Offshore wind power operation and maintenance large-scale operation safety control identification method based on YOLOv8

By combining the YOLOv8 deep learning model and video surveillance system, all-weather automated safety supervision of offshore wind power operations is achieved, and the problem of inability to identify safety equipment and violations in the existing technology is solved, and the operation safety and management efficiency are improved.

CN120340123APending Publication Date: 2025-07-18THREE GORGES NEW ENERGY OFFSHORE WIND POWER OPERATION & MAINTENANCE JIANGSU CO LTD
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Patent Information

Application Number
CN202510383501.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-03-28
Publication Date
2025-07-18

AI Technical Summary

Technical Problem

It is difficult to achieve intelligent real-time monitoring and early warning in offshore wind farm operation management. The existing video surveillance system cannot judge the wear of safety protective equipment and violations of the operator in real time, resulting in low efficiency in operation safety management and many risks and hidden dangers.

Method used

The YOLOv8 deep learning model is combined with the video surveillance system, and through data acquisition and labeling, model training, real-time monitoring, abnormal detection and alarm, and daily report generation, we realize all-weather automated operation safety supervision and violation identification.

Benefits of technology

It realizes all-weather intelligent monitoring, real-time detection of the wearing and behavior of safety equipment, triggers instant alarms, improves the safety and management efficiency of offshore wind power operations, reduces human negligence, and supports centralized management and visual data analysis.

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Abstract

The invention discloses a YOLOv8-based offshore wind power operation and maintenance large-scale operation safety management and control identification method. The method comprises the following steps of 1, performing YOLOv8 model training and safety identification; step 2, docking and real-time monitoring of a video monitoring system; step 3, anomaly detection and alarm; step 4, daily report generation and display; according to the invention, a deep learning model is combined with existing monitoring equipment, all-weather and automatic operation safety supervision and violation behavior identification are provided, and the safety and management efficiency of offshore wind power operation are improved.
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Citation Information

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