Issue |
BIO Web Conf.
Volume 105, 2024
IV International Conference on Agricultural Engineering and Green Infrastructure for Sustainable Development (AEGISD-IV 2024)
|
|
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Article Number | 03007 | |
Number of page(s) | 10 | |
Section | Digital Technologies and Automation in Agriculture | |
DOI | https://doi.org/10.1051/bioconf/202410503007 | |
Published online | 26 April 2024 |
Mathematical modeling of food production technological processes in a computer training complex for operator education
1 Russian Biotechnological University, Moscow, Russia
2 Moscow State University of Technology and Management named after K.G. Razumovsky, Moscow, Russia
3 Perm National Research Polytechnic University, Perm, Russia
* Corresponding author: i.s.polevshchikov@mail.ru
A computer training complex (CTC) structure has been designed for training food production operators, comprising interconnected subsystems generating output parameters. This CTC structure enables the configuration and storage of knowledge on specific technological processes, facilitating the customization of exercises by instructors. Individualized exercises are generated for students to perform in a virtual production environment, utilizing computer programs, tablets, smartphones, or simulated production equipment, including VR/AR technologies. During exercises, student actions are automatically recorded, and a quantitative assessment is provided based on standards, forming a comprehensive evaluation. A mathematical model, represented by a Markov transition graph, captures various states of the virtual production environment during simulated processes at the CTC. This model serves as the basis for constructing specific technological process models in the food industry, tailored to CTC hardware, software, and training methodologies. The methodology presented streamlines the development of training courses for CTC exercises, enhancing personnel knowledge and skills in food production. By utilizing these models, methods, and algorithms, CTCs can be tailored for training operators in diverse food production sectors. The approach outlined simplifies course creation, ensuring personnel development at the desired proficiency level through exercise quality assessment algorithms.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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