Communication signal electromagnetic radiation evaluation processing method and system, and storage medium

Abstract

The invention provides a communication signal electromagnetic radiation evaluation processing method and system and a storage medium. The method comprises the following steps: acquiring electromagnetic radiation data monitored in real time; analyzing the preprocessed electromagnetic radiation data by using a pre-trained anomaly detection model so as to identify electromagnetic radiation signals with abnormal intensity; calculating characteristic parameters of the electromagnetic radiation signal with the abnormal intensity; according to the characteristic parameters, evaluating the abnormal level of the electromagnetic radiation signal with the abnormal intensity through a preset evaluation rule; and outputting an evaluation result, wherein the evaluation result at least comprises an abnormal level and a processing measure corresponding to the abnormal level. According to the technical scheme provided by the invention, the electromagnetic radiation signal with abnormal intensity can be automatically identified and evaluated, the requirement of manual intervention is reduced, and the working efficiency and the processing speed are improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of data processing technology, and in particular to a communication signal electromagnetic radiation evaluation processing method, system and storage medium. Background Art

[0002] In communication technology, communication signals refer to electromagnetic waves that carry information. These signals can be analog or digital, and they propagate in space in the form of radio waves for wireless communication. With the development of modern science and technology, electromagnetic radiation has become an inevitable part of people's daily lives. In application scenarios such as communication base stations, radar systems, and industrial equipment, the monitoring of electromagnetic radiation has become increasingly important. The reason for the generation of electromagnetic radiation: any accelerated charge will generate an electromagnetic field, and when these electromagnetic fields propagate in the form of waves, electromagnetic radiation is formed. In communication base stations, radar systems, industrial equipment, etc., the operation of electronic equipment will generate electromagnetic radiation.

[0003] The purpose of monitoring electromagnetic radiation is to ensure that its level is within a safe range to avoid any impact on human health and electronic equipment. However, traditional electromagnetic radiation assessment methods often rely on simple detection and judgment of electromagnetic radiation intensity. Even if abnormal signals can be identified, there is a lack of targeted treatment measures, resulting in the inability to effectively control risks. Summary of the invention

[0004] The embodiments of the present invention provide a communication signal electromagnetic radiation evaluation and processing method, system and storage medium, which are used to solve the problem that the automatic identification and evaluation of electromagnetic radiation signals of abnormal intensity are poor in the prior art.

[0005] In a first aspect, an embodiment of the present invention provides a communication signal electromagnetic radiation evaluation and processing method, comprising:

[0006] Obtain real-time monitoring of electromagnetic radiation data;

[0007] Using a pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity, wherein the pre-processing at least includes: denoising, smoothing and frequency conversion;

[0008] Calculating characteristic parameters of the electromagnetic radiation signal of abnormal intensity, wherein the characteristic parameters at least include time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy;

[0009] According to the characteristic parameters, evaluating the abnormal level of the electromagnetic radiation signal of abnormal intensity by using a preset evaluation rule;

[0010] Output an evaluation result, wherein the evaluation result at least includes an abnormality level and a treatment measure corresponding to the abnormality level.

[0011] Optionally, the preprocessing includes at least: denoising, smoothing and frequency conversion;

[0012] Before using the pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity, the method further includes:

[0013] Performing Fourier transformation on the electromagnetic radiation data to convert it into a frequency domain signal;

[0014] Using a bandpass filter to filter out noise signals outside a specified frequency range, wherein the specified frequency range is determined according to the frequency range of an electromagnetic radiation signal of normal intensity, and is used to retain frequency components related to the electromagnetic radiation signal of abnormal intensity;

[0015] A sliding window smoothing algorithm is applied to reduce signal fluctuations of the electromagnetic radiation data.

[0016] Optionally, the using a pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity includes:

[0017] Using a long short-term memory network for time series prediction to capture long-term dependencies in the electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity; and / or,

[0018] In combination with an autoencoder, the electromagnetic radiation signal of abnormal intensity is identified by reconstructing the difference between the electromagnetic radiation signal of normal intensity and the electromagnetic radiation data; and / or,

[0019] Support vector machine is used for classification, and the optimal hyperplane is constructed to distinguish electromagnetic radiation signals of normal intensity from electromagnetic radiation signals of abnormal intensity.

[0020] Optionally, the calculating the characteristic parameter of the electromagnetic radiation signal of abnormal intensity includes:

[0021] By formula: Calculate the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity; wherein, represents the time-weighted average radiation intensity, I i It is represented as the radiation intensity at the i-th moment, N is the number of sampling points, and w t (i) is expressed as a time-based weight function, where λ is the attenuation coefficient, t i It is expressed as the time difference between the i-th moment and the reference moment;

[0022] By formula: P = max(I1, I2, ..., I N), calculate the peak intensity of the electromagnetic radiation signal of abnormal intensity, where P represents the peak intensity, I i It is expressed as the radiation intensity at the i-th moment, i = {1, 2, ..., N};

[0023] By formula: Calculate the energy density of the electromagnetic radiation signal of abnormal intensity; where E represents the energy density, Δt represents the sampling interval, and w e (i) is expressed as an energy-based weight function, where

[0024] By formula: Calculate the spectrum entropy of the electromagnetic radiation signal of abnormal intensity; where S represents the spectrum entropy, p j It is represented as the power ratio of the jth frequency component, and M is represented as the total number of frequency components.

[0025] Optionally, the evaluating the abnormal level of the electromagnetic radiation signal of abnormal intensity by a preset evaluation rule according to the characteristic parameter includes:

[0026] When the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T1, it is determined to be a first-level abnormality;

[0027] When the peak intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T2, it is determined to be a secondary abnormality;

[0028] When the energy density of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T3, it is determined as a third-level abnormality;

[0029] When the spectrum entropy of the electromagnetic radiation signal of abnormal intensity is lower than the preset threshold value T4, it is determined to be a level 4 abnormality.

[0030] Optionally, the evaluating the abnormal level of the electromagnetic radiation signal of abnormal intensity by a preset evaluation rule according to the characteristic parameter includes:

[0031] Define the abnormal level calculation formula: G = w1·log(T1+1)+w2·log(T2+1)+w3·log(T3+1)+w4·log(T4+1);

[0032] Among them, G represents the abnormality level, w1, w2, w3 and w4 represent the importance weights of time-weighted average radiation intensity, peak intensity, energy density and spectral entropy respectively, and T1, T2, T3 and T4 represent the preset thresholds of the corresponding characteristic parameters respectively.

[0033] Optionally, the calculation formula of the importance weights w1, w2, w3 and w4 is:

[0034]

[0035] Among them, λ is the parameter for adjusting the steepness of the curve, σ1, σ2, σ3 and σ4 are the calibration factors of the corresponding characteristic parameters, μ P , μ E , μ S are respectively expressed as the average values ​​of the corresponding characteristic parameters, represents the time-weighted average radiation intensity, P represents the peak intensity, E represents the energy density, and S represents the spectral entropy.

[0036] In a second aspect, an embodiment of the present invention provides a communication signal electromagnetic radiation evaluation and processing system, comprising:

[0037] An acquisition module is used to acquire electromagnetic radiation data for real-time monitoring;

[0038] A detection module, used to analyze the pre-processed electromagnetic radiation data using a pre-trained anomaly detection model to identify electromagnetic radiation signals of abnormal intensity, wherein the pre-processing at least includes: denoising, smoothing and frequency conversion;

[0039] A calculation module, used for calculating characteristic parameters of the electromagnetic radiation signal of abnormal intensity, wherein the characteristic parameters at least include time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy;

[0040] An evaluation module, used to evaluate the abnormal level of the electromagnetic radiation signal of abnormal intensity according to the characteristic parameters and through a preset evaluation rule;

[0041] The output module is used to output the evaluation result, and the evaluation result at least includes the abnormality level and the processing measures corresponding to the abnormality level.

[0042] In a third aspect, an embodiment of the present invention provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement the communication signal electromagnetic radiation evaluation and processing method as described in the first aspect above.

[0043] In a fourth aspect, an embodiment of the present invention provides a storage medium storing a computer program, which, when executed by a computer, implements the communication signal electromagnetic radiation evaluation and processing method as described in the first aspect above.

[0044] In an embodiment of the present invention, real-time monitored electromagnetic radiation data is acquired; the preprocessed electromagnetic radiation data is analyzed using a pre-trained anomaly detection model to identify electromagnetic radiation signals of abnormal intensity, wherein the preprocessing includes at least: denoising, smoothing, and frequency conversion; characteristic parameters of the electromagnetic radiation signal of abnormal intensity are calculated, and the characteristic parameters include at least time-weighted average radiation intensity, peak intensity, energy density, and spectral entropy; based on the characteristic parameters, the abnormal level of the electromagnetic radiation signal of abnormal intensity is evaluated by preset evaluation rules; and an evaluation result is output, and the evaluation result includes at least the abnormal level and the treatment measures corresponding to the abnormal level.

[0045] The present invention provides a communication signal electromagnetic radiation evaluation and processing method, which has the following significant beneficial effects:

[0046] By preprocessing the electromagnetic radiation data, including denoising, smoothing and frequency conversion, the detection accuracy of abnormal intensity electromagnetic radiation signals is effectively improved. Denoising in the preprocessing process can filter out irrelevant noise signals, smoothing can reduce signal fluctuations, and frequency conversion helps to identify abnormal frequency components in the signal.

[0047] The technical solution of the embodiment of the present invention adopts a comprehensive evaluation index, that is, calculating the characteristic parameters of the abnormal intensity electromagnetic radiation signal, including the time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy. These parameters can comprehensively evaluate the abnormal degree of the signal from multiple angles, thereby improving the accuracy and comprehensiveness of the evaluation. By evaluating the electromagnetic radiation signal of abnormal intensity through the preset evaluation rules, different thresholds can be flexibly configured to distinguish abnormal signals of different levels, making the evaluation more accurate and reliable.

[0048] The technical solution of the embodiment of the present invention implements targeted processing measures: the output evaluation results include not only the abnormal level, but also the processing measures corresponding to the abnormal level, which helps to take timely and effective response measures and reduce potential risks. The method can automatically identify and evaluate electromagnetic radiation signals of abnormal intensity, reduce the need for manual intervention, and improve work efficiency and processing speed.

[0049] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A flow chart of a communication signal electromagnetic radiation evaluation and processing method provided by an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of the structure of an electromagnetic radiation assessment process provided by an embodiment of the present invention;

[0053] Figure 3 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0055] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be performed in the order in which they appear in this document or may be performed in parallel. In addition, these processes may include more or fewer operations, and these operations may be performed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0056] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0057] Figure 1 A flow chart of a method for evaluating and processing electromagnetic radiation of a communication signal is provided in accordance with an embodiment of the present invention. Figure 1 As shown, the method includes:

[0058] 101. Obtain real-time monitoring of electromagnetic radiation data;

[0059] This step refers to the process of obtaining electromagnetic radiation data in real time from sensors or monitoring equipment. This data usually includes the strength, frequency and other relevant parameters of the electromagnetic field. The electromagnetic radiation data can be analog or digital signals, depending on the type and configuration of the monitoring equipment.

[0060] In the embodiment of the present invention, it is assumed that the application scenario is an electromagnetic radiation monitoring system in an industrial production environment. The system is intended to monitor the electromagnetic radiation level around the production equipment in real time to ensure the safety of employees and the normal operation of the equipment.

[0061] Monitoring equipment: The system is equipped with multiple electromagnetic radiation monitoring sensors, which are distributed in key locations of production facilities, such as around equipment, at entrances, etc. These sensors can monitor the intensity of electromagnetic radiation in real time and send data to the central processing unit.

[0062] Data collection: The monitoring sensor continuously collects electromagnetic radiation data and transmits the data to the central processing unit at a certain sampling frequency (for example, once per second). These data usually include information such as the change trend of electromagnetic radiation intensity over time and frequency components.

[0063] Data format: The collected data can be digital signals, such as stored in the form of CSV files or database records, or it can be analog signals, which need to be converted to digital signals by analog-to-digital converters for processing.

[0064] Specific embodiment: Assume that 5 electromagnetic radiation monitoring sensors are installed in a factory workshop, and these sensors collect data every 1 second. The monitoring system collects data uninterruptedly 24 hours a day. After receiving the data from these sensors, the central processing unit pre-processes them, including denoising and smoothing, to reduce noise interference and improve the accuracy of subsequent analysis.

[0065] In this way, the system can monitor the electromagnetic radiation level in real time, ensuring that abnormal situations can be detected in time and appropriate measures can be taken.

[0066] 102. Analyze the preprocessed electromagnetic radiation data using a pre-trained anomaly detection model to identify electromagnetic radiation signals of abnormal intensity;

[0067] Wherein, the preprocessing at least includes: denoising, smoothing and frequency conversion;

[0068] Before executing step 102, it also includes: performing Fourier transform on the electromagnetic radiation data to convert it into a frequency domain signal; using a bandpass filter to filter out noise signals outside a specified frequency range, wherein the specified frequency range is determined based on the frequency range of an electromagnetic radiation signal of normal intensity and is used to retain frequency components related to the electromagnetic radiation signal of abnormal intensity; and applying a sliding window smoothing algorithm to reduce signal fluctuations of the electromagnetic radiation data.

[0069] Among them, Fourier transform is a mathematical tool used to convert time domain signals into frequency domain signals. Through Fourier transform, electromagnetic radiation data can be converted from time domain to frequency domain, making it easier to identify different frequency components in the signal.

[0070] A bandpass filter is a filter that allows signals within a specified frequency range to pass through while suppressing signals of other frequencies. In the present invention, the bandpass filter is used to filter out noise signals outside the specified frequency range and retain frequency components associated with electromagnetic radiation signals of abnormal intensity.

[0071] The sliding window smoothing algorithm is an algorithm used to reduce signal fluctuations. By moving a window over the data and averaging the data within the window, the random fluctuations in the signal can be reduced and the smoothness of the signal can be improved.

[0072] In the embodiment of the present invention, it is assumed that the application scenario is an electromagnetic radiation monitoring system in an industrial production environment. The system is intended to monitor the electromagnetic radiation level around the production equipment in real time to ensure the safety of employees and the normal operation of the equipment.

[0073] The system is equipped with multiple electromagnetic radiation monitoring sensors, which are distributed in key locations of the production facilities. The raw electromagnetic radiation data collected by the sensors are first converted into frequency domain signals through Fourier transform to analyze the frequency components of the signals. Next, a bandpass filter is used to filter out noise signals outside the specified frequency range, which is determined based on the frequency range of electromagnetic radiation signals of normal intensity to ensure that the frequency components related to electromagnetic radiation signals of abnormal intensity are retained. Finally, a sliding window smoothing algorithm is applied to reduce the signal fluctuations of the electromagnetic radiation data and improve the smoothness and continuity of the signal.

[0074] Based on the above foundation, the execution of step 102 may specifically include: using a long short-term memory network for time series prediction, capturing the long-term dependencies in the electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity; and / or, combining an autoencoder, reconstructing the difference between the electromagnetic radiation signal of normal intensity and the electromagnetic radiation data to identify the electromagnetic radiation signal of abnormal intensity; and / or, using a support vector machine for classification, distinguishing between electromagnetic radiation signals of normal intensity and electromagnetic radiation signals of abnormal intensity by constructing an optimal hyperplane.

[0075] In this step, the Long Short-Term Memory (LSTM) network is a special recurrent neural network (RNN) that can capture long-term dependencies in data. In the present invention, LSTM is used to capture long-term dependencies in electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity.

[0076] Autoencoder (AE) is an unsupervised learning model that can be used for data dimension reduction and feature learning. In the present invention, the autoencoder reconstructs the difference between the electromagnetic radiation signal of normal intensity and the electromagnetic radiation data to identify the electromagnetic radiation signal of abnormal intensity.

[0077] Support Vector Machine (SVM) is a supervised learning model used for classification and regression analysis. In the present invention, SVM distinguishes electromagnetic radiation signals of normal intensity from electromagnetic radiation signals of abnormal intensity by constructing an optimal hyperplane.

[0078] In an embodiment of the present invention, after the above data collection and preprocessing, the system uses a pre-trained anomaly detection model to analyze the preprocessed electromagnetic radiation data. Specifically, a long short-term memory network (LSTM) is used for time series prediction to identify electromagnetic radiation signals of abnormal intensity by capturing long-term dependencies in electromagnetic radiation data. At the same time, an autoencoder (AE) is combined to identify electromagnetic radiation signals of abnormal intensity by reconstructing the difference between electromagnetic radiation signals of normal intensity and electromagnetic radiation data. In addition, a support vector machine (SVM) is used for classification to distinguish electromagnetic radiation signals of normal intensity from electromagnetic radiation signals of abnormal intensity by constructing an optimal hyperplane.

[0079] For example, suppose that five electromagnetic radiation monitoring sensors are installed in a factory workshop, and these sensors collect data every 1 second. The monitoring system collects data non-stop 24 hours a day. After receiving the data from these sensors, the central processing unit pre-processes them, including Fourier transform, bandpass filtering, and sliding window smoothing. Then, it uses a pre-trained anomaly detection model for analysis, which includes components such as LSTM, AE, and SVM. Through the joint work of these components, the system is able to identify electromagnetic radiation signals of abnormal intensity and evaluate them.

[0080] In this way, the system can monitor the electromagnetic radiation level in real time, ensuring that abnormal situations can be detected in time and appropriate measures can be taken.

[0081] 103. Calculate characteristic parameters of the electromagnetic radiation signal of abnormal intensity;

[0082] In this step, the characteristic parameters include at least time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy;

[0083] The specific implementation process of step 103 may include:

[0084] By formula: Calculate the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity; wherein, represents the time-weighted average radiation intensity, I i It is represented as the radiation intensity at the i-th moment, N is the number of sampling points, and w t (i) is expressed as a time-based weight function, where λ is the attenuation coefficient, t i It is expressed as the time difference between the i-th moment and the reference moment;

[0085] By formula: P = max(I1, I2, ..., I N ), calculate the peak intensity of the electromagnetic radiation signal of abnormal intensity, where P represents the peak intensity, I i It is expressed as the radiation intensity at the i-th moment, i = {1, 2, ..., N};

[0086] By formula: Calculate the energy density of the electromagnetic radiation signal of abnormal intensity; where E represents the energy density, Δt represents the sampling interval, and w e (i) is expressed as an energy-based weight function, where

[0087] By formula: Calculate the spectrum entropy of the electromagnetic radiation signal of abnormal intensity; where S represents the spectrum entropy, p j It is represented as the power ratio of the jth frequency component, and M is represented as the total number of frequency components.

[0088] In this step, the time-weighted average radiation intensity is a weighted average of the radiation intensity of the electromagnetic radiation data at different time points to take into account the importance of different time points. Weight function According to the time difference t from the reference time i is attenuated, where λ is the attenuation coefficient.

[0089] Peak intensity refers to the maximum radiation intensity value in the electromagnetic radiation data, which reflects the maximum strength of the signal.

[0090] Energy density is a calculation of the energy of electromagnetic radiation data, taking into account the radiation intensity at each sampling point and the sampling interval. Energy-based weighting function Weighting is performed according to the ratio of radiation intensity to time-weighted average radiation intensity.

[0091] Spectral entropy is a statistic based on spectrum distribution, which is used to measure the uniformity of spectrum distribution. It is obtained by calculating the logarithmic weighted sum of the power ratios of different frequency components.

[0092] In the embodiment of the present invention, it is assumed that the application scenario is an electromagnetic radiation monitoring system in an industrial production environment. The system is intended to monitor the electromagnetic radiation level around the production equipment in real time to ensure the safety of employees and the normal operation of the equipment.

[0093] First, the system is equipped with multiple electromagnetic radiation monitoring sensors, which are distributed in key locations of the production facility. The raw electromagnetic radiation data collected by the sensor is first converted into a frequency domain signal through Fourier transform to analyze the frequency components of the signal. Next, a bandpass filter is used to filter out noise signals outside the specified frequency range. The specified frequency range here is determined according to the frequency range of the electromagnetic radiation signal of normal intensity to ensure that the frequency components related to the electromagnetic radiation signal of abnormal intensity are retained. Finally, a sliding window smoothing algorithm is applied to reduce the signal fluctuations of the electromagnetic radiation data and improve the smoothness and continuity of the signal.

[0094] Furthermore, the system uses the pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data and further calculates the characteristic parameters of the electromagnetic radiation signal with abnormal intensity. The specific steps are as follows:

[0095] Time-weighted average radiation intensity: By formula: Calculate the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity; wherein, λ is the attenuation coefficient, t i It is expressed as the time difference between the i-th moment and the reference moment;

[0096] Peak intensity: by formula: P = max (I1, I2, ..., I N ), calculating the peak intensity of the electromagnetic radiation signal of abnormal intensity;

[0097] Energy density: Through the formula: Calculating the energy density of the electromagnetic radiation signal of abnormal intensity; wherein,

[0098] Spectral entropy: By formula: Calculate the spectrum entropy of the electromagnetic radiation signal of abnormal intensity, where p j Expressed as the power ratio of the jth frequency component;

[0099] For example, suppose that five electromagnetic radiation monitoring sensors are installed in a factory workshop, and these sensors collect data every 1 second. The monitoring system collects data non-stop 24 hours a day. After receiving the data from these sensors, the central processing unit pre-processes them, including Fourier transform, bandpass filtering, and sliding window smoothing. Then, the pre-trained anomaly detection model is used for analysis. For each electromagnetic radiation signal of abnormal intensity, the system calculates its characteristic parameters, including time-weighted average radiation intensity, peak intensity, energy density, and spectral entropy.

[0100] Time-weighted average radiation intensity: Assuming the attenuation coefficient λ = 0.1, calculate the time-weighted average radiation intensity of each abnormal signal.

[0101] Peak intensity: Directly calculate the maximum radiation intensity of each abnormal signal.

[0102] Energy density: Considering the sampling interval Δt=1 second, the energy density of each abnormal signal is calculated.

[0103] Spectral entropy: Calculate the spectral entropy of each abnormal signal to evaluate the uniformity of the spectrum distribution.

[0104] In this way, the system can calculate the characteristic parameters of electromagnetic radiation signals of abnormal intensity, providing data support for subsequent evaluation and processing.

[0105] 104. Evaluate the abnormal level of the electromagnetic radiation signal of abnormal intensity according to the characteristic parameters using a preset evaluation rule;

[0106] As a possible implementation method, step 104 may include: when the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T1, it is determined as a first-level abnormality; when the peak intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T2, it is determined as a second-level abnormality; when the energy density of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T3, it is determined as a third-level abnormality; when the spectrum entropy of the electromagnetic radiation signal of abnormal intensity is lower than the preset threshold value T4, it is determined as a fourth-level abnormality.

[0107] In this implementation, the preset threshold is a value set according to a specific standard and is used to determine whether the electromagnetic radiation signal has reached an abnormal state. These thresholds can be determined based on historical data, industry standards or other factors.

[0108] The abnormality level is determined based on the comparison result between the characteristic parameter and the preset threshold value, and is used to indicate the abnormality degree of the electromagnetic radiation signal. Different abnormality levels represent different degrees of abnormal conditions.

[0109] As another possible implementation method, step 104 may include: defining an abnormality level calculation formula: G=w1·log(T1+1)+w2·log(T2+1)+w3·log(T3+1)+w4·log(T4+1);

[0110] Among them, G represents the abnormality level, w1, w2, w3 and w4 represent the importance weights of time-weighted average radiation intensity, peak intensity, energy density and spectral entropy respectively, and T1, T2, T3 and T4 represent the preset thresholds of the corresponding characteristic parameters respectively.

[0111] The calculation formulas of the importance weights w1, w2, w3 and w4 are as follows:

[0112]

[0113] Among them, λ is the parameter for adjusting the steepness of the curve, σ1, σ2, σ3 and σ4 are the calibration factors of the corresponding characteristic parameters, μ P , μ E , μ S are respectively expressed as the average values ​​of the corresponding characteristic parameters, represents the time-weighted average radiation intensity, P represents the peak intensity, E represents the energy density, and S represents the spectral entropy.

[0114] In this implementation, importance weights are used to represent the relative importance of different feature parameters in evaluating the anomaly level. These weights can be calculated using a formula to ensure that the evaluation results are more reasonable.

[0115] The abnormality level calculation formula is a method for calculating the abnormality level based on the threshold and importance weight of the feature parameters. Through this formula, multiple feature parameters can be comprehensively considered to determine the abnormality level.

[0116] In the embodiment of the present invention, it is assumed that the application scenario is an electromagnetic radiation monitoring system in an industrial production environment. The system is intended to monitor the electromagnetic radiation level around the production equipment in real time to ensure the safety of employees and the normal operation of the equipment.

[0117] The system is equipped with multiple electromagnetic radiation monitoring sensors, which are distributed in key locations of the production facilities. The raw electromagnetic radiation data collected by the sensors are first converted into frequency domain signals through Fourier transform to analyze the frequency components of the signals. Next, a bandpass filter is used to filter out noise signals outside the specified frequency range, which is determined based on the frequency range of electromagnetic radiation signals of normal intensity to ensure that the frequency components related to electromagnetic radiation signals of abnormal intensity are retained. Finally, a sliding window smoothing algorithm is applied to reduce the signal fluctuations of the electromagnetic radiation data and improve the smoothness and continuity of the signal.

[0118] After analyzing the preprocessed electromagnetic radiation data using a pre-trained anomaly detection model, the system further calculates the characteristic parameters of the electromagnetic radiation signal with abnormal intensity, including time-weighted average radiation intensity, peak intensity, energy density, and spectral entropy.

[0119] Based on the calculated characteristic parameters, the system evaluates the abnormal level of the electromagnetic radiation signal of abnormal intensity through preset evaluation rules. The specific steps are as follows:

[0120] Threshold comparison: The system sets preset thresholds T1, T2, T3 and T4, which are used for time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy respectively. When the characteristic parameters exceed these thresholds, the system determines the corresponding abnormal level.

[0121] Abnormal level calculation formula: The system can also use the abnormal level calculation formula: G = w1·log(T1+1)+w2·log(T2+1)+w3·log(T3+1)+w4·log(T4+1);

[0122] Among them, G represents the abnormality level, w1, w2, w3 and w4 represent the importance weights of time-weighted average radiation intensity, peak intensity, energy density and spectral entropy respectively, and the system calculates the importance weights w1, w2, w3 and w4 according to the calculation formula of the above weights, and T1, T2, T3 and T4 represent the preset thresholds of the corresponding characteristic parameters respectively.

[0123] For example, suppose that five electromagnetic radiation monitoring sensors are installed in a factory workshop, and these sensors collect data every 1 second. The monitoring system collects data non-stop 24 hours a day. After receiving the data from these sensors, the central processing unit pre-processes them, including Fourier transform, bandpass filtering, and sliding window smoothing. Then, the pre-trained anomaly detection model is used for analysis, and the characteristic parameters of the electromagnetic radiation signal of abnormal intensity are calculated. Then, the system evaluates the abnormal level based on these characteristic parameters.

[0124] Threshold comparison: Assuming the preset thresholds T1=10, T2=20, T3=500 and T4=0.5, the system determines the abnormality level based on these thresholds.

[0125] Abnormality level calculation formula: The system calculates the importance weight according to the formula. Assuming λ = 0.5, σ1 = 1.2, σ2 = 1.1, σ3 = 1.0 and σ4 = 0.9, the average values ​​of the corresponding characteristic parameters are μ P =15, μ E =300, μ S=0.7, and is divided into the above importance weight calculation formula to obtain the importance weights w1=0.4, w2=0.3, w3=0.2 and w4=0.1 respectively. Then the system uses the anomaly level calculation formula: G=w1·log(T1+1)+w2·log(T2+1)+w3·log(T3+1)+w4·log(T4+1) to calculate the anomaly level.

[0126] In this way, the system can evaluate the abnormality level based on the characteristic parameters and preset thresholds, and take corresponding measures based on the abnormality level.

[0127] 105. Output an evaluation result, where the evaluation result at least includes an abnormality level and a treatment measure corresponding to the abnormality level.

[0128] In this step, outputting the evaluation results means presenting the abnormal level obtained by the evaluation and the corresponding treatment measures in an appropriate form. This step is the last step of the entire evaluation process, and its purpose is to enable relevant personnel to clearly understand the monitoring results and their response measures.

[0129] The abnormality level is determined based on the comparison result between the characteristic parameter and the preset threshold value, and is used to indicate the abnormality degree of the electromagnetic radiation signal. Different abnormality levels represent different degrees of abnormal conditions.

[0130] The treatment measures are corresponding measures taken for different abnormal levels. These measures are aimed at reducing or eliminating the potential risks brought by abnormal situations.

[0131] In the embodiment of the present invention, it is assumed that the application scenario is an electromagnetic radiation monitoring system in an industrial production environment. The system is intended to monitor the electromagnetic radiation level around the production equipment in real time to ensure the safety of employees and the normal operation of the equipment.

[0132] The system outputs the abnormal level and corresponding treatment measures based on the evaluation results. The specific steps are as follows:

[0133] Abnormal level output: The system outputs corresponding level information according to the abnormal level obtained by evaluation, such as "level 1 abnormality", "level 2 abnormality", etc.

[0134] Treatment measures: The system outputs corresponding treatment measures according to the abnormality level. For example, for "level 1 abnormality", it is recommended to take emergency measures, such as shutting down related equipment and evacuating personnel; for "level 2 abnormality", it is recommended to strengthen monitoring and regularly check the equipment status; for "level 3 abnormality", it is recommended to perform equipment maintenance and inspection; for "level 4 abnormality", it is recommended to adjust the equipment working status or parameter settings.

[0135] In this way, the system can evaluate the abnormality level based on characteristic parameters and preset thresholds, and take corresponding measures according to the abnormality level, ensuring that abnormal situations can be discovered in time and appropriate actions can be taken.

[0136] Figure 2 A schematic diagram of the structure of a communication signal electromagnetic radiation evaluation and processing system is provided for an embodiment of the present invention. Figure 2 As shown, the device comprises:

[0137] An acquisition module 21 is used to acquire electromagnetic radiation data monitored in real time;

[0138] The detection module 22 is used to analyze the pre-processed electromagnetic radiation data using a pre-trained anomaly detection model to identify electromagnetic radiation signals of abnormal intensity, wherein the pre-processing at least includes: denoising, smoothing and frequency conversion;

[0139] A calculation module 23, used to calculate characteristic parameters of the electromagnetic radiation signal of abnormal intensity, wherein the characteristic parameters at least include time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy;

[0140] An evaluation module 24, configured to evaluate the abnormal level of the electromagnetic radiation signal of abnormal intensity according to the characteristic parameters and through a preset evaluation rule;

[0141] The output module 25 is used to output the evaluation result, which at least includes the abnormality level and the processing measures corresponding to the abnormality level.

[0142] In the embodiment of the present invention, the preprocessing at least includes: denoising, smoothing and frequency conversion; the device also includes: a preprocessing module 26;

[0143] The preprocessing module 26 is used to perform Fourier transform on the electromagnetic radiation data to convert it into a frequency domain signal; use a bandpass filter to filter out noise signals outside a specified frequency range, wherein the specified frequency range is determined based on the frequency range of electromagnetic radiation signals of normal intensity and is used to retain frequency components related to electromagnetic radiation signals of abnormal intensity; and apply a sliding window smoothing algorithm to reduce signal fluctuations of the electromagnetic radiation data.

[0144] In an embodiment of the present invention, the detection module 22 is specifically used to use a long short-term memory network to perform time series prediction, by capturing the long-term dependencies in the electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity; and / or, in combination with an autoencoder, by reconstructing the difference between the electromagnetic radiation signal of normal intensity and the electromagnetic radiation data to identify the electromagnetic radiation signal of abnormal intensity; and / or, using a support vector machine for classification, by constructing an optimal hyperplane to distinguish between electromagnetic radiation signals of normal intensity and electromagnetic radiation signals of abnormal intensity.

[0145] In the embodiment of the present invention, the calculation module 23 is specifically used to calculate the following formula: Calculate the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity; wherein, represents the time-weighted average radiation intensity, I i It is represented as the radiation intensity at the i-th moment, N is the number of sampling points, and w t (i) is expressed as a time-based weight function, where λ is the attenuation coefficient, t i It is expressed as the time difference between the i-th moment and the reference moment;

[0146] By formula: P = max(I1, I2, ..., I N ), calculate the peak intensity of the electromagnetic radiation signal of abnormal intensity, where P represents the peak intensity, I i It is expressed as the radiation intensity at the i-th moment, i = {1, 2, ..., N};

[0147] By formula: Calculate the energy density of the electromagnetic radiation signal of abnormal intensity; where E represents the energy density, Δt represents the sampling interval, and w e (i) is expressed as an energy-based weight function, where

[0148] By formula: Calculate the spectrum entropy of the electromagnetic radiation signal of abnormal intensity; where S represents the spectrum entropy, p j It is represented as the power ratio of the jth frequency component, and M is represented as the total number of frequency components.

[0149] In an embodiment of the present invention, the evaluation module 24 is specifically used to determine as a first-level abnormality when the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T1; to determine as a second-level abnormality when the peak intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T2; to determine as a third-level abnormality when the energy density of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T3; and to determine as a fourth-level abnormality when the spectrum entropy of the electromagnetic radiation signal of abnormal intensity is lower than the preset threshold value T4.

[0150] In the embodiment of the present invention, the evaluation module 24 is specifically used to define the abnormality level calculation formula: G=w1·log(T1+1)+w2·log(T2+1)+w3·log(T3+1)+w4·log(T4+1);

[0151] Where G represents the abnormality level, w1, w2, w3 and w4 represent the importance weights of time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy, respectively, and T1, T2, T3 and T4 represent the preset thresholds of the corresponding characteristic parameters, respectively;

[0152] The calculation formulas of the importance weights w1, w2, w3 and w4 are as follows:

[0153]

[0154] Among them, λ is the parameter for adjusting the steepness of the curve, σ1, σ2, σ3 and σ4 are the calibration factors of the corresponding characteristic parameters, μ P , μ E , μ S are respectively expressed as the average values ​​of the corresponding characteristic parameters, represents the time-weighted average radiation intensity, P represents the peak intensity, E represents the energy density, and S represents the spectral entropy.

[0155] Figure 3 The communication signal electromagnetic radiation evaluation and processing system can perform Figure 1 The implementation principle and technical effect of the communication signal electromagnetic radiation evaluation and processing method described in the illustrated embodiment will not be described in detail. The specific manner in which each module and unit performs operations in the communication signal electromagnetic radiation evaluation and processing system in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0156] In one possible design, Figure 2 The communication signal electromagnetic radiation evaluation and processing system of the illustrated embodiment may be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0157] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0158] The processing component 32 is used to: obtain real-time monitored electromagnetic radiation data; use a pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity, wherein the preprocessing includes at least: denoising, smoothing and frequency conversion; calculate the characteristic parameters of the electromagnetic radiation signal of abnormal intensity, the characteristic parameters include at least time-weighted average radiation intensity, peak intensity, energy density and spectral entropy; based on the characteristic parameters, evaluate the abnormal level of the electromagnetic radiation signal of abnormal intensity through preset evaluation rules; output an evaluation result, the evaluation result includes at least the abnormal level and the treatment measures corresponding to the abnormal level.

[0159] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0160] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0161] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0162] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0163] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0164] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0165] The embodiment of the present invention further provides a storage medium storing a computer program, which can achieve the above-mentioned Figure 1 The communication signal electromagnetic radiation evaluation and processing method of the embodiment shown.

[0166] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0167] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0168] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0169] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A communication signal electromagnetic radiation evaluation and processing method, characterized in that: include: Obtain real-time monitoring of electromagnetic radiation data; Using a pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity, wherein the pre-processing at least includes: denoising, smoothing and frequency conversion; Calculating characteristic parameters of the electromagnetic radiation signal of abnormal intensity, wherein the characteristic parameters at least include time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy; According to the characteristic parameters, evaluating the abnormal level of the electromagnetic radiation signal of abnormal intensity by using a preset evaluation rule; Output an evaluation result, wherein the evaluation result at least includes an abnormality level and a treatment measure corresponding to the abnormality level.

2. The method according to claim 1, characterized in that The preprocessing at least includes: denoising, smoothing and frequency conversion; Before using the pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity, the method further includes: Performing Fourier transformation on the electromagnetic radiation data to convert it into a frequency domain signal; Using a bandpass filter to filter out noise signals outside a specified frequency range, wherein the specified frequency range is determined according to the frequency range of an electromagnetic radiation signal of normal intensity, and is used to retain frequency components related to the electromagnetic radiation signal of abnormal intensity; A sliding window smoothing algorithm is applied to reduce signal fluctuations of the electromagnetic radiation data.

3. The method according to claim 2, characterized in that The method of using a pre-trained anomaly detection model to analyze the pre-processed electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity includes: Using a long short-term memory network for time series prediction to capture long-term dependencies in the electromagnetic radiation data to identify electromagnetic radiation signals of abnormal intensity; and / or, In combination with an autoencoder, the electromagnetic radiation signal of abnormal intensity is identified by reconstructing the difference between the electromagnetic radiation signal of normal intensity and the electromagnetic radiation data; and / or, Support vector machine is used for classification, and the optimal hyperplane is constructed to distinguish electromagnetic radiation signals of normal intensity from electromagnetic radiation signals of abnormal intensity.

4. The method according to claim 1, characterized in that: The step of calculating the characteristic parameter of the electromagnetic radiation signal of abnormal intensity comprises: By formula: Calculate the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity; wherein, represents the time-weighted average radiation intensity, I i It is represented as the radiation intensity at the i-th moment, N is the number of sampling points, and w t (i) is expressed as a time-based weight function, where λ is the attenuation coefficient, t i It is expressed as the time difference between the i-th moment and the reference moment; By formula: P = max(I1, I2, ..., I N ), calculate the peak intensity of the electromagnetic radiation signal of abnormal intensity, where P represents the peak intensity, I i It is expressed as the radiation intensity at the i-th moment, i = {1, 2, ..., N}; By formula: Calculate the energy density of the electromagnetic radiation signal of abnormal intensity; where E represents the energy density, Δt represents the sampling interval, and w e (i) is expressed as an energy-based weight function, where By formula: Calculate the spectrum entropy of the electromagnetic radiation signal of abnormal intensity; where S represents the spectrum entropy, p j It is represented as the power ratio of the jth frequency component, and M is represented as the total number of frequency components.

5. The method according to claim 1, characterized in that: The step of evaluating the abnormal level of the electromagnetic radiation signal of abnormal intensity according to the characteristic parameter by using a preset evaluation rule includes: When the time-weighted average radiation intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T1, it is determined to be a first-level abnormality; When the peak intensity of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T2, it is determined to be a secondary abnormality; When the energy density of the electromagnetic radiation signal of abnormal intensity exceeds the preset threshold value T3, it is determined as a third-level abnormality; When the spectrum entropy of the electromagnetic radiation signal of abnormal intensity is lower than the preset threshold value T4, it is determined to be a level 4 abnormality.

6. The method according to claim 1, characterized in that The step of evaluating the abnormal level of the electromagnetic radiation signal of abnormal intensity according to the characteristic parameter by using a preset evaluation rule includes: Define the abnormal level calculation formula: G = w1·log(T1+1)+w2·log(T2+1)+w3·log(T3+1)+w4·log(T4+1); Among them, G represents the abnormality level, w1, w2, w3 and w4 represent the importance weights of time-weighted average radiation intensity, peak intensity, energy density and spectral entropy respectively, and T1, T2, T3 and T4 represent the preset thresholds of the corresponding characteristic parameters respectively.

7. The method according to claim 6, characterized in that The calculation formula of the importance weights w1, w2, w3 and w4 is: Among them, λ is the parameter for adjusting the steepness of the curve, σ1, σ2, σ3 and σ4 are the calibration factors of the corresponding characteristic parameters, μ P , μ E , μ S are respectively expressed as the average values ​​of the corresponding characteristic parameters, represents the time-weighted average radiation intensity, P represents the peak intensity, E represents the energy density, and S represents the spectral entropy.

8. A communication signal electromagnetic radiation evaluation and processing system, characterized in that: include: An acquisition module is used to acquire electromagnetic radiation data for real-time monitoring; A detection module, used to analyze the pre-processed electromagnetic radiation data using a pre-trained anomaly detection model to identify electromagnetic radiation signals of abnormal intensity, wherein the pre-processing at least includes: denoising, smoothing and frequency conversion; A calculation module, used for calculating characteristic parameters of the electromagnetic radiation signal of abnormal intensity, wherein the characteristic parameters at least include time-weighted average radiation intensity, peak intensity, energy density and spectrum entropy; An evaluation module, used to evaluate the abnormal level of the electromagnetic radiation signal of abnormal intensity according to the characteristic parameters and through a preset evaluation rule; The output module is used to output the evaluation result, and the evaluation result at least includes the abnormality level and the processing measures corresponding to the abnormality level.

9. A storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, the communication signal electromagnetic radiation evaluation and processing method as described in any one of claims 1 to 7 is implemented.

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