Communication interference detection system based on power spectrum and information entropy

Abstract

The invention belongs to the technical field of communication interference detection, and particularly relates to a communication interference detection system based on a power spectrum and information entropy, which comprises the following specific steps: S1, signal acquisition: acquiring a received signal in a communication system by using signal acquisition equipment, and acquiring the received signal according to a working frequency band and a signal bandwidth of the communication system; the sampling frequency and the sampling precision of the acquisition equipment are set, it is ensured that information of received signals can be obtained completely and accurately, and meanwhile, in order to reduce the influence of the external environment on signal acquisition, the acquisition equipment can be shielded and grounded; s2, signal preprocessing: preprocessing the collected original signal, wherein the preprocessing comprises filtering and de-noising operations; according to the method, power spectrum analysis and an information entropy theory are combined, detection is carried out from two dimensions of signal frequency distribution characteristics and statistical characteristics, interference of different types can be detected more comprehensively and accurately, the false detection rate and the omission ratio are reduced, and a complex interference environment is effectively dealt with.

Description

Technical Field

[0001] The present invention relates to the technical field of communication interference detection, and in particular to a communication interference detection system based on power spectrum and information entropy. Background Art

[0002] In today's digital age, communication technology has become a critical means of information transmission and interaction, widely used in mobile communications, satellite communications, wireless local area networks, and other fields. With the continuous development and popularization of communication networks, the communication environment has become increasingly complex, and various interference signals have emerged. These interference signals not only degrade communication quality and affect the user experience, but in severe cases, they can even cause communication interruptions, resulting in significant economic losses and safety risks. Therefore, how to efficiently and accurately detect interference signals in communication systems has become a key issue to ensure their stable operation.

[0003] In actual communications, interference signals come in a variety of forms, and interference scenarios are complex and ever-changing. For example, in crowded urban environments, the simultaneous use of numerous wireless devices interweaves signals from multiple frequency bands, generating co-channel interference and adjacent-channel interference. When base stations from multiple operators operate in similar frequency bands, co-channel interference can occur, leading to unstable mobile phone signals, degraded call quality, and even call interruptions. In satellite communications, natural phenomena such as electromagnetic radiation and solar storms, as well as malicious interference from humans, can interfere with satellite communication signals, causing freezes and pixelation in satellite TV images and reducing the positioning accuracy of satellite navigation systems. Traditional interference detection methods, such as those based on threshold comparison, often struggle to adapt to complex and changing communication environments. These methods rely on pre-set fixed thresholds. When the communication environment changes, such as fluctuations in background noise intensity or changes in interference signal characteristics, these fixed thresholds cannot accurately distinguish between interference signals and normal signals, resulting in false detections and missed detections. This approach fails to meet the requirements of modern communication systems for accurate and real-time interference detection. Therefore, a communication interference detection system based on power spectrum and information entropy is proposed. Summary of the Invention

[0004] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0005] A communication interference detection system based on power spectrum and information entropy includes the following specific steps:

[0006] S1: Signal Acquisition: The signal acquisition equipment is used to collect the received signals in the communication system. The sampling frequency and sampling accuracy of the acquisition equipment can be set according to the operating frequency band and signal bandwidth of the communication system to ensure that the information of the received signal can be obtained completely and accurately. At the same time, in order to reduce the impact of the external environment on signal acquisition, the acquisition equipment can also be shielded and grounded;

[0007] S2: Signal preprocessing: preprocessing the collected original signal, including filtering and denoising operations;

[0008] S3: Power spectrum calculation: Use the fast Fourier transform algorithm to perform spectrum analysis on the preprocessed signal to convert the time domain signal into the frequency domain signal and obtain the signal spectrum. Then, by squaring the spectrum and normalizing it, the power spectrum of the signal is calculated. In addition, to improve the accuracy and resolution of the power spectrum calculation, windowing can be used to reduce spectrum leakage.

[0009] S4: Information entropy calculation: First, divide the preprocessed signal into several signal segments of equal length. For each signal segment, calculate its probability distribution. Then, use the information entropy formula to calculate the information entropy of each signal segment based on the probability distribution. Then, average the information entropy of all signal segments to obtain the information entropy of the entire received signal.

[0010] S5, interference judgment and identification: First, set the power spectrum threshold and information entropy threshold, then compare the calculated power spectrum with the power spectrum threshold. If the power spectrum exceeds the threshold in a certain frequency range, it is preliminarily judged that there is an interference signal in that frequency range. At the same time, the calculated information entropy is compared with the information entropy threshold. If the information entropy exceeds the threshold, it indicates that there may be interference in the signal. After that, the judgment results of the power spectrum and information entropy are combined to determine whether there is an interference signal. If interference exists, the type of interference signal will be identified and classified according to the characteristics of the power spectrum and the size of the information entropy.

[0011] S6, Interference Signal Strength Assessment: After determining the presence of an interference signal, the intensity level of the interference signal is calculated based on the power distribution of the interference signal in the power spectrum and the size of the information entropy, combined with a preset intensity assessment model;

[0012] S7, historical data comparison and analysis: First, a historical interference detection database is established. Then, the characteristics of the interference signal detected this time are compared and analyzed with historical data. Data mining and machine learning algorithms are used to analyze the changing trends and patterns of the interference signals.

[0013] S8, result output and feedback: The results of interference detection and identification are output in an intuitive manner. At the same time, the detection results are fed back to the communication system so that the communication system can take corresponding anti-interference measures to improve the anti-interference capability and communication quality of the communication system.

[0014] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the information entropy formula is:

[0015]

[0016] Among them, p(x i ) indicates that the signal value is x i probability.

[0017] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the specific steps of S5 are as follows:

[0018] S51, threshold setting: setting the power spectrum threshold and information entropy threshold;

[0019] S52, power spectrum comparison judgment: The calculated power spectrum of the received signal is compared with the set power spectrum threshold point by point to traverse each frequency point in the power spectrum. If the power spectrum value exceeds the power spectrum threshold for multiple consecutive points within a certain frequency range, and the frequency range exceeds the set width, it is preliminarily determined that there is an interference signal in the frequency range. At the same time, the frequency range where the interference signal exists is recorded to provide a basis for subsequent interference type identification;

[0020] S53, information entropy comparison judgment: The calculated information entropy of the received signal is compared with the set information entropy threshold. If the information entropy value is greater than the information entropy threshold, it indicates that the uncertainty of the signal has increased and the possibility of interference is high. At the same time, combined with the result of power spectrum comparison judgment, if the power spectrum judges that interference exists and the information entropy also exceeds the threshold, it is further confirmed that there is an interference signal;

[0021] S54, interference type identification: after determining the presence of an interference signal, identify the interference type based on the characteristics of the power spectrum and the size of the information entropy;

[0022] S55, result integration and recording: Integrate the results of interference judgment and identification, record the existence of interference signals, frequency range, and interference type information, and provide complete and accurate data for subsequent result output and feedback links, so that the communication system can take effective anti-interference measures in a timely manner according to the interference situation.

[0023] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the specific steps of S51 are as follows:

[0024] S511, Power Spectrum Threshold: The power spectrum threshold is set based on the power spectrum distribution during normal operation of the communication system, combined with historical data and actual application scenarios. The threshold is dynamically adjusted to ensure accuracy, taking into account the impact of background noise in different environments.

[0025] S512, information entropy threshold: Analyze the information entropy characteristics of normal communication signals, calculate the mean and standard deviation of their information entropy, and on this basis, set the information entropy threshold to distinguish normal signals from signals that may have interference; the setting formula of the information entropy threshold is:

[0026] A=H0+kσ

[0027] Among them, A represents the information entropy threshold, H0 represents the mean of information entropy, σ represents the standard deviation of information entropy, and k represents the empirical coefficient.

[0028] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the specific steps of S54 are as follows:

[0029] S541, Narrowband Interference: If there is a clear power peak in the power spectrum that is concentrated in a narrow frequency range, and the peak power is higher than the power of the surrounding frequency points, and the information entropy increases compared to the normal signal but the increase is small, it is judged to be narrowband interference;

[0030] S542, broadband interference: When the power spectrum increases overall over a wide frequency range and the information entropy is higher than the information entropy threshold of a normal signal, it can be determined to be broadband interference;

[0031] S543, other interference types: For complex interference signals, including pulse interference and swept frequency interference, the system can further analyze and judge them by combining the signal's time domain characteristics and high-order cumulative quantity characteristics. At the same time, it also uses machine learning classification algorithms to train and classify the extracted multiple signal features to accurately identify the interference type.

[0032] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the specific steps of S6 are as follows:

[0033] S61, determining evaluation indicators: determining power-related indicators, information entropy indicators, and time-domain feature indicators;

[0034] S62, Establish an evaluation model: Use the hierarchical analysis method to construct a judgment matrix, compare the importance of each indicator pairwise, and calculate the weight. At the same time, it can combine the regression algorithm in machine learning with the historical interference data to train the model and establish the mathematical relationship between the evaluation indicators and the interference signal strength;

[0035] S63, calculating the intensity value: Substitute the extracted power-related index, information entropy index, and time-domain characteristic index data into the established evaluation model for calculation; if a weighted summation method is used, the formula is expressed as:

[0036]

[0037] Where I represents the strength of the interference signal, w i Expressed as the weight of the i-th indicator, x i It is represented as the value of the ith indicator, and n is the total number of indicators;

[0038] S64, intensity level classification: based on the calculated interference signal strength value, combined with the actual tolerance of the communication system and historical experience, different intensity levels are classified;

[0039] S65, output result: upload the calculated interference signal strength level to the result output and feedback link for result output.

[0040] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the specific steps of S61 are as follows:

[0041] S611, power-related indicators: Extract the maximum power value, average power value, and total power within the interference frequency band of the interference signal in the power spectrum. The maximum power value reflects the most severe interference, the average power value reflects the overall energy level of the interference, and the total power comprehensively considers the total energy within the interference frequency band.

[0042] S612, Information Entropy Index: The interference signal is separated and its information entropy is calculated. The information entropy reflects the disorder and uncertainty of the interference signal. The higher the information entropy, the more complex the interference signal is and the greater its intensity may be.

[0043] S613, time domain characteristic indicators: Analyze the peak amplitude and duration characteristics of the interference signal in the time domain. The peak amplitude reflects the impact intensity of the interference on the signal, and the duration indicates the duration of the interference impact.

[0044] As a preferred solution of the communication interference detection system based on power spectrum and information entropy described in the present invention, the specific steps of S7 are as follows:

[0045] S71, data collection and storage: After each interference detection is completed, the complete data of this detection is collected, and the time of the detection, the working status of the communication system and the environmental parameters are recorded. The collected data is then stored;

[0046] S72, Feature Extraction and Screening: First, extract key features from the stored historical data, including the power spectrum characteristics, information entropy characteristics, time domain characteristics, interference occurrence time characteristics, and environmental characteristics of the interference signal; then use a feature selection algorithm to screen the extracted features, remove redundant and irrelevant features, retain the most valuable features for interference analysis, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis;

[0047] S73, data comparison and matching: First, a corresponding similarity measurement method is used to calculate the similarity between the characteristics of the current detection data and the historical data. Different measurement methods can be selected according to the characteristics of different types of features. The similarity measurement methods include Euclidean distance and cosine similarity. Then, a similarity threshold is set, and historical data with similarity above the threshold is selected as comparison data with similar characteristics to the current detection data.

[0048] S74, Pattern Mining and Trend Analysis: First, cluster analysis is performed on the matched historical data to aggregate interference data with similar characteristics into different categories, and the characteristics and patterns of the interference data in each category are observed. The cluster analysis includes K-means clustering and DBSCAN clustering. Then, for time-related features, time series analysis methods are used to analyze the variation patterns of interference signals in the time dimension and predict the time, frequency, and intensity trends of possible future interference. The time series analysis methods include ARIMA models and LSTM neural networks.

[0049] S75, output results: upload the results obtained from the historical data comparison and analysis to the result output and feedback link for result output.

[0050] Compared with existing technologies:

[0051] 1. Deal with complex interference scenarios and signal forms to improve detection accuracy

[0052] In view of the diverse forms and complex scenarios of interference signals in actual communications, traditional methods are difficult to fully address. The present invention combines power spectrum analysis and information entropy theory to perform detection from two dimensions: signal frequency distribution characteristics and statistical characteristics. For example, narrowband interference presents power peaks at specific frequency points on the power spectrum, while broadband interference increases the power spectrum as a whole. Power spectrum analysis can capture the interference frequency range; at the same time, the randomness of the interference signal leads to higher information entropy, and information entropy calculation can assist in judgment from a statistical perspective. The combination of the two can more comprehensively and accurately detect different types of interference, reduce false detection and missed detection rates, and effectively deal with complex interference environments.

[0053] 2. Solve the limitations of fixed thresholds and enhance environmental adaptability

[0054] Traditional threshold comparison-based methods rely on fixed thresholds, which can easily lead to misjudgments when the communication environment changes, such as when background noise fluctuates or when interference signal characteristics change. The present invention dynamically evaluates signal characteristics by comprehensively analyzing the power spectrum and information entropy. When calculating the power spectrum, windowing and other methods are used to improve accuracy and resolution; when calculating information entropy, the signal is segmented and averaged to make the results more representative. In practical applications, the power spectrum threshold and information entropy threshold can be flexibly adjusted according to different communication scenarios and environments, so that the detection method can adapt to situations such as changes in background noise and differences in interference signal characteristics, thus overcoming the limitations of fixed thresholds.

[0055] 3. Meet the requirements of modern communication systems and improve anti-interference capabilities

[0056] Traditional methods cannot meet the accuracy and real-time requirements of modern communication systems for interference detection. However, this invention rationally sets the sampling frequency and accuracy during the signal acquisition phase to ensure complete signal acquisition. The preprocessing phase improves signal quality through filtering and denoising. Power spectrum and information entropy calculations employ efficient algorithms to ensure real-time detection. The interference judgment and identification phase integrates the results of these two dimensions to improve accuracy. Detection results are promptly fed back to the communication system, enabling the system to rapidly adjust communication frequencies and modulation methods, effectively improving the system's anti-interference capabilities and communication quality to meet modern communication needs. DETAILED DESCRIPTION

[0057] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below.

[0058] The present invention provides a communication interference detection system based on power spectrum and information entropy, which includes the following specific steps:

[0059] S1: Signal Acquisition: The signal acquisition equipment is used to collect the received signals in the communication system. The sampling frequency and sampling accuracy of the acquisition equipment can be set according to the operating frequency band and signal bandwidth of the communication system to ensure that the information of the received signal can be obtained completely and accurately. At the same time, in order to reduce the impact of the external environment on signal acquisition, the acquisition equipment can also be shielded and grounded;

[0060] S2: Signal preprocessing: preprocessing the collected original signal, including filtering and denoising operations;

[0061] S3: Power spectrum calculation: Use the fast Fourier transform algorithm to perform spectrum analysis on the preprocessed signal to convert the time domain signal into the frequency domain signal and obtain the signal spectrum. Then, by squaring the spectrum and normalizing it, the power spectrum of the signal is calculated. In addition, to improve the accuracy and resolution of the power spectrum calculation, windowing can be used to reduce spectrum leakage.

[0062] S4: Information entropy calculation: First, divide the preprocessed signal into several signal segments of equal length. For each signal segment, calculate its probability distribution. Then, use the information entropy formula to calculate the information entropy of each signal segment based on the probability distribution. Then, average the information entropy of all signal segments to obtain the information entropy of the entire received signal.

[0063] The information entropy formula is:

[0064]

[0065] Among them, p(x i ) indicates that the signal value is x i probability;

[0066] S5, interference judgment and identification: First, set the power spectrum threshold and information entropy threshold, then compare the calculated power spectrum with the power spectrum threshold. If the power spectrum exceeds the threshold in a certain frequency range, it is preliminarily judged that there is an interference signal in that frequency range. At the same time, the calculated information entropy is compared with the information entropy threshold. If the information entropy exceeds the threshold, it indicates that there may be interference in the signal. After that, the judgment results of the power spectrum and information entropy are combined to determine whether there is an interference signal. If interference exists, the type of interference signal will be identified and classified according to the characteristics of the power spectrum and the size of the information entropy.

[0067] The specific steps of S5 are as follows:

[0068] S51, threshold setting: setting the power spectrum threshold and information entropy threshold;

[0069] The specific steps of S51 are as follows:

[0070] S511, Power Spectrum Threshold: The power spectrum threshold is set based on the power spectrum distribution during normal operation of the communication system, combined with historical data and actual application scenarios. The threshold is dynamically adjusted to ensure accuracy, taking into account the impact of background noise in different environments.

[0071] S512, information entropy threshold: Analyze the information entropy characteristics of normal communication signals, calculate the mean and standard deviation of their information entropy, and on this basis, set the information entropy threshold to distinguish normal signals from signals that may have interference; the setting formula of the information entropy threshold is:

[0072] A=H0+kσ

[0073] Among them, A represents the information entropy threshold, H0 represents the mean of information entropy, σ represents the standard deviation of information entropy, and k represents the empirical coefficient;

[0074] S52, power spectrum comparison judgment: The calculated power spectrum of the received signal is compared with the set power spectrum threshold point by point to traverse each frequency point in the power spectrum. If the power spectrum value exceeds the power spectrum threshold for multiple consecutive points within a certain frequency range, and the frequency range exceeds the set width, it is preliminarily determined that there is an interference signal in the frequency range. At the same time, the frequency range where the interference signal exists is recorded to provide a basis for subsequent interference type identification;

[0075] S53, information entropy comparison judgment: The calculated information entropy of the received signal is compared with the set information entropy threshold. If the information entropy value is greater than the information entropy threshold, it indicates that the uncertainty of the signal has increased and the possibility of interference is high. At the same time, combined with the result of the power spectrum comparison judgment, if the power spectrum judges that interference exists and the information entropy also exceeds the threshold, it is further confirmed that there is an interference signal. If only the information entropy exceeds the threshold and the power spectrum shows no abnormality, the signal will be further analyzed, such as re-checking the signal preprocessing link or adopting a more sophisticated feature extraction method to eliminate the possibility of misjudgment;

[0076] S54, interference type identification: after determining the presence of an interference signal, identify the interference type based on the characteristics of the power spectrum and the size of the information entropy;

[0077] The specific steps of S54 are as follows:

[0078] S541, Narrowband Interference: If there is a clear power peak in the power spectrum that is concentrated in a narrow frequency range, and the peak power is higher than the power of the surrounding frequency points, and the information entropy increases compared to the normal signal but the increase is small, it is judged to be narrowband interference;

[0079] S542, broadband interference: When the power spectrum increases overall over a wide frequency range and the information entropy is higher than the information entropy threshold of a normal signal, it can be determined to be broadband interference;

[0080] S543, Other Interference Types: For complex interference signals, including pulse interference and swept frequency interference, the system can further analyze and judge the interference signals by combining their time domain characteristics and high-order cumulants. Furthermore, a machine learning classification algorithm uses the extracted signal features for training and classification to accurately identify the interference type.

[0081] S55, result integration and recording: Integrate the results of interference judgment and identification, record the existence of interference signals, frequency range, and interference type information, and provide complete and accurate data for subsequent result output and feedback links, so that the communication system can take effective anti-interference measures in a timely manner according to the interference situation;

[0082] S6, Interference Signal Strength Assessment: After determining the presence of an interference signal, the intensity level of the interference signal is calculated based on the power distribution of the interference signal in the power spectrum and the size of the information entropy, combined with a preset intensity assessment model;

[0083] The specific steps of S6 are as follows:

[0084] S61, determining evaluation indicators: determining power-related indicators, information entropy indicators, and time-domain feature indicators;

[0085] The specific steps of S61 are as follows:

[0086] S611, power-related indicators: Extract the maximum power value, average power value, and total power within the interference frequency band of the interference signal in the power spectrum. The maximum power value reflects the most severe interference, the average power value reflects the overall energy level of the interference, and the total power comprehensively considers the total energy within the interference frequency band.

[0087] S612, Information Entropy Index: The interference signal is separated and its information entropy is calculated. The information entropy reflects the disorder and uncertainty of the interference signal. The higher the information entropy, the more complex the interference signal is and the greater its intensity may be.

[0088] S613, Time Domain Characteristic Indicators: Analyze the peak amplitude and duration characteristics of the interference signal in the time domain. The peak amplitude reflects the impact intensity of the interference on the signal, and the duration indicates the duration of the interference impact.

[0089] S62, Establish an evaluation model: Use the hierarchical analysis method to construct a judgment matrix, compare the importance of each indicator pairwise, and calculate the weight. At the same time, it can combine the regression algorithm in machine learning with the historical interference data to train the model and establish the mathematical relationship between the evaluation indicators and the interference signal strength;

[0090] S63, calculating the intensity value: Substitute the extracted power-related index, information entropy index, and time-domain characteristic index data into the established evaluation model for calculation; if a weighted summation method is used, the formula is expressed as:

[0091]

[0092] Where I represents the strength of the interference signal, w i Expressed as the weight of the i-th indicator, x i It is represented as the value of the ith indicator, and n is the total number of indicators;

[0093] S64, intensity level classification: based on the calculated interference signal strength value, combined with the actual tolerance of the communication system and historical experience, different intensity levels are classified;

[0094] S65, output result: upload the calculated interference signal strength level to the result output and feedback link for result output;

[0095] By setting the interference signal strength assessment, the interference signal strength can be quantitatively assessed, and the communication system can take more targeted anti-interference measures. For weak interference, a simple filtering method can be used; for strong interference, the communication frequency can be adjusted in time or a more complex anti-interference algorithm can be enabled, thereby improving the flexibility and effectiveness of the communication system in dealing with interference and further ensuring communication quality.

[0096] S7, historical data comparison and analysis: First, a historical interference detection database is established. Then, the characteristics of the interference signal detected this time are compared and analyzed with historical data. Data mining and machine learning algorithms are used to analyze the changing trends and patterns of the interference signals.

[0097] The specific steps of S7 are as follows:

[0098] S71, data collection and storage: After each interference detection is completed, the complete data of this detection is collected, and the time of the detection, the working status of the communication system and the environmental parameters are recorded. The collected data is then stored;

[0099] S72, Feature Extraction and Screening: First, extract key features from the stored historical data, including the power spectrum characteristics, information entropy characteristics, time domain characteristics, interference occurrence time characteristics, and environmental characteristics of the interference signal; then use a feature selection algorithm to screen the extracted features, remove redundant and irrelevant features, retain the most valuable features for interference analysis, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis;

[0100] S73, data comparison and matching: First, a corresponding similarity measurement method is used to calculate the similarity between the characteristics of the current detection data and the historical data. Different measurement methods can be selected according to the characteristics of different types of features. The similarity measurement methods include Euclidean distance and cosine similarity. Then, a similarity threshold is set, and historical data with similarity above the threshold is selected as comparison data with similar characteristics to the current detection data.

[0101] S74, Pattern Mining and Trend Analysis: First, cluster analysis is performed on the matched historical data to aggregate interference data with similar characteristics into different categories, and the characteristics and patterns of the interference data in each category are observed. The cluster analysis includes K-means clustering and DBSCAN clustering. Then, for time-related features, time series analysis methods are used to analyze the variation patterns of interference signals in the time dimension and predict the time, frequency, and intensity trends of possible future interference. The time series analysis methods include ARIMA models and LSTM neural networks.

[0102] S75, output results: upload the results obtained from the historical data comparison and analysis to the result output and feedback link for result output;

[0103] By setting up historical data comparison and analysis, it is possible to predict potential interference risks in advance and provide a forward-looking decision-making basis for the communication system. For example, if a certain type of interference signal is found to appear frequently and gradually increase in intensity during a specific time period or environment, the communication system can adjust its operation strategy in advance and take preventive anti-interference measures to reduce the impact of interference on communication and improve the stability and reliability of the communication system.

[0104] S8, result output and feedback: The results of interference detection and identification are output in an intuitive manner. At the same time, the detection results are fed back to the communication system so that the communication system can take corresponding anti-interference measures to improve the anti-interference capability and communication quality of the communication system.

[0105] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A communication interference detection system based on power spectrum and information entropy, characterized in that: The specific steps are as follows: S1, signal acquisition: The signal acquisition equipment is used to collect the received signals in the communication system. The sampling frequency and sampling accuracy of the acquisition equipment can be set according to the operating frequency band and signal bandwidth of the communication system to ensure that the information of the received signal can be obtained completely and accurately. At the same time, in order to reduce the impact of the external environment on signal acquisition, the acquisition equipment can also be shielded and grounded; S2, signal preprocessing: preprocessing the collected original signal, including filtering and denoising operations; S3, power spectrum calculation: Use the fast Fourier transform algorithm to perform spectrum analysis on the preprocessed signal to convert the time domain signal into the frequency domain signal and obtain the signal spectrum. Then, by squaring the spectrum and normalizing it, the power spectrum of the signal is calculated. In addition, to improve the accuracy and resolution of the power spectrum calculation, windowing can be used to reduce spectrum leakage. S4, information entropy calculation: First, divide the preprocessed signal into several signal segments of equal length. For each signal segment, calculate its probability distribution. Then, use the information entropy formula to calculate the information entropy of each signal segment based on the probability distribution. Then, average the information entropy of all signal segments to obtain the information entropy of the entire received signal. S5, interference judgment and identification: First, set the power spectrum threshold and information entropy threshold, then compare the calculated power spectrum with the power spectrum threshold. If the power spectrum exceeds the threshold in a certain frequency range, it is preliminarily judged that there is an interference signal in that frequency range. At the same time, the calculated information entropy is compared with the information entropy threshold. If the information entropy exceeds the threshold, it indicates that there may be interference in the signal. After that, the judgment results of the power spectrum and information entropy are combined to determine whether there is an interference signal. If interference exists, the type of interference signal will be identified and classified according to the characteristics of the power spectrum and the size of the information entropy. S6, Interference Signal Strength Assessment: After determining the presence of an interference signal, the intensity level of the interference signal is calculated based on the power distribution of the interference signal in the power spectrum and the size of the information entropy, combined with a preset intensity assessment model; S7, historical data comparison and analysis: First, a historical interference detection database is established. Then, the characteristics of the interference signal detected this time are compared and analyzed with historical data. Data mining and machine learning algorithms are used to analyze the changing trends and patterns of the interference signals. S8, result output and feedback: The results of interference detection and identification are output in an intuitive manner. At the same time, the detection results are fed back to the communication system so that the communication system can take corresponding anti-interference measures to improve the anti-interference capability and communication quality of the communication system.

2. A communication interference detection system based on power spectrum and information entropy according to claim 1, characterized in that: The information entropy formula is: Among them, p(x i ) indicates that the signal value is x i probability.

3. The communication interference detection system based on power spectrum and information entropy according to claim 1, characterized in that: The specific steps of S5 are as follows: S51, threshold setting: setting the power spectrum threshold and information entropy threshold; S52, power spectrum comparison judgment: The calculated power spectrum of the received signal is compared with the set power spectrum threshold point by point to traverse each frequency point in the power spectrum. If the power spectrum value exceeds the power spectrum threshold for multiple consecutive points within a certain frequency range, and the frequency range exceeds the set width, it is preliminarily determined that there is an interference signal in the frequency range. At the same time, the frequency range where the interference signal exists is recorded to provide a basis for subsequent interference type identification; S53, information entropy comparison judgment: The calculated information entropy of the received signal is compared with the set information entropy threshold. If the information entropy value is greater than the information entropy threshold, it indicates that the uncertainty of the signal has increased and the possibility of interference is high. At the same time, combined with the result of power spectrum comparison judgment, if the power spectrum judges that interference exists and the information entropy also exceeds the threshold, it is further confirmed that there is an interference signal; S54, interference type identification: after determining the presence of an interference signal, identify the interference type based on the characteristics of the power spectrum and the size of the information entropy; S55, result integration and recording: Integrate the results of interference judgment and identification, record the existence of interference signals, frequency range, and interference type information, and provide complete and accurate data for subsequent result output and feedback links, so that the communication system can take effective anti-interference measures in a timely manner according to the interference situation.

4. A communication interference detection system based on power spectrum and information entropy according to claim 3, characterized in that: The specific steps of S51 are as follows: S511, Power Spectrum Threshold: The power spectrum threshold is set based on the power spectrum distribution during normal operation of the communication system, combined with historical data and actual application scenarios. The threshold is dynamically adjusted to ensure accuracy, taking into account the impact of background noise in different environments. S512, information entropy threshold: Analyze the information entropy characteristics of normal communication signals, calculate the mean and standard deviation of their information entropy, and on this basis, set the information entropy threshold to distinguish normal signals from signals that may have interference; the setting formula of the information entropy threshold is: A=H0+kσ Among them, A represents the information entropy threshold, H0 represents the mean of information entropy, σ represents the standard deviation of information entropy, and k represents the empirical coefficient.

5. The communication interference detection system based on power spectrum and information entropy according to claim 3, characterized in that: The specific steps of S54 are as follows: S541, Narrowband Interference: If there is a clear power peak in the power spectrum that is concentrated in a narrow frequency range, and the peak power is higher than the power of the surrounding frequency points, and the information entropy increases compared to the normal signal but the increase is small, it is judged to be narrowband interference; S542, broadband interference: When the power spectrum increases overall over a wide frequency range and the information entropy is higher than the information entropy threshold of a normal signal, it can be determined to be broadband interference; S543, other interference types: For complex interference signals, including pulse interference and swept frequency interference, the system can further analyze and judge them by combining the signal's time domain characteristics and high-order cumulative quantity characteristics. At the same time, it also uses machine learning classification algorithms to train and classify the extracted multiple signal features to accurately identify the interference type.

6. The communication interference detection system based on power spectrum and information entropy according to claim 1, characterized in that: The specific steps of S6 are as follows: S61, determining evaluation indicators: determining power-related indicators, information entropy indicators, and time-domain feature indicators; S62, Establish an evaluation model: Use the hierarchical analysis method to construct a judgment matrix, compare the importance of each indicator pairwise, and calculate the weight. At the same time, it can combine the regression algorithm in machine learning with the historical interference data to train the model and establish the mathematical relationship between the evaluation indicators and the interference signal strength; S63, calculating the intensity value: Substitute the extracted power-related index, information entropy index, and time-domain characteristic index data into the established evaluation model for calculation; if a weighted summation method is used, the formula is expressed as: Where, I represents the strength value of the interference signal, W i Expressed as the weight of the i-th indicator, x i It is represented as the value of the ith indicator, and n is the total number of indicators; S64, intensity level classification: based on the calculated interference signal strength value, combined with the actual tolerance of the communication system and historical experience, different intensity levels are classified; S65, output result: upload the calculated interference signal strength level to the result output and feedback link for result output.

7. The communication interference detection system based on power spectrum and information entropy according to claim 6, characterized in that: The specific steps of S61 are as follows: S611, power-related indicators: Extract the maximum power value, average power value, and total power within the interference frequency band of the interference signal in the power spectrum. The maximum power value reflects the most severe interference, the average power value reflects the overall energy level of the interference, and the total power comprehensively considers the total energy within the interference frequency band. S612, Information Entropy Index: The interference signal is separated and its information entropy is calculated. The information entropy reflects the disorder and uncertainty of the interference signal. The higher the information entropy, the more complex the interference signal is and the greater its intensity may be. S613, time domain characteristic indicators: Analyze the peak amplitude and duration characteristics of the interference signal in the time domain. The peak amplitude reflects the impact intensity of the interference on the signal, and the duration indicates the duration of the interference impact.

8. The communication interference detection system based on power spectrum and information entropy according to claim 1, characterized in that: The specific steps of S7 are as follows: S71, data collection and storage: After each interference detection is completed, the complete data of this detection is collected, and the time of the detection, the working status of the communication system and the environmental parameters are recorded. The collected data is then stored; S72, Feature Extraction and Screening: First, extract key features from the stored historical data, including the power spectrum characteristics, information entropy characteristics, time domain characteristics, interference occurrence time characteristics, and environmental characteristics of the interference signal; then use a feature selection algorithm to screen the extracted features, remove redundant and irrelevant features, retain the most valuable features for interference analysis, reduce data dimensions, and improve the efficiency and accuracy of subsequent analysis; S73, data comparison and matching: First, a corresponding similarity measurement method is used to calculate the similarity between the characteristics of the current detection data and the historical data. Different measurement methods can be selected according to the characteristics of different types of features. The similarity measurement methods include Euclidean distance and cosine similarity. Then, a similarity threshold is set, and historical data with similarity above the threshold is selected as comparison data with similar characteristics to the current detection data. S74, Pattern Mining and Trend Analysis: First, cluster analysis is performed on the matched historical data to aggregate interference data with similar characteristics into different categories, and the characteristics and patterns of the interference data in each category are observed. The cluster analysis includes K-means clustering and DBSCAN clustering. Then, for time-related features, time series analysis methods are used to analyze the variation patterns of interference signals in the time dimension and predict the time, frequency, and intensity trends of possible future interference. The time series analysis methods include ARIMA models and LSTM neural networks. S75, output results: upload the results obtained from the historical data comparison and analysis to the result output and feedback link for result output.

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