This article is introduced into the perspective tendencies of the digital transformation of chemical enterprises which allow to improve the process of managing enterprises of the branch. Presented the algorithms of managing and technological information processing based on deep neural network apparatus. New approaches to data processing known as video analytics are applied; it allows to automate the registration process of visual data at chemical enterprises and reduce the impact of the human factor on the objectivity of the decisions. The worked out algorithmic structures of managing and technological information processing allows to carry out the identification of hidden regularities there and ensure the effective achievement of goals at chemical enterprises.

The article is devoted to the analysis of problems and search of directions of development of digitalization of oil industry in Russia. The study analyzed the current state of digitalization of the oil industry, methods used and directions of development of Industry 4.0 in oil industry. The main problems of oil industry digitalization development in the Russian Federation were identified. The main problems in the development of oil industry digitalization in Russia include the lack of clear priorities for technological development of the industry, an acute shortage of funding for early stages of research and development, underdevelopment of venture capital investment in industry projects, institutional barriers, etc. As a result of the research, a systematic approach was proposed, which includes three priorities for the development of digitalization of oil industry in Russia, the implementation of which will stimulate and develop sector R&D, venture capital investment, and the institutional environment. The study concludes with a presentation of the main findings and results.

The article is devoted to managing the development of digital technologies in agriculture of the Russian Federation. The study analysed the current state of digitalization of agriculture, identified the main directions of development. During the work, factors and risks that impede the development of digital technologies in agriculture were identified, associated with the strong fragmentation of the Russian agricultural market, problems of confidentiality, security and regulation of data handling, and other factors. As a result of the study, a model for the interaction of smart agriculture objects in the Russian Federation was proposed, the implementation of which will provide a qualitative and quantitative transition to the use of digital technologies. The characteristic is given to the objects of smart agriculture in the Russian Federation in the framework of the interaction model and the indicators of digitalization of agriculture of the Russian Federation are determined. The study concludes with key findings and results.

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A feature of energy systems (ESs) is the diversity of objects, as well as the variety and manifold of the interconnections between them. A method for monitoring ESs clusters is proposed based on the combined use of a fuzzy cognitive approach and dynamic clustering. A fuzzy cognitive approach allows one to represent the interdependencies between ESs objects in the form of fuzzy impact relations, the analysis results of which are used to substantiate indicators for fuzzy clustering of ESs objects and to analyze the stability of clusters and ESs. Dynamic clustering methods are used to monitor the cluster structure of ESs, namely, to assess the drift of cluster centers, to determine the disappearance or emergence of new clusters, and to unite or separate clusters of ESs.