Real-time monitoring and early warning system for hydropower safety based on the internet of things

Abstract

With the development of Internet of Things (IoT) technology, the current system has the problem of data processing delay, making it challenging to capture complex long-term dependencies and identify potential risks and failures in advance. This paper introduces the LSTM (Long Short-Term Memory) model in combination with the IoT, aiming to process time series data effectively, dynamically adjust warning thresholds, and predict potential risks. The real-time monitoring and early warning system for hydropower safety based on the IoT combines NB-IoT (Narrowband Internet of Things) technology and the LSTM model to achieve key parameter monitoring, data transmission, anomaly detection, and dynamic threshold adjustment. Sensors are deployed to cover important areas of the hydropower station, and LSTM captures long-term dependencies and predicts potential risks. After preprocessing, the data is transmitted through a lightweight protocol to ensure safety and accuracy. The early warning system integrates multiple modules, supports dynamic alarms and continuous optimization, and improves the safety and efficiency of hydropower station operations. Experimental results show that the LSTM model is superior to the comparison model in multiple indicators. In water level monitoring, the LSTM accuracy rate is as high as 98.50%, and the F1 score is 96.14%, significantly better than linear regression and decision trees. In gas concentration monitoring, the LSTM delay is only 70.8 ms, and the real-time rate is 99%. In the system stability assessment, the LSTM error rate was 1.8% under pressure monitoring, and the normal operation stability reached 99.6%, showing strong robustness and rapid recovery capabilities, suitable for scenarios with high real-time and high stability requirements. The real-time monitoring and early warning system for hydropower safety based on the IoT, combined with NB-IoT technology and LSTM model, can efficiently process complex time series data, adapt to high-load environments, and significantly improve the performance and reliability of the hydropower safety monitoring and early warning system.