Environmental risk perception and warning strategy of power metering laboratory based on LSTM network

This study proposes an environmental risk perception and early warning method for metrology laboratories that combines information acquisition, feature extraction, deep learning, and bias analysis. First, various sensors are used to collect operating data from the laboratory and extract its operating characteristics. The data is then processed using long and short-term memory neural network (LSTM) to predict the laboratory's operational state. Finally, the generalized extreme value theory is employed to establish the alarm threshold based on the normal running state, enabling early risk warning of the equipment. The experimental results demonstrate that the LSTM model is highly effective, achieving stable risk prediction accuracy above 98%, surpassing other neural network models.