Safety Hazard Prediction System for Nuclear Power Construction Stage Based on Deep Learning

The construction of nuclear power projects spans the entire cycle from survey, civil engineering, installation to commissioning, and the various stages of construction activities are complex and varied. Faced with these challenges, the application of computer technology has become crucial. Through the efficient data processing capabilities of computers, massive amounts of data generated at each stage can be collected and analyzed in real-time, providing strong support for security management. This not only enhances the timeliness of hazard identification, but also promotes the precise implementation of risk prevention and control measures, providing a solid guarantee for the smooth progress of nuclear power engineering construction. Therefore, this article intends to conduct research on a risk warning system for nuclear power construction based on deep learning, in order to accurately predict safety hazards in the process of nuclear power construction and timely prevent and control them. By collecting and organizing typical safety accident case data in the domestic nuclear power construction process, a comprehensive and rich safety accident database will be established. This article takes the hidden danger database associated with the smart construction site system of a nuclear power project under construction as a specific example and analysis object, and automatically extracts features from massive data and uses advanced deep learning algorithms such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to train and optimize samples, establishing an efficient and accurate prediction model. During the time periods of 8:00 to 9:00 and 17:15 and 17:30 on October 23, 2023, the predicted results of the model were consistent with the true labels, and the prediction confidence was high, indicating that the model had a high degree of confidence in the safety status at these times. The research results of this article will provide strong technical support for accident risk prevention and control in the process of nuclear power construction.