Novel interference frequency point detection method and device, storage medium and electronic equipment
By applying machine learning technology in interference point detection and building an interference classification model, the problems of low accuracy and limited application range of traditional detection methods are solved, and high-precision and robust interference point detection are achieved.
Patent Information
- Application Number
- CN202411807083.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-10
- Publication Date
- 2025-02-28
AI Technical Summary
Traditional interference frequency point detection methods have problems such as low accuracy, limited application range and susceptible to environmental information interference.
Using a machine learning-based method, by obtaining frequency hopping communication frame data of the communication system, hierarchical sampling and supervised learning algorithm training are carried out, and an interference classification model is constructed to identify interference frequency points.
High-precision interference frequency point detection is realized, the anti-interference ability is improved, and the detection robustness is enhanced.
Smart Images
Figure CN119544115A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of interference frequency detection, and in particular to a novel interference frequency detection method, device, storage medium and electronic device. Background Art
[0002] As the electromagnetic environment faced by wireless communication systems becomes more and more complex, the interference during communication is becoming more and more serious. Rapidly and accurately detecting interference frequencies helps improve the anti-interference ability of communication systems.
[0003] Traditional interference detection methods such as energy detection, continuous mean elimination, and matched filter detection have achieved interference frequency detection to a certain extent, but they require additional operations such as setting threshold values and manually designing features, and are easily affected by environmental information of the communication system. They have problems such as low accuracy and limited application scope. Summary of the invention
[0004] The embodiments of the present application provide a novel interference frequency detection method, device, storage medium and electronic device. The method automatically learns the hidden rules in communication data based on machine learning technology to achieve high-precision interference frequency detection.
[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.
[0006] According to a first aspect of an embodiment of the present application, a novel interference frequency point detection method is provided, including:
[0007] Obtain frequency hopping communication frame data of the communication system, and generate a data set by stratified sampling after splitting it according to the set frequency set;
[0008] Construct an interference classification model based on a supervised learning algorithm and set model parameters;
[0009] Based on the data set, the interference classification model is trained and verified to obtain a frequency point interference state recognition model;
[0010] Based on the frequency interference state recognition model, the interference frequency points in the communication frame are detected, and a set of interfered frequency points in the communication frame is output.
[0011] In some embodiments of the present application, based on the above scheme, the step of acquiring the frequency hopping communication frame data of the communication system and generating a data set by stratified sampling after splitting it according to the set frequency set includes:
[0012] Based on the set simulation communication system, communication frame data is obtained as sampling data;
[0013] Divide all sampled data into training sets and test sets in proportion;
[0014] Splitting the training set data by frequency point to obtain all sampling data of a single frequency point;
[0015] Marking the category labels corresponding to all the frequency points contained in the test set at the end to obtain a marked test set;
[0016] A category label is added to the end of the sampling data of each frequency point obtained by splitting the training set, and a labeled training set and validation set are generated for the sampling data of each frequency point by stratified sampling.
[0017] In some embodiments of the present application, based on the aforementioned solution, the interference classification model is constructed based on the supervised learning algorithm, and the model parameters are set, including:
[0018] Statistical analysis of the data set, including maximum value analysis, mean and variance analysis;
[0019] Based on the task type, select the interference classification model from the supervised learning algorithm;
[0020] For the selected interference classification model, set appropriate model parameters and convergence conditions.
[0021] In some embodiments of the present application, based on the aforementioned solution, training the interference classification model based on the data set includes:
[0022] Reading the training set;
[0023] Model training is performed based on the training set, the selected interference classification model and the model parameters until the model is iteratively converged and then saved.
[0024] In some embodiments of the present application, based on the aforementioned solution, verifying the interference classification model based on the data set includes:
[0025] Reading the verification set;
[0026] Obtain the trained interference classification model;
[0027] The trained interference classification model is used to perform classification prediction on the verification set to verify the generalization performance of the model, and the verified interference classification model is used as the frequency point interference state recognition model.
[0028] In some embodiments of the present application, based on the aforementioned solution, detecting the interference frequency in the communication frame based on the frequency interference state recognition model includes:
[0029] Acquire the test data set and the frequency point interference state recognition model;
[0030] Splitting each test sample data in the test data set according to the frequency point to obtain test data of multiple frequency points;
[0031] Based on the frequency interference state recognition model, test data of multiple frequency points are classified and predicted in turn to obtain multiple classification results;
[0032] The frequency point number combination whose classification result is interference is extracted as the interference frequency point subset.
[0033] In some embodiments of the present application, based on the above solution, the set of interfered frequency points in the output communication frame includes:
[0034] The interference frequency subset is compared with the frequency set to obtain the interfered frequency in the specific communication frame and output it.
[0035] According to a second aspect of an embodiment of the present application, a novel interference frequency point detection device is provided, including:
[0036] An acquisition unit, used for acquiring frequency hopping communication frame data of a communication system;
[0037] A generation unit, used to generate a data set by stratified sampling after splitting according to a set frequency set;
[0038] A construction unit, used to construct an interference classification model based on a supervised learning algorithm and set model parameters;
[0039] A training and verification unit, used to train and verify the interference classification model based on the data set to obtain a frequency point interference state recognition model;
[0040] The detection unit is used to detect the interference frequency points in the communication frame based on the frequency interference state recognition model, and output the set of interfered frequency points in the communication frame.
[0041] According to a third aspect of an embodiment of the present application, a computer-readable storage medium is provided, wherein the storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer executes the method according to the first aspect.
[0042] According to a fourth aspect of the embodiments of the present application, there is provided an electronic device, including: a memory and a processor;
[0043] The memory is used to store computer instructions;
[0044] The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in the first aspect.
[0045] The technical solution of the present application utilizes machine learning technology to overcome the shortcomings of traditional interference frequency detection methods, greatly improves the accuracy and robustness of interference frequency detection, and provides technical support for intelligent detection of interference frequencies and intelligent anti-interference communication applications.
[0046] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0047] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:
[0048] Figure 1 A schematic diagram of a flow chart of a novel interference frequency point detection method according to an embodiment of the present application is shown;
[0049] Figure 2 A block diagram of a novel interference frequency point detection device according to an embodiment of the present application is shown;
[0050] Figure 3 A block diagram of an electronic device according to an embodiment of the present application is shown;
[0051] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION
[0052] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.
[0053] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.
[0054] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.
[0055] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.
[0056] It should be noted that the “plurality” mentioned in this article refers to two or more.
[0057] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0058] Some embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.
[0059] See also Figure 1 , shows a flow chart of a novel interference frequency detection method according to an embodiment of the present application.
[0060] like Figure 1 As shown, a novel interference frequency detection method is demonstrated, including steps S100 to S400.
[0061] refer to Figure 1 , step S100, obtaining the frequency hopping communication frame data of the communication system, and generating a data set by stratified sampling after splitting it according to the set frequency set.
[0062] In some feasible embodiments, based on the above solution, step S100 includes:
[0063] Step S101 : based on a set simulated communication system, obtaining communication frame data as sampling data.
[0064] It should be noted that the simulated communication system includes an interference node and two communication nodes, and the signal received by the receiving end is the superposition of the useful signal s(t) sent by the sending end, the noise signal n(t) of the communication system and the interference signal j(t) sent by the interference end;
[0065] The simulation communication system needs to set the number of frequency hopping points fn and the frequency hopping center frequency f c , frequency hopping bandwidth bw, interference signal bandwidth and power, channel noise and other main parameters.
[0066] Based on the simulation communication system, the communication frame simulation data of the interfered signal can be obtained by simulating the frequency hopping signal and the interference signal.
[0067] It should be noted that the communication frame data at least includes fields such as sampling timestamp, communication rate, signal-to-noise ratio, transmission signal power, frequency hopping frequency signal power and signal-to-noise ratio.
[0068] Step S102: Divide all sampled data into a training set and a test set in proportion.
[0069] Specifically, the ratio is usually 8:2.
[0070] Step S103, splitting the training set data by frequency point to obtain all sampled data of a single frequency point.
[0071] Specifically, the training set data is split according to a single frequency point to obtain all sampling data of fn frequency points, and the dimension of the sampling data of a single frequency point is n*m, wherein n is the amount of data, and m is the number of dimensions of the data field; the sample data amount n of the frequency point is not a fixed value, and it depends on the frequency hopping distribution of the frequency point fi in the sampling data of the simulation system.
[0072] Step S104, marking the category labels corresponding to all the frequency points contained in the test set at the end to obtain a marked test set.
[0073] Step S105, adding a category label to the sampling data of the single frequency point in step S103 and placing it in the last field of the data sample; and then generating a labeled training set and a validation set for the sampling data of the single frequency point by stratified sampling.
[0074] It should be noted that the category label uses 1 and 0 to represent interference and non-interference respectively.
[0075] It should be noted that the stratified sampling is to sample the frequency sampling data in a balanced ratio of interference and non-interference to generate data sets in sequence.
[0076] It should be noted that the ratio of the training set to the validation set generated based on the sampling data of a single frequency point is usually set to 8:2.
[0077] Continue to refer Figure 1 , step S200, constructing an interference classification model based on a supervised learning algorithm and setting model parameters.
[0078] In some feasible embodiments, based on the above solution, step S200 includes:
[0079] Step S201 , performing maximum value analysis, mean value analysis and variance analysis on the data set of each frequency point to understand the statistical distribution of the sampled data.
[0080] Step S202: Select an interference classification model from a supervised learning algorithm based on the task type.
[0081] Specifically, the interference frequency detection task is converted into a classification judgment of whether a single frequency point is interfered with;
[0082] Based on the classification task, at least one interference classification model is selected from the supervised learning algorithm, taking the support vector machine interference classification model as an example.
[0083] Step S203: setting appropriate model parameters and convergence conditions based on the selected interference classification model.
[0084] Taking the support vector machine interference classification model as an example, it is necessary to set two key parameters: penalty factor and kernel function. The penalty factor value range is usually [0.01, 0.1, 1, 10]. The kernel function is usually Sigmoid and RBF.
[0085] Continue to refer Figure 1 , step S300, training and verifying the interference classification model based on the data set to obtain a frequency point interference state recognition model.
[0086] In some feasible embodiments, based on the above scheme, training the interference classification model based on the data set includes:
[0087] Reading the training set;
[0088] Model training is performed based on the training set, the selected interference classification model and the model parameters until the model is iteratively converged and then saved.
[0089] Exemplarily, for a single frequency point, the process is as follows:
[0090] The training set in step S105 is read, and based on the interference classification model selected in step S200 and the set model parameters and convergence conditions, an interference classification model for a single frequency point is trained, and the trained interference classification model is saved locally when the optimal effect is achieved.
[0091] In some feasible embodiments, based on the above scheme, verifying the interference classification model based on the data set includes:
[0092] Reading the verification set;
[0093] Obtain the trained interference classification model;
[0094] The trained interference classification model is used to perform classification prediction on the verification set to verify the generalization performance of the model, and the verified interference classification model is used as the frequency point interference state recognition model.
[0095] Exemplarily, for a single frequency point, the process is as follows:
[0096] The verification set in step S105 is read, and the saved interference classification model with the best effect is loaded; classification prediction is performed on the verification set based on the loaded interference classification model to verify the generalization performance of the model.
[0097] It can be understood that by repeating the above model training and model verification process, fn frequency point interference state recognition models are obtained respectively.
[0098] Continue to refer Figure 1 , step S400, detecting interference frequencies in a communication frame based on the frequency interference state recognition model, and outputting a set of interfered frequencies in the communication frame.
[0099] In some feasible embodiments, based on the above solution, detecting the interference frequency in the communication frame based on the frequency interference state recognition model includes:
[0100] Acquire the test data set and the frequency point interference state recognition model;
[0101] Splitting each test sample data in the test data set according to the frequency point to obtain test data of multiple frequency points;
[0102] Based on the frequency interference state recognition model, test data of multiple frequency points are classified and predicted in turn to obtain multiple classification results;
[0103] The frequency point number combination whose classification result is interference is extracted as the interference frequency point subset.
[0104] Exemplarily, the specific process of this step is as follows:
[0105] Read the test dataset;
[0106] Load fn frequency interference state identification models;
[0107] Each test sample data is split by frequency point to obtain test data of fn frequency points;
[0108] Use the fn frequency interference state recognition model to classify and predict the test data of fn frequency points in turn, and obtain fn classification results;
[0109] The frequency point number combination with the classification result of 1 is extracted as a subset, denoted as f gr , whose quantitative dimension is no greater than fn.
[0110] It should be noted that 1 represents interference, and the corresponding 0 represents non-interference.
[0111] In some feasible embodiments, based on the above solution, the set of interfered frequency points in the output communication frame includes:
[0112] Compare the interference frequency subset f gr With the frequency set f, the frequency of the interfered frequency in the specific communication frame is obtained and output.
[0113] The following describes an apparatus embodiment of the present application, which can be used to execute a novel interference frequency detection method in the above-mentioned embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, please refer to the above-mentioned method embodiment of the present application.
[0114] Reference Figure 2 As shown, a novel interference frequency point detection device 200 according to an embodiment of the present application includes:
[0115] An acquisition unit 201 is used to acquire frequency hopping communication frame data of a communication system;
[0116] A generating unit 202, configured to generate a data set by stratified sampling after splitting according to a set frequency set;
[0117] A construction unit 203, used to construct an interference classification model based on a supervised learning algorithm and set model parameters;
[0118] A training and verification unit 204 is used to train and verify the interference classification model based on the data set to obtain a frequency point interference state recognition model;
[0119] The detection unit 205 is used to detect interference frequencies in the communication frame based on the frequency interference state recognition model, and output a set of interfered frequencies in the communication frame.
[0120] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor, and when the processor 320 executes the computer program 311, the steps of the above-mentioned new interference frequency detection method are implemented.
[0121] Since the electronic device introduced in this embodiment is a device used to implement a new interference frequency detection device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation mode of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.
[0122] During the specific implementation process, when the computer program 311 is executed by a processor, any implementation method in the embodiments corresponding to the first aspect can be implemented.
[0123] Figure 4 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.
[0124] It should be noted that Figure 4 The computer system 400 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0125] like Figure 4 As shown, the computer system 400 includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 to the random access memory (RAM) 403, such as executing the method described in the above embodiment. In the RAM 403, various programs and data required for system operation are also stored. The CPU 401, ROM 402 and RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.
[0126] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom is installed into the storage section 408 as needed.
[0127] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 409, and / or installed from a removable medium 411. When the computer program is executed by a central processing unit (CPU) 401, various functions defined in the system of the present application are executed.
[0128] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.
[0129] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.
[0130] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.
[0131] As another aspect, the present application further provides a computer program product or a computer program, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes a novel interference frequency point detection method described in the above embodiment.
[0132] As another aspect, the present application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being assembled into the electronic device. The above computer-readable medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements a novel interference frequency detection method described in the above embodiment.
[0133] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.
[0134] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.
[0135] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary techniques in the art that are not disclosed in the present application. It should be understood that the present application is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.
Claims
1. A novel interference frequency detection method, characterized in that: include: Obtain frequency hopping communication frame data of the communication system, and generate a data set by stratified sampling after splitting it according to the set frequency set; Construct an interference classification model based on a supervised learning algorithm and set model parameters; Based on the data set, the interference classification model is trained and verified to obtain a frequency point interference state recognition model; Based on the frequency interference state recognition model, the interference frequency points in the communication frame are detected, and a set of interfered frequency points in the communication frame is output.
2. The method according to claim 1, characterized in that: The step of acquiring the frequency hopping communication frame data of the communication system and splitting the data according to the set frequency set to generate a data set in a stratified sampling manner includes: Based on the set simulation communication system, communication frame data is obtained as sampling data; Divide all sampled data into training sets and test sets in proportion; Splitting the training set data by frequency point to obtain all sampling data of a single frequency point; Marking the category labels corresponding to all the frequency points contained in the test set at the end to obtain a marked test set; A category label is added to the end of the sampling data of each frequency point obtained by splitting the training set, and a labeled training set and validation set are generated for the sampling data of each frequency point by stratified sampling.
3. The method according to claim 2, characterized in that The interference classification model is constructed based on the supervised learning algorithm, and the model parameters are set, including: Statistical analysis of the data set, including maximum value analysis, mean and variance analysis; Based on the task type, select the interference classification model from the supervised learning algorithm; For the selected interference classification model, set appropriate model parameters and convergence conditions.
4. The method according to claim 3, characterized in that Training the interference classification model based on the data set includes: Reading the training set; Model training is performed based on the training set, the selected interference classification model and the model parameters until the model is iteratively converged and then saved.
5. The method according to claim 4, characterized in that Validating the interference classification model based on the data set includes: Reading the verification set; Obtain the trained interference classification model; The trained interference classification model is used to perform classification prediction on the verification set to verify the generalization performance of the model, and the verified interference classification model is used as the frequency point interference state recognition model.
6. The method according to claim 5, characterized in that The detecting the interference frequency in the communication frame based on the frequency interference state recognition model includes: Acquire the test data set and the frequency point interference state recognition model; Splitting each test sample data in the test data set according to the frequency point to obtain test data of multiple frequency points; Based on the frequency interference state recognition model, test data of multiple frequency points are classified and predicted in turn to obtain multiple classification results; The frequency point number combination whose classification result is interference is extracted as the interference frequency point subset.
7. The method according to claim 6, characterized in that The set of interfered frequency points in the output communication frame includes: The interference frequency subset is compared with the frequency set to obtain the interfered frequency in the specific communication frame and output it.
8. A novel interference frequency detection device, characterized in that: include: An acquisition unit, used for acquiring frequency hopping communication frame data of a communication system; A generation unit, used to generate a data set by stratified sampling after splitting according to a set frequency set; A construction unit, used to construct an interference classification model based on a supervised learning algorithm and set model parameters; A training and verification unit, used to train and verify the interference classification model based on the data set to obtain a frequency point interference state recognition model; The detection unit is used to detect the interference frequency points in the communication frame based on the frequency interference state recognition model, and output the set of interfered frequency points in the communication frame.
9. A computer-readable storage medium, characterized in that: The storage medium stores computer instructions, and when the computer instructions are executed on a computer, the computer is enabled to execute the method according to any one of claims 1 to 7.
10. An electronic device, characterized in that: include: Memory and processor; The memory is used to store computer instructions; The processor is used to call the computer instructions stored in the memory so that the electronic device executes the method as described in any one of claims 1-7.