Design of data acquisition and analysis system for deep vertical shaft

ZHAO Baiting; PANG Meng; JIA Xiaofen

doi:10.13272/j.issn.1671-251x.2021120088

Volume 48 Issue 5

May 2022

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Journal of Mine Automation > 2022 > 48(5): 118-122. > DOI: 10.13272/j.issn.1671-251x.2021120088

ZHAO Baiting, PANG Meng, JIA Xiaofen. Design of data acquisition and analysis system for deep vertical shaft[J]. Journal of Mine Automation，2022，48(5)：118-122. DOI: 10.13272/j.issn.1671-251x.2021120088

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Design of data acquisition and analysis system for deep vertical shaft

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

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Received Date: December 26, 2021
Revised Date: April 26, 2022
Available Online: March 04, 2022

Abstract

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.
- deep shaft,
- cage guide inspection robot,
- wheel lock,
- cloud server,
- convolutional neural network,
- crack detection,
- Ajax

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References

[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

[2]	DU Qing-jie, WANG Qiang, TANG Quan-hai, JIANG Zhi-long. Improvement of three-proofing lacquer coating process for mine-used PCB assembly[J]. Journal of Mine Automation, 2013, 39(6): 109-112.
[3]	ZHANG Ning, REN Mao-wen, LIU Ping. Identification of coal-rock interface based on principal component analysis and BP neural network[J]. Journal of Mine Automation, 2013, 39(4): 55-58.
[4]	ZHU Shang-song, YU Chun-lei. A Wireless Network Structure Combining ZigBee and GPRS[J]. Journal of Mine Automation, 2010, 36(10): 88-90.
[5]	DAI Xue-fe. Finite Element Analysis and Optimization Design of Movable Arm of Loader's Working Equipment Based on Pro/E[J]. Journal of Mine Automation, 2010, 36(4): 50-52.
[6]	TAN Guo-jun, ZHU Chong, LI Guang-chao, KUAI Song-yan, Han Yao-fei. Research of Key Parameters Identification of Asynchronous Motor Based on DSP[J]. Journal of Mine Automation, 2009, 35(9): 41-45.
[7]	NIU Ju-fe. Key Technologies of Development of Mine Safety Information System Based on MapX[J]. Journal of Mine Automation, 2008, 34(5): 103-106.
[8]	CHEN Rong-guang~, YAN Xiao-feng~, LI Juan~. Research of Mine-used GEPON System and Its Implementatio[J]. Journal of Mine Automation, 2008, 34(5): 44-47.
[9]	YUAN Xiang-ke, PEI Ji-tian, YAN Shu-wei. Application of Model KJZ-1500/1140Z Assembled Switches in Control and Protection of Belt COnveyor of Coal Mine[J]. Journal of Mine Automation, 2007, 33(5): 80-81.

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JIA Xiaofen

School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

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Design of data acquisition and analysis system for deep vertical shaft