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

Abstract

Background: Nowadays, the function of information construction in construction project quality supervision and management is increasingly prominent, and it has become a task that cannot be ignored by administrative departments. Objective: To supervise and manage engineering safety data effectively and display the system construction more intuitively, a method based on computer network technology is proposed. Methods: K-means clustering, random forest, neural network, and other artificial intelligence algorithms were used for data modelling, and classification model evaluation, regression model evaluation, and other evaluation tools were used to evaluate the quality of the built model, and the power engineering monitoring system was established. The functions of engineering safety supervision and management, data storage and query, deformation graphical display, data analysis and forecast, results report output, and so on are realized. Results: The results showed that the mean square error of K-means was 7.74, the mean square error of random forest was 27.5, and the error of neural network was 4.4. Conclusion: Neural network has the smallest error and the closest data. The establishment of the system provides a new research platform for power engineering safety supervision and management.