Foreign experience in the use of generative machine learning networks for technical support of fire protection unit exercises

the article reveals an actual problem of studying the foreign experience of using generative machine learning networks in exercises process of fire protection units. The research goal is to identify the main features of the use of generative machine learning networks for technical support of firefighter exercises in such foreign countries as the USA, Canada, Germany, France, Japan and China. This experience can be useful for improving the efficiency of the State Fire Service of the Russian Federation. The research objectives are to analyze the main theoretical approaches to the use of generative machine learning networks in the field of fire extinguishing; as well as in the consideration of the main types of neural networks used in the training of fire protection units. The methodology is based on a systematic approach, within the framework of which such general scientific methods as synthesis, analysis, systematization and formal-logical approach were used. The study also involved a number of special methods: historiographical analysis of scientific literature and methods of descriptive analysis. As a research result, the following conclusions were formulated: the successful use of generative machine learning networks in the field of technical support for fire drills directly depends on the quality of the data on which these networks are trained. Foreign examples demonstrate the need for careful preparation and verification of information arrays, as well as the need for constant updating of data on the basis of which the network is trained. Nevertheless, generative machine learning networks have proven to be effective for the fastest possible response of fire departments to various types of incidents and effective training of personnel based on simulated real situations.

virtual reality, augmented reality, fire protection, firefighting, innovative technologies