Treffer: An integrated approach to designing an automated processing system for Big Data on the haulage process by transport vehicles

The article discusses the issues of assessing the haulage efficiency by mining dump trucks using an algorithm for an automated system to process Big Data collected from sensors and GLONASS/GPS-based devices mounted on the vehicles in real time mode. Techniques are proposed to reduce the time required for on-line data processing and analysis, including the design of distributed processing infrastructure, real-time optimization algorithms, and machine learning methods for managing and analyzing the data from sensors, GPS systems and other intelligent devices of the vehicles. The developed algorithm to process Big Data of the haulage process takes into account the collection, storage, analysis, visualization of the data as well as a structural model for determining the technical and operational performance indicators of the haulage process, i.e. the delivery time, vehicle load factor, volume of cargo transported, mileage utilization factor, fuel consumption, idle time and availability factor.