ellibsЭлектронные библиотекиRussian Digital Libraries Journal1562-5419Казанский (Приволжский) федеральный университет10.26907/1562-5419-2025-28-5-1015-1035ellibs-606Research ArticleСтатьиПроектирование динамической экспертной системы по анализу влияния климатических воздействий на малые и средние предприятияDesign of a Dynamic Expert System for Analyzing the Impact of Climate Effects on Small and Medium Sized EnterprisesБурнашевРустам АрифовичBurnashevRustam Arifovichr.burnashev@inbox.ruСергеевЯрослав ВладиславовичSergeevYaroslav Vladislavovichsergeevyarik7@yandex.ruКазанский (Приволжский) федеральный университетKazan (Volga region) Federal University20251912202528510151035Copyright © Бурнашев Р.А., Сергеев Я.В., 20252025Бурнашев Р.А., Сергеев Я.В.Burnashev R.A., Sergeev Y.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ellibs.elpub.ru/jour/article/view/606

Растущая нестабильность климата создает новые вызовы и риски для устойчивости малых и средних предприятий. В работе предложена архитектура прототипа динамической экспертной системы, интегрирующей несколько ключевых модулей: пользовательский интерфейс, базу знаний, серверное приложение и модуль динамического обновления данных с API-интерфейсами реального времени. Особенностью системы является применение аппарата Z⁺-чисел, реализованного на основе программной библиотеки scikit-fuzzy, что позволяет учитывать градуированную уверенность в оценках. Этот подход дает более обоснованные и адаптивные оценки рисков, чувствительные к изменению качества исходных данных. Интерактивная визуализация результатов реализована на основе картографической платформы OpenStreetMap. Приведены примеры агрегации экспертных оценок в формате Z-чисел, а также описана методика адаптации функций уверенности системы на основе исторических данных.

Growing climate instability is creating new challenges and risks for the resilience of small and medium-sized enterprises (SMEs). This article proposes a prototype architecture for a dynamic expert system comprising several key modules: a user interface, a knowledge base, a server application, and a dynamic data update module with real-time APIs. A distinctive feature of the system is the application of Z⁺-number calculus, implemented using the scikit-fuzzy library, which allows for accounting of graded confidence in evaluations. This approach provides more robust and adaptive risk assessments that are sensitive to changes in the quality of input data. Interactive visualization of the results is built upon OpenStreetMap. The system's methodology for self-adaptation of confidence measures based on historical data is described.

Z-числанечеткая логикаэкспертная системанеопределенностьклиматические рискималые и средние предприятиявизуализация данныхпринятие решенийZ-numbersfuzzy logicexpert systemuncertaintyclimate riskssmall and medium enterprisesdata visualizationdecision makingReferencesZadeh L.A. A Note on Z-numbers // Information Sciences. 2011. Vol. 181. P. 2923–2932.Zadeh L.A. A Note on Z-numbers // Information Sciences. 2011. Vol. 181. P. 2923–2932.Aliev R.A., Alizadeh A.V., Huseynov O.H. The arithmetic of discrete Z-numbers // Information Sciences. 2015. Vol. 290. P. 134–155.Aliev R.A., Alizadeh A.V., Huseynov O.H. The arithmetic of discrete Z-numbers // Information Sciences. 2015. Vol. 290. P. 134–155.Zeinalova L.M. Choquet aggregation based decision making under Z-information // ICTACT Journal on Soft Computing. 2014. Vol. 4, № 4. P. 819–824.Zeinalova L.M. Choquet aggregation based decision making under Z-information // ICTACT Journal on Soft Computing. 2014. Vol. 4, № 4. P. 819–824.Burnashev R.A., Sergeev Y.V., Nazipova A.F. Metody granulyatsii nechetkikh vremennykh ryadov dlya analiza dannykh // Ontologiya proektirovaniya. 2025. T. 15, No. 3(57). P. 404-417. https://doi.org/10.18287/2223-9537-2025-15-3-404-417.Burnashev R.A., Sergeev Y.V., Nazipova A.F. Metody granulyatsii nechetkikh vremennykh ryadov dlya analiza dannykh // Ontologiya proektirovaniya. 2025. T. 15, No. 3(57). P. 404-417. https://doi.org/10.18287/2223-9537-2025-15-3-404-417.Enikeeva A.I., Burnashev R.A., Farahov R.R. Development of an Expert System Based on Fuzzy Logic for Pneumonia Diagnostics // Automatic Documentation and Mathematical Linguistics. 2024. Vol. 58, No. S4. P. S202–S215. https://doi.org/10.3103/S000510552470027X.Enikeeva A.I., Burnashev R.A., Farahov R.R. Development of an Expert System Based on Fuzzy Logic for Pneumonia Diagnostics // Automatic Documentation and Mathematical Linguistics. 2024. Vol. 58, No. S4. P. S202–S215. https://doi.org/10.3103/S000510552470027X.Enikeev A.I., Burnashev R.A., Vakhitov G.Z. Software tools and techniques for the expert systems building // Advances in Intelligent Systems and Computing. 2020. Vol. 1041. P. 191-199. https://doi.org/10.1007/978-981-15-0637-6_16Enikeev A.I., Burnashev R.A., Vakhitov G.Z. Software tools and techniques for the expert systems building // Advances in Intelligent Systems and Computing. 2020. Vol. 1041. P. 191-199. https://doi.org/10.1007/978-981-15-0637-6_16Poleshchuk O.M., Poyarkov N.G., Tumor S.V. Prinyatiye resheniy na osnove bayesovskogo podkhoda i Z-chisel // Lesnoy vestnik. 2019. T. 23, No. 4. S. 112–116. https://doi.org/10.18698/2542-1468-2019-4-112-116.Poleshchuk O.M., Poyarkov N.G., Tumor S.V. Prinyatiye resheniy na osnove bayesovskogo podkhoda i Z-chisel // Lesnoy vestnik. 2019. T. 23, No. 4. S. 112–116. https://doi.org/10.18698/2542-1468-2019-4-112-116.Poleshchuk O.M., Chernova T.V. Z - chisla i ikh novyye vozmozhnosti dlya modelirovaniya real'nogo mira // Sovremennyye problemy fiziko-matematicheskogo obrazovaniya: sbornik materialov VI Mezhdunarodnoy zaochnoy nauchno-prakticheskoy konferentsii, Orekhovo-Zuyevo, 12–13 dekabrya 2016 goda / Gosudarstvennyy gumanitarno-tekhnologicheskiy universitet. Orekhovo-Zuyevo: Gosudarstvennyy gumanitarno-tekhnologicheskiy universitet, 2016. P. 33–35.Poleshchuk O.M., Chernova T.V. Z - chisla i ikh novyye vozmozhnosti dlya modelirovaniya real'nogo mira // Sovremennyye problemy fiziko-matematicheskogo obrazovaniya: sbornik materialov VI Mezhdunarodnoy zaochnoy nauchno-prakticheskoy konferentsii, Orekhovo-Zuyevo, 12–13 dekabrya 2016 goda / Gosudarstvennyy gumanitarno-tekhnologicheskiy universitet. Orekhovo-Zuyevo: Gosudarstvennyy gumanitarno-tekhnologicheskiy universitet, 2016. P. 33–35.Kostikova A.V., Tereliansky P.V., Shuvaev A.V. [i dr.] Expert Fuzzy Modeling of Dynamic Properties of Complex Systems // ARPN Journal of Engineering and Applied Sciences. 2016. Vol. 11, No. 17. P. 10601–10608.Kostikova A.V., Tereliansky P.V., Shuvaev A.V. [i dr.] Expert Fuzzy Modeling of Dynamic Properties of Complex Systems // ARPN Journal of Engineering and Applied Sciences. 2016. Vol. 11, No. 17. P. 10601–10608.Mozgachev A.V., Rybina G.V., Shantser D.I., Blokhin Y.M. Dinamicheskiye intellektual'nyye sistemy na osnove integrirovannykh ekspertnykh sistem // Pribory i sistemy. Upravleniye, kontrol', diagnostika. 2012. No. 5. S. 13–20.Mozgachev A.V., Rybina G.V., Shantser D.I., Blokhin Y.M. Dinamicheskiye intellektual'nyye sistemy na osnove integrirovannykh ekspertnykh sistem // Pribory i sistemy. Upravleniye, kontrol', diagnostika. 2012. No. 5. S. 13–20.Titov N.A., Makrushin S.V. Tekhnologiya sozdaniya domennoy bazy znaniy vopros-otvetnoy sistemy na osnove krupnomasshtabnoy universal'noy bazy znaniy // Computational Nanotechnology. 2022. V. 9, № 1. P. 115–124. https://doi.org/10.33693/2313-223X-2022-9-1-115-124.Titov N.A., Makrushin S.V. Tekhnologiya sozdaniya domennoy bazy znaniy vopros-otvetnoy sistemy na osnove krupnomasshtabnoy universal'noy bazy znaniy // Computational Nanotechnology. 2022. V. 9, № 1. P. 115–124. https://doi.org/10.33693/2313-223X-2022-9-1-115-124.Davydenko I.T. Semantic models, method and tools of knowledge bases coordinated development based on reusable components // Otkrytyye semanticheskiye tekhnologii proektirovaniya intellektual'nykh sistem. 2018. No. 8. P. 99–119.Davydenko I.T. Semantic models, method and tools of knowledge bases coordinated development based on reusable components // Otkrytyye semanticheskiye tekhnologii proektirovaniya intellektual'nykh sistem. 2018. No. 8. P. 99–119.Bochkarev A.M. Effektivnost' ispol'zovaniya informatsionnykh platform razrabotki klient-servernykh prilozheniy dlya informatsionnykh sistem promyshlennykh predpriyatiy // Finansovyy biznes. 2021. № 4(214). P. 17–19.Bochkarev A.M. Effektivnost' ispol'zovaniya informatsionnykh platform razrabotki klient-servernykh prilozheniy dlya informatsionnykh sistem promyshlennykh predpriyatiy // Finansovyy biznes. 2021. № 4(214). P. 17–19.Grinyuk D.A., Sukhorukova I.G., Oliferovich N.M. Ispol'zovaniye algoritmov approksimatsii dlya sglazhivaniya trendov izmeritel'nykh preobrazovateley // Trudy BGТU. Seriya 3: Fiziko-matematicheskiye nauki i informatika. 2017. № 2(200). P. 82–87.Grinyuk D.A., Sukhorukova I.G., Oliferovich N.M. Ispol'zovaniye algoritmov approksimatsii dlya sglazhivaniya trendov izmeritel'nykh preobrazovateley // Trudy BGТU. Seriya 3: Fiziko-matematicheskiye nauki i informatika. 2017. № 2(200). P. 82–87.Bi L., Cao W., Hu W., Wu M. A Dynamic-Attention-Based Heuristic Fuzzy Expert System for the Tuning of Microwave Cavity Filters // IEEE Transactions on Fuzzy Systems. 2022. Vol. 30, No. 9. P. 3695–3707. https://doi.org/10.1109/tfuzz.2021.3124643.Bi L., Cao W., Hu W., Wu M. A Dynamic-Attention-Based Heuristic Fuzzy Expert System for the Tuning of Microwave Cavity Filters // IEEE Transactions on Fuzzy Systems. 2022. Vol. 30, No. 9. P. 3695–3707. https://doi.org/10.1109/tfuzz.2021.3124643.Livio J., Hodhod R. AI Cupper: A Fuzzy Expert System for Sensorial Evaluation of Coffee Bean Attributes to Derive Quality Scoring. IEEE Transactions on Fuzzy Systems.2018. Vol. 26 (6). P. 3418–3427. https://doi.org/10.1109/TFUZZ.2018.2832611.Livio J., Hodhod R. AI Cupper: A Fuzzy Expert System for Sensorial Evaluation of Coffee Bean Attributes to Derive Quality Scoring. IEEE Transactions on Fuzzy Systems.2018. Vol. 26 (6). P. 3418–3427. https://doi.org/10.1109/TFUZZ.2018.2832611.Samanta S., Pratama M., Sundaram S. Bayesian Neuro-Fuzzy Inference System for Temporal Dependence Estimation. IEEE Transactions on Fuzzy Systems. 2021. Vol. 29 (9). P. 2479–2490. https://doi.org/10.1109/TFUZZ.2020.3001667.Samanta S., Pratama M., Sundaram S. Bayesian Neuro-Fuzzy Inference System for Temporal Dependence Estimation. IEEE Transactions on Fuzzy Systems. 2021. Vol. 29 (9). P. 2479–2490. https://doi.org/10.1109/TFUZZ.2020.3001667.Giiven M.K., Passino K.M. Avoiding exponential parameter growth in fuzzy systems. IEEE Transactions on Fuzzy Systems. 2001. Vol. 9 (1). P. 194–199 https://doi.org/10.1109/91.917125.Giiven M.K., Passino K.M. Avoiding exponential parameter growth in fuzzy systems. IEEE Transactions on Fuzzy Systems. 2001. Vol. 9 (1). P. 194–199 https://doi.org/10.1109/91.917125.

The authors declare that there are no conflicts of interest present.