Prediction of rock mixed ratio in image-based intelligent control of drawing open-ing in longwall top coal caving face-Part Ⅰ:Surface rock mixed ratio of coal flow

WANG Jiachen; YANG Shengli; LI Lianghui; AN Bochao; SONG Shixiong; WANG Yubing

doi:10.13225/j.cnki.jccs.2024.0207

Abstract：Image-based intelligent control of longwall top coal caving(LTCC)is a critical technology for achieving intelli-gent coal mining.Its core lies in monitoring the rock mixed ratio(RMR)of coal flow to control the drawing opening.However,challenges such as low illumination in underground environments,the irregular shapes of coal-rock particles,and their accumulation and compression significantly hinder the prediction of RMR in coal flow.To address these issues,this study focuses on the surface RMR of coal flow.A coal-rock image database was constructed under various illumina-tion conditions to investigate the influence of illumination and coal-rock types on image segmentation performance.A high-fidelity experimental platform for the"caving-transport"process in LTCC was developed,and a high-precision re-cognition method for surface projection area-based RMR of coal flow under complex scenarios was proposed,based on multi-illuminance fusion and optical flow optimization.Furthermore,the impact of different projection angles and meth-ods on the extraction of two-dimensional morphological features of coal-rock particles was explored,and the quantitative relationships between morphological features across dimensions were clarified.A"size+shape"feature fusion model was developed to reasonably predict the surface volume-based RMR of coal flow.Finally,the proposed methods were valid-ated using both laboratory and field data.The research findings indicate that as illumination increases,the recognition ac-curacy of the two coal-rock combination forms initially increases,stabilizes,and then slightly decreases.By setting a reas-onable illumination level(e.g.,17 730 lx),the mAP@0.5 for the coal+mudstone combination improved from 88.7％(3 180 lx)to 92.3％.The introduction of multi-illuminance fusion and optical flow analysis further enhanced the recogni-tion accuracy and adaptability of surface projection area-based RMR under complex scenarios.A reasonable selection of light source wavelength can amplify the relative diffuse reflectance differences of coal-rock particles,increasing the distin-guishability of image features.Moreover,appropriate projection methods improve the accuracy of predicting the projec-tion area of irregularly shaped coal-rock particles,reducing the prediction error from 60％to less than 10％.By incorporat-ing shape features into the volume prediction model,the prediction accuracy was significantly improved,with the coeffi-cient of determination increasing from 0.941 6(size-only model)to 0.969 2.