Sustainable Development of Mountain Territories,
2024,
цитирований: 0,
open access

,
doi.org,
Abstract
Production volumes of the Russian agro-industrial complex (AIC) increased by 61% from 2016 to 2022, while plans for self-sufficiency were exceeded in many indicators of food security, and since 2020 exports have shown stable growth, exceeding imports. In the domestic agricultural sector, especially in the remote mountainous regions of the Urals, Altai, and the Caucasus, there are a number of scientific and technical tasks that can be solved through the use of digital technologies. When implementing such solutions, they should take into account the changing employment structure, minimize the negative effects of digital transformation, increase the productivity of mining agriculture, the level and quality of life in the territory of implementation. The development of an effective key information parsing system is of high importance for monitoring and analyzing modern technologies for the development of the agro-industrial complex of mountainous territories, which in turn is a complex and dynamic area, including many interrelated components – from agricultural practices to infrastructure, technology and social factors. The data parser will allow you to systematically monitor an extensive pool of information and identify key trends, relationships and patterns in the development of modern agricultural technologies. In the context of rapid technological changes, it is important to have a tool that allows you to quickly analyze new information from various sources – scientific publications, news, industry reports. The parsing system ensures timely collection, structuring and analysis of relevant data. The system analysis of data obtained through the parser can provide decision makers with valuable analytical information, which will allow them to justify management decisions related to investments, policy development, and innovation in the agro-industrial complex of mountainous territories. The parser can also be used to monitor successful practices and technological solutions in agriculture applied in other regions or countries. Such solutions will allow identifying promising options for technology transfer and adaptation to local conditions. It is also worth noting that the parsing system can be in demand by the scientific community as a tool for data collection and analysis for fundamental and applied research in the field of agricultural development, including technology efficiency assessment, scenario modeling and forecasting. Materials and methods. As part of the study, a digital platform is proposed for use, which, through a data parsing tool, will allow automated monitoring of advanced technologies for the agro-industrial complex. The research methodology includes the following stages: selection and integration of data sources; development of a database structure for storing and analyzing information; creation of an algorithm for processing and analyzing data, including machine learning and text analysis methods; testing and evaluating the effectiveness of the parser on real data. Results. An analysis of digital platforms for solving the problems of modern agriculture was carried out, and a data parsing tool was developed that will allow automated monitoring of advanced technologies for agriculture in mountainous areas, the parser can be used to collect data for the sustainable development of the agro-industrial complex of mountainous territories. The application of language models in parsing systems of advanced agricultural technologies is also considered. The combination of a deep understanding of natural language, the ability to identify hidden patterns, information structuring and visualization skills, as well as predictive capabilities allows us to develop an effective tool for analyzing and making informed management decisions in agriculture in mountainous areas. Discussion. When working with large amounts of information about the state and dynamics of technology development in modern agriculture, decision support systems can not only quickly analyze publications and news, but also present key indicators in an interactive form. In addition, the language model is able to build predictive scenarios for the development of the economy based on the studied data, helping to make effective management decisions. In general, the introduction of advanced language models into data parsing systems for the agro-industrial complex of mountainous territories opens up unique opportunities for rapid, comprehensive and comprehensive analysis of significant amounts of information about technologies and performance indicators. Conclusion. The article presents the results of research on tools for searching and systematizing targeted information about advanced and applicable agricultural technologies in mountainous regions. The concept of a digital parser tool is presented, which will continuously scan current sources of information: scientific publications, expert blogs, industry media and databases to identify new developments, innovations and technological trends. Resume. The article presents the results of research on tools for searching and systematizing targeted information about advanced and applicable agricultural technologies in mountainous regions. The concept of a digital parser tool is presented, which will continuously scan current sources of information: scientific publications, expert blogs, industry media and databases to identify new developments, innovations and technological trends. Suggestions for practical applications and directions for future research. The research results can be useful in solving the tasks of monitoring and analyzing modern agricultural technologies based on information from open or closed-targeted information sources. Such solutions will make it possible to identify promising options for technology transfer and adaptation. The developed parsing system can be in demand by the scientific community as a data collection and analysis tool for fundamental and applied research in the field of agricultural development, including technology efficiency assessment, scenario modeling and forecasting.