Signal anti-interference processing method based on parameter modulation

Abstract

The invention relates to the technical field of signal processing, and discloses a signal anti-interference processing method based on parameter modulation, and the method comprises the steps: obtaining original signal data and interference noise signal data; according to the original signal data and the interference noise signal data, performing root-mean-square value calculation to obtain an original root-mean-square value and an interference noise root-mean-square value; performing factor calculation according to the original root mean square value and the interference noise root mean square value to obtain an interference identification factor; when the interference identification factor is greater than a preset interference threshold value, determining the original signal data corresponding to the interference identification factor as to-be-processed signal data; preprocessing the to-be-processed signal data to obtain preprocessed signal data; performing noise separation operation on the preprocessed signal data to obtain descrambled signal data and pre-corrected signal data; and performing correction processing operation on the descrambled signal data and the pre-corrected signal data to obtain clear signal data. According to the method, interference noise can be effectively separated, and signal quality is improved.

Description

Technical Field

[0001] This invention relates to the field of signal processing technology, and in particular to a signal anti-interference processing method based on parameter modulation. Background Technology

[0002] Currently, signal processing is an important branch of information technology, widely used in communications, medicine, audio and video processing, radar, sensors, and many other fields. Its core objective is to extract useful information from raw signals while suppressing interference and noise to improve signal quality and accuracy.

[0003] In existing technologies, interference signals are separated and removed by multi-channel sampling of the signal, followed by synchronous processing and subtraction using signal analysis and processing circuits. While this method effectively separates interference signals, the complexity of the anti-interference circuit increases system cost, and the added interference during processing affects the system's accuracy, resulting in an unclear final signal. Summary of the Invention

[0004] This invention provides a signal anti-interference processing method based on parameter modulation to effectively separate interference noise and improve signal quality.

[0005] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a signal anti-interference processing method based on parameter modulation, comprising:

[0006] Acquire raw signal data and interference noise signal data;

[0007] Based on the original signal data and the interference noise signal data, the root mean square value is calculated to obtain the original root mean square value and the interference noise root mean square value.

[0008] Based on the original root mean square value and the root mean square value of the interference noise, a factor calculation is performed to obtain the interference identification factor;

[0009] The interference identification factor is compared with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed.

[0010] The signal data to be processed is preprocessed to obtain preprocessed signal data;

[0011] The preprocessed signal data is subjected to noise separation to obtain scrambled signal data and pre-corrected signal data;

[0012] The scrambled signal data and the pre-corrected signal data are processed to obtain clear signal data.

[0013] Preferably, the formula for calculating the original root mean square value is:

[0014]

[0015] In the formula, This is the original root mean square value; For the first One original signal data; The number of original signal data;

[0016] The formula for calculating the root mean square value of the interference noise is:

[0017]

[0018] In the formula, This represents the root mean square value of the interference noise. For the first One interference noise signal data; This represents the number of interference noise signal data.

[0019] Preferably, the formula for calculating the interference identification factor is:

[0020]

[0021] In the formula, This represents the root mean square value of the interference noise. This is the original root mean square value; This is an interference identification factor.

[0022] Preferably, the preprocessing operation on the signal data to be processed to obtain preprocessed signal data includes:

[0023] Extract the maximum and minimum values ​​of the signal data to be processed;

[0024] Based on the signal data to be processed, the maximum value data, and the minimum value data, normalization calculation is performed to obtain normalized signal data;

[0025] The normalized signal data and the signal data to be processed are merged to determine the preprocessed signal data.

[0026] Preferably, the formula for calculating the normalized signal data is:

[0027]

[0028] In the formula, For normalized signal data; The signal data to be processed; This represents the maximum value. This represents the minimum value data.

[0029] Preferably, the step of performing noise separation on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data includes:

[0030] Based on the wavelet domain transform function, the preprocessed signal data is transformed in the wavelet domain to obtain wavelet domain preprocessed signal data.

[0031] Based on the preprocessed signal data in the wavelet domain and the wavelet domain transform function, the coefficients are calculated to obtain the wavelet coefficients;

[0032] The wavelet coefficients are compared with a preset wavelet coefficient threshold. When the wavelet coefficients are greater than the preset wavelet coefficient threshold, the preprocessed signal data corresponding to the wavelet coefficients is determined as scrambling signal data.

[0033] When the wavelet coefficients are less than a preset wavelet coefficient threshold, the preprocessed signal data corresponding to the wavelet coefficients is determined as pre-corrected signal data.

[0034] Preferably, the formula for calculating the wavelet coefficients is:

[0035]

[0036] In the formula, These are wavelet coefficients; Preprocess signal data in the wavelet domain; This is the wavelet domain transform function.

[0037] Preferably, the step of performing correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data includes:

[0038] Based on the output value of the fitting function, the fitting accuracy is calculated according to the pre-corrected signal data to obtain the fitting accuracy;

[0039] The fitting accuracy is compared with a preset accuracy threshold. When the fitting accuracy is greater than the preset accuracy threshold, the pre-corrected signal data corresponding to the fitting accuracy is determined as the first clear signal data.

[0040] Based on the preset correction threshold, soft thresholding is performed according to the wavelet coefficients to obtain the corrected wavelet coefficients;

[0041] Based on the wavelet basis function, signal adjustment calculations are performed according to the scrambled signal data and the modified wavelet coefficients to obtain the second clear signal data;

[0042] The first clear signal data and the second clear signal data are merged to obtain clear signal data.

[0043] Preferably, the formula for calculating the fitting accuracy is:

[0044]

[0045] In the formula, For fitting accuracy; For the first One pre-correction signal data; For the first Each fitted function outputs a value; The amount of pre-corrected signal data.

[0046] In a second aspect, the present invention provides a signal anti-interference processing device, comprising:

[0047] The data acquisition module is used to acquire raw signal data and interference noise signal data;

[0048] The root mean square (RMS) calculation module is used to calculate the RMS value based on the original signal data and the interference noise signal data to obtain the original RMS value and the interference noise RMS value.

[0049] The factor calculation module is used to perform factor calculation based on the original root mean square value and the root mean square value of the interference noise to obtain the interference identification factor;

[0050] The signal data judgment module is used to compare the interference identification factor with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed.

[0051] The data processing module is used to perform preprocessing operations on the signal data to be processed to obtain preprocessed signal data;

[0052] The noise separation module is used to perform noise separation operations on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data.

[0053] The clear signal output module is used to perform correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data.

[0054] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement a signal anti-interference processing method based on parameter modulation as described in any one of the above.

[0055] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform any one of the above-described signal anti-interference processing methods based on parameter modulation.

[0056] Compared to existing technologies, the signal anti-interference processing method provided by this invention effectively separates interference noise and improves signal quality. First, by calculating the root mean square value of the original signal data and the interference noise signal data, the intensity difference between the original signal and the interference signal is quantified. Based on this quantification result, the signal data to be processed is identified by comparing the interference identification factor with a preset threshold. In the preprocessing stage, the maximum and minimum values ​​are extracted and normalized to ensure that the signal data to be processed is processed on a consistent scale. This operation avoids data distortion problems that occur during normalization. By merging the normalized signal data with the original signal, the stability of the processing process and the recoverability of the signal are improved. Regarding noise separation, this invention uses wavelet transform to analyze the signal, separating the scrambled signal and the pre-corrected signal. Compared with traditional filtering methods, wavelet transform can more effectively handle high-frequency noise and low-frequency interference in the signal, especially in noise suppression of complex signals. Finally, in the signal correction stage, this invention improves signal clarity, removes residual interference, and restores the true characteristics of the signal by calculating the accuracy of the fitting function and the soft threshold of the corrected wavelet coefficients. By merging the first clear signal data with the second clear signal data, the original signal can be reconstructed, ensuring that the signal is de-distorted while removing interference. In summary, the method described can effectively separate interference noise and improve signal quality. Attached Figure Description

[0057] Figure 1 This is a schematic flowchart of the signal anti-interference processing method provided in the first embodiment of the present invention;

[0058] Figure 2 This is a schematic diagram of the signal anti-interference processing device provided in the second embodiment of the present invention. Detailed Implementation

[0059] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Reference Figure 1The first embodiment of the present invention provides a signal anti-interference processing method based on parameter modulation, comprising the following steps:

[0061] S11, acquire raw signal data and interference noise signal data;

[0062] S12, calculate the root mean square value based on the original signal data and the interference noise signal data to obtain the original root mean square value and the interference noise root mean square value;

[0063] S13, Based on the original root mean square value and the root mean square value of the interference noise, perform factor calculation to obtain the interference identification factor;

[0064] S14, compare the interference identification factor with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, determine the original signal data corresponding to the interference identification factor as the signal data to be processed.

[0065] S15, perform preprocessing operation on the signal data to be processed to obtain preprocessed signal data;

[0066] S16, perform noise separation operation on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data;

[0067] S17, perform correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data.

[0068] In step S11, it is necessary to acquire the original signal data and the interference noise signal data, including:

[0069] In one specific embodiment, raw signal data refers to unprocessed signals acquired by sensors or instruments. These signals contain the actual signal from the measurement system as well as interference noise from the environment, equipment, etc. For example, when measuring the current signal of a device, it may be affected by electromagnetic interference, power supply noise, etc., resulting in the signal containing both valid information and noise.

[0070] Specifically, voltage or current signals are acquired from sensors (such as temperature sensors, pressure sensors, etc.) using an oscilloscope or data acquisition card (such as an NI DAQ device). In practice, a specific sampling frequency can be set, such as 1kHz or 10kHz, to ensure that no frequency components of the signal are missed. The sampling time interval is determined by the sampling frequency.

[0071] For example, if it is necessary to acquire the temperature signal of a sensor, and the sensor samples 100 times per second (i.e., a sampling frequency of 100Hz), the voltage signal output by the sensor will be converted into digital data. At this time, the temperature change signal is superimposed with the interference noise caused by the power supply to form the original signal data.

[0072] It should be noted that the raw signal data contains both the desired effective signal and interference signals, and must be effectively denoised in subsequent steps in order to recover a clear and useful signal.

[0073] In one specific embodiment, interference noise signal data refers to noise components in the original signal that cannot be ignored. These components are caused by external factors such as environmental interference, equipment failure, or electromagnetic interference. For example, factors such as power supply interference, external vibration, or electromagnetic radiation can introduce additional noise into the signal.

[0074] Specifically, interference noise can be obtained through simulation or direct measurement using specialized noise detection equipment. For example, it can be acquired by introducing known noise sources (such as electromagnetic interference generators or white noise generators) into the measurement environment, or by collecting ambient noise from areas without signal input during the same time period. During noise acquisition, the sampling frequency needs to be consistent with the original signal to ensure data consistency and comparability.

[0075] For example, suppose that when measuring the current of an electrical device, a noise generator can be placed near the device to simulate the interference of power supply noise on the signal. The noise signal will then be recorded and, after being combined with the original signal, form noisy signal data.

[0076] It should be noted that the interference noise signal data provides a standard reference for subsequent noise separation and identification. By comparing the characteristics of the original signal and the noise signal, noise components can be effectively identified and denoised.

[0077] In step S12, the root mean square (RMS) value needs to be calculated based on the original signal data and the interference noise signal data to obtain the original RMS value and the interference noise RMS value, including:

[0078] In a specific embodiment, the root mean square (RMS) value is a commonly used metric in signal processing, representing the magnitude of signal energy and aiding in subsequent interference identification and denoising. The original RMS value reflects the overall energy of the original signal data, and the formula for calculating the original RMS value is as follows:

[0079]

[0080] In the formula, This is the original root mean square value; For the first One original signal data; The number of original signal data;

[0081] It should be noted that the root mean square value of the original signal represents the signal strength or energy, and is a quantitative indicator of signal strength.

[0082] In a specific embodiment, the root mean square value of the interference noise is also an important indicator describing the intensity of the noise signal. The formula for calculating the root mean square value of the interference noise is as follows:

[0083]

[0084] In the formula, This represents the root mean square value of the interference noise. For the first One interference noise signal data; This represents the number of interference noise signal data.

[0085] It should be noted that the root mean square (RMS) value of interference noise reflects the intensity of the noise. During interference identification and denoising, the degree of noise impact can be determined by comparing the RMS value of the noise signal with that of the original signal.

[0086] In step S13, factor calculation is performed based on the original root mean square value and the root mean square value of the interference noise to obtain the interference identification factor, including:

[0087] In one specific embodiment, the interference identification factor is calculated using the root mean square value of the original signal and the root mean square value of the interference noise. The formula for calculating the interference identification factor is:

[0088]

[0089] In the formula, This represents the root mean square value of the interference noise. This is the original root mean square value; This is an interference identification factor.

[0090] It should be noted that the interference identification factor is the ratio of noise intensity to signal intensity, used to judge the signal quality. When the interference identification factor exceeds a preset interference threshold (e.g., 1.0), it indicates that the interference noise is strong, the original signal quality is poor, and further noise reduction processing is required.

[0091] In step S14, the interference identification factor needs to be compared with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed, including:

[0092] In one specific embodiment, an interference identification factor is compared with a preset interference threshold to determine whether the original signal requires further processing. When the interference identification factor is greater than the preset interference threshold, it indicates that the signal contains strong interference components and requires subsequent noise separation and correction processing.

[0093] First, in step S13, the interference identification factor is obtained, which represents the ratio of the interference intensity to the original signal intensity. If the interference identification factor is large (e.g., 2.8), it indicates that the interference component in the signal accounts for a large proportion, which will affect the signal quality.

[0094] Specifically, the preset interference threshold is a pre-defined standard used to determine whether a signal has been interfered with. Setting the interference threshold first requires analyzing the characteristics of both the signal and the interference. This includes the root mean square values ​​of the original signal and the interference signal. and The interference signals differ in strength, frequency, and noise type. The amplitude of the interference signal varies over time, and the interference noise exhibits different characteristics in the frequency domain. For example, in a common power system, the interference signal is high-frequency noise, while the original signal is a low-frequency current signal. Data acquisition of both the original and interference signals can be performed in real-time using sensors, data acquisition cards, or other devices. For example, suppose the root mean square value of the original signal acquired by a sensor... The root mean square value of the interference signal According to the aforementioned formula, the interference identification factor for:

[0095]

[0096] In this situation, the interference signal accounts for 40% of the original signal strength.

[0097] In one specific embodiment, the interference threshold should be set based on the system's tolerance to interference. The threshold setting should consider the system's signal quality requirements, the common strength of interfering signals, and the actual application scenario. For example, in some signal processing systems, if the strength of the interfering signal exceeds 30% of the original signal (i.e., ... If the interference level is greater than 0.3, the signal quality is considered to be affected and requires intervention. In other scenarios, a higher threshold is set, such as 0.5 or 0.6, indicating that intervention is only required when the interference signal strength is greater than 50%.

[0098] For example, suppose that after experiments and system testing, it is found that when the strength of the interference signal exceeds 40% of the original signal, the signal quality will be affected, then the interference threshold can be set to 0.4.

[0099] In one specific embodiment, if If the value exceeds the interference threshold, it indicates that there is interference in the signal. In this case, the original signal data will be marked as signal data to be processed and will enter the subsequent signal processing flow.

[0100] In step S15, the signal data to be processed needs to be preprocessed to obtain preprocessed signal data, including:

[0101] First, regarding the signal data to be processed It is necessary to extract its maximum value. and minimum value These values ​​are used in subsequent normalization processing to help standardize the range of the signal.

[0102] Specifically, obtaining the maximum and minimum values ​​is a standard data preprocessing step, especially when dealing with signal data of different scales. Normalization can reduce the impact of scale differences. Assuming the signal data to be processed is a set of discrete signal values, represented as... By iterating through the dataset, we obtain the maximum and minimum values. This represents the maximum amplitude of the signal, while This represents the minimum amplitude of the signal. This step can be implemented programmatically, for example, using the max() and min() functions in Python.

[0103] Next, the normalization operation standardizes the signal data to be processed, scaling its value range to between [0, 1] and [0, 1]. This operation helps eliminate the influence of different signal data due to differences in units or ranges. The formula for calculating the normalized signal data is as follows:

[0104]

[0105] In the formula, For normalized signal data; The signal data to be processed; This represents the maximum value. This represents the minimum value data.

[0106] For example, after the normalization calculation is completed, the normalized signal data is merged with the original signal data to be processed. The merged dataset is the preprocessed signal data, and the merging operation can include simple concatenation or organizing the data in a specific way. For example, the normalized signal data can be directly concatenated with the original signal data to form a new feature vector, or the normalized data and the original data can be placed in different columns in sequence.

[0107] In step S16, noise separation is required on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data, including:

[0108] In one specific embodiment, wavelet domain transform is a processing technique that converts signals from the time domain to the frequency domain. Its key feature is its ability to analyze signals at multiple scales (i.e., multiple frequencies) and resolutions. Unlike the traditional Fourier transform, wavelet transform can simultaneously provide time and frequency information, making it suitable for processing non-stationary signals (i.e., signals whose frequency and amplitude vary over time). Wavelet transform decomposes a signal into different frequency bands, allowing for the analysis of local signal features at different scales. In this way, it can suppress high-frequency noise components while preserving the effective information of the signal. Its advantage lies in its ability to focus on local changes in the signal over time, making it particularly suitable for processing signals with instantaneous changes or abrupt changes, such as edges in images, pulse signals in audio, or rapid fluctuations in sensor data.

[0109] Specifically, in signal processing, especially in noise separation, wavelet transform can effectively distinguish between stationary and transiently changing parts of a signal, thereby identifying and suppressing high-frequency noise. After preprocessing signal data, wavelet transform separates noise components from useful signals, facilitating further processing.

[0110] In one specific embodiment, wavelet coefficients are obtained through wavelet transform, representing the local characteristics of the signal at different frequencies and time scales. Each set of wavelet coefficients corresponds to the signal's variation at a specific scale, reflecting the signal's amplitude and frequency information at that scale. The magnitude of the wavelet coefficients reflects the signal's importance at that scale.

[0111] Specifically, wavelet coefficients are used to distinguish between the effective components and noise components of a signal. During noise separation, larger wavelet coefficients correspond to the effective components of the signal, while smaller coefficients correspond to noise or minor signal variations. By thresholding the wavelet coefficients, noise can be effectively removed while retaining useful signal information.

[0112] In one specific embodiment, the formula for calculating the wavelet coefficients is:

[0113]

[0114] In the formula, These are wavelet coefficients; Preprocess signal data in the wavelet domain; This is the wavelet domain transform function.

[0115] It's important to note that wavelet basis functions are the core of wavelet transform. They are used to generate a series of waveforms with different frequencies and time characteristics, which are then used for signal analysis. Different wavelet basis functions have different time-frequency characteristics, such as polynomial, wavelet, and Haar wavelets. The choice of wavelet basis functions directly affects the effectiveness of the wavelet transform and the analysis results. The role of wavelet basis functions is to convolve the signal with different basis functions, thereby extracting different frequency components from the signal. By using different wavelet basis functions, detailed information about the signal can be obtained at multiple scales. For example, some wavelet basis functions are more sensitive to high-frequency changes (such as abrupt changes or edges), while others are more sensitive to low-frequency changes (such as stationary parts). In noise separation, choosing an appropriate wavelet basis function can help analyze different components of the signal. For example, if the signal contains many instantaneous changes (such as noise), a wavelet basis function sensitive to high-frequency changes can be chosen to better separate the noise components. If the signal changes relatively smoothly, a wavelet basis function sensitive to low-frequency components can be chosen to preserve the effective parts of the signal.

[0116] In a specific embodiment, common methods for setting wavelet coefficient thresholds include fixed thresholding and threshold setting based on signal-to-noise ratio (SNR) or adaptive algorithms. For fixed thresholding, the threshold can be set to a constant, such as 0.05, meaning all coefficients smaller than this value are considered noise and need to be discarded. For example, when a wavelet coefficient is less than 0.05, its corresponding signal component is considered to contribute little to the scrambling signal and is therefore treated as noise. For thresholds based on SNR, if the signal's SNR is high, the threshold can be set smaller, such as 0.02 or 0.03, to retain more effective signal components; while in cases of low SNR, a higher threshold can be set, such as 0.1 or 0.2, to avoid excessive noise interference. In adaptive algorithms, the threshold is dynamically adjusted according to the signal's statistical characteristics. For example, the threshold can be set by calculating the signal's standard deviation and mean. When the standard deviation is large, indicating drastic signal changes, the threshold can be set lower (e.g., 0.05) to retain more detail; while in more stable signals, the threshold can be set higher (e.g., 0.1) to remove more noise. Therefore, the appropriate selection of the threshold depends not only on the noise intensity of the signal, but also on the specific application scenario, to ensure that noise can be removed while retaining the effective components of the signal.

[0117] In one specific embodiment, after wavelet transform, wavelet coefficients and signal components at various scales are obtained. Next, by comparing these coefficients with preset wavelet coefficient thresholds, it is possible to distinguish which signal components are useful, valid signals and which are noise. During noise separation, if the wavelet coefficient is large (1.5), it indicates that the corresponding signal component is a valid signal (descrambled signal data); if the wavelet coefficient is small (0.02), the component is considered noise and requires further correction.

[0118] In step S17, the descrambled signal data and the pre-corrected signal data need to be corrected to obtain clear signal data, including:

[0119] First, the pre-corrected signal data needs to be fitted using a fitting function to obtain the output value of the fitting function.

[0120] Specifically, a polynomial fitting model is selected, and the least squares method is used to solve for the fitting parameters, and the pre-corrected signal data is used. And the corresponding independent variable data (e.g., time or signal sampling points). Assume the data points are... .

[0121] Choose a polynomial fitting function:

[0122]

[0123] Construct the least squares matrix, where the goal of least squares is to minimize the sum of squared errors. This is done by constructing the design matrix. and target vector The fitting parameters can be solved using the following formula. :

[0124]

[0125] in, It includes independent variables. The matrix, It includes observation values Column vectors.

[0126] Next, the fitted output value is calculated, and the coefficients obtained from the fitting are used. , the independent variable Substituting into the fitting function, we obtain the first... Output values ​​of the fitted function:

[0127]

[0128] Next, calculate the first... Pre-correction signal data With the Fitted values The error between the two sides is used, and the average of the absolute errors is used to measure the fitting accuracy. The formula for calculating the fitting accuracy is:

[0129]

[0130] In the formula, For fitting accuracy; For the first One pre-correction signal data; For the first Each fitted function outputs a value; The amount of pre-corrected signal data.

[0131] It's important to note that fitting accuracy reflects the magnitude of the error in the fitting process. The fitting accuracy should be less than a preset accuracy threshold. The setting of the fitting accuracy threshold can be determined based on the signal's noise characteristics and application requirements. A reasonable accuracy range can be determined through experiments or experience. For example, for signals with high noise, a higher fitting accuracy threshold can be set to ensure that the fitting result effectively removes noise even with large fluctuations. Conversely, for signals with stable changes and low noise, a lower fitting accuracy threshold can be set. Specifically, by analyzing the fitting accuracy distribution of historical data, a suitable interval can be selected, and the fitting accuracy threshold can be set as a multiple of the standard deviation or mean of the signal data fitting error. For example, if the mean fitting accuracy in historical data is 1.5 and the standard deviation is 0.5, the fitting accuracy threshold can be set to 2.0, indicating that when the fitting accuracy is greater than 2.0, the signal can be considered reliable.

[0132] Specifically, the fitting accuracy is compared with a preset accuracy threshold. When the fitting accuracy is greater than the preset accuracy threshold, the pre-corrected signal data corresponding to the fitting accuracy is determined as the first clear signal data.

[0133] In one specific embodiment, soft thresholding is used to further adjust the wavelet coefficients and reduce unnecessary noise components. In this step, the calculated wavelet coefficients are first compared with a preset correction threshold. If the absolute value of the wavelet coefficient is lower than the preset threshold, the coefficient is considered to represent noise and should be set to zero; if the absolute value of the wavelet coefficient is greater than the preset threshold, the coefficient is retained and scaled according to the threshold. Specifically, the corrected wavelet coefficients... The calculation formula is:

[0134]

[0135] in, These are wavelet coefficients. The preset correction threshold, The sign of the wavelet coefficients is indicated (i.e., their positive or negative sign is preserved). Soft thresholding can effectively remove the small-value portion representing noise from the wavelet coefficients while retaining the large-value portion, thereby enhancing the true information of the signal.

[0136] It should be noted that the preset correction threshold is determined based on the signal's noise level and denoising requirements. The correction threshold is a key parameter used to distinguish between valid and noisy signals; its setting must ensure that noise is removed without over-correcting the valid signal. The correction threshold can be set by analyzing historical or experimental data. In practice, a reasonable range can be set based on the distribution characteristics of wavelet coefficients. For example, assuming that the standard deviation of the signal coefficients extracted by wavelet transform is 0.4 and the maximum value of the signal coefficients is 2.0, then the correction threshold can be set to 1.2. This means that when the wavelet coefficient is less than 1.2, the component is considered noise and needs correction; while the portion greater than 1.2 is considered a valid signal and can be retained. In practical applications, the correction threshold can be dynamically adjusted according to different signal characteristics and noise intensity to ensure optimized correction results.

[0137] In one specific embodiment, after obtaining the corrected wavelet coefficients, the scrambled signal data is adjusted using a wavelet basis function. The wavelet basis function is a periodic waveform with good localization properties. During signal adjustment, the corrected wavelet coefficients are combined with the wavelet basis function to recover a denoised and clearer signal. This process is essentially performing an inverse wavelet transform on the signal to obtain the second, clearer signal data. The role of the wavelet basis function is to ensure that the signal is reconstructed in the time-frequency domain and to effectively reduce noise interference.

[0138] Finally, the first and second clear signal data will be merged into the final clear signal data. The first clear signal data comes from the result of fitting accuracy calculation, and the second clear signal data comes from the signal adjusted by soft thresholding and wavelet basis functions. The merging process can be accomplished through weighted averaging. For example, different weights can be assigned based on the fitting accuracy and threshold calculation results, or the signals can be merged by direct addition. The merged signal will contain the advantages of both the scrambled signal and the corrected pre-corrected signal, ultimately resulting in a clear signal data with higher quality and lower noise.

[0139] In summary, the signal anti-interference processing method provided by this invention effectively separates interference noise and improves signal quality. First, by calculating the root mean square value of the original signal data and the interference noise signal data, the intensity difference between the original signal and the interference signal is quantified. Based on this quantification result, the signal data to be processed is identified by comparing the interference identification factor with a preset threshold. In the preprocessing stage, the extraction of maximum and minimum values ​​and normalization calculations ensure that the signal data to be processed can be processed on a consistent scale. This operation avoids data distortion problems that occur during normalization. By merging the normalized signal data with the original signal, the stability of the processing process and the recoverability of the signal are improved. Regarding noise separation, this invention utilizes wavelet transform to analyze the signal, separating the scrambled signal and the pre-corrected signal. Compared with traditional filtering methods, wavelet transform can more effectively handle high-frequency noise and low-frequency interference in signals, especially in noise suppression of complex signals. Finally, in the signal correction stage, this invention improves signal clarity, removes residual interference, and restores the true characteristics of the signal by calculating the accuracy of the fitting function and the soft threshold of the corrected wavelet coefficients. By merging the first clear signal data with the second clear signal data, the original signal can be reconstructed, ensuring that the signal is not distorted while de-interference is achieved.

[0140] Reference Figure 2 The second embodiment of the present invention provides a signal anti-interference processing device, comprising:

[0141] The data acquisition module is used to acquire raw signal data and interference noise signal data;

[0142] The root mean square (RMS) calculation module is used to calculate the RMS value based on the original signal data and the interference noise signal data to obtain the original RMS value and the interference noise RMS value.

[0143] The factor calculation module is used to perform factor calculation based on the original root mean square value and the root mean square value of the interference noise to obtain the interference identification factor;

[0144] The signal data judgment module is used to compare the interference identification factor with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed.

[0145] The data processing module is used to perform preprocessing operations on the signal data to be processed to obtain preprocessed signal data;

[0146] The noise separation module is used to perform noise separation operations on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data.

[0147] The clear signal output module is used to perform correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data.

[0148] Preferably, the data acquisition module is specifically used to acquire raw signal data and interference noise signal data;

[0149] Preferably, the root mean square (RMS) calculation module is specifically used to calculate the RMS value based on the original signal data and the interference noise signal data to obtain the original RMS value and the interference noise RMS value, including:

[0150] The formula for calculating the original root mean square value is:

[0151]

[0152] In the formula, This is the original root mean square value; For the first One original signal data; The number of original signal data;

[0153] The formula for calculating the root mean square value of the interference noise is:

[0154]

[0155] In the formula, This represents the root mean square value of the interference noise. For the first One interference noise signal data; This represents the number of interference noise signal data.

[0156] Preferably, the factor calculation module is specifically used to perform factor calculation based on the original root mean square value and the root mean square value of the interference noise to obtain the interference identification factor, including:

[0157] The formula for calculating the interference identification factor is as follows:

[0158]

[0159] In the formula, This represents the root mean square value of the interference noise. This is the original root mean square value; This is an interference identification factor.

[0160] Preferably, the signal data judgment module is specifically used to compare the interference identification factor with a preset interference threshold, and when the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed;

[0161] Preferably, the data processing module is specifically used to perform preprocessing operations on the signal data to be processed to obtain preprocessed signal data, including:

[0162] The preprocessing operation on the signal data to be processed to obtain preprocessed signal data includes:

[0163] Extract the maximum and minimum values ​​of the signal data to be processed;

[0164] Based on the signal data to be processed, the maximum value data, and the minimum value data, normalization calculation is performed to obtain normalized signal data;

[0165] The normalized signal data and the signal data to be processed are merged to determine the preprocessed signal data.

[0166] The formula for calculating the normalized signal data is:

[0167]

[0168] In the formula, For normalized signal data; The signal data to be processed; This represents the maximum value. This represents the minimum value data.

[0169] Preferably, the noise separation module is specifically used to perform noise separation operations on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data, including:

[0170] The step of performing noise separation on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data includes:

[0171] Based on the wavelet domain transform function, the preprocessed signal data is transformed in the wavelet domain to obtain wavelet domain preprocessed signal data.

[0172] Based on the preprocessed signal data in the wavelet domain and the wavelet domain transform function, the coefficients are calculated to obtain the wavelet coefficients;

[0173] The wavelet coefficients are compared with a preset wavelet coefficient threshold. When the wavelet coefficients are greater than the preset wavelet coefficient threshold, the preprocessed signal data corresponding to the wavelet coefficients is determined as scrambling signal data.

[0174] When the wavelet coefficients are less than a preset wavelet coefficient threshold, the preprocessed signal data corresponding to the wavelet coefficients is determined as pre-corrected signal data.

[0175] The formula for calculating the wavelet coefficients is as follows:

[0176]

[0177] In the formula, These are wavelet coefficients; Preprocess signal data in the wavelet domain; This is the wavelet domain transform function.

[0178] Preferably, the clear signal output module is specifically used to perform correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data, including:

[0179] The step of performing correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data includes:

[0180] Based on the output value of the fitting function, the fitting accuracy is calculated according to the pre-corrected signal data to obtain the fitting accuracy;

[0181] The fitting accuracy is compared with a preset accuracy threshold. When the fitting accuracy is greater than the preset accuracy threshold, the pre-corrected signal data corresponding to the fitting accuracy is determined as the first clear signal data.

[0182] Based on the preset correction threshold, soft thresholding is performed according to the wavelet coefficients to obtain the corrected wavelet coefficients;

[0183] Based on the wavelet basis function, signal adjustment calculations are performed according to the scrambled signal data and the modified wavelet coefficients to obtain the second clear signal data;

[0184] The first clear signal data and the second clear signal data are merged to obtain clear signal data.

[0185] The formula for calculating the fitting accuracy is:

[0186]

[0187] In the formula, For fitting accuracy; For the first One pre-correction signal data; For the first Each fitted function outputs a value; The amount of pre-corrected signal data.

[0188] It should be noted that the signal anti-interference processing device provided in this embodiment of the invention is used to execute all the process steps of the signal anti-interference processing method based on parameter modulation in the above embodiment. The working principle and beneficial effect of the two are one-to-one, so they will not be described again.

[0189] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a fitting accuracy calculation program. When the processor executes the computer program, it implements the steps in the above-described signal anti-interference processing method embodiments, for example... Figure 1 The step S11 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the data acquisition module.

[0190] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0191] The electronic device may be a desktop computer, laptop, handheld computer, or smart tablet, etc. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, etc.

[0192] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0193] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, at least one application program required for a function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the mobile phone (such as audio data, phonebook, etc.). In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, memory, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0194] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0195] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0196] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A signal anti-interference processing method based on parameter modulation, characterized in that, include: Acquire raw signal data and interference noise signal data; Based on the original signal data and the interference noise signal data, the root mean square value is calculated to obtain the original root mean square value and the interference noise root mean square value. Based on the original root mean square value and the root mean square value of the interference noise, a factor calculation is performed to obtain the interference identification factor; The interference identification factor is compared with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed. The signal data to be processed is preprocessed to obtain preprocessed signal data; The preprocessed signal data is subjected to noise separation to obtain scrambled signal data and pre-corrected signal data; The scrambled signal data and the pre-corrected signal data are processed to obtain clear signal data.

2. The signal anti-interference processing method based on parameter modulation according to claim 1, characterized in that, The formula for calculating the original root mean square value is: In the formula, This is the original root mean square value; For the first One original signal data; The number of original signal data; The formula for calculating the root mean square value of the interference noise is: In the formula, This represents the root mean square value of the interference noise. For the first One interference noise signal data; This represents the number of interference noise signal data.

3. The signal anti-interference processing method based on parameter modulation according to claim 1, characterized in that, The formula for calculating the interference identification factor is as follows: In the formula, This represents the root mean square value of the interference noise. This is the original root mean square value; This is an interference identification factor.

4. The signal anti-interference processing method based on parameter modulation according to claim 1, characterized in that, The preprocessing operation on the signal data to be processed to obtain preprocessed signal data includes: Extract the maximum and minimum values ​​of the signal data to be processed; Based on the signal data to be processed, the maximum value data, and the minimum value data, normalization calculation is performed to obtain normalized signal data; The normalized signal data and the signal data to be processed are merged to determine the preprocessed signal data.

5. The signal anti-interference processing method based on parameter modulation according to claim 4, characterized in that, The formula for calculating the normalized signal data is: In the formula, For normalized signal data; The signal data to be processed; This represents the maximum value. This represents the minimum value data.

6. The signal anti-interference processing method based on parameter modulation according to claim 1, characterized in that, The step of performing noise separation on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data includes: Based on the wavelet domain transform function, the preprocessed signal data is transformed in the wavelet domain to obtain wavelet domain preprocessed signal data. Based on the preprocessed signal data in the wavelet domain and the wavelet domain transform function, the coefficients are calculated to obtain the wavelet coefficients; The wavelet coefficients are compared with a preset wavelet coefficient threshold. When the wavelet coefficients are greater than the preset wavelet coefficient threshold, the preprocessed signal data corresponding to the wavelet coefficients is determined as scrambling signal data. When the wavelet coefficients are less than a preset wavelet coefficient threshold, the preprocessed signal data corresponding to the wavelet coefficients is determined as pre-corrected signal data.

7. The signal anti-interference processing method based on parameter modulation according to claim 6, characterized in that, The formula for calculating the wavelet coefficients is as follows: In the formula, These are wavelet coefficients; Preprocess signal data in the wavelet domain; This is the wavelet domain transform function.

8. The signal anti-interference processing method based on parameter modulation according to claim 6, characterized in that, The step of performing correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data includes: Based on the output value of the fitting function, the fitting accuracy is calculated according to the pre-corrected signal data to obtain the fitting accuracy; The fitting accuracy is compared with a preset accuracy threshold. When the fitting accuracy is greater than the preset accuracy threshold, the pre-corrected signal data corresponding to the fitting accuracy is determined as the first clear signal data. Based on the preset correction threshold, soft thresholding is performed according to the wavelet coefficients to obtain the corrected wavelet coefficients; Based on the wavelet basis function, signal adjustment calculations are performed according to the scrambled signal data and the modified wavelet coefficients to obtain the second clear signal data; The first clear signal data and the second clear signal data are merged to obtain clear signal data.

9. The signal anti-interference processing method based on parameter modulation according to claim 8, characterized in that, The formula for calculating the fitting accuracy is: In the formula, For fitting accuracy; For the first One pre-correction signal data; For the first Each fitted function outputs a value; The amount of pre-corrected signal data.

10. A signal anti-interference processing device, characterized in that, include: The data acquisition module is used to acquire raw signal data and interference noise signal data; The root mean square (RMS) calculation module is used to calculate the RMS value based on the original signal data and the interference noise signal data to obtain the original RMS value and the interference noise RMS value. The factor calculation module is used to perform factor calculation based on the original root mean square value and the root mean square value of the interference noise to obtain the interference identification factor; The signal data judgment module is used to compare the interference identification factor with a preset interference threshold. When the interference identification factor is greater than the preset interference threshold, the original signal data corresponding to the interference identification factor is determined as the signal data to be processed. The data processing module is used to perform preprocessing operations on the signal data to be processed to obtain preprocessed signal data; The noise separation module is used to perform noise separation operations on the preprocessed signal data to obtain scrambled signal data and pre-corrected signal data. The clear signal output module is used to perform correction processing on the descrambled signal data and the pre-corrected signal data to obtain clear signal data.

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