Аннотация
The subject of this study is the digital transformation of the occupational safety management system in civil aviation. Owing to the country’s unique geopolitical position in the centre of Eurasia, the Republic of Kazakhstan’s rapid growth in cargo and passenger traffic is associated with increasing employee risks, making it critically necessary to review existing occupational safety management systems. Traditional approaches to occupational safety management, which often focus on reactive elimination of consequences, are insufficiently effective in the context of the dynamic development of a high-tech industry, where every incident has serious economic, reputational, and social repercussions. It is possible to improve occupational safety by applying advanced digital technologies, in particular digital twin and AI agent technologies, which are capable of continuously accumulating, processing, and transmitting vast amounts of data in real time through self-learning, creating a comprehensive picture of an aviation enterprise’s entire occupational safety management system’s functioning. This study aims to justify the feasibility of modifying the occupational safety management system in civil aviation in the Republic of Kazakhstan by integrating digital twin and AI agent technologies into key functional processes. Results. The proposed algorithm for the developed AI agent, which is explicitly designed for integration into the occupational safety management systems of aviation enterprises in Kazakhstan, is the key contribution of this study. Its architecture, operating principles, and algorithms for interacting with big data received from digital twins of various elements of the aviation system are described in detail, ranging from the condition of aircraft and ground equipment to the personnel’s psychophysiological indicators and the working environment’s characteristics. This algorithm enables the AI agent to detect anomalies and build predictive models, signalling potential threats in advance. The results of the AI agent’s risk assessment in the civil aviation occupational safety system have been visualised, demonstrating its high efficiency in identifying vulnerabilities, predicting critical situations and forming informed, personalised recommendations for their prevention. The research results demonstrate how proactive monitoring and analysis performed by an AI agent based on digital twin data can significantly reduce the likelihood of injuries and occupational diseases. Conclusions. The proposed approach to modifying the occupational safety management system at civil aviation enterprises is based on the synergistic integration of digital twins and AI agents, whereby risk management shifts from reactive elimination to preventive modelling and mitigation of potential threats. The creation of an occupational safety management system at the country’s aviation enterprises, based on the use of digital twins and AI agents, will significantly increase the competitiveness of civil aviation in the Republic of Kazakhstan on the world market, positioning it as a leader in the application of high-tech solutions for ensuring occupational safety and sustainable development.