Intelligent Analysis of "Four in Place" Data for Electricity Safety Services Based on Deep Learning

In response to the problems of difficulty in real-time monitoring, insufficient warning mechanisms, and slow processing response in the current field of electricity safety monitoring, this study introduces a deep convolutional neural network (CNN) model to improve the accuracy and real-time performance of data analysis, thereby optimizing the efficiency of electricity safety services and reducing safety hazards. This article first extracts features by alternately using convolutional layers and pooling layers, and integrates them through fully connected layers to construct a deep CNN model. We need to use this model to preprocess data such as electricity consumption, electricity consumption time, and load type, including data cleaning and feature extraction; subsequently, the CNN automatically learns the spatial hierarchical features of electricity consumption data by applying multiple filters for feature learning and pattern recognition, in order to identify potential security risks; finally, by using backpropagation algorithm to adjust the weights and bias parameters of the neural network, this study achieves a real-time dynamic warning system with "four in place" (prevention measures in place, monitoring system in place, emergency response in place, and education and training in place). The deep learning (DL) model in this study significantly improves the real-time monitoring and accuracy of early warning through real-time data processing and precise anomaly detection, making the response time 50% faster than traditional systems and effectively reducing the incidence of safety accidents. This article provides stronger technical support for the intelligent analysis of "four in place" data.