Technoeconomics

UNK RUS 4-20 Peter the Great St.Petersburg Polytechnic University Pochetniy Vasiliy

Saint Petersburg, Russia

Integrating generative AI for technological trend analysis and patent research automation This study explores the development and application of generative artificial intelligence (AI) for technological trend analysis and patent research automation. The research addresses the inefficiencies in traditional patent research, which is labour-intensive and costly, proposing a solution based on AI technologies such as machine learning, natural language processing (NLP), and vector database systems. The proposed solution incorporates MLOps and RAG frameworks for data collection, analysis, and integration, enabling the automation of patent data processing and keyword extraction through modified TF-IDF algorithms and semantic embeddings. The architecture includes tools for clustering patents by thematic and contextual similarities, significantly reducing the time required for research and enhancing accuracy. Experimental results demonstrate the effectiveness of the developed system, achieving significant improvements in the speed of generating patent studies (30–60 minutes) and the precision of information retrieval. The study highlights the transformative potential of generative AI in streamlining intellectual property analysis and fostering technological innovation. 10.57809/2025.4.2.13.1 330.47 patent research automation Generative Artificial Intelligence Natural Language Processing (NLP) Machine Learning Operationalization (MLOps) Retrieval- Augmented Generation (RAG) TF-IDF Algorithm Large Language Models (LLMs) vector databases https://technoeconomics.spbstu.ru/article/2025.13.1/ 1-Pochetniy.pdf

UNK RUS 21-31 Peter the Great St.Petersburg Polytechnic University Klunduk Daria

Saint Petersburg, Russia

Peter the Great St.Petersburg Polytechnic University Tikhomirova Maria

Saint Petersburg, Russia

Digital tourism platforms as a means to promote industrial tourism in Russia: current status and improvement suggestions This research considers the main features of digital travel platforms aimed at promoting industrial tourism in Russia. The authors study four currently operating Russian platforms—Travel.RU, PromTourism, the Industrial Tourism section of the Visit Petersburg portal, and “Svoe Za Gorodom” [Your Own Countryside]—in order to assess their functions, strengths, and weaknesses. According to the findings, while these platforms provide helpful functions such as aggregating information and filtering tours, they do lack important features such as direct booking, detailed multimedia content, and user interaction, e.g., reviews and ratings. As a result, the authors conclude that efficient digital platforms for industrial tourism are supposed to incorporate comprehensive information, e-commerce capabilities, personalized user experience, and robust multimedia support to see visible improvements in their overall performance and user engagement. 10.57809/2025.4.2.13.2 330.47 digital tourism platforms industrial tourism e-commerce user experience booking systems multimedia content tourism digitalization https://technoeconomics.spbstu.ru/article/2025.13.2/ 2-Klunduk-Tikhomirova.pdf

UNK RUS 32-41 0000-0002-5153-7727 Peter the Great St.Petersburg Polytechnic University Lyamin Boris

Saint Petersburg, Russia

Peter the Great St.Petersburg Polytechnic University Yanchevskaya Margarita

Saint Petersburg, Russia

A model for satisfaction improvement in participants of the educational process via the introduction of lean tools in the administration of universities This paper considers a model to boost satisfaction in all educational stakeholders at higher education institutions via the introduction of lean manufacturing techniques into administrative departments. The authors emphasize the importance of using lean methodology due to increasing competition in the education market and the need for resource optimization. The authors propose a model that illustrates the relationship between resources, processes, lean manufacturing tools, and the level of satisfaction among students, faculty, and staff. The research method involves analyzing current processes, identifying areas for improvement, and implementing techniques such as 5S, value stream mapping, Kanban, and standardization. According to the results, the implementation of lean techniques can reduce time and cost, decrease bureaucratic burden, and enhance service quality. In turn, it would have a positive impact on the satisfaction of all participants in the educational process. The practical significance of this research lies in the potential to apply the proposed model in order to enhance the university performance and ensure sustainable development or higher education. 10.57809/2025.4.2.13.3 330.47 lean manufacturing higher education institution dean's office participant satisfaction process optimization standardization operational efficiency management model https://technoeconomics.spbstu.ru/article/2025.13.3/ 3-Lyamin-Yanchevskaya.pdf

UNK RUS 42-49 G1 Software Li Artem

Saint Petersburg, Russia

Peter the Great St.Petersburg Polytechnic University Barakina Polina

Saint Petersburg, Russia

Industry-specific application of methods for requirements management in tourism and hospitality This paper aims to identify the most effective methods for requirements management in tourism and hospitality, which is a highly relevant issue within the growing complexity of managing hotel and tourism enterprises. In the course of the research, the authors define the major bottlenecks and limitations of current approaches and articulate a set of improvement suggestions for requirements management in hotels at different levels with due consideration of existing GOSTs. 10.57809/2025.4.2.13.4 330.47 digitalization enterprise architecture requirements management tourism hospitality https://technoeconomics.spbstu.ru/article/2025.13.4/ 4-Li-Barakina.pdf

UNK RUS 50-59 Financial University Bialeckaia Elena

Novorossiysk, Russia

State University of Architecture and Civil Engineering Kudryavceva Olga

Astrakhan, Russia

Operating algorithm of the strategic center for digital technologies in the fishing industry This article considers the technological development in the fishing industry and the use of digital technology in this sector. The study examines the main challenges of digitalization in the economy of the fishing industry, and presents conclusions and recommendations for the implementation of digital technologies. The use of a digital platform, as a system for interaction between independent participants in the economy through an algorithm, in a unified information environment, reduces transaction costs by using digital information processing techniques and optimizing the division of labour. The unique aspect of this digital platform is the feedback from participants, which ensures sustainable development and minimizes risks in a turbulent economic climate. This process ensures the smooth operation of the program and prevents errors. As a result of the research, the authors propose a functional model of a situational centre that reflects the structure and functions of the system, as well as the information flows and material objects that connect these functions. 10.57809/2025.4.2.13.5 330.47 fishing industry situation centre information society process control https://technoeconomics.spbstu.ru/article/2025.13.5/ 5-Bialeckaia-Kudryavceva.pdf

UNK RUS 60-69 Peter the Great St.Petersburg Polytechnic University Khamzina Karina

Saint Petersburg, Russia

0000-0003-1032-7173 Peter the Great St.Petersburg Polytechnic University Voronova Olga

St. Petersburg, Russia

PMS-systems in the hospitality industry: specifics of implementation This article aims to assess the specifics and key properties of PMS-systems in order to evaluate the prospects for their adoption in the hospitality industry. In the course of the research, the authors have reviewed the theoretical foundation of cloud-based PMS and studied the market for IT solutions available in the Russian Federation. They have also analyzed and classified the automated control systems of the hotel market and concluded that the implementation of cloud-based PMS systems and process automation can optimize hotel management and improve service quality. Based on these findings, it will be possible to design a project for the PMS-systems implementation with due consideration of the unique operational needs of hospitality industry. 10.57809/2025.4.2.13.6 330.47 process automation hotel business cloud PMS implementation project multi properties process unification management company https://technoeconomics.spbstu.ru/article/2025.13.6/ 6-Khamzina-Voronova.pdf

UNK RUS 70-79 Peter the Great St.Petersburg Polytechnic University Klimentov Andrei

Saint Petersburg, Russia

Investigation of platinum price seasonality using high-order autoregression This research investigates platinum price seasonality using high-order autoregressive modeling. The research object is daily platinum price dynamics (LME data, 2015–2024), focusing on long-term dependencies and cyclical patterns. The method employs stepwise decomposition of a 270-day lag autoregression AR(270) into computationally efficient 15-day lag sub-models, enabling significance testing of all coefficients while minimizing resource demands. Results identify the one-day lag as the dominant predictor, with marginal effects at 6–15-day lags and MAPE (1.15%) confirm model robustness. Conclusions indicate no statistically significant weekly cycles due to the overwhelming influence of short-term lags, though the method’s applicability in low-resource environments (e.g., Microsoft Excel) facilitates accessible highorder autoregression. 10.57809/2025.4.2.13.7 330.47 platinum price forecasting high-order autoregression seasonal cycles stepwise decomposition computational efficiency lagged coefficients time series analysis https://technoeconomics.spbstu.ru/article/2025.13.7/ 7-Klimentov.pdf