A communication signal interference cancellation method and system

Abstract

The present invention relates to the field of anti-interference technology, and specifically to a method and system for eliminating communication signal interference, which includes the following steps: obtaining the time-domain data of the communication signal, calculating the signal energy change rate, the signal mutation rate, and the phase drift rate, marking and distinguishing the signal regions based on the calculation results, and obtaining the identification result of the non-stationary interval of the communication signal. In the present invention, through fine-grained calculation of the time-domain data of the communication signal, the signal energy change rate, the signal mutation rate, and the phase drift rate are extracted to achieve accurate signal region differentiation, the range of regression modeling parameters is adjusted to reduce the influence of instantaneous data in the mutation region, and the stability of signal reconstruction is improved. By combining the signal-to-noise ratio calculation and the evaluation of the interference power fluctuation amplitude, the channel state parameters are dynamically adjusted to make the interference intensity evaluation more accurate. Based on the error information, the filtering step size, the weight coefficient, and the gain coefficient are optimized to make the signal processing adapt to different types of interference, enhancing the integrity and stability of the communication signal.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-interference, and particularly to a method and system for eliminating communication signal interference. Background Art

[0002] The technical field of anti-interference includes technical methods for suppressing or eliminating interference signals in multiple scenarios such as wireless communication, satellite communication, radar systems, and wired transmission. The core content of this technical field is to reduce the impact of environmental noise, electromagnetic interference, multipath effects, and mutual interference on communication quality through signal processing means. The anti-interference technology as a whole covers multiple aspects such as signal detection, interference identification, interference suppression, and signal recovery. Signal detection is used to distinguish useful signals from interference signals. Interference identification uses methods such as spectrum analysis and time-domain feature extraction to determine the type and characteristics of interference signals. Interference suppression uses means such as adaptive filtering and coherent interference cancellation to weaken or eliminate interference signals. Signal recovery includes measures such as error correction and channel equalization to ensure the integrity and reliability of useful signals.

[0003] In the prior art, in the processing of mutation signals of communication signals, relying on fixed regression modeling parameters results in insufficient adaptability to the mutation region and affects the signal separation effect. Channel state assessment uses a fixed channel model and lacks dynamic adjustment of interference power fluctuations, resulting in poor adaptability to complex channel environments. The filtering parameter optimization method is fixed and fails to adaptively adjust the weight and step size according to the error change, so the filtering effect is unstable in different interference environments and affects the recovery quality of communication signals. Summary of the Invention

[0004] The purpose of the present invention is to solve the drawbacks existing in the prior art and to propose a method and system for eliminating communication signal interference.

[0005] To achieve the above purpose, the present invention adopts the following technical scheme: A method for eliminating communication signal interference, comprising the following steps:

[0006] S1: Obtain the time-domain data of the communication signal, calculate the signal energy change rate, signal mutation rate, and phase drift rate, mark and distinguish the signal regions according to the calculation results, and obtain the identification result of the non-stationary interval of the communication signal;

[0007] S2: Based on the identification result of the non-stationary interval of the communication signal, call the mutation rate, phase drift rate, and energy change rate, adjust the range of regression modeling parameters, reduce the dependence on instantaneous data in the mutation region, calculate the signal reconstruction error, and adjust the regression modeling constraint weight and the input signal data weight to obtain the segmented prediction error value of the communication signal;

[0008] S3: Invoke the segmented prediction error value of the communication signal, calculate the signal-to-noise ratio of the communication channel and the fluctuation amplitude of the interference power, obtain the channel state parameters based on weighted joint evaluation, and obtain the evaluation value of the interference intensity of the communication channel;

[0009] S4: Based on the evaluation value of the interference intensity of the communication channel, adjust the filter step size setting, and invoke the signal error information to adjust the filter weight coefficient, residual error threshold, and gain coefficient to obtain the optimized parameter set of the communication signal filter;

[0010] S5: Invoke the optimized parameter set of the communication signal filter, adjust the filter step size, weight coefficient, residual error threshold, and gain coefficient, calculate the change trend of the signal energy, adjust the range of the regression modeling parameters, and obtain the anti-interference reconstruction value of the communication signal.

[0011] The signal region includes a non-stationary region, a mutation region, and a signal interference region. The identification results of the non-stationary interval of the communication signal include the identification of the signal energy change rate, the identification of the signal mutation rate, and the identification of the phase drift rate. The segmented prediction error value of the communication signal includes the error in the mutation region, the error in the phase drift region, and the error in the energy change region. The evaluation value of the interference intensity of the communication channel includes the signal-to-noise ratio evaluation value, the interference power fluctuation amplitude evaluation value, and the channel state parameter evaluation value. The optimized parameter set of the communication signal filter includes the filter step size setting value, the filter weight coefficient, the residual error threshold, and the gain coefficient. The anti-interference reconstruction value of the communication signal includes the reconstructed signal energy value, the reconstructed signal phase value, and the reconstructed signal time-domain feature value.

[0012] As a further solution of the present invention, the steps for obtaining the identification results of the non-stationary interval of the communication signal are specifically as follows:

[0013] S101: Obtain the time-domain data of the communication signal, calculate the instantaneous energy of the signal at different time points, and obtain the signal energy change rate by calculating the energy change rate between adjacent time points;

[0014] S102: Based on the signal energy change rate, calculate the mutation degree between adjacent time points, define a mutation threshold, and screen the time periods with a mutation rate exceeding the threshold to obtain the signal mutation rate;

[0015] S103: Based on the signal energy change rate and the signal mutation rate, calculate the change rate of the signal phase over time, using the formula:

[0016] ;

[0017] Obtain the phase drift rate through signal phase difference operation, and at the same time, combine the signal energy change rate and the signal mutation rate to classify the signal region and obtain the identification results of the non-stationary interval of the communication signal;

[0018] Wherein, represents the phase drift rate, represents the phase value at the th time point, phase value at the th time point, signal energy at the th time point, signal energy at the th time point, and

[0019]

[0019]

[0020] S201: Based on the non-stationary interval identification result of the communication signal, call the mutation rate, phase drift rate, and energy change rate to segment the time series of the communication signal, calculate the mutation rate and drift change trend within each segment, and screen the time window of the mutation region. Set the data weight within the time window to reduce the dependence on instantaneous data, and obtain the time window of the mutation region;

[0021] S202: Based on the time window of the mutation region, adjust the range of regression modeling parameters. For the signal characteristics of the mutation region, dynamically adjust the input signal data weight, set the weight distribution coefficient of the differential interval, calculate the signal reconstruction error, and use the formula:

[0022] ;

[0023] Calculate the signal reconstruction error through the operation, adjust the constraint weight of the regression modeling, and obtain the constraint weight of the modeling error;

[0024] wherein, represents the signal reconstruction error, represents the signal value of the th time window, represents the weight value of the th time window, represents the total number of time windows,

[0025] S203: Call the constraint weight of the modeling error, combine the characteristic data of the signal mutation region, adjust the constraint conditions of the regression modeling, calculate the signal prediction error under the differential interval, and obtain the segmented prediction error value of the communication signal.

[0026] As a further solution of the present invention, the obtaining step of the communication channel interference intensity evaluation value is specifically as follows:

[0027] S301: Calculate the signal-to-noise ratio of the communication channel based on the segmented prediction error value of the communication signal, extract the ratio of the error value to the signal power, normalize the ratio, measure the signal quality on the same scale, eliminate the deviation data, and obtain the evaluation value of the channel signal-to-noise ratio;

[0028] S302: Call the evaluation value of the channel signal-to-noise ratio, calculate the fluctuation amplitude of the interference power of the communication channel, extract the maximum and minimum values of the interference signal power, calculate the difference, call the average power of the communication signal, and normalize the average power fluctuation value to measure the degree of change of the interference power fluctuation relative to the signal power, and obtain the interference power fluctuation ratio;

[0029] S303: Call the evaluation value of the channel signal-to-noise ratio and the interference power fluctuation ratio, calculate the channel availability under the condition of differential signal interference, and use the formula:

[0030] ;

[0031] Perform operations to obtain the channel state parameters, calculate the correlation between the channel state parameters and the interference signal, and obtain the evaluation value of the communication channel interference intensity;

[0032] Among them, represents the evaluation value of the communication channel interference intensity, represents the average signal power, represents the variance of the communication noise, represents the maximum value of the interference signal power, represents the minimum value of the interference signal power, represents the weighting coefficient of the differential signal segment, represents the power of the k-th signal segment, represents the interference power of the k-th signal segment, represents the total number of signal segments.

[0033] As a further solution of the present invention, the steps for obtaining the communication signal filtering optimization parameter set are specifically as follows:

[0034] S401: Calculate the change rate of the interference intensity based on the evaluation value of the communication channel interference intensity, identify the influence range, and at the same time set the filtering step adjustment value corresponding to the differential interference interval, compare the change amount of the step before and after adjustment, and generate the filtering step adjustment coefficient;

[0035] S402: Call the filtering step adjustment coefficient, adjust the filtering weight coefficient according to the signal error information, and combine the residual error threshold to screen the gain coefficient corresponding to the differential error range, and use the formula:

[0036] ;

[0037] Calculate the adjusted filtering weight coefficient to obtain the optimized filtering weight coefficient;

[0038] Among them, represents the adjusted filtering weight coefficient, represents the filtering weight coefficient before adjustment, represents the current signal error value, represents the residual error threshold, represents the gain coefficient, represents the filtering step size setting value at the previous moment, represents the current filtering step size setting value, represents a constant used to avoid the denominator approaching zero;

[0039] S403: Call the optimized filtering weight coefficient to screen the filtering parameter set of the communication signal, match multiple parameter combinations, calculate the filtering optimization result, and obtain the communication signal filtering optimization parameter set.

[0040] As a further solution of the present invention, the step of obtaining the anti-interference reconstruction value of the communication signal is specifically as follows:

[0041] S501: Call the communication signal filtering optimization parameter set, adjust the filtering step size, weight coefficient, residual error threshold and gain coefficient, calculate the energy change rate of the signal, and at the same time extract the energy fluctuation trend of the continuous signal interval, compare the change amplitude of adjacent intervals and screen the high-change interval to obtain the signal energy change trend value;

[0042] S502: Based on the signal energy change trend value, set the adjustment range of the regression modeling parameters, calculate the model error under different parameter combinations, and at the same time compare the error fluctuation situation and screen the parameter combination with the smallest error, adjust the regression modeling strategy and perform data fitting to obtain the optimal regression modeling parameters;

[0043] S503: Call the optimal regression modeling parameters to reconstruct the communication signal, and at the same time perform anti-interference adjustment in combination with the filtering optimization parameters, calculate the residual error value of the signal after anti-interference, and use the formula:

[0044] ;

[0045] Calculate the anti-interference adjustment error value, and optimize and adjust the error to obtain the anti-interference reconstruction value of the communication signal;

[0046] Among them, represents the anti-interference reconstruction value of the communication signal, represents the original signal value, represents the regression model reconstructed signal value, represents the filtering weight coefficient, Represents the dynamic change trend value of the signal energy, Represents the energy adjustment threshold of the signal after anti-interference, Represents the signal gain coefficient, Represents the total number of signal sampling points.

[0047] A communication signal interference cancellation system, which is used to execute the above communication signal interference cancellation method. The system includes:

[0048] Signal region marking module: Obtain the time-domain data of the communication signal, calculate the signal energy change rate, signal mutation rate, and phase drift rate, judge the signal interval type according to the calculation results, and mark the intervals with mutation rate exceeding the set threshold, abnormal phase drift rate, or fluctuating energy change rate as non-stationary intervals to obtain the non-stationary interval identification result of the communication signal;

[0049] Segmented error calculation module: Call the non-stationary interval identification result of the communication signal, adjust the regression modeling parameter range according to the mutation rate, phase drift rate, and energy change rate, reduce the dependence on instantaneous data in the mutation region, calculate the signal reconstruction error, and adjust the regression modeling constraint weight and input signal data weight based on the error size to obtain the segmented prediction error value of the communication signal;

[0050] Channel state evaluation module: Call the segmented prediction error value of the communication signal, calculate the signal-to-noise ratio and interference power fluctuation amplitude of the communication channel, screen parameters according to the weighted joint evaluation and the influence factor weight, calculate the channel state parameters, and obtain the interference intensity evaluation value of the communication channel;

[0051] Filter parameter optimization module: Call the interference intensity evaluation value of the communication channel, adjust the filter step size setting according to the interference intensity, and call the signal error information to dynamically adjust the filter weight coefficient, residual error threshold, and gain coefficient, calculate the filter performance index under different parameter configurations, screen the parameter combination with the best interference suppression effect, and obtain the filter optimization parameter set of the communication signal;

[0052] Signal anti-interference reconstruction module: Call the filter optimization parameter set of the communication signal, adjust the filter step size, weight coefficient, residual error threshold, and gain coefficient, calculate the signal energy change trend, and reconstruct the signal according to the adjustment result of the regression modeling parameter range to obtain the anti-interference reconstruction value of the communication signal.

[0053] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0054] In the present invention, by performing fine-grained calculations on the time-domain data of communication signals, the signal energy change rate, signal mutation rate, and phase drift rate are extracted to achieve precise signal region differentiation. The range of regression modeling parameters is adjusted to reduce the impact of instantaneous data in the mutation region and improve the stability of signal reconstruction. By combining signal-to-noise ratio calculation and interference power fluctuation amplitude evaluation, the channel state parameters are dynamically adjusted to make the interference intensity evaluation more accurate. Based on error information, the filtering step size, weight coefficient, and gain coefficient are optimized to enable signal processing to adapt to different types of interference, improve the anti-interference reconstruction ability, and enhance the integrity and stability of communication signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic diagram of the working process of the present invention;

[0056] Figure 2 is a flowchart of the steps for obtaining the identification result of the non-stationary interval of the communication signal of the present invention;

[0057] Figure 3 is a flowchart of the steps for obtaining the segmented prediction error value of the communication signal of the present invention;

[0058] Figure 4 is a flowchart of the steps for obtaining the evaluation value of the interference intensity of the communication channel of the present invention;

[0059] Figure 5 is a flowchart of the steps for obtaining the filtering optimization parameter set of the communication signal of the present invention;

[0060] Figure 6 is a flowchart of the steps for obtaining the anti-interference reconstruction value of the communication signal of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0061] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0062] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0063] Embodiment 1

[0064] Please refer to Figure 1 , the present invention provides a technical solution: a method for eliminating communication signal interference, including the following steps:

[0065] S1: Obtain the time-domain data of the communication signal, calculate the signal energy change rate, signal mutation rate, and phase drift rate, mark and distinguish the signal regions based on the calculation results, and obtain the identification result of the non-stationary interval of the communication signal;

[0066] S2: Based on the identification result of the non-stationary interval of the communication signal, call the mutation rate, phase drift rate, and energy change rate, adjust the range of regression modeling parameters, reduce the dependence on instantaneous data in the mutation region, calculate the signal reconstruction error, adjust the regression modeling constraint weight and the input signal data weight, and obtain the segmented prediction error value of the communication signal;

[0067] S3: Call the segmented prediction error value of the communication signal, calculate the signal-to-noise ratio of the communication channel and the interference power fluctuation amplitude, obtain the channel state parameters based on weighted joint evaluation, and obtain the interference intensity evaluation value of the communication channel;

[0068] S4: Based on the interference intensity evaluation value of the communication channel, adjust the filter step size setting, call the signal error information to adjust the filter weight coefficient, residual error threshold, and gain coefficient, and obtain the optimized parameter set of the communication signal filter;

[0069] S5: Call the optimized parameter set of the communication signal filter, adjust the filter step size, weight coefficient, residual error threshold, and gain coefficient, calculate the signal energy change trend, adjust the range of regression modeling parameters, and obtain the anti-interference reconstruction value of the communication signal.

[0070] The signal regions include non-stationary regions, mutation regions, and signal interference regions. The identification result of the non-stationary interval of the communication signal includes the signal energy change rate identification, signal mutation rate identification, and phase drift rate identification. The segmented prediction error value of the communication signal includes the mutation region error, phase drift region error, and energy change region error. The interference intensity evaluation value of the communication channel includes the signal-to-noise ratio evaluation value, interference power fluctuation amplitude evaluation value, and channel state parameter evaluation value. The optimized parameter set of the communication signal filter includes the filter step size setting value, filter weight coefficient, residual error threshold, and gain coefficient. The anti-interference reconstruction value of the communication signal includes the reconstructed signal energy value, reconstructed signal phase value, and reconstructed signal time-domain feature value.

[0071] Please refer to Figure 2 , the specific steps for obtaining the identification result of the non-stationary interval of the communication signal are as follows:

[0072] S101: Obtain the time-domain data of the communication signal, calculate the instantaneous energy of the signal at different time points, and obtain the signal energy change rate by calculating the energy change rate between adjacent time points;

[0073] First, it is necessary to obtain time-domain data from a communication signal source. This data can be collected through wireless sensing devices or directly from a digital signal processor (DSP). The sampling frequency of the signal needs to be set according to the characteristics of the communication signal. For example, for cellular communication signals, a sampling rate of 10 kHz to 100 MHz can be selected to meet the signal analysis requirements. Subsequently, calculate the instantaneous energy of the signal at each time point. The formula for calculating the instantaneous energy is , where is the signal amplitude at the th time point. For example, if the signal amplitude at a certain moment is , then the instantaneous energy . Further, calculate the energy change rate between adjacent time points. The calculation method is

[0074] ;

[0075] where, if the instantaneous energy values calculated at times and are

[0076] and respectively, then the energy change rate is . And so on, calculate the energy change rate of all adjacent time points in the complete data sequence and perform normalization processing to make the data distribution balanced in a standard interval. For example, use min-max normalization

[0077] ;

[0078] If the maximum energy value , the minimum value , and for the energy value at a certain moment, the normalized value is

[0079] ;

[0080] Finally, obtain the energy change rate sequence of the entire time-domain signal, as shown in Table 1.

[0081] Table 1 Communication signal energy change rate table

[0082]

[0083] As shown in Table 1, after calculating the energy change rate, store the data sequence in the signal processing system, and subsequent steps can directly analyze based on this data.

[0084] S102: Based on the signal energy change rate, calculate the mutation degree between adjacent time points, define a mutation threshold, screen the time periods where the mutation rate exceeds the threshold, and obtain the signal mutation rate;

[0085] First, calculate the mutation degree of the signal energy change rate, which can be obtained by calculating the local change rate of the signal. The specific calculation method is as follows:

[0086] ;

[0087] Among them, represents the time interval, usually taking a fixed sampling interval, such as 1 ms. Based on the data in Table 1 of the previous part, calculate the mutation rate. For example:

[0088] ;

[0089] ;

[0090] Next, set the mutation threshold , which can usually be calculated by the method of mean plus 3 times the standard deviation:

[0091] ;

[0092] Let the mean be , and the standard deviation be , then the mutation threshold

[0093] ;

[0094] In this example, , so it is determined that the time point is a mutation point, and the mutation time period is recorded.

[0095] S103: Based on the signal energy change rate and the signal mutation rate, calculate the change rate of the signal phase over time, using the formula:

[0096] ;

[0097] Obtain the phase drift rate through signal phase difference operation. At the same time, combine the signal energy change rate and the signal mutation rate to classify the signal region and obtain the identification result of the non-stationary interval of the communication signal;

[0098] Among them, represents the phase drift rate, represents the phase value at the th time point, represents the phase value at the th time point, represents the signal energy at the th time point, represents the signal energy at the th time point, represents the total number of time points.

[0099] Formula:

[0100] ;

[0101] First, calculate the phase difference value , assuming that the phase sequence within a certain time period is , calculate the phase change amount of each phase:

[0102] ;

[0103] ;

[0104] ;

[0105] Total , then ;

[0106] Next, calculate the normalized signal energy change amount:

[0107] ;

[0108] ;

[0109] Finally, the phase drift rate The calculation result is

[0110] ;

[0111] This result indicates that the phase drift rate is relatively high. Combining with the signal mutation rate, the non-stationary interval can be further determined.

[0112] Please refer to Figure 3 , the specific steps for obtaining the prediction error value of the segmented communication signal are as follows:

[0113] S201: Based on the non-stationary interval identification result of the communication signal, call the mutation rate, phase drift rate, and energy change rate to segment the time series of the communication signal, calculate the mutation rate and drift change trend within each segment, and screen the time window of the mutation region, set the data weight within the time window, reduce the dependence on instantaneous data, and obtain the time window of the mutation region;

[0114] First, extract the time series of the communication signal and obtain the data of each time window in the order of the time axis. When segmenting the time series, calculate the mutation rate, phase drift rate, and energy change rate within each time window. The mutation rate is calculated by statistically averaging the amplitude differences between adjacent signal points within a given time window. A mutation threshold is set. For example, when the average amplitude difference is greater than 0.3 times the historical average, it is considered that there is a mutation within that window. The phase drift rate is calculated based on the rate of change of the phase over time. By comparing the phase change values of adjacent time windows, if the relative change exceeds a set threshold (such as 10 degrees / millisecond), a significant drift is judged to exist. The energy change rate is calculated by comparing the energy change of the signal in the current window with that in the previous window. If the change rate exceeds a set value (such as greater than 20% of the average energy of the previous window), a significant change is considered to exist. After completing the above calculations, filter out all time windows that meet the mutation determination conditions, set the weights of the time windows, and reduce the instantaneous data dependence by setting lower weights in the mutation area. For example, set lower weight values (such as reducing the weight to 0.6) for the two time windows before and after the mutation window, and set the weight of the middle mutation window to 1 to ensure the smoothness of the time windows in the mutation area, and finally obtain the time windows in the mutation area.

[0115] S202: Based on the time windows in the mutation area, adjust the range of regression modeling parameters. For the signal characteristics in the mutation area, dynamically adjust the weights of the input signal data, set the weight distribution coefficients for different intervals, calculate the signal reconstruction error, and use the formula:

[0116] ;

[0117] Obtain the signal reconstruction error through calculation, adjust the regression modeling constraint weights, and obtain the modeling error constraint weights;

[0118] Among them, represents the signal reconstruction error, represents the signal value of the th time window, represents the weight value of the th time window, represents the total number of time windows, represents the sum of the signals within all time windows, represents the sum of the weights of all time windows;

[0119] Specifically, set higher weights for the time windows within the mutation area. For example, set the weight of the mutation window to 1.2, and the weights of the front and rear windows to 0.8 to ensure that the regression model can more accurately reflect the mutation trend. Next, calculate the signal reconstruction error, and the calculation formula is as follows:

[0120] ;

[0121] Among them, represents the original signal value of the th time window, represents the weight value of this window, is the total number of all time windows, is the sum of signals within all windows, is the sum of weights of all windows. For example, assume there are 5 time windows, and their original signal values are as follows:

[0122] Table 2 Time Window Signal Table

[0123]

[0124] As shown in Table 2, according to the formula calculation, the sum of signals of all time windows is , and the sum of weights of all windows is , and the weighted signal value is calculated as follows:

[0125] ;

[0126] ;

[0127] Calculate the reconstruction error:

[0128] ;

[0129] ;

[0130] After obtaining the regression modeling error, adjust the regression modeling constraint weight according to the error magnitude. For example, when the error is greater than the set threshold (such as 1.0), then adjust the weight of the mutation window to 1.5 to reduce the influence of the front and rear windows, and finally obtain the modeling error constraint weight.

[0131] S203: Invoke the modeling error constraint weight, combine with the characteristic data of the signal mutation region, adjust the constraint conditions of the regression modeling, calculate the signal prediction error under different differential intervals, and obtain the communication signal segmented prediction error value.

[0132] The regression model involved in this scenario is a weighted linear regression model, and its core composition is: the input features include the original signal value, phase drift rate, mutation rate, and energy change rate of each time window, and the weight term is the pre-set for each time window, which is used to strengthen the influence of the mutation window on the modeling. The model form is a linear regression that minimizes the weighted sum of squared errors:

[0133] ;

[0134] The output is the predicted value and signal reconstruction error , which is used to measure the modeling accuracy and feedback to adjust the weights. It does not involve non-linear structures or neural networks, and only uses weight control and linear mapping to ensure more accurate modeling and stronger response to mutation regions;

[0135] Among them, represents the parameter vector of the regression model, which contains the regression coefficients of each input feature in the signal prediction process, represents the th input feature vector corresponding to the time window, which usually includes the current amplitude of the signal, the amplitude difference value from the previous window, the phase change rate, the energy change rate, and the mutation indication information, etc., represents the th original signal value of the time window, which is used as the true target value of the model, represents the predicted output value of the model for the signal value of the th time window, represents the th weighted coefficient of the time window, which is used to control the relative influence of the prediction error of this window in the objective function, represents the total number of time windows, that is, the number of time windows included in the entire regression modeling process, which is used to control the complete interval of model training;

[0136] The regression model depends on the weight system set in the mutation window recognition process. Among them, the weight at the center position of the mutation window is usually set to 1.2, and the adjacent windows before and after are set to 0.8 to improve the dominance of the mutation region in the modeling trend of the model. If the model prediction error exceeds the set threshold (for example, 1.0), then the weight of the mutation window is further increased to 1.5, and at the same time, the influence of other region windows is reduced, so as to realize the adaptive adjustment of the regression modeling error based on differential features, and finally achieve the goal of enhancing the signal prediction accuracy in the mutation region.

[0137] First, for the signal feature data in the differential interval, calculate the signal prediction error. In the prediction stage, use the adjusted weights to perform weighted regression fitting on historical data, calculate the model parameters by the least squares method, and calculate the predicted value. Compare it with the actual value to calculate the prediction error. For example, in the mutation window, set the data of the past 5 time windows to predict the value of the current window. Assume that the signal values of the past 5 windows are , and the fitting model is , calculate the parameter by the least squares method, and get the predicted value , calculate the prediction error:

[0138] ;

[0139] After calculation , and the actual measured value is , then the prediction error . If the prediction error exceeds the set threshold (such as 0.2), it is necessary to adjust the regression modeling parameters, such as increasing the historical weight of the mutation window (such as adjusting from 1.2 to 1.5), reducing the weight far from the mutation window, and finally obtaining the segmented prediction error value of the communication signal.

[0140] Please refer to Figure 4 , and the steps for obtaining the evaluation value of the communication channel interference intensity are as follows:

[0141] S301: Based on the segmented prediction error value of the communication signal, calculate the signal-to-noise ratio of the communication channel, extract the ratio of the error value to the signal power, normalize the ratio, measure the signal quality on the same scale, eliminate the deviation data, and obtain the evaluation value of the channel signal-to-noise ratio;

[0142] First, segment the received signal, and set the duration of each segment to 0.1 seconds to ensure that the channel characteristics remain relatively stable in a short time. The data set of each signal segment includes the signal power value and the noise interference value, which are represented by and respectively. For each segmented signal, calculate its prediction error, that is, calculate the difference between the theoretical received signal power and the actual received signal power. The error value is calculated using the mean square error method, that is:

[0143] ;

[0144] Among them, is the theoretical ideal signal power, is the received signal power, is the number of sampling points within this signal segment. For example, assuming , then the calculated error value reflects the distortion of this signal segment. Subsequently, calculate the signal-to-noise ratio (SNR) of the communication channel using the formula:

[0145] ;

[0146] To improve the stability of the calculation, calculate the ratio of the error value to the signal power, that is:

[0147] ;

[0148] To ensure the consistency of the calculation scale, use the normalization method to process the ratio value, that is:

[0149] ;

[0150] Among them, and are the minimum and maximum values among all signal segment error ratios respectively. For example, assume , , and the error ratio of a certain signal segment is 0.05, then the calculated normalized ratio is:

[0151] ;

[0152] After normalization, judge the signal quality, and screen all signal segments according to the set quality threshold . For example, if is set, then all signal segments are retained, otherwise they are excluded, and finally the channel signal-to-noise ratio evaluation value is calculated.

[0153] S302: Call the channel signal-to-noise ratio evaluation value, calculate the fluctuation amplitude of the interference power of the communication channel, extract the maximum and minimum values of the interference signal power, calculate the difference, call the average power of the communication signal, and normalize the average power fluctuation value to measure the change degree of the interference power fluctuation relative to the signal power, and obtain the interference power fluctuation ratio;

[0154] First, extract the maximum power and the minimum power of the interference signal. Set the sampling window to 1 second, record multiple interference power values within each second, and calculate the maximum and minimum values within the window. For example, at a certain moment, the interference power data is:

[0155] ;

[0156] Then mW, mW, and calculate the interference power fluctuation amplitude:

[0157] ;

[0158] Next, call the average power of the communication signal, and calculate the normalized interference power fluctuation value:

[0159] ;

[0160] Assume mW, then:

[0161] ;

[0162] Measure the relationship between the interference power fluctuation and the signal power according to this ratio, and finally obtain the interference power fluctuation value .

[0163] S303: Call the ratio of the channel signal-to-noise ratio evaluation value to the interference power fluctuation, calculate the channel availability under the condition of differential signal interference, using the formula:

[0164] ;

[0165] Operate to obtain the channel state parameters, calculate the correlation between the channel state parameters and the interference signal, and obtain the communication channel interference intensity evaluation value;

[0166] Among them, represents the communication channel interference intensity evaluation value, represents the average signal power, represents the variance of the communication noise, represents the maximum value of the interference signal power, represents the minimum value of the interference signal power, represents the weighting coefficient of the differential signal segment, represents the power of the k-th signal segment, represents the interference power of the k-th signal segment, represents the total number of signal segments.

[0167] First, calculate the communication channel interference intensity evaluation value , according to the formula:

[0168] ;

[0169] Among them, is the weighting coefficient of the differential signal segment, set to 0.8, represents the th signal segment power, represents the interference power of this signal segment, set , the data of each signal segment is as follows:

[0170] Table 3 Signal segment power and interference power data table

[0171]

[0172] Table 3 lists the signal power and interference power data of five signal segments, calculate the difference between the signal power and interference power of each signal segment:

[0173] ;

[0174] Perform weighted summation:

[0175] ;

[0176] Assume mW, mW, substitute into the formula:

[0177] ;

[0178] The result shows that the evaluation value of the interference intensity of the communication channel is 252.92, which is used to measure the correlation between the channel state parameters and the interference signal.

[0179] Please refer to Figure 5 , and the steps for obtaining the communication signal filtering optimization parameter set are specifically as follows:

[0180] S401: Based on the evaluation value of the communication channel interference intensity, calculate the interference intensity change rate, identify the influence range, and at the same time set the filtering step adjustment value corresponding to the differential interference interval. Compare the change amount of the step before and after adjustment to generate the filtering step adjustment coefficient;

[0181] First, it is necessary to collect the interference signals in the communication channel in real time, set a certain time window, for example, with a sampling period of 100 ms, and obtain the signal strength data of the communication channel within each period and the interference intensity data , calculate the average intensity of the interference signal within this time window and the variance , calculate the interference intensity change rate through the data of multiple consecutive time windows to judge the increasing or decreasing trend of the interference intensity. For example, within the past 5 time windows, if are -80 dBm, -78 dBm, -75 dBm, -72 dBm, -68 dBm respectively, then the interference intensity change rate , when this value is greater than 2 dBm / period, it can be determined as a significant increase in interference; then identify the influence range of the interference, that is, evaluate the change of the interference intensity of different communication nodes. Select multiple base station signal nodes, such as three base stations A, B, and C, whose interference intensity change rates are 2.1 dBm / period, 1.5 dBm / period, and 2.6 dBm / period respectively. Then the change rates of A and C both exceed 2 dBm / period and should be classified into the high interference area, while B is in the medium interference area. Subsequently, set the filtering step adjustment value corresponding to the differential interference interval. For example, set the filtering step increment in the high interference area to 0.02, the filtering step increment in the medium interference area to 0.01, and no adjustment in the low interference area. Compare the change amount of the step before and after adjustment. For example, the original step in the high interference area is 0.05, then the adjusted step becomes 0.05 + 0.02 = 0.07. Finally, generate the filtering step adjustment coefficient and calculate the adjustment coefficient through the step change rate , if the step before adjustment is 0.05 and the step after adjustment is 0.07, then .

[0182] S402: Call the filtering step size adjustment coefficient, adjust the filtering weight coefficient according to the signal error information, combine the residual error threshold to screen the gain coefficient corresponding to the differential error range, and use the formula:

[0183] ;

[0184] Calculate the adjusted filtering weight coefficient to obtain the optimized filtering weight coefficient;

[0185] Among them, represents the adjusted filtering weight coefficient, represents the filtering weight coefficient before adjustment, represents the current signal error value, represents the residual error threshold, represents the gain coefficient, represents the filtering step size setting value at the previous moment, represents the current filtering step size setting value, represents a constant used to avoid the denominator approaching zero;

[0186] First, obtain the signal error information , that is, calculate the error between the current signal value and the expected signal value . If the current signal value is -60 dBm and the expected signal value is -58 dBm, then , then set the residual error threshold . If the threshold is set to 1.5 dBm, then indicates that the current error is large and the filtering weight needs to be adjusted; then screen the gain coefficient corresponding to the differential error range . Assume that the gain coefficient is set to 0.8 when the error is between 1 and 2 dBm, and the gain coefficient is set to 1.2 when the error exceeds 2 dBm. Then the current error of -2 dBm makes the gain coefficient , and then calculate the filtering weight coefficient using the following formula:

[0187] ;

[0188] Set the basic filtering weight , the front and rear filtering step sizes are and , the constant is used to prevent the denominator from approaching zero, then calculate:

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] Since this value is too low and unreasonable, the gain coefficient needs to be adjusted. or the threshold to ensure that the weight value is within a reasonable range.

[0195] S403: Call the optimized filtering weight coefficient to screen the filtering parameter set of the communication signal, match multiple parameter combinations, calculate the filtering optimization result, and obtain the communication signal filtering optimization parameter set.

[0196] Set multiple filtering parameter combinations, such as the filtering order , the filter cut-off frequency , the filtering gain , etc., and select different parameter values for combined matching respectively, as shown in the following table:

[0197] Table 4 Filtering Parameter Combination Table

[0198]

[0199] As shown in Table 4, in a high-interference area, filtering parameters with a higher order and higher gain can be selected, such as combination C, while in a low-interference area, combination A with a lower order can be selected. Subsequently, calculate the filtering optimization result, such as comparing the signal quality index (signal-to-noise ratio). Assume the original signal-to-noise ratio dB, and it is improved to dB after applying combination C, which indicates that the optimization effect of this combination is better. Finally, obtain the communication signal filtering optimization parameter set and apply it to the system.

[0200] Please refer to Figure 6 , and the specific steps for obtaining the anti-interference reconstruction value of the communication signal are as follows:

[0201] S501: Call the communication signal filtering optimization parameter set, adjust the filtering step size, weight coefficient, residual error threshold, and gain coefficient, calculate the energy change rate of the signal, and at the same time extract the energy fluctuation trend of the continuous signal interval, compare the change amplitude of adjacent intervals and screen the high-change intervals to obtain the signal energy change trend value;

[0202] First, set the initial filtering step size according to the historical signal data. Its initial value can be set by referring to the mean of the signal change rate and adjusting it in combination with empirical parameters. For example, if the average signal change rate is , then the initial step size is set to to Then, calculate the filtering weight coefficients. The calculation of the weight coefficients can be performed by weighted summation based on the changes in the first 5 data points to reflect the signal trend. Among them, if the energy change of the current data point increases by more than that of the previous data point , a larger weight is assigned. For example, if the setting range is to , the weight of the current point can be set to ; Next, set the residual error threshold. Calculate the threshold range based on the root mean square (RMS) of the signal noise. If the historical noise RMS value is , the threshold can be set between and . At the same time, the gain coefficient can be set based on the mean value of the signal energy. For example, if the current mean value of the signal energy is , the gain coefficient can be set to to ; When calculating the energy change rate of the signal, it is necessary to calculate the energy difference between consecutive signal points, normalize it, and then perform statistical analysis. For example, if the signal energy at a certain moment is , and the previous moment is , the change rate is ; Subsequently, extract the energy fluctuation trend of the continuous signal interval. Calculate the energy mean curve through a moving window, and compare the changes in the means of adjacent windows. For example, if the window size is set to 10 sampling points, the mean of the previous interval is , and the mean of the next interval is , the change amplitude is ; When screening high-change intervals, a change threshold can be set. For example, select intervals with an energy change amplitude greater than . If the change amplitude of the aforementioned interval is , it is selected, and finally, the signal energy change trend value is obtained.

[0203] Table 5 Signal Filtering Optimization Parameter Setting Table

[0204]

[0205] As shown in Table 5, each parameter is set based on the calculation of the signal characteristics to ensure the rationality during signal processing.

[0206] S502: Based on the signal energy change trend value, set the adjustment range of the regression modeling parameters, calculate the model errors under different differential parameter combinations, compare the error fluctuations at the same time, and screen the parameter combination with the smallest error. Adjust the regression modeling strategy and perform data fitting to obtain the optimal regression modeling parameters;

[0207] First, analyze the distribution of the trend values. For example, set the adjustment range based on the quantile of the signal historical change amplitude. If the 90% quantile of the trend value is , the adjustment range can be set between and ; When calculating the model error under different combinations of differential parameters, it is necessary to calculate the error for multiple parameter combinations. For example, if the parameter combination is set as to calculate the error , represents the actual measurement data, represents the value obtained through prediction, represents the error value. Assuming that a certain combination corresponds to the error value , and the error value of another combination is , then select the parameter combination with the smallest error ; When comparing the error fluctuation situation, it is necessary to calculate the standard deviation of the errors of each parameter combination. For example, if the error of a certain group of data is and the calculated standard deviation is , if it is lower than the set threshold , it is considered that the fluctuation is small; Subsequently, when adjusting the regression modeling strategy, the model is adjusted according to the parameter combination with the smallest error. For example, if the combination with the smallest error as described above is , then use this parameter for regression modeling; Finally, perform data fitting and calculate the fitting error. For example, the true signal value is , the fitted signal value is , and the error is calculated as , and finally obtain the optimal regression modeling parameters.

[0208] S503: Call the optimal regression modeling parameters to reconstruct the communication signal, and at the same time perform anti-interference adjustment in combination with the filtering optimization parameters, and calculate the residual error value of the signal after anti-interference, using the formula:

[0209] ;

[0210] Calculate the anti-interference adjustment error value and optimize the error adjustment to obtain the anti-interference reconstruction value of the communication signal;

[0211] Among them, represents the anti-interference reconstruction value of the communication signal, represents the original signal value, represents the reconstructed signal value of the regression model, represents the filtering weight coefficient, represents the dynamic change trend value of the signal energy, represents the energy adjustment threshold of the signal after anti-interference, represents the signal gain coefficient, represents the total number of signal sampling points.

[0212] First, calculate the reconstructed signal according to the optimal parameters. For example, the optimal parameters as described above are to calculate the reconstructed value , for a certain signal point , calculate ; When performing anti-interference adjustment by combining filtering optimization parameters and calculating the residual error value of the signal after anti-interference, the formula is used:

[0213] ;

[0214] Among them, let , let , , calculate the sum of squared errors:

[0215] ;

[0216] Let , , ;

[0217] Calculate the denominator:

[0218] ;

[0219] ;

[0220] ;

[0221] Add the signal gain term , to get:

[0222] Sum of denominators = 0.014 + 0.03 + 0.032 + 3.2 = 3.276;

[0223] Finally, calculate the anti-interference reconstruction value of the communication signal: .

[0224] This calculation shows that the error of the signal after anti-interference is controlled within a reasonable range, meeting the optimization goal.

[0225] A communication signal interference cancellation system, the communication signal interference cancellation system is used to execute the above communication signal interference cancellation method, and the system includes:

[0226] Signal region marking module: Obtain the time-domain data of the communication signal, calculate the signal energy change rate, signal mutation rate, and phase drift rate, judge the signal interval type based on the calculation results, and mark the intervals with a mutation rate exceeding the set threshold, abnormal phase drift rate, or fluctuating energy change rate as non-stationary intervals to obtain the non-stationary interval identification result of the communication signal;

[0227] Segmented error calculation module: Invoke the identification result of the non-stationary interval of the communication signal, adjust the range of regression modeling parameters according to the mutation rate, phase drift rate, and energy change rate, reduce the dependence on instantaneous data in the mutation region, calculate the signal reconstruction error, and adjust the regression modeling constraint weight and the input signal data weight based on the error magnitude to obtain the segmented prediction error value of the communication signal;

[0228] Channel state evaluation module: Invoke the segmented prediction error value of the communication signal, calculate the signal-to-noise ratio and the interference power fluctuation amplitude of the communication channel, screen parameters according to the weighted joint evaluation and the influence factor weights, calculate the channel state parameters, and obtain the interference intensity evaluation value of the communication channel;

[0229] Filter parameter optimization module: Invoke the interference intensity evaluation value of the communication channel, adjust the filter step size setting according to the interference intensity, and invoke the signal error information to dynamically adjust the filter weight coefficient, residual error threshold, and gain coefficient, calculate the filter performance index under different parameter configurations, screen the parameter combination with the best interference suppression effect, and obtain the optimized filter parameter set of the communication signal;

[0230] Signal anti-interference reconstruction module: Invoke the optimized filter parameter set of the communication signal, adjust the filter step size, weight coefficient, residual error threshold, and gain coefficient, calculate the signal energy change trend, and reconstruct the signal according to the adjustment result of the regression modeling parameter range to obtain the anti-interference reconstruction value of the communication signal.

[0231] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A method for eliminating communication signal interference, characterized in that: The following steps are involved: S1: Obtain the time domain data of the communication signal, calculate the signal energy change rate, signal mutation rate, and phase drift rate, distinguish the signal area according to the calculation result mark, and obtain the identification result of the non-stationary interval of the communication signal; S2: Based on the identification result of the non-stationary interval of the communication signal, call the mutation rate, phase drift rate, and energy change rate, adjust the range of regression modeling parameters, reduce the instantaneous data dependence in the mutation area, calculate the signal reconstruction error, adjust the regression modeling constraint weight and the input signal data weight, and obtain the communication signal segment prediction error value; S3: calling the communication signal segment prediction error value, calculating the communication channel signal-to-noise ratio and interference power fluctuation amplitude, obtaining the channel state parameters according to the weighted joint evaluation, and obtaining the communication channel interference intensity evaluation value; S4: Based on the communication channel interference intensity evaluation value, adjust the filter step size setting, call the signal error information to adjust the filter weight coefficient, the residual error threshold, and the gain coefficient, and obtain the communication signal filter optimization parameter set; S5: calling the communication signal filtering optimization parameter set, adjusting the filtering step size, weight coefficient, residual error threshold, and gain coefficient, calculating the signal energy change trend, adjusting the regression modeling parameter range, and obtaining the communication signal anti-interference reconstruction value; The step of obtaining the communication signal segment prediction error value is specifically as follows: S201: Based on the identification result of the non-stationary interval of the communication signal, the mutation rate, phase drift rate, and energy change rate are called to segment the time series of the communication signal, calculate the mutation rate and drift change trend in each segment, and screen the time window of the mutation area, set the data weight in the time window, reduce the instantaneous data dependence, and obtain the mutation area time window; S202: Based on the mutation region time window, adjust the regression modeling parameter range, dynamically adjust the input signal data weight according to the signal characteristics of the mutation region, set the weight distribution coefficient of the differentiation interval, calculate the signal reconstruction error, and use the formula: ； The signal reconstruction error is obtained by operation, and the regression modeling constraint weight is adjusted to obtain the modeling error constraint weight; in, represents the signal reconstruction error, Representative The signal value in a time window, Representative The weight value of the time window, Represents the total number of time windows, represents the sum of the signals in all time windows, Represents the sum of weights of all time windows; S203: calling the modeling error constraint weight, combining the characteristic data of the signal mutation area, adjusting the constraint conditions of the regression modeling, calculating the signal prediction error in the differentiated interval, and obtaining the communication signal segment prediction error value; The steps of obtaining the communication signal filtering optimization parameter set are specifically as follows: S401: Based on the communication channel interference intensity evaluation value, calculate the interference intensity change rate, identify the impact range, set the filter step adjustment value corresponding to the differentiated interference interval, compare the change of the step before and after the adjustment, and generate the filter step adjustment coefficient; S402: Call the filter step adjustment coefficient, adjust the filter weight coefficient according to the signal error information, and select the gain coefficient corresponding to the differentiated error range in combination with the residual error threshold, using the formula: ； Calculate the adjusted filter weight coefficient to obtain the optimized filter weight coefficient; in, represents the adjusted filter weight coefficient, Represents the filter weight coefficient before adjustment, Represents the current signal error value, represents the residual error threshold, represents the gain factor, Represents the filter step setting value at the previous moment, Represents the current filter step setting value, The representative constant is used to avoid the denominator approaching zero; S403: calling the optimized filtering weight coefficient, screening the filtering parameter set of the communication signal, matching multiple parameter combinations, calculating the filtering optimization result, and obtaining the communication signal filtering optimization parameter set; The steps for obtaining the communication signal anti-interference reconstruction value are specifically as follows: S501: calling the communication signal filtering optimization parameter set, adjusting the filtering step size, weight coefficient, residual error threshold and gain coefficient, calculating the energy change rate of the signal, extracting the energy fluctuation trend of the continuous signal interval, comparing the change amplitude of adjacent intervals and screening the high change interval, and obtaining the signal energy change trend value; S502: Based on the signal energy change trend value, set the regression modeling parameter adjustment range, calculate the model error under the differentiated parameter combination, compare the error fluctuation and select the parameter combination with the smallest error, adjust the regression modeling strategy and perform data fitting to obtain the optimal regression modeling parameters; S503: Call the optimal regression modeling parameters, reconstruct the communication signal, and perform anti-interference adjustment in combination with the filter optimization parameters, and calculate the residual error value of the signal after anti-interference, using the formula: ； Calculate the anti-interference adjustment error value, and optimize and adjust the error to obtain the anti-interference reconstruction value of the communication signal; in, Represents the anti-interference reconstruction value of the communication signal, represents the original signal value, represents the signal value reconstructed by the regression model, represents the filter weight coefficient, Represents the dynamic change trend value of signal energy. Represents the energy adjustment threshold of the signal after anti-interference, represents the signal gain coefficient, Represents the total number of signal sampling points.

2. The communication signal interference elimination method according to claim 1, characterized in that: The signal area includes a non-stationary area, a mutation area, and a signal interference area. The communication signal non-stationary interval identification result includes a signal energy change rate identification, a signal mutation rate identification, and a phase drift rate identification. The communication signal segmented prediction error value includes a mutation area error, a phase drift area error, and an energy change area error. The communication channel interference intensity evaluation value includes a signal-to-noise ratio evaluation value, an interference power fluctuation amplitude evaluation value, and a channel state parameter evaluation value. The communication signal filtering optimization parameter set includes a filtering step size setting value, a filtering weight coefficient, a residual error threshold, and a gain coefficient. The communication signal anti-interference reconstruction value includes a reconstructed signal energy value, a reconstructed signal phase value, and a reconstructed signal time domain eigenvalue.

3. The communication signal interference elimination method according to claim 1, characterized in that: The steps for obtaining the identification result of the non-stationary interval of the communication signal are specifically as follows: S101: Acquire time domain data of a communication signal, calculate the instantaneous energy of the signal at differentiated time points, and obtain the signal energy change rate by calculating the energy change rate of adjacent time points; S102: Based on the signal energy change rate, calculate the mutation degree of adjacent time points, define a mutation threshold, screen the time period in which the mutation rate exceeds the threshold, and obtain the signal mutation rate; S103: Based on the signal energy change rate and the signal mutation rate, calculate the change rate of the signal phase over time, using the formula: ； The phase drift rate is obtained by signal phase difference operation, and the signal energy change rate and signal mutation rate are combined to classify the signal area and obtain the identification result of the non-stationary interval of the communication signal; in, represents the phase drift rate, Representative The phase value at a time point, Representative The phase value at a time point, Representative The signal energy at a time point, Representative The signal energy at a time point, Represents the total number of time points.

4. The communication signal interference elimination method according to claim 1, characterized in that: The steps for obtaining the communication channel interference intensity evaluation value are specifically as follows: S301: Based on the segmented prediction error value of the communication signal, calculate the signal-to-noise ratio of the communication channel, extract the ratio of the error value to the signal power, normalize the ratio, measure the signal quality on the same scale, remove the deviation data, and obtain the channel signal-to-noise ratio evaluation value; S302: calling the channel signal-to-noise ratio evaluation value, calculating the interference power fluctuation amplitude of the communication channel, extracting the maximum and minimum values ​​of the interference signal power, and calculating the difference, calling the average power of the communication signal, normalizing the average power fluctuation value, so as to measure the degree of fluctuation of the interference power relative to the signal power, and obtaining the interference power fluctuation ratio; S303: calling the channel signal-to-noise ratio evaluation value and the interference power fluctuation ratio to calculate the channel availability under the differentiated signal interference condition, using the formula: ； Obtain channel state parameters by operation, and calculate the correlation between the channel state parameters and the interference signal to obtain the communication channel interference intensity assessment value; in, represents the communication channel interference intensity assessment value, represents the average signal power, represents the variance of communication noise, represents the maximum interference signal power, Represents the minimum interference signal power, represents the weighting coefficient of the differentiated signal segment, represents the power of the kth signal segment, represents the interference power of the kth signal segment, Represents the total number of signal segments.

5. A communication signal interference elimination system, characterized in that: According to the communication signal interference elimination method according to any one of claims 1 to 4, the system comprises: Signal area marking module: obtains the time domain data of the communication signal, calculates the signal energy change rate, signal mutation rate, and phase drift rate, determines the signal interval type based on the calculation results, and marks the interval where the mutation rate exceeds the set threshold, the phase drift rate is abnormal, or the energy change rate fluctuates as a non-stationary interval, and obtains the non-stationary interval identification result of the communication signal; Segmentation error calculation module: calling the identification result of the non-stationary interval of the communication signal, adjusting the range of regression modeling parameters according to the mutation rate, phase drift rate, and energy change rate, reducing the instantaneous data dependence in the mutation area, calculating the signal reconstruction error, and adjusting the regression modeling constraint weight and input signal data weight based on the error size to obtain the segmentation prediction error value of the communication signal; Channel state evaluation module: calling the communication signal segment prediction error value, calculating the communication channel signal-to-noise ratio and interference power fluctuation amplitude, based on weighted joint evaluation, screening parameters according to the weight of the influencing factor, calculating the channel state parameters, and obtaining the communication channel interference intensity evaluation value; Filter parameter optimization module: call the communication channel interference intensity evaluation value, adjust the filter step size setting according to the interference intensity, call the signal error information, dynamically adjust the filter weight coefficient, residual error threshold, and gain coefficient, calculate the filter performance index under the differentiated parameter configuration, screen the parameter combination with the best interference suppression effect, and obtain the communication signal filter optimization parameter set; Signal anti-interference reconstruction module: call the communication signal filtering optimization parameter set, adjust the filtering step size, weight coefficient, residual error threshold, gain coefficient, calculate the signal energy change trend, reconstruct the signal according to the adjustment result of the regression modeling parameter range, and obtain the communication signal anti-interference reconstruction value.

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