ANALYSIS OF SOFTWARE PLATFORMS FOR CREATING DIGITAL TWINS IN ECONOMIC SECTORS
Abstract
The article explores the landscape of software solutions for creating digital twins—virtual models of physical objects or processes that are essential in modern industries, including manufacturing, energy, transportation, and healthcare. The authors discuss challenges such as the absence of standardized frameworks, the complexity of integrating heterogeneous data sources, and scalability limitations affecting deployment. Through an analysis of scientific publications (IEEE Xplore, ScienceDirect), technical reports (Gartner, IBM), and industrial case studies (2020–2024), the article proposes a classification of technological approaches to digital twin development, covering physics-based, data-driven, hybrid, and cloud-edge models. A comparative study of leading platforms (Siemens Digital Industries, Azure Digital Twins, PTC ThingWorx, Ansys Twin Builder, among others) evaluates key criteria, including functionality, interoperability, computational performance, security mechanisms, and total cost of ownership. To facilitate objective software selection, the study introduces a weighted evaluation methodology, which is tested in the energy sector, providing insights into practical adoption strategies. The findings suggest that Azure Digital Twins excels in cloud security and integration, while Siemens MindSphere is well-suited for industrial applications, whereas domain-specific tools like Autodesk Revit demonstrate restricted flexibility. The conclusions provide strategic recommendations for implementing digital twins, emphasizing the need for a combined edge-cloud computing approach, the adoption of zero-trust security frameworks, and the application of AutoML for model adaptation and optimization. Additionally, the study outlines future trends, including the standardization of protocols (OPC UA, ISO 23247), the emergence of DTaaS (Digital Twin as a Service) solutions, and the advancement of 5G/TSN networks for real-time simulations. Ultimately, the article underscores the importance of tailoring platform selection to industry requirements, technical constraints, and financial considerations to ensure optimal digital twin deployment across various sectors.
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