首页|期刊导航|工矿自动化|带式输送机煤流量计量方法

带式输送机煤流量计量方法OA

Coal flow rate measurement method for belt conveyors

中文摘要英文摘要

目前煤流量计量方法大多以轮廓重建与体积计算为核心,普遍将煤流堆积密度视为恒定值,忽视了粒径分布变化导致的计量偏差,同时传统的图像分割模型对小样本场景下多尺度煤粒分割精度不足,体积求解方法难以精准拟合不规则煤流轮廓,进一步增大了煤流量计量误差.针对上述问题,从多类别图像分割、多模态数据融合及三维体积求解3个维度,提出了基于深度相机RGB图像与点云融合的带式输送机煤流量计量方法.针对单元煤流RGB图像分割问题,通过在UNet3+中嵌入多尺度注意力模块(MAM)和自适应多感受野模块(AMFM),并采用H-Swish激活函数,设计改进UNet3+模型,实现了多粒径煤粒精准分割;提出基于定位板像素匹配的图像拼接方法,提升单元煤流RGB图像与对应点云的拼接效率与配准精度;采用分治法与自适应法优化Delaunay三角剖分算法,精准拟合煤流三维轮廓,提升体积计算精度.基于煤流图像分割结果,采用面积加权融合法求解单元煤流等效平均密度,结合体积计算结果求得煤流量.实验结果表明:改进UNet3+模型的平均交并比与平均像素准确率分别为85.22%和90.56%,较UNet3+分别提升4.71%和3.88%,较UNet分别提升15.07%和12.91%;与传统的恒定密度法相比,所提方法的煤流量计量值更接近真实值.

Most existing coal flow rate calculation methods focus on contour reconstruction and volume calculation.They generally regard the bulk density of coal flow as a constant,overlooking measurement deviations caused by changes in particle-size distribution.Moreover,conventional image segmentation models provide insufficient accuracy for multi-scale coal particle segmentation in small-sample scenarios,and volume calculation methods have difficulty accurately fitting irregular coal flow contours,further increasing coal flow rate calculation errors.To address these problems,a coal flow rate measurement method for belt conveyors integrating depth-camera RGB images and point clouds was proposed from three perspectives:multiclass image segmentation,multimodal data fusion,and three-dimensional volume calculation.To segment RGB images of unit coal flow,an improved UNet3+model was developed by embedding a Multi-Scale Attention Module(MAM)and an Adaptive Multi-Receptive Field Module(AMFM)into UNet3+and using the H-Swish activation function,thereby achieving accurate segmentation of coal particles with multiple particle sizes.An image stitching method based on positioning-plate pixel matching was proposed to improve the stitching efficiency and registration accuracy of unit coal flow RGB images and their corresponding point clouds.The Delaunay triangulation algorithm was optimized using divide-and-conquer and adaptive methods to accurately fit three-dimensional coal flow contours and improve volume calculation accuracy.Based on the coal flow image segmentation results,area-weighted fusion was used to determine the equivalent average density of a unit coal flow,and the coal flow rate was then calculated in combination with the volume calculation result.Experimental results showed that the mean intersection over union and mean pixel accuracy of the improved UNet3+model were 85.22%and 90.56%,respectively,representing increases of 4.71%and 3.88%over UNet3+and 15.07%and 12.91%over UNet,respectively.Compared with the conventional constant-density calculation method,the coal flow rate measurement value obtained by the proposed method was closer to the true value.

李希峰;赵昌伟;郭琳;王新宇;陈梦婷;丁国伟;陈文旭;李东民

山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019山东科技大学智能装备学院,山东泰安 271019||山东聚多士能源科技有限公司,山东泰安 271000

矿业与冶金

带式输送机煤流量计量深度相机图像分割图像拼接改进UNet3+点云三角剖分

belt conveyorcoal flow rate calculationdepth cameraimage segmentationimage stitchingimproved UNet3+point cloud triangulation

《工矿自动化》 2026 (7)

65-74,10

国家自然科学基金项目(52174145).

10.13272/j.issn.1671-251x.2026040088

评论