Background & Challenges
A coal mine located in Zaozhuang, Shandong Province, faced significant challenges in safety management and operational efficiency.
Traditional safety monitoring systems were unable to cope with the complexity of the mining environment, especially in real-time monitoring and accident prevention.
Project Objectives
To address these issues, the mine adopted an advanced centralized control system enhanced with multiple AI-powered video analytics modules.
The goal was to improve safety supervision, reduce risks, and streamline operations across the mining area.
AI Algorithm Applications
Hard Hat Detection
Monitors workers in real time to ensure proper helmet usage and compliance with safety protocols.
Smoking Detection
Identifies smoking behavior within the mining area, helping prevent fire hazards.
Workwear Detection
Confirms personnel are wearing required protective uniforms, supporting standardized management and quick identification.
Fire & Smoke Detection
Provides instant detection of flames or smoke to prevent fire-related incidents.
Personnel Intrusion Detection
Detects unauthorized entry into restricted zones to enhance on-site security.
Sleeping / Absence Detection
Monitors critical operation posts to ensure workers do not fall asleep or leave their stations during duty hours.
Vehicle Intrusion Detection
Identifies vehicles entering sensitive areas to prevent accidents and ensure compliance.
Conveyor Belt Foreign Object Detection
Detects debris or foreign objects on conveyor belts, preventing damage and operational downtime.
Conveyor Belt Misalignment Detection
Continuously monitors belt alignment to maintain safe and stable operation.
Coal Pile Monitoring
Analyzes coal pile size and distribution to optimize storage and transportation management.
Integrated Data Management Platform
Centralizes all monitoring data, providing real-time analytics, alerts, and comprehensive reports for decision support.
Results & Impact
The implementation of this centralized AI-enhanced control system significantly improved both safety performance and operational efficiency.
Key benefits include:
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Dramatically reduced safety risks through automated early-warning mechanisms.
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Improved workforce compliance and reduced human-error incidents.
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Optimized resource utilization and equipment protection.
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Real-time data insights supporting faster and more informed decision-making.
Future Outlook
The project plans to further enhance algorithm accuracy and system intelligence through continuous optimization and machine learning.
Additionally, the platform will be expanded to cover more safety and operational domains, driving the modernization of coal mine safety management and digital transformation.