Iron ore blending in an open-pit mine is an important means to ensure ore grade balance and resource recycling in iron mine industrial production. With the comprehensive recovery and utilisation of resource mining, the multi-source and multi-target ore blending method has become one of the focuses of the mining industry. Scientific and reasonable ore blending can effectively reduce the transportation cost of the enterprise. It can also ensure that the ore grade, washability index and iron carbonate content meet the requirements of the concentrator and significantly improve the comprehensive utilisation rate and economic benefits of the ore. An ore blending method for open-pit iron ore is proposed in this paper. The blending method is realised by establishing the ore blending model. This model aims to achieve maximum ore output and the shortest transportation distance, ore washability index, total iron grade, ferrous iron grade and iron carbonate content after the ore blending meets the requirements. This method can meet the situation of a single mine to a single concentrator and that of a single mine to multiple concentrators. According to the results of ore blending, we can know the bottleneck of current production. Through targeted optimisation management, we can tap the production potential of an open-pit mine.

Belts are widely applied in mine production for conveying ores. Understanding ore granularity, which is a crucial factor in determining the effectiveness of crushers, is vital for optimising production efficiency throughout the crushing process and ensuring the success of subsequent operations. Based on edge computing technology, an online detection method is investigated to rapidly and accurately obtain ore granularity information on high-speed conveyor belts. The detection system utilising machine vision technology is designed in this paper. The high-speed camera set above the belt is used to collect the image of the ore flow, and the collected image is input into the edge computing device. After binary, grey morphology and convex hull algorithm processing, the particle size distribution of ore is obtained by statistical analysis. Finally, a 5G router is used to output the settlement result to a cloud platform. In the GUANBAOSHAN mine of Ansteel Group, the deviation between manual screening and image particle size analysis was studied. Experimental results show that the proposed method can detect the ore granularity, ore flow width and ore flow terminal in real-time. It can provide a reference for the staff to adjust the parameters of the crushing equipment, reduce the mechanical loss and the energy consumption of the equipment, improve the efficiency of crushing operation and reduce the failure rate of the crusher.