Built in Power Status Detection Component Based on Deep Learning in Multi source Data Acquisition Devices

Today, multi-source data acquisition devices play an increasingly important role in industry, environmental monitoring and health surveillance. However, the long-term stable operation and reliability of these devices rely heavily on the stability and continuity of the built-in power supply. Therefore, it is particularly important to accurately and efficiently monitor and predict the status of power supplies. In this study, we propose a deep learning-based built-in power supply state detection method to automatically learn and extract key features of power supply performance from data collected by multiple sensors by designing and implementing an end-to-end neural network model. This paper combines a convolutional neural network (CNN) and a long short-term memory network (LSTM) in order to process time-series data and predict the immediate state of the power supply in real time. The system also contains an adaptive learning mechanism that automatically adjusts the prediction model according to environmental changes and equipment aging to maintain high sensitivity and accuracy of detection. Even in the case of $\mathbf{2 5 \%}$ data loss, the accuracy of the model remains around 93.5%, which indicates the robustness of the model in dealing with incomplete data. This study not only optimizes the power state detection of multi-source data acquisition devices, but also provides a valuable empirical foundation and theoretical support for the application of deep learning technology in the field of energy management, demonstrating its wide application prospects and practical value.