Design of data acquisition and analysis system for deep vertical shaft
-
摘要: 目前用于深立井井筒数据采集的罐道巡检机器人能够搭载的传感器数量较少，且没有安全保护装置，存在安全隐患，此外，深立井井筒数据可视化方案大多采用3D GIS进行渲染和显示，存在不易移植、开发周期长等问题。针对上述问题，设计了一种深立井井筒数据采集及分析系统。对罐道巡检机器人进行改进，通过增加车轮锁装置保证机器人运行过程中的安全性，以带车轮锁的罐道巡检机器人作为移动平台，通过搭载红外摄像头、光电编码器、超声波测距模块及各种传感器实现深立井井筒数据采集。采用云服务器加前端可视化面板的方式进行数据处理和显示，通过云服务器接收罐道巡检机器人发送的各类数据，并对数据进行分类处理：温湿度传感器和气体传感器数据直接存储在相应文件夹内；视频数据采用卷积神经网络（CNN）进行处理，分析罐道和井壁是否出现裂缝、形变等，再将分析结果存储在相应文件夹内。对前端可视化面板进行轻量化处理，采用异步JavaScript和XML（Ajax）从云服务器定时读取数据，并采用JavaScript编写上位机界面显示程序，以提高系统的便携性和可移植性。测试结果表明：以带车轮锁的罐道巡检机器人作为数据采集装置，提高了数据采集的可靠性和安全性；可视化面板加云服务器的数据处理和显示方式将上位机软件所占内存缩小至5 MB以内，页面刷新快，可移植性强。Abstract: At present, the cage guide inspection robot used for data acquisition of deep vertical shaft can only carry a small number of sensors. There is no safety protection device, so there are potential safety hazards. In addition, most of the deep vertical shaft data visualization programs use 3D GIS for rendering and display. It is difficult to transplant and has long development cycle. In order to solve the above problems, a data acquisition and analysis system for deep vertical shaft is designed. The cage guide inspection robot is improved by adding a wheel lock device to ensure the safety of the robot during operation. The cage guide inspection robot with wheel lock is used as mobile platform. The data acquisition of deep vertical shaft is realized by infrared camera, photoelectric encoder, ultrasonic ranging module and various sensors on the robot. The cloud server plus front-end visualization panel is used for data processing and display. The cloud server receives various data sent by the cage guide inspection robot and classifies and processes the data. The data of temperature and humidity sensor and the gas sensor is directly stored in the corresponding folder. The convolutional neural network (CNN) is applied to process the video data and analyze whether there are cracks or deformation in the cage guide and the shaft wall. The analysis results are stored in the corresponding folder. The front-end visual panel is lightweighted. Asynchronous JavaScript and XML (Ajax) are used to read data from the cloud server regularly. And JavaScript is used to write the host computer interface display program to improve the portability of the system. The test results show that the cage guide inspection robot with wheel lock used as the data acquisition device can improve the reliability and safety of the system. The data processing and display method of the visualization panel and cloud server reduces the memory occupied by the host computer software to less than 5 MB. The page refreshes quickly and the portability is strong.
-
Keywords:
- deep shaft /
- cage guide inspection robot /
- wheel lock /
- cloud server /
- convolutional neural network /
- crack detection /
- Ajax
-
-
![]()
图 1 深立井井筒数据采集及分析系统框架
Figure 1. Framework of data acquisition and analysis system for deep vertical shaft
![]()
图 4 深立井内部情况可视化面板
Figure 4. The visualization panel of internal situation of deep vertical shaft
-
[1] 袁亮. 煤炭精准开采科学构想[J]. 煤炭学报,2017,42(1):1-7. YUAN Liang. Scientific conception of precision coal mining[J]. Journal of China Coal Society,2017,42(1):1-7.
[2] 武强,涂坤,曾一凡,等. 打造我国主体能源(煤炭)升级版面临的主要问题与对策探讨[J]. 煤炭学报,2019,44(6):1625-1636. WU Qiang,TU Kun,ZENG Yifan,et al. Discussion on the main problems and countermeasures for building an upgrade version of main energy (coal) industry in China[J]. Journal of China Coal Society,2019,44(6):1625-1636.
[3] 许金,何桥. 煤矿安全监控系统自诊断方法研究[J]. 煤矿安全,2021,52(1):127-130. XU Jin,HE Qiao. Research on self-diagnosis method of coal mine safety monitoring system[J]. Safety in Coal Mines,2021,52(1):127-130.
[4] 袁亮. 面向煤炭精准开采的物联网架构及关键技术[J]. 工矿自动化,2017,43(10):1-6. YUAN Liang. Framework and key technologies of Internet of things for precision coal mining[J]. Industry and Mine Automation,2017,43(10):1-6.
[5] 崔俊飞. 安全监控超限报警信息融合系统建设[J]. 煤炭技术,2021,40(8):168-170. CUI Junfei. Construction of information fusion system for safety monitoring overrun alarm[J]. Coal Technology,2021,40(8):168-170.
[6] 孙亚娟,经来旺. 黄淮地区井筒破裂致因分析及防治措施[J]. 安徽理工大学学报(自然科学版),2010,30(1):25-29. DOI: 10.3969/j.issn.1672-1098.2010.01.007 SUN Yajuan,JING Laiwang. Analysis of shaft lining rupture in Huanghuai region and its control[J]. Journal of Anhui University of Science and Technology(Natural Science),2010,30(1):25-29. DOI: 10.3969/j.issn.1672-1098.2010.01.007
[7] 刘超林,李扬,许延春. 煤矿立井井筒稳定性评价及破裂预测[J]. 煤炭与化工,2013,36(7):12-14,17. LIU Chaolin,LI Yang,XU Yanchun. Stability appraisal and fracture forecast of coal mine shaft[J]. Coal and Chemical Industry,2013,36(7):12-14,17.
[8] 孙继平. 煤矿监控新技术与新装备[J]. 工矿自动化,2015,41(1):1-5. SUN Jiping. New technologies and new equipments of coal mine monitoring[J]. Industry and Mine Automation,2015,41(1):1-5.
[9] 谭章禄,吴琦,肖懿轩,等. 智慧矿山信息可视化研究[J]. 工矿自动化,2020,46(1):26-31. TAN Zhanglu,WU Qi,XIAO Yixuan,et al. Research on information visualization of smart mine[J]. Industry and Mine Automation,2020,46(1):26-31.
[10] 封华, 李强, 陈朋朋. 磁轮驱动的钢罐道巡检机器人研究[J/OL]. 煤炭科学技术: 1-11[2021-12-07]. http://kns.cnki.net/kcms/detail/11.2402.TD.20210924.1433.002.html. FENG Hua, LI Qiang, CHEN Pengpeng. Research on inspection robot of steel cage guide driven by magnetic wheel[J/OL]. Coal Science and Technology: 1-11[2021-12-07]. http://kns.cnki.net/kcms/detail/11.2402.TD.20210924.1433.002.html.
[11] 贺耀宜,刘丽静,赵立厂,等. 基于工业物联网的智能矿山基础信息采集关键技术与平台[J]. 工矿自动化,2021,47(6):17-24. HE Yaoyi,LIU Lijing,ZHAO Lichang,et al. Key technology and platform of intelligent mine basic information acquisition based on industrial Internet of things[J]. Industry and Mine Automation,2021,47(6):17-24.
[12] 成董浩,浑宝炬,靳凯. 增强现实技术在矿井通风系统中的应用前景分析[J]. 工矿自动化,2018,44(8):10-14. CHENG Donghao,HUN Baoju,JIN Kai. Application prospect analysis of augmented reality technology in mine ventilation system[J]. Industry and Mine Automation,2018,44(8):10-14.
[13] 王国法,刘峰,孟祥军,等. 煤矿智能化(初级阶段)研究与实践[J]. 煤炭科学技术,2019,47(8):1-36. WANG Guofa,LIU Feng,MENG Xiangjun,et al. Research and practice on intelligent coal mine construction (primary stage)[J]. Coal Science and Technology,2019,47(8):1-36.
[14] 韩安,陈晓晶,贺耀宜,等. 智能矿山综合管控平台建设构思[J]. 工矿自动化,2021,47(8):7-14. HAN An,CHEN Xiaojing,HE Yaoyi,et al. Construction conception of intelligent mine integrated management and control platform[J]. Industry and Mine Automation,2021,47(8):7-14.
[15] LU Haoxiang,LIU Zhenbing,PAN Xipeng. An adaptive detail equalization for infrared image enhancement based on multi-scale convolution[J]. IEEE Access,2020,8:156763-156773. DOI: 10.1109/ACCESS.2020.3017499
[16] DAIS D,BAL İ E,SMYROU E,et al. Automatic crack classification and segmentation on masonry surfaces using convolutional neural networks and transfer learning[J]. Automation in Construction,2021,125:103606. DOI: 10.1016/j.autcon.2021.103606
-
期刊类型引用(5)
1. 张凯. 液压支架自动化后人工调控策略推荐系统. 山西焦煤科技. 2024(02): 33-36+41 . 
百度学术
2. 刘萌，付翔，姜玉龙，刘彬，杨宇琪，秦一凡，孙岩. 液压支架姿态数字孪生精准快速映射方法. 工矿自动化. 2024(06): 136-141+158 . 本站查看
3. 虞婧，魏红磊，周亘儒，孟龙，陈浩. 基于数字孪生技术的低碳选煤厂能源监控平台应用研究. 煤炭工程. 2024(12): 12-18 . 百度学术
4. 王栎淇，范维，陈传军，李森，付文阳. 基于实时数据驱动的车间输送线数字孪生系统研究与实现. 制造业自动化. 2023(08): 171-177 . 百度学术
5. 鲍久圣，张可琨，王茂森，阴妍，杨磊，葛世荣. 矿山数字孪生MiDT:模型架构、关键技术及研究展望. 绿色矿山. 2023(01): 166-177 . 百度学术
其他类型引用(1)
下载:
计量
- 文章访问数: 270
- HTML全文浏览量: 42
- PDF下载量: 38
- 被引次数: 6
