Automated construction and visualization of reliability model algorithms using Google Colab and Simintech

This paper presents a comprehensive approach to solving Kolmogorov differential equation systems using the Google Colab cloud platform. The research aims to create an algorithmic solution implementing the Runge – Kutta method in Python, including the development of program code that accurately estimates the number of integrations, enabling work both with and without the specialized scipy. integrate library. To enhance modeling efficiency, a structural scheme for solving these equations using SimInTech software has been developed. The methodology includes the development and testing of numerical integration, as well as the creation of visualizations for dynamic reliability models. The authors' automation and visualization methods are highly adaptable and can be integrated into educational programs for students studying reliability theory and automatic control theory. The application of the mathematical framework of Markov random processes expands the capabilities for analyzing and forecasting the behavior of complex systems. The authors demonstrate that the proposed approaches reduce the time required for complex calculations and significantly improve the clarity and informativeness of the visualizations of the created models. These advantages are evident when working with large datasets and resilience stimulation methods, where traditional methods either require significantly more resources or provide insufficient efficiency. The conclusions confirm the high effectiveness and flexibility of the proposed approach to automation and process management, utilizing practice-oriented tools aimed at enhancing adaptability and resilience.