State division method of zinc flotation based on heterogeneous texture features

Abstract

The present invention proposes a zinc flotation state division method based on heterogeneous texture features, which integrates the gray level co-occurrence matrix algorithm that can better effect on high-frequency texture features, and the algorithm that has better modeling effects on medium and low-frequency texture images The Gaussian Markov random field algorithm extracts the texture features of zinc flotation images, and normalizes them as texture feature vectors. In the ensemble clustering algorithm, first use relatively high-efficiency partition clustering to eliminate the influence of noise points and outliers, and then use a hierarchical clustering algorithm with better clustering quality and higher stability to divide the cluster output The clustering centers are combined to obtain the final clustering result. Experiments have proved that the texture feature quantity extracted by the present invention has good mode separability, and the integrated clustering algorithm can well distinguish foams in different states, and this method can be directly implemented on a computer, and the cost is low. High efficiency and easy implementation.

Description

technical field [0001] The invention belongs to the technical field of froth flotation, and in particular relates to a method for dividing zinc flotation working conditions. Background technique [0002] Froth flotation is one of the most important beneficiation methods in zinc smelting today. The flotation method is a method that uses the different physical and chemical properties of the surface of mineral particles to cause different hydrophilicity, and then separates the minerals. It has a strong Practical value. Through continuous stirring and aeration during the flotation process, a large number of bubbles with different sizes, colors, shapes and textures can be formed, and mineral particles adhere to the surface of the bubbles to achieve mineral separation. Correct identification of flotation conditions is the basis and key to realize the optimal operation of flotation production. The visual characteristics of flotation froth contain a large amount of information rel...

AI Technical Summary

Problems solved by technology

However, the micro-heterogeneity, complexity, and ambiguity of the concept of the foam texture have brought great challenges to the texture extraction. It is difficult to identify the state of the zinc flotation foam with a single method. To classify and identify the selected state, it is necessary to analyze the texture features from multiple aspects to further realize the automatic classification and identification of flotation production conditions

At the same time, traditional foam image clustering mostly uses a single clustering model, which is difficult to meet the requirements of clustering accuracy and efficiency at the same time. Therefore, it is necessary to combine the advantages of multiple clustering methods to achieve a more accurate zinc flotation image clustering algorithm

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