Gas Alarm Ventilation Device for Closed Environment Based on Digital Twin and Artificial Intelligence Recognition

In order to solve the problem of untimely gas leak detection and delayed alarm response in a closed environment, this study introduces digital twin and artificial intelligence recognition technology to develop an intelligent gas alarm ventilation device. The device achieves rapid response and automatic ventilation by real-time monitoring of changes in gas concentration. This study first uses digital twin technology to virtually model the confined space. The gas concentration, temperature, humidity, and pressure data collected by sensors in real time are synchronously transmitted to the virtual model to achieve dynamic updating of the environment. Next, the Long Short-Term Memory (LSTM) algorithm is used to identify and analyze gas leaks, determine whether the gas concentration exceeds the standard, and immediately sound an alarm if it approaches the warning threshold. The algorithm can optimize analysis accuracy and improve recognition results through interactive learning of real-time data and historical data. Finally, when the gas concentration exceeds the safety threshold, the system automatically starts the ventilation device and adjusts the power and time of the exhaust fan according to the leakage situation, so as to effectively discharge the harmful gas. The device also has a self-correction function, which can optimize the ventilation mode as the environment changes, making the alarm and ventilation more intelligent and accurate. The actual gas concentration starts from 50 ppm and steadily increases by about 2 ppm every 10 s. The system finally succeeds in reducing the concentration of harmful gases below 83%. The intelligent gas alarm and ventilation device designed in this study improves the intelligent level of gas leakage monitoring and response in closed environments, significantly reduces the burden of manual monitoring, and can effectively ensure the safe management of confined spaces.