Energy Mines
- The accuracy of underground risk identification is provided to over 95%, with a false alarm rate of ≤ 5%
- Complete the deployment of the kilometer tunnel AI system in 3 weeks, with zero production interruption
- Exclusive algorithm library for mines, supporting multi-dimensional warning of landslides/gases/personnel behavior
A certain mining group was established in July 2002 with a registered capital of 200 million yuan. It owns 4 iron mines, 1 large tin mine, 1 medium-sized gold lead zinc mine, and 2 non-metallic mines, mainly focusing on the development of black metals in Guangdong, non-ferrous metals, and precious metals in Jiangxi. The annual sales of iron concentrate in China are nearly one million tons, and the potential total value of controlled mineral resources is over 100 billion yuan. It is one of the largest iron ore suppliers in southern China and also the largest private mine in southern Guangdong (with an annual output of 12 million tons of large-scale iron ore). There are over 15 underground working faces, with a total length of 8.6 kilometers of tunnels. In order to implement the national "Construction Specification for the Six Major Systems of Safety and Risk Avoidance in Metal and Non Metal Underground Mines", we will initiate the upgrade of AI intelligent safety systems and build a fully closed-loop management system of "monitoring warning disposal".
| Traditional Management Pain Points | AI Intelligent Upgrade Solution | Actual Results |
|
There Are Many Blind Spots in Risk Monitoring: |
● Deploy AI server+surveillance camera (covering 100% of the work area) ● The underground personnel positioning system enables real-time online positioning of personnel positions |
Reduce monitoring blind spots by 30%, |
|
Inefficient Personnel Management: |
● Intelligent behavior analysis (trespassing into restricted areas/hot work/carrying people on mining trucks/crossing conveyor belts/smoking and playing with mobile phones/personnel falling/illegal operations) ● Equipment wearing identification (not wearing a safety helmet/not wearing safety clothing/not carrying a self rescue device)） ● Sound and light alarm linkage |
The proactive interception rate for violations has increased to 54%, and the equipment compliance rate is 100% |
|
Emergency Response Lag: |
● Personnel locator (centimeter level UWB positioning) ● Multi modal emergency communication system (supporting Mesh networking in disconnected environments) ● Optimal escape route AI planning |
The response time for police reports has been shortened to 8 minutes, |
- Hardware Architecture:
Domestic AI server
Explosion proof AI camera (IP68 protection/gas environment adaptive)
Distributed edge computing nodes (underground intrinsically safe equipment) - Algorithm Matrix:
Personnel safety: fall detection, restricted area intrusion recognition, self rescue devices, safety helmets, safety clothing wearing monitoring, mine car carrying/crossing conveyor belts and other violations
Equipment safety: Conveyor belt tear warning, abnormal vibration detection of drainage pump
- Digital Twin Platform:
1: 3D tunnel modeling (real-time display of personnel positioning/equipment/risk points)
Thermal map shows gas accumulation area/abnormal rock pressure area - Hierarchical Response Mechanism:
Level 1 warning (sound and light alarm+work order push)
Level 2 emergency (automatic power cut off+emergency broadcast activated)
Level 3 disaster (triggering escape route guidance+synchronizing rescue coordinates)
- Model Iteration: Training based on 200+hours of real scene data underground
- Simulation Testing: Building 12 types of digital emergency drill scenarios such as permeable/landslide/fire
8/5000
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