Design and Implementation of Anti-electric Shock Warning System Based on Computer Algorithms

In daily life and industrial production environments, frequent electric shock accidents can seriously threaten people's lives. Traditional anti-electric shock measures often rely on physical isolation and manual supervision, which has great limitations in real-time and accuracy. In order to reduce the risk of electric shock, this paper designs and implements an electric shock warning system based on computer algorithms. The system adopts a modular architecture and integrates algorithms such as convolutional neural network (CNN), long short-term memory (LSTM) network and support vector machine (SVM), mainly including data acquisition module, data processing module, feature extraction module, model prediction module and warning response module. The data acquisition module integrates millimeter wave radar and other modules to monitor key parameters such as current and voltage in real-time. The data processing module is responsible for signal preprocessing, including normalization, filtering and denoising. The feature extraction module uses CNN to identify key features related to the risk of electric shock. The model prediction module combines LSTM and SVM to analyze the extracted features and predict the risk of electric shock. The early warning response module triggers the early warning mechanism when a risk is detected, sends early warning information to relevant personnel through sound and light alarms, SMS (short message service) notifications, etc., and links with the automatic power-off device. The final result analysis shows that the average early warning accuracy of the anti-electric shock early warning system in 20 simulated electric shock scenarios is as high as 97.715%, and the maximum warning time is 292 milliseconds, which is significantly better than the traditional early warning system, improving the accuracy and response speed of the early warning.