Computer Network Technology for the Construction of Engineering Safety Supervision and Management Systems

Background: Currently, the function of information construction in the supervision and management of construction project quality has become increasingly prominent and cannot be ignored by administrative departments. Objective: This study aimed to effectively supervise and manage engineering safety data and display the system construction intuitively. Moreover, a method based on computer network technology was also proposed. Methods: K-means clustering, random forest, neural network, and other artificial intelligence algorithms were used for data modelling. Evaluation tools, such as the classification model and regression model, were used to evaluate the quality of the developed model, and a power engineering monitoring system was established. The functions of engineering safety supervision and management, data storage and query, graphical deformation display, data analysis and forecast, and report outputs were analyzed. Results: The mean square error of K-means was 7.74, that of the random forest was 27.5, and that of the neural network was 4.4. Conclusion: Neural network offered the smallest error and closest data. The establishment of the system provides a new research platform for the supervision and management of power engineering safety.