A Method of Zinc Flotation Dosing State Evaluation Based on Probabilistic Semantic Analysis Model

Abstract

The invention discloses a zinc flotation dosing state evaluation method based on a probabilistic semantic analysis model. The dynamic features of zinc flotation images in different dosing states are obtained through an accelerated robust feature algorithm, and the foam is extracted by using a gray level co-occurrence matrix For the underlying texture features of the image, the dynamic feature vectors of the acquired foam images are clustered by the precise European locality-sensitive hash clustering algorithm, and a relevant visual dictionary is constructed to correlate the acquired dynamic features with the underlying texture features using the Pearson correlation coefficient. degree measurement, optimize the original visual dictionary, and finally use the probabilistic semantic analysis model to evaluate the state of zinc flotation dosing on different foam images. The invention solves the problem of inaccurate and time-consuming evaluation of the zinc flotation dosing state by workers, can accurately evaluate the zinc flotation dosing state, shortens the calculation time, and thereby realizes the overall optimization of the zinc flotation process.

Description

technical field [0001] The invention relates to the technical field of zinc flotation automation, in particular to a zinc flotation dosing state evaluation method based on a probabilistic semantic analysis model. Background technique [0002] In the field of zinc flotation, the evaluation of dosing status is mainly done by experienced workers on site observing the foam morphology on the surface of the flotation tank to complete the on-site operation. The operation error of the dosing state is large and the efficiency is low, and it is impossible to objectively evaluate and recognize the dosing state in the zinc flotation process, which easily leads to low work efficiency and loss of mineral raw materials. [0003] In the field of zinc flotation dosing status evaluation, it was mainly identified by adaptively learning the surface characteristics of the bubbles. By converting the PDF of the bubble size into a cumulative distribution histogram feature, the typical reagents were...

AI Technical Summary

Problems solved by technology

[0002] In the field of zinc flotation, the evaluation of dosing status is mainly done by experienced workers on site observing the foam morphology on the surface of the flotation tank to complete the on-site operation. The operation error of the dosing state is large and the efficiency is low, and it is impossible to objectively evaluate and recognize the dosing state in the zinc flotation process, which easily leads to low work efficiency and loss of mineral raw materials

[0003] In the field of zinc flotation dosing status evaluation, it was mainly identified by adaptively learning the surface characteristics of the bubbles. By converting the PDF of the bubble size into a cumulative distribution histogram feature, the typical reagents were obtained by using the unsupervised furthest neighbor clustering learning method. Under the clustering feature set of the cumulative distribution histogram of foam size, the typical distribution of a section of foam image is marked and the Bayesian reasoning principle is used to obtain the evaluation of the dosing state under the current dosage. This type of method mainly has complex calculations and evaluation inaccurate deficiency

Images