Construction and Analysis of a Low Fuel Consumption Scheduling Model for Mining Trucks in Open-pit Mines

This paper presents a scheduling scheme based on a convolutional neural network to predict the fuel consumption of mining trucks and select the most fuel-efficient path. By collecting the driving data of mining trucks, taking into account the different situations of different factors affecting fuel consumption of mining trucks under different states of full load and no load, the dispatching system can predict the path with the lowest fuel consumption of the mining trucks. The scheme can achieve the same task with the lowest fuel consumption. The scheduling scheme can help the mine control the cost and improve the utilization rate of mine resources and has certain significance for the economic and green development of open-pit mines.