Internet of vehicles data sharing method based on double-layer block chain

Abstract

The invention provides an Internet of Vehicles data sharing and reputation management method based on a double-layer block chain. In order to solve the problem that the data reliability in the Internet of Vehicles is difficult to guarantee, the invention designs a data sharing mechanism based on a reputation value: a double-layer block chain architecture comprising a bottom-layer chain and a top-layer chain is constructed, the bottom-layer chain is composed of vehicle nodes and is responsible for local data sharing, and the top-layer chain is composed of RSU nodes with high reputation values and is responsible for whole-network data synchronization; a direct reputation value between nodes is calculated based on a time decay factor, a global reputation value is calculated by adopting a dynamic weight iteration algorithm in combination with a recommended reputation value of path credibility, and the reputation value is used as an important basis for data request matching to ensure the reliability of a data source; and carrying out distribution between the double-layer block chains based on the data influence range. According to the method, a reputation management mechanism and a data sharing process are deeply fused, the sharing efficiency is improved while the data reliability is ensured, and the system deployment cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Vehicles data sharing, and specifically to an Internet of Vehicles data sharing method based on a double-layer blockchain. Background Art

[0002] With the rapid development of intelligent transportation systems, Internet of Vehicles technology is becoming more mature and gradually becoming practical. 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 status, and driving behavior through on-board sensors. After analysis and sharing, these data can provide traffic participants with diversified services such as real-time road condition information, safety warnings, and path planning. Therefore, establishing an efficient and reliable data sharing mechanism is of great significance to the healthy operation of the Internet of Vehicles.

[0003] At present, a variety of solutions have been proposed for the problem of data sharing in the Internet of Vehicles. Traditional solutions mainly rely on roadside units (RSUs) as data relays and storage nodes, and use a centralized architecture for data management. With the development of blockchain technology, researchers have begun to apply it to data sharing in the Internet of Vehicles, using the decentralized and tamper-proof characteristics 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 value, which quantifies the credibility of nodes by evaluating their historical behavior.

[0004] However, the existing technical solutions still have the following problems: first, the traditional RSU deployment and maintenance costs are high and the coverage is limited; second, the existing blockchain architecture has low consensus efficiency in large-scale networks and is difficult to meet the needs of real-time data sharing in the Internet of Vehicles scenario; third, the existing reputation mechanism does not fully consider the highly dynamic characteristics of nodes in the Internet of Vehicles environment and is easily attacked by malicious nodes. In response to these problems, the present invention proposes a data sharing solution based on a two-layer blockchain, and constructs a hierarchical blockchain architecture to significantly reduce the system deployment cost and data synchronization overhead. At the same time, the present invention designs a dynamic reputation evaluation mechanism that integrates the time decay factor, which effectively improves the security and reliability of the system. Summary of the invention

[0005] The purpose of the present invention is to provide a method for sharing Internet of Vehicles data based on a double-layer blockchain. By constructing a hierarchical blockchain architecture, redesigning the data sharing process mechanism and integrating a dynamic reputation evaluation mechanism, the problems of high system deployment cost, low data sharing efficiency and imperfect trust evaluation mechanism in the prior art are solved.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for sharing data in an Internet of Vehicles based on a double-layer blockchain includes the following steps:

[0008] S1. System initialization: Build a two-layer blockchain architecture, which includes two levels: the top chain and the bottom chain. The bottom chain consists of vehicle nodes and RSU nodes, which are responsible for local data sharing; the top chain consists of RSU nodes with higher reputation values, which are responsible for data synchronization across the entire network. The system uses a trusted certification authority (TA) to authenticate the vehicle identity and issue digital certificates. During the authentication process, TA will automatically generate a unique PID for each vehicle.

[0009] S2. Data collection and upload: Data collectors collect data through vehicle-mounted equipment and generate data blocks DATA based on data characteristics. BC 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={d 1 ,d 2 ,d 3 ,d 4 ...} indicates the characteristic information of the data, Urg indicates the urgency of the data, Url indicates the data address, PID Vx Vx represents the unique identity information of the vehicle, Sig vec Indicates the vehicle's signature on the data. The data collector uploads the encapsulated data block to the underlying chain for storage and sharing.

[0012] S3. Data hierarchical processing: RSU nodes act as a bridge between the two-layer chains and receive data DATA from the bottom chain BC And calculate its influence range S based on the characteristic information D of the data data When the data impact range exceeds the preset threshold (i.e. S data ≥θ), the RSU node repackages the data as:

[0013]

[0014] Where S data Indicates that the data impact range is calculated based on D, Sig RSU Represents the digital signature corresponding to RSU, φ data ={DATA BC, Gid}, Gid represents the identity index of the RSU node, DATA BC It is the data block of the bottom chain. It is uploaded to the top chain for full network synchronization to achieve hierarchical storage and dissemination 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 best one from M candidate data providers according to the set evaluation mechanism. The selection criteria are:

[0016]

[0017] in Taking into account the provider V x Global reputation value Location distance d, data timeliness t, trajectory similarity The specific calculation formula will be introduced later.

[0018] S5. Request data sharing: After selecting the best data provider, the requester V y To data providers It sends an encrypted data sharing request Req, requesting data data_1:

[0019]

[0020] Req represents a request packet. Indicates the use Perform public key encryption, at the same time V y Send a transaction request Tx to the blockchain network id , start the smart contract to automate the data exchange:

[0021]

[0022] S6. Data transfer: Data providers After receiving the request Req, the transaction Tx is first confirmed through the blockchain id The validity of the request is then encrypted and packaged into a Package and sent to

[0023]

[0024] Transmitted to the data requester through a secure channel to ensure the security and integrity of the data transmission process.

[0025] S7. Rating update: After the data exchange is completed, the requester rates the provider based on the quality of the received data, and the satisfactory evaluation is recorded as Rate Vy→Vx=positive Vy→Vx = +1, unsatisfactory evaluation is recorded as Rate Vy→Vx =negative Vy→Vx =-1. The rating results are verified by the smart contract RVSC, including steps such as certificate legitimacy check, transaction ID duplication check, PID matching verification, etc. After the verification is passed, the provider's reputation value record is updated.

[0026] Furthermore, the reputation management system based on the double-layer blockchain in the Internet of Vehicles environment described in S1 includes three types of participant nodes: a trusted authentication authority TA (TrustAuthority), an RSU node, and a vehicle (Vehicle);

[0027] The blockchain system includes multiple bottom chains (BC) based on geographical location 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 in the area and the reputation value and certificate status information of the vehicles in the area; 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 area ID and reputation value status information;

[0028] The RSU node acts as a bridge between the bottom chain and the top chain, is responsible for forwarding and encapsulating data, and uploads the data of the bottom chain to the top chain according to the impact 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 revokes the public key and blacklists malicious vehicles by monitoring the reputation value of the vehicle to ensure the security and stability of the system;

[0030] The vehicle acts as a terminal node of the underlying chain, responsible for data collection, processing and sharing, and automatically completes data requests and credit rating updates through smart contracts;

[0031] Furthermore, the process of establishing the Internet of Vehicles data sharing system in step S1 is as follows:

[0032] During the system initialization phase, elliptic curve digital signature technology (ECDSA) and asymmetric encryption algorithms are used to ensure transaction security and vehicle identity verification. Each vehicle must be certified by a trusted certification authority (TA) before joining the network to become a legal vehicle. During the certification process, the TA will automatically generate a unique Assign an anonymous blockchain address, a pair of public and private keys and corresponding digital certificates Used to encrypt shared data. The vehicle then synchronizes blockchain data from the neighboring edge nodes 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] where σ data = {D, Urg, Url} is the label vector of data data, where D = {d 1 ,d 2 ,d 3 ,d 4 …} indicates the characteristic information of the data (for example: type, size, sampling frequency, collection time, sharing permissions, vehicle identity information, vehicle geographic location, sensor data, etc.) to identify and index the data. Urg is the urgency of the data, and Url is the location of the data storage. Different storage address types can be designed according to the data type, such as: 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. Only the URL and metadata of the data are saved on the chain. The metadata provides descriptive information of the data. Hash(data) and Hash(σ data ) indicates that the hash value obtained by hashing the original data and the data tag vector ensures that the data cannot be tampered with. vec Represents the vehicle’s signature on the data, ensuring the reliability of the data source, S data The scope of influence of the data;

[0036] Further, in step S4 The instructions are as follows:

[0037]

[0038] Said Indicates vehicle V y For vehicle V x The evaluation value of the data reliability. 1 ,w 2 ,w 3 ,w 4 is the weight coefficient and w 1 +w 2 +w 3 +w 4 =1, b is the weight coefficient of the control distance d, d represents the data providing vehicle Vx The distance 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] where t curr Indicates the current time, t mess It indicates the time when the message occurs, and λ is an attenuation coefficient, which determines the influence of the time difference on timeliness.

[0042] Said It refers to the similarity between vehicles. This scheme uses Fréchet distance for calculation. Assume that the trajectories of the two vehicles are standardized and represented by continuous curves A(t) and B(t), respectively, where t varies on the closed interval [0,1]. Then the similarity of the trajectories of the two vehicles can be expressed as:

[0043]

[0044] Where d is the metric function, this scheme represents the Euclidean distance between the positions of the two vehicles after time remapping, and α(t) and β(t) are two reparameterized functions.

[0045] For 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. The specific steps are as follows:

[0046] S41, Vehicle V y Calculate the vehicle V x The normalized direct reputation value is as shown in S411-S412:

[0047] 411. Vehicle V y Calculate the vehicle V x Positive reviews Negative evaluation

[0048]

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

[0050]

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

[0052] S412, Vehicle V y Calculate the vehicle V x The normalized direct reputation value

[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 Ask with V z Intermediate node V that has conducted direct transactions i To evaluate V z The specific steps are shown in S421-S423:

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

[0058]

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

[0060] S422: Based on the path credibility evaluation, vehicle V y Filter trusted paths, the filtering conditions are:

[0061] T_rec(path)>δ

[0062] Only paths whose credibility exceeds the threshold will be accepted;

[0063] S423. When there are multiple credible recommendation paths, it is necessary to reasonably aggregate these recommendation values. This solution adopts a weighted average method based on path credibility:

[0064]

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

[0066] Malicious nodes may conduct collusion attacks by constructing seemingly independent recommendation paths in an attempt 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, the 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, the path weights are adjusted:

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

[0071] Where f(Corr,σ) is a penalty function based on path relevance 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] in is the global reputation value of node i in the kth iteration, and each element of the vector ranges from [-1, 1];

[0075] The global reputation value iteration formula is:

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

[0077] Where C is the standardized trust matrix c ji , P is the pre-trust vector, α(T (k) ) is the dynamic weight function calculation formula:

[0078]

[0079] Where β is the scaling factor, θ is the reputation threshold, σ is the smoothing factor, and γ is the basic weight;

[0080] Global reputation value calculation convergence conditions:

[0081] ∥T (k+1) -T (k) ∥ 2 <∈

[0082] S44. Calculate vehicle transaction reputation value And select the best data provider Selection criteria The advantages and beneficial effects of the present invention are as follows:

[0083] 1. Through the layered blockchain architecture, data is stored and synchronized in layers according to the scope of influence, reducing redundant consensus and transmission, and significantly improving the system's consensus efficiency and data sharing efficiency.

[0084] 2. A dynamic reputation value model was designed, and a time decay factor and asymmetric penalty mechanism were introduced to more accurately reflect the real-time reputation status of nodes and effectively identify and suppress malicious behavior. Finally, the data request and rating process was automatically executed through smart contracts to ensure the security and reliability of data sharing. While ensuring efficient data sharing, the overall solution enhances the security, scalability and anti-attack capabilities of the system, and is suitable for large-scale Internet of Vehicles environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 Flowchart of Internet of Vehicles Data Sharing

[0086] Figure 2 System Throughput Analysis

[0087] Figure 3 Recommended reputation value

[0088] Figure 4 Two-layer blockchain architecture design

[0089] Figure 5 Comparison of consensus delays at different network scales DETAILED DESCRIPTION

[0090] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0092] S1. System initialization: Build a two-layer blockchain architecture, which includes two levels: the top chain and the bottom chain. The bottom chain consists of vehicle nodes and RSU nodes, which are responsible for local data sharing; the top chain consists of RSU nodes with higher credibility, which are responsible for data synchronization across the entire network. The system uses a trusted certification authority (TA) to authenticate the identity of the vehicle and issue digital certificates. It uses elliptic curve digital signature technology (ECDSA) and asymmetric encryption algorithms to ensure the security of transactions and verification of vehicle identity. Each vehicle must be certified by a trusted certification authority (TA) before joining the network to become a legal vehicle. During the authentication process, TA will automatically generate a unique Assign an anonymous blockchain address, a pair of public and private keys and corresponding digital certificates Used to encrypt shared data.

[0093] S2, data collection and upload: Data collectors collect data through vehicle-mounted equipment and generate data blocks DATA based on data characteristics BC 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={d 1 ,d 2 ,d 3 ,d 4 ...} indicates the characteristic information of the data, Urg indicates the urgency of the data, Url indicates the data address, PID Vx Vx represents the unique identity information of the vehicle, Sig vec Indicates the vehicle's signature on the data. The data collector uploads the encapsulated data block to the underlying chain for storage and sharing.

[0096] S3, data hierarchical processing: RSU node acts as a bridge between the two-layer chains and receives data DATA from the bottom chain BC And calculate its influence range S based on the characteristic information D of the data data When the data impact range exceeds the preset threshold (i.e. S data ≥θ), the RSU node repackages the data into Format, where S data Indicates that the data impact range is calculated based on D, SigRSU Represents the digital signature corresponding to RSU, φ data ={DATA BC , Gid}, Gid represents the identity index of the RSU node, DATA BC It is the data block of the bottom chain. It is uploaded to the top chain for full network synchronization to achieve hierarchical storage and dissemination of data.

[0097] 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 best one from M candidate data providers according to the set evaluation mechanism. The selection criteria are:

[0098]

[0099] in Taking into account the provider V x Global reputation value Location distance d, data timeliness t, trajectory similarity And other factors.

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

[0101] S411, Vehicle V y Calculate the vehicle V x Positive reviews Negative evaluation

[0102]

[0103] Positive evaluation of data requester Vy→Vx The value is +1, negative evaluation Vy→Vx is -1, r is the time attenuation factor, and the Gaussian function is used for calculation:

[0104]

[0105] Where t is the current time, t 0 is the transaction occurrence time, c is the decay rate parameter;

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

[0107]

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

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

[0110] S421, Vehicle V y Ask with V z Intermediate node V that has conducted direct transactions i To evaluate V z Credibility:

[0111]

[0112] in: Indicates V y V z Recommended reputation value, Represents the direct trust value between adjacent nodes on the path, n is the path length (number of nodes - 1)

[0113] S422: Based on the path credibility evaluation, vehicle V y Filter trusted paths, the filtering conditions are:

[0114] T_rec(path)>δ

[0115] Only paths whose credibility exceeds the threshold will be accepted;

[0116] S423. When there are multiple credible recommendation paths, it is necessary to reasonably aggregate these recommendation values. This solution adopts a weighted average method based on path credibility:

[0117]

[0118] Where P is the set of all paths that meet the credibility threshold, T_rec p is the recommended value provided for path p, Trust(p) is the trustworthiness of path p as the weight;

[0119] For any two recommended paths, the correlation coefficient is calculated as follows:

[0120]

[0121] Where V(p) represents the set of nodes contained in path p.

[0122] When suspicious patterns are detected, the path weights are adjusted:

[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] in is the global reputation value of node i in the kth iteration, and each element of the vector ranges from [-1, 1];

[0127] The global reputation value iteration formula is:

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

[0129] Where C is the standardized trust matrix c ji , P is the pre-trust vector, α(T (k) ) is the dynamic weight function calculation formula:

[0130] Among them, α(T (k) ) is the dynamic weight function calculation formula:

[0131]

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

[0133] Global reputation value calculation convergence conditions:

[0134] ∥T (k+1) -T (k) ∥ 2 <∈

[0135] S44. Calculate vehicle transaction reputation value And select the best data provider Selection criteria

[0136] S5. Request data sharing: After selecting the best data provider, the requester sends an encrypted data sharing request Req to it, requesting the provision of data data_1:

[0137]

[0138] Req represents a request packet. Indicates the use Perform public key encryption, at the same time V y Send a transaction request Tx to the blockchain network id , start the smart contract to automate the data exchange:

[0139]

[0140] S6. Data transfer: Data providers After receiving the request Req, the transaction Tx is first confirmed through the blockchain id The validity of the request is then encrypted and packaged into a Package and sent to

[0141]

[0142] Transmitted to the data requester through a secure channel to ensure 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 satisfactory evaluation is recorded as Rate Vy→Vx =positive Vy→Vx = +1, unsatisfactory evaluation is recorded as Rate Vy→Vx =negative Vy→Vx =-1. The rating results are verified by the smart contract RVSC, including steps such as certificate legitimacy check, transaction ID duplication check, PID matching verification, etc. After the verification is passed, the provider's reputation value record is updated.

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

Claims

1. A vehicle networking data sharing method based on a double-layer blockchain, characterized in that , including the following steps: S1. System initialization: Build a two-layer blockchain architecture, which includes two levels: the top chain and the bottom chain. The bottom chain consists of vehicle nodes and RSU nodes, responsible for local data sharing; The top chain is composed of RSU nodes with high reputation values, which are responsible for data synchronization across the entire network. The system uses a trusted certification authority (TA) to authenticate the vehicle identity and issue digital certificates. During the authentication process, the TA will automatically generate a unique PID for each vehicle. S2. Data collection and upload: Data collectors collect data through vehicle-mounted equipment and generate data blocks DATA based on data characteristics. BC The format of the data block is: DATA BC ={σ data ,Hash(s data ),PID Vx ,Sig vec } It contains information such as data tag vector, data hash value, impact range, and digital signature. The data collector uploads the encapsulated data block to the underlying chain for storage and sharing. S3. Data hierarchical processing: RSU nodes act as a bridge between the two-layer chains, receiving data from the bottom chain and calculating its impact range S data When the data impact range exceeds the preset threshold (i.e. S data ≥θ), the RSU node repackages the data as: And upload it to the top-level chain for full network synchronization to achieve hierarchical storage and dissemination of data. 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 best one from M candidate data providers according to the set evaluation mechanism. The selection criteria are: in Multiple factors are taken into consideration, including the provider’s reputation, location distance, data timeliness, etc. S5. Request data sharing: After selecting the best data provider, the requester sends a data sharing request to it: At the same time, send a transaction request to the blockchain network: Initiate smart contracts to automate data exchange. S6. Data transmission: After receiving the request, the data provider first confirms the transaction Tx through the blockchain id The validity of the request is then encrypted and packaged: 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 satisfactory evaluation is recorded as Rate Vy→Vx =positive Vy→Vx = +1, unsatisfactory evaluation is recorded as Rate Vy→Vx =negative Vy→Vx =-1. The rating results are verified by the smart contract RVSC, including steps such as certificate legitimacy check, transaction ID duplication check, PID matching verification, etc. After the verification is passed, the provider's reputation value record is updated.

2. The method according to claim 1, characterized in that The data block DATA in S2 BC The format is: DATA BC ={σ data ,Hash(s data ),PID Vx ,Sig dev } Among them, σ data It is a data tag vector, which contains data feature information, urgency, and storage location; The data block DATA TC The format is: in, Gid is the RSU node identity index.

3. The method according to claim 1, characterized in that ,In the data sharing process, the data requester initiates the request through the ,smart contract and selects the best data provider based on the ,reputation of candidate data providers: in is the best data provider, M is the set of data providers containing the required information, and Cred is the data reliability evaluation value: Said Indicates vehicle V y For vehicle V x The evaluation value of data reliability. w1, w2, w3, w4 are weight coefficients and w1+w2+w3+w4=1, b is the weight coefficient of the control distance d, and d represents the data providing vehicle V x The distance to the event location, t represents the timeliness of the message, It refers to the similarity between vehicles. This scheme uses Fréchet distance for calculation.

4. A method for calculating the reputation value of Internet of Vehicles based on time decay, characterized in that , including the following steps: S41, Vehicle V y Calculate the vehicle V x The normalized direct reputation value is as shown in S411-S412: S411, Vehicle V y Calculate the vehicle V x Positive reviews Negative evaluation S412, Vehicle V y Calculate the vehicle V x The normalized direct reputation value S42. In the absence of prior experience and direct reputation value, vehicle V y Ask with V z Intermediate node V that has conducted direct transactions i To evaluate V z The specific steps are shown in S421-S423: S421, Vehicle V y Ask with V z Intermediate node V that has conducted direct transactions i To evaluate V z Credibility S422: Based on the path credibility evaluation, vehicle V y Filter the trusted paths, the filtering conditions are: T_rec(path)>δ Only paths whose credibility exceeds the threshold δ will be accepted; S423. When there are multiple credible recommendation paths, it is necessary to reasonably aggregate these recommendation values. This solution adopts a weighted average method based on path credibility: Where P is the set of all paths that meet the credibility threshold, T_rec p is the recommended value provided for path p, Trust(p) is the credibility of path p as the weight, For vehicle V y V z Recommended reputation value; S43, the system iteratively calculates the global reputation value: T (k+1) =(1-α(T (k) ))CT (k) +α(T (k) )P Until the convergence condition is met: ∥T (k+1) -T (k) ∥2<∈ S44, calculating the vehicle transaction reputation value: And select the best data provider Selection criteria:

5. The method according to claim 4, characterized in that , the asymmetric weight satisfies: 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 vehicle V x Positive reviews Negative evaluation Positive evaluation of data requester Vy→Vx The value is +1, negative evaluation Vy→Vx is -1, r is the time attenuation factor, and the Gaussian function is used for calculation: Where t is the current time, t0 is the time when the transaction occurs, and c is the decay rate parameter.

6. The method according to claim 4, characterized in that , the path credibility is calculated by the following formula: When T_rec(path)>δ, the path is considered to be credible.

7. The method according to claim 4, characterized in that , the dynamic weight is calculated by the following function: Among them, α(T (k) ) is the dynamic weight function calculation formula; Define the global reputation value vector for the kth iteration: The global reputation value iteration formula is: T (k+1) =(1-α(T (k) ))CT (k) +α(T (k) )P 8. The method according to claim 4, characterized in that , the iteration process continues until the convergence condition is met: ∥T (k +1) -T (k) ∥2<∈where ∈ is the preset convergence threshold.

9. The method according to claim 4, characterized in that , the calculation of the recommendation reputation value includes correlation coefficient analysis: Used to identify potential collusion attacks.

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