A vehicle networking data sharing method based on double-layer blockchains

Abstract

The application provides a kind of based on double-layer block chain's Internet of Vehicles data sharing and credit management method.For the problem that data reliability is difficult to guarantee in Internet of Vehicles, the application designs a data sharing mechanism based on credit value: by constructing a double-layer block chain architecture including bottom layer chain and top layer chain, wherein the bottom layer chain is composed of vehicle nodes responsible for local data sharing, and the top layer chain is composed of RSU nodes with high credit value responsible for network data synchronization; based on time decay factor, the direct credit value between nodes is calculated, combined with the recommended credit value of path credibility, the global credit value is calculated by using dynamic weight iterative algorithm, the credit value is used as an important basis for data request matching, to ensure the reliability of data source; based on data influence range, distribution is carried out between double-layer block chains.The application deeply integrates the credit management mechanism with the data sharing process, improves the sharing efficiency while ensuring the data reliability, and reduces the system deployment cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of Internet of Vehicles data sharing, and in particular to an Internet of Vehicles data sharing method based on double-layer blockchains. BACKGROUND

[0002] With the rapid development of intelligent transportation systems, Internet of Vehicles technology is becoming mature and gradually moving towards practicality. Internet of Vehicles can effectively improve road traffic efficiency, reduce traffic accidents and improve driving experience by realizing real-time communication and data sharing between vehicles and between vehicles and infrastructure. In the Internet of Vehicles environment, vehicles continuously collect multi-source heterogeneous data such as road conditions, vehicle states and driving behaviors through on-board sensors. These data, after analysis and sharing, can provide real-time traffic information, safety warnings, path planning and other diversified services for traffic participants. Therefore, establishing an efficient and reliable data sharing mechanism is of great significance for the benign operation of Internet of Vehicles.

[0003] At present, a variety of solutions have been proposed for Internet of Vehicles data sharing. Traditional solutions mainly rely on roadside units (RSUs) as data relay and storage nodes, and use centralized architecture for data management. With the development of blockchain technology, researchers have begun to apply it to Internet of Vehicles data sharing, using the decentralized and tamper-proof features of blockchain to ensure the security and credibility of data sharing. At the same time, in order to encourage vehicles to actively participate in data sharing and curb malicious behavior, researchers have proposed an incentive mechanism based on reputation values, which quantifies the credibility of nodes by evaluating their historical behavior.

[0004] However, the existing technical solutions still have the following problems: first, the deployment and maintenance cost of traditional RSUs is high, and the coverage is limited; second, the existing blockchain architecture has low consensus efficiency in large-scale networks, making it difficult to meet the real-time data sharing needs in Internet of Vehicles scenarios; third, the existing reputation mechanism does not fully consider the characteristics of high dynamicity of nodes in Internet of Vehicles environment, and is vulnerable to attacks by malicious nodes. In view of these problems, the present application proposes a data sharing scheme based on double-layer blockchains, and constructs a hierarchical blockchain architecture to significantly reduce the system deployment cost and data synchronization overhead. At the same time, the present application designs a dynamic reputation evaluation mechanism that integrates a time decay factor, effectively improving the security and reliability of the system. SUMMARY

[0005] The present application aims to provide an Internet of Vehicles data sharing method based on double-layer blockchains, which solves the problems of high system deployment cost, low data sharing efficiency and imperfect trust evaluation mechanism in the prior art by constructing a hierarchical blockchain architecture, redesigning the data sharing process mechanism and integrating a dynamic reputation evaluation mechanism.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0007] A double-layer blockchain-based Internet of Vehicles data sharing method comprising the following steps:

[0008] S1. System initialization: a double-layer blockchain architecture is constructed, including two layers of a top chain and a bottom chain. The bottom chain is composed of vehicle nodes and RSU nodes, responsible for local data sharing; the top chain is composed of RSU nodes with high reputation values, responsible for network-wide data synchronization. The system uses a trusted authentication agency (TA) to authenticate and issue digital certificates for vehicle identities. During the authentication process, the TA automatically generates a unique PID for each vehicle.

[0009] S2. Data collection and uploading: data collectors collect data through vehicle-mounted devices and generate data blocks DATA BC according to the data characteristics. The format of the data block is:

[0010] DATA BC ={σ data ,PID Vx ,Hash(σ data ),Sig vec}

[0011] where σ data ={D,Urg,Url}, D={d1,d2,d3,d4…} represents the characteristic information of the data, Urg represents the data urgency, Url represents the data address, PID Vx represents the unique identity information of the vehicle Vx, and Sig vec represents the vehicle's signature on the data. The data collector uploads the encapsulated data block to the bottom chain for storage and sharing.

[0012] S3. Data hierarchical processing: RSU nodes act as a bridge between the double-layer chains, receiving data DATA BC from the bottom chain and calculating its influence range S data according to the data characteristic information D. When the data influence range exceeds the preset threshold (i.e., S data ≥θ), the RSU node re-encapsulates the data as:

[0013]

[0014] where S data represents the data influence range calculated according to D, Sig RSU represents the digital signature of the RSU, φ data ={DATA BC ,Gid}, and Gid represents the identity index of the RSU node, DATA BCThe bottom layer chain data block is uploaded to the top layer chain for network synchronization, realizing hierarchical storage and propagation of data.

[0015] S4. Initiate data request: the data requester initiates a data query request through the smart contract IRSC deployed on the blockchain. The smart contract selects the optimal one from M candidate data providers according to the set evaluation mechanism The selection criteria are:

[0016]

[0017] Wherein The global reputation value of the provider V x is comprehensively considered. The position distance d, data timeliness t, and trajectory similarity The specific calculation formula is introduced later.

[0018] S5. Request data sharing: after selecting the optimal data provider, the requester V y sends an encrypted data sharing request Req to the data provider , requesting to provide data data_1:

[0019]

[0020] Wherein Req represents the request information package, represents public key encryption using , at the same time V y sends a transaction request Tx id to the blockchain network, starting the smart contract to automatically process data exchange:

[0021]

[0022] S6. Data transmission: after receiving the request Req, the data provider first confirms the validity of the transaction Tx id through the blockchain, then encrypts and packages the requested data as Package and sends it to

[0023]

[0024] Through a secure channel to the data requester, ensuring the security and integrity of the data transmission process.

[0025] S7. Rating update: after the data exchange is completed, the requester rates the provider according to the quality of the received data, and the satisfied evaluation is recorded as Rate Vy→Vx = positive Vy→Vx = +1, and the unsatisfied evaluation is recorded as RateVy→Vx = negative Vy→Vx = -1. The rating result is verified by the smart contract RVSC, including certificate legality check, transaction ID duplication check, PID matching verification, etc. After verification, the provider's reputation value record is updated.

[0026] Further, the reputation management system based on double-layer blockchain in the vehicle networking environment in S1 includes three types of participant nodes: a trusted authentication authority TA, RSU nodes, and vehicles (Vehicle);

[0027] The blockchain system includes a plurality of bottom chains (BC) based on geographical location division and a top chain (TC) for managing global data. The bottom chain is maintained by RSU nodes and vehicles in each geographical area, and the bottom chain only maintains shared data within the region and the reputation value and certificate status information of vehicles within the region. The top chain is maintained by RSU nodes and TA, and is used to manage and update the status information of global vehicles, including new vehicle registration, malicious vehicle removal, and updating of vehicle region id and reputation value status information;

[0028] The RSU node serves as a bridge between the bottom chain and the top chain, is responsible for data forwarding and encapsulation, and uploads the data of the bottom chain to the top chain according to the influence range of the data;

[0029] The trusted authentication authority TA is responsible for the initial identity authentication, key distribution, and trust management of the vehicle, and performs public key revocation and blacklist management on malicious vehicles by monitoring the reputation value of the vehicle, to ensure the security and stability of the system;

[0030] The vehicle serves as a terminal node of the bottom chain, is responsible for data collection, processing, and sharing, and automatically completes data request and reputation evaluation update through a smart contract;

[0031] Further, the vehicle networking data sharing system establishment process of step S1 is as follows:

[0032] In the system initialization phase, elliptic curve digital signature technology (ECDSA) and asymmetric encryption algorithm are used to ensure the security of the transaction and the verification of the vehicle identity. Each vehicle must obtain the authentication of the trusted authentication authority (TA) before joining the network and become a legal vehicle. During the authentication process, the TA will automatically generate a unique anonymous blockchain address for each vehicle, as well as a pair of public and private keys and corresponding digital certificates for encrypting shared data. The vehicle then synchronizes the blockchain data from the adjacent edge node to complete the joining process;

[0033] Further, the data block in step S2:

[0034] DATA BC = {σ data , Hash(σ data ), PID Vx , Sig vec}

[0035] wherein σ data = {D, Urg, Url} is a label vector of the data data, wherein D = {d1, d2, d3, d4…} represents feature information of the data (for example: type, size, sampling frequency, collection time, sharing permission, vehicle identity information identifier, vehicle geographic location, sensor data, etc.), used to identify and index the data, Urg is the urgency of the data data, and Url is the location of the data storage. Different storage address types can be designed according to the data type, for example: the address of the data storage server, the ID of the shared vehicle, or the hash value of the IPFS (Interplanetary File System) data. The original information of the shared data in the system is not directly stored on the blockchain, and only the url and metadata of the data are saved on the chain. The metadata provides the description information of the data. Hash(data) and Hash(σ data ) represent the hash values obtained by performing hash operations on the original data and the data label vector, which ensure that the data is tamper-proof. Sig vec represents the signature of the vehicle on the data, which ensures the reliability of the source of the data, and S data represents the influence range of the data.

[0036] Further, the data block in step S4 is described as follows:

[0037]

[0038] The represents the evaluation value of the data reliability of the vehicle V y to the vehicle V x . w1, w2, w3, and w4 are weight coefficients and w1 + w2 + w3 + w4 = 1, b is a weight coefficient for controlling the distance d, d represents the distance from the data providing vehicle V x to the event location, and t represents the timeliness of the message.

[0039] The t represents the timeliness of the message, and the calculation formula is as follows:

[0040]

[0041] wherein t curr represents the current time, t mess ​represents the time when the message occurs, and λ is a decay coefficient that determines the degree of influence of the time difference on timeliness.

[0042] The refers to the similarity between vehicles, and the Fréchet distance is used for calculation. Assuming that the trajectories of two vehicles are represented by continuous curves A(t) and B(t) after standardization, where t varies in the closed interval [0, 1], the similarity of the trajectories of the two vehicles can be represented as:

[0043]

[0044] where d is a metric function, and the Euclidean distance between the positions of the two vehicles after time remapping is used in the present scheme, and α(t), β(t) are two reparameterization functions.

[0045] is the global reputation value of the vehicle V x . The reputation value calculation process includes the calculation of the direct reputation value C, the calculation of the recommended reputation value T_rec, and the calculation of the global reputation value T, and the specific steps are as follows:

[0046] S41, the vehicle V y calculates the normalized direct reputation value of the vehicle V x , and the steps are shown in S411-S412:

[0047] 411, the vehicle V y calculates the positive evaluation x and the negative evaluation of the vehicle V Vy→Vx :

[0048]

[0049] The positive evaluation positive Vy→Vx of the data requester is +1, the negative evaluation negative Vy→Vx is -1, r is the time decay factor, and the Gaussian function is used for calculation:

[0050]

[0051] where t is the current time, t0 is the transaction time, and c is the decay rate parameter;

[0052] S412, the vehicle V y calculates the normalized direct reputation value of the vehicle V x :

[0053]

[0054] Where a+b=1 and b>a;

[0055] Furthermore, for vehicles V that have not been directly traded... y With V z Vehicle V y You can ask V z Intermediate node V that has conducted direct transactions i To evaluate V z Credibility.

[0056] S42. In the absence of prior experience and direct reputation value, vehicle V y Inquiry and V z Intermediate node V that has conducted direct transactions i To evaluate V z The credibility of the assessment is determined through the following steps, as shown in S421-S423:

[0057] S421, Vehicle V y Inquiry and V z Intermediate node V that has conducted direct transactions i To evaluate V z Credibility:

[0058]

[0059] in: V represents y For V z Recommended reputation value, This represents the direct trust value between adjacent nodes on the path, where n is the path length (number of nodes - 1).

[0060] S422. Based on the route reliability assessment, vehicle V y Filter trusted paths based on the following criteria:

[0061] T_rec(path)>δ

[0062] Only paths with a credibility exceeding the threshold will be accepted;

[0063] S423. When multiple reliable recommendation paths exist, these recommendation values ​​need to be aggregated reasonably. This solution adopts a weighted average method based on path reliability:

[0064]

[0065] Where P is the set of all paths that satisfy the credibility threshold, and T_rec p The recommended value provided for path p, where Trust(p) is the credibility of path p as a weight;

[0066] Malicious nodes can implement collusion attacks by constructing recommendation paths that appear independent, trying to manipulate the final reputation evaluation results. To this end, we introduce a defense mechanism based on path correlation analysis. For any two recommendation paths, their correlation coefficient is calculated as follows:

[0067]

[0068] where V(p) represents the set of nodes contained in path p.

[0069] When suspicious patterns are detected, adjust the path weight:

[0070] Trust ′ (p) = Trust(p) f(Corr, sigma)

[0071] where f(Corr, sigma) is a penalty function based on path correlation and recommendation value dispersion

[0072] S43, the system calculates the global reputation value. Define the global reputation value vector of the kth iteration:

[0073]

[0074] where is the global reputation value of node i in the kth iteration, and each element of the vector takes a value in the range [-1, 1];

[0075] The iteration formula of the global reputation value is:

[0076] T (k+1) = (1 - a(T (k) ))CT (k) + a(T (k) )P

[0077] where C is the normalized trust matrix c ji , P is the pre-trust vector, and a(T (k) ) is a dynamic weight function. The calculation formula is:

[0078]

[0079] where beta is the scaling factor, theta is the reputation threshold, sigma is the smoothing factor, and gamma is the base weight;

[0080] Convergence condition for global reputation value calculation:

[0081] || T (k+1) - T (k) ||2 < epsilon

[0082] S44, calculate the vehicle transaction reputation value and select the optimal data provider Selection criteria The advantages and beneficial effects of the present application are as follows:

[0083] 1. By layered blockchain architecture, data is stored and synchronized according to the influence range, reducing redundant consensus and transmission, and significantly improving the consensus efficiency and data sharing efficiency of the system.

[0084] 2. A dynamic reputation value model is designed, a time decay factor and an asymmetric punishment mechanism are introduced, which can more accurately reflect the real-time reputation of the node, effectively identify and suppress malicious behavior; finally, the data request and rating process are automatically executed through the smart contract, ensuring the security and reliability of data sharing. The overall scheme ensures efficient data sharing while enhancing the security, scalability and attack resistance of the system, suitable for large-scale vehicle networking environment. BRIEF DESCRIPTION OF DRAWINGS

[0085] Figure 1 Vehicle networking data sharing flow chart

[0086] Figure 2 System throughput analysis

[0087] Figure 3 Recommended reputation value

[0088] Figure 4 Double-layer blockchain architecture design

[0089] Figure 5 Comparison of consensus delay under different network sizes DETAILED DESCRIPTION

[0090] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0091] The present application provides a kind of vehicle networking data sharing method based on double-layer blockchain, which comprises the following steps:

[0092] S1, system initialization: build a double-layer blockchain architecture, including top chain and bottom chain two levels. The bottom chain is composed of vehicle nodes and RSU nodes, responsible for local data sharing; the top chain is composed of RSU nodes with high reputation value, responsible for network data synchronization. The system uses trusted authentication agency (TA) to authenticate and issue digital certificates for vehicle identity, uses elliptic curve digital signature technology (ECDSA) and asymmetric encryption algorithm to ensure the security of transaction and the verification of vehicle identity. Each vehicle must obtain the authentication of the trusted authentication agency (TA) before joining the network, and become a legal vehicle. In the authentication process, TA will automatically generate a unique anonymous blockchain address for each vehicle, and assign a pair of public and private keys and corresponding digital certificates for encrypted shared data.

[0093] S2, data collection and upload: data collectors collect data through vehicle-mounted devices, and generate data blocks DATA BC according to data characteristics. The format of the data block is:

[0094] DATA BC ={σ data ,Hash(σ data ),PID Vx ,Sig data}

[0095] Where σ data ={D,Urg,Url}, D={d1,d2,d3,d4…} represents the characteristic information of data, Urg represents the data urgency, Url represents the data address, PID Vx represents the unique identity information of vehicle Vx, and Sig vec represents the signature of the vehicle on the data. The data collector uploads the encapsulated data block to the bottom chain for storage and sharing.

[0096] S3, data hierarchical processing: RSU nodes act as a bridge between the double-layer chains, receiving data DATA BC from the bottom chain and calculating its influence range S data according to the characteristic information D of the data. When the data influence range exceeds the preset threshold (i.e. S data ≥θ), the RSU node re-encapsulates the data into format, where S data represents the data influence range calculated according to D, Sig RSU represents the digital signature of the RSU, φ data ={DATA BC ,Gid}, Gid represents the identity index of the RSU node, and DATA BCThis is the underlying blockchain data block. It is then uploaded to the top-level blockchain for network-wide synchronization, achieving hierarchical storage and propagation of data.

[0097] S4. Initiating a Data Request: The data requester initiates a data query request through the IRSC smart contract deployed on the blockchain. The smart contract selects the best data provider from M candidate data providers according to the set evaluation mechanism. The selection criteria are:

[0098]

[0099] in Taking into account provider V x Global reputation score Location distance d, data timeliness t, trajectory similarity Multiple factors, including...

[0100] S41, Vehicle V y Calculate the value of vehicle V x The normalized direct reputation value is calculated using the steps shown in S411-S412:

[0101] S411, Vehicle V y Calculate the value of vehicle V x Positive evaluation negative evaluation

[0102]

[0103] The data requester's positive feedback Vy→Vx A value of +1 indicates a negative evaluation. Vy→Vx The value is -1, and r is the time decay factor, calculated using a Gaussian function:

[0104]

[0105] Where t is the current time, t0 is the time the transaction occurred, and c is the decay rate parameter;

[0106] S412, Vehicle V y Calculate the value of vehicle V x Normalized direct reputation score:

[0107]

[0108] Where a+b=1 and b>a;

[0109] S42. In the absence of prior experience and direct reputation value, vehicle V y Inquiry and V z Intermediate node V that has conducted direct transactions iTo evaluate the credibility of V z , the specific steps are shown in S421-S243:

[0110] S421, vehicle V y asks the intermediate nodes V z that have conducted direct transactions with V i to evaluate the credibility of V z :

[0111]

[0112] wherein: represents the recommended reputation value of V y to V z , represents the direct trust value between adjacent nodes on the path, and n is the path length (node number-1)

[0113] S422, on the basis of path credibility evaluation, vehicle V y filters the trusted path, and the filtering condition is:

[0114] T_rec(path)>δ

[0115] Only the path with credibility exceeding the threshold value will be accepted;

[0116] S423, when there are multiple trusted recommended paths, it is necessary to reasonably aggregate these recommended values. This scheme adopts a weighted average method based on path credibility:

[0117]

[0118] wherein P is the set of all paths meeting the credibility threshold, T_rec p is the recommended value provided by path p, and Trust(p) is the credibility of path p as a weight;

[0119] For any two recommended paths, calculate their correlation coefficient as follows:

[0120]

[0121] wherein V(p) represents the node set contained in path p.

[0122] When a suspicious pattern is detected, adjust the path weight:

[0123] Trust ′ (p) = Trust(p)·f(Corr,σ)

[0124] S43, the system calculates the global reputation value. Define the global reputation value vector of the kth iteration:

[0125]

[0126] wherein is the global reputation value of node i in the kth iteration, each element of the vector takes value in the range [-1, 1];

[0127] The iteration formula of the global reputation value is:

[0128] T (k+1) = (1 - a(T (k) ))CT (k) + a(T (k) )P

[0129] wherein C is the normalized trust matrix c ji , P is the pre-trust vector, a(T (k) ) is a dynamic weight function, and the calculation formula is:

[0130] wherein a(T (k) ) is a dynamic weight function, and the calculation formula is:

[0131]

[0132] wherein β is a scaling factor, θ is a reputation threshold, σ is a smoothing factor, and γ is a basic weight;

[0133] The convergence condition of the global reputation value calculation is:

[0134] ||T (k+1) - T (k) ||2 < ε

[0135] S44, calculating the vehicle transaction reputation value and selecting the optimal data provider selection criteria

[0136] S5. Requesting data sharing: after selecting the optimal data provider, the requester sends an encrypted data sharing request Req to it, requesting to provide data data_1:

[0137]

[0138] wherein Req represents a request information package, is encrypted using public key encryption, and at the same time, V y sends a transaction request Tx id to the blockchain network to start the intelligent contract to automatically process data exchange:

[0139]

[0140] S6. Data Transmission: Data Provider Upon receiving the request (Req), the transaction (Tx) is first confirmed via the blockchain. id The validity of the request is verified, and then the requested data is encrypted, packaged into a package, and sent.

[0141]

[0142] Data is transmitted to the data requester through a secure channel, ensuring the security and integrity of the data transmission process.

[0143] S7. Rating Update: After the data exchange is completed, the requester rates the provider based on the quality of the received data, and the satisfaction rating is recorded as Rate. Vy→Vx =positive Vy→Vx =+1, unsatisfactory rating is recorded as Rate Vy→Vx =negative Vy→Vx = -1. The rating result is verified through the smart contract RVSC, which includes steps such as certificate legality check, transaction ID deduplication, and PID matching verification. After successful verification, the provider's reputation value record is updated.

[0144] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for sharing vehicle network data based on a two-layer blockchain, characterized in that, Includes the following steps: S1. System Initialization: Construct a two-layer blockchain architecture, which includes a top-level chain and a bottom-level chain; the bottom-level chain consists of vehicle nodes and RSU nodes, which are responsible for local data sharing. The top-level chain consists of RSU nodes with high reputation values, which are responsible for synchronizing data across the entire network. The system uses a Trusted Certification Authority (TA) to authenticate vehicle identities and issue digital certificates. During the authentication process, the TA will automatically generate a unique PID for each vehicle. S2. Data Acquisition and Upload: Data collectors acquire data through onboard equipment and generate data blocks (DATA) based on data characteristics. BC The format of the data block is: It contains the data tag vector σ data Data hash value Hash(σ) data ), Scope of influence Sig vec and vehicle V x Digital signature information Data collectors upload the packaged database to the underlying blockchain for storage and sharing. S3. Data Hierarchical Processing: The Road Test Unit (RSU) node acts as a bridge between the two-layer chains, receiving data from the lower-level chain and calculating its influence range S. data When the range of data influence exceeds the preset threshold θ, i.e., S data ≥θ, the RSU node will repackage the data as: It is then uploaded to the top-level blockchain for full network synchronization, enabling hierarchical storage and dissemination of data. S4. Initiating a Data Request: The data requester initiates a data query request through the smart contract IRSC deployed on the blockchain; the smart contract selects the best data provider from M candidate data providers according to the set evaluation mechanism. The selection criteria are: in The system comprehensively considers multiple factors, including the provider's reputation score, distance information, and data timeliness; w1, w2, w3, and w4 are weighting coefficients, and w1 + w2 + w3 + w4 = 1; b is the weighting coefficient controlling the distance d; and t represents message timeliness. Let x be the global reputation value of vehicle x; S5. Request Data Sharing: Select the Optimal Data Provider Afterwards, the requester V y Send a data sharing request Req to it: in Indicates the use of X x of Public key encryption is used; data_1 is the transmitted data, timestamp is the timestamp; and V... y Send transaction request Tx to the blockchain network id : Initiate smart contracts to automate data exchange processes; S6. Data Transmission: Upon receiving the request, the data provider first verifies the validity of the transaction via the blockchain, and then encrypts and packages the requested data. Data is transmitted to the data requester through a secure channel to ensure the security and integrity of the data transmission process. S7. Rating Update: After the data exchange is completed, the requester rates the provider based on the quality of the received data, and the satisfaction rating is recorded as follows: Unsatisfactory reviews are recorded as follows ; The rating results are verified through smart contracts, including certificate validity checks, transaction ID deduplication, and PID matching verification steps. Once the verification is successful, the provider's reputation value record is updated.

2. The method as described in claim 1, characterized in that, Data block DATA as described in S2 BC The format is: Where σ data A data tag vector containing data characteristic information, urgency level, and storage location; The data block DATA TC Format: in Gid is the RSU node identity index, Sig RSU For RSU signature.

3. The method as described in claim 1, characterized in that, During the data sharing process, data requesters initiate requests through smart contracts and select the optimal data provider based on the reputation of candidate data providers. Where M is the set of data providers containing the required information, and Cred is the data reliability assessment value: The Indicates vehicle V y For vehicle V x The data reliability assessment value; w1, w2, w3, w4 are weighting coefficients and w1+w2+w3+w4=1, b is the weighting coefficient of the control distance d, and d represents the data provider V. x Examples of times and locations, where t represents the message's timeliness. Let x be the global reputation value of vehicle x. This refers to the similarity between vehicles, calculated using the Fréchet distance.

4. A method for calculating the reputation value of a vehicle-to-everything (V2X) network based on time decay, characterized in that... Includes the following steps: S41, Vehicle V y Calculate the value of vehicle V x The normalized direct reputation value is calculated using the steps shown in S411-S412: S411, Vehicle V y Calculate the V of a vehicle x Positive evaluation negative evaluation S412, Vehicle V y Calculate the V of a vehicle x Normalized direct reputation value The asymmetric weights a and b satisfy a+b=1 and b>a; this design makes the reputation loss caused by negative behavior significantly greater than the reputation gain caused by positive behavior. S42. In the absence of prior experience and direct reputation value, vehicle V y Inquiry and V z Intermediate node V that has conducted direct transactions i To evaluate V z The credibility of the information is determined by the specific steps S421-S423: S421, Vehicle V y Inquiry and V z Intermediate node V that has conducted direct transactions i To evaluate V z Credibility in V represents y For V z Recommended reputation value, This represents the direct trust value between adjacent nodes on the path, where n = number of nodes - 1, and is the path length. S422. Based on the route reliability assessment, vehicle V y The criteria for selecting reliable paths are: T_rec(path)>δ Only paths with a credibility T_rec exceeding the threshold δ will be accepted; S423. When multiple reliable recommendation paths exist, it is necessary to reasonably aggregate these recommendation values ​​using a weighted average method based on path reliability: Where P is the set of all paths that satisfy the credibility threshold, and T-rec p The recommended value provided for path p, where Trust(p) is the credibility of path p as a weight. For vehicle V y For V z Credibility; Malicious nodes may collude to launch attacks by constructing seemingly independent recommendation paths, attempting to manipulate the final reputation assessment results. To address this, we introduce a defense mechanism based on path correlation analysis. The correlation coefficient for any two recommendation paths is calculated as follows: Where V(p) represents the set of nodes contained in path p; When a suspicious pattern is detected, adjust the path weight: Trust′(p)=Trust(p)·f(Corr,σ) Where f(Corr,σ) is a penalty function based on path relevance and the dispersion of recommendation values; S43. The system calculates the global reputation value iteratively: T k+1 =(1-α(T k ))CT k +α(T k )P Until the convergence condition ||T is met k+1 -T k ||2<∈; Where ∈ is a predefined convergence threshold, α(T) k ) represents the dynamic weights; C is the standardized trust matrix, and P is the pre-trust vector; S44. Calculate the vehicle transaction credit score: And select the optimal data provider Selection criteria:

5. The method as described in claim 4, characterized in that... The asymmetric weights satisfy: a + b = 1 and b > a, where a is the positive evaluation weight and b is the negative evaluation weight; Vehicle V y Calculate the value of vehicle V x Positive evaluation negative evaluation Positive feedback from data requesters A value of +1 indicates a negative evaluation. The value is -1, and r is the time decay factor, calculated using a Gaussian function: Where t is the current time, t0 is the time the transaction occurred, and c is the decay rate parameter.

6. The method as described in claim 4, characterized in that... The dynamic weights are calculated using the following function: Where α(T) k () is the formula for calculating the dynamic weight function; Define the global reputation value vector for the k-th iteration: The formula for iterating the global reputation value is: T k+1 =(1-α(T k ))CT k +α(T k )P。

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