kaz29Вестник Казахстанско-Британского технического университетаHerald of the Kazakh-British Technical University1998-66882959-8109Казахстанско-Британский Технический Университет10.55452/1998-6688-2024-21-2-65-82kaz29-1255Research ArticleКОМПЬЮТЕРНЫЕ НАУКИCOMPUTER SCIENCEРАЗРАБОТКА WEB-ПРИЛОЖЕНИЯ АНАЛИЗА И ВИЗУАЛИЗАЦИИ ДАННЫХ ДЛЯ ОБЕСПЕЧЕНИЯ БЕЗОПАСНОСТИ ТЕХНОЛОГИЧЕСКИХ ПРОЦЕССОВDEVELOPMENT OF A WEB APPLICATION FOR DATA ANALYSIS AND VISUALIZATION TO ENSURE THE SAFETY OF TECHNOLOGICAL PROCESSEShttps://orcid.org/0000-0002-2117-5390УвалиеваИ.UvaliyevaI.

PhD

070004, г. Усть-Каменогорск

PhD

070004, Ust-Kamenogorsk

iuvalieva@ektu.kzhttps://orcid.org/0000-0002-3247-5882РустамовС.RustamovS.

PhD, ассоциированный профессор

г. Баку

PhD, Associate Professor

Baku

Srustamov@ada.edu.azhttps://orcid.org/0000-0002-7238-6016БельгиноваС.BelginovaS.

PhD

050013, г. Алматы

PhD

050013, Almaty

sbelginova@gmail.comhttps://orcid.org/0000-0002-0554-7684Рахметуллинаж.RakhmetullinaZh.

канд.физ.-мат.наук

070004, г. Усть-Каменогорск

Candidate of Physical- Mathematical Sciences

070004, Ust-Kamenogorsk

zhrakhmetullina@ektu.kzВосточно-Казахстанский технический университет им. Д. СерикбаеваКазахстанD. Serikbayev East Kazakhstan Technical UniversityKazakhstanУниверситет АDAАзербайджанADA UniversityAzerbaijanУниверситет «Туран»КазахстанUniversity «Turan»Kazakhstan2024010720242126582Copyright © Увалиева И., Рустамов С., Бельгинова С., Рахметуллина ж., 20242024Увалиева И., Рустамов С., Бельгинова С., Рахметуллина ж.Uvaliyeva I., Rustamov S., Belginova S., Rakhmetullina Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://vestnik.kbtu.edu.kz/jour/article/view/1255

Современные технологии довольно хорошо развились за последнее время, и кроме банального сбора и обработки данных современные алгоритмы способны предугадывать поведение оборудования и предупреждать возникновение неполадок и выход из строя заблаговременно и предпринимать действия по снижению вероятности возникновения аварии и предупреждению соответствующих сотрудников. В рамках данного исследования авторами предложена разработка web-приложения анализа и визуализации данных УК-ТЭЦ для обеспечения безопасности технологических процессов. Усть-Каменогорская ТЭЦ является тепловой электростанцией регионального назначения, выработка тепла и электричества покрывает 80% нагрузки жилищно-коммунального сектора города и промышленных предприятий. ТЭЦ входит в единую энергосистему Казахстана. Разрабатываемое приложение имеет веб-интерфейс, который будет отображать и визуализировать данные основных датчиков и приборов УК ТЭЦ, а также предупреждать возникновение неисправности оборудования и анализировать поведение датчиков с помощью машинного обучения. В качестве основной используемой программной платформы была выбрана node.js, построенная на языке программирования java script, с использованием дополнительных модулей, расширяющих возможности данного языка программирования, написанных на c++, что ускоряет работу с массивами данных, их получение и отправку

Modern technologies have developed quite well lately, and in addition to the banal data collection and processing, modern algorithms are able to predict the behavior of equipment and prevent the occurrence of malfunctions and failures in advance and take actions to reduce the likelihood of an accident and warn the relevant employees. Within the framework of this study, the authors proposed the development of a web-application for the analysis and visualization of data from UK-TPP to ensure the safety of technological processes. Ust-Kamenogorsk TPP is a regional thermal power plant, heat and electricity generation covers 80% of the load of the housing and communal sector of the city and industrial enterprises. The TPP is part of the unified energy system of Kazakhstan. The application being developed has a web interface that will display and visualize data from the main sensors and instruments of the UK TPP, as well as prevent equipment malfunctions and analyze the behavior of sensors using machine learning. Node.js was chosen as the main software platform used, built on the java script programming language, using additional modules that expand the capabilities of this programming language, written in c ++, which speeds up working with data arrays, receiving, and sending them.

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