/202338001035
Balandin, D.V., et al.: Educational and research project “Optimization of the sugar beet processing schedule”. In: Voevodin V., Sobolev S., Yakobovsky M., Shagaliev R. (eds). Supercomputing. LNCS, vol. 13708, pp. 409–422. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-22941-1_30
Tuzhilkin, V.I., et al.: Mathematical model of operational accounting and control of sugar beet production. Izvestiya vuzov. Pishchevaya tekhnologiya. (News Uiversit. Food Technol.) 2–3, 117–121 (2018). (In Russian)
Tuzhilkin, V.I., et al.: Operational accounting and control of sugar beet production. Theor. Aspects Storage Process. Agricult. Prod. 1, 20–34 (2019)
Kharchenko, S.V.: Formation of primary accounting and analytical information for accounting and control during sugar beet processing at sugar industry enterprises. Ekonomika: vchera, segodnya, zavtra. (Econ. Yesterday, Today, Tomorrow) 10(2–1), 407–419 (2020). (In Russian)
Saprykin, M.Yu., Saprykina, N.A.: Analysis of the concept of “information” from the standpoint of an object-oriented approach. Sci. Sci. 8(2), 1–10 (2016). (In Russian). https://doi.org/10.15862/36TVN216
Simonovich, S.V.: Computer Science. Basic course: Textbook for Universities, 3rd edn. The Third Generation Standard, 640p. Peter, St. Petersburg (2011). (In Russian)
Makarova, N.V., Volkov, V.B.: Informatics: Textbook for Universities, 576p. St. Petersburg, St. Petersburg (2011). (In Russian)
Junqueira, R., Morabito, R.: Modeling and solving a sugarcane harvest front scheduling problem. Int. J. Prod. Econ. 231(1), 150–160 (2019)
Kaplan, A., Haenlein, M.: Siri, Siri, in my hand: who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Bus. Horiz. 62(1), 15–25 (2019)
Gorbachenko, V.I., Akhmetov, B.S., Kuznetsova, O.Yu.: Intelligent systems: fuzzy systems and networks. In: Textbook for Universities, 2nd edn., Corr. and Add, 105p. Yurayt Publishing House, Moscow (2019)
Flach, P.: Machine learning. In: The Science and Art of Building Algorithms that Extract Knowledge from Data, 400p. DMK Press Publishing House (2015)
Gruzdev, A.V.: Predictive Modeling in IBM SPSS Statistics, R and Python: The Method of Decision Trees and a Random Forest. DMK Press Publishing House, 642p. (2018). ISBN: 978-5-97060-539-4
Shah, S.N.R., Siddiqui, G.R., Pathan, N.: Predicting the behaviour of self-compacting concrete incorporating agro-industrial waste using experimental investigations and comparative machine learning modelling. Structures 52, 536–548 (2023). https://doi.org/10.1016/j.istruc.2023.04.009
Taskiner, T., Bilgen, B.: Optimization models for harvest and production planning in agri-food supply chain: a systematic review. Logistics 5(3), 52 (2021). https://doi.org/10.3390/logistics5030052
Li, J., et al.: Production plan for perishable agricultural products with two types of harvesting. Inf. Process. Agricult. 7(1), 83–92 (2020). https://doi.org/10.1016/j.inpa.2019.05.001
Varasa, M., Bassob, F., Maturana, S., Osorio, D., Pezoa, R.: A multi-objective approach for supporting wine grape harvest operations. Comput. Indust. Eng. 145, 106497 (2020). https://doi.org/10.1016/j.cie.2020.106497
Armin, C.A., Emad, R.: Review of optimization researches in the field of agricultural supply chain. Mod. Concep. Dev. Agrono. 5(4), 556–560 (2020). https://doi.org/10.31031/MCDA.2020.05.000619
Morozov, A.Y., Sandhu, S.K., Kuzenkov, O.A.: Global optimization in Hilbert spaces using the survival of the fittest algorithm. Commun. Nonl. Sci. Numer. Simul. 103, 106007 (2021)
Grishagin, V.A., Barkalov, K.A., Kozinov, E.A.: ML-based approach for accelerating global search algorithm for solving multicriteria problems. In: Learning and Intelligent Optimization (LION 2022). LNCS, vol. 13621, pp. 123–129. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-24866-5_9
Nguyen, T.-D., et al.: Mathematical programming models for fresh fruit supply chain optimization: a review of the literature and emerging trends. AgriEngineering 3, 519–541 (2021). https://doi.org/10.3390/agriengineering3030034
Kuzenkov, O.A., Kuzenkova, G.V. Identification of the fitness function using neural networks. Procedia Comput. Sci. 169, 692–697 (2020). https://doi.org/10.1016/j.procs.2020.02.179
Anichin, V.L.: Theory and Practice of Production Resources Management in the Beet Sugar Subcomplex of the Agro-industrial Complex. Publication House of the BelGSHA, Belgorod (2005). (In Russian)
Kukhar, V.N., Chernyavsky, A.P., Chernyavskaya, L.I., Mokanyuk, Y.A.: Methods for assessing the technological properties of sugar beet using indicators of the content of potassium, sodium and \(\alpha \)-amine nitrogen determined in beetroot and its processing products. Sugar 1, 18–36 (2019). (In Russian)
Rafgarden, T.: Perfect algorithm. In: Greedy Algorithms and Dynamic Programming, 256p. St. Petersburg (2020) (In Russian)
Chernyavskaya, L.I., Mokanyuk, Yu.A., Kuhar, V.N., Chernyavsky, A.P.: The efficiency of sugar beet processing depends on the loss of sugar during the storage of root crops. Part 3. Chemical and phytopathological indicators of sugar beet mechanized harvesting after storage in kagats. Sugar 1, 36–45 (2021). https://doi.org/10.24411/2413-5518-2021-10103. (In Russian)
Acknowledgments
The article was carried out under the contract No. SSZ-1771 dated 22.04.2021 on the implementation of R &D on the topic: “Creation of high-tech sugar production on the basis of JSC “Sergach Sugar Plant”, within the framework of the Agreement on the provision of subsidies from the federal budget for the development of cooperation between the Russian educational organization of higher education and the organization of the real sector of the economy in order to implement a comprehensive project to create high-tech production No. 075-11-2021-038 of 24.06.2021. (IGC 000000S407521QLA0002).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Balandin, D., Kuzenkov, O., Egamov, A. (2023). Development of Components for Monitoring and Control Intelligent Information System. In: Voevodin, V., Sobolev, S., Yakobovskiy, M., Shagaliev, R. (eds) Supercomputing. RuSCDays 2023. Lecture Notes in Computer Science, vol 14389. Springer, Cham. https://doi.org/10.1007/978-3-031-49435-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-49435-2_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-49434-5
Online ISBN: 978-3-031-49435-2
eBook Packages: Computer ScienceComputer Science (R0)