Research on Carbon Footprint Diagnosis System for Power Equipment Based on Improved Yolov5
With the increasingly prominent global carbon emissions issue,carbon footprint diagnosis and control of power equip-ment has become a key issue.Traditional carbon footprint diagnosis methods face difficulties in data acquisition and insufficient diag-nostic accuracy.Therefore,the study proposes a Yolov5 power equipment carbon footprint diagnosis system model that integrates power flow tracking.The results show that the average accuracy of the proposed model is 84.91%,and the average delay of carbon footprint detection is 7.16s.The performance of the proposed model is significantly higher than that of the control model.Compared to tradition-al carbon footprint diagnosis methods,this system has significantly improved in accuracy,real-time performance,and processing effi-ciency.The proposed model has shown significant advantages in terms of accuracy.In addition,this model can more accurately predict the trend of carbon footprint changes and monitor the changes in carbon footprint of electrical equipment in real time through trend tracking,providing more reliable data support for environmental decision-making.At the same time,the methods and ideas of this study also provide new possibilities for the application of artificial intelligence in the field of environmental protection.
万方数据
维普
