Fuhua Artificial Intelligence has independently developed a multimodal large model and successfully launched the Transmission Line Abnormal Detection System, specifically designed for power operation and maintenance scenarios. This system relies on advanced image recognition and video analysis technology to monitor and intelligently analyze the operating environment of transmission lines in real-time. It accurately identifies various potential risks, including:
- Flames: Quickly detects open flames near transmission lines and equipment, providing timely fire hazard warnings.
- Smoke: Differentiates between natural fog and fire smoke to reduce false alarms.
- Foreign Objects on the Line: Identifies foreign objects such as branches, plastic bags, and kites hanging or entangled on the power lines.
- Cranes: Detects the proximity and potential collision risks of construction or lifting equipment with transmission lines.
- Wind Turbine Blades: Monitors abnormal conditions such as damage or detachment of wind turbine blades.
In joint testing with Southern Power Grid, the system achieved an overall detection accuracy of over 91%, maintaining high efficiency and stability even under complex environments and variable weather conditions.
Key Advantages:
- Multimodal Large Model Driven: Combines visual, semantic, and temporal information to enhance recognition robustness.
- Real-Time Monitoring and Early Warning: Low-latency processing ensures quick alerts as soon as abnormalities occur.
- Wide Adaptability: Supports both fixed monitoring equipment and drone inspection video inputs.
- High Reliability: Verified through large-scale field testing, maintaining high detection rates across different lighting, weather, and angle conditions.
The implementation of this system is expected to significantly reduce the risk of transmission line faults and accidents, improve the efficiency and safety of power system operation and maintenance, and provide solid technical support for smart grid construction.