This article introduces an ontological model of digital twins and explores their classification specifically in the context of ensuring human life safety. The paper delves into how digital twins - virtual replicas of physical systems or processes - can be used to improve safety standards, especially in high-risk industries like oil and gas. By simulating real-world scenarios and potential hazards, digital twins provide valuable insights that can help predict and mitigate risks before they impact human life. The article also highlights various examples of digital twins successfully applied to enhance safety measures, such as monitoring dangerous environments, predicting equipment failures, and providing real-time data to improve decision-making during emergencies. Moreover, the prospects for developing digital twins are discussed, particularly in integrating advanced technologies like artificial intelligence, machine learning, and IoT. These advancements could significantly enhance the capabilities of digital twins in safeguarding human life, making them an indispensable tool in industrial safety and risk management practices.
Classification of Digital Twins in Safety Digital twins can be classified based on their application area, maturity level, and used models. The main types of digital twins for human safety include:
Infrastructure digital twins (for smart cities and transport systems);
Medical digital twins (for monitoring health and preventing diseases) [
Personal safety digital twins (for smart homes and accident prevention). 3. Here are examples of how digital twins can enhance safety in daily life: a) Emergency Services: Digital twins are used to create accurate models of buildings and fire safety systems, allowing real-time monitoring of fire sensors and predicting fire developments. Virtual simulations of emergencies (fires, technological disasters, transport accidents) enable responders to practice action scenarios, modeling various critical situations in real time.
b) Personal Safety in Smart Homes: A digital twin can predict potential threats, such as gas leaks or power supply failures, and take preemptive measures. Additionally, digital twins of buildings and individuals can simulate evacuation processes in real time, considering building parameters, people’s locations, available exits, and crowd behavior to determine optimal evacuation routes) Industrial safety. In industries, digital twins help minimize accidents related to equipment wear and production efficiency. For example, at dangerous facilities, they can track workplace conditions and predict accidents.
c) Industrial Safety: In the industrial sector, digital twins help minimize accidents related to
equipment wear and decreased production efficiency. In hazardous environments (such as chemical
and oil and gas plants), digital twins can monitor workplace conditions, predict accidents, and model
emergency scenarios. This capability allows employees to train and prevent potential threats before
they occur, enhancing overall safety and operational effectiveness. A notable example of a digital
twin in the oil and gas sector is the implementation by Tengizchevroil in 2023 of the "Digital Twin of
the KTL 1 and KTL 2 Complex Technological Line". This digital representation allows for the
exploration of various scenario possibilities, optimizing processes to use existing resources more
effectively while reducing waste and environmental harm [
Additionally, a digital twin of a worker can be used to monitor health status, ensure proper use of personal protective equipment, track the worker's location at the oil field, identify potential hazards, and predict negative outcomes if risks are not addressed promptly.
d) Education in Schools and Educational Institutions: For schoolchildren and students studying safety (such as the Basics of Life Safety), digital twins can be utilized in educational simulations and training exercises.
Feng Tao [
4. Perspectives on the Development of Digital Twins in Human Safety
Modern technologies in artificial intelligence and the Internet of Things (IoT) significantly expand the capabilities of digital twins. The use of AI enables the analysis of vast amounts of data and the prediction of scenarios that are difficult to account for using traditional methods.
According to the Gartner Hype Cycle for Emerging Technologies (see Fig. 2), it is clear that digital twins are trending and are just beginning to develop.
In this regard, the key promising directions include [
еnhancing the accuracy of threat predictions;
integrating with machine learning technologies for the automatic adaptation of models;
expanding applications within smart cities and transportation systems to improve public
safety;
developing a digital twin of individuals [
Considering the previously mentioned points, it becomes evident that digital twins are evolving into a highly effective tool for ensuring safety across various domains. From fire protection to health condition monitoring and forecasting, this technology is instrumental in minimizing risks, improving control systems, and enhancing overall quality of life. Digital twins allow for the creation of virtual replicas of physical systems or processes, enabling real-time analysis and the prediction of potential failures or hazards before they occur in reality. This proactive approach not only significantly contributes to accident prevention but also instills a sense of confidence in the technology's ability to ensure safety.
In sectors like healthcare, industrial safety, and urban planning, digital twins provide invaluable insights that help optimize operations and ensure safer environments for people. For instance, in healthcare, digital twins can simulate a patient's condition, allowing doctors to predict health outcomes and personalize treatment. In industrial settings, they help monitor equipment, predict failures, and prevent accidents, while in urban environments, digital twins assist in optimizing traffic management and ensuring public safety.
Looking toward the future, we can anticipate even greater integration of digital twins into everyday life. As technology advances, it will likely lead to higher safety, security, and comfort levels in both professional and personal spheres. Integrating artificial intelligence, IoT, and machine learning into digital twin systems will further enhance their capabilities, making them indispensable in safeguarding human life. Ultimately, digital twins have the potential to revolutionize how we manage risks and ensure safety across various industries, contributing to a more secure and comfortable world for everyone.
The authors have not employed any Generative AI tools.