E.Yu. Davydov1, S.A. Malgina2, M.P. Farkhadov3, O.V. Blinova4, D.V. Komanich5
1, 2 FSUE RFNC-VNIIEF (Sarov, Russia)
3–5 V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences (Moscow, Russia)
1 eyudavydov@vniief.ru, 2 samalgina@vniief.ru, 3 mais@ipu.ru, 4 blinovaov@ipu.ru, 5 komanich.d@ipu.ru
In order to make decisions on the management of scientific projects, specialists need to obtain data slices from various sources related to one or more scientific projects in a short time. Structured repositories for this data are under development: storage structures and data processing methods are being upgraded and supplemented. It is necessary to develop the architecture and layout of a system that provides comprehensive processing of information on the progress of scientific projects.
Structure the storage and automate the processing of incoming and stored documentation in order to optimize the process of planning and monitoring scientific projects and support management decisions. The entire cycle of document processing and analysis should be integrated into a single system called the ISU NCFM. The system should enable interaction between existing modules and subsystems and should ensure scalability and sustainability. An architecture has been proposed and a functional mock-up of the NCFM information management system has been created, automating a number of tasks for processing and analyzing documentation. The layout is undergoing user testing, work is underway to improve the quality of completed tasks and develop modules that expand functionality.
The developed layout of the ISU NCFM is deployed on the basis of the RFNC NCFM, used in the processing of incoming documentation and its analysis. The parsing module is used both in conjunction with the intelligent processing platform and separately to extract information from documents, and is registered as a software product.
The publication was carried out within the framework of the scientific program of the National Center for Physics and Mathematics, direction No. 9 "Artificial intelligence and big data in technical, industrial, natural and social systems".
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