A detection system and method for weak ripple and noise signals of DC regulated power supply

Abstract

This application relates to a system and method for detecting weak ripple and noise signals in a DC regulated power supply. The system comprises a control module, a waveform processing module, a waveform analysis module, and a storage module. The control module automatically adjusts the operating parameters of the power supply and detection device to automatically match the power supply, load, and detection device. The waveform processing module obtains and processes the signal output by the detection device, and analyzes and extracts the processed signal through a voltage amplification unit to obtain and store the weak ripple and noise signal of the DC regulated power supply. This system can improve the detection accuracy of weak ripple and noise signals, simplify the detection process, and reduce costs.

Description

Technical Field

[0001] The present application relates to the technical field of DC regulated power supply detection, and in particular to a system and method for detecting weak ripple and noise signals of a DC regulated power supply. Background Art

[0002] A DC stabilized power supply is generally formed by an AC power supply through rectification and voltage stabilization. This inevitably results in some AC components in the DC stabilized quantity. This AC component superimposed on the DC stabilized quantity is called ripple and noise. The current method of measuring power supply ripple to weak ripples in the μV level requires multiple debugging and measurement of the power supply output, loop control and detection devices. The process is cumbersome and there is a technical problem that weak waveform components are easily lost. Summary of the Invention

[0003] Based on this, it is necessary to provide a detection system and method for weak ripple and noise signals of a DC regulated power supply that can improve the accuracy of weak waveform detection in order to address the above technical problems.

[0004] A detection system for weak ripple and noise signals of a DC regulated power supply, the system comprising: a control module, a waveform processing module, a waveform analysis module and a storage module;

[0005] The control module is configured to set a power control range according to the received DC regulated power supply and load requirements, configure a power measurement range within the power control range using the Nyquist sampling theorem, and adjust operating parameters of its own control unit according to the power control range and the power measurement range to output a waveform signal to be processed to the waveform processing module;

[0006] The waveform processing module is configured to calculate the voltage amplitude of the received waveform signal to be processed, perform noise reduction processing on the waveform signal to be processed based on the voltage amplitude and a filtering algorithm to obtain a waveform signal to be separated, perform frequency domain or time domain separation on the waveform signal to be separated using an intelligent separation algorithm, and output a weak waveform signal to the waveform analysis module; the weak waveform signal includes: a weak ripple signal and a noise signal;

[0007] The waveform analysis module is configured to extract spectral features and amplify the voltage amplitude of the received weak waveform signal by adjusting filter parameters to obtain a first waveform signal, perform secondary waveform feature extraction on the first waveform signal using wavelet transform to obtain a second waveform signal, and input the result of calibrating the second waveform signal with the original waveform signal of the DC regulated power supply into the storage module;

[0008] The storage module is used to store the detection signal corresponding to the received calibration result.

[0009] A method for detecting weak ripple and noise signals of a DC regulated power supply, the method comprising:

[0010] Get the original waveform signal of the DC regulated power supply.

[0011] The original waveform signal is configured with detection and control basic information through the control module, and the original waveform signal is separated in the frequency domain or time domain according to the detection and control basic information to obtain a ripple signal and a noise signal.

[0012] The waveform analysis module amplifies the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and extracts the weak noise waveform characteristics of the waveform signal to be measured to obtain a first waveform signal.

[0013] The first waveform signal and the original waveform signal are calibrated to obtain a standard waveform signal.

[0014] A computer device includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0015] Get the original waveform signal of the DC regulated power supply.

[0016] The original waveform signal is configured with detection and control basic information through the control module, and the original waveform signal is separated in the frequency domain or time domain according to the detection and control basic information to obtain a ripple signal and a noise signal.

[0017] The waveform analysis module amplifies the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and extracts the weak noise waveform characteristics of the waveform signal to be measured to obtain a first waveform signal.

[0018] The first waveform signal and the original waveform signal are calibrated to obtain a standard waveform signal.

[0019] The above-mentioned system and method for detecting weak ripple and noise signals of a DC regulated power supply automatically adjusts the operating parameters of the power supply, load and detection device through the control module to automatically match the power supply, load and detection device. The waveform processing module obtains the signal output by the detection device and processes the output signal. The processed signal is analyzed and extracted by the voltage amplification unit to obtain the weak ripple and noise signal of the DC regulated power supply and store it. There is no need to debug and measure the power supply, load and detection device multiple times, and the overall process is simple. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 Schematic diagram of the structure of a detection system for weak ripple and noise signals of a DC regulated power supply according to one embodiment;

[0021] Figure 21 is a flow chart of a method for detecting weak ripple and noise signals of a DC regulated power supply in one embodiment;

[0022] Figure 3 1 is a flow chart of the steps for detecting weak ripple and noise signals of a DC regulated power supply in one embodiment;

[0023] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0025] In one embodiment, Figure 1 As shown, a detection system for weak ripple and noise signals of a DC regulated power supply is provided, comprising: a control module 1, a waveform processing module 2, a waveform analysis module 3 and a storage module 4, wherein:

[0026] Control module 1 is used to set a power control range according to the received DC regulated power supply and load requirements, configure a power measurement range within the power control range using the Nyquist sampling theorem, and adjust the operating parameters of its own control unit according to the power control range and the power measurement range to output a waveform signal to be processed to the waveform processing module.

[0027] Specifically, the control module 1 includes a loop control unit 11, a power output unit 12 and a detection device control unit 13, which respectively control the operation of the power supply and the detection device, and the operating parameters of the load, power supply and detection device match each other. The operating parameters include real-time current and real-time voltage. Through the adjustment of the loop control unit 11, the power control unit 12 and the detection device control unit 13, the operating parameters of the load, power supply and detection device match each other, so that the detection device can operate normally and stably.

[0028] It should be noted that the output end of the power supply is respectively connected to the input end of the load and the detection end of the detection device. The specific models and specifications of the load, power supply and detection device need to be selected and determined according to the actual specifications of the device. The operating parameters include real-time current and real-time voltage.

[0029] Waveform Processing Module 2 is configured to calculate the voltage amplitude of the received waveform signal to be processed, perform noise reduction on the waveform signal based on the voltage amplitude and a filtering algorithm to obtain a waveform signal to be separated, perform frequency or time domain separation on the separated waveform signal using an intelligent separation algorithm, and output a weak waveform signal to the Waveform Analysis Module. The weak waveform signal includes a weak ripple signal and a noise signal.

[0030] Specifically, the waveform processing module 2 includes a waveform acquisition unit 21, a voltage calculation unit 22 and a separation unit 23. The waveform acquisition unit 21 is used to acquire the output waveform signal from the detection device. The output waveform signal includes all waveforms. The voltage calculation unit 22 is used to calculate the voltage amplitude of the waveform signal. The separation unit 23 is used to separate the acquired waveform signal to obtain different waveform signals. After the waveform acquisition unit 21 acquires the output waveform signal from the detection device, the voltage amplitude of different waveforms is calculated by the voltage calculation unit 22. The separation unit 23 separates the waveform signal according to the amplitude calculated by the voltage calculation unit 22 to obtain multiple different waveform signals.

[0031] The waveform analysis module 3 is used to extract the spectral characteristics and amplify the voltage amplitude of the received weak waveform signal by adjusting the parameters of the filter to obtain a first waveform signal, perform secondary waveform feature extraction on the first waveform signal using wavelet transform to obtain a second waveform signal, and input the result of calibrating the second waveform signal with the original waveform signal of the DC regulated power supply into the storage module.

[0032] Specifically, the waveform analysis module 3 includes a filtering unit 31, a voltage amplification unit 32, an extraction unit 33 and a calibration unit 34. The filtering unit 31 separates the different waveform signals obtained by the separation unit 23 to obtain a ripple waveform signal. The voltage amplification unit 32 is used to amplify the amplitude of the obtained ripple and noise waveform signals. The extraction unit 33 is used to extract the weak signal therein. The calibration unit 34 is used to reversely process the ripple and noise waveform signals according to the processing of the voltage amplification unit 32 to obtain a weak noise signal, and output it to the storage module 4 for storage.

[0033] It should be noted that the filtering unit 31 is preset with a first waveform feature, and the ripple waveform signal is filtered and separated by analyzing the different waveform signals separated by the separation unit 23. The voltage amplification unit 32 amplifies the ripple waveform signal separated by filtering by a multiple amplification method. The extraction unit 33 is preset with a second waveform feature, and analyzes, separates and extracts the amplified ripple and noise waveform signal to extract the weak noise signal. The calibration unit 34 performs calibration based on the initially sampled ripple and noise signal and the weak noise signal output by the filtering unit, the voltage amplification unit and the extraction unit.

[0034] The storage module 4 is configured to store the detection signal corresponding to the received calibration result.

[0035] The stored weak ripple and noise signals can also be synchronously output to a display device for real-time display, allowing for more intuitive observation of the weak ripple and noise signals.

[0036] In one embodiment, the control module includes: a load control unit, a detection device control unit, and a power supply control unit. The load control unit is configured to set a power supply control range based on the received DC regulated power supply and load requirements and output it to the power supply control unit. The power supply control range includes a voltage range and a current range. The detection device control unit is configured to configure a power supply measurement range within the power supply control range using the Nyquist sampling theorem and output the power supply measurement range to the power supply control unit. The power supply control unit is configured to adjust the operating parameters of the control module based on the received DC regulated power supply, the power supply control range, and the power supply measurement range, and reset the control module based on the operating parameters to output a waveform signal to be processed to the waveform processing module.

[0037] In one embodiment, the waveform processing module includes: a waveform acquisition unit, a voltage calculation unit, and a separation unit. The waveform acquisition unit is used to collect the waveform signal to be processed output by the DC regulated power supply from the detection device control unit. The voltage calculation unit is used to calculate the voltage amplitude of the waveform signal to be processed, reduce the noise of the waveform signal to be processed using a filtering algorithm, and output the waveform signal to be separated to the separation unit. The separation unit is used to perform frequency domain or time domain separation on the received waveform signal to be separated using an intelligent separation algorithm, and output the weak waveform signal to the waveform analysis module.

[0038] In one embodiment, the separation unit is further used to extract fingerprint features of the waveform signal to be separated based on the constructed comprehensive waveform fingerprint library, establish a separation model of the waveform signal using the fingerprint features and the gray wolf optimization algorithm, separate the ripple and noise of the waveform signal to be separated according to the separation model, and output the weak waveform signal to the waveform analysis module.

[0039] In one embodiment, the waveform analysis module includes: a filtering unit, a voltage amplification unit, an extraction unit, and a calibration unit. The filtering unit is used to filter and extract spectrum features of the separated ripple signal and noise signal through a preset filter using a spectrum analysis method to obtain a first waveform signal, and output the first waveform signal to the voltage amplification unit. The voltage amplification unit is used to amplify the amplitude feature of the ripple signal of the first waveform signal to a required threshold value through an adjustable gain amplification circuit, and then output the amplified first waveform signal to the extraction unit. The extraction unit is used to extract the waveform features of the amplified first waveform signal using a wavelet transform method to obtain a second waveform signal, and output the second waveform signal to the calibration unit. The calibration unit is used to input the result of calibrating the second waveform signal and the original waveform signal of the DC regulated power supply into the storage module.

[0040] It is worth noting that the operating parameters of the power supply and the detection device are automatically adjusted by the control module so that the power supply, the load, and the detection device are automatically matched. The waveform processing module obtains the signal output by the detection device and processes the output signal. The processed signal is analyzed and extracted by the voltage amplification unit to obtain the weak ripple and noise signals of the DC regulated power supply and store them. This achieves the effect of not needing to debug and measure the power supply, the load, and the detection device multiple times, and the overall process is simple. On the other hand, the present invention effectively solves the problem of the existing technology easily losing weak waveform components by constructing a comprehensive waveform fingerprint library, adopting separation model parameter optimization based on the gray wolf optimization algorithm, and introducing various advanced signal processing technologies such as wavelet threshold denoising, thereby achieving high-precision detection of weak ripple and noise signals output by the DC regulated power supply, and can provide accurate and reliable basic data for subsequent power supply performance analysis and fault diagnosis.

[0041] In one embodiment, Figure 2 As shown in the figure, a method for detecting weak ripple and noise signals of a DC regulated power supply is provided. Figure 1 The following steps are used as an example to illustrate the system:

[0042] Step 202: Acquire the original waveform signal of the DC regulated power supply.

[0043] In step 204 , the original waveform signal is configured with detection control basic information through the control module, and the original waveform signal is separated in the frequency domain or time domain according to the detection control basic information to obtain a ripple signal and a noise signal.

[0044] The basic control information includes the operating parameters and power control range of the control module configuration.

[0045] Specifically, it's necessary to determine the normal operating range of the DC regulated power supply's output, including the output voltage and current ranges. The output voltage range is typically determined by the power supply's design rating, for example, 0-30V, while the output current range is determined by the load requirements and output power. The output voltage and current can be adjusted using potentiometers or encoders. During actual adjustment, a binary approach can be used to approximate the output voltage and current until they stabilize at the midpoint of the desired range, ensuring the power supply operates within its design range. This step lays the foundation for subsequent waveform capture, ensuring the power supply is in normal operating condition and capturing the true, weak waveform characteristics of its output.

[0046] Furthermore, the detection device must have a certain measurement range and sampling rate to ensure it can capture weak ripple and noise signals in the power supply output waveform. The measurement range is primarily determined based on the determined power supply output range. For example, if the voltage output range is 0-30V, the detection device's measurement range can be set to ±50V. Furthermore, the measurement range must be sufficiently fine, and a high-resolution analog-to-digital converter must be used to minimize quantization error. The sampling rate setting is crucial for capturing high-frequency components. According to the Nyquist sampling theorem, the sampling rate must be greater than twice the highest frequency component, typically 3-5 times or more. For example, if you need to capture a 1MHz high-frequency ripple signal, the sampling rate must be set to at least 3MHz. In practical applications, the frequency range of the output signal can be pre-tested and the sampling rate appropriately set accordingly. Appropriate use of techniques such as oversampling and digital filtering can help improve the ability to capture weak signals. This step aims to ensure that the detection device can capture the weak waveform characteristics of the power supply output with a sufficient measurement range and sampling rate.

[0047] Furthermore, the operating parameters of the load, power supply, and detection device must match each other to avoid signal distortion or distorted detection caused by mismatches. The operating parameters here primarily refer to real-time current and voltage. Specifically, the following steps are: First, determine the normal output voltage and current range of the power supply, for example, 0-30V and 0-10A. Then, select a load that matches these values, such as a 20V, 5A constant voltage and constant current load. Then, set the detection device's measurement range based on the corresponding voltage and current values ​​of this load, for example, 0-40V and 0-20A, to ensure a sufficiently large detection range. Furthermore, impedance matching between the detection device and the load must be ensured to avoid unexpected voltage drops or current shunting. Furthermore, during actual operation, the voltage and current values ​​between the power supply, load, and detection device must be monitored in real time to ensure they are within the normal operating range and match the respective parameters. Based on the measured data, the power supply, load, or detection device can be fine-tuned in a timely manner until the operating parameters are fully matched and optimal detection results are achieved. The purpose of this step is to eliminate the influence of external factors, so that the system is in normal working condition and provide an accurate and reliable data source for subsequent weak waveform detection.

[0048] Step 206 , amplifying the voltage amplitude of the ripple signal by the waveform analysis module to obtain a waveform signal to be measured, extracting the weak noise waveform feature of the waveform signal to be measured, and obtaining a first waveform signal.

[0049] Specifically, the waveform acquisition unit collects the waveform signal output by the power supply from the detection device, the voltage calculation unit calculates the voltage amplitude of the collected waveform signal, and the voltage calculation unit is used to calculate the voltage amplitude information of the waveform signal. At the same time, a filtering algorithm is used to preprocess the waveform to reduce the impact of noise on amplitude measurement. An intelligent separation algorithm is used to separate the acquired waveform signal in the frequency domain or time domain to retain weak waveform components as much as possible.

[0050] Furthermore, a comprehensive waveform fingerprint library with weak ripple and noise features is constructed:

[0051] 1) Collect a large amount of DC power supply output waveform sample data with varying degrees of weak ripple and noise characteristics. This sample data can be obtained from actual measurements or generated through simulation.

[0052] 2) Feature extraction is performed on these waveform sample data, including time domain features (such as amplitude, period, etc.), frequency domain features (such as harmonic distribution, spectrum envelope, etc.) and time-frequency features (such as wavelet coefficients, etc.).

[0053] 3) The extracted features are combined into a high-dimensional feature vector as the fingerprint feature of each waveform sample.

[0054] 4) The fingerprint features of all waveform samples are constructed into a comprehensive fingerprint library to provide a reference for subsequent separation algorithms.

[0055] Furthermore, the Grey Wolf Optimization Algorithm (GWO algorithm) is used to establish a waveform signal separation model:

[0056] 1) Define the objective function as the sum of the errors between the original waveform signal and the separated ripple signal and noise signal, that is, minimize the difference between the original signal and the separated signal.

[0057] 2) The GWO algorithm was used to iteratively optimize the parameters of the separation model. The GWO algorithm simulates the leadership hierarchy of gray wolves and their behavioral characteristics during hunting, including the roles of alpha, beta, delta, and omega wolves.

[0058] 3) In each iteration, the alpha wolf, beta wolf, and delta wolf guide the omega wolf to approach the global optimal solution until the convergence condition of the objective function is met.

[0059] 4) The optimized separation model parameters are the final separation algorithm, which can be used to separate the ripple and noise components of the new waveform signal to be measured.

[0060] Furthermore, the objective function is to minimize the error between the original waveform signal and the separated waveform signal; the separation model parameters are iteratively optimized until the separation result meets the requirement of retaining the weak waveform component, and the optimized separation model is applied to the separation of the waveform signal to be measured to obtain the ripple signal and noise signal that retain the weak components.

[0061] Furthermore, spectrum analysis methods are used to further filter the separated ripple and noise signals. First, the amplitude distribution characteristics of the ripple and noise signals in the frequency domain need to be observed. By analyzing the spectrograms of these two signals, the frequency range corresponding to the weak components can be identified. For example, for ripple signals, weak components are typically concentrated at the fundamental frequency and its integer multiples; whereas for noise signals, weak components may be dispersed over a wider frequency band. Next, a suitable frequency domain filter is designed to extract these weak spectral components. The filter can be a Butterworth, Chebyshev, or elliptic filter, with a passband frequency range that covers the identified weak component frequencies. To avoid introducing additional distortion, the amplitude within the filter passband should be kept as flat as possible. Applying the designed filter to the original ripple and noise signals yields the filtered signals. Carefully examine the filtered signals to ensure that the weak spectral characteristics are well preserved, and adjust the filter parameters for optimization if necessary. Through the above spectrum analysis and filtering processing, the weak components in the ripple signal and noise signal can be effectively extracted, laying a good foundation for subsequent further analysis. The purpose of this step is to preserve the weak waveform characteristics as much as possible and reduce the distortion effect of the filtering process on the original signal.

[0062] Furthermore, the filtered ripple signal is appropriately amplified, and after completing the frequency domain filtering process, a relatively pure ripple signal is obtained. However, since the ripple component in the original waveform is very weak, it is necessary to amplify it appropriately to highlight its characteristics and facilitate subsequent analysis:

[0063] First, we need to measure the peak-to-peak value of the filtered ripple signal. Because the ripple component is weak, this value is usually very small, making it difficult to directly observe and analyze. Therefore, we need to design an adjustable gain amplifier circuit to amplify the ripple signal to an appropriate range.

[0064] The selection of magnification should meet the following principles:

[0065] - The peak-to-peak value after amplification should be within the measurement range of the detection device to avoid overload.

[0066] - The amplification factor should not be too large, otherwise it may amplify the original noise components and affect the signal-to-noise ratio.

[0067] - The magnification can usually be set between 10x and 100x, and can be adjusted appropriately based on actual conditions.

[0068] Apply the amplifier circuit to the filtered ripple signal to generate an amplified signal. Measure the peak-to-peak value of the amplified signal to confirm that it meets measurement requirements. Simultaneously, observe the amplified ripple signal to confirm that weak components are effectively amplified without significant distortion. If necessary, adjust the amplification factor for optimization. Proper amplification makes the ripple signal amplitude easier to observe and measure, facilitating subsequent analysis and evaluation. The goal of this step is to maximize the ripple signal amplitude without distortion, creating favorable conditions for downstream extraction of weak components.

[0069] Furthermore, wavelet transforms are used to extract weak ripple and noise signal features from the amplified signal. First, an appropriate wavelet basis function must be selected. Common wavelet bases include Haar wavelets, Daubechies wavelets, and Symlets wavelets. The selection of the wavelet basis should take into account the characteristics of the signal to be measured, such as periodicity and transient characteristics. Next, wavelet transforms are performed on the amplified ripple and noise signals. By observing the distribution characteristics of the wavelet coefficients, key coefficients corresponding to weak ripple and noise components can be identified. For example, ripple components are often characterized by strong correlation between coefficients at adjacent scales, while noise components exhibit a more dispersed coefficient distribution. The identified key wavelet coefficients are extracted and the weak characteristic signals of ripple and noise are reconstructed through an inverse wavelet transform. The reconstructed signal should be carefully examined to confirm that it effectively reflects the weak features of the original waveform. If necessary, the wavelet basis function and analysis parameters can be adjusted and optimized. Wavelet transform analysis effectively extracts weak characteristic components from the amplified ripple and noise signals, providing more refined data support for subsequent analysis and evaluation. The purpose of this step is to fully explore the weak waveform characteristics and provide a reliable basis for the final detection results.

[0070] Step 208: calibrate the first waveform signal and the original waveform signal to obtain a standard waveform signal.

[0071] Specifically, calibration is performed based on the initial sampled signal and the processed signal to obtain the final weak ripple and noise signal. First, the original ripple and noise signals obtained from the initial sampling need to be recorded. Then, the weak ripple and noise signals processed in the previous step are compared and analyzed with the initial sampled signal. Next, the difference between the initial and processed signals needs to be calculated, including amplitude, phase, and harmonic characteristics. Based on this difference information, a correction algorithm can be used to inversely compensate the processed weak signal to restore the ripple and noise characteristics closest to the original waveform. Common correction algorithms include least squares method and Kalman filtering. The corrected ripple and noise signals should more accurately reflect the true characteristics of the weak components in the original waveform. Both signals need to be carefully examined to confirm that their distortion has been reduced to an acceptable level. If necessary, the correction parameters can be further optimized. Finally, the corrected weak ripple and noise signals are output as the final result of the detection system. This step ensures that the entire waveform processing chain can maximize the preservation and restoration of weak characteristic components, providing a reliable data foundation for subsequent performance analysis.

[0072] In the above-mentioned method for detecting weak ripple and noise signals of a DC regulated power supply, the operating parameters of the power supply and the detection device are automatically adjusted by the control module so that the power supply, the load, and the detection device are automatically matched. The waveform processing module obtains the signal output by the detection device and processes the output signal. The processed signal is analyzed and extracted by the voltage amplification unit to obtain the weak ripple and noise signals of the DC regulated power supply and store them. This achieves the effect of not requiring multiple debugging and measurements of the power supply, the load, and the detection device, and simplifies the overall process. On the other hand, the present invention effectively solves the problem of the existing technology easily losing weak waveform components by constructing a comprehensive waveform fingerprint library, adopting separation model parameter optimization based on the gray wolf optimization algorithm, and introducing various advanced signal processing technologies such as wavelet threshold denoising, thereby achieving high-precision detection of weak ripple and noise signals output by the DC regulated power supply, and can provide accurate and reliable basic data for subsequent power supply performance analysis and fault diagnosis.

[0073] In one embodiment, the original waveform signal is configured with operating parameters through the load control module and the power control module of the control module, and the basic detection control information of the detection device control unit in the control module is adjusted according to the operating parameters. The amplitude of the original waveform signal is denoised according to the detection control information to obtain a waveform signal to be separated. A separation model of the waveform signal to be separated is constructed using the gray wolf optimization algorithm. The ripple component and noise component of the waveform signal to be separated are obtained by optimizing the parameters of the separation model. After extracting the spectral features of the ripple signal corresponding to the ripple component and the noise signal corresponding to the noise component using a spectrum analysis method, the ripple signal is amplified to obtain a ripple signal and a noise signal.

[0074] In one embodiment, the waveform analysis module amplifies the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and a wavelet transform method is used to extract the weak noise waveform characteristics of the waveform signal to be measured to obtain a first waveform signal. The weak noise waveform characteristics include: weak ripple characteristics and noise characteristics.

[0075] In one embodiment, the standard waveform signal is stored in the storage module as a real signal.

[0076] It is worth noting that, first of all, it is necessary to record the original ripple signal obtained by initial sampling and noise signal Then, the weak ripple signal obtained by the previous processing and noise signal Compare and analyze with the initial sampling signal.

[0077] Next, the difference between the initial and processed signals needs to be calculated, including amplitude, phase, and harmonic characteristics. Based on this difference information, a correction algorithm can be used to inversely compensate the processed weak signal, restoring the ripple and noise characteristics as close to the original waveform as possible. Common correction algorithms include least squares method and Kalman filtering.

[0078] The ripple signal obtained after correction and noise signal , which should be able to more accurately reflect the true characteristics of the weak components in the original waveform. It is necessary to carefully check the two signals to confirm that their distortion has been reduced to an acceptable level. If necessary, the correction parameters can be further optimized.

[0079] Finally, the corrected weak ripple and noise signal and The output is the final result of the detection system. Through this step, it is ensured that the entire chain of waveform processing can retain and restore the weak characteristic components to the greatest extent possible, providing a reliable data basis for subsequent performance analysis.

[0080] In one embodiment, Figure 3 As shown, a detection step for weak ripple and noise signals of a DC regulated power supply is provided, specifically including:

[0081] S1, adjustment, the load control unit, the power control unit and the detection device control unit respectively monitor the real-time load, the power loop control and the operation of the detection device, and the real-time load, power output and the operating parameters of the detection device match each other;

[0082] Specifically, the output end of the power supply is connected to the input end of the load and the detection end of the detection device respectively. The monitoring of the load detection unit, the power supply control unit and the detection device control unit is used to match the operating parameters of the real-time load, power supply loop control and the detection device with each other, so that the detection device can operate normally and stably and obtain a stable signal.

[0083] S2, waveform processing, the waveform acquisition unit obtains the output waveform signal from the detection device, the voltage calculation unit calculates the voltage amplitude of the waveform signal, and the separation unit separates the obtained waveform signal to obtain different waveform signals;

[0084] S3, waveform analysis: The filtering unit separates the different waveform signals obtained by the separation unit to obtain a ripple signal and a noise signal. The amplitude amplification unit amplifies the amplitude of the obtained ripple signal. The extraction unit extracts the weak noise signal. The calibration unit calibrates the initial sampled ripple and noise signals and the weak noise signal output by the filtering unit, voltage amplification unit, and extraction unit, and outputs the calibration to the storage module for storage.

[0085] In one embodiment, the waveform processing step includes:

[0086] S2-1, the waveform acquisition unit collects the waveform signal of the power output from the detection device;

[0087] S2-2, the voltage calculation unit calculates the voltage amplitude of the collected waveform signal;

[0088] S2-3, using the voltage calculation unit to calculate the voltage amplitude information of the waveform signal, and using the filtering algorithm to pre-process the waveform to reduce the impact of noise on the amplitude measurement;

[0089] S2-4. Use intelligent separation algorithm to separate the acquired waveform signal in frequency domain or time domain, and try to retain the weak waveform components.

[0090] In one embodiment, the specific steps of waveform analysis include:

[0091] S3-1, the filtering unit uses the spectrum analysis method to further filter the separated ripple signal and noise signal to preserve the spectrum characteristics as much as possible;

[0092] S3-2, the voltage amplification unit appropriately amplifies the filtered ripple signal to highlight the weak ripple signal, but it cannot be over-amplified to cause distortion;

[0093] S3-3, the extraction unit uses the wavelet transform method to extract the weak ripple and noise signal features from the amplified signal;

[0094] S3-4. The calibration unit performs calibration based on the initial sampled signal and the processed signal to obtain the final weak ripple and noise signal to ensure that the waveform distortion is minimized.

[0095] The step of performing frequency domain or time domain separation on the acquired waveform signal using an intelligent separation algorithm specifically includes:

[0096] S2-4-1. Build a comprehensive waveform fingerprint library with weak ripple and noise features;

[0097] S2-4-2. Use the GWO algorithm to establish a waveform signal separation model, and the objective function is to minimize the error between the original waveform signal and the separated waveform signal;

[0098] S2-4-3, iteratively optimizing the separation model parameters until the separation result meets the requirement of retaining the weak waveform component;

[0099] S2-4-4. Apply the optimized separation model to the separation of the waveform signal to be measured to obtain the ripple signal and noise signal with weak components retained.

[0100] It is worth mentioning that the construction of a comprehensive waveform fingerprint library with weak ripple and noise features is as follows:

[0101] 1. Collect waveform sample data

[0102] First, it is necessary to collect a large amount of DC regulated power supply output waveform sample data with different degrees of weak ripple and noise characteristics. These sample data can come from actual measurements or be generated through simulation.

[0103] For actual measurement, you can use a high-performance digital oscilloscope or data acquisition equipment to sample DC regulated power supplies of various models and specifications and collect their output waveforms. The following points should be noted during the sampling process:

[0104] The sampling frequency should be much higher than the highest frequency component of the power supply output waveform to meet the Nyquist sampling theorem and avoid spectrum aliasing.

[0105] The sampling time should cover multiple complete ripple cycles to ensure that complete waveform information is collected.

[0106] For waveforms containing weak ripples and noise, a low-pass filter needs to be used for preprocessing to improve the signal-to-noise ratio.

[0107] During the sampling process, external interference should be minimized to ensure the stability of the measurement environment.

[0108] For simulation generation, a mathematical model of the DC regulated power supply can be established, including the rectifier circuit, filter circuit, load, and other components, and the model can be parameterized. Then, through Monte Carlo simulation, various parameters (such as filter capacitor value and switching frequency) can be changed to generate waveform sample data with varying degrees of ripple and noise characteristics.

[0109] 2. Extract waveform features

[0110] For the collected waveform sample data, it is necessary to extract its time domain, frequency domain and time-frequency domain features to form a high-dimensional feature vector as the fingerprint feature of each waveform sample.

[0111] Time domain characteristics:

[0112] Waveform Amplitude( ): Peak-to-peak value

[0113] Waveform period ( ):Ripple / Noise Period

[0114] Rise time( ) and falling edge time ( )

[0115] RMS value ( )

[0116] These time domain features directly reflect information such as the waveform amplitude, periodic characteristics, and waveform slope, and are very important for analyzing weak ripples and noise.

[0117] Frequency domain characteristics:

[0118] Fourier transform amplitude spectrum ( ): reflects the spectrum distribution

[0119] Harmonic content ( ): Total harmonic distortion, defined as

[0120]

[0121] in, is the fundamental amplitude, is the amplitude of the nth harmonic.

[0122] Spectral envelope ( ): describes the changing trend of the spectrum envelope

[0123] Frequency domain features can reveal the periodicity, harmonic structure and spectrum distribution characteristics of the waveform, which is very helpful for identifying weak ripples and noise.

[0124] Time-frequency domain features:

[0125] Wavelet transform coefficients ( ): Using wavelet basis functions of different scales to analyze the waveform can effectively extract weak transient features. Wavelet transform is defined as:

[0126]

[0127] in, is the wavelet basis function, is the scale factor, is the translation factor.

[0128] The time-frequency domain features combine the time domain and frequency domain information, which can better describe the time-varying characteristics of weak ripples and noise in the waveform and are an effective feature extraction method.

[0129] Through the above feature extraction, each waveform sample can be represented as a high-dimensional feature vector:

[0130]

[0131] This eigenvector is the fingerprint feature of the waveform sample, reflecting the comprehensive characteristics of its weak ripple and noise.

[0132] 3. Build a fingerprint library

[0133] The fingerprint feature vectors of all waveform samples Form a matrix , which is the comprehensive waveform fingerprint library constructed:

[0134]

[0135] in, is the total number of waveform samples. This fingerprint library provides a rich reference for the subsequent separation algorithm, which can better identify and separate weak ripple and noise components.

[0136] In one embodiment, S2-4-2 uses the Grey Wolf Optimization (GWO) algorithm to establish a separation model for waveform signals.

[0137] 1. Define the objective function

[0138] The goal is to convert the original waveform signal Separation into ripple signal and noise signal The two parts are combined so that their superposition is as close to the original signal as possible. Therefore, the objective function can be defined as:

[0139]

[0140] That is, the original signal needs to be minimized And the separated ripple signal and noise signal The squared integral of the error between .

[0141] 2. Establishing a separation model

[0142] To solve the objective function, the Gray Wolf Optimization (GWO) algorithm is used to optimize the parameters of the separation model. GWO is a population-based heuristic optimization algorithm that simulates the social behavior and hunting strategies of gray wolves.

[0143] Assume that the separation model can be expressed as:

[0144]

[0145] in, and is the separation function to be optimized, and is the corresponding model parameter vector.

[0146] The optimization goal is to find and , so that the objective function Minimize. The iterative process of the GWO algorithm is as follows:

[0147] 1) Initialize the population: randomly generate Gray wolf individuals, each individual corresponds to a set of separation model parameters and .

[0148] 2) Calculate fitness: For each individual gray wolf, calculate the corresponding objective function value , as the fitness of the individual.

[0149] 3) Update Alpha, Beta, and Delta wolves: Select the three individuals with the highest fitness so far as Alpha, Beta, and Delta wolves respectively.

[0150] 4) Update Omega Wolf: The remaining individuals are treated as Omega wolves and updated according to the positions of Alpha, Beta, and Delta wolves:

[0151]

[0152] in, is the position vector of the Omega wolf individual (i.e., separation model parameter), is the random coefficient vector, obey Evenly distributed.

[0153] 5) Determine convergence: If the objective function If the value is less than the preset threshold or reaches the maximum number of iterations, the algorithm converges and outputs the optimal separation model parameters. and ; Otherwise, return to step 2) and continue iterative optimization.

[0154] Through the iterative optimization of the GWO algorithm, the objective function can be found Minimize the separation model parameters to obtain the final ripple signal and noise signal This separation model can be applied to any new waveform signal to be measured to achieve effective separation of weak ripple and noise components.

[0155] S2-4-3 Iterative optimization of separation model parameters:

[0156] In S2-4-2, a separation model based on the GWO algorithm was established. Now it is necessary to iteratively optimize the parameters of this model so that the separation results meet the requirements of retaining weak waveform components.

[0157] The specific iterative optimization steps are as follows:

[0158] 1) Initialize the population: randomly generate Gray wolf individuals, each individual corresponds to a set of separation model parameters and .

[0159] 2) Calculate fitness: For each individual gray wolf, calculate the corresponding objective function value , as the fitness of the individual. Objective function The definitions are as follows:

[0160]

[0161] in, and is a regularization parameter used to control the energy of ripple signal and noise signal.

[0162] 3) Update Alpha, Beta, and Delta wolves: Select the three individuals with the highest fitness so far as Alpha, Beta, and Delta wolves respectively.

[0163] 4) Update Omega Wolf: The remaining individuals are treated as Omega wolves and updated according to the positions of Alpha, Beta, and Delta wolves:

[0164]

[0165] 5) Determine convergence: If the objective function If the value is less than the preset threshold or reaches the maximum number of iterations, the algorithm converges and outputs the optimal separation model parameters. and ; Otherwise, return to step 2) and continue iterative optimization.

[0166] In the iterative optimization process, two regularization parameters are introduced and , their functions are:

[0167] 1) Control ripple signal The energy can ensure that the separated ripple components are not overly suppressed, thereby retaining the weak ripple characteristics.

[0168] 2) Controlling noise signals The energy of the separated noise components can be ensured not to be over-amplified, thus avoiding distortion.

[0169] By reasonably selecting these two regularization parameters, we can minimize the error between the original signal and the separated signal while properly balancing the ripple and noise components, making the separation result closer to the subtle features of the actual waveform.

[0170] It should be noted that the regularization parameter and The selection of needs to be adjusted according to the characteristics of the actual waveform sample to achieve the best separation effect. This requires a lot of simulation experiments and test verification to determine the appropriate parameter value range.

[0171] Through the above iterative optimization process, the separation model parameters that meet the requirements of retaining weak waveform components are finally obtained. and , which can be used to separate the ripple and noise components of the new waveform signal to be measured.

[0172] S2-4-4 Apply the separation model to separate the waveform signal to be measured:

[0173] In the previous steps, we constructed a comprehensive waveform fingerprint library and optimized the separation model parameters using the GWO algorithm, ensuring that the separation results can effectively preserve the weak ripple and noise characteristics. Now we can apply this separation model to a new waveform signal to separate the ripple and noise components.

[0174] The specific steps are as follows:

[0175] 1. Input the waveform signal to be measured ;

[0176] 2. According to the features extracted in S2-4-1, the waveform signal to be tested Represented as a feature vector ;

[0177] 3. In the fingerprint database Find and The most similar sample feature vector;

[0178] 4. Use the separation model parameters corresponding to the most similar sample and , calculate the ripple signal of the signal to be measured and noise signal :

[0179]

[0180] in, and is the separation model function.

[0181] 5. Output separated ripple signal and noise signal .

[0182] It should be noted that when searching for the most similar sample in step 3, a distance-based similarity metric, such as Euclidean distance or cosine similarity, can be used. In addition, if there is no completely matching sample in the fingerprint library, interpolation or extrapolation methods can be used to estimate the separation result of the signal to be tested based on the separation model parameters of the nearest neighbor samples.

[0183] By applying the optimized separation model, the weak ripple and noise components can be effectively extracted from the new waveform signal to be measured, providing valuable information for subsequent power supply performance analysis and optimization.

[0184] In summary, S2-4-1 through S2-4-4 describe a complete system for detecting weak ripple and noise in DC power supplies based on a waveform fingerprint library and the GWO algorithm. This system effectively extracts and separates weak ripple and noise characteristics, providing important technical support for accurately measuring and optimizing power supply performance.

[0185] It should be noted that the relevant formulas or variables are explained as follows:

[0186] : The peak-to-peak value of the waveform, that is, the difference between the maximum and minimum values ​​of the waveform amplitude;

[0187] : The period of the waveform, that is, the length of time for a complete waveform cycle;

[0188] : Waveform rising edge time, that is, the time required for the waveform to rise from the minimum value to the maximum value;

[0189] : Waveform falling edge time, that is, the time required for the waveform to drop from the maximum value to the minimum value;

[0190] : The root mean square value of the waveform, reflecting the effective value of the waveform;

[0191] : The Fourier transform amplitude spectrum of the waveform reflects the spectrum distribution of the waveform at different frequencies;

[0192] : Total harmonic distortion, defined as the square root of the ratio of the fundamental amplitude to the amplitude of higher harmonics;

[0193] : The spectrum envelope of the waveform describes the overall change trend of the spectrum distribution;

[0194] : Wavelet transform coefficients of the waveform, where is the scale factor, is the translation factor, which can effectively extract the weak transient features in the waveform;

[0195] : The original waveform signal to be separated;

[0196] : Separated ripple signal;

[0197] : The noise signal obtained by separation;

[0198] : parameter vector of the ripple signal separation model;

[0199] : parameter vector of the noise-signal separation model;

[0200] : Ripple signal separation model function;

[0201] : Noise signal separation model function;

[0202] : Population size, that is, the number of gray wolf individuals;

[0203] : The position vector of the gray wolf individual, corresponding to the separation model parameters;

[0204] : Position vector of individual Alpha wolf;

[0205] : Beta wolf individual position vector;

[0206] :Delta wolf individual position vector;

[0207] :No. random coefficient vectors, obey Even distribution;

[0208] :No. A random number vector, subject to Even distribution;

[0209] : Objective function, which represents the square integral of the error between the original signal and the separated signal;

[0210] : Regularization parameter of the ripple signal, used to control the energy of the ripple signal;

[0211] : Regularization parameter of the noise signal, used to control the energy of the noise signal;

[0212] : feature vector of waveform sample;

[0213] : Fingerprint library, a matrix composed of all waveform sample feature vectors;

[0214] : The total number of waveform samples in the fingerprint library.

[0215] In one embodiment, a spectrum analysis method is used to further filter the separated ripple signal and noise signal:

[0216] After completing the initial separation of the waveform signal, the separated ripple signal and noise signal need to be further filtered to better retain the weak spectrum characteristics. Here we will use the spectrum analysis method for filtering.

[0217] First, the ripple signal needs to be and noise signal Perform Fourier transform respectively to obtain their amplitude spectrum in the frequency domain and By observing these two amplitude spectra, we can identify the frequency range corresponding to the weak components. For example, for a ripple signal, the weak components are usually concentrated at the fundamental frequency and its integer multiples; while for a noise signal, the weak components may be dispersed over a wider frequency band.

[0218] Next, a suitable frequency-domain filter must be designed to extract these weak spectral components. This filter can be a Butterworth, Chebyshev, or elliptic filter, with a passband frequency range that covers the frequencies of the identified weak components. To avoid introducing additional distortion, the amplitude within the filter's passband must be as flat as possible.

[0219] Apply the designed filter to the original ripple signal and noise signal to obtain the filtered signal. and The filtered signal needs to be carefully checked to ensure that the weak spectral features are well preserved. If necessary, the filter parameters can be adjusted appropriately for optimization.

[0220] Through the above spectrum analysis and filtering processing, the weak components in the ripple signal and noise signal can be effectively extracted, laying a good foundation for subsequent further analysis. The purpose of this step is to preserve the weak waveform characteristics as much as possible and reduce the distortion effect of the filtering process on the original signal.

[0221] Step S3-2: Amplify the filtered ripple signal appropriately

[0222] After completing the frequency domain filtering process, a relatively pure ripple signal is obtained. However, since the ripple component in the original waveform is very weak, it needs to be amplified appropriately to highlight its characteristics for subsequent analysis.

[0223] First, it is necessary to measure the filtered ripple signal Peak-to-peak value Since the ripple component is weak, this value is usually very small and difficult to observe and analyze directly. Therefore, it is necessary to design an adjustable gain amplifier circuit. Amplify the amplitude to the appropriate range.

[0224] Magnification The selection should meet the following principles:

[0225] Amplified peak-to-peak value It should be within the measuring range of the detection device to avoid overload.

[0226] Magnification It should not be too large, otherwise it may amplify the original noise components and affect the signal-to-noise ratio.

[0227] Usually the magnification Set it between 10 times and 100 times and adjust it appropriately according to the actual situation.

[0228] Apply the amplifier circuit to , get the amplified ripple signal . Need to measure Peak-to-peak value , confirm that it meets the measurement requirements. At the same time, observe the amplified ripple signal to confirm that the weak components are effectively amplified and there is no serious distortion. If necessary, the amplification factor can be adjusted Optimize.

[0229] Proper amplification can make the amplitude of the ripple signal easier to observe and measure, facilitating subsequent analysis and evaluation. The purpose of this step is to amplify the amplitude of the ripple signal as much as possible without distortion, creating favorable conditions for downstream weak component extraction.

[0230] Step S3-3: Use wavelet transform to extract weak ripple and noise signal features from the amplified signal.

[0231] In the previous steps, the separated ripple and noise signals have been frequency-domain filtered and amplitude-amplified. Now, we need to further extract the weak characteristic components of these two signals to provide a basis for the final analysis results. Here, we will use the wavelet transform method to achieve this goal.

[0232] First, we need to choose a suitable wavelet basis function Commonly used wavelet bases include Haar wavelet, Daubechies wavelet, Symlets wavelet, etc. The selection of wavelet base should take into account the characteristics of the signal to be measured, such as periodicity and transient characteristics.

[0233] Next, the amplified ripple signal and noise signal Perform wavelet transform respectively to obtain their wavelet coefficients at different scales and times and The wavelet transform is defined as follows:

[0234]

[0235] in, is the scale factor, is the translation factor.

[0236] By observing the distribution characteristics of wavelet coefficients, we can identify the key coefficients corresponding to weak ripple and noise components. For example, ripple components are usually characterized by strong correlation between coefficients at adjacent scales, while noise components are characterized by a more dispersed distribution of coefficients.

[0237] Extract the identified key wavelet coefficients and reconstruct the weak characteristic signals of ripple and noise through inverse wavelet transform and The reconstructed signal needs to be carefully checked to confirm that it can effectively reflect the weak features in the original waveform. If necessary, the wavelet basis function and analysis parameters can be appropriately adjusted for optimization.

[0238] Through wavelet transform analysis, it is possible to effectively extract weak characteristic components from the amplified ripple and noise signals, providing more refined data support for subsequent analysis and evaluation. The purpose of this step is to fully explore the weak waveform characteristics and provide a reliable basis for the final detection results.

[0239] It should be understood that although Figure 2-3 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2-3 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0240] Each module in the aforementioned system for detecting weak ripple and noise signals in a DC regulated power supply can be implemented in whole or in part via software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a computer device memory in software form, allowing the processor to call and execute the corresponding operations of each module.

[0241] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a method for detecting weak ripple and noise signals of a DC regulated power supply is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0242] Those skilled in the art will understand that Figure 1 、 Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0243] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0244] Get the original waveform signal of the DC regulated power supply.

[0245] The original waveform signal is configured with detection and control basic information through the control module, and the original waveform signal is separated in the frequency domain or time domain according to the detection and control basic information to obtain a ripple signal and a noise signal.

[0246] The waveform analysis module amplifies the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and extracts the weak noise waveform characteristics of the waveform signal to be measured to obtain a first waveform signal.

[0247] The first waveform signal and the original waveform signal are calibrated to obtain a standard waveform signal.

[0248] In one embodiment, when executing the computer program, the processor further implements the following steps: configuring operating parameters of the load control module and the power control module of the control module, adjusting basic detection control information of the detection device control unit in the control module based on the operating parameters, performing noise reduction on the amplitude of the original waveform signal based on the detection control information to obtain a waveform signal to be separated, constructing a separation model for the waveform signal to be separated using the Gray Wolf Optimization Algorithm, and optimizing the parameters of the separation model to obtain the ripple component and noise component of the waveform signal to be separated. Using a spectrum analysis method, spectral features are extracted from the ripple signal corresponding to the ripple component and the noise signal corresponding to the noise component, and then the ripple signal is amplified to obtain a ripple signal and a noise signal.

[0249] In one embodiment, when executing the computer program, the processor further implements the following steps: amplifying the voltage amplitude of the ripple signal using a waveform analysis module to obtain a waveform signal to be measured, and extracting weak noise waveform features of the waveform signal to be measured using a wavelet transform method to obtain a first waveform signal. The weak noise waveform features include: weak ripple features and noise features.

[0250] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: storing the standard waveform signal as a real signal in a storage module.

[0251] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0252] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0253] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art could make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.

Claims

1. A detection system for weak ripple and noise signals of a DC regulated power supply, characterized in that: The system includes: a control module, a waveform processing module, a waveform analysis module and a storage module; The control module is configured to set a power control range according to the received DC regulated power supply and load requirements, configure a power measurement range within the power control range using the Nyquist sampling theorem, and adjust operating parameters of its own control unit according to the power control range and the power measurement range to output a waveform signal to be processed to the waveform processing module; The waveform processing module is used to calculate the voltage amplitude of the received waveform signal to be processed, perform noise reduction processing on the waveform signal to be processed according to the voltage amplitude and the filtering algorithm to obtain the waveform signal to be separated, use the intelligent separation algorithm to perform frequency domain or time domain separation on the waveform signal to be separated, and output the weak waveform signal to the waveform analysis module, specifically: the original waveform signal is configured with operating parameters through the load control unit and the power control unit of the control module, and the detection control basic information of the detection device control unit in the control module is regulated according to the operating parameters; the amplitude of the original waveform signal is denoised according to the detection control basic information to obtain the waveform signal to be separated, the gray wolf optimization algorithm is used to construct a separation model of the waveform signal to be separated, and the ripple component and noise component of the waveform signal to be separated are obtained by optimizing the parameters of the separation model; the ripple signal corresponding to the ripple component and the noise signal corresponding to the noise component are respectively extracted by the spectrum analysis method, and then the ripple signal is amplified to obtain the ripple signal and the noise signal; the weak waveform signal includes: a weak ripple signal and a noise signal; The waveform analysis module is configured to extract spectral features and amplify the voltage amplitude of the received weak waveform signal by adjusting filter parameters to obtain a first waveform signal, perform secondary waveform feature extraction on the first waveform signal using wavelet transform to obtain a second waveform signal, and input the result of calibrating the second waveform signal with the original waveform signal of the DC regulated power supply into the storage module; The storage module is used to store the detection signal corresponding to the received calibration result.

2. The system according to claim 1, wherein: The control module includes: a load control unit, a detection device control unit and a power supply control unit; The load control unit is used to set a power control range according to the received DC regulated power supply and load requirements and output it to the power control unit; the power control range includes: a voltage range and a current range; The detection device control unit is configured to configure a power measurement range within the power control range using the Nyquist sampling theorem, and output the power measurement range to the power control unit; The power control unit is used to adjust the operating parameters of the control module according to the received DC regulated power supply, the power control range and the power measurement range, and reset the control module according to the operating parameters to output the waveform signal to be processed to the waveform processing module.

3. The system according to claim 1, wherein: The waveform processing module includes: a waveform acquisition unit, a voltage calculation unit and a separation unit; The waveform acquisition unit is used to collect the waveform signal to be processed output by the DC regulated power supply from the detection device control unit; The voltage calculation unit is used to calculate the voltage amplitude of the waveform signal to be processed, and use a filtering algorithm to reduce the noise of the waveform signal to be processed, and output the waveform signal to be separated to the separation unit; The separation unit is used to use an intelligent separation algorithm to perform frequency domain or time domain separation on the received waveform signal to be separated, and output the weak waveform signal to the waveform analysis module.

4. The system according to claim 3, characterized in that The separation unit is further used to extract the fingerprint features of the waveform signal to be separated based on the constructed comprehensive waveform fingerprint library, establish a waveform signal separation model using the fingerprint features and the gray wolf optimization algorithm, separate the ripple and noise of the waveform signal to be separated according to the separation model, and output the weak waveform signal to the waveform analysis module.

5. The system according to claim 1, wherein: The waveform analysis module includes: a filtering unit, a voltage amplification unit, an extraction unit and a calibration unit; The filtering unit is configured to filter the separated ripple signal and noise signal through a preset filter using a spectrum analysis method and extract spectrum features to obtain a first waveform signal, and output the first waveform signal to the voltage amplification unit; The voltage amplifying unit is configured to amplify the amplitude characteristic of the ripple signal of the first waveform signal to a required threshold value through an adjustable gain amplifying circuit, and then output the amplified first waveform signal to the extraction unit; The extraction unit is configured to extract waveform features of the amplified first waveform signal using a wavelet transform method to obtain a second waveform signal, and output the second waveform signal to the calibration unit; The calibration unit is used to input the result of calibrating the second waveform signal and the original waveform signal of the DC regulated power supply into the storage module.

6. A method for detecting weak ripple and noise signals of a DC regulated power supply, characterized in that: Applicable to the detection system for weak ripple and noise signals of a DC regulated power supply according to any one of claims 1 to 5, the method comprising: Obtaining the original waveform signal of the DC regulated power supply; The original waveform signal is configured with detection control basic information by the control module, and the original waveform signal is separated in the frequency domain or time domain according to the detection control basic information to obtain a ripple signal and a noise signal; A waveform analysis module is used to amplify the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and a weak noise waveform feature of the waveform signal to be measured is extracted to obtain a first waveform signal; The first waveform signal and the original waveform signal are calibrated to obtain a standard waveform signal.

7. The method according to claim 6, characterized in that The original waveform signal is configured with detection control basic information by the control module, and the original waveform signal is separated in the frequency domain or time domain according to the detection control basic information to obtain a ripple signal and a noise signal, including: The original waveform signal is configured with operating parameters by the load control unit and the power control unit of the control module, and the detection control basic information of the detection device control unit in the control module is regulated according to the operating parameters; Denoising the amplitude of the original waveform signal according to the detection control basic information to obtain a waveform signal to be separated, constructing a separation model of the waveform signal to be separated using a gray wolf optimization algorithm, and obtaining a ripple component and a noise component of the waveform signal to be separated by optimizing parameters of the separation model; After extracting spectrum features of the ripple signal corresponding to the ripple component and the noise signal corresponding to the noise component respectively by using a spectrum analysis method, the ripple signal is amplified to obtain a ripple signal and a noise signal.

8. The method according to claim 7, characterized in that The waveform analysis module amplifies the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and extracts the weak noise waveform characteristics of the waveform signal to be measured to obtain a first waveform signal, including: A waveform analysis module is used to amplify the voltage amplitude of the ripple signal to obtain a waveform signal to be measured, and a wavelet transform method is used to extract the weak noise waveform characteristics of the waveform signal to be measured to obtain a first waveform signal; The weak noise waveform characteristics include: weak ripple characteristics and noise characteristics.

9. The method according to claim 8, characterized in that After the step of calibrating the first waveform signal and the original waveform signal to obtain a standard waveform signal, the method further includes: The standard waveform signal is stored as a real signal in a storage module.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 6 to 9 are implemented.

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