Coal-rock-gangue sorting method and system based on multi-source data fusion analysis

A coal-rock-gangue sorting method based on multi-source data fusion analysis, comprising:

• collecting different categories of coal, rock, and gangue, and processing the collected different categories of coal, rock, and gangue into rock blocks and coal blocks of uniform size;
• conducting identical load-bearing tests on the processed rock blocks and coal blocks, monitoring strain responses of the rock blocks and the coal blocks in the tests, correlating different the monitored strain responses with corresponding categories of coal, rock, and gangue, and building a strain-based multi-classification model;
• monitoring a heat released from the coal blocks and the rock blocks during the load-bearing tests, correlating emitted heat under identical size and loading conditions with the corresponding categories of coal, rock, and gangue, and building a heat-based multi-classification model;
• performing a spectral analysis on the different categories of coal, rock, and gangue to identify spectral absorption peak features, correlating the identified spectral absorption peak features with the corresponding categories of coal, rock, and gangue, and building a spectral-based multi-classification model;
• and
• employing the strain-based, heat-based, and spectral-based multi-classification models to independently classify coal-rock-gangue mixtures, and by fusing sorting results of the strain-based, heat-based, and spectral-based multi-classification models, determining and outputting a final sorting result;
• wherein, determining and outputting the final sorting result by fusing the sorting results of the strain-based, heat-based, and spectral-based multi-classification models, specifically comprising:
• applying a weighted averaging method to three the sorting results, selecting a category with the highest probability after the weighted averaging as the final sorting result, and separately conveying and transporting the separated coal and rock;
• a process of the weighting is a dynamic weighting based on an uncertainty of prediction of a fusion model, the uncertainty is quantified by using an entropy value, and weights are assigned according to entropy values;
• the entropy value is computed using the following formula:
• H
• ⁡
• (
• p
• )
• =
• -
• ∑
• i
• =
• 1
• c
• P
• i
• ⁢
• log
• ⁡
• (
• P
• i
• )
• ,
• wherein, p denotes the probability distribution of the categories predicted by the model, C denotes a total number of the categories, and Pi denotes a probability that the model classifies a sample into category i.