Construction and application of the dynamic supervision system for the mining of mineral resources

Konferenz: AIIPCC 2021 - The Second International Conference on Artificial Intelligence, Information Processing and Cloud Computing
26.06.2021 - 28.06.2021 in Hangzhou, China

Tagungsband: AIIPCC 2021

Seiten: 8Sprache: EnglischTyp: PDF

Autoren:
Xie, Zhiying; Lu, Hang (Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, Fujian Xiame, China)
Yang, Wenfu; He, Yuanrong (Big Data Institute of Digital Natural Disaster Monitoring in Fujian, Xiamen University of Technology, Fujian Xiame, China & Key Laboratory of Resource Environment and Disaster Monitoring in Shanxi, Shanxi Jinzhong, China)

Inhalt:
The high-speed development of mineral resources not only drives the industrialization process, but also produces many illegal behaviours, such as overburden mining, etc. The risks and future troubles caused by this can not be ignored. But at present, the supervision of mineral resources only relies on the routine field investigation, which is time-consuming, laborious and inefficient. Based on the advantages of high-resolution images and WebGL technology, this paper built a dynamic supervision system for the mining of mineral resources by using B/S architecture. The system breaks the dependence of traditional image interpretation on professional software and professionals, directly carries out multitemporal image comparison and human-computer interactive interpretation of suspected cross-boundary map through the Web terminations, and supports multi-person cooperative operation, automatically and quickly assemble interpretation map and attribute table, mass production interpretation information table and interpretation thematic map. During the outbreak, the supervisory authorities used the system to screen more than 500 open-pit mines in the Ningxia area under their jurisdiction one by one, quickly obtaining suspected cases of mining across the border in the mines, and providing strong data support for follow-up field verification, the supervision efficiency of mineral resources has been greatly improved.