Super-capacitor data denoising method and system based on low-pass filtering

Abstract

The invention relates to the technical field of data processing, in particular to a super-capacitor data denoising method and system based on low-pass filtering, and the method comprises the steps: obtaining an abnormal factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of an adjacent frequency in a spectrogram; according to a trend between each reference frequency and an adjacent minimum value in the spectrogram, adjusting an abnormal factor of each reference frequency to obtain a noise interference factor of each reference frequency; obtaining a noise factor of each reference frequency according to the periodic interference factor, the noise interference factor and the size of each reference frequency; and obtaining the cut-off frequency of each kind of signal data through the noise factor of each reference frequency, and denoising each kind of signal data through the cut-off frequency to obtain each kind of denoised signal data. According to the method, the high-frequency noise is analyzed, so that the accuracy of data denoising is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a supercapacitor data denoising method and system based on low-pass filtering. Background Art

[0002] A supercapacitor is an electrical energy storage device with a high capacitance value, capable of providing fast charge and discharge, featuring high power density, long lifespan, etc., and is widely used in fields such as electric vehicle energy recovery, instantaneous electrical energy supplementation, and backup power supplies. To monitor the supercapacitor, various data need to be collected through sensors. Due to the instability of charge transfer during the electrochemical reaction process, the interaction between the battery and the capacitor, electromagnetic interference from the external environment, and noise in the internal circuit of the system, the collected data has poor credibility. Therefore, it is necessary to use various data in the supercapacitor system for denoising processing to improve the measurement accuracy of the sensor, ensure the accuracy of signals such as the voltage and current of the capacitor; optimize the control system to maintain the stability and efficiency of the charge and discharge process; improve the reliability of the system and reduce the risk of failures caused by noise; reduce false alarms and ensure the accuracy of fault diagnosis; and improve the user experience to make the monitored data more stable and credible. Therefore, denoising of supercapacitor data plays a very important role. Summary of the Invention

[0003] The present invention provides a supercapacitor data denoising method and system based on low-pass filtering to solve existing problems.

[0004] The objective of the present invention can be achieved through the following technical solutions: In the first aspect of the present invention, a supercapacitor data denoising method based on low-pass filtering is provided, including: Obtain various signal data in the supercapacitor; Obtain the spectrogram corresponding to each signal data, obtain the maximum value and the minimum value in the spectrogram corresponding to each signal data, record the frequency corresponding to the maximum value in the spectrogram as the reference frequency, and obtain the anomaly factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrogram; adjust the anomaly factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrogram to obtain the noise interference factor of each reference frequency. Obtain the periodic interference factor of each reference frequency, obtain the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor, and the magnitude of each reference frequency; obtain the cut-off frequency of each signal data through the noise factor of each reference frequency, and perform denoising on each signal data through the cut-off frequency to obtain each denoised signal data.

[0005] Further, the obtaining of the spectrogram corresponding to each signal data includes: Perform Fourier transform on each signal data in the supercapacitor to transform the frequency-domain signal, and obtain the spectrogram corresponding to each signal data.

[0006] Further, obtaining the anomaly factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrogram includes:

[0007] In the formula, represents the amplitude of each reference frequency, represents the amplitude of the left adjacent frequency of each reference frequency, represents the amplitude of the right adjacent frequency of each reference frequency, represents the absolute value symbol, represents the anomaly factor of each reference frequency; wherein, the horizontal axis of the spectrogram is frequency and the vertical axis is amplitude.

[0008] Further, adjusting the anomaly factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrogram to obtain the noise interference factor of each reference frequency includes:

[0009] In the formula, represents the slope between the maximum point of each reference frequency and the left adjacent minimum point, represents the slope between the maximum point of each reference frequency and the right adjacent minimum point, represents the anomaly factor of each reference frequency, represents the exponential function with the natural constant as the base, represents the noise interference factor of each reference frequency, represents the absolute value symbol.

[0010] Further, obtaining the periodic interference factor of each reference frequency includes: Obtain the fundamental frequency in the spectrogram corresponding to each signal data, and then judge whether each reference frequency is an integer multiple of the fundamental frequency. When it is an integer multiple, record the periodic interference factor of each reference frequency as the preset low periodic interference factor , and when it is not an integer multiple, record the periodic characteristic of each reference frequency as the preset high periodic interference factor .

[0011] Further, obtaining the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor and the magnitude of each reference frequency includes:

[0012] In the formula, represents the magnitude of each reference frequency, represents the periodic interference factor of each reference frequency, represents the noise interference factor of each reference frequency, represents the linear normalization function, represents the noise factor of each reference frequency.

[0013] Furthermore, obtaining the cut-off frequency of each type of signal data through the noise factor of each reference frequency, and denoising each type of signal data through the cut-off frequency to obtain each type of denoised signal data includes: Filter out the reference frequencies whose noise factors of all reference frequencies are greater than a preset threshold and denote them as target frequencies, and select the frequency with the smallest frequency among the target frequencies as the cut-off frequency in low-pass filtering; According to the cut-off frequency of each type of signal data, denoise each type of signal data through the low-pass filtering algorithm to obtain each type of denoised signal data.

[0014] The second aspect of the present invention is to provide a supercapacitor data denoising system based on low-pass filtering, including: Data acquisition module: used to acquire various signal data in the supercapacitor; Data analysis module: used to obtain the spectrogram corresponding to each type of signal data, obtain the maximum and minimum values in the spectrogram corresponding to each type of signal data, denote the frequency corresponding to the maximum value in the spectrogram as the reference frequency, and obtain the anomaly factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrogram; adjust the anomaly factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrogram to obtain the noise interference factor of each reference frequency; Denoising module: used to obtain the periodic interference factor of each reference frequency, obtain the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor and the magnitude of each reference frequency; obtain the cut-off frequency of each type of signal data through the noise factor of each reference frequency, and denoise each type of signal data through the cut-off frequency to obtain each type of denoised signal data.

[0015] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, it implements the supercapacitor data denoising method based on low-pass filtering.

[0016] A fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor implements the supercapacitor data denoising method based on low-pass filtering.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrogram, the anomaly factor of each reference frequency is obtained, improving the accuracy of data anomaly analysis; according to the trend between each reference frequency and the adjacent minimum value in the spectrogram, the anomaly factor of each reference frequency is adjusted to obtain the noise interference factor of each reference frequency, improving the accuracy of trend change analysis; the periodic interference factor of each reference frequency is obtained, and according to the periodic interference factor, the noise interference factor and the magnitude of each reference frequency, the noise factor of each reference frequency is obtained, improving the accuracy of noise analysis; the cut-off frequency of each type of signal data is obtained through the noise factor of each reference frequency, and each type of signal data is denoised through the cut-off frequency to obtain each type of signal data after denoising, analyzing the high-frequency noise, and improving the accuracy of data denoising. Description of the Drawings

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0019] Figure 1 It is a schematic flowchart of the steps of the supercapacitor data denoising method based on low-pass filtering provided by the present invention; Figure 2 It is a schematic module flowchart of the supercapacitor data denoising system based on low-pass filtering provided by the present invention. Detailed Embodiments

[0020] In order to enable those skilled in the art to better understand the solutions of the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] It should be noted that the terms "first", "second", etc. in the description, claims and above-mentioned drawings of the present invention are used to distinguish similar objects, and do not necessarily have to be used to describe a specific order or sequence. It should be understood that such data used can be interchanged under appropriate circumstances, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0022] In view of the problems existing in the background technology, it is of great practical significance to research and design a supercapacitor data denoising method and system based on low-pass filtering.

[0023] As Figure 1 shown, the first aspect of the present invention is to provide a supercapacitor data denoising method based on low-pass filtering, including the following steps: Step S001: Collect various signal data in the supercapacitor.

[0024] It should be noted that in order to realize the health assessment of the supercapacitor, the prediction of the service life, the optimization of the control accuracy of the supercapacitor, and the fault diagnosis of the supercapacitor, it is necessary to collect various signal data of the supercapacitor through various sensors, and analyze the various signal data of the supercapacitor to realize the health assessment, life prediction, control optimization and fault diagnosis of the supercapacitor, so as to ensure its efficient and stable operation in various application scenarios.

[0025] Specifically, various signal data in the supercapacitor are obtained through various sensors; among them, the various signal data in the supercapacitor include: current signal data, voltage signal data, temperature signal data, internal resistance signal data, and power signal data.

[0026] Among them, the current signal data is collected by a current sensor, the voltage signal data is collected by a voltage sensor, the temperature signal data is collected by a temperature sensor, the internal resistance signal data is simply calculated by an AC impedance spectrometer, and the power signal data is collected by a power meter.

[0027] Thus, various signal data in the supercapacitor are obtained.

[0028] Step S002: Obtain the spectrogram corresponding to each type of signal data, obtain the maximum and minimum values in the spectrogram corresponding to each type of signal data, record the frequency corresponding to the maximum value in the spectrogram as the reference frequency, and obtain the anomaly factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrogram; adjust the anomaly factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrogram to obtain the noise interference factor of each reference frequency.

[0029] It should be noted that since the supercapacitor is an energy storage element with a high power density, the energy of the supercapacitor is generally released instantaneously. Therefore, the charging and discharging of the supercapacitor are generally completed in a short time. During the charging and discharging process of the supercapacitor, especially when the capacitor is charged and discharged with a large current in a short time, the instantaneous change of the current is very drastic. According to the current-voltage relationship, the higher the charging and discharging rate, the more drastic the current change. The rapid current change may cause rapid energy exchange in components such as inductors and capacitors in the circuit, thus generating high-frequency noise. At the moment of capacitor charging and discharging, the current fluctuation not only affects the capacitor itself, but may also be transmitted to other parts through power lines, cables, etc., generating electromagnetic radiation or conductive noise. Such high-frequency changes are essentially a transient phenomenon, usually manifested as noise in the circuit at a relatively high frequency. And during the operation of the supercapacitor, if there are frequent switching actions, high-frequency noise will also be manifested.

[0030] It should be further noted that in order to remove the high-frequency noise caused by the short-time charging and discharging of the supercapacitor, it is necessary to first obtain the spectrogram information corresponding to various signal data in the frequency domain and further determine the high-frequency noise through the spectrogram.

[0031] Specifically, perform a frequency-domain signal transformation on each type of signal data in the supercapacitor through Fourier transform to obtain the spectrogram corresponding to each type of signal data; among them, Fourier transform is a well-known technology and will not be specifically described here.

[0032] It should be noted that since the horizontal axis of the spectrogram is frequency and the vertical axis is amplitude, the high-frequency noise is at a position with a relatively large frequency in the spectrogram, and the amplitude corresponding to the high-frequency noise shows a convex peak-like characteristic relative to the amplitude of the adjacent frequencies nearby. Therefore, the frequency corresponding to the high-frequency noise can be determined by analyzing the amplitude difference and frequency between each peak frequency and the adjacent frequencies.

[0033] Specifically, obtain the maximum and minimum values in the spectrogram corresponding to each type of signal data; record the frequencies corresponding to all the maximum values as reference frequencies, obtain the two frequencies adjacent to the left and right of the reference spectrum, and analyze the anomaly factor of each reference frequency based on the amplitude differences between the reference frequency and the two adjacent frequencies on the left and right; the anomaly factor of each reference frequency is specifically expressed by the formula:

[0034] In the formula, represents the amplitude of each reference frequency, represents the amplitude of the frequency adjacent to the left of each reference frequency, represents the amplitude of the frequency adjacent to the right of each reference frequency, represents the absolute value symbol, represents the anomaly factor of each reference frequency.

[0035] Among them, represents the difference between the amplitude of each reference frequency and the amplitude of the frequency adjacent to the left, represents the difference between the amplitude of each reference frequency and the amplitude of the frequency adjacent to the right; the greater the two differences, the more the corresponding reference frequency shows the characteristics of a protruding spike in the spectrogram, that is, the greater the anomaly factor corresponding to each reference frequency.

[0036] It should be noted that since high-frequency noise can be quantified through the spike characteristics, but similar spike characteristics may also exist in normal signal data, further analysis is required; and because the peak value of high-frequency noise is relatively low compared to other normal frequency signal data, and it is relatively flat compared to normal frequency signal data, that is, the difference between the maximum value and the adjacent minimum value is small, so analysis is carried out through the difference between each maximum value and the adjacent minimum value.

[0037] Specifically, obtain the two minimum values adjacent to each reference frequency, and correct the anomaly factor of each reference frequency according to the difference between the amplitude of each reference frequency and the two adjacent minimum values to obtain the noise interference factor of each reference frequency; the noise interference factor of each reference frequency is specifically expressed by the formula:

[0038] In the formula, represents the slope between the maximum value point of each reference frequency and the minimum value point adjacent to the left, represents the slope between the maximum value point of each reference frequency and the minimum value point adjacent to the right, represents the anomaly factor of each reference frequency, represents the exponential function with the natural constant as the base, represents the noise interference factor for each reference frequency, represents the absolute value symbol.

[0039] Among them, represents the average value of the absolute value of the slope between the maximum point and the left adjacent minimum point and the absolute value of the slope between the right adjacent minimum point for each reference frequency. When the average value of the two absolute values of the slopes is smaller, that is, the changing trend intersects slowly, it indicates that each reference frequency is relatively flat compared to the normal frequency signal data. Therefore, the greater the possibility that each reference frequency is high-frequency noise. Thus, through performs a negative correlation mapping on to obtain the noise interference factor for each reference frequency.

[0040] So far, the noise interference factor for each reference frequency is obtained.

[0041] Step S003: Obtain the noise factor for each reference frequency according to the periodic interference factor, the noise interference factor, and the magnitude of each reference frequency; obtain the cut-off frequency of each type of signal data through the noise factor of each reference frequency, and perform denoising on each type of signal data through the cut-off frequency to obtain the denoised signal data of each type.

[0042] It should be noted that, because noise is irregular and the corresponding frequency is also higher, and the irregularity of noise can be analyzed through its periodicity. Therefore, noise can be analyzed through the high and low frequencies and periodicity.

[0043] Furthermore, it should be noted that when a frequency is a frequency signal that normally exhibits periodic changes, its frequency is an integer multiple of the fundamental frequency. Therefore, the periodic interference factor of each frequency can be analyzed by analyzing whether each frequency is an integer multiple of the fundamental frequency.

[0044] Specifically, obtain the fundamental frequency in the spectrogram corresponding to each type of signal data, and then determine whether each reference frequency is an integer multiple of the fundamental frequency. When it is an integer multiple, record the periodic interference factor of each reference frequency as the preset low periodic interference factor , and when it is not an integer multiple, record the periodic characteristic of each reference frequency as the preset high periodic interference factor . Among them, in this embodiment, the preset low periodic interference factor and the preset high periodic interference factor , among which, in this embodiment, the preset low periodic interference factor and the preset high periodic interference factor are not specifically limited, and the implementer can determine according to the specific situation.

[0045] The noise factor of each reference frequency is obtained through the magnitude of each reference frequency, the periodic interference factor, and the noise interference factor; the noise factor of each reference frequency is specifically expressed by the formula:

[0046] In the formula, represents the magnitude of each reference frequency, represents the periodic interference factor of each reference frequency, represents the noise interference factor of each reference frequency, represents the linear normalization function, represents the noise factor of each reference frequency.

[0047] The reference frequencies with the noise factors of all reference frequencies greater than the preset threshold are screened out and denoted as target frequencies. The frequency with the smallest frequency among the target frequencies is selected as the cut-off frequency in the low-pass filter. Thus, the cut-off frequency of each type of signal data is obtained. Among them, in this embodiment, the preset threshold In this embodiment, no specific limitation is imposed on the preset threshold and the implementer can determine it according to the specific situation.

[0048] According to the cut-off frequency of each type of signal data, each type of signal data is denoised through a low-pass filter algorithm to obtain each type of denoised signal data. Among them, the low-pass filter algorithm is a well-known technology and will not be specifically described here.

[0049] As Figure 2 shown, the second aspect of the present invention is to provide a supercapacitor data denoising system based on low-pass filtering, including the following modules: Data acquisition module 101: used to obtain various signal data in the supercapacitor; Data analysis module 102: used to obtain the spectrogram corresponding to each type of signal data, obtain the maximum and minimum values in the spectrogram corresponding to each type of signal data, record the frequency corresponding to the maximum value in the spectrogram as the reference frequency, and obtain the anomaly factor of each reference frequency according to the difference between the amplitude of each reference frequency in the spectrogram and the amplitude of the adjacent frequency; adjust the anomaly factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrogram to obtain the noise interference factor of each reference frequency; Denoising module 103: used to obtain the periodic interference factor of each reference frequency, obtain the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor, and the magnitude of each reference frequency; obtain the cut-off frequency of each type of signal data through the noise factor of each reference frequency, and denoise each type of signal data through the cut-off frequency to obtain each type of denoised signal data.

[0050] The third aspect of the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a supercapacitor data denoising method based on low-pass filtering is implemented.

[0051] The fourth aspect of the present invention is to provide a computer-readable storage medium storing a computer program, which when executed by a processor, implements a supercapacitor data denoising method based on low-pass filtering.

[0052] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) containing computer-usable program code.

[0053] The present invention is described with reference to the flowcharts and / or block diagrams of methods, systems, and computer program products according to the embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for realizing the functions specified in one Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0054] These computer program instructions can also be stored in a computer-readable memory capable of guiding a computer or other programmable data processing devices to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including an instruction device, and the instruction device realizes the functions specified in one Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0055] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, so that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable devices provide steps for realizing the functions specified in one Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: it is still possible to modify the specific implementation manners of the present invention or make equivalent replacements, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the present invention.

Claims

1. A supercapacitor data denoising method based on low-pass filtering, characterized in that: include: Obtain various signal data in supercapacitors; Obtain a spectrum graph corresponding to each signal data, obtain the maximum and minimum values ​​in the spectrum graph corresponding to each signal data, record the frequency corresponding to the maximum value in the spectrum graph as the reference frequency, and obtain the abnormal factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrum graph; adjust the abnormal factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrum graph to obtain the noise interference factor of each reference frequency; Obtain the periodic interference factor of each reference frequency, and obtain the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor and the size of each reference frequency; obtain the cutoff frequency of each signal data through the noise factor of each reference frequency, and denoise each signal data through the cutoff frequency to obtain each signal data after denoising.

2. The supercapacitor data denoising method based on low-pass filtering according to claim 1, characterized in that: The obtaining of a frequency spectrum diagram of each signal data comprises: The frequency domain signal of each signal data in the supercapacitor is transformed by Fourier transform to obtain the frequency spectrum diagram corresponding to each signal data.

3. The supercapacitor data denoising method based on low-pass filtering according to claim 1, characterized in that: The method of obtaining the abnormal factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrum diagram comprises: In the formula, represents the amplitude of each reference frequency, represents the amplitude of the left adjacent frequency of each reference frequency, represents the amplitude of the right adjacent frequency of each reference frequency, represents the absolute value symbol, represents the anomaly factor for each reference frequency; Among them, the horizontal axis of the spectrum graph is frequency, and the vertical axis is amplitude.

4. The supercapacitor data denoising method based on low-pass filtering according to claim 1, characterized in that: The adjusting of the abnormal factor of each reference frequency according to the trend between each reference frequency and the adjacent minimum value in the spectrum diagram to obtain the noise interference factor of each reference frequency includes: In the formula, Represents the slope between the maximum point and the left adjacent minimum point of each reference frequency, Represents the slope between the maximum point and the right adjacent minimum point of each reference frequency, represents the anomaly factor for each reference frequency, represents an exponential function with a natural constant as base, represents the noise interference factor for each reference frequency, Represents the absolute value symbol.

5. The supercapacitor data denoising method based on low-pass filtering according to claim 1, characterized in that: The obtaining of the periodic interference factor of each reference frequency includes: Obtain the base frequency in the spectrum graph corresponding to each signal data, and then determine whether each reference frequency is an integer multiple of the base frequency. If it is an integer multiple, the periodic interference factor of each reference frequency is recorded as the preset low periodic interference factor. , when it is not an integer multiple, the periodic characteristics of each reference frequency are recorded as the preset high periodic interference factor .

6. The supercapacitor data denoising method based on low-pass filtering according to claim 1, characterized in that: The step of obtaining the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor and the magnitude of each reference frequency includes: In the formula, Indicates the magnitude of each reference frequency, represents the periodic interference factor of each reference frequency, represents the noise interference factor for each reference frequency, represents the linear normalization function, Represents the noise factor for each reference frequency.

7. The supercapacitor data denoising method based on low-pass filtering according to claim 1, characterized in that: The method of obtaining a cutoff frequency for each signal data by using a noise factor of each reference frequency, and denoising each signal data by using the cutoff frequency to obtain each signal data after denoising, comprises: The noise factor of all reference frequencies is greater than the preset threshold The reference frequency is screened out and recorded as the target frequency, and the frequency with the smallest frequency among the target frequencies is selected as the cutoff frequency in the low-pass filter; According to the cutoff frequency of each signal data, each signal data is denoised by a low-pass filtering algorithm to obtain each signal data after denoising.

8. The supercapacitor data denoising system based on low-pass filtering is characterized by: include: Data acquisition module: used to obtain various signal data in the supercapacitor; Data analysis module: used to obtain the spectrum graph corresponding to each signal data, obtain the maximum and minimum values ​​in the spectrum graph corresponding to each signal data, record the frequency corresponding to the maximum value in the spectrum graph as the reference frequency, and obtain the abnormal factor of each reference frequency according to the difference between the amplitude of each reference frequency and the amplitude of the adjacent frequency in the spectrum graph; according to the trend between each reference frequency and the adjacent minimum value in the spectrum graph, adjust the abnormal factor of each reference frequency to obtain the noise interference factor of each reference frequency; Denoising module: used to obtain the periodic interference factor of each reference frequency, and obtain the noise factor of each reference frequency according to the periodic interference factor, the noise interference factor and the size of each reference frequency; obtain the cutoff frequency of each signal data through the noise factor of each reference frequency, and denoise each signal data through the cutoff frequency to obtain each signal data after denoising.

9. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the supercapacitor data denoising method based on low-pass filtering according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the supercapacitor data denoising method based on low-pass filtering according to any one of claims 1 to 7 is implemented.

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