Vehicle identity anonymous authentication method in space-air-ground fusion vehicle-mounted network

Abstract

The invention provides a vehicle identity anonymous authentication method in a space-air-ground fusion vehicle-mounted network. The method comprises the following steps: carrying out multi-source data acquisition; generating a dynamic area identifier based on the vehicle real-time position data, and performing dynamic updating according to a vehicle moving threshold value; vehicle equipment identification information is acquired, a salted hash value is generated in combination with the dynamic region identifier, and an anonymous certificate containing dynamic effective duration is generated; extracting spatio-temporal data fluctuation parameters according to the environmental perception data, and extracting delay trend parameters and channel stability parameters according to the regional network state data; the weight is dynamically adjusted according to the link type and the time period type, and the dynamic trust quantized value of the vehicle is calculated through weighted summation; differential authentication is executed based on the dynamic trust quantized value of the vehicle and the anonymous voucher, and the differential authentication comprises the steps that lightweight verification is adopted when the vehicle is highly trusted, two-factor authentication is started when the vehicle is low trusted, and link timeout redundancy authentication is combined. According to the method, the trust evaluation accuracy can be improved, and the dynamism of a privacy protection mechanism is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking, and in particular to a method for anonymous vehicle identity authentication. Background Art

[0002] Because the Air-Ground-Earth-Fused Vehicle Network (AIV) builds a heterogeneous network that collaborates across wide and local areas through satellite communications, ground-based roadside units, and onboard terminals, vehicle identity authentication must meet the real-time requirements of highly dynamic node access, the anonymity requirements of user privacy protection, and the ability to dynamically adjust authentication strength to accommodate switching between satellite and terrestrial links. Traditional authentication mechanisms in single network environments struggle to adapt to the multi-source data fusion and complex topology characteristics of this scenario. Existing solutions employ a consortium chain authentication mechanism based on a distributed ledger. Their trust assessment model uses fixed parameters and fails to fully account for the differences in transmission characteristics between satellite and terrestrial links in AIV-Ground-Earth-Fused Networks, easily leading to authentication misjudgments in scenarios dominated by satellite communications. Furthermore, their fixed anonymous identity credential replacement cycle makes them unable to adapt to the identity tracking risks brought about by the rapid movement of nodes in high-density vehicular networks, and they also suffer from insufficiently dynamic privacy protection mechanisms.

[0003] Patent publication number CN 112039870 A discloses a privacy-preserving blockchain-based in-vehicle network authentication method. By integrating blockchain and smart contracts to build a distributed in-vehicle network environment, the method combines blockchain with the secure multi-party computation (STD) encryption method to ensure anonymous identity authentication even when a trusted third party is offline. The implementation process includes the following: a system initialization process, in which a cloud server generates system parameters and initializes the consortium chain; a smart contract deployment process, in which a preset smart contract and its access policy are initialized into the consortium chain to obtain the smart contract address; a vehicle registration process, in which a vehicle owner registers with the cloud server and obtains an authentication script; a vehicle login and message authentication process, in which a vehicle sends real-time traffic messages accompanied by identity authentication information to nearby roadbed facilities and vehicles. The receiving party queries the consortium chain administrator node for verification credentials. Multiple administrator nodes perform distributed computing to decrypt the verification credentials and return the verification credentials to the message recipient to complete message authentication; a vehicle deregistration process, in which the cloud server deregisters the vehicle account; and an identity tracking process, in which a cloud server auditor node tracks the vehicle identity if a malicious message is detected. This method achieves anonymous identity authentication and privacy protection, ensuring the accuracy and reliability of message transmission, and is suitable for self-organizing intelligent application scenarios. However, this method is limited in scope, lacks dynamic optimization, and ignores link characteristics, making it more suitable for static scenarios in small-scale ground vehicle networks. Summary of the Invention

[0004] In response to the technical problems of insufficient dynamic adaptability and limited privacy protection capabilities of existing methods in multi-source data fusion scenarios, the present invention proposes an anonymous vehicle identity authentication method in an air-space-ground-integrated vehicle network. Through dynamic area identification generation, dynamic configuration of the valid time in the anonymous identity credential, precise extraction of multi-source data features, dynamic trust evaluation and dynamic adjustment of authentication strength, it can adapt to the multi-source data fusion and complex topology characteristics of the air-space-ground-integrated network, improve the accuracy of trust evaluation, and enhance the dynamic nature of the privacy protection mechanism.

[0005] In order to achieve the above object, the technical solution of the present invention is achieved as follows:

[0006] A method for anonymous vehicle identity authentication in an air-space-ground integrated vehicle network, comprising the following steps:

[0007] S1: Collect multi-source data, including real-time vehicle location data, environmental perception data, and regional network status data, and perform preprocessing;

[0008] S2: Generate dynamic area identification based on real-time vehicle location data and dynamically update it according to vehicle movement thresholds;

[0009] S3: Obtain vehicle device identification information and combine it with the dynamic area identifier to generate a salted hash value. The salted hash value is combined with the regional network status data and group signature technology to generate an anonymous credential with a dynamic validity period.

[0010] S4: Extract spatiotemporal data fluctuation parameters based on environmental perception data, and extract delay trend parameters and channel stability parameters based on regional network status data;

[0011] S5: Dynamically adjust the weights according to link type and time period based on spatiotemporal data fluctuation parameters, delay trend parameters, and channel stability parameters, and calculate the dynamic trust quantization value of the vehicle through weighted summation;

[0012] S6: Perform differentiated authentication based on the vehicle's dynamic trust quantification value and anonymous credentials. The differentiated authentication includes using lightweight verification when the trust is high, starting two-factor authentication when the trust is low, and combining link timeout redundant authentication to finally obtain the authentication result.

[0013] Furthermore, the vehicle real-time location data is real-time location information including latitude and longitude coordinates and UTC timestamp obtained by a satellite positioning module carried by the vehicle;

[0014] The environmental perception data is the environmental information of traffic light status and road obstacles perceived by the ground roadside unit through the fusion of millimeter-wave radar and camera;

[0015] The regional network status data is a communication delay sequence, a channel quality index sequence, a network security level and a network load factor including a vehicle adjacent node list transmitted by a space orbit satellite;

[0016] The preprocessing uses the PTP protocol to synchronize the timestamps of vehicle real-time location data, environmental perception data, and regional network status data, and aggregates the aligned three types of data into structured data frames in time series.

[0017] Further, the implementation method of step S2 is:

[0018] S2.1: The onboard processing terminal divides the latitude and longitude coordinates in the real-time location information into grids to obtain a spatial grid ID for each grid, which is used to identify the spatial location of the vehicle.

[0019] S2.2: Divide the UTC timestamp corresponding to the structured data frame into time slices according to a first preset granularity; concatenate the spatial grid ID and the time slice ID to generate a dynamic region identifier in the format of "grid ID_time slice ID" for associating vehicle location with time dimension features;

[0020] S2.3: Set a movement threshold. When the vehicle movement exceeds the movement threshold or the time slice is updated, the dynamic area identification is regenerated.

[0021] Further, the implementation method of step S3 is:

[0022] S3.1: The on-board processing terminal extracts the device identification information of the on-board unit (OBU), including the device fingerprint and the device unique identifier. The device unique identifier is combined with the dynamic area identifier through a binary XOR operation to generate a random salt value. The device fingerprint and the random salt value are concatenated and calculated using a hash algorithm to obtain the salted hash value H.

[0023] S3.2: Generate a group signature private key based on the network security level, dynamic zone identifier, and device unique identifier;

[0024] S3.3: Generate an anonymous identity credential with a signature timestamp and validity period using the network load factor, group signature private key, salted hash value H, and dynamic zone identifier.

[0025] Further, the implementation method of step S4 is:

[0026] S4.1: Divide the time slice obtained in step S2.2 into multiple sub-time windows according to a second preset time granularity, each sub-time window including a data segment; perform temporal continuity segmentation on the environmental perception data by determining the time interval and number of consecutive data points in each data segment, thereby obtaining data segments that have not been temporally segmented and data segments that have been temporally segmented;

[0027] S4.2: For each data segment before and after temporal continuity partitioning, calculate spatiotemporal data fluctuation parameters based on the environmental perception data, and calculate delay trend parameters and channel stability parameters based on the regional network status data;

[0028] S4.3: Use the wavelet denoising algorithm to filter outliers and map the parameters to a uniform range through Min-Max normalization to generate a multidimensional spatiotemporal data vector F = [f1, f2, f3], where f1 represents the spatiotemporal data fluctuation parameter, f2 represents the delay trend parameter, and f3 represents the channel stability parameter.

[0029] Furthermore, the implementation method of step S3.2 is:

[0030] S3.21: Extract some fields from the device unique identifier and perform a binary XOR operation with the grid ID in the dynamic area identifier to generate the initial value of the basic key;

[0031] S3.22: Calculate the time slice ID in the dynamic area identifier using a hash function to generate a key factor;

[0032] S3.23: Concatenate the base key and the key factor bit by bit to obtain a combined key, and then perform a modulo operation on the combined key to obtain the initial value of the group signature private key;

[0033] S3.24: Based on the current network security level, the initial value of the group signature private key is encrypted using the AES algorithm and the encrypted initial value of the group signature private key is obfuscated by changing the byte order of the encrypted initial value of the group signature private key using a random permutation algorithm to obtain the group signature private key SK' that contains the obfuscated spatiotemporal characteristics and security level.

[0034] The implementation method of step S3.3 is:

[0035] S3.31: Use the BLS algorithm to sign the salted hash value H and the dynamic zone identifier using the group signature private key SK' to generate a signature value S, and record the current UTC time as the signature timestamp T q , combined with the signature value S and signature timestamp T q , dynamic zone identifier and salted hash value H form the preliminary certificate P = (H, dynamic zone identifier, S, T q );

[0036] S3.32: Obtain the network load factor LoadCoef from the regional network status data, combine it with the preset basic validity period BaseTime to calculate the final validity period E, and combine it with the preliminary credential P to generate the final anonymous identity credential {P, E}; the validity period E is calculated as follows:

[0037]

[0038] Among them, α e and β e is the load regulation coefficient, α e The value range is 0.5~1.0, β e The value ranges from 1.0 to 1.5.

[0039] Furthermore, the method for temporally segmenting the environmental perception data described in step S4.1 is as follows: for environmental perception data points within a data segment, the time intervals between adjacent data points are calculated; if the time intervals between N consecutive data points are all less than the segmentation time threshold t, the data points are segmented into the same data segment, thereby obtaining a data segment segmented for temporal continuity, wherein the value of N×t is less than the second preset time granularity; for consecutive data points spanning multiple data segments, if the time intervals are always less than the segmentation time threshold t, the data points are merged into the same data segment, thereby obtaining a data segment segmented for temporal continuity;

[0040] In step S4.2:

[0041] For environmental perception data, after adding a local timestamp, the vehicle terminal calculates the mean square error of the longitude and latitude of the location data points in each data segment to obtain the spatiotemporal data fluctuation parameter;

[0042] The communication delay sequence of adjacent nodes and the channel quality index sequence are extracted from the regional network status data, the sliding average of the communication delay sequence is calculated to obtain the delay trend parameter, and the mean of the absolute differences between adjacent values ​​in the channel quality index sequence is calculated to obtain the channel stability parameter.

[0043] Further, the implementation method of step S5 is:

[0044] S5.1: Dynamic calculation of network topology indicators: First, count the number of adjacent nodes in the same grid as the current vehicle in the regional network status data and divide it by a preset base to obtain the node connectivity indicator. Then, obtain the historical data of the first M time slices of the communication delay sequence and the channel quality indicator sequence, respectively, and calculate the ratio of the historical data fluctuation range to the time slice length to obtain the first link stability indicator and the second link stability indicator, respectively.

[0045] S5.2: Define the weight matrix W = [w1,w2,w3] corresponding to the spatiotemporal feature vector F = [f1,f2,f3], where w1 is the weight of the spatiotemporal data fluctuation parameter, w2 is the weight of the delay trend parameter, and w3 is the weight of the channel stability parameter.

[0046] S5.3: Divide the node connectivity index into at least three intervals and configure corresponding spatiotemporal data fluctuation parameter weights; compare the first link stability index and the second link stability index with the preset first fluctuation threshold P1 and the second fluctuation threshold P2 respectively, and adjust the delay trend parameter weight and the channel stability parameter weight according to the comparison results, with the weight unchanged, the preset first weight attenuation ratio α, or the preset second weight attenuation ratio β; if the current link type is a satellite link, the first weight attenuation ratio α and the second weight attenuation ratio β are multiplied by the coefficient λ to perform the delay trend parameter weight and the channel stability parameter weight adjustment.

[0047] S5.4: Obtain the current time period type from the dynamic area identifier to determine whether the current time slice belongs to the traffic peak period. Dynamically adjust the spatiotemporal data fluctuation parameter weight, delay trend parameter weight, and channel stability parameter weight based on the time period type using the peak factor γ and the off-peak factor δ.

[0048] S5.5: Perform weighted summation on the corrected spatiotemporal data fluctuation parameter weight, delay trend parameter weight, and channel stability parameter weight with the corresponding characteristic parameters in the spatiotemporal characteristic vector F to obtain the dynamic trust quantization value T, and set a high confidence threshold.

[0049] Further, the implementation method of step S6 is:

[0050] S6.1: When receiving the authentication request, the vehicle processing terminal verifies whether the signature timestamp of the anonymous credential is within the valid time and matches the current time, and extracts the remaining valid time E re , the dynamic trust quantization value and the effective time remaining E re As a certification decision factor;

[0051] S6.2: If the dynamic trust quantization value T is higher than the preset first trust threshold and the remaining effective time is greater than the preset first time threshold, the lightweight authentication process is executed; if the dynamic trust quantization value is lower than the preset second trust threshold or the remaining effective time is less than the preset second time threshold, the enhanced authentication process including biometric verification is triggered;

[0052] S6.3: Set different response timeout thresholds based on the current communication link type. If dual links exist at the same time, use the dual links to perform enhanced authentication processes separately to achieve redundant authentication. Set the master and slave links. When the master link times out, it automatically switches to the slave link to complete the authentication.

[0053] Furthermore, the lightweight authentication process is as follows: the on-board processing terminal obtains the vehicle OBD parameters and splices them, and then calculates the hash value through the hash function as the lightweight authentication token U, splices the lightweight authentication token U with the dynamic area identifier, and sends it to the ground roadside unit RSU, and the ground roadside unit RSU completes the authentication;

[0054] The enhanced authentication process is as follows: start the biometric feature acquisition module in the vehicle, obtain the driver's fingerprint and iris features, generate an enhanced authentication feature vector through a hash function, send it to the authentication center through the current communication link, and the authentication center completes the authentication.

[0055] The beneficial effects of the present invention are:

[0056] The present invention realizes the association of spatiotemporal features in the air-ground-space fusion network through the collaborative collection of multi-source data and the generation of dynamic regional identifiers. Compared with the existing alliance chain authentication mechanism based on distributed ledgers, its trust assessment model no longer adopts fixed parameter design, but dynamically adjusts the weights in combination with spatiotemporal data fluctuations and network status, fully considering the differences in transmission characteristics between satellite links and ground links, and effectively reducing the authentication misjudgment rate in satellite communication-dominated scenarios; the anonymous credential mechanism based on salted hashing and group signatures enables the anonymous identity credential replacement cycle to be dynamically adjusted with the network load, adapting to the identity tracking risk brought about by the rapid movement of nodes in high-density vehicle networks, and solving the problem of insufficient dynamics of the privacy protection mechanism of the existing scheme; the differentiated authentication process based on trust value and link characteristics reduces the authentication overhead of high-trust nodes while ensuring security, and improves the adaptability and security of the authentication mechanism in complex vehicle network environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 The figure is a flow chart of the anonymous identity authentication method based on the air-space-ground-integrated vehicle network of the present invention.

[0059] Figure 2 This is a detailed flow chart of the anonymous identity authentication method based on the air-space-ground-integrated vehicle network of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without creative work are within the scope of protection of the present invention.

[0061] A method for anonymous vehicle identity authentication in a space-ground integrated vehicle network, the system includes a space orbit satellite, an onboard unit (OBU), a ground roadside unit (RSU) and an onboard processing terminal, the space orbit satellite, the onboard unit (OBU), and the ground roadside unit (RSU) are all connected to the onboard processing terminal, and the method is implemented by the onboard processing terminal, such as Figure 1 、 Figure 2 The implementation steps are as follows:

[0062] S1: Collect multi-source data, including real-time vehicle location data, environmental perception data, and regional network status data, and perform preprocessing.

[0063] The vehicle real-time location data is real-time location information including latitude and longitude coordinates and UTC timestamp obtained by the satellite positioning module carried by the vehicle;

[0064] The environmental perception data is traffic light status, road obstacles and other environmental information perceived by the ground roadside unit through the fusion of millimeter-wave radar and camera;

[0065] The regional network status data is a communication delay sequence (satellite link delay) containing a vehicle adjacent node list transmitted by a space orbit satellite, a channel quality indicator sequence (bandwidth utilization), a network security level and a network load factor.

[0066] Furthermore, the PTP protocol is used to synchronize and align the timestamps of the vehicle's real-time location data, environmental perception data, and regional network status data, ensuring consistent data point times and avoiding data misalignment due to varying acquisition frequencies. The aligned data is then aggregated into structured data frames based on time series and transmitted to the onboard processing terminal via a converged communication link. This addresses the heterogeneity of multi-source data, ensuring temporal and spatial consistency through timestamp alignment, and providing standardized input for dynamic identifier generation.

[0067] In an embodiment of the present application, the vehicle's satellite positioning module samples NMEA protocol positioning data from space orbit satellites at a sampling frequency of 10 Hz (such as the current longitude 116.4°, latitude 39.9°, and speed 100 km / h). The ground roadside unit (RSU) detects the construction obstacle 200 meters ahead through a lidar and pre-processes it through an edge computing node. The space satellite transmits the current road section through the DVB-S2 standard with a satellite link delay of 500ms and an available bandwidth of 2Mbps. When the vehicle is connected to the on-board processing terminal, the on-board processing terminal synchronously collects the positioning data of the satellite positioning module, the obstacle detection information of the ground roadside unit (RSU), and the satellite link network status parameters. After timestamp alignment, the structured data frame records the vehicle's real-time position (116.4°E, 39.9°N), the distance of the construction obstacle ahead of 200 meters, and the satellite link delay of 500ms, etc., providing multi-dimensional data support for the subsequent generation of dynamic area identification.

[0068] S2: Generate dynamic area identifiers based on real-time vehicle location data and dynamically update them based on vehicle movement thresholds. Through latitude and longitude gridding, time slice segmentation, spatiotemporal feature combination, and movement thresholds, this approach enables the correlation of spatiotemporal features under highly dynamic vehicle movement in a space-ground-air integrated vehicle network.

[0069] Specifically, the implementation method is:

[0070] S2.1: The onboard processing terminal divides the latitude and longitude coordinates in the real-time location information into grids to obtain a spatial grid ID for each grid, which is used to identify the spatial location of the vehicle.

[0071]

[0072] Where GridID is the spatial grid ID, Lon is the longitude coordinate, Lat is the latitude coordinate, and Δ is the grid resolution, which is 0.01 degrees. 1 km is converted to 1 degree = 111 km.

[0073] S2.2: Divide the UTC timestamp corresponding to the structured data frame into time slices according to a first preset granularity; concatenate the spatial grid ID and the time slice ID to generate a dynamic area identifier in the format of "grid ID_time slice ID" for associating vehicle location with time dimension features.

[0074] S2.3: Set a movement threshold. When the vehicle movement exceeds the movement threshold or the time slice is updated, recalculate the dynamic area identification to adapt to the high-speed movement of the vehicle.

[0075] In the embodiment of the present application, the spatial grid ID is divided into 1km×1km equidistant grids based on the WGS84 coordinate system, and the timestamp preset granularity is 30 seconds. When the vehicle travels to longitude 116.405° and latitude 39.902°, the grid ID is 3991164 obtained by dividing the grid into 1km×1km equidistant grids. The first preset granularity is 30 seconds. The current UTC time is 1685234567 seconds. The current UTC time is used as the time slice ID, and the generated dynamic area identifier is "3991164_1685234567". If the vehicle moves 550 meters within 30 seconds, the on-board processing terminal recalculates the spatial grid ID and updates the time slice, generating a new dynamic area identifier to ensure the dynamic association of spatiotemporal features.

[0076] S3: Obtain vehicle device identification information and combine it with the dynamic region identifier to generate a salted hash value. This salted hash value is then combined with regional network status data and group signature technology to generate an anonymous credential with a dynamic validity period. Through salted hash value calculation, group signature private key generation, and validity period configuration, the requirements for device identity privacy protection and key security in the integrated air-ground-space vehicle network are met.

[0077] Specifically, the implementation method is:

[0078] S3.1: The onboard processing terminal extracts the device identification information of the onboard unit (OBU), including the device fingerprint (the device's MAC address) and the device unique identifier (the OBU hardware serial number). The device unique identifier is combined with the grid ID and time slice ID in the dynamic area identifier through a binary XOR operation to generate a random salt value. The device fingerprint and the random salt value are then concatenated and hashed to obtain a salted hash value H. The random salt value is generated by combining the device unique identifier with the grid number in the dynamic area identifier to enhance the uniqueness of the hash result. The salted hash combines the device fingerprint and the random salt value to prevent hash collision attacks.

[0079] In the embodiment of the present application, the device fingerprint, i.e., the MAC address, is 00:1A:2B:3C:4D:5E, the device unique identifier, i.e., the OBU hardware serial number, is OBU20230512001 (last 8 digits), the generated random salt value is 5a3f2b1e7d9c..., and the salted hash value H is H=sha256(00:1A...5E_3991164_1685234567_5a3f...), where sha256() represents SHA-256 hash calculation.

[0080] S3.2: Generate a group signature private key based on the network security level, the grid ID in the dynamic area identifier, the time slice ID, and the device unique identifier.

[0081] S3.21: Extract some fields from the device unique identifier and perform a binary XOR operation with the grid ID in the dynamic area identifier to generate the initial value of the basic key, thereby achieving a preliminary association between the device identity and the spatial location.

[0082] In this embodiment, the last 8 characters of the device unique identifier, i.e., the hardware serial number OBU20230512001, are extracted and a binary XOR operation is performed with the grid ID 3991164 in the dynamic area identifier to obtain a 128-bit random salt value to generate the initial value of the basic key.

[0083] Device unique identifier: OBU20230512001, extract the last 8 characters: "05012001";

[0084] Grid ID: 3991164 (from dynamic region ID);

[0085] Operation: binary exclusive OR operation (XOR);

[0086] "05012001" → 00110000 00110101 00110001 00110010 00110000 00110001001100000110001 (64 bits);

[0087] "3991164" → 00110011 00111001 00111001 00110001 00110001 0011011000110100 (56 bits);

[0088] Fill to the same length (128 bits), and perform bitwise XOR operation to output the initial value of the base key:

[0089] 10101010 11001100 10101010 11001100 10101010 11001100 1010101011001100 (128 bits).

[0090] S3.22: The time slice ID in the dynamic zone identifier is calculated using the SHA-256 hash algorithm to generate a 256-bit key factor. This factor changes dynamically as the time slice is updated, ensuring the time dimension uniqueness of the private key and preventing key association between the same device at different time periods.

[0091] In this example, the timeslice ID is 1685234567 (UTC timestamp); the SHA-256 hash calculation output is:

[0092] "a1b2c3d4e5f6a7b8c9d0e1f2a3b4c5d6e7f8a9b0c1d2e3f4a5b6c7d8e9f0a1b2" (64 characters hexadecimal).

[0093] S3.23: Concatenate the base key and the key factor bit by bit to obtain a combined key, and then perform a modulo operation (mod2^256) on the combined key to obtain the initial value of the 256-bit group signature private key, thereby realizing the key fusion of spatiotemporal dimension features and ensuring that the private key simultaneously contains dynamic information of device characteristics, spatial location, and time dimensions.

[0094] In this embodiment, the input parameters are: the base key (128 bits) generated in step S3.21, the key factor (256 bits) generated in step S3.22; the total length of the concatenated binary string is 384 bits, and the lower 256 bits are taken to output the initial value of the group signature private key: "e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2" (256 bits).

[0095] S3.24: Based on the current network security level in the regional network status data, the initial value of the group signature private key is encrypted using the AES algorithm. The encrypted initial value of the group signature private key is then byte-ordered using a random permutation algorithm to achieve obfuscation. The obfuscation algorithm (AES + random permutation) dynamically adjusts the complexity of the group signature private key based on network load, ultimately yielding a group signature private key that incorporates temporal and spatial characteristics and security level obfuscation. This ensures the independence of private keys for the same device across time and space, enhancing the group signature private key's anti-attack capabilities.

[0096] In this example, the network security level is divided into three levels: low, medium, and high. The greater the network load coefficient in the regional network status data, the higher the security level. Obfuscation transformation is performed according to the network security level. The AES algorithm uses the AES-128 algorithm, with 1 pass for low level, 3 passes for medium level, and 5 passes for high level. The higher the security level, the greater the obfuscation intensity, ensuring the security of the key in a high-risk network environment.

[0097] Input the initial value of the group signature private key: "e5f6a1b2..." and perform AES-128 encryption and random permutation; output the group signature private key SK': "3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2c3d4e5f6a1b2" (256 bits after obfuscation).

[0098] S3.3: Generate an anonymous identity credential with a signature timestamp and validity period using the network load factor, group signature private key, salted hash value H, and dynamic zone identifier.

[0099] S3.31 uses the BLS algorithm to use the group signature private key to sign the salted hash value H and the dynamic zone identifier to generate a signature value S, and records the current UTC time as the signature timestamp T q , combined with the signature value S and signature timestamp T q , dynamic zone identifier and salted hash value H form the preliminary credential {P=(H, dynamic zone identifier, S, T q )}, to achieve digital signature protection of identity information.

[0100] In an embodiment of the present application, the salted hash value H and the dynamic area identifier are first merged into a unique message string using the SHA-256 function, and then the BLS algorithm (such as the mapping rule of the BLS12-381 curve) is used to map the message string to a point G on the elliptic curve. The group signature private key SK' is multiplied by the elliptic curve point G to obtain the signature value S, and a preliminary credential {H,3991164_1685234567,S,1685234600} is generated. The validity of this credential can be verified at the roadside unit, but it cannot be traced back to a specific vehicle, thereby achieving identity privacy protection.

[0101] S3.32, obtain the network load coefficient LoadCoef from the regional network status data, combine it with the preset basic validity period BaseTime to calculate the final validity period E, and combine it with the preliminary credential P to generate the final anonymous identity credential {P, E}, realizing dynamic optimization of credential validity. The calculation method of the validity period E is:

[0102]

[0103] Among them, α e and β e is the load regulation coefficient. In this embodiment, α e =0.8,β e =1.2. e The value ranges from 0.5 to 1.0, less than 1, β e The value ranges from 1.0 to 1.5, and is greater than 1. The validity period is dynamically configured to adjust the validity period of the certificate according to the network load. When the load is high, the validity period is shortened to reduce the verification pressure, and when the load is low, the validity period is extended to improve efficiency.

[0104] S4: Extracts spatiotemporal data fluctuation parameters based on environmental perception data, and extracts delay trend parameters and channel stability parameters based on regional network status data. By dividing time slices into sub-windows, dynamically coupling data continuity, and merging data across windows, this approach resolves inconsistent timestamps in multi-source data in a space-ground-ground fusion vehicle network, while also enabling feature extraction from multi-source spatiotemporal data.

[0105] The specific implementation method is:

[0106] First, the time slice obtained in step S2.2 is divided into multiple sub-time windows according to the second preset time granularity, and each sub-time window includes a data segment; by determining the time interval and the number of consecutive data points in each data segment, the environmental perception data is divided into data segments with time continuity. Specifically, for environmental perception data points in a data segment, the time interval of adjacent data points is calculated. If the time interval of N = 3 consecutive data points is less than t = 2 seconds, N = 3 is an engineering experience value, which is used to avoid both misjudgment caused by single-point jitter (N = 1 is too sensitive) and response delay (N = 5 may cause missed events), 2 seconds × 3 = 6 seconds < 10 seconds, ensuring that continuity judgment can be triggered within a single time window, then the data is divided into the same data segment; for continuous data points across multiple data segments, if the time interval is always less than t = 2 seconds, they are merged into the same data segment to ensure the temporal continuity of the data.

[0107] Furthermore, in each data segment without time continuity division and each data segment obtained after time continuity division, for environmental perception data, the on-board terminal adds a local timestamp and calculates the mean square error of the longitude and latitude of the location data points in each data segment to obtain the spatiotemporal data fluctuation parameter; the adjacent node communication delay sequence and channel quality index sequence are extracted from the regional network status data, the sliding average of the communication delay sequence is calculated to obtain the delay trend parameter, and the mean of the absolute differences between adjacent values ​​in the channel quality index sequence is calculated to obtain the channel stability parameter.

[0108] Furthermore, a wavelet denoising algorithm is used to filter outliers. Here, the Symlet wavelet basis function is used, and the parameters are mapped to a uniform range through Min-Max normalization to generate a multidimensional spatiotemporal data vector F = [f1, f2, f3], where f1 represents the spatiotemporal data fluctuation parameter, f2 represents the delay trend parameter, and f3 represents the channel stability parameter.

[0109] S5: Dynamically adjusts weights based on spatiotemporal data fluctuation parameters, delay trend parameters, and channel stability parameters according to link type and time period, and calculates the vehicle's dynamic trust quantification value through weighted summation. This dynamic calculation of network topology indicators and dynamic weight configuration addresses the issue of inaccurate trust assessments caused by differences in transmission characteristics between satellite and terrestrial links in integrated air-ground-space networks.

[0110] The specific implementation method is:

[0111] S5.1: Dynamic calculation of network topology indicators: First, count the number of adjacent nodes in the regional network status data that are in the same grid as the current vehicle, and divide it by the preset base to obtain the node connectivity indicator. The node connectivity indicator is used to quantify the node density in the current grid. The basic weight configuration assigns different weights to the spatiotemporal data fluctuation parameters according to the difference in node density. Then, obtain the historical data of the first M time slices of the communication delay sequence and the channel quality indicator sequence respectively, calculate the ratio of the historical data fluctuation range to the time slice length, and obtain the first link stability indicator and the second link stability indicator respectively. The link stability indicator reflects the historical fluctuation degree of communication delay and channel quality.

[0112] S5.2: Define the spatiotemporal feature vector F = [f1, f2, f3] and the corresponding weight matrix W = [w1, w2, w3], where w1 is the spatiotemporal data fluctuation parameter weight, w2 is the delay trend parameter weight, and w3 is the channel stability parameter weight.

[0113] S5.3: First, divide the node connectivity index into at least three intervals and configure the corresponding spatiotemporal data fluctuation parameter weights.

[0114] Specifically, in an embodiment of the present application, the number of adjacent nodes in the same grid as the current vehicle in the regional network status data is first counted, and the number is divided by the preset base 10 to obtain the node connectivity index, and the node connectivity index is divided into three intervals: a low-density interval (0, T1], a medium-density interval (T1, T2], and a high-density interval (T2, +∞), where T1 = 0.3, T2 = 0.7, T1 takes [0.2, 0.4] and T2 takes [0.6, 0.8], and the basic weight coefficients K1, K2, and K3 are respectively configured, K1>K2>K3, K1=0.4, K2=0.3, K3=0.2), forming a basic mapping rule that "the higher the node density, the lower the weight of the spatiotemporal data fluctuation parameter".

[0115] Furthermore, the first link stability indicator and the second link stability indicator are compared with a preset first fluctuation threshold and a preset second fluctuation threshold, respectively. Based on the comparison results, the weight of the delay trend parameter and the weight of the channel stability parameter are adjusted according to the weight unchanged, the preset first weight decay ratio, or the preset second weight decay ratio. If the current link type is a satellite link, the first weight decay ratio and the second weight decay ratio are multiplied by the coefficient λ before the parameter adjustment is performed. The weight decay adjustment dynamically reduces the weights of the delay trend parameter and the channel stability parameter based on the link stability to reduce the interference of unstable links on the trust assessment.

[0116] Specifically, in an embodiment of the present application, the fluctuation range (maximum value - minimum value) of the historical data of the communication delay sequence N = 5 time slots before the current time slot is calculated, and the result is divided by the time slot length of 30 seconds to obtain a first link stability index; the fluctuation range (maximum value - minimum value) of the historical data of the channel quality index sequence N = 5 time slots before the current time slot is calculated, and the result is divided by the time slot length of 30 seconds to obtain a second link stability index; the first fluctuation threshold P1 = 0.5, the second fluctuation threshold P2 = 0.3, P2 is greater than 0 and less than 1, the first weight attenuation ratio α = 0.1, the second weight attenuation ratio β = 0.3 (β>α), β is greater than 0 and less than 1, if the stability index > P1, the weight remains unchanged, if P2 < stability index ≤ P1, the weight is reduced according to the first weight attenuation ratio α, if the stability index ≤ P2, the weight is reduced according to β, if the current link is a satellite link, the attenuation ratio is multiplied by the coefficient λ and then adjusted, and the ground link is not processed. In this embodiment, the coefficient λ is 1.5.

[0117] S5.4: Obtain the time period type from the dynamic area identifier to determine whether the current time slice belongs to the traffic peak period. Dynamically correct the spatiotemporal data fluctuation parameter weight, delay trend parameter weight, and channel stability parameter weight using the peak factor and non-peak factor based on the time period type. The peak factor and non-peak factor adjust the weight coefficient of the specific node density interval upward or downward according to the time period characteristics to adapt to the special network load characteristics of the peak period.

[0118] Specifically, in an embodiment of the present application, UTC time is extracted from the time slice of the dynamic area identifier to determine whether it belongs to the peak period (7:00-9:00 / 17:00-19:00); if it is a peak period and the node connectivity index is in a low-density interval, the weights of the three parameters are multiplied by the peak factor γ=1.2; if it is a non-peak period and the node connectivity index is in a high-density interval, the weights of the three parameters are multiplied by the non-peak factor δ.

[0119] S5.5: Perform weighted summation on the corrected spatiotemporal data fluctuation parameter weights, delay trend parameter weights, and channel stability parameter weights with the corresponding characteristic parameters in the spatiotemporal characteristic vector to obtain the dynamic trust quantization value T, and set 0.7 as the high confidence threshold, and T ≥ 0.7 as high confidence.

[0120] T=Σ(w i ×f i ).

[0121] S6: Differentiated authentication is performed based on the vehicle's dynamic trust quantification value and anonymous credentials. The differentiated authentication process implements different authentication strategies of varying strength based on the decision factor results, achieving a balance between security and efficiency. This differentiated authentication includes lightweight verification for high-trust conditions and two-factor authentication combined with link timeout redundancy for low-trust conditions. The final authentication result achieves dynamic adjustment of authentication strength. Through the dynamic coupling calculation of trust quantification value and credential expiration, threshold-driven authentication level division, and link-specific adaptive redundant transmission decisions, the system achieves matching of authentication strength and node trustworthiness in the air-ground-ground converged vehicle network.

[0122] S6.1: When receiving the authentication request, the onboard processing terminal verifies the signature timestamp T of the anonymous credential q Whether it is within the valid time and matches the current time, extract the remaining valid time, and use the dynamic trust quantization value T and the remaining valid time as authentication decision factors.

[0123] S6.2: If the dynamic trust quantization value is higher than the preset first trust threshold and the remaining effective time is greater than the preset first time threshold, the lightweight authentication process is executed; if the dynamic trust quantization value is lower than the preset second trust threshold or the remaining effective time is less than the preset second time threshold, the enhanced authentication process including biometric verification is triggered.

[0124] The lightweight authentication process is as follows: the on-board processing terminal obtains the vehicle's on-board diagnostic system (OBD) parameters (such as engine speed and vehicle speed) and splices them, then calculates the hash value as the lightweight authentication token U through the SHA-256 hash function, splices the lightweight authentication token U with the dynamic area identifier and sends it to the ground roadside unit (RSU), which completes the authentication.

[0125] The enhanced authentication process is as follows: start the biometric feature acquisition module in the vehicle, obtain the driver's fingerprint and iris features, generate an enhanced authentication feature vector through the SHA-256 hash algorithm, send it to the authentication center through the current communication link, and the authentication center completes the authentication.

[0126] S6.3: Set different response timeout thresholds based on the current communication link type (ground or satellite link). If dual links (ground and satellite links) exist at the same time, use the dual links to execute the enhanced authentication process separately to achieve redundant authentication. Set up a master-slave link. When the master link (satellite) times out, it automatically switches to the slave link (ground) to complete the authentication, ensuring the reliability of the authentication process.

[0127] Specifically, in the embodiment of the present application, the valid time parameter E in the anonymous credential generated in step S3 is extracted and the current time T is used. now Subtract the signature timestamp T qGet the elapsed time E y , then use the effective duration parameter E to subtract the elapsed time E y Get the remaining effective time E re The timeout threshold is set according to the link type, 1000ms for satellite link and 500ms for terrestrial link. When the dynamic trust quantization value T = 0.8 (high trust) is calculated in step S5 and the remaining valid time of the certificate is E re =6min is greater than the first time threshold of 5min, lightweight authentication is started, and the ground roadside unit (RSU) completes the comparison within 100ms (<500ms ground threshold); if the dynamic trust quantization value T=0.4 (low confidence), the fingerprint hash collected by the vehicle is sent to the authentication center via a satellite link. The authentication center refers to the authoritative authentication platform deployed in the ground command and control center, and is redundantly transmitted through the ground 5G link. If the satellite link does not respond within 1000ms, it will automatically switch to the ground 5G link to complete two-factor authentication to ensure the authentication success rate.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0129] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system or product that includes 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 are inherent to these processes, methods, systems or products.

[0130] It should be understood that the above are only preferred embodiments of the present invention and the principles of the technology used. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention is described in detail through the above embodiments, the present invention is not limited to the specific embodiments described herein. Without departing from the concept of the present invention, it may also include many other effective embodiments, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for anonymous vehicle identity authentication in an air-space-ground integrated vehicle network, characterized in that: The steps are: S1: Collect multi-source data, including real-time vehicle location data, environmental perception data, and regional network status data, and perform preprocessing; S2: Generate dynamic area identification based on real-time vehicle location data and dynamically update it according to vehicle movement thresholds; S3: Obtain vehicle device identification information and combine it with the dynamic area identifier to generate a salted hash value. The salted hash value is combined with the regional network status data and group signature technology to generate an anonymous credential with a dynamic validity period. S4: Extract spatiotemporal data fluctuation parameters based on environmental perception data, and extract delay trend parameters and channel stability parameters based on regional network status data; S5: Dynamically adjust the weights according to link type and time period based on spatiotemporal data fluctuation parameters, delay trend parameters, and channel stability parameters, and calculate the dynamic trust quantization value of the vehicle through weighted summation; S6: Perform differentiated authentication based on the vehicle's dynamic trust quantification value and anonymous credentials. The differentiated authentication includes using lightweight verification when the trust is high, starting two-factor authentication when the trust is low, and combining link timeout redundant authentication to finally obtain the authentication result.

2. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 1 is characterized in that: The vehicle real-time location data is real-time location information including latitude and longitude coordinates and UTC timestamp obtained by the satellite positioning module carried by the vehicle; The environmental perception data is the environmental information of traffic light status and road obstacles perceived by the ground roadside unit through the fusion of millimeter-wave radar and camera; The regional network status data is a communication delay sequence, a channel quality index sequence, a network security level and a network load factor including a vehicle adjacent node list transmitted by a space orbit satellite; The preprocessing uses the PTP protocol to synchronize the timestamps of vehicle real-time location data, environmental perception data, and regional network status data, and aggregates the aligned three types of data into structured data frames in time series.

3. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 2 is characterized in that: The implementation method of step S2 is: S2.1: The onboard processing terminal divides the latitude and longitude coordinates in the real-time location information into grids to obtain a spatial grid ID for each grid, which is used to identify the spatial location of the vehicle. S2.2: Divide the UTC timestamp corresponding to the structured data frame into time slices according to a first preset granularity; concatenate the spatial grid ID and the time slice ID to generate a dynamic region identifier in the format of "grid ID_time slice ID" for associating vehicle location with time dimension features; S2.3: Set a movement threshold. When the vehicle movement exceeds the movement threshold or the time slice is updated, the dynamic area identification is regenerated.

4. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 3 is characterized in that: The implementation method of step S3 is: S3.1: The on-board processing terminal extracts the device identification information of the on-board unit (OBU), including the device fingerprint and the device unique identifier. The device unique identifier is combined with the dynamic area identifier through a binary XOR operation to generate a random salt value. The device fingerprint and the random salt value are concatenated and calculated using a hash algorithm to obtain the salted hash value H. S3.2: Generate a group signature private key based on the network security level, dynamic zone identifier, and device unique identifier; S3.3: Generate an anonymous identity credential with a signature timestamp and validity period using the network load factor, group signature private key, salted hash value H, and dynamic zone identifier.

5. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 3 is characterized in that: The implementation method of step S4 is: S4.1: Divide the time slice obtained in step S2.2 into multiple sub-time windows according to a second preset time granularity, each sub-time window including a data segment; By determining the time interval and the number of consecutive data points in each data segment, the environmental perception data is divided into time continuity segments, and the data segments without time continuity division and the data segments after time continuity division are obtained; S4.2: For each data segment before and after temporal continuity partitioning, calculate spatiotemporal data fluctuation parameters based on the environmental perception data, and calculate delay trend parameters and channel stability parameters based on the regional network status data; S4.3: Use the wavelet denoising algorithm to filter outliers and map the parameters to a uniform range through Min-Max normalization to generate a multidimensional spatiotemporal data vector F = [f1, f2, f3], where f1 represents the spatiotemporal data fluctuation parameter, f2 represents the delay trend parameter, and f3 represents the channel stability parameter.

6. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 4 is characterized in that: The implementation method of step S3.2 is: S3.21: Extract some fields from the device unique identifier and perform a binary XOR operation with the grid ID in the dynamic area identifier to generate the initial value of the basic key; S3.22: Calculate the time slice ID in the dynamic area identifier using a hash function to generate a key factor; S3.23: Concatenate the base key and the key factor bit by bit to obtain a combined key, and then perform a modulo operation on the combined key to obtain the initial value of the group signature private key; S3.24: Based on the current network security level, the initial value of the group signature private key is encrypted using the AES algorithm and the encrypted initial value of the group signature private key is obfuscated by changing the byte order of the encrypted initial value of the group signature private key using a random permutation algorithm to obtain the group signature private key SK' that contains the obfuscated spatiotemporal characteristics and security level. The implementation method of step S3.3 is: S3.31: Use the BLS algorithm to sign the salted hash value H and the dynamic zone identifier using the group signature private key SK' to generate a signature value S, and record the current UTC time as the signature timestamp T q , combined with the signature value S and signature timestamp T q , dynamic zone identifier and salted hash value H form the preliminary certificate P = (H, dynamic zone identifier, S, T q ); S3.32: Obtain the network load factor LoadCoef from the regional network status data, combine it with the preset basic validity period BaseTime to calculate the final validity period E, and combine it with the preliminary credential P to generate the final anonymous identity credential {P, E}; the validity period E is calculated as follows: Among them, α e and β e is the load regulation coefficient, α e The value range is 0.5~1.0, β e The value ranges from 1.0 to 1.

5.

7. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 5 is characterized in that: The method for temporally segmenting the environmental perception data described in step S4.1 is as follows: for environmental perception data points within a data segment, the time intervals between adjacent data points are calculated. If the time intervals between N consecutive data points are all less than the segmentation time threshold t, they are segmented into the same data segment, thereby obtaining a data segment segmented for temporal continuity, where the value of N×t is less than the second preset time granularity. For consecutive data points spanning multiple data segments, if the time intervals between the consecutive data points are always less than the segmentation time threshold t, they are merged into the same data segment, thereby obtaining a data segment segmented for temporal continuity. In step S4.2: For environmental perception data, after adding a local timestamp, the vehicle terminal calculates the mean square error of the longitude and latitude of the location data points in each data segment to obtain the spatiotemporal data fluctuation parameter; The communication delay sequence of adjacent nodes and the channel quality index sequence are extracted from the regional network status data, the sliding average of the communication delay sequence is calculated to obtain the delay trend parameter, and the mean of the absolute differences between adjacent values ​​in the channel quality index sequence is calculated to obtain the channel stability parameter.

8. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 5 or 7, characterized in that: The implementation method of step S5 is: S5.1: Dynamic calculation of network topology indicators: First, count the number of adjacent nodes in the same grid as the current vehicle in the regional network status data and divide it by a preset base to obtain the node connectivity indicator. Then, obtain the historical data of the first M time slices of the communication delay sequence and the channel quality indicator sequence, respectively, and calculate the ratio of the historical data fluctuation range to the time slice length to obtain the first link stability indicator and the second link stability indicator, respectively. S5.2: Define the weight matrix W = [w1,w2,w3] corresponding to the spatiotemporal feature vector F = [f1,f2,f3], where w1 is the weight of the spatiotemporal data fluctuation parameter, w2 is the weight of the delay trend parameter, and w3 is the weight of the channel stability parameter. S5.3: Divide the node connectivity index into at least three intervals and assign corresponding spatiotemporal data fluctuation parameter weights; compare the first link stability index and the second link stability index with a preset first fluctuation threshold P1 and a preset second fluctuation threshold P2, respectively; and adjust the delay trend parameter weight and the channel stability parameter weight according to the comparison results, with the weights remaining unchanged, and with a preset first weight decay ratio α, or a preset second weight decay ratio β. If the current link type is a satellite link, the first weight attenuation ratio α and the second weight attenuation ratio β are multiplied by the coefficient λ to perform the delay trend parameter weight and the channel stability parameter weight adjustment. S5.4: Obtain the current time period type from the dynamic area identifier to determine whether the current time slice belongs to the traffic peak period. Dynamically adjust the spatiotemporal data fluctuation parameter weight, delay trend parameter weight, and channel stability parameter weight based on the time period type using the peak factor γ and the off-peak factor δ. S5.5: Perform weighted summation on the corrected spatiotemporal data fluctuation parameter weight, delay trend parameter weight, and channel stability parameter weight with the corresponding characteristic parameters in the spatiotemporal characteristic vector F to obtain the dynamic trust quantization value T, and set a high confidence threshold.

9. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to any one of claims 4, 6 or 8, characterized in that: The implementation method of step S6 is: S6.1: When receiving the authentication request, the vehicle processing terminal verifies whether the signature timestamp of the anonymous credential is within the valid time and matches the current time, and extracts the remaining valid time E re , the dynamic trust quantization value and the effective time remaining E re As a certification decision factor; S6.2: If the dynamic trust quantization value T is higher than the preset first trust threshold and the remaining effective time is greater than the preset first time threshold, execute the lightweight authentication process; If the dynamic trust quantification value is lower than the preset second trust threshold or the remaining effective time is less than the preset second time threshold, the enhanced authentication process including biometric verification is triggered; S6.3: Set different response timeout thresholds based on the current communication link type. If dual links exist at the same time, use the dual links to perform enhanced authentication processes separately to achieve redundant authentication. Set the master and slave links. When the master link times out, it automatically switches to the slave link to complete the authentication.

10. The anonymous vehicle identity authentication method in the air-space-ground fusion vehicle network according to claim 9 is characterized in that: The lightweight authentication process is as follows: the vehicle processing terminal obtains the vehicle OBD parameters and splices them, and then calculates the hash value through the hash function as the lightweight authentication token U, splices the lightweight authentication token U with the dynamic area identifier, and sends it to the ground roadside unit RSU, which completes the authentication; The enhanced authentication process is as follows: start the biometric feature acquisition module in the vehicle, obtain the driver's fingerprint and iris features, generate an enhanced authentication feature vector through a hash function, send it to the authentication center through the current communication link, and the authentication center completes the authentication.

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