A method and system for automatically labeling low-voltage power distribution safety operation specifications
Through the YOLOV9 neural network recognition model, the risk level in low-voltage power distribution operations is automatically monitored, which solves the problem of low efficiency of manual monitoring and improves the safety of low-voltage power distribution operations.
CN119516457BActive Publication Date: 2025-09-16GUANGDONG POWER GRID CO LTD +1
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Patent Information
- Application Number
- CN202411425053.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-12
- Publication Date
- 2025-09-16
- Estimated Expiration
- 2044-10-12
AI Technical Summary
Technical Problem
In the existing technology, the safety operation standard monitoring of low-voltage power distribution systems relies on manual observation, resulting in a waste of manpower and material resources and the failure to discover potential risks in a timely manner, posing a safety hazard.
Method used
A recognition model based on the YOLOV9 neural network is used to identify the position and clothing of staff through images, calculate risk scores and automatically label risk levels, thereby realizing automatic monitoring of risk levels.
Benefits of technology
Accurately identify the position and clothing of workers, reduce manual observation errors, take preventive measures in advance, improve work safety, and reduce the possibility of accidents.
✦ Generated by Eureka AI based on patent content.
Abstract
A method for automatically labeling the safety standards of low-voltage power distribution operations includes the following steps: Step S1: Obtaining a power distribution operation screen and inputting the screen into a recognition model; Step S2: The recognition model identifies the worker and their work status based on the power distribution operation screen, wherein the work status includes the worker's position and clothing; Step S3: Obtaining a risk score for the current worker based on their work status, and determining the worker's risk level based on the risk score falling within a preset level range; and assigning a risk level to the worker's vicinity in the power distribution operation screen. The recognition model of the present invention, based on advanced algorithms and technologies, can accurately identify key information such as the worker's position and clothing, reducing errors that may be introduced by manual observation. This allows for proactive preventive measures, reduces the likelihood of accidents, and improves operational safety.
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