Vehicle communication data processing method, vehicle and storage medium

Abstract

The embodiment of the invention provides a vehicle communication data processing method, a vehicle and a storage medium, and the method comprises the steps: obtaining target feature data and real-time state data of a target vehicle, the target feature data being used for representing multi-dimensional physical features associated with the target vehicle, and the real-time state data being used for representing real-time state data of the target vehicle; the real-time state data is used for representing an engine running state and an ignition state of the target vehicle; generating key seed data based on the target feature data and the real-time state data; a vehicle session key is generated according to the key seed data, and the vehicle session key is used for encrypting and decrypting the session data packet; and performing data communication of the target vehicle by using the vehicle session key. According to the method and the device, the technical problems of low security of a fixed key management scheme and great influence of an encryption authentication mechanism on real-time performance in related technologies are solved.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a vehicle communication data processing method, a vehicle, and a storage medium. Background Technology

[0002] With the intelligent transformation of the automotive industry, the security of in-vehicle networks has become an increasingly important focus. As the infrastructure for data transmission, the Controller Area Network (CAN) bus's network security protection technology is crucial for ensuring vehicle information security. Currently, encryption authentication and physical feature recognition schemes constitute the two pillars of in-vehicle network protection. Traditional encryption schemes suffer from communication delays due to high computational complexity, making it difficult to meet the real-time requirements of in-vehicle networks. Authentication methods based on single physical features are greatly affected by environmental factors, resulting in unstable authentication accuracy. Fixed key management schemes are vulnerable to cracking and lack dynamic update mechanisms. Furthermore, most physical feature detection schemes require dedicated hardware, increasing costs and reducing system compatibility. The lack of adaptive mechanisms makes the system unable to cope with changes in network topology and the evolution of attack methods, thus limiting the practical application effectiveness of in-vehicle network security technologies.

[0003] There is currently no good solution to the above problems. Summary of the Invention

[0004] This application provides a vehicle communication data processing method, a vehicle, and a storage medium to at least solve the technical problems of low security of fixed key management schemes and the significant impact of encryption authentication mechanisms on real-time performance in related technologies.

[0005] According to one aspect of the embodiments of this application, a vehicle communication data processing method is provided, comprising: acquiring target feature data and real-time status data of a target vehicle, wherein the target feature data is used to represent multi-dimensional physical features associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle; generating key seed data based on the target feature data and real-time status data; generating a vehicle session key based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets; and using the vehicle session key to perform data communication of the target vehicle.

[0006] Optionally, the target feature data includes: clock offset feature data, voltage ripple feature data, and impedance feature data of the target vehicle. The clock offset feature data is used to describe the difference between the local clock and the network clock of the target vehicle. The voltage ripple feature data is used to reflect the voltage fluctuation in the on-board power system. The impedance feature data is used to characterize the electrical characteristics of the signal transmission path in the controller area network of the target vehicle.

[0007] Optionally, generating key seed data based on target feature data and real-time status data includes: weighting clock offset feature data, voltage ripple feature data, and impedance feature data according to preset weight coefficients to obtain feature fusion results; concatenating the feature fusion results and real-time status data to obtain data concatenation results; and performing hash calculation on the data concatenation results to generate key seed data.

[0008] Optionally, obtaining the clock offset characteristic data of the target vehicle includes: obtaining the cumulative clock offset data corresponding to multiple pulse cycles; calculating the average of the cumulative clock offset data to obtain the offset average result; performing compensation processing on the offset average result to obtain the offset compensation result; and determining the clock offset characteristic data based on the offset compensation result.

[0009] Optionally, obtaining voltage ripple feature data of the target vehicle includes: obtaining an initial voltage sampling signal according to preset sampling parameters; filtering the initial voltage sampling signal to obtain a signal filtering result; and extracting frequency band energy features based on the signal filtering result to obtain voltage ripple feature data.

[0010] Optionally, obtaining the impedance characteristic data of the target vehicle includes: performing impedance detection on the controller local area network bus of the target vehicle based on time-domain reflection to obtain impedance detection results; and extracting features from the impedance detection results to obtain impedance characteristic data.

[0011] Optionally, generating a vehicle session key based on the key seed data includes: expanding the key seed data using a key derivation function to obtain a key expansion result; and performing parameter mapping based on the key expansion result to obtain the vehicle session key.

[0012] Optionally, the vehicle communication data processing method further includes: updating the key seed data according to a preset period; or: updating the key seed data in response to the feature change data corresponding to the target feature data exceeding a preset threshold.

[0013] According to another aspect of the embodiments of this application, a vehicle communication data processing apparatus is also provided, comprising: an acquisition module, configured to acquire target feature data and real-time status data of a target vehicle, wherein the target feature data is used to represent multi-dimensional physical features associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle; a first generation module, configured to generate key seed data based on the target feature data and the real-time status data; a second generation module, configured to generate a vehicle session key based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets; and a communication module, configured to perform data communication of the target vehicle using the vehicle session key.

[0014] Optionally, the target feature data includes: clock offset feature data, voltage ripple feature data, and impedance feature data of the target vehicle. The clock offset feature data is used to describe the difference between the local clock and the network clock of the target vehicle. The voltage ripple feature data is used to reflect the voltage fluctuation in the on-board power system. The impedance feature data is used to characterize the electrical characteristics of the signal transmission path in the controller area network of the target vehicle.

[0015] Optionally, the first generation module is further configured to: weight the clock offset feature data, voltage ripple feature data, and impedance feature data according to preset weighting coefficients to obtain feature fusion results; concatenate the feature fusion results and real-time status data to obtain data concatenation results; and perform hash calculation on the data concatenation results to generate key seed data.

[0016] Optionally, the acquisition module is also used to: acquire the cumulative clock offset data corresponding to multiple pulse cycles; calculate the average of the cumulative clock offset data to obtain the offset average result; perform compensation processing on the offset average result to obtain the offset compensation result; and determine the clock offset feature data based on the offset compensation result.

[0017] Optionally, the acquisition module is also used to: acquire an initial voltage sampling signal according to preset sampling parameters; perform filtering processing on the initial voltage sampling signal to obtain signal filtering results; and extract frequency band energy features based on the signal filtering results to obtain voltage ripple feature data.

[0018] Optionally, the acquisition module is also used to: perform impedance detection on the controller local area network bus of the target vehicle based on time-domain reflection to obtain impedance detection results; and extract features from the impedance detection results to obtain impedance feature data.

[0019] Optionally, the second generation module is further configured to: expand the key seed data using a key derivation function to obtain a key expansion result; and perform parameter mapping based on the key expansion result to obtain a vehicle session key.

[0020] Optionally, the vehicle communication data processing device further includes: an update module, used to: update the key seed data according to a preset period; or; update the key seed data in response to the feature change data corresponding to the target feature data exceeding a preset threshold.

[0021] According to another aspect of the embodiments of this application, a vehicle is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0022] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0023] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0024] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0025] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0026] In this embodiment, target feature data and real-time status data of the target vehicle are acquired, and then key seed data is generated based on the target feature data and real-time status data. Subsequently, a vehicle session key is generated based on the key seed data, and finally, the vehicle session key is used to conduct data communication with the target vehicle. This achieves the goal of generating a dynamic vehicle session key by integrating the multi-dimensional physical features of the vehicle with real-time status data, thereby achieving the technical effect of improving the security of vehicle network communication, enhancing user trust and user experience. This solves the technical problems of low security of fixed key management schemes and the significant impact of encryption authentication mechanisms on real-time performance in related technologies. Attached Figure Description

[0027] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0028] Figure 1 This is a flowchart of a vehicle communication data processing method according to an embodiment of this application;

[0029] Figure 2 This is a structural block diagram of a vehicle communication data processing device according to an embodiment of this application. Detailed Implementation

[0030] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0031] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0032] According to an embodiment of this application, an embodiment of a vehicle communication data processing method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0033] This method embodiment can be executed in an electronic device or similar computing device that includes memory and a processor. Taking operation on a computer terminal as an example, the computer terminal may include one or more processors (processors may include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field-programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processors (TPUs), artificial intelligence (AI) type processors, etc.) and memory for storing data. Optionally, the computer terminal may also include transmission devices, input / output devices, and display devices for communication functions. Those skilled in the art will understand that the above structural description is merely illustrative and does not limit the structure of the computer terminal. For example, the computer terminal may include more or fewer components than described above, or have a different configuration than described above.

[0034] The memory can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle communication data processing method in this embodiment. The processor executes various functional applications and data processing by running the computer program stored in the memory, thereby implementing the aforementioned vehicle communication data processing method. The memory may include high-speed random access memory (RAM) and non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks (LANs), mobile communication networks, and combinations thereof.

[0035] The transmission device is used to receive or send data via a network. Specific examples of the network mentioned above may include a wireless network provided by the mobile terminal's communication provider. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device may be a Radio Frequency (RF) module, used for wireless communication with the Internet.

[0036] Display devices can be, for example, touchscreen liquid crystal displays (LCDs) and touch displays (also referred to as "touchscreens" or "touch displays"). The LCD allows users to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), which allows users to interact with the GUI through finger contact and / or gestures on a touch-sensitive surface. Optional human-computer interaction functions include: creating web pages, drawing, word processing, creating electronic documents, playing games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital video, playing digital music, and / or web browsing, etc. Executable instructions for performing the above human-computer interaction functions are configured / stored in one or more processor-executable computer program products or readable storage media.

[0037] This embodiment provides a vehicle communication data processing method. Figure 1 This is a flowchart of a vehicle communication data processing method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps:

[0038] Step S11: Obtain target feature data and real-time status data of the target vehicle. The target feature data is used to represent the multi-dimensional physical features associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle.

[0039] Step S12: Generate key seed data based on target feature data and real-time status data;

[0040] Step S13: Generate a vehicle session key based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets.

[0041] Step S14: Use the vehicle session key to conduct data communication with the target vehicle.

[0042] The aforementioned target feature data can specifically be the physical fingerprint information of the target vehicle, including but not limited to multi-dimensional hardware features such as clock skew, voltage ripple, and impedance characteristics. Target feature data are inherent hardware attributes of the ECU that are not easily copied and are used to construct the hardware identity of the target vehicle. For example, clock skew refers to the slight difference between the internal clock of the vehicle ECU and the external reference clock. This difference is caused by differences in precision during the manufacturing process and is unique to each ECU.

[0043] The aforementioned real-time status data can be the operating status information of the target vehicle at a certain moment. Specifically, in this embodiment, it mainly includes the engine operating status and ignition status. For example, engine speed, ignition switch position, etc. The real-time status data reflects the immediate operating situation of the target vehicle and can be used to dynamically adjust the individual key generation strategy.

[0044] Target feature data can be continuously collected and stored through built-in sensors, while real-time status data is typically updated in real time by the vehicle control system, which can obtain this information via the ECU through the CAN bus. The acquisition process involves sensor readings, bus monitoring, and data logging to ensure the timeliness and accuracy of both target feature data and real-time status data.

[0045] Key seed data is generated based on target feature data and real-time state data. This key seed data is the starting point of the key generation process, formed by fusing the target feature data and real-time state data, and is used to derive subsequent session keys. The generation process involves data fusion algorithms, such as hash functions, to ensure the unpredictability and security of the key seed data.

[0046] The vehicle session key is generated based on the key seed data. This key is used to encrypt and decrypt session data packets, has a time limit, and is used in a single communication session or a group of communication sessions. The generation process involves a key derivation function (HKDF) that uses the key seed data and additional parameters to generate the vehicle session key.

[0047] Finally, the vehicle session key is used for data communication with the target vehicle. This key is used to encrypt and decrypt data packets sent and received on the target vehicle's CAN bus, ensuring the security of the in-vehicle network communication. Specifically, the session key is loaded into the encryption module for encrypting data packets, and the decryption process is reversed at the decryption end.

[0048] Taking the engine ECU as an example, the ECU first collects its own clock offset data through built-in sensors, then reads engine speed and ignition status information from the CAN bus, and merges these data to generate target feature data and real-time status data. Subsequently, key seed data is generated based on the target feature data and real-time status data. The key seed data and the current timestamp are mapped using the HKDF function to generate the vehicle session key. Finally, the ECU uses the generated vehicle session key to encrypt the control commands to be sent. The receiving end also uses the session key to decrypt the received data packet, ensuring the authenticity and confidentiality of the data.

[0049] Based on the above steps S11 to S14, by acquiring the target feature data and real-time status data of the target vehicle, key seed data is generated based on the target feature data and real-time status data. Subsequently, a vehicle session key is generated based on the key seed data. Finally, the vehicle session key is used for data communication with the target vehicle. This achieves the goal of generating a dynamic vehicle session key by integrating the multi-dimensional physical characteristics of the vehicle with real-time status data, thereby achieving the technical effect of improving the security of vehicle network communication, enhancing user trust and user experience. It also solves the technical problems of low security of fixed key management schemes and the significant impact of encryption authentication mechanisms on real-time performance in related technologies.

[0050] The vehicle communication data processing method in the embodiments of this application will be further described below.

[0051] In one optional embodiment, the target feature data includes: clock offset feature data, voltage ripple feature data, and impedance feature data of the target vehicle, wherein the clock offset feature data is used to describe the difference between the local clock and the network clock of the target vehicle, the voltage ripple feature data is used to reflect the voltage fluctuation in the on-board power system, and the impedance feature data is used to characterize the electrical characteristics of the signal transmission path in the controller area network of the target vehicle.

[0052] The aforementioned clock offset characteristic data of the target vehicle refers to the time difference between the internal clock of the target vehicle's ECU and the network reference clock, reflecting the individual differences in ECU hardware. For example, if the local clock of the target vehicle's ECU differs from the network clock by 10 nanoseconds per second, then this 10 nanosecond difference constitutes the clock offset characteristic data, which is a key basis for hardware identity authentication.

[0053] The voltage ripple characteristic data described above describes the fluctuation and ripple level of the output voltage in the vehicle power supply system, reflecting the characteristics of the power module and the vehicle's operating status. For example, when the vehicle is stationary, the voltage ripple characteristic data is relatively stable, while when the vehicle starts or accelerates, the voltage ripple characteristic data will change accordingly due to load changes. The fluctuation data can be used to generate dynamic and unpredictable key seeds.

[0054] The aforementioned impedance characteristic data characterizes the electrical properties of the signal transmission path in the target vehicle's CAN bus, including but not limited to parameters such as the bus's characteristic impedance, node impedance, and cable capacitance. For example, impedance characteristic data may vary due to the vehicle's age, ambient temperature changes, and cable aging, thus providing an indicator reflecting the vehicle's physical environment status and facilitating dynamic authentication based on the physical environment.

[0055] In a typical vehicle startup scenario, when the ignition switch is turned to the ON position and the ECU begins operation, this embodiment first collects clock offset characteristic data between the ECU's internal clock and the network clock using a high-speed timer. Then, a high-precision analog-to-digital converter (ADC) is used to sample and analyze the vehicle's power system, extracting voltage ripple characteristic data. Simultaneously, time-domain reflectometry (TDR) technology is used to detect the impedance characteristic data of the CAN bus signal transmission path. After data fusion, a dynamic feature set reflecting the target vehicle's current physical state and operating environment is formed. This feature set is used to generate the key seed data, which is then used in subsequent steps to generate the vehicle session key, ensuring the security of each data communication.

[0056] Based on the above optional embodiments, by comprehensively collecting and analyzing the clock offset, voltage ripple, and impedance characteristic data of the target vehicle, it is possible to effectively capture the individual hardware differences and operating environment changes of the target vehicle. The generated key seed data has a high degree of randomness and unpredictability, thereby ensuring the security of the vehicle session key derived from it, and significantly enhancing the anti-attack capability and key management flexibility of the vehicle network communication.

[0057] In one optional embodiment, generating key seed data based on target feature data and real-time state data includes:

[0058] The clock offset feature data, voltage ripple feature data, and impedance feature data are weighted according to preset weighting coefficients to obtain feature fusion results; the feature fusion results and real-time status data are concatenated to obtain data concatenation results; the data concatenation results are hashed to generate key seed data.

[0059] The aforementioned preset weighting coefficients represent the relative importance of clock offset characteristic data, voltage ripple characteristic data, and impedance characteristic data in constituting the target characteristic data. These coefficients are determined based on experimental data and theoretical analysis and are used to balance the contribution of different characteristics to the final certification decision. For example, assuming that clock offset characteristic data is the most important, followed by voltage ripple characteristic data, and finally impedance characteristic data, then the preset weighting coefficients can be set to α, β, and y, respectively, satisfying α > β > y and α + β + y = 1. Weighting is used to combine the various indicators of the target characteristic data according to the preset weighting coefficients to highlight the more decisive characteristics in security certification.

[0060] Each type of feature data can be continuously monitored and collected through built-in sensors and circuit modules to ensure the real-time nature and accuracy of the data. Weighted processing is implemented at the software level, performing mathematical operations on the collected feature data according to preset weight coefficients to obtain a comprehensive score, i.e., the feature fusion result.

[0061] The feature fusion results and real-time status data are spliced ​​together to obtain the spliced ​​data result. The splicing process is a data combination operation that connects the feature fusion results and real-time status data in a predetermined format to form a more comprehensive data set describing the current status of the target vehicle, i.e., the spliced ​​data result.

[0062] A hash calculation is performed on the concatenated data to generate key seed data. Hash calculation can compress datasets of any size into a fixed-length hash value, which is unique; even minute data variations will result in significantly different hash values. A specific hash algorithm is used to process the concatenated data, outputting a fixed-length binary string as the starting point for generating the vehicle session key. This ensures the randomness and unpredictability of the key seed data, enhancing the security of subsequent key generation.

[0063] Based on the above optional embodiments, the feature weighting, data concatenation, and hash calculation steps in this application embodiment achieve a comprehensive consideration and transformation of the target vehicle's physical characteristics and real-time status data, generating unique key seed data. This effectively integrates individual hardware differences and changes in the vehicle's operating environment, ensuring the high randomness and unpredictability of the key seed data, thus laying a secure foundation for the subsequent generation of vehicle session keys. This application embodiment can significantly improve the security and stability of in-vehicle network communication without significantly increasing the hardware burden of the in-vehicle system, providing strong technical support for vehicle information security.

[0064] In one optional embodiment, acquiring the clock offset characteristic data of the target vehicle includes:

[0065] Obtain the cumulative clock offset data corresponding to multiple pulse cycles; calculate the average of the cumulative clock offset data to obtain the offset average result; perform compensation processing on the offset average result to obtain the offset compensation result; determine the clock offset characteristic data based on the offset compensation result.

[0066] The aforementioned multiple pulse cycles represent the number of CAN bus synchronization pulses over a period of time, used to measure the accumulated deviation of the ECU clock relative to the bus clock. For example, the ECU can record the time difference between its clock and the CAN bus clock within 100 pulse cycles.

[0067] The aforementioned cumulative clock offset data specifically refers to the cumulative deviation between the ECU's local clock and the CAN bus clock. For example, assuming the ECU clock is slower than the bus clock, the difference measured at the end of each pulse cycle will be accumulated, forming a series of data points reflecting the deviation trend.

[0068] This embodiment of the application uses a built-in high-speed timer to trigger timing in each pulse cycle, recording the instantaneous deviation between the ECU's local clock and the bus clock. This acquisition process typically begins immediately after the ECU starts and continues until the vehicle is shut down or the authentication mechanism is activated.

[0069] Furthermore, the cumulative clock offset data is averaged to obtain the offset mean. The average calculation is used to determine the average level of a set of data, reflecting the general characteristics of the dataset without being affected by individual extreme values. For example, assuming the clock offsets measured over 100 pulse cycles are d1, d2, ..., d100, the offset mean can be calculated using the formula: (d1 + d2 + ... + d100) / 100. A mathematical average is then applied to all the acquired cumulative clock offset data points to obtain a value representing the average offset over a long period.

[0070] The offset mean result is compensated to obtain the offset compensation result. Given that clock offset may be affected by factors such as vehicle ambient temperature and ECU aging, this embodiment introduces environmental parameters such as temperature and aging, and adjusts the offset mean result through a preset compensation algorithm to eliminate errors caused by changes in external conditions. By considering the temperature compensation model and other environmental factors, the offset mean result is corrected so that the final offset compensation result more accurately reflects the physical characteristics of the ECU and is unaffected by changes in the operating environment.

[0071] Clock offset feature data is determined based on the offset compensation results. This clock offset feature data is clock offset data that has been compensated and represents the characteristics of the ECU hardware. It is used to construct a physical fingerprint and is a key component of security authentication.

[0072] In this embodiment, to achieve accurate measurement of clock offset data, a high-speed timer with a high sampling rate of 100MHz can be used. The measurement process begins with ECU power-on initialization. The high-speed timer is triggered by receiving the CAN bus synchronization pulse, recording the cumulative clock deviation Δt between the local crystal oscillator t_local and the bus clock t_bus over multiple pulse cycles. The deviation value is calculated using the formula Δt = |t_local - t_bus|. To obtain a more stable average clock offset result, the system can continuously collect deviation data for 100 cycles and calculate its average value Δt_avg, using the following formula:

[0073] Δt_avg = (∑|t_local - t_bus|) / 100

[0074] The above average values ​​typically range from 12 to 50 ns at a temperature of 25°C. Given that temperature fluctuations have a significant impact on crystal oscillator frequency, this embodiment further utilizes ambient temperature information fed back by a built-in temperature sensor to perform temperature compensation calculations. The temperature compensation model can be:

[0075] Δt_corr = Δt_raw [1 + Σ(a_n (T - T0)^n)]

[0076] Where n = 0~2, a_n is a pre-set calibration coefficient. The system can adjust the crystal oscillator frequency according to the difference between the current temperature T and the standard temperature T0, as well as the frequency-temperature characteristics of the crystal oscillator, thereby achieving frequency compensation and ensuring that the measurement results of clock deviation are not affected by temperature changes. Finally, to improve the stability and reliability of the measurement results, this embodiment can also use a sliding window averaging algorithm to filter the clock offset measurement results and eliminate instantaneous jitter introduced by noise. Through a systematic and refined measurement and compensation process, this embodiment significantly improves the measurement accuracy of clock offset characteristic data, providing a solid foundation for the synchronization and reliability of signal transmission in vehicle communication networks.

[0077] Based on the above optional embodiments, by collecting cumulative clock offset data, calculating the average offset, performing temperature compensation, and finally determining the clock offset characteristic data, the accuracy and robustness of ECU identity authentication are effectively improved. This process not only captures individual hardware differences within the ECU but also eliminates the impact of environmental changes on clock offset characteristics through dynamic compensation, ensuring accurate node identity verification even under harsh conditions and enhancing the security of in-vehicle network communication. Furthermore, the key seed generated based on the compensated clock offset characteristic data is more stable and reliable, reducing key generation errors caused by hardware fluctuations and further enhancing the protection capabilities of vehicle secure communication.

[0078] In one optional embodiment, obtaining voltage ripple characteristic data of the target vehicle includes:

[0079] The initial voltage sampling signal is obtained according to the preset sampling parameters; the initial voltage sampling signal is filtered to obtain the signal filtering result; based on the signal filtering result, the frequency band energy feature is extracted to obtain the voltage ripple feature data.

[0080] The aforementioned preset sampling parameters include sampling rate, sampling resolution, input impedance, and sampling duration, which are used to ensure the quality and integrity of the initial voltage sampling signal. In this embodiment, the ECU uses a built-in high-precision ADC to monitor the vehicle power system in real time and collects data according to the preset sampling parameters. After determining the preset sampling parameters, the ECU starts the ADC to sample the power supply voltage, records the voltage change sequence, and forms the initial voltage sampling signal.

[0081] The initial voltage sampling signal is filtered to obtain the signal filtering result. For the initial voltage sampling signal, digital signal processing techniques, such as bandpass filtering, DC component removal, and spectral leakage compensation, are used to eliminate noise and irrelevant signals, retaining and focusing on key information reflecting voltage ripple. In this embodiment, after completing the filtering process, the output is a voltage signal that has been noise-removed, spectral leakage compensated, and retains only the voltage signal within a 10-100kHz bandwidth, providing a clean data foundation for subsequent feature extraction.

[0082] Frequency band energy feature extraction is performed based on the signal screening results to obtain voltage ripple feature data. Frequency band energy feature extraction involves further analysis of the screened signals to calculate the signal energy in a specific frequency band, which characterizes the intensity and distribution characteristics of the voltage ripple. For example, the signal energy within a bandwidth of 10-100kHz is calculated, and features such as peak frequency, spectral centroid, and bandwidth are further extracted.

[0083] In this embodiment, a high-precision data acquisition and processing scheme is employed for the voltage ripple characteristic analysis of the DC power supply in the vehicle system. The aim is to deeply explore the spectral characteristics and time-domain behavior of the ripple signal, thereby optimizing power supply performance and eliminating potential interference. In terms of technical specifications, a high-speed ADC with a resolution of 16 bits or higher, a sampling rate of 1 MSPS (millions of samples per second), and an input impedance set to 1 MΩ||10 pF can be selected to ensure high-precision voltage measurement. A low-temperature drift reference such as the LTZ1000 can be used as the reference source, with a temperature drift of only 0.05 ppm / ℃, providing an extremely stable and accurate reference for ripple analysis. Furthermore, to prevent the input signal from being impacted by transient voltage, a transient voltage suppression diode is used for front-end protection, and a resistor-capacitor low-pass filter circuit (resistor R=100Ω, capacitor C=100nF) is added to effectively suppress high-frequency noise, ensuring signal quality while maintaining good input impedance matching.

[0084] In the hardware decoupling stage, to ensure accurate voltage ripple measurement, a 0.1μF X7R ceramic capacitor and a 10μF tantalum capacitor can be connected in parallel on the ECU's power pin. The star grounding layout controls the grounding impedance to within the range of <10mΩ, effectively suppressing electromagnetic interference and signal reflection. For sampling control, a timer-triggered mode is used, with a sampling duration set to 100ms, equivalent to collecting 100k data points. The first 5% of the data is specifically retained to capture the instantaneous details of signal changes.

[0085] In the digital signal processing stage, the DC component is first removed from the initial voltage sampling signal. Then, the Hanning window is used to reduce spectral leakage. Finally, a 2048-point Fast Fourier Transform is used to convert the time-domain signal into a frequency-domain representation x[k], facilitating subsequent feature analysis. Frequency band energy calculation focuses on a bandwidth of 10-100kHz. By integrating the energy at each frequency point, the energy distribution of the signal in the target frequency band is determined. In the frequency band energy calculation process, the frequency band boundaries k_start and k_end are first determined, and then the energy is calculated according to the following formula:

[0086] E = Σ|X[k]|² (2 / N^2S_1) Δf

[0087] Where, Δf = 488Hz, =0.375 is the energy compensation coefficient for the Hanning window, and N is the number of points in the Fast Fourier Transform. To further improve the analysis accuracy, this application also employs optimization strategies such as double spectrum energy calculation, rapid frequency point location, interpolation correction, and noise floor subtraction.

[0088] To eliminate the negative impact of power grid frequency interference on ripple analysis, this embodiment introduces a 50Hz notch filter. A second-order infinite impulse response notch filter is used to form an extremely narrow stopband at 50Hz, significantly weakening the signal at this frequency without affecting other components. The transfer function of the notch filter is expressed by H(z). The pole and zero parameters are calculated to effectively suppress the 50Hz power frequency component. The transfer function H(z) is as follows:

[0089] H(z)=(1-2cos(2π f_0 / f_s ) z^(-1)+z^(-2)) / (1-2r cos(2π f_0 / f_s ) z^(-1)+r^2 z^(-2))

[0090] Where f_0 is the notch center frequency (e.g., 50Hz), f_s is the sampling frequency (e.g., 1MHz), and r is the pole radius.

[0091] In the key feature calculation stage, the focus is on energy feature extraction, ripple waveform characteristic analysis, and spectral feature quantization. First, the energy of the signal within a 10-100kHz bandwidth is integrated to determine the total energy E and the peak frequency f_peak where the energy is most concentrated. Total energy E = Σ|X[k]|² (2 / N² ) Δf, peak frequency location f_peak = argmax(P[k]). Next, extract the average energy density E_avg = E_total / (k_end - k_start + 1) from the spectrum, and the spectral centroid C = (Σ(k P[k])) / E_total, Spectrum bandwidth BW =sqrt((Σ((kC)^2) Indices such as P[k]) / E_total and spectral flatness = (geometric mean P[k]) / (arithmetic mean P[k]) are used. In time-domain analysis, the peak-to-peak value of the ripple is measured as V_pp = max(v(t)) - min(v(t)), and the overshoot rate is evaluated as Overshoot = (V_max - V_steady) / V_steady. These indicators can intuitively reflect the transient characteristics of the ripple signal. Finally, through feature normalization and dimensionality reduction, the extracted features are converted into a standardized and dimensionality-reduced form, which facilitates subsequent analysis and comparison.

[0092] Based on the above optional embodiments, by acquiring the initial voltage sampling signal according to preset sampling parameters, filtering the signal to remove interference and focusing on key frequency bands, and then extracting frequency band energy features based on the signal filtering results, specific information of voltage ripple in the vehicle power system is effectively captured. This not only ensures the quality and signal-to-noise ratio of the obtained voltage ripple feature data, but also provides accurate hardware feature input for subsequent physical fingerprint fusion and key seed generation. The embodiments of this application can ensure the accuracy and stability of voltage ripple feature data, providing a reliable foundation for node authentication and data anti-tampering in the vehicle network, and significantly improving the communication security performance based on hardware fingerprints.

[0093] In one optional embodiment, obtaining the impedance characteristic data of the target vehicle includes:

[0094] Impedance detection of the controller local area network bus of the target vehicle is performed using the time-domain reflection method to obtain the impedance detection results; feature extraction is performed on the impedance detection results to obtain impedance feature data.

[0095] Time-domain reflectometry (TDR) is used to measure the impedance of cables or transmission lines. It analyzes the impedance characteristics of the cable by sending a high-speed pulse signal to the cable and measuring the amplitude and time delay of the reflected signal. In this embodiment, TDR technology is used to detect impedance changes in the target vehicle's CAN bus to obtain information reflecting the physical characteristics of the vehicle network.

[0096] The impedance detection results described above are measurements of the target vehicle's CAN bus impedance characteristics obtained through time-domain reflectometry, including the location of impedance discontinuities, impedance values, and their variations. For example, if the impedance at a certain point in the cable suddenly increases from 120Ω to 150Ω, it indicates that a connector or ECU is connected at that location.

[0097] In this embodiment, the TDR circuit module built into the ECU sends a known pulse signal to the target CAN bus, and then the module receives and analyzes the reflected signal. The time delay and amplitude change of the reflected signal are used to calculate the impedance detection result.

[0098] Feature extraction is performed on the impedance detection results to obtain impedance feature data. Key impedance parameters are extracted from the impedance detection results to construct the physical fingerprint of the target vehicle. For example, statistical features such as the bus characteristic impedance, node impedance, and the average and standard deviation of cable capacitance are extracted from the detection results.

[0099] Impedance characteristic data specifically refers to a dataset extracted from impedance detection results that reflects the electrical characteristics of the target vehicle's CAN bus, used to characterize individual differences in the vehicle's network physical layer. For example, impedance characteristic data may include information such as an average characteristic impedance of 120Ω with a standard deviation of 0.5Ω; an average node impedance of 150Ω with a standard deviation of 1Ω; and an average cable capacitance of 200pF / m with a standard deviation of 10pF / m.

[0100] In this embodiment, the impedance detection results are processed using statistical methods to extract key indicators reflecting impedance characteristics, such as average value, standard deviation, maximum value, and minimum value, which constitute impedance characteristic data.

[0101] Based on the above optional embodiments, this application embodiment uses time-domain reflection to perform impedance detection on the target vehicle's controller area network bus and extracts features from the detection results. This effectively captures the individual differences in the electrical characteristics of the target vehicle's CAN bus and generates impedance feature data. This provides a solid foundation for physical fingerprint authentication of the vehicle network and enhances the stability and reliability of hardware-based secure communication. Through the impedance detection and feature extraction of this application embodiment, accurate identification of physical features can be achieved in the existing vehicle network architecture without relying on additional hardware devices, significantly improving the engineering applicability and economy of vehicle network communication security protection.

[0102] In one optional embodiment, generating a vehicle session key based on key seed data includes:

[0103] The key seed data is expanded using a key derivation function to obtain the key expansion result; based on the key expansion result, parameter mapping is performed to obtain the vehicle session key.

[0104] Key derivation functions are used to derive a series of subkeys from the key seed data, ensuring the randomness and security of the subkeys. Key derivation functions ensure that even if the original key material is leaked, the derived keys are difficult to reverse engineer.

[0105] The HKDF algorithm can be used to expand the key seed data using a key derivation function, taking the key seed data as input to generate a key expansion result of sufficient length. HKDF mainly consists of three stages: extraction, expansion, and information injection.

[0106] For example, in the extraction phase, HKDF uses a hash function to extract a fixed-length random seed from the key seed data; in the expansion phase, the random seed is expanded into the desired key expansion result by calling HMAC again and injecting specific information parameters.

[0107] The vehicle session key is obtained by parameter mapping based on the key expansion result. Parameter mapping is used to transform the information in the key expansion result into a specific key format or required key length to adapt to the key requirements of a specific encryption algorithm or communication framework. For example, if the length of the key expansion result exceeds the 128 bits required by AES-128, the parameter mapping process will truncate an appropriate length from the key expansion result to generate the vehicle session key.

[0108] Based on the above optional embodiments, by expanding the key seed data using a key derivation function, and then obtaining the vehicle session key through parameter mapping based on the key expansion result, dynamic key generation and high security are ensured. The HKDF algorithm can derive a sufficiently long key expansion result from the key seed data containing physical characteristics such as clock offset, voltage ripple, and impedance, as well as the vehicle's operating status. Even if the original data is leaked, the derived key is difficult to crack. The parameter mapping step further ensures that the derived key can seamlessly match specific encryption algorithms and communication frameworks. Therefore, the embodiments of this application can independently realize dynamic key updates and format adaptation, providing a real-time updated key management mechanism that is closely integrated with the vehicle's physical characteristics for vehicular network communication, significantly reducing the risk of key prediction and replay attacks, and improving communication security.

[0109] In an optional embodiment, the vehicle communication data processing method further includes:

[0110] The key seed data is updated according to a preset period; or, in response to the feature change data corresponding to the target feature data exceeding a preset threshold, the key seed data is updated.

[0111] Based on system security requirements and communication efficiency requirements, a pre-set time interval for updating the key seed data is determined, i.e., the preset period. For example, an update process is performed every 10 seconds or every minute to ensure the timeliness and security level of the key.

[0112] According to a preset cycle, the ECU or other safety components in the vehicle network will re-collect the target feature data and generate new key seed data based on the new physical feature data, ensuring the dynamic nature of the key seed data and improving communication security.

[0113] During normal vehicle operation, the security system in this embodiment is configured with an update cycle of 10 seconds. Whenever the preset cycle ends, the ECU initiates a physical feature acquisition process, rereading target feature data such as clock offset, voltage ripple, and impedance characteristics. Subsequently, the ECU performs weighted, concatenated, and hash calculations on this data according to the previously described steps to generate new key seed data. That is, every 10 seconds, the security node in the target vehicle updates the key seed based on the current physical state, thereby ensuring timely updates and security of the communication key.

[0114] In response to a change in the target feature data exceeding a preset threshold, the key seed data is updated. A change in the target feature data exceeding the preset threshold means that the magnitude of the change exceeds a system-preset security threshold. For example, if the clock offset changes by more than 30% from the average value, this embodiment will trigger an update process.

[0115] The vehicle system's safety monitoring module continuously monitors changes in target feature data. Once a change is detected that exceeds a preset threshold, the ECU immediately re-collects the target feature data and generates new key seed data. This ensures a rapid response and maintains key security when significant changes occur in physical characteristics.

[0116] Based on the above optional embodiments, by updating the key seed data according to a preset period or in response to changes in target feature data exceeding a threshold, the dynamic security protection capability of vehicle network communication is effectively enhanced. It can not only refresh the key seed regularly to prevent potential attackers from eavesdropping and predicting for a long time, but also react quickly when abnormal changes in physical characteristics are detected and update the key in real time, increasing the flexibility and security of key management.

[0117] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0118] According to an embodiment of this application, an apparatus embodiment for a vehicle communication data processing method is provided. It should be noted that the apparatus can be used to execute the above-described vehicle communication data processing method.

[0119] Figure 2 This is a structural block diagram of a vehicle communication data processing device according to an embodiment of this application, such as... Figure 2 As shown, the device includes:

[0120] The acquisition module 201 is used to acquire target feature data and real-time status data of the target vehicle, wherein the target feature data is used to represent the multi-dimensional physical characteristics associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle; the first generation module 202 is used to generate key seed data based on the target feature data and real-time status data; the second generation module 203 is used to generate a vehicle session key based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets; and the communication module 204 is used to conduct data communication of the target vehicle using the vehicle session key.

[0121] Optionally, the target feature data includes: clock offset feature data, voltage ripple feature data, and impedance feature data of the target vehicle. The clock offset feature data is used to describe the difference between the local clock and the network clock of the target vehicle. The voltage ripple feature data is used to reflect the voltage fluctuation in the on-board power system. The impedance feature data is used to characterize the electrical characteristics of the signal transmission path in the controller area network of the target vehicle.

[0122] Optionally, the first generation module 202 is further configured to: perform weighted processing on clock offset feature data, voltage ripple feature data and impedance feature data according to preset weight coefficients to obtain feature fusion results; perform concatenation processing on feature fusion results and real-time status data to obtain data concatenation results; and perform hash calculation on data concatenation results to generate key seed data.

[0123] Optionally, the acquisition module 201 is further configured to: acquire clock cumulative offset data corresponding to multiple pulse cycles; calculate the average of the clock cumulative offset data to obtain the offset average result; perform compensation processing on the offset average result to obtain the offset compensation result; and determine clock offset characteristic data based on the offset compensation result.

[0124] Optionally, the acquisition module 201 is further configured to: acquire an initial voltage sampling signal according to preset sampling parameters; perform filtering processing on the initial voltage sampling signal to obtain signal filtering results; and extract frequency band energy features based on the signal filtering results to obtain voltage ripple feature data.

[0125] Optionally, the acquisition module 201 is further configured to: perform impedance detection on the controller local area network bus of the target vehicle based on time-domain reflection to obtain impedance detection results; and extract features from the impedance detection results to obtain impedance feature data.

[0126] Optionally, the second generation module 203 is further configured to: expand the key seed data using a key derivation function to obtain a key expansion result; and perform parameter mapping based on the key expansion result to obtain a vehicle session key.

[0127] Optionally, the vehicle communication data processing device further includes: an update module 205, used to: update the key seed data according to a preset period; or; update the key seed data in response to the feature change data corresponding to the target feature data exceeding a preset threshold.

[0128] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0129] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0130] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program:

[0131] S1, acquire the target feature data and real-time status data of the target vehicle, wherein the target feature data is used to represent the multi-dimensional physical features associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle.

[0132] S2, generate key seed data based on target feature data and real-time status data;

[0133] S3, Generate a vehicle session key based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets;

[0134] S4 uses the vehicle session key to conduct data communication with the target vehicle.

[0135] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0136] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0137] S1, acquire the target feature data and real-time status data of the target vehicle, wherein the target feature data is used to represent the multi-dimensional physical features associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle.

[0138] S2, generate key seed data based on target feature data and real-time status data;

[0139] S3, Generate a vehicle session key based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets;

[0140] S4 uses the vehicle session key to conduct data communication with the target vehicle.

[0141] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0142] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0143] Embodiments of this application also provide a computer program that, when executed by a processor, implements the methods described in the various embodiments of this application.

[0144] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0145] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0146] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0147] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0148] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0149] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A vehicle communication data processing method, characterized in that, include: Acquire target feature data and real-time status data of the target vehicle, wherein the target feature data is used to represent the multi-dimensional physical features associated with the target vehicle, and the real-time status data is used to represent the engine operating status and ignition status of the target vehicle. Generate key seed data based on the target feature data and the real-time status data; A vehicle session key is generated based on the key seed data, wherein the vehicle session key is used to encrypt and decrypt session data packets; The target vehicle uses the vehicle session key for data communication.

2. The method according to claim 1, characterized in that, The target feature data includes: clock offset feature data, voltage ripple feature data, and impedance feature data of the target vehicle. The clock offset feature data is used to describe the difference between the local clock and the network clock of the target vehicle. The voltage ripple feature data is used to reflect the voltage fluctuation in the vehicle power system. The impedance feature data is used to characterize the electrical characteristics of the signal transmission path in the controller area network of the target vehicle.

3. The method according to claim 2, characterized in that, Generating key seed data based on the target feature data and the real-time status data includes: The clock offset feature data, voltage ripple feature data, and impedance feature data are weighted according to preset weighting coefficients to obtain feature fusion results; The feature fusion result and the real-time status data are spliced ​​together to obtain a data splicing result. The data concatenation result is hashed to generate the key seed data.

4. The method according to claim 2, characterized in that, Obtaining the clock offset feature data of the target vehicle includes: Obtain the cumulative clock offset data corresponding to multiple pulse cycles; The average value of the accumulated clock offset data is calculated to obtain the average offset result; The offset mean result is compensated to obtain the offset compensation result; The clock offset feature data is determined based on the offset compensation result.

5. The method according to claim 2, characterized in that, Obtaining the voltage ripple characteristic data of the target vehicle includes: The initial voltage sampling signal is obtained according to the preset sampling parameters; The initial voltage sampling signal is filtered to obtain the signal filtering result; Based on the signal filtering results, frequency band energy features are extracted to obtain the voltage ripple feature data.

6. The method according to claim 2, characterized in that, Obtaining the impedance characteristic data of the target vehicle includes: Impedance detection of the controller local area network bus of the target vehicle is performed based on time-domain reflection to obtain the impedance detection result; The impedance detection results are subjected to feature extraction to obtain the impedance feature data.

7. The method according to claim 1, characterized in that, Generating the vehicle session key based on the key seed data includes: The key seed data is expanded using a key derivation function to obtain the key expansion result; The vehicle session key is obtained by performing parameter mapping based on the key expansion result.

8. The method according to claim 1, characterized in that, The vehicle communication data processing method further includes: The key seed data is updated according to a preset period; or; In response to the feature change data corresponding to the target feature data exceeding a preset threshold, the key seed data is updated.

9. A vehicle, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 8.

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