The present disclosure provides an energy identification method for a micro-energy device based on back propagation (BP) neural network, which includes the following steps: S1, sampling a dynamic voltage of a micro-energy device in an open-circuit state to obtain an original voltage signal, and denoising the original voltage signal by an adaptive threshold wavelet transform; S2, extracting an R wave peak value of the denoised voltage signal so as to obtain model input data; S3, establishing a BP neural network model, inputting data to train the model, and stopping training when a training error is smaller than a preset value, to obtain a qualified BP neural network model; and S4, identifying a to-be-identified voltage signal by using the BP neural network model obtained in the step S3. According to the present disclosure, accurate and rapid energy identification and classification can be carried out, and the classification result is reliable.
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