A vehicle data management method, system, and medium based on combined public keys.

Abstract

This application provides a vehicle data management method, system, and medium based on combined public keys. It calculates the vehicle's dynamic encryption requirement coefficient based on driving behavior data and generates the vehicle's key state. Based on the key state and link stability index, it generates multiple key fragments and distribution paths. Cross-verification of old and new keys is employed during distribution. A dynamic association is established between driving behavior and key management requirements. The key lifecycle is adjusted by triggering the dynamic encryption requirement coefficient calculated in real time, ensuring that the key update frequency matches the vehicle's operating status. Furthermore, a fragment distribution mechanism combined with real-time network topology awareness optimizes key distribution path selection, significantly reducing network transmission latency for key updates. Simultaneously, adaptive verification strength adjustment is performed during key distribution, maintaining communication security while ensuring business continuity, thus balancing the timeliness and security of key updates.

Description

Technical Field

[0001] This application relates to the field of vehicle network data management technology, specifically to a vehicle data management method, system, and medium based on combined public keys. Background Technology

[0002] Vehicle-to-everything (V2X) data management technology collects and analyzes massive amounts of information generated during vehicle operation, combining communication protocols and key management mechanisms to build a dynamic and reliable data interaction system. Raw data collected by vehicle terminals, such as driving status and environmental perception data, is encrypted and transmitted to the cloud platform. Algorithms such as Combined Public Key (CPK) are used to achieve identity authentication and data integrity verification, preventing unauthorized access or tampering. Distributed storage and collaborative computing process multi-source heterogeneous data, satisfying both real-time requirements and ensuring privacy and security, ultimately forming a traceable basis for vehicle-road cooperative decision-making, providing underlying support for intelligent transportation systems.

[0003] However, due to the highly dynamic nature of vehicle nodes and frequent changes in network topology in the connected vehicle environment, the CPK key management center struggles to respond promptly to key update requests from all vehicles. This results in some vehicles continuing to communicate using old keys even after their keys have expired. Attackers can exploit this time lag to forge legitimate identities and launch man-in-the-middle attacks, posing a traffic safety hazard. Therefore, there is an urgent need for a method to improve the security of CPK key management in connected vehicle environments. Summary of the Invention

[0004] To address the aforementioned technical problems, this application is proposed. Embodiments of this application provide a vehicle data management method, system, and medium based on a combined public key.

[0005] According to one aspect of this application, a vehicle data management method based on a combined public key is provided, comprising: collecting vehicle operation data; wherein the operation data includes driving behavior data; calculating a dynamic encryption requirement coefficient of the vehicle based on the driving behavior data; wherein the dynamic encryption requirement coefficient characterizes the degree of key update requirement of the vehicle; generating a key state of the vehicle based on the dynamic encryption requirement coefficient; calculating a link stability index of the vehicle; wherein the link stability index characterizes the network stability of the vehicle; generating multiple key fragments and distribution paths of the vehicle based on the key state of the vehicle and the link stability index; calculating a key update urgency score of the vehicle based on the link stability index; distributing multiple key fragments, and using cross-validation of old and new keys during the distribution process; wherein the verification strength of the cross-validation of old and new keys is determined according to the key update urgency score; and recombining the multiple key fragments after the distribution of the multiple key fragments to obtain a new key.

[0006] In one embodiment, the acquisition of vehicle operating data includes: acquiring accelerator pedal depth, braking pressure, and steering wheel angle data of the vehicle via an onboard bus; and acquiring the relative speed and distance matrix of a target object relative to the vehicle via millimeter-wave radar.

[0007] In one embodiment, generating the key state of the vehicle based on the dynamic encryption requirement coefficient includes: calculating the duration thresholds of the active period, transition period, and obsolescence period of the current key of the vehicle based on the dynamic encryption requirement coefficient; and generating the key state of the vehicle based on the duration thresholds of the active period, transition period, and obsolescence period of the current key.

[0008] In one embodiment, calculating the link stability index of the vehicle includes: measuring the base station signal strength corresponding to the vehicle; calculating the key update success rate based on the base station signal strength to obtain the link stability index.

[0009] In one embodiment, generating multiple key fragments and distribution paths for the vehicle based on the vehicle's key state and the link stability index includes: calculating the distribution priority of the multiple key fragments based on the vehicle's key state and the link stability index; and determining the distribution path of the multiple key fragments according to the distribution priority when network congestion occurs.

[0010] In one embodiment, the cross-verification of new and old keys during the distribution process includes: alternating between new and old keys for communication in a round-robin manner during the distribution process and recording the verification success rate; and dynamically adjusting the zero-knowledge proof rounds based on the verification success rate and the current network latency to update the verification strategy.

[0011] In one embodiment, the step of reassembling the multiple key fragments to obtain a new key after the multiple key fragments have been distributed includes: after the multiple key fragments have been distributed, using a threshold decryption mechanism to verify the consistency of the multiple key fragments; if the verification passes, then reassembling the multiple key fragments and activating the new key state.

[0012] In one embodiment, the vehicle data management method based on combined public keys further includes: collecting key update logs and verification records of the vehicle; constructing a relationship network of key update events based on the key update logs and verification records; extracting spatiotemporal features from the relationship network; and training a model based on the spatiotemporal features to obtain an anomaly detection model.

[0013] According to another aspect of this application, a vehicle data management system based on combined public keys is provided, comprising: an operation data acquisition module for acquiring vehicle operation data; wherein the operation data includes driving behavior data; a demand coefficient calculation module for calculating a dynamic encryption demand coefficient of the vehicle based on the driving behavior data; wherein the dynamic encryption demand coefficient characterizes the degree of key update demand of the vehicle; a key state generation module for generating the key state of the vehicle based on the dynamic encryption demand coefficient; and a stability index calculation module for calculating a link stability index of the vehicle; wherein the link stability index characterizes the... The network stability of the vehicle; a distribution path generation module, used to generate multiple key fragments and distribution paths for the vehicle based on the vehicle's key status and the link stability index; an urgency score calculation module, used to calculate the key update urgency score for the vehicle based on the link stability index; a distribution process verification module, used to distribute multiple key fragments and perform cross-verification of the old and new keys during the distribution process; wherein the verification strength of the cross-verification of the old and new keys is determined according to the key update urgency score; and a new key reassembly module, used to reassemble the multiple key fragments to obtain a new key after the distribution of the multiple key fragments is completed.

[0014] According to another aspect of this application, a computer-readable storage medium is provided, the storage medium storing a computer program for performing any of the methods described above.

[0015] This application provides a vehicle data management method, system, and medium based on combined public keys. The method involves collecting vehicle operational data, including driving behavior data; calculating a dynamic encryption requirement coefficient based on the driving behavior data, whereby the dynamic encryption requirement coefficient characterizes the vehicle's key update requirement; generating the vehicle's key state based on the dynamic encryption requirement coefficient; calculating the vehicle's link stability index, whereby the link stability index characterizes the vehicle's network stability; generating multiple key fragments and distribution paths for the vehicle based on the vehicle's key state and link stability index; calculating the vehicle's key update urgency score based on the link stability index; and distributing multiple key fragments during the distribution process. The system employs cross-verification of old and new keys; the verification strength of this cross-verification is determined based on a key update urgency score; after multiple key fragments are distributed, they are reassembled to obtain a new key; a dynamic correlation is established between driving behavior and key management needs, and a dynamic encryption requirement coefficient calculated in real time triggers key lifecycle adjustments to match the key update frequency with the vehicle's operating status; a fragment distribution mechanism combined with real-time network topology perception optimizes key distribution path selection, significantly reducing network transmission latency for key updates; and adaptive verification strength adjustment is performed during key distribution to maintain communication security while ensuring business continuity, thus balancing the timeliness and security of key updates. Attached Figure Description

[0016] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0017] Figure 1 This is a flowchart illustrating a vehicle data management method based on a combined public key provided in an exemplary embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the structure of a vehicle data management system based on a combined public key provided in an exemplary embodiment of this application.

[0019] Figure 3 This is a structural diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation

[0020] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0021] Figure 1 This is a flowchart illustrating a vehicle data management method based on a combined public key, provided in an exemplary embodiment of this application. Figure 1 As shown, the vehicle data management method based on combined public keys includes the following steps:

[0022] Step 110: Collect vehicle operation data.

[0023] The operational data includes driving behavior data. This application uses the Internet of Things to collect real-time operational data from vehicles (which may include multiple vehicles) and filters out the driving behavior data to determine the driver's actions.

[0024] Step 120: Calculate the dynamic encryption requirement coefficient of the vehicle based on driving behavior data.

[0025] The dynamic encryption demand coefficient characterizes the degree of key update requirement for a vehicle. This application determines the vehicle's key replacement requirement based on driving behavior data, and quantitatively calculates its dynamic encryption demand coefficient.

[0026] Step 130: Generate the vehicle's key status based on the dynamic encryption requirement coefficient.

[0027] This application determines the demand for vehicle key updates based on a dynamic encryption demand coefficient, thereby adjusting the lifecycle of the current key to generate the vehicle's key status, including information such as the vehicle's key lifespan and current status.

[0028] Step 140: Calculate the link stability index of the vehicle.

[0029] The link stability index characterizes the network stability of a vehicle. This application calculates the link stability index—the network stability parameter for each vehicle—to determine the network stability of each vehicle, thereby determining the smoothness of its data transmission.

[0030] Step 150: Based on the vehicle's key status and link stability index, generate multiple key fragments and distribution paths for the vehicle.

[0031] When the vehicle needs to update its key, this application generates multiple key fragments and distribution paths by combining the vehicle's key status and link stability index, ensuring transmission efficiency and stability while taking into account the stability of the transmission link.

[0032] Step 160: Calculate the key update urgency score for the vehicle based on the link stability index.

[0033] This application calculates the key update urgency score based on the link stability index to dynamically adjust the scheduling strategy (i.e., the key transmission scheduling strategy for multiple vehicles) and generate key fragment distribution instructions, thereby balancing the key updates of multiple vehicles.

[0034] Step 170: Distribute multiple key fragments, and cross-verify the old and new keys during the distribution process.

[0035] The verification strength of the cross-verification between the old and new keys is determined based on a key update urgency score. During the key fragment distribution phase, to prevent malicious attacks, this application employs a cross-verification method between the old and new keys to verify the vehicle, thereby reducing the risk of information leakage.

[0036] Step 180: After the multiple key fragments have been distributed, reassemble the multiple key fragments to obtain a new key.

[0037] After receiving multiple key fragments, the vehicle reassembles them to obtain a new key, and then activates the new key after the reassembly is complete, so as to achieve a secure replacement of the old and new keys.

[0038] This application provides a vehicle data management method based on combined public keys. The method involves collecting vehicle operational data, including driving behavior data; calculating a dynamic encryption requirement coefficient based on the driving behavior data, whereby the dynamic encryption requirement coefficient characterizes the vehicle's key update requirement; generating the vehicle's key state based on the dynamic encryption requirement coefficient; calculating the vehicle's link stability index, whereby the link stability index characterizes the vehicle's network stability; generating multiple key fragments and distribution paths for the vehicle based on the vehicle's key state and link stability index; calculating the vehicle's key update urgency score based on the link stability index; and distributing multiple key fragments, employing a method that... Cross-validation of old and new keys; the verification strength of cross-validation is determined based on the urgency score of key updates; after multiple key fragments are distributed, they are recombined to obtain a new key; driving behavior is dynamically linked to key management needs, and key lifecycle adjustments are triggered by real-time calculated dynamic encryption requirement coefficients to match the key update frequency with the vehicle's operating status; a fragment distribution mechanism combined with real-time network topology perception optimizes key distribution path selection, significantly reducing network transmission latency for key updates; and adaptive verification strength adjustment is performed during key distribution to maintain communication security while ensuring business continuity, balancing the timeliness and security of key updates.

[0039] In one embodiment, step 110 can be implemented by: collecting data on the accelerator pedal depth, braking pressure, and steering wheel angle of the vehicle via the vehicle bus; and collecting the relative speed and distance matrix of the target object relative to the vehicle via millimeter-wave radar.

[0040] This application acquires data such as accelerator pedal depth, brake pressure, and steering wheel angle of the vehicle via the vehicle bus. At the same time, it uses millimeter-wave radar to collect the relative speed and distance matrix of the target object relative to the vehicle, and processes the collected data in real time. Specifically, it uses the sliding window method to calculate the characteristic value of the rate of change of acceleration, and uses the DBSCAN clustering algorithm (Density-Based Spatial Clustering of Applications with Noise) to analyze the spatial distribution pattern of the radar point cloud, and finally outputs the dynamic encryption requirement coefficient in the 0-1 interval.

[0041] In one embodiment, step 130 can be implemented as follows: based on the dynamic encryption requirement coefficient, calculate the duration thresholds of the active period, transition period, and obsolescence period of the vehicle's current key; based on the duration thresholds of the active period, transition period, and obsolescence period of the current key, generate the vehicle's key status.

[0042] This application adjusts the key lifecycle based on a dynamic encryption demand coefficient, dividing it into an active period, a transition period, and an obsolescence period, and generating a key state matrix. Specifically, the dynamic encryption demand coefficient is transmitted as a key input parameter to a key lifecycle management system. This system uses a Lorentz attractor to generate a chaotic sequence and calculates the duration thresholds of the active, transition, and obsolescence periods using Lyapunov exponents. The key state encoder encodes the calculated threshold parameters into a Hermitian matrix with a time-sensitivity weight factor and appends a timestamp verification signature to generate the final key state matrix. The key state matrix contains the key's validity period information, providing a decision-making basis for subsequent key distribution.

[0043] In one embodiment, step 140 can be implemented by: measuring the base station signal strength corresponding to the vehicle; calculating the key update success rate based on the base station signal strength to obtain the link stability index.

[0044] This application monitors changes in the vehicle network topology in real time, constructs a channel quality matrix by measuring the received power of the reference signal and the signal-to-interference-plus-noise ratio, calculates the key update success rate during base station handover, establishes a Markov chain model to predict the link-keeping probability, outputs the expected path lifetime value, and combines the received key state matrix with the Dijkstra algorithm to calculate the optimal distribution path, generates a node selection scheme with weighted factors, and forms a complete key distribution path optimization scheme.

[0045] In one embodiment, step 150 can be implemented as follows: based on the vehicle's key status and link stability index, calculate the distribution priority of multiple key fragments; and determine the distribution path of multiple key fragments according to the distribution priority when the network is congested.

[0046] This application receives dynamic encryption demand coefficients and key status matrices, determines the key update urgency score based on the link stability index, determines the key fragment distribution priority based on the key update urgency score, initiates a priority transmission mechanism when the network is congested, and prioritizes the distribution of key key fragments to target nodes. During the execution of the distribution instruction, the transmission status is monitored in real time and feedback information is returned, forming a dynamic scheduling closed loop.

[0047] In one embodiment, step 170 above can be implemented by: using the old and new keys alternately in a round-robin manner during the distribution process and recording the verification success rate; and dynamically adjusting the zero-knowledge proof rounds based on the verification success rate and the current network latency to update the verification strategy.

[0048] During the key transition period (i.e. the distribution of key fragments), this application obtains the old and new keys, uses a polling mechanism to alternate between the two keys for communication, and records the verification success rate. Based on the real-time monitored verification success rate and network latency parameters, it dynamically adjusts the verification rounds of the Schnorr protocol zero-knowledge proof, updates the adjusted parameters to the verification strategy library, and achieves adaptive adjustment of the verification strength.

[0049] In one embodiment, step 180 can be implemented as follows: after multiple key fragments are distributed, a threshold decryption mechanism is used to verify the consistency of the multiple key fragments; if the verification is successful, the multiple key fragments are reassembled and a new key state is activated.

[0050] This application uses the Lagrange interpolation method to decompose the complete key into multiple fragments, adds topological constraints to each fragment, selects relay nodes according to the path optimization scheme, establishes a mapping relationship between fragments and nodes, and the receiving node uses a threshold decryption mechanism to verify the consistency of key fragments, performs Shamir polynomial coefficient verification, activates the new key state after verification, and feeds back the activation result to the scheduling system to update the system key state record.

[0051] In one embodiment, the above-mentioned vehicle data management method based on combined public keys may further include: collecting vehicle key update logs and verification records; constructing a relationship network of key update events based on the key update logs and verification records; extracting spatiotemporal features from the relationship network; and training a model based on the spatiotemporal features to obtain an anomaly detection model.

[0052] This application analyzes the security situation of the key update process, constructs an anomaly detection model, outputs defense strategies, and feeds these defense strategies back to the driving behavior feature extraction process through a strategy loading interface. Specifically, the security situation analysis process first collects key update logs and verification records, constructs a relationship network of key update events, extracts spatiotemporal feature patterns, updates the anomaly detection model under a federated learning framework, employs differential privacy technology to protect training data, and finally outputs a priority list of defense rules (i.e., defense strategies). These defense strategies are then fed back to the feature extraction unit through the strategy loading interface, and the feature extraction unit dynamically adjusts the feature extraction rules and computation frequency according to the risk level.

[0053] Each unit or system forms a closed-loop system through parameter coupling and feedback control. This application can automatically shorten the key transition period when the vehicle accelerates rapidly, preload the next area key fragment during base station handover, and increase the verification strength level when abnormal access is detected. Through the multi-module collaborative working mechanism, it effectively solves the problem of CPK key update delay in the dynamic environment of vehicle networking and achieves a balance between the timeliness and security of key management.

[0054] Figure 2 This is a schematic diagram of the structure of a vehicle data management system based on a combined public key, provided in an exemplary embodiment of this application. Figure 2 As shown, the vehicle data management system 20 based on combined public keys includes: an operation data acquisition module 21 for collecting vehicle operation data, including driving behavior data; a demand coefficient calculation module 22 for calculating the vehicle's dynamic encryption demand coefficient based on the driving behavior data, wherein the dynamic encryption demand coefficient characterizes the vehicle's key update demand; a key state generation module 23 for generating the vehicle's key state based on the dynamic encryption demand coefficient; and a stability index calculation module 24 for calculating the vehicle's link stability index, wherein the link stability index characterizes the vehicle's network stability. The system includes: a network stability module; a distribution path generation module 25, used to generate multiple key fragments and distribution paths for a vehicle based on its key status and link stability index; an urgency score calculation module 26, used to calculate the urgency score of a vehicle's key update based on the link stability index; a distribution process verification module 27, used to distribute multiple key fragments and perform cross-verification of the old and new keys during the distribution process; wherein the verification strength of the cross-verification of the old and new keys is determined according to the key update urgency score; and a new key reassembly module 28, used to reassemble multiple key fragments to obtain a new key after the distribution of multiple key fragments.

[0055] This application provides a vehicle data management system based on a combined public key. The system collects vehicle operation data, including driving behavior data, through a data acquisition module 21. A demand coefficient calculation module 22 calculates the vehicle's dynamic encryption demand coefficient based on the driving behavior data, whereby the dynamic encryption demand coefficient characterizes the vehicle's key update demand. A key state generation module 23 generates the vehicle's key state based on the dynamic encryption demand coefficient. A stability index calculation module 24 calculates the vehicle's link stability index, whereby the link stability index characterizes the vehicle's network stability. A distribution path generation module 25 generates multiple key fragments and distribution paths for the vehicle based on its key state and link stability index. An urgency score calculation module 26 calculates the vehicle's key update urgency based on the link stability index. The scoring and distribution process involves the verification module 27 distributing multiple key fragments, with cross-verification of old and new keys during the distribution process. The verification strength of the cross-verification is determined based on the key update urgency score. After the distribution of multiple key fragments, the new key reassembly module 28 reassembles the multiple key fragments to obtain a new key. A dynamic correlation is established between driving behavior and key management requirements. A dynamic encryption requirement coefficient calculated in real-time triggers key lifecycle adjustments, ensuring that the key update frequency matches the vehicle's operating status. Furthermore, a fragment distribution mechanism combined with real-time network topology perception optimizes key distribution path selection, significantly reducing network transmission latency for key updates. Simultaneously, adaptive verification strength adjustment during key distribution maintains communication security while ensuring business continuity, balancing the timeliness and security of key updates.

[0056] In one embodiment, the above-mentioned operational data acquisition module 21 can be further configured to: acquire vehicle accelerator pedal depth, braking pressure and steering wheel angle data via vehicle bus; and acquire the relative speed and distance matrix of the target object relative to the vehicle via millimeter-wave radar.

[0057] In one embodiment, the key state generation module 23 can be further configured to: calculate the duration thresholds of the active period, transition period and obsolescence period of the vehicle's current key based on the dynamic encryption demand coefficient; and generate the vehicle's key state based on the duration thresholds of the active period, transition period and obsolescence period of the current key.

[0058] In one embodiment, the stability index calculation module 24 can be further configured to: measure the base station signal strength corresponding to the vehicle; calculate the key update success rate based on the base station signal strength, and obtain the link stability index.

[0059] In one embodiment, the above-mentioned distribution path generation module 25 can be further configured to: calculate the distribution priority of multiple key fragments based on the vehicle's key status and link stability index; and determine the distribution path of multiple key fragments according to the distribution priority when the network is congested.

[0060] In one embodiment, the above-mentioned distribution process verification module 27 can be further configured to: use the old and new keys alternately for communication in a round-robin manner during the distribution process and record the verification success rate; and dynamically adjust the zero-knowledge proof rounds based on the verification success rate and the current network latency to update the verification strategy.

[0061] In one embodiment, the new key reassembly module 28 can be further configured to: after the distribution of multiple key fragments is completed, use a threshold decryption mechanism to verify the consistency of the multiple key fragments; if the verification is successful, reassemble the multiple key fragments and activate the new key state.

[0062] In one embodiment, the vehicle data management system 20 based on combined public keys can be further configured to: collect vehicle key update logs and verification records; construct a relational network of key update events based on the key update logs and verification records; extract spatiotemporal features from the relational network; and train a model based on the spatiotemporal features to obtain an anomaly detection model.

[0063] Below, for reference Figure 3 This application describes an electronic device according to embodiments thereof. The electronic device may be either or both of a first device and a second device, or a standalone device independent of them, which may communicate with the first device and the second device to receive acquired input signals from them.

[0064] Figure 3 A block diagram of an electronic device according to an embodiment of this application is illustrated.

[0065] like Figure 3 As shown, the electronic device 10 includes one or more processors 11 and memory 12.

[0066] The processor 11 may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 10 to perform desired functions.

[0067] The memory 12 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may include, for example, random access memory (RAM) and / or cache memory. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium, and the processor 11 may execute the program instructions to implement the methods of the various embodiments of this application described above and / or other desired functions. Various contents such as input signals, signal components, and noise components may also be stored in the computer-readable storage medium.

[0068] In one example, the electronic device 10 may also include an input device 13 and an output device 14, which are interconnected via a bus system and / or other forms of connection mechanism (not shown).

[0069] When the electronic device is a standalone device, the input device 13 can be a communication network connector for receiving the collected input signals from the first device and the second device.

[0070] In addition, the input device 13 may also include, for example, a keyboard, a mouse, etc.

[0071] The output device 14 can output various information to the outside, including determined distance information, direction information, etc. The output device 14 may include, for example, a display, a speaker, a printer, and a communication network and its connected remote output devices, etc.

[0072] Of course, for the sake of simplicity, Figure 3 Only some of the components of the electronic device 10 relevant to this application are shown in this illustration; components such as buses, input / output interfaces, etc., are omitted. In addition, the electronic device 10 may include any other suitable components depending on the specific application.

[0073] In addition to the methods and apparatus described above, embodiments of this application may also be computer program products, which include computer program instructions that, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0074] The computer program product can be written in any combination of one or more programming languages ​​to perform the operations of the embodiments of this application. The programming languages ​​include object-oriented programming languages ​​such as Java and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0075] Furthermore, embodiments of this application may also be computer-readable storage media storing computer program instructions thereon, which, when executed by a processor, cause the processor to perform the steps in the methods according to various embodiments of this application described in the "Exemplary Methods" section above.

[0076] The computer-readable storage medium may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0077] The basic principles of this application have been described above with reference to specific embodiments. However, it should be noted that the advantages, benefits, and effects mentioned in this application are merely examples and not limitations, and should not be considered as essential features of each embodiment of this application. Furthermore, the specific details disclosed above are for illustrative and facilitative purposes only, and are not limitations. These details do not limit the application to the necessity of employing the aforementioned specific details for implementation.

[0078] The block diagrams of devices, apparatuses, devices, and systems involved in this application are merely illustrative examples and are not intended to require or imply that they must be connected, arranged, or configured in the manner shown in the block diagrams. As those skilled in the art will recognize, these devices, apparatuses, devices, and systems can be connected, arranged, and configured in any manner. Words such as “comprising,” “including,” “having,” etc., are open-ended terms meaning “including but not limited to,” and are used interchangeably with them. The terms “or” and “and” as used herein refer to the terms “and / or,” and are used interchangeably with them unless the context clearly indicates otherwise. The term “such as” as used herein refers to the phrase “such as but not limited to,” and is used interchangeably with it.

[0079] It should also be noted that in the apparatus, equipment, and methods of this application, the components or steps can be disassembled and / or recombined. These disassemblies and / or recombinations should be considered as equivalent solutions of this application.

[0080] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use this application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein can be applied to other aspects without departing from the scope of this application. Therefore, this application is not intended to be limited to the aspects shown herein, but rather to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0081] The above description has been given for purposes of illustration and description. Furthermore, this description is not intended to limit the embodiments of this application to the forms disclosed herein. Although numerous exemplary aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, alterations, additions, and sub-combinations thereof.

Claims

1. A vehicle data management method based on a combined public key, characterized by, The method comprises: collecting operation data of the vehicle; wherein the operation data comprises driving behavior data; calculating a dynamic encryption demand coefficient of the vehicle based on the driving behavior data; wherein the dynamic encryption demand coefficient represents the degree of secret key update demand of the vehicle; generating a secret key state of the vehicle based on the dynamic encryption demand coefficient; calculating a link stability index of the vehicle; wherein the link stability index represents the network stability of the vehicle; generating a plurality of secret key segments and a distribution path of the vehicle based on the secret key state and the link stability index of the vehicle; calculating a secret key update urgency score of the vehicle based on the link stability index; distributing a plurality of the secret key segments, and using new and old secret key cross verification in the distribution process; wherein the verification strength of the new and old secret key cross verification is determined according to the secret key update urgency score; after the distribution of a plurality of the secret key segments is completed, recombining a plurality of the secret key segments to obtain a new secret key; the generating of the secret key state of the vehicle based on the dynamic encryption demand coefficient comprises: calculating the duration threshold of the active period, the transition period and the discard period of the current secret key of the vehicle based on the dynamic encryption demand coefficient; generating the secret key state of the vehicle based on the duration threshold of the active period, the transition period and the discard period of the current secret key.

2. The group public key-based vehicle data management method according to claim 1, characterized by, The collecting of the operation data of the vehicle comprises: collecting the throttle pedal depth, brake pressure and steering wheel angle data of the vehicle through the vehicle-mounted bus; collecting the relative speed and distance matrix of the target object relative to the vehicle through the millimeter wave radar.

3. The group public key-based vehicle data management method according to claim 1, characterized by, The calculating of the link stability index of the vehicle comprises: measuring the signal strength of the base station corresponding to the vehicle; calculating the secret key update success rate based on the base station signal strength to obtain the link stability index.

4. The group public key-based vehicle data management method according to claim 1, characterized by, The generating of a plurality of secret key segments and a distribution path of the vehicle based on the secret key state and the link stability index of the vehicle comprises: calculating the distribution priority of a plurality of the secret key segments based on the secret key state and the link stability index of the vehicle; determining the distribution path of a plurality of the secret key segments according to the distribution priority when the network is congested.

5. The group public key-based vehicle data management method according to claim 1, characterized by, The using of new and old secret key cross verification in the distribution process comprises: alternately using new and old secret keys for communication in a polling manner during the distribution process, and recording the verification success rate; based on the verification success rate and the current network delay, dynamically adjusting the rounds of zero-knowledge proof to update the verification strategy.

6. The group public key-based vehicle data management method according to claim 1, characterized by, The recombining of a plurality of the secret key segments to obtain a new secret key after the distribution of a plurality of the secret key segments is completed comprises: after the distribution of a plurality of the secret key segments is completed, verifying the consistency of a plurality of the secret key segments using a threshold decryption mechanism; if the verification is passed, recombining a plurality of the secret key segments and activating a new secret key state.

7. The group public key-based vehicle data management method according to claim 1, characterized by, The vehicle data management method based on combined public key further comprises: collecting secret key update logs and verification records of the vehicle; constructing a relationship network of secret key update events based on the secret key update logs and the verification records; extracting spatiotemporal features in the relationship network; training a model based on the spatiotemporal features to obtain an anomaly detection model.

8. A vehicle data management system based on a combined public key, characterized by The method comprises: The running data collection module is configured to collect running data of the vehicle, wherein the running data comprises driving behavior data. The demand coefficient calculation module is configured to calculate a dynamic encryption demand coefficient of the vehicle based on the driving behavior data, wherein the dynamic encryption demand coefficient represents a degree of secret key update demand of the vehicle. The secret key state generation module is configured to generate a secret key state of the vehicle based on the dynamic encryption demand coefficient. The stability index calculation module is configured to calculate a link stability index of the vehicle, wherein the link stability index represents network stability of the vehicle. The distribution path generation module is configured to generate a plurality of secret key segments and distribution paths of the vehicle based on the secret key state and the link stability index of the vehicle. The urgency score calculation module is configured to calculate a secret key update urgency score of the vehicle based on the link stability index. The distribution process verification module is configured to distribute the plurality of secret key segments and perform new-old secret key cross verification during the distribution process, wherein a verification strength of the new-old secret key cross verification is determined according to the secret key update urgency score. The new secret key recombination module is configured to recombine the plurality of secret key segments to obtain a new secret key after the distribution of the plurality of secret key segments is completed. The secret key state generation module is further configured to: calculate duration threshold values of an active period, a transition period and a discard period of a current secret key of the vehicle based on the dynamic encryption demand coefficient; and generate the secret key state of the vehicle based on the duration threshold values of the active period, the transition period and the discard period of the current secret key.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is configured to execute the method of any one of claims 1-7.

Images