사물인터넷융복합논문지 (Journal of Internet of Things and Convergence)

한국사물인터넷학회 (The Korea Internet of Things Society)
권호 표지
DOI QR코드

DOI QR Code

노천광산의 월경 채굴 조기경보 모니터링시스템의 설계 및 구현

Design and Implementation of Early Warning Monitoring System for Cross-border Mining in Open-pit Mines

  • 이크 (목원대학교 정보통신용합공학부) ;
  • 민병원 (목원대학교 정보통신용합공학부 )
  • Li Ke (Division of Information and Communication Convergence Engineering, Mokwon University) ;
  • Byung-Won Min (Division of Information and Communication Convergence Engineering, Mokwon University)
  • 투고 : 2024.02.28
  • 심사 : 2024.03.21
  • 발행 : 2024.04.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

노천 광산 채굴 시나리오와 관련하여 현재 중국에서는 주요 수동 및 정기 검사를 위한 비디오 모니터링을 사용하는 것으로 인건비를 지속적으로 투자해야 하며 적시성이 낮다. 이 조기경보 모니터링의 문제를 해결하기 위해 이 글에서는 공간화 알고리즘 모델을 개발하여 노천광산의 월경채굴 조기경보시스템을 설계하고 광산채굴장비의 지리적 정보를 산출하고 실시간으로 광산 승인 범위의 레이어 좌표와 비교하고, 자동으로 광산의 월경 채굴 행동을 예측한다. 장시 핑샹 지역을 연구 대상으로 하여 노천 광산 채굴 엔지니어링 기계 장비를 식별 및 추적 대상으로 선정하였으며, 현장 실험을 통해 시스템이 안정적이고 신뢰할 수 있으며 검증 시스템의 목표 추적 정확도가 높은 것으로 나타났으며, 광산 채굴 감독의 적시성과 정확성을 향상시킬 수 있고 감독의 인건비를 크게 절감할 수 있다.

For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

키워드

참고문헌

  1. W.J.Song, Sh.Zh.Li and T.Min, "Research and implementation of remote Monitoring system for Mineral Resources Mining," Scientific and technological management of land and resources, Vol.30, No.3, pp.93-97, 2013. 
  2. J.K. Liu, "Remote sensing monitoring and analysis of mine geological mining based on geophysical technology," World non-ferrous metals, Vol.18, No.22, pp.27-29, 2018. 
  3. Pingxiang Local Chronicles compilation Committee, "Pingxiang City Chronicles," China: local Records Publishing House, pp.12-45, 2007. 
  4. H.K.Lin, "Motion state analysis and recognition of excavator in complex scene," Guangzhou: South China Agricultural University, 2016.
  5. Zh.Y.Zhang, "A Flexible New Technique for Camera Calibration," IEEE Tran sactions on Pattern Analysis & Machine Intelligence, Vol.22, No.11, pp.1330-1334, 2000.  https://doi.org/10.1109/34.888718
  6. L.X.Wu, "Progress of digital mines in China," Geographic information world, Vol.6, No.5, pp.6-13, 2008. 
  7. J.Y.Gong, "Development opportunities and challenges of surveying and Mapping remote Sensing Technology in the era of artificial Intelligence," Journal of Wuhan University Information Science Edition, Vol.43, No.12, pp. 1788-1796, 2018. 
  8. C.B.Choy, D.F.Xu, Gwak J., K.Chen and Savarese S, "A unified approach for single and multi-view 3D object reconstruction," in Proceedings of 2016 European Conference on Computer Vision, Amsterdam, 2016, pp.628-644. Dissertations. 
  9. B.Yang, Stefano R and Andrew M, "Dense 3D Object Reconstruction from a Single Depth View," IEEE transactions on pattern analysis and machine intelligence,Vol.41, No.12, pp.2820-2834. 
  10. S.K.Liu, Giles L and Ororbia A, "Learning a hierarchical latent variable model of 3D shapes," in Proceedings of 2018 International Conference on 3D Vision, Italy, 2018, pp.542-551. Dissertations. 
  11. Tulsiani S, Efros A.A and Malik J, "Multi-view consistency as supervisory signal for learning shape and pose prediction," in Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, USA, 2018, pp.2897-2905. Dissertations. 
  12. H.Yu and J.Oh, "Anytime 3D object reconstruction using multi modal variational autoencoder," IEEE Robotics and Automation Leters, Vol.7, No.02, pp.2162-2169, 2022.  https://doi.org/10.1109/LRA.2022.3142439
  13. L.Feng, J.S.Xie and G.Li, "Summary of the principles and methods of camera calibration," Mechanical Engineer, Vol.16, No.01, pp.18-20, 2016. 
  14. Y.Chai and J.K.Xu, "Target recognition and location system based on machine vision," Computer engineering and design, Vol.40, No.12, pp.3557-3562, 2019. 
  15. Z.M.Pan, K.W.Jin and K.X.Pan, "Research on keyhole imaging," Physics and engineering, Vol.28,No.4, pp.102-108, 2018. 
  16. M.W.Zhang, W.T.Leng and H.Shen, "Determination of the position relationship between privacy-protected points and arbitrary polygons," Journal of cryptography, Vol.6, No.04, pp.443-454, 2019.
  17. L.Lv, T.Z,Yao and J.T.Song, "Design and implementation of 3D reconstruction system based on monocular vision," Computer engineering, Vol.44, No.12, pp.233-239, 2018. 
  18. F.Yang, "Research on large-field-of-view camera calibration technology for large aeronautical component measurement," Dalian University of Technology, 2017. 
  19. Y.L Ji, H.T Duan. "Research on key Technologies of Video Image Analysis in Intelligent Video Surveillance system," China's informationization, Vol.19, No.02, pp.46-49, 2019. 
  20. R.Zh.Guo, H.T.Lin and B.He, "GIS framework for smart city," Journal of Wuhan University, Vol.45, No.12, pp.1829-1835, 2020.