Oil production is increasing. This increases the number of accidents. Oil spills are increasing. Since oil has special physical and chemical properties and parameters, contamination of water resources by oil and oil products causes man-made disasters. The authors made the assumption that a robot with artificial intelligence will be used to purify the water surface from oil (oil products) by biological methods. This robot will be located directly on the ship and will monitor and analyze oil pollution. In order to carry out clean-up activities at the site of the accident, it is necessary to have information on the main parameters of oil pollution. The authors of this article propose a structure for the monitoring and analysis of oil pollution in water resources. According to this structure, analysis and monitoring must be carried out by an intelligent decision support system. An intelligent decision support system includes a database of oil pollution parameters and a knowledge base. The aim of this work is to analyze oil pollution on the water surface using an intelligent decision support system. In order to achieve this objective, the article proposes the structure of the oil pollution parameter database, which is part of an intelligent system to support decision-making on oil pollution analysis and assessment. This scheme includes the main parameters of oil pollution affecting the decision on the choice of type and quantity of treatment products for the biological treatment method. An algorithm for determining the capacity of oil spill is proposed. The main elements of the oil pollution parameters database are: type of oil products, capacity of oil spill, water flow, wave height and velocity, wind direction and speed. In future, the analysis and monitoring scheme for oil-based water pollution will be expanded to include special technical, measuring and meteorological instruments that will allow the immediate presence of the oil (oil products) spill investigate oil contamination parameters.

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The article is devoted to the development of methodological foundations for constructing an optimal system of stochastic stabilization of cutting power based on the results of structural identification of models of the dynamics of the system '' woodworking machine-cutting process '' and uncontrolled disturbance. In order to solve the problem of structural identification of the '' woodworking machine-cutting process ' system and the disturbance acting in the process of wood-cutting, the article proposes a special technology, the use of which made it possible to determine the transfer function of the '' woodworking machine-cutting process '' and estimate the spectral density of the disturbance acting during the processing. It has been established that when the physical and mechanical properties of wood and the state of the cutting tool change, the structure of the transfer function and spectral density does not change, but only the parameters change.As a result of solving the synthesis problem, the structure and parameters of the optimal controller are determined, which ensures the specified quality of the processed surface with minimal energy consumption. To assess the quality of control, it is proposed to use a quadratic criterion, which is the sum of two weighted variances of the stator current deviation of the main motion motor (characterizes energy costs) and the variance of the feed drive speed control signal.Studies of the robust stability of the optimal system with the obtained controller under the influence of unstructured disturbances made it possible to determine the class and estimate the maximum norms of unstructured disturbances at which the system maintains stability and a given control quality. The use of the proposed approach to the construction of an optimal system of stochastic stabilization of cutting power makes it possible to achieve a reduction in energy costs by 12% for a given quality of the processed surface by increasing the stabilization accuracy by two orders of magnitude.

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