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.

Advancements in excavator technology play a critical role in improving safety, and productivity in nonmetallic material extraction. This study investigated the rational placement of LiDAR sensors on excavators to improve hazard detection and operational stability. The proposed method integrates side-and top-mounted LiDAR sensors to identify hazards, classify objects, and secure excavator positioning on sloped terrain. Advanced data processing techniques, including ground plane removal, region-of-interest restriction, and dynamic object detection, were implemented to improve hazard identification. A custom algorithm was developed to process the LiDAR data, filter irrelevant objects, and provide real-time recommendations for improving excavator stability. The results indicate that the system achieved a 95% hazard detection accuracy in high-risk zones with a radius of (0-5 m), reduced positioning errors by 20%, and reduced the excavation cycle time by 10-15%. The findings suggest that side-mounted LiDAR placement offers superior detection capabilities and stability benefits compared with top-mounted configurations. The integration of 3D LiDAR with AI-driven analytics enables more precise depth perception, obstacle recognition, and adaptive positioning, thereby significantly enhancing operational accuracy.

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.