CN114598403B - Data link broadband noise electromagnetic signal interference prediction method and system - Google Patents
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Abstract
The invention relates to a data link broadband noise electromagnetic signal interference prediction method and a system, which relate to the field of electromagnetic interference effects, wherein the method comprises the following steps: acquiring a frequency spectrum image and a data chain performance parameter histogram during broadband electromagnetic interference; constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram; dividing the sample set into a training set and a testing set; training the two-channel CNN model by using the training set to obtain a two-channel CNN prediction model; the double-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are connected in sequence; and predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result. The invention utilizes the double-channel CNN model to realize the prediction of the interference degree of the data link.
Description
Technical Field
The invention relates to the field of electromagnetic interference effects, in particular to a data link broadband noise electromagnetic signal interference prediction method and system.
Background
Unmanned plane as an emerging air combat force opens up a new modern war mode with the advantages of multiple loads, high efficiency and low cost. The data link is a communication hub for information interaction between the unmanned aerial vehicle and the ground, and is also a neuron which is most sensitive to electromagnetic interference. Because various electromagnetic signals are densely staggered in a complex battlefield electromagnetic environment, the unmanned aerial vehicle data link is threatened by multiple parties, and the communication performance of the data link is seriously affected, so that the overall combat efficiency of the unmanned aerial vehicle is reduced.
The broadband noise signal is a typical interference signal pattern in a complex battlefield electromagnetic environment, and can be regarded as low-power unintentional noise interference on an unmanned aerial vehicle data link when a large number of electronic devices work simultaneously and space electromagnetic signals are randomly combined in an interlaced manner in the battlefield environment; in addition, enemy electromagnetic interference machines also can transmit high power noise electromagnetic interference targeted to the communication frequency band of the my data link. These intentional or unintentional noise interferences can cause the signal-to-noise ratio of the data link to be reduced and the error rate to be increased to different degrees, and influence the transmission of control instructions of the unmanned aerial vehicle from the my.
At present, machine learning methods have also attracted extensive attention and application in the fields of electromagnetic compatibility and communication. The convolution neural network can directly input an original image, avoids complex pretreatment of the image, has a certain deep learning capability, and is widely applied. There is no method of applying machine learning to electromagnetic interference prediction.
Disclosure of Invention
The invention aims to provide a data link broadband noise electromagnetic signal interference prediction method and a system, which are used for predicting the interference degree of a data link by using a double-channel CNN model.
In order to achieve the above object, the present invention provides the following solutions:
a data link broadband noise electromagnetic signal interference prediction method comprises the following steps:
acquiring a frequency spectrum image and a data chain performance parameter histogram during broadband electromagnetic interference;
constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram;
dividing the sample set into a training set and a testing set;
training the two-channel CNN model by using the training set to obtain a two-channel CNN prediction model; the double-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are connected in sequence;
and predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result.
Optionally, the acquiring the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram specifically includes:
acquiring IQ information and data link performance parameters of electromagnetic signals on a communication link;
performing STFT (space time Fourier transform) on the IQ signal to obtain a broadband electromagnetic interference time spectrum image;
and carrying out normalization processing on the data link performance parameters and drawing a data link performance parameter histogram according to the normalized data link performance parameters.
Optionally, training the two-channel CNN model by using the training set to obtain a two-channel CNN prediction model, which specifically includes:
inputting the broadband electromagnetic interference time spectrum image of the training set into a first feature extraction module of the feature extraction layer to obtain a first local feature;
inputting the data chain performance parameter histogram of the training set into a second feature extraction module of the feature extraction layer to obtain a second local feature;
inputting the first local feature and the second local feature into the feature fusion layer to obtain a feature vector;
inputting the feature vector into the predictive regression layer to obtain a data link performance predictive value;
determining a loss function according to the predicted value of the data link performance and a target value in the training set;
optimizing the two-channel CNN model by utilizing SGDM, RMSProp and Adam optimization methods according to the loss function respectively to obtain an evaluation index and a plurality of optimization results; the evaluation index includes root mean square error and accuracy;
and determining a dual-channel CNN prediction model according to the evaluation index and the plurality of optimization results.
Optionally, the expression of the loss function is:
where loss is a loss function, t i Is the target value under the i-th prediction result, y i For model predictions, k is the number of batches.
Optionally, the expression of the root mean square error is:
wherein RMSE is root mean square error, y i As a model predictive value, t i N is the target value under the i-th type prediction result train Is the total number of samples of the channel.
Optionally, the accuracy is expressed as:
wherein Acc (y, t) is the accuracy, y i As a model predictive value, t i N is the target value under the i-th type prediction result train Sign (y i ,t i ) As a sign function.
A data link broadband noise electromagnetic signal interference prediction system, comprising:
the acquisition module is used for acquiring a frequency spectrum image and a data chain performance parameter histogram during broadband electromagnetic interference;
the construction module is used for constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram;
the splitting module is used for dividing the sample set into a training set and a testing set;
the training module is used for training the two-channel CNN model by utilizing the training set to obtain a two-channel CNN prediction model; the double-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are connected in sequence;
and the prediction module is used for predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result.
Optionally, the acquiring module specifically includes:
the acquisition unit is used for acquiring IQ information and data link performance parameters of electromagnetic signals on the communication link;
the STFT conversion unit is used for carrying out STFT conversion on the IQ signal to obtain a frequency spectrum image when broadband electromagnetic interference occurs;
and the normalization processing and drawing unit is used for carrying out normalization processing on the data link performance parameters and drawing a data link performance parameter histogram according to the normalized data link performance parameters.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention obtains the frequency spectrum image and the data chain performance parameter histogram during broadband electromagnetic interference; constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram; dividing the sample set into a training set and a testing set; training the two-channel CNN model by using the training set to obtain a two-channel CNN prediction model; the double-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are connected in sequence; and predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result. The invention can accurately predict the interference effect level of the data link under the interference of broadband noise, so that the data link can evaluate the threat of the interference of the broadband noise.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a data link broadband noise electromagnetic signal interference prediction method provided by the invention;
fig. 2 is a graph of variation trend of interference power-performance parameters according to the present invention;
FIG. 3 is a schematic diagram of an electromagnetic interference acquisition system for an injection data link;
FIG. 4 is a normalized histogram of data link performance parameters;
fig. 5 is a schematic structural diagram of a two-channel CNN prediction model.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a data link broadband noise electromagnetic signal interference prediction method and a system, which are used for predicting the interference degree of a data link by using a double-channel CNN model.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
According to the invention, a double-channel CNN network is established, a broadband electromagnetic interference time-frequency spectrogram and a data link state parameter histogram are used as model input, a set data link communication performance boundary is used as output, the communication performance of the unmanned aerial vehicle data link under different broadband noise interferences is predicted, and the interference effect evaluation problem of the data link under the broadband noise interferences is solved.
As shown in fig. 1, the method for predicting electromagnetic signal interference of broadband noise of a data link provided by the invention comprises the following steps:
as shown in fig. 2, when the interference power is within 1dB from the lock loss power, the signal-to-noise ratio of the data link is rapidly reduced, the bit error rate is exponentially increased, and the communication performance may be deteriorated to communication interruption at any time; when the interference power is 1-3 dB away from the unlocking power, the data link can work normally, but the signal to noise ratio is reduced compared with the normal working state, the error code rises faster along with the increase of the interference power, and the communication quality is reduced; when the interference power distance is 3-6 dB, the data link can work normally, but a lower error code number exists; when the interference power distance is greater than 6dB, the data link works normally, and the error code number is 0.
The unmanned aerial vehicle communication performance boundary is specified. According to the data link communication mechanism and electromagnetic compatibility sensitivity test experience, using the out-of-lock as a sign of data link communication interruption, and dividing the electromagnetic interference level of the data link according to the power value of the distance out-of-lock. The interference levels are respectively: interruption: losing lock; disturbance: the interference power is 1dB away from the lock; interference: the interference power is 3dB away from the lock; early warning: the interference power is 6dB from the lock-out.
As shown in fig. 3, an injection type unmanned aerial vehicle data link electromagnetic interference data acquisition system is adopted to obtain a spectrum image and a data link performance parameter histogram sample set during broadband electromagnetic interference, the sample set is preprocessed, and the preprocessed sample set is subjected to preprocessing according to a ratio of 7: the scale of 3 is divided into training and test sets. The airborne data link and the ground data link are connected through a radio frequency cable, and two 40dB fixed attenuators and an adjustable attenuator are connected in series in the middle: the fixed attenuator is used for attenuating a larger working signal output by the radio frequency port, simulating a long-distance transmission condition and protecting the radio frequency front end from being broken down by a high-power signal; the adjustable attenuator is used for adjusting the working signal and simulating the working signal of the UAV at different flight distances. The interference signal is generated by a signal generator and injected into a communication link through a combiner to simulate electromagnetic interference to a data link of the unmanned aerial vehicle in space. The ground-end data chain is connected with a computer through a network cable, and the state information of the data chain is read in real time through state monitoring software, wherein the data chain comprises qualitative information and three quantitative data: the qualitative information is whether the data link is out of lock; the quantitative information is: automatic gain control voltage (AGC) indication, bit Error Rate (BER), signal to noise ratio (SNR). The electromagnetic space signal is measured by a monitoring receiver, and the receiver is connected to the receiving end of the airborne data chain antenna and records the IQ data of the analog space electromagnetic signal.
Step 101: and acquiring a frequency spectrum image and a data chain performance parameter histogram during broadband electromagnetic interference. Step 101 specifically includes:
IQ information and data link performance parameters of electromagnetic signals on a communication link are acquired.
And performing STFT on the IQ signal to obtain a broadband electromagnetic interference time spectrum image.
And carrying out normalization processing on the data link performance parameters and drawing a data link performance parameter histogram according to the normalized data link performance parameters.
IQ data of electromagnetic signals on a communication link and performance parameters of a data link are obtained through an acquisition system. Because the IQ data and the data chain performance parameter structure are different, the direct fusion modeling is inconvenient, and therefore, the visualization processing is carried out on the two data. Performing STFT (space time Fourier transform) on a time domain signal formed by the IQ data, then squaring a modulus value of the time domain signal, and then combining according to a time sequence to obtain a time-frequency spectrogram of the electromagnetic signal; the data link performance parameters AGC, SNR and BER are normalized and a histogram is plotted as shown in fig. 4. Both the time-frequency spectrogram and the data chain performance parameter histogram will change in real time with interference.
Step 102: and constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram.
And acquiring a spectrum image and a data chain performance parameter histogram sample set during broadband electromagnetic interference, visualizing and preprocessing the sample set, and dividing the preprocessed sample set into a training set and a testing set. The spectrum image is obtained by performing STFT transformation on IQ data and then overlapping the IQ data in a time domain, and the data link performance histogram is drawn in real time by normalized data link performance parameters.
Step 103: the sample set is divided into a training set and a test set.
And obtaining a model sample through visual conversion of the data, and preprocessing a sample set. 440 data link performance parameter samples and 440 electromagnetic interference spectrogram samples are obtained through testing, images of the samples are divided by 255, and data are normalized between (0 and 1), so that interference caused by difference of the value ranges of data in each dimension is reduced. The above samples are marked according to the prescribed unmanned aerial vehicle communication performance boundary and interference level, and 440 samples are divided into four groups of {0,1,3,6}, 110 samples each. Samples for each channel were prepared according to 7: the scale of 3 is divided into training and test sets.
Step 104: training the two-channel CNN model by using the training set to obtain a two-channel CNN prediction model; the dual-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are sequentially connected.
As shown in fig. 5, a two-channel CNN model is constructed for predicting the interference degree of the data link. The dual-channel CNN model consists of a feature extraction layer, a feature fusion layer and a predictive regression layer. The characteristic extraction layer is two parallel branches and is used for extracting the partial characteristics of the data link performance parameter sample and the electromagnetic interference spectrogram respectively, and the layer consists of a convolution layer, a pooling layer and a full connection layer; the feature fusion layer consists of an addition layer and a full connection layer and is used for fusing the image features to obtain feature vectors corresponding to each image; and the prediction regression layer calculates the half mean square error loss of the regression task and obtains a prediction result.
The feature extraction layer is two parallel CNN network branches and is used for extracting the data link performance parameter samples and the local features of an electromagnetic interference spectrogram respectively, and the feature extraction layer consists of a convolution layer, a pooling layer and a full connection layer. The specific process for extracting the characteristics is as follows: the feature mapping of the input image is convolved with the convolution kernel, the local features of the input image sample set are extracted, and then the pooling layer is connected for reducing the dimension of the image features, so that the model overfitting is reduced to a certain extent. The convergence rate of the network is increased by a modified linear unit (corrected LinearUnit, RELU) activation function, and the image characteristics are further extracted by a combination of a convolution layer and a full connection layer.
The feature fusion layer is composed of an addition layer and a full connection layer and is used for fusing the image features to obtain feature vectors corresponding to each image, and for regression problems, the feature number of the full connection layer is 1.
The predictive regression layer calculates the semi-mean square error loss of the regression task. Through neural network back propagation, each neuron is continuously trained to update the network weight and offset value, so that the error gradient is reduced, and the model is continuously optimized.
Step 104 specifically includes:
inputting the broadband electromagnetic interference time spectrum image of the training set into a first feature extraction module of the feature extraction layer to obtain a first local feature;
inputting the data chain performance parameter histogram of the training set into a second feature extraction module of the feature extraction layer to obtain a second local feature;
inputting the first local feature and the second local feature into the feature fusion layer to obtain a feature vector;
inputting the feature vector into the predictive regression layer to obtain a data link performance predictive value;
determining a loss function according to the predicted value of the data link performance and a target value in the training set; wherein, the expression of the loss function is:
where loss is a loss function, t i Is the target value under the i-th prediction result, y i As a result of the model predictive value,k is the number of batches.
The loss function loss is used for measuring the degree of inconsistency between the predicted value and the target value, small batches are set for accelerating the iteration speed, the number of the batches is assumed to be k, and the half-mean square error in each small batch observation is the loss function.
Optimizing the two-channel CNN model by utilizing SGDM, RMSProp and Adam optimization methods according to the loss function respectively to obtain an evaluation index and a plurality of optimization results; the evaluation index includes root mean square error and accuracy; wherein, the expression of the root mean square error is:
wherein RMSE is root mean square error, y i As a model predictive value, t i N is the target value under the i-th type prediction result train Is the total number of samples of the channel.
And optimizing the value of the loss function in the training set by using SGDM, RMSProp and Adam optimization methods, and then evaluating the prediction accuracy through index parameters. The evaluation indexes are Root Mean Square Error (RMSE) and accuracy.
Accuracy is defined as the ratio of the number of correctly predicted samples to the total number of samples. The samples in the model are uniformly distributed, so that the prediction accuracy index can be used for evaluation. In the present model, it is defined that the prediction result is correct when the absolute value of the difference between the prediction result and the true value is smaller than 1. Let y be i As a model predictive value, t i Is the target value, N train Is the total number of samples of the channel.
The expression of the accuracy is:
wherein Acc (y, t) is the accuracy, y i As a model predictive value, t i N is the target value under the i-th type prediction result train Sign (y i ,t i ) As a sign function.
RMSE was used to evaluate the predictive effect of the final model. The smaller the RMSE, the better the predictive effect of the model.
The predicted structure of the two-channel CNN model optimized using SGDM, RMSProp, and Adam is shown in table 1. It can be obtained that the two-channel convolution model using the SGDM solver has the highest prediction accuracy, the lowest RMSE, and the model optimized by the RMSProp, and the model optimized by the Adam has relatively poor result. The accuracy of the two-channel CNN model optimized by the three solvers is above 90%.
Table 1 prediction results of network on test set
| Solver | Accuracy rate of | RMSE |
| SGDM | 0.9697 | 0.5189 |
| Adam | 0.9091 | 0.7542 |
| RMSProp | 0.9621 | 0.5214 |
And determining a dual-channel CNN prediction model according to the evaluation index and the plurality of optimization results.
Step 105: and predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result.
The invention provides a data link electromagnetic interference effect level, and a visualized sample set is obtained by acquiring a broadband noise electromagnetic interference time-frequency spectrogram and a data link performance parameter normalization histogram; preprocessing the sample set, dividing the preprocessed sample set into a training set and a testing set, then constructing a double-channel CNN prediction model, and inputting the training set and the testing set into the model for training to obtain a trained CNN prediction model; and finally, inputting the test set into the two-channel CNN prediction model, and predicting the input image of the test set to obtain a prediction result of the performance boundary level of the data link. The method can accurately predict the interference effect level of the data link under the interference of broadband noise, so that the data link can evaluate the threat of the interference of the broadband noise, thereby timely taking certain anti-interference measures and improving the intelligent interference cognitive ability of the data link.
The invention also provides a data link broadband noise electromagnetic signal interference prediction system, which comprises:
and the acquisition module is used for acquiring the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram. The acquisition module specifically comprises: the acquisition unit is used for acquiring IQ information and data link performance parameters of electromagnetic signals on the communication link; the STFT conversion unit is used for carrying out STFT conversion on the IQ signal to obtain a frequency spectrum image when broadband electromagnetic interference occurs; and the normalization processing and drawing unit is used for carrying out normalization processing on the data link performance parameters and drawing a data link performance parameter histogram according to the normalized data link performance parameters.
And the construction module is used for constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram.
And the splitting module is used for dividing the sample set into a training set and a testing set.
The training module is used for training the two-channel CNN model by utilizing the training set to obtain a two-channel CNN prediction model; the dual-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are sequentially connected.
And the prediction module is used for predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result.
The invention is suitable for the technical field of electromagnetic interference effect evaluation, and provides an unmanned aerial vehicle data link broadband noise electromagnetic interference effect evaluation method and device. And (3) acquiring a spectrum image and a data chain performance parameter histogram sample set during broadband electromagnetic interference by adopting an injection type unmanned aerial vehicle data chain electromagnetic interference data acquisition system according to a specified unmanned aerial vehicle communication performance boundary, and dividing the preprocessed sample set into a training set and a testing set. And then constructing a two-channel CNN regression prediction model, inputting the training set into the two-channel CNN prediction model to obtain a trained two-channel CNN prediction model, and finally inputting the test set into the trained two-channel CNN prediction model to carry out regression prediction on the input image of the test set to obtain a prediction result of the performance boundary level of the data link. The method and the device can accurately predict the interference effect level of the data link under the broadband noise interference, so that the data link can evaluate the threat of the broadband noise interference, thereby timely taking certain anti-interference measures and improving the intelligent interference cognitive ability of the data link.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.
Claims (8)
1. The method for predicting the electromagnetic signal interference of the broadband noise of the data link is characterized by comprising the following steps of:
acquiring a frequency spectrum image and a data chain performance parameter histogram during broadband electromagnetic interference;
constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram;
dividing the sample set into a training set and a testing set;
training the two-channel CNN model by using the training set to obtain a two-channel CNN prediction model; the double-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are connected in sequence;
and predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result.
2. The method for predicting electromagnetic signal interference of broadband noise of data link according to claim 1, wherein the acquiring the spectral image and the histogram of the performance parameters of the data link during broadband electromagnetic interference specifically comprises:
acquiring IQ information and data link performance parameters of electromagnetic signals on a communication link;
performing STFT (space time Fourier transform) on the IQ information to obtain a broadband electromagnetic interference time spectrum image;
and carrying out normalization processing on the data link performance parameters and drawing a data link performance parameter histogram according to the normalized data link performance parameters.
3. The method for predicting electromagnetic signal interference of broadband noise of data link according to claim 1, wherein the training the two-channel CNN model by using the training set to obtain the two-channel CNN prediction model specifically comprises:
inputting the broadband electromagnetic interference time spectrum image of the training set into a first feature extraction module of the feature extraction layer to obtain a first local feature;
inputting the data chain performance parameter histogram of the training set into a second feature extraction module of the feature extraction layer to obtain a second local feature;
inputting the first local feature and the second local feature into the feature fusion layer to obtain a feature vector;
inputting the feature vector into the predictive regression layer to obtain a data link performance predictive value;
determining a loss function according to the predicted value of the data link performance and a target value in the training set;
optimizing the two-channel CNN model by utilizing SGDM, RMSProp and Adam optimization methods according to the loss function respectively to obtain an evaluation index and a plurality of optimization results; the evaluation index includes root mean square error and accuracy;
and determining a dual-channel CNN prediction model according to the evaluation index and the plurality of optimization results.
4. A data link broadband noise electromagnetic signal interference prediction method according to claim 3, wherein the expression of the loss function is:
where loss is a loss function, t i Is the target value under the i-th prediction result, y i For model predictions, k is the number of batches.
5. The method for predicting electromagnetic signal interference of broadband noise of data link according to claim 3, wherein the expression of root mean square error is:
wherein RMSE is root mean square error, y i As a model predictive value, t i N is the target value under the i-th type prediction result train Is the total number of samples of the channel.
6. The method for predicting electromagnetic signal interference of broadband noise of data link according to claim 3, wherein the expression of accuracy is:
wherein Acc (y, t) is the accuracy, y i As a model predictive value, t i N is the target value under the i-th type prediction result train Sign (y i ,t i ) As a sign function.
7. A data link broadband noise electromagnetic signal interference prediction system, comprising:
the acquisition module is used for acquiring a frequency spectrum image and a data chain performance parameter histogram during broadband electromagnetic interference;
the construction module is used for constructing a sample set according to the broadband electromagnetic interference time spectrum image and the data link performance parameter histogram;
the splitting module is used for dividing the sample set into a training set and a testing set;
the training module is used for training the two-channel CNN model by utilizing the training set to obtain a two-channel CNN prediction model; the double-channel CNN model comprises a feature extraction layer, a feature fusion layer and a predictive regression layer which are connected in sequence;
and the prediction module is used for predicting the dual-channel CNN prediction model by using the test set to obtain a data chain performance prediction result.
8. The data link broadband noise electromagnetic signal interference prediction system according to claim 7, wherein the obtaining module specifically includes:
the acquisition unit is used for acquiring IQ information and data link performance parameters of electromagnetic signals on the communication link;
the STFT conversion unit is used for carrying out STFT conversion on the IQ information to obtain a frequency spectrum image in broadband electromagnetic interference;
and the normalization processing and drawing unit is used for carrying out normalization processing on the data link performance parameters and drawing a data link performance parameter histogram according to the normalized data link performance parameters.
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