Adaptive management of innovation implementation in the Fire and Rescue Sector from design to implementation using the example of automatic fire control systems

Introduction. Fire and rescue services operate amid uncertainty and technological challenges, increasing the need for engineering innovations. Their effect depends on implementation governance, regulatory approval, and integration into operational practice. Adaptive management models are required, relying on effectiveness metrics and feedback. The approach is illustrated with automatic fire containment systems (AFCS) for facilities where water suppression is unacceptable or ineffective.

Aim and objectives. The aim of this study is to justify an adaptive approach to managing the implementation of engineering and technical innovations in the fire and rescue sector based on mathematical modelling. The study addresses the tasks of formalizing the functioning of the fire and rescue service, constructing a Markov model of the facility’s states during a fire, assessing the probabilities of damage levels, comparing discrete and continuous models, and developing an adaptive model for the transfer of innovations.

Methods and research. Discrete- and continuous-time Markov chains, graph and matrix modelling, and numerical computation of steady-state probabilities were used. Life-cycle analysis and synthesis of regulatory, technical, and operational experience in AFCS deployment were applied.

Results and discussion. A Markov model of AFCS functioning, including operating modes and damage levels, was developed. Transition probabilities (discrete time) and Kolmogorov equations (continuous time) were obtained, and equality of limiting state probabilities was shown. Modelling quantified the influence of AFCS reliability and organizational factors. A closed-loop adaptive innovation transfer model was proposed. The research supported development of a GOST standard for AFCS.

Conclusions. Markov chains are suitable for assessing AFCS performance and damage probabilities; limiting probabilities are invariant to the time representation. Technology transfer depends on embedding innovations into regulations and operation. The proposed adaptive transfer model supports sectoral self-renewal within the sector overall.

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