基于线性模型划分的煤流体积测量

陈湘源; 薛旭升

doi:10.13272/j.issn.1671-251x.2022090085

基于线性模型划分的煤流体积测量

doi: 10.13272/j.issn.1671-251x.2022090085

陈湘源^1,,
薛旭升²

1.
国能榆林能源有限责任公司, 陕西榆林　719000
2.
西安科技大学机械工程学院, 陕西西安　710054

基金项目: 陕西省科技厅秦创原“科学家+工程师”项目（2023KXJ-238）。

详细信息

作者简介:
陈湘源（1972—），男，内蒙古鄂尔多斯人，高级工程师，硕士，主要研究方向为智能化与机电系统安全，E-mail:shyscxy@163

中图分类号: TD634
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- 文章访问数: 132
- HTML全文浏览量: 42
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2022-09-28
- 修回日期: 2023-07-14
- 网络出版日期: 2023-08-03

Coal flow volume measurement based on linear model partitioning

CHEN Xiangyuan^1
,,
XUE Xusheng²

1.
CHN Energy Yulin Energy Co., Ltd., Yulin 719000, China
2.
College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China

摘要

摘要: 针对基于线激光条纹的带式输送机煤量测量精度和计算效率较低、胶带运行过程中存在拖尾现象及跑偏、飘带造成的数据不对齐问题，提出了一种基于线性模型划分的煤流体积测量方法。首先，利用高速线激光相机进行煤流数据采集；然后，通过基于线性模型划分的点云配准算法将胶带底部点云数据与煤流表面数据融合，形成完整的三维煤流数据；最后，通过煤流体积测量模型实现对煤流体积的测量。试验结果表明，基于线性模型划分的煤流体积测量方法在高粉尘环境、煤流表面洒水环境、昏暗环境及正常光照环境下测量粗糙表面铁块、光滑表面铁块及实物（煤和矸石）所得结果精度均在95%以上；且光滑表面铁块较粗糙表面铁块在4种模拟环境下的测量精度高，说明物体表面平整度越好测量精度越高，环境对测量精度影响不大。实际测试结果表明，基于线性模型划分的煤流体积测量方法的测量精度均在97%以上，对应平均耗时均在80 ms以内；与基于KD树的测量方法相比，整体精度提高了6%以上，处理时效性提高了1倍。
- 带式输送机煤量测量 /
- 线性模型划分 /
- 煤流体积测量 /
- 点云配准 /
- 点云三角网格化 /
- 线激光条纹
Abstract: The precision and computational efficiency of coal quantity measurement for belt conveyors based on linear laser stripes are low. There is a trailing phenomenon during belt operation, as well as data misalignment caused by deviation and drifting. In order to solve the above problems, a coal flow volume measurement method based on linear model partitioning is proposed. Firstly, the method uses a high-speed line laser camera to collect coal flow data. Secondly, a point cloud registration algorithm based on linear model partitioning is used to fuse the point cloud data at the bottom of the belt with the surface data of the coal flow, forming a complete three-dimensional coal flow data. Finally, the coal flow volume measurement is achieved through a coal flow volume measurement model. The experimental results show that the coal flow volume measurement method based on linear model partitioning has a precision of over 95% when measuring rough surface iron blocks, smooth surface iron blocks, and physical objects (coal and gangue) in high dust environment, coal flow surface watering environment, dim environment, and normal lighting environment. Moreover, the measurement precision of smooth-surface iron blocks is higher than that of rough-surface iron blocks in four simulated environments. It indicates that the better the flatness of the object surface, the higher the measurement precision. The environment has little impact on measurement precision. The actual test results show that the coal flow volume measurement method based on linear model partitioning has a measurement precision of over 97%. The corresponding average time is within 80 ms. Compared with the measurement method based on the KD tree, the overall precision has been improved by more than 6% and the processing timeliness has been doubled.
- belt conveyor coal quantity measurement /
- linear model partitioning /
- coal flow volume measurement /
- point cloud registration /
- point cloud triangle mesh /
- line laser stripe

HTML全文

图 1 煤流边界

Figure 1. Coal flow boundary

下载: 全尺寸图片幻灯片

图 2 胶带煤流三角化模型

Figure 2. Triangulation model of belt coal flow

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图 3 试验平台

Figure 3. Experimental platform

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图 4 带式输送机工作环境

Figure 4. Working environment of belt conveyor

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图 5 试验素材

Figure 5. Experimental material

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图 6 实际场景安装效果

Figure 6. Actual scene installation rendering

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表 1 各环境下煤流体积测量精度

Table 1. Coal flow volume measurement precision in various environments %

试验素材	测量精度
试验素材	正常光照环境	高粉尘环境	表面洒水环境	昏暗环境
粗糙表面铁块	97.5	97.1	97.3	97.7
光滑表面铁块	98.2	98.3	98.5	98.7
煤和矸石	96.1	95.9	95.8	96.3

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表 2 实际场景煤流体积测量结果

Table 2. Coal flow volume measurement results in actual scenarios

时间/min	测量结果/m³			测量精度/%		平均处理时间/ms
时间/min	本文测量算法	基于KD树测量算法	电子胶带秤	本文测量方法	基于KD树测量方法	本文测量方法	基于KD树测量方法
5	509.635	462.301	510.267	99.9	90.6	75	167
11	1 208.700	1 060.454	1 175.67	97.3	90.2	73	158
30	3 115.830	2 767.120	3 084.86	99.0	89.7	76	178
50	5 520.710	4 866.070	5 400.74	97.8	90.1	75	169
80	8 420.480	7 402.580	8 271.04	98.2	89.5	74	163
120	12 639.210	11 255.050	12 409.1	98.2	90.7	78	189

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