This article examines the concept of the Digital Twin as a key element of the Industry 4.0 paradigm, based on the integration of the Internet of Things (IoT) and data analytics. The authors analyze existing definitions of the digital twin, trace their evolution, and identify common misconceptions. Particular attention is given to distinguishing between Digital Model, Digital Shadow, and Digital Twin, which allows for a more precise understanding of the level of interaction between physical and digital systems.
The paper further describes the main application areas of the technology, including manufacturing, healthcare, and smart cities. It is shown that Digital Twin has gained the widest adoption in industrial environments, where it is used for monitoring, predictive maintenance, and process optimization. The authors also discuss перспективы применения in healthcare and urban infrastructure, and provide examples of existing industrial platforms and solutions.
In this part of the article, key challenges related to the development and implementation of Digital Twin are also addressed, including limitations of IT infrastructure, issues of data security and privacy, and challenges arising from the high level of connectivity in IoT systems. The authors emphasize the importance of these aspects for the further development of the technology and lay the groundwork for subsequent analysis.
