In this paper, we consider the need to create an algorithmized digital platform for the development of electric charging networks of road transport and to optimize the load on the current power grid complex. In modern conditions, this platform can become an example of a mechanism for public-private partnerships and the development of environmentally friendly transport. The purpose of the platform is to provide users with up-to-date information about the state of the electric vehicle battery, reduce the cost of creating a network charging infrastructure for electric vehicle transport, automated resource accounting for power supply companies, and other services. The use of information technologies to combine the processes of economic relations in the field of energy sales services will attract funds for infrastructure development by increasing the load factor of generating capacities and unloading power centers. In the article, the authors propose a digital platform architecture using modern technologies such as IIoT, Vehicle to Grid (V2G), Machine learning, Big Data.

Big Data analysis is becoming an everyday task for companies all over the world, including Russian companies. Due to advances in technology and the reduction in the cost of storage systems, companies can now collect and store large volumes of heterogeneous data. The important step of extracting knowledge and value from such data is a challenge that will eventually be met by all companies seeking to maintain their competitiveness and place in the market. However, companies face several challenges when it comes to collecting, pre-processing, and integrating data into cohesive data sets designed to deliver analytics. In this article, the above problems and possible solutions are illustrated using the example of cleaning, integration, and normalization of data obtained in the measurement of indicators for the Vanyukov melting furnace process. The article considers an approach to the study of metallurgical processes using the analysis of large operational control data sets. Standard methods of processing the data sets of operational process control are used. The correlation analysis of the main process parameters is carried out. The results are interpreted for their further practical application.

The maintenance of oil pumps is a complex task for any operating organization, and for an industrial enterprise in the oil and gas sector of the economy, this issue has a high degree of urgency. One of the reasons for this is a wide spread of pumping equipment in all areas of oil and gas enterprises. At the same time, an aggressive environment, uneven load, remote facilities, and harsh climatic zones (especially in the areas of the Arctic region or production platforms) are factors that make it relevant to develop special systems that help or simplify the maintenance of pumping equipment. Dynamic modeling is one of the modern technologies which allows for solving the urgent issue of assessing the technical condition of equipment. It is the basis of systems that carry out diagnostics and prognostic calculations and allow for assessing the dynamic state of objects under various conditions of their operation, among other functions. Augmented reality technology is a technology that allows for reducing the time for equipment maintenance by reducing the time for searching and processing various information required in the maintenance process. This paper presents an investigation of the effectiveness of an augmented reality and a dynamic simulation system collaboration in oil pump maintenance. Since there is insufficient research on the joint application of these two technologies, the urgent issue is to prove the effectiveness of such collaboration. For this purpose, this paper provides a description of the system structure, gives a description of the development process of the augmented reality system application and tests the application using Microsoft HoloLens 2.