Wireless key identification method and device, electronic equipment and storage medium
By extracting the radio frequency signals of the vehicle's wireless key and identifying the radio frequency fingerprint, the problem of fixed code technology being easily replayed is solved, and the safety of the vehicle is enhanced.
Patent Information
- Application Number
- CN202411227502.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-03
- Publication Date
- 2024-12-24
AI Technical Summary
The fixed code technology of existing vehicle wireless keys is easily replayed and attacked, resulting in low vehicle security.
By obtaining the radio frequency signal to be authenticated, performing feature extraction processing, and using the authentication model for radio frequency fingerprint recognition, preventing the wireless key from being played back during use, and enhancing the security protection capability of the physical layer.
Effectively protect the vehicle wireless key from being played and attacked during use, enhancing the security protection capability of the physical layer, preventing control signals from being played and used, thereby ensuring the safety of users.
Smart Images
Figure CN119181159A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of vehicle safety technology, and in particular to a wireless key identification method, device, electronic device and storage medium. Background Art
[0002] With the continuous development of communication technology, vehicle wireless keys can be used to allow users to access vehicles, and send codes to the vehicle through the vehicle wireless key to instruct the vehicle to unlock the vehicle. In the related art, fixed code technology is used to process the signal of the wireless key. However, the key signal of the fixed code technology is easy to be replayed to unlock the vehicle, resulting in low security of the vehicle.
[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the invention
[0004] The main purpose of the embodiments of the present application is to provide a wireless key identification method, device, electronic device and storage medium, which can improve the safety of the vehicle.
[0005] To achieve the above object, one aspect of an embodiment of the present application provides a wireless key identification method, the method comprising:
[0006] Acquire a radio frequency signal to be authenticated;
[0007] Performing feature extraction processing on the radio frequency signal to obtain signal features;
[0008] Performing radio frequency fingerprint recognition processing on the signal characteristics according to the authentication model to obtain an authentication result;
[0009] The radio frequency signal is interactively transmitted and processed according to the authentication result to obtain a wireless key recognition result.
[0010] In some embodiments, the obtaining of the radio frequency signal to be authenticated comprises the following steps:
[0011] An initial signal is received through a hardware system;
[0012] Performing down-conversion and filtering amplification processing on the initial signal to obtain a conversion signal;
[0013] The converted signal is modulated and demodulated to obtain the radio frequency signal to be authenticated.
[0014] In some embodiments, performing feature extraction processing on the radio frequency signal to obtain signal features comprises the following steps:
[0015] Performing time-frequency analysis on the radio frequency signal to obtain time-frequency characteristics;
[0016] Performing frequency domain analysis on the radio frequency signal to obtain frequency domain features;
[0017] The signal feature is obtained according to the time-frequency feature and the frequency domain feature.
[0018] In some embodiments, before performing radio frequency fingerprint recognition processing on the signal feature according to the authentication model to obtain the authentication result, the method further includes pre-training the authentication model, including the following steps:
[0019] Get training data;
[0020] Perform deep learning modeling processing according to the training data to obtain an initial model;
[0021] Performing model compression processing on the initial model to obtain the certification model;
[0022] Inputting the training data into the authentication model to obtain a radio frequency signal prediction result;
[0023] The parameters of the authentication model are updated according to the radio frequency signal prediction result.
[0024] In some embodiments, performing deep learning modeling processing according to the training data to obtain an initial model includes the following steps:
[0025] Preprocessing the training data to obtain in-phase orthogonal data;
[0026] The in-phase and quadrature data are subjected to radio frequency fingerprint recognition processing based on a deep learning model, and the initial model is obtained through training.
[0027] In some embodiments, the deep learning modeling process is performed according to the training data to obtain an initial model, and the method further comprises the following steps:
[0028] Preprocessing the training data to obtain in-phase orthogonal data;
[0029] Performing expert feature transformation processing on the in-phase orthogonal data to obtain expert feature data;
[0030] The expert feature data is subjected to radio frequency fingerprint recognition processing based on a deep learning model, and the initial model is obtained through training.
[0031] In some embodiments, performing model compression processing on the initial model to obtain the certification model includes the following steps:
[0032] Determine the student model based on the application scenario and computing resources;
[0033] The student model is trained according to the initial model, and the parameters of the student model are adjusted by minimizing the prediction difference between the student model and the initial model to obtain the certification model.
[0034] To achieve the above object, another aspect of the embodiment of the present application provides a wireless key identification device, the device comprising:
[0035] The first module is used to obtain the radio frequency signal to be authenticated;
[0036] The second module is used to perform feature extraction processing on the radio frequency signal to obtain signal features;
[0037] The third module is used to perform radio frequency fingerprint recognition processing on the signal characteristics according to the authentication model to obtain an authentication result;
[0038] The fourth module is used to perform interactive transmission processing on the radio frequency signal according to the authentication result to obtain a wireless key recognition result.
[0039] In some embodiments, the first module is used to obtain the radio frequency signal to be authenticated, including:
[0040] The first unit is used to receive an initial signal through a hardware system;
[0041] The second unit is used to perform down-conversion and filtering and amplification processing on the initial signal to obtain a conversion signal;
[0042] The third unit is used to perform modulation and demodulation processing on the converted signal to obtain the radio frequency signal to be authenticated.
[0043] In some embodiments, the second module is used to perform feature extraction processing on the radio frequency signal to obtain signal features, including:
[0044] The fourth unit is used to perform time-frequency analysis on the radio frequency signal to obtain time-frequency features;
[0045] A fifth unit is used to perform frequency domain analysis processing on the radio frequency signal to obtain frequency domain features;
[0046] The sixth unit is used to obtain the signal feature according to the time-frequency feature and the frequency domain feature.
[0047] In some embodiments, the apparatus further includes a fifth module for pre-training the authentication model, including:
[0048] Unit 7, used to obtain training data;
[0049] An eighth unit is used to perform deep learning modeling processing according to the training data to obtain an initial model;
[0050] A ninth unit is used for performing model compression processing on the initial model to obtain the certification model;
[0051] A tenth unit is used to input the training data into the authentication model to obtain a radio frequency signal prediction result;
[0052] The eleventh unit is used to update the parameters of the authentication model according to the radio frequency signal prediction result.
[0053] In some embodiments, the eighth unit is used to perform deep learning modeling processing according to the training data to obtain an initial model, including:
[0054] A first subunit is used to pre-process the training data to obtain in-phase and orthogonal data;
[0055] The second subunit is used to perform radio frequency fingerprint recognition processing on the in-phase orthogonal data based on a deep learning model to train and obtain the initial model.
[0056] In some embodiments, the eighth unit, used to perform deep learning modeling processing according to the training data to obtain an initial model, further includes:
[0057] A first subunit is used to pre-process the training data to obtain in-phase orthogonal data;
[0058] A third subunit is used to perform expert feature transformation processing on the in-phase orthogonal data to obtain expert feature data;
[0059] The fourth subunit is used to perform radio frequency fingerprint recognition processing on the expert feature data based on a deep learning model to train and obtain the initial model.
[0060] In some embodiments, the ninth unit is used to perform model compression processing on the initial model to obtain the certification model, including the following steps:
[0061] The fifth subunit is used to determine the student model according to the application scenario and computing resources;
[0062] The sixth subunit is used to train the student model according to the initial model, and adjust the parameters of the student model by minimizing the prediction difference between the student model and the initial model to obtain the certification model.
[0063] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, the electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing the above-mentioned method when executing the computer program.
[0064] To achieve the above objective, another aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described above is implemented.
[0065] The embodiments of the present application include at least the following beneficial effects: The present application provides a wireless key identification method, device, electronic device and storage medium, which obtains the radio frequency signal to be authenticated; performs feature extraction processing on the radio frequency signal to obtain the signal feature; performs radio frequency fingerprint identification processing on the signal feature according to the authentication model to obtain the authentication result; performs interactive transmission processing on the radio frequency signal according to the authentication result to obtain the wireless key identification result. The embodiments of the present application perform radio frequency fingerprint identification on the radio frequency signal through the authentication model, which can effectively protect the vehicle's wireless key from being replayed during use, enhance the security protection capability of the physical layer, and prevent its control signal from being replayed and utilized, thereby ensuring the safety of user use. BRIEF DESCRIPTION OF THE DRAWINGS
[0066] Figure 1 is a flow chart of a wireless key identification method provided by an embodiment of the present application;
[0067] Figure 2 yes Figure 1 Flow chart of step S101 in FIG.
[0068] Figure 3 It is a schematic diagram of drawing a constellation diagram provided in an embodiment of the present application;
[0069] Figure 4 yes Figure 1 Flow chart of step S102 in FIG.
[0070] Figure 5 is a schematic diagram of a radio frequency fingerprint recognition provided by an embodiment of the present application;
[0071] Figure 6 It is a schematic diagram of an authentication model training provided in an embodiment of the present application;
[0072] Figure 7 It is a schematic diagram of a model lightweight processing provided in an embodiment of the present application;
[0073] Figure 8 is a structural schematic diagram of a wireless key identification device provided in an embodiment of the present application;
[0074] Fig. 9 It is a schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application;
[0075] Fig.10 It is a structural schematic diagram of a storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION
[0076] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.
[0077] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".
[0078] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.
[0079] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.
[0080] Before describing the embodiments of the present application in detail, some nouns and terms involved in the embodiments of the present application are first described. The nouns and terms involved in the embodiments of the present application are subject to the following explanations.
[0081] 1) Radio frequency signals are a form of electromagnetic waves, with a frequency range of usually from tens of kilohertz (kHz) to hundreds of gigahertz (GHz), and are widely used in wireless communications, radar, radio and television, satellite communications, microwave heating, and other fields. These signals can carry information (such as sound, images, data, etc.), propagate through the air or other media, and achieve long-distance wireless transmission and reception.
[0082] 2) Deep learning is an important research direction in the field of machine learning. It is based on the study of artificial neural networks and captures complex structures and abstract features in data by building multi-layer neural networks. Deep learning models can automatically learn and extract useful information from large amounts of data without manually specifying features, which makes them perform well in complex tasks such as computer vision, natural language processing, and speech recognition.
[0083] 3) An intelligent vehicle is a comprehensive device that integrates environmental perception, planning and decision-making, multi-level assisted driving and other functions. It concentrates on the use of computers, modern sensors, information fusion, communications, artificial intelligence and automatic control technologies. It can exchange information with people, vehicles, roads, etc. through on-board environmental perception devices and information terminals, so that the vehicle has intelligent environmental perception capabilities and can automatically analyze the safety and dangerous status of vehicle driving.
[0084] In the related art, the vehicle wireless key can be used to allow the user to access the vehicle, and a code is sent to the vehicle through the vehicle wireless key to instruct the vehicle to unlock the vehicle. However, in actual applications, it is found that when the fixed code technology is used to process the signal of the wireless key, an attacker can use a device to capture the vehicle's radio frequency signal, and then replay the radio frequency signal through the device. The attacker can use the replayed signal to unlock the door, control the window, open the trunk, etc., resulting in the safety of the vehicle being affected.
[0085] In view of this, a wireless key identification method, device, electronic device and storage medium are provided in the embodiments of the present application. The scheme obtains the radio frequency signal to be authenticated; performs feature extraction processing on the radio frequency signal to obtain the signal feature; performs radio frequency fingerprint identification processing on the signal feature according to the authentication model to obtain the authentication result; and performs interactive transmission processing on the radio frequency signal according to the authentication result to obtain the wireless key identification result. The embodiments of the present application perform radio frequency fingerprint identification on the radio frequency signal through the authentication model, which can effectively protect the wireless key of the vehicle from being replayed during use, enhance the security protection capability of the physical layer, and prevent its control signal from being replayed and utilized, thereby ensuring the safety of user use.
[0086] A wireless key identification method provided in an embodiment of the present application relates to the field of vehicle safety technology. A wireless key identification method provided in an embodiment of the present application can be applied to a terminal, can be applied to a server, or can be software running in a terminal or a server. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, and a vehicle-mounted terminal, etc., but is not limited to this; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, and can also be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The server can also be a node server in a blockchain network; the software can be an application that implements a wireless key identification method, etc., but is not limited to the above forms.
[0087] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.
[0088] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.
[0089] Figure 1 is an optional flow chart of a wireless key identification method provided in an embodiment of the present application. Figure 1 The method may include but is not limited to steps S101 to S104.
[0090] Step S101, obtaining a radio frequency signal to be authenticated;
[0091] Step S102, performing feature extraction processing on the radio frequency signal to obtain signal features;
[0092] Step S103, performing radio frequency fingerprint recognition processing on the signal feature according to the authentication model to obtain an authentication result;
[0093] Step S104: performing interactive transmission processing on the radio frequency signal according to the authentication result to obtain a wireless key recognition result.
[0094] Steps S101 to S104 shown in the embodiment of the present application are as follows: obtaining the radio frequency signal to be authenticated; performing feature extraction processing on the radio frequency signal to obtain signal features; performing radio frequency fingerprint recognition processing on the signal features according to the authentication model to obtain the authentication result; performing interactive transmission processing on the radio frequency signal according to the authentication result to obtain the wireless key recognition result. The embodiment of the present application performs radio frequency fingerprint recognition on the radio frequency signal through the authentication model, which can effectively protect the wireless key of the vehicle from being replayed during use, enhance the security protection capability of the physical layer, prevent its control signal from being replayed, and thus ensure the safety of user use.
[0095] See also Figure 2 In step S101 of some embodiments, obtaining the radio frequency signal to be authenticated includes the following steps:
[0096] Step S201, receiving an initial signal through a hardware system;
[0097] Step S202, performing down-conversion and filtering amplification processing on the initial signal to obtain a conversion signal;
[0098] Step S203: modulate and demodulate the converted signal to obtain the radio frequency signal to be authenticated.
[0099] In an embodiment of the present application, an initial signal is received by a hardware system, wherein the hardware system may be received by an antenna, and the electromagnetic wave is converted into a radio frequency signal by the antenna to obtain an initial signal, which is the signal obtained by the antenna conversion. Then, the received initial signal is down-converted and filtered and amplified to obtain a converted signal. It may be preliminarily amplified by a low noise amplifier (LNA) to reduce the influence of noise on the signal, and then down-converted to convert the high frequency signal into an intermediate frequency or baseband signal for subsequent processing. This may be achieved by a frequency converter, where the radio frequency signal is mixed with the local oscillator signal generated by the local oscillator, and then filtered and amplified to remove the stray signals and noise generated during the mixing process, and further amplified to meet the requirements of the subsequent processing circuit for the signal amplitude, to obtain a converted signal. Finally, the converted signal is modulated by loading the baseband signal onto the radio frequency carrier, and demodulation is the inverse process, that is, recovering the original baseband signal from the received radio frequency signal to obtain the radio frequency signal to be authenticated. It can be imagined that the embodiment of the present application can obtain a constellation diagram based on the radio frequency signal, such as Figure 3 As shown, the radio frequency signal 301 to be authenticated is collected to obtain an in-phase signal 302 and an orthogonal signal 303, and a constellation diagram 304 is drawn based on the in-phase signal and the orthogonal signal. The constellation diagram can intuitively show the distribution of the signal on the complex plane, and can clearly reflect key factors such as the modulation quality, phase noise, signal interference, and channel distortion of the signal. The embodiment of the present application provides a basis for subsequent identification by obtaining the radio frequency signal to be authenticated.
[0100] See also Figure 4 In step S102 of some embodiments, the feature extraction process of the radio frequency signal to obtain the signal feature includes the following steps:
[0101] Step S401, performing time-frequency analysis on the radio frequency signal to obtain time-frequency features;
[0102] Step S402, performing frequency domain analysis processing on the radio frequency signal to obtain frequency domain features;
[0103] Step S403: obtaining the signal feature according to the time-frequency feature and the frequency domain feature.
[0104] In the embodiment of the present application, the radio frequency signal is subjected to time-frequency analysis to obtain time-frequency features, wherein the time domain is a general summary of the signal changes over time expressed on the time axis, and the time-frequency features may include the waveform, amplitude, time relationship, etc. of the signal. The radio frequency signal is subjected to frequency domain analysis to obtain frequency domain features, wherein the frequency domain is a coordinate system that describes the characteristics of the signal in terms of frequency. The time domain signal is converted to the frequency domain for analysis through mathematical tools such as Fourier transform, and the frequency domain features include amplitude information and phase information, etc. The embodiment of the present application provides a data basis for subsequent authentication model recognition processing by using the extracted time-frequency features and frequency domain features as signal features.
[0105] In some embodiments, before performing radio frequency fingerprint recognition processing on the signal feature according to the authentication model to obtain the authentication result, the method further includes pre-training the authentication model, including the following steps:
[0106] Get training data;
[0107] Perform deep learning modeling processing according to the training data to obtain an initial model;
[0108] Performing model compression processing on the initial model to obtain the certification model;
[0109] Inputting the training data into the authentication model to obtain a radio frequency signal prediction result;
[0110] The parameters of the authentication model are updated according to the radio frequency signal prediction result.
[0111] In the embodiment of the present application, the signal characteristics are subjected to radio frequency fingerprint recognition processing according to the authentication model, see Figure 5 , G is the controller, T is the key, and the illustrated process is the RF fingerprint recognition process of the key and the controller. The RF fingerprint recognition is performed through the authentication model deployed in the controller, so as to identify whether the key is the correct key for subsequent processing. Prior to this, the embodiment of the present application also needs to pre-train the authentication model, specifically by preparing real signal acquisition data and simulation data to form a training data set, and construct an initial model based on the training data. The initial model can adopt a deep learning model such as a convolutional neural network, a recurrent neural network, and a transformer model. Then, the deep learning model is subjected to model compression processing, so as to transfer information from a large model or an integrated model to a small model for training, and will not significantly cause a decrease in accuracy, so as to obtain an authentication model. The training data is then input into the authentication model for training, and the parameters of the authentication model are updated through the RF signal prediction results. The parameters of the model can be updated by setting a preset training round. The embodiment of the present application facilitates subsequent RF fingerprint recognition authentication by pre-training the authentication model, thereby improving the accuracy of authentication.
[0112] In some embodiments, performing deep learning modeling processing according to the training data to obtain an initial model includes the following steps:
[0113] Preprocessing the training data to obtain in-phase orthogonal data;
[0114] The in-phase and quadrature data are subjected to radio frequency fingerprint recognition processing based on a deep learning model, and the initial model is obtained through training.
[0115] In the embodiments of this application, please refer to Figure 6 , the embodiment of the present application can perform deep learning modeling processing based on I / Q signals on the RF signals collected by the RF front end and ADC (analog / digital converter) to obtain an initial model, wherein the I / Q signals are in-phase and quadrature signals, i.e., include in-phase signals and quadrature signals. By preprocessing the training data to obtain in-phase and quadrature data, the in-phase and quadrature data are directly processed for RF fingerprint recognition based on the deep learning model, which can have the ability to learn protocol-independent RF fingerprints, and has good scalability and flexible deployment, and is more suitable for large-scale and ultra-large-scale individual recognition.
[0116] In some embodiments, the deep learning modeling process is performed according to the training data to obtain an initial model, and the method further comprises the following steps:
[0117] Preprocessing the training data to obtain in-phase orthogonal data;
[0118] Performing expert feature transformation processing on the in-phase orthogonal data to obtain expert feature data;
[0119] The expert feature data is subjected to radio frequency fingerprint recognition processing based on a deep learning model, and the initial model is obtained through training.
[0120] In an embodiment of the present application, the training data can be preprocessed to obtain in-phase orthogonal data, and then the in-phase orthogonal data can be transformed by expert features, that is, the in-phase orthogonal data can be transformed by short-time Fourier transform, wavelet transform, constellation diagram and other methods to obtain expert feature data, and then the expert feature data can be processed for radio frequency fingerprint recognition based on the deep learning model to train and obtain an initial model. The embodiment of the present application can improve data processing efficiency by modeling through expert feature data. By converting radio frequency signals into expert feature data, the model can more accurately identify and classify different signal sources or signal modes, and use these feature data for adaptive learning to continuously optimize its own recognition algorithm and decision model.
[0121] In some embodiments, performing model compression processing on the initial model to obtain the certification model includes the following steps:
[0122] Determine the student model based on the application scenario and computing resources;
[0123] The student model is trained according to the initial model, and the parameters of the student model are adjusted by minimizing the prediction difference between the student model and the initial model to obtain the certification model.
[0124] In the embodiment of the present application, the student model is determined according to the application scenario and computing resources. The student model is a small model with a relatively simple structure and a small number of parameters. The student model is obtained by designing the model based on the computing resources, deployability and computing efficiency of the vehicle controller. Figure 7 , the student model is trained according to the initial model, and the difference between the student model prediction and the initial model prediction is minimized, usually using metrics such as mean square error or cross entropy loss function, so that the student model can learn the knowledge in the teacher model, and can also use the intermediate layer features or attention map of the teacher model as additional supervision information to further improve the performance of the student model, and adjust the parameters of the student model by regularly evaluating the performance of the student model and adjusting the training parameters or optimizing the model structure according to the evaluation results to obtain a certification model. The embodiment of the present application can significantly improve the reasoning speed of the model and reduce the consumption of computing resources by reducing the model size and reducing the computational complexity.
[0125] The following is a detailed description of the embodiments of the present invention with reference to specific application examples:
[0126] The embodiments of the present application can be applied to the field of wireless signal authentication technology, and specifically can be applied to the unlocking scenario of smart vehicles. When a user wants to communicate with a vehicle controller through a wireless key, the radio frequency signal of the wireless key is obtained, and the features of the radio frequency signal are extracted and radio frequency fingerprint recognition is performed on the signal features based on the authentication model deployed on the vehicle terminal. The signal is compared with a pre-established key database. When the authentication result output by the authentication module is passed, a signal interaction channel can be established between the wireless key and the vehicle controller according to the radio frequency signal for data transmission, thereby unlocking the vehicle and other operations. The embodiments of the present application will only establish signal transmission if the radio frequency signal emitted by the wireless key is confirmed to be legitimate through authentication. When the authentication fails, the owner can be notified by a sound alarm or SMS prompt, thereby improving vehicle safety.
[0127] See also Figure 8 The present application also provides a wireless key identification device, the device comprising:
[0128] The first module 801 is used to obtain a radio frequency signal to be authenticated;
[0129] The second module 802 is used to perform feature extraction processing on the radio frequency signal to obtain signal features;
[0130] The third module 803 is used to perform radio frequency fingerprint recognition processing on the signal characteristics according to the authentication model to obtain an authentication result;
[0131] The fourth module 804 is used to perform interactive transmission processing on the radio frequency signal according to the authentication result to obtain a wireless key recognition result.
[0132] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0133] The embodiment of the present application also provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the above wireless key identification method when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.
[0134] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0135] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:
[0136] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.
[0137] The memory 902 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 902 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 902, and the processor 901 calls and executes the wireless key identification method of the embodiment of this application;
[0138] Input / output interface 903, used to implement information input and output;
[0139] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized through wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WI FI, Bluetooth, etc.);
[0140] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);
[0141] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .
[0142] See also Fig.10 The embodiment of the present application further provides a computer-readable storage medium 1001, which stores a computer program 1002, and when the computer program is executed by a computer processor 1003, the wireless key identification method is implemented.
[0143] It can be understood that the contents of the above method embodiments are all applicable to the present storage medium embodiments, the functions specifically implemented by the present storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.
[0144] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
[0145] The embodiments of the present application provide a wireless key identification method, device, electronic device and storage medium. The scheme obtains the radio frequency signal to be authenticated; performs feature extraction processing on the radio frequency signal to obtain the signal feature; performs radio frequency fingerprint identification processing on the signal feature according to the authentication model to obtain the authentication result; and performs interactive transmission processing on the radio frequency signal according to the authentication result to obtain the wireless key identification result. The embodiments of the present application perform radio frequency fingerprint identification on the radio frequency signal through the authentication model, which can effectively protect the vehicle's wireless key from being replayed during use, enhance the security protection capability of the physical layer, and prevent its control signal from being replayed and utilized, thereby ensuring the safety of user use.
[0146] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.
[0147] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.
[0148] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0149] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.
[0150] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.
[0151] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.
[0152] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.
[0153] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
[0154] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.
[0155] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.
[0156] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.
Claims
1. A wireless key identification method, characterized in that: The method comprises the following steps: Acquire a radio frequency signal to be authenticated; Performing feature extraction processing on the radio frequency signal to obtain signal features; Performing radio frequency fingerprint recognition processing on the signal characteristics according to the authentication model to obtain an authentication result; The radio frequency signal is interactively transmitted and processed according to the authentication result to obtain a wireless key recognition result.
2. The method according to claim 1, characterized in that: The step of obtaining the radio frequency signal to be authenticated comprises the following steps: An initial signal is received through a hardware system; Performing down-conversion and filtering amplification processing on the initial signal to obtain a conversion signal; The converted signal is modulated and demodulated to obtain the radio frequency signal to be authenticated.
3. The method according to claim 1, characterized in that The step of performing feature extraction processing on the radio frequency signal to obtain signal features comprises the following steps: Performing time-frequency analysis on the radio frequency signal to obtain time-frequency characteristics; Performing frequency domain analysis on the radio frequency signal to obtain frequency domain features; The signal feature is obtained according to the time-frequency feature and the frequency domain feature.
4. The method according to claim 1, characterized in that: Before performing radio frequency fingerprint recognition processing on the signal feature according to the authentication model to obtain the authentication result, the method further includes pre-training the authentication model, including the following steps: Get training data; Perform deep learning modeling processing according to the training data to obtain an initial model; Performing model compression processing on the initial model to obtain the certification model; Inputting the training data into the authentication model to obtain a radio frequency signal prediction result; The parameters of the authentication model are updated according to the radio frequency signal prediction result.
5. The method according to claim 4, characterized in that The deep learning modeling process is performed according to the training data to obtain an initial model, comprising the following steps: Preprocessing the training data to obtain in-phase and orthogonal data; The in-phase and quadrature data are subjected to radio frequency fingerprint recognition processing based on a deep learning model, and the initial model is obtained through training.
6. The method according to claim 4, characterized in that The deep learning modeling process is performed according to the training data to obtain an initial model, and the method further comprises the following steps: Preprocessing the training data to obtain in-phase orthogonal data; Performing expert feature transformation processing on the in-phase orthogonal data to obtain expert feature data; The expert feature data is subjected to radio frequency fingerprint recognition processing based on a deep learning model, and the initial model is obtained through training.
7. The method according to claim 4, characterized in that The step of performing model compression processing on the initial model to obtain the authentication model comprises the following steps: Determine the student model based on the application scenario and computing resources; The student model is trained according to the initial model, and the parameters of the student model are adjusted by minimizing the prediction difference between the student model and the initial model to obtain the certification model.
8. A wireless key recognition device, characterized in that: The device comprises: The first module is used to obtain the radio frequency signal to be authenticated; The second module is used to perform feature extraction processing on the radio frequency signal to obtain signal features; The third module is used to perform radio frequency fingerprint recognition processing on the signal characteristics according to the authentication model to obtain an authentication result; The fourth module is used to perform interactive transmission processing on the radio frequency signal according to the authentication result to obtain a wireless key recognition result.
9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.