Flotation Condition Recognition Based on HGNN and Forth Image Dynamic Feature

Abstract

The quality of flotation conditions directly affects the flotation efficiency. Aiming at the problems of difficult online detection, strong subjective arbitrariness, and low recognition efficiency of various flotation conditions in actual flotation work, a flotation condition recognition method based on hypergraph neural network (HGNN) and dynamic feature of forth images is proposed in this paper. Firstly, an improved local binary mode (LBP-TOP) algorithm is introduced to extract the dynamic features of forth sequence containing time information, and then features such as kurtosis and skewness are extracted as supplements to integrate the dynamic features of forth with the supplementary features. By utilizing the aforementioned characteristics and constructing a hypergraph, we have developed an HGNN model that facilitates high-order complex data correlation encoding, thus accomplishing accurate identification of flotation conditions. Finally, simulation shows the effectiveness of the proposed method.