## 3 Proposed LSTM Model
The LSTM network model can only classify the ripple spectrum, and it is difficult to calculate the center frequency corresponding to the spectrum. In order to solve this problem, this paper proposes a data acquisition and processing method based on a sliding window. The center frequency is estimated by the position of the window, so that the designed neural network model can not only identify the branch power supply, but also realize the branch circuit. An estimate of the actual output power of the power supply. The design of the sliding window is shown in the figure, where the window length \(N_{window}\) is determined by the maximum width of the switching frequency ripple spectrum of all SMPSs in the system; the window sliding step \(N_{step}\) satisfies the condition \(1\leq N_{step}\leq N_{window}\), when the \(N_{step}\) is smaller, the sliding The more times, the higher the resolution of frequency positioning, the more accurate the estimation of the actual output power of the power supply, but at the same time, the more the number of windows, the greater the amount of calculation required; otherwise, the amount of calculation would be reduced, and the estimation accuracy would be lower. When \(N_{step}<N_{window}\), there will be incomplete overlap between the sliding window and the ripple spectrum waveform window. In order to avoid introducing signals with non-ripple spectrum features into the training process and affecting the training results, the window coverage parameter \(N_{conv}\) is defined. The value of this parameter is the number of coincident data points between the sliding window and the label window. Only when \(N_{conv}>a*N_{window}\), the sliding window is marked with the same label for training, otherwise the data label of the window is defined as empty. Among them, the threshold coefficient is \(0<a\leq 1\). When \(a\) is larger, the number of labeled windows is smaller, and the training accuracy is higher, and the convergence is easier, but the amount of original data required is larger; otherwise, the smaller the amount of original data required. In addition, the accuracy will decrease, so this parameter needs to be balanced according to the actual training scenario. Since the position of the sliding window needs to be marked to invert the actual power of the power supply, the entire training and detection process needs to be adapted accordingly. The adaptation method is shown in the figure:  
The overall identification method process mainly includes three parts:  
1. Data preparation: 1. Collect the ripple signal at the load end and perform spectrum transformation, and extract the spectrum data S in the window of the switching spectrum variation range; 2. Adjust the output voltage of the SMPS of the \(i\)-\(th\) branch so that its actual output power decreases, while the actual output power of other branches increases. Therefore, there will be a frequency component waveform shiftedto the left in the frequency spectrum, while the other frequency component waveforms are shifted to the right. From this, it can be determined that the left-shifted frequency component is the spectral waveform corresponding to the _i-th_ branch; (c) Repeat step b until the spectral components corresponding to each branch power supply are determined; (d) Adjust the load resistance to make each power supply work under different output power conditions, collect ripple spectrum data at the same time, and label each frequency component waveform in the spectrum data. For the coincident spectral components, count the number m of spectrum quantities, and the standard is the coincident power supply m; (e) Repeat step d to realize the collection of data sets under different working conditions; (f) Adjust the power supply voltage of a certain branch to change the overall current sharing state of the system, and then repeat steps d to e to sample and label the spectrum data in the new current sharing state; (g) Repeat step f to complete data collection for different current sharing states.