Usynin Andrey Vyacheslavovich, Mashanov Ilya Vladimirovich, Dudarev Dmitriy Konstantinovich, Medvedev Maksim Aleksandrovich
COMPUTER TOOL FOR AUTOMATION OF DESIGNING OF PRODUCTION PLANNING SYSTEMS

Rozanova Anna Vyacheslavovna, Dostovalov Andrey Andreevich, Kapustin Savely Andreevich
CREATING A TEMPLATE FOR INTEGRATION AND PROCESSING OF BUSINESS EVENTS IN PRODUCTION

Tiptev Aleksey Leonidovich, Kuksa Leonid Vasilievich, Pastukhov Roman Maksimovich, Saif Mujahed Abdullah Hayel
TEMPLATE BUSINESS APPLICATION BASED ON THE TOTUM INTERFACE BUILDER FOR THE ORT/CI PLATFORM

Hussein Asharf Adil Said, Balungu Daniel Musafiri
DEVELOPMENT OF A DIGITAL TWIN FOR MATERIAL FLOW BASED ON THE EXAMPLE OF A LONG ROLLING PRODUCTION USING THE DATA-TRACK PLATFORM

Kuts Dmitry Vladimirovich, Porshnev Sergey Vladimirovich, Kuts Maria Petrovna
DEVELOPMENT OF A SOFTWARE ENVIRONMENT FOR AN EDUCATIONAL AND TRAINING CYBERPOLYGON

Andreeva Kristina Aleksandrovna, Solod Anastasia Vasil’evna, Balungu Daniel Musafiri
SOLARA LIBRARY FOR PROCESS ANALYTICS: OPTIMIZATION AND DATA VISUALIZATION USING APACHE SUPERSET

Malykh Maksim Aleksandrovich, Predein Nikita Sergeevich, Burdin Dmitrij Evgen’evich, Medvedev Maksim Aleksandrovich
CREATING A DEVOPS TEMPLATE FOR AN INDUSTRIAL PLATFORM OF CONTINUOUS INTELLIGENCE

Mashanov Ilya Vladimirovich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Dudarev Dmitriy Konstantinovich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Medvedev Maksim Aleksandrovich, Associate Professor, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

This paper presents a tool for automating the construction of production planning systems, which is designed to convert the conditions of mathematical programming problems described in the Univer table framework into executable Python code using the PuLP library. The implementation of this tool solves the actual problem associated with the need to manually transfer models developed by analysts into industrial code, which often leads to lost time, inaccuracies and increased development costs. The proposed solution provides an automated and error-free transition from analytical prototypes to production implementation, reducing implementation time and increasing the efficiency of development teams and analysts. As a result of automation, development costs are reduced, the probability of errors is reduced, and the cycle of implementation of mathematical optimization models into production practice is significantly accelerated.

KEYWORDS: Python, PuLP, Excel, Univer, TypeScript, code generation, mathematical programming, linear programming, digitalization of manufacturing, automation.

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Dostovalov Andrey Andreevich, Master’s student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Kapustin Savely Andreevich, Master’s student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

In the context of global digitalization, enterprises are increasingly turning to modern platforms for data integration and management. One of the promising areas is the integration of Apache NiFi and Data-track, which provide many services to create a unified data management system. This article is devoted to the study of the capabilities of these platforms and their joint application in a production environment. Digitalization of production is becoming increasingly relevant, allowing for increased efficiency and quality through the introduction of information technologies such as the Internet of Things, big data and AI. As a result, powerful platforms Apache Kafka, Apache NiFi, Apache Flink, AWS Kinesis and RabbitMQ were created, and a DATA tracking system (DATA-CI) was selected that allows data integration with other applications. systems and monitor user activity in real time.

KEYWORDS: Data-track, digitalization of production, Continuous Intelligence, automation, Apache NiFi, Data-track, data integration, automation of data flows, data monitoring.

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Kuksa Leonid Vasilievich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Pastukhov Roman Maksimovich, Student, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Saif Mujahed Abdullah Hayel, Senior Lecturer, Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

This article presents a method for creating a template business application for industrial tasks on the ORT/CI platform using the Totum no-code interface builder. It outlines the solution’s architectural and technological features, the implementation sequence, and the advantages of the no-code approach for developing interfaces in an industrial environment. The suitability of the resulting template for enterprise digital-transformation initiatives is assessed.

KEYWORDS: interface builder, Totum, ORT, CI, DATA-TRACK, digital transformation, no-code, business application.

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Balungu Daniel Musafiri, Postgraduate student, Assistant Basic Department of Big Data Analytics and Video Analysis Methods, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

This article describes the process of building a digital twin of material flows for long-range production using a domestic DATA-TRACK solution and the object relations method (ORT). First, a digital model of the site is created: accounting objects are determined, product movement trajectories are modeled, and virtual sensors are installed (such as the speed of transportation, the position of the workpiece). Then, through the Swagger interface, entities and their attributes are formed, such as the temperature in the furnace, set by float data types, and linked to the product processing process. The monitoring process is automated using the Nuclio serverless architecture: a special Python function monitors changes in the status of the accounting object. When the workpiece enters the furnace, a trigger is triggered (HTTP hook in conjunction with the FBP pipeline of the DATA-TRACK platform), which initiates the execution of the function. It requests the current values of the temperature parameter through the API and updates the corresponding indicator of the accounting object through internal DATA-TRACK REST requests. In addition, the same function generates metrics in the Prometheus format (workpiece_id, temperature), which are immediately displayed in the Grafana toolbar. Due to this, the operator continuously sees the dynamics of temperature changes in the furnace and promptly takes measures when the values exceed the permissible limits.

KEYWORDS: digital twin, material flow, long rolling production, DATA-TRACK, ORT, production automation.

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Porshnev Sergey Vladimirovich, Doctor of Technical Sciences, Full Professor, Head of Unit, Training and Scientific Center “Information Security”, Ural Federal University named after the first President of Russia B.N. Yeltsin

Kuts Maria Petrovna, teacher of the Department of Foreign Languages and Educational Technologies, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

The article analyzes the problem of optimizing the educational process in the field of training information security specialists through the use of the created software environment of the cyberpolygon training center. The use of cyber polygons allows for the comprehensive development of professional competencies in the field of information security in the Russian Federation, aimed at developing students, specialists and managers of information security and information technology areas with practical skills in protecting against cyber threats and computer attacks, improving the security of software and hardware components of information and industrial automated infrastructure of Russian organizations, including software products from the Unified Register of Russian computer programs. In addition, the use of cyber polygons contributes to the improvement of the organizational and methodological base of organizations. In the work, the authors implemented their ideas of the cyberpolygon structure, its programmatic implementation and filling with educational and methodological materials. The developed cyberpolygon is designed to develop practical skills in working with security tools, study tools and techniques for attacking information infrastructure, administer the security subsystem of operating systems, and work with file systems.

KEYWORDS: cyberpolygon, information security, virtual infrastructure, Proxmox, cybersecurity, KVM, LXC, Astra Linux.

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Solod Anastasia Vasil’evna, Student, Basic Department «Big Data Analytics and Video Analysis Methods», Ural Federal University named after the first President of Russia B.N. Yeltsin

Balungu Daniel Musafiri, Assistant, Basic Department «Big Data Analytics and Video Analysis Methods», Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

The study is devoted to the joint use of the Solara Python framework and the Apache Superset BI platform for analyzing industrial systems data (SCADA, MES, ERP, IoT). Solara is used to create interactive mnemonic diagrams, and Superset is used for analytical dashboards. The combination of these tools increases the flexibility of data processing and the efficiency of visualization, which contributes to operational transparency and optimization of solutions in the context of Industry 4.0.

KEYWORDS: Solara, analytics, manufacturing processes, Apache Superset, data visualization.

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Predein Nikita Sergeevich, Student, Department of Big Data Analytics and Video Analysis Methods, Institute of Radioelectronics and Information Technology, Ural Federal University named after the first President of Russia B.N. Yeltsin

Burdin Dmitrij Evgen’evich, Student, Department of Big Data Analytics and Video Analysis Methods, Institute of Radioelectronics and Information Technology, Ural Federal University named after the first President of Russia B.N. Yeltsin

Medvedev Maksim Aleksandrovich, Associate Professor, Department of Big Data Analytics and Video Analysis Methods, Institute of Radioelectronics and Information Technology, Ural Federal University named after the first President of Russia B.N. Yeltsin

Abstract

This article describes the development of a DevOps template for the ORT/CI platform, which supports the concept of Continuous Intelligence in industrial applications. The ORT/CI platform integrates tools like Apache NiFi, JupyterLab, and Totum, enabling data integration and analytics within operational processes. The project’s goal was to create a general template for version control, deployment, and maintenance of user applications on this platform, and to set up collection and visualization of metrics and logs using Prometheus, Grafana, and Loki. A CI/CD pipeline based on GitLab CI was implemented, leveraging containerization (Docker, Kubernetes), automating deployment of functions developed in JupyterLab via the Nuclio serverless platform. The developed template simplifies onboarding to the ORT/CI environment by standardizing development and operations workflows. Results indicate that integrating modern DevOps practices increases automation and visibility of the platform’s operations while retaining configuration flexibility and scalability.

KEYWORDS: DevOps, ORT/CI, Continuous Intelligence, CI/CD, JupyterLab, Nuclio, Apache NiFi, Prometheus, Grafana, Loki, integration platforms, monitoring, automation.

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