Product Overview
The Mine AI Brain is a comprehensive intelligent platform built on real-time video image data from underground cameras, with AI image intelligent recognition technology at its core, machine deep learning models and alarm systems as support, and automation fuzzy control technology as its method. It monitors and recognizes personnel, equipment, and environmental operating conditions, achieving end-to-end intelligent operation.
Product Description
The Mine AI Brain represents the apex of mining intelligence — a platform that not only sees what is happening underground but understands it, predicts consequences, and takes action. By fusing AI image recognition, deep learning, and automated fuzzy control, the platform creates a closed-loop intelligent system that monitors, analyzes, decides, and acts without human intervention for routine safety management.
The platform's hazard recognition capabilities are extensive. It identifies large coal pieces or anchor bolts on conveyor belts and triggers alarms, detects unsupported working-face canopies, recognizes unclosed ventilation doors and windows, and monitors dozens of other safety-critical conditions. Coal flow detection uses visual recognition to quantify belt conveyor coal volume, automatically triggering protective shutdowns or speed adjustments to reduce equipment wear and energy consumption — an intelligent optimization that no manual operator could match in real time.
Personnel behavior recognition is another pillar of the platform. It intelligently identifies violations, speeding, red-light running, and other unsafe behaviors, automatically generating alarms, snapshots, and video recordings for evidence and training. Specialized equipment detection covers hoist head and tail rope inspection, conveyor belt longitudinal tear and surface damage detection, and scraper conveyor chain-break detection — conditions that, if undetected, can lead to catastrophic equipment failure.
The platform's linkage control capability is where recognition meets action. When an anomaly is identified, the system issues control commands to third-party systems through communication interfaces — for example, slowing or stopping a conveyor when a hazard is detected on the belt, or triggering ventilation adjustments when gas-related visual indicators are observed. The system achieves detection and recognition accuracy of ≥95% with a false alarm rate controlled to ≤2%, performance levels that make autonomous safety management practical. The cloud-edge-device collaborative computing architecture distributes processing across the network for optimal latency and reliability, and supports B/S, C/S, and mobile app access methods.
Key Features
- Hazard recognition: belt large-piece/anchor-bolt detection, canopy support, ventilation door status
- Coal flow detection with automatic protective shutdown and speed adjustment
- Personnel behavior recognition: violations, speeding, red-light running with auto-alarm/snapshot/recording
- Specialized equipment detection: hoist rope, belt tear, scraper chain-break
- Linkage control: automatic command issuance to third-party systems
- Recognition accuracy ≥95%, false alarm rate ≤2%
- Cloud-edge-device collaborative computing architecture
- B/S, C/S, and mobile app access support
Technical Specifications
| Parameter | Value |
|---|
| System Components | Main control computer, AI analysis unit, AI management server, NVR, AI platform software, IS digital HD cameras, intelligent recognition cameras, face recognition cameras, industrial ring network |
| Recognition Accuracy | ≥95% |
| False Alarm Rate | ≤2% |
| Architecture | Cloud-edge-device collaborative computing |
| Access Methods | B/S, C/S, mobile app |
Applications
Coal mine safety production intelligent management, underground video intelligent analysis, equipment operating condition monitoring, and transport system intelligent speed regulation.
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