A blockchain-based data sharing method and system in a vehicle internet of things under an edge cloud computing environment

Abstract

The application discloses a kind of edge cloud computing environment under Internet of Vehicles based on blockchain data sharing method and system, the method includes: collecting the user information and data demand of data sharing party and receiver;According to user information, the utility function of receiver is calculated, the utility function of sharing party and the overall utility function of proxy node;Set constraint, determine target equation, establish optimization equation under the condition of constraint;Solve the extreme value of optimization equation by Lagrange multiplier method;Design the iterative two-way auction algorithm based on game theory to transform the problem;According to the bid price of user, the direction and result of data sharing are determined.The application can improve the enthusiasm of user sharing data, ensure that user shares data safely and efficiently, protect the private information of user, and can realize the maximization of overall utility function.

Description

TECHNICAL FIELD

[0001] The application provides a blockchain-based data sharing method and system in an edge cloud computing environment of Internet of Vehicles, and belongs to the field of data sharing in Internet of Vehicles. BACKGROUND

[0002] With the introduction of smart cities and the increase in traffic pressure, Internet of Vehicles (IoV) that combines mobile cloud computing and vehicle ad hoc networks (VANETs) has attracted extensive attention from academia and industry, providing a large number of Internet of Vehicles services. As a major mode of transportation, autonomous vehicles can generate 1 GB of environmental data per second from cameras, radars, GPS, etc. Sharing this data with the surrounding environment can improve the driving experience and efficiency of users and ensure driving safety. In this context, Internet of Vehicles can integrate the computing and communication capabilities of vehicles together as a new paradigm for sharing and processing information between vehicles, pedestrians and infrastructure. Through Internet of Vehicles, vehicles can share information with other vehicles or infrastructure, effectively utilize all dynamic information in Internet of Vehicles, and obtain safe, efficient and entertaining services in driving. For example, vehicles can send warning information to other vehicles in advance to avoid traffic accidents and improve safety driving, improve traffic efficiency through platooning and speed guidance, and provide news, environment, social entertainment and other entertainment services for drivers or passengers to improve user experience and satisfaction, etc.

[0003] With the rapid increase in the size of vehicle data and the high-speed mobility of Internet of Vehicles and the insecurity of communication networks, data cannot be effectively transmitted to vehicles. In addition, some sensor data have their spatial range and limited service life, such as current traffic information, intersection traffic congestion information, etc. Realizing vehicle data transaction requires a lower waiting time, and traditional data transmission through remote cloud transmission cannot guarantee the transmission quality. Edge computing can greatly reduce data transmission costs and reduce the burden on data centers by processing data close to users at the edge rather than uploading all data to the cloud. However, edge computing still faces many risks in security. For example, edge devices are placed at the edge, which is physically closer to attackers, and the data to be transmitted generally contains very sensitive information for the creator, so car users may not be willing to store their content in untrusted cache storage space. Considering the extreme importance of security issues, blockchain technology is introduced into Internet of Vehicles data sharing as a powerful method that can solve the trust problem of Internet of Things and provide secure data storage. In addition, vehicles or infrastructure in Internet of Vehicles systems will face many challenges when sharing data.

[0004] Efficiency, the data shared by vehicles conveys road conditions to drivers, helps them drive safely, and improves their driving experience, and due to the high speed of vehicles, it is necessary to ensure the timeliness of data transmission. For example, if the road condition data transmission is delayed for 5 minutes, it will not have any meaning to the driver. Therefore, it is crucial to ensure the timeliness of data transmission, that is, the data of the Internet of Vehicles system must be transmitted within a short time.

[0005] Privacy, since the data generated by vehicles contains a large amount of private information of vehicle users, such as frequently visited places, work and rest time arrangements, and their hobbies, the location of their home and work, and the route they usually take can be easily inferred by attackers, and the data shared in the Internet of Vehicles system requires that the privacy of users cannot be leaked. Reliability, the quality of data collected by vehicles is uneven, and sharing low-quality data is meaningless in the Internet of Vehicles, because the services brought by low-quality data sharing are often unreliable, which will reduce the driving experience of users, and may also cause traffic accidents, so it is very important to ensure the reliability of data. Overall utility, in the traditional data sharing market, the selfishness of data agents pursuing their own utility maximization rather than overall utility maximization and the problem of easy leakage of user privacy hinder the development of data transactions. SUMMARY

[0006] The purpose of the application is to provide a blockchain-based data sharing method and system in the Internet of Vehicles in an edge cloud computing environment, which aims to improve the enthusiasm of users in data transactions. The proposed scheme can protect the private information of users, and the security of data sharing can be guaranteed by the smart contract and encryption technology in the blockchain. The scheme can also improve the enthusiasm of users in sharing data and maximize the overall utility function.

[0007] Technical scheme: In order to achieve the above application purpose, the application provides a blockchain-based data sharing method in the Internet of Vehicles in an edge cloud computing environment, which comprises the following steps:

[0008] S1, assuming that the data sharing scene involves P users, wherein the number of sharing parties participating in sharing is represented by P A , the number of receiving parties is represented by P B , P A +P B , each user has an index value, wherein the index value of the sharing party is represented by a, a∈{1,2,...,P A}; the index value of the receiving party is represented by b, b∈{1,2,...,P B};

[0009] S2. Calculate the quality of the data shared by the recipient from the sharer based on the data retention time and data volume. Determine the update of the sharer's data based on the data's age. Establish a data update equation. Based on the data update equation, determine the user's purchase intention W. Calculate the receiver b's utility function U based on the data quality and data transmission quality of each sharer and the recipient's data purchase cost. b ;

[0010] S3. Calculate the privacy loss of the data based on the amount of available data collected by the system and the collection cost, and further calculate the total cost of the sharing party sharing the data with the receiving party to obtain the sharing party's utility function U a ;

[0011] S4. Calculate the overall utility function of the proxy node based on the utility functions of both data sharing parties. Establish an optimization equation Objective 1 based on the constraints that the data sharing system should satisfy on both data sharing parties. The optimization goal is to maximize the overall utility function while satisfying the demand constraints.

[0012] S5. Use Taylor expansion to transform the optimization equation Objective 1 into the overall utility function maximization problem Objective 2. Use the Lagrange multiplier method to solve the data sharing rules of the data sharing parties when the overall utility function is maximized.

[0013] S6. Based on the data sharing rules of both parties, design a utility maximization problem for the sharing party and the receiving party respectively, so that each participant shares data in a way that maximizes the overall utility function.

[0014] Furthermore, the quality of the data purchased by the receiver b from the sharing party a in step S2 is expressed as:

[0015]

[0016] Among them, τ1 and τ2 are the data quality parameters of the purchased data, f(t) represents the instantaneous value of the data when the retention time is t, is the total value obtained during the t time of holding the data, N b,a The amount of data that the receiver b needs to provide from the sharing party a;

[0017] When the data retention time t meets certain conditions, it means that the data is outdated and should be updated. The sharing party needs to collect data again and update the available data volume and data collection cost. The data update equation of the sharing party is as follows:

[0018]

[0019] Among them, C is the cost of updating given data, and W is the user's purchase intention. W = 1 when W = 0 when

[0020] The transmission quality of the sharer a is denoted by q a , which is related to the transmission cost of the sharer a:

[0021] q a = Wlog2(1 + ηk a )

[0022] where k a represents the transmission cost of the sharer a, and η is a predefined parameter.

[0023] The utility function of the receiver is defined as:

[0024]

[0025] where PD b,a is the cost of the receiver receiving data.

[0026] Further, the specific method of the step S3 is as follows:

[0027] Define the available data amount provided by the sharer a to the receiver b as N a,b , and l1 and l2 are two cost factors, and the data collection cost is c(N a,b ), which is denoted as:

[0028]

[0029] The loss of data privacy in the transaction process is defined as:

[0030] PL = pc a · N a,b

[0031] where pc a represents the privacy protection coefficient of the sharer a, and the total cost h(N a,b ) of the sharer a sharing data to the receiver b is:

[0032] h(N a,b ) = c(N a,b ) + PL

[0033] The utility function of the sharer is:

[0034]

[0035] where PS a,b is the income obtained by the sharer selling data.

[0036] Further, in step S4, the benefit w of the agent node and the overall utility function SW of the agent node are calculated as follows:

[0037]

[0038] where PD b,a is the cost of the receiver receiving data, PS a,b is the income obtained by the sharer sharing data, p is the data quality purchased by the receiver b from the sharer a, q a is the transmission quality of the sharer a;

[0039] The overall utility maximization problem Objective1 is in the form of:

[0040]

[0041]

[0042] where S is the demand matrix of all receivers, B is the sharing matrix of all sharers, BD b is the data demand of the receiver bd, SD a is the data sharing amount of the sharer a, c1-c4 are constraints that the optimization problem should satisfy, c1 constraint indicates that the data demand range of each receiver b is between the minimum value and the maximum value ; c2 constraint means that the sharing amount of the sharer is less than the maximum data amount it has; c3 constraint means that when the market reaches equilibrium, the demand and supply of the data sharing parties are equal; and c4 constraint means that the sharing amount of the sharer is not negative.

[0043] Further, in step S5, Objective1 is converted into the overall utility function maximization problem Objective2:

[0044]

[0045]

[0046] where x ba is the bid of the receiver, y ab is the bid of the sharer. Objective1 and Objective2 are different, but they have the same constraint conditions. When solving the overall utility function maximization by using the Lagrange multiplier method, the data sharing rules of the data sharing parties are obtained. When the data sharing rules satisfy the following conditions, solving Objective2 is equivalent to solving Objective1:

[0047]

[0048] Furthermore, according to the data sharing rules of both parties, a utility maximization problem BMP is designed for the receiver as follows:

[0049]

[0050] Among them, x b is the bid vector submitted by receiver b, U b is the utility function of the receiver b, P b (x b ) is the service fee paid by the recipient to the agent;

[0051] The utility maximization problem SMP of the sharing party is as follows:

[0052]

[0053] Among them, y a is the bid vector submitted by sharing party a, U a is the utility function of sharing party a, R a (y a ) is the remuneration paid by the agent to the sharing party.

[0054] In addition, the present invention proposes a data sharing system based on blockchain in the Internet of Vehicles under an edge cloud computing environment, which includes the following modules:

[0055] Scenario sharing building block, assuming that the data sharing scenario involves P users, where the number of sharing parties is P A Indicates the number of receivers as P B It means that P=P A +P B , each user has an index value, where the shared party index value is represented by a, a∈{1,2,...,P A}; The receiver index value is represented by b, b∈{1,2,...,P B};

[0056] The data update module calculates the quality of the data shared by the receiver from the sharing party based on the data retention time and data volume, determines the update of the sharing party's data based on the obsoleteness of the data, establishes a data update equation, and determines the user's purchase intention W based on the data update equation. The utility function U of the receiver b is calculated based on the data quality and data transmission quality of each sharing party and the purchase data cost of the receiver. b ;

[0057] The utility function building module calculates the privacy loss of data based on the amount of available data collected by the system and the collection cost, and further calculates the total cost of the sharing party sharing the data with the receiving party to obtain the sharing party's utility function U a;

[0058] The maximization objective module calculates the overall utility function of the proxy node based on the utility functions of both data sharing parties, and establishes the optimization equation Objective 1 based on the constraints that the data sharing system should meet on both data sharing parties. The optimization goal is to maximize the overall utility function while meeting the demand constraints.

[0059] The solution module uses Taylor expansion to transform the optimization equation Objective1 into the overall utility function maximization problem Objective2, and uses the Lagrange multiplier method to solve the data sharing rules of the data sharing parties when the overall utility function is maximized;

[0060] The data sharing module designs a utility maximization problem for the sharing party and the receiving party respectively according to the data sharing rules of the two parties, so that each participant shares data in a way that maximizes the overall utility function.

[0061] Beneficial effects: Compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

[0062] To address the issue of Internet of Vehicles (IoV) data sharing in edge cloud computing environments, we propose an iterative double-auction data sharing algorithm to ensure the security and trustworthiness of both parties involved, protect their privacy during the sharing process, and maximize system utility. This blockchain-based data sharing algorithm can reduce data transmission costs. The integrated blockchain and edge cloud computing solution can overcome each other's limitations, eliminating the scalability barriers often encountered by traditional blockchain frameworks, as well as the decentralized management, security, coordination, and computational issues often encountered by cloud-edge computing systems. Therefore, blockchain-enabled cloud-edge solutions can provide reliable access and control, as well as efficient storage and computation, in large-scale networks, maximizing the utility function while ensuring security. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a structural diagram of a data sharing system model based on blockchain according to the present invention. DETAILED DESCRIPTION

[0064] Reference Figure 1 The present invention proposes a data sharing method based on blockchain in the Internet of Vehicles under an edge cloud computing environment, which includes the following steps:

[0065] S1. Assume that the data sharing scenario involves P users, where the number of participants is P. A Indicates the number of receivers as P B It means that P=P A +P Beach user has an index value, where the index value of the sharing party is denoted by a, a∈{1,2,...,P A};the index value of the receiving party is denoted by b, b∈{1,2,...,P B};

[0066] S2, the data quality of the receiving party shared from the sharing party is calculated according to the retention time of the data and the data quantity, the update of the data of the sharing party is determined based on the obsolescence of the data, a data update equation is established, the purchase willingness W of the user is determined according to the data update equation, and the utility function U of the receiving party b is calculated according to the data quality of each sharing party, the data transmission quality and the purchase data cost of the receiving party b ;

[0067] S3, the loss of privacy of the data is calculated according to the available data quantity and the collection cost collected by the system, and the total cost of the sharing party sharing the data to the receiving party is further calculated, so as to obtain the utility function U of the sharing party a ;

[0068] S4, the overall utility function of the proxy node is calculated according to the utility functions of the data sharing parties, an optimization equation Objective1 is established based on the constraint conditions that the data sharing system should meet, and the optimization target is to maximize the overall utility function under the condition of meeting the demand constraint conditions;

[0069] S5, the optimization equation Objective1 is converted into an overall utility function maximization problem Objective2 by using Taylor expansion, and the data sharing rules of the data sharing parties when maximizing the overall utility function are solved by using the Lagrange multiplier method;

[0070] S6, according to the data sharing rules of the data sharing parties, an utility maximization problem is designed for the sharing party and the receiving party respectively, so that each participant carries out data sharing in the manner of maximizing the overall utility function.

[0071] Further, the data quality of the receiving party b purchased from the sharing party a in the step S2 is represented as:

[0072]

[0073] Wherein, τ1 and τ2 are data quality parameters of the purchased data, f(t) represents the instantaneous value of the data when the retention time is t, is the total value obtained by holding the data for t time, N b,a is the data quantity required by the receiving party b to be provided by the sharing party a;

[0074] When the data retention time t meets certain conditions, it means that the data is outdated and should be updated. The sharing party needs to collect data again and update the available data amount and data collection cost. The sharing party data update equation is as follows:

[0075]

[0076] Where C is the cost of updating the given data, W represents the user's purchase intention, when W = 1; W = 0;

[0077] The transmission quality of the sharing party a is represented by q a , which is related to the transmission cost of the sharing party a:

[0078] q a = Wlog2(1+ηk a )

[0079] Where k a represents the transmission cost of the sharing party a, and η is a predefined parameter.

[0080] The utility function of the receiving party is defined as:

[0081]

[0082] Where PD b,a is the cost of the receiving party receiving data.

[0083] Further, the specific method of the step S3 is as follows:

[0084] Define the available data amount provided by the sharing party a to the receiving party b as N a,b , and l1 and l2 are two cost factors, and the data collection cost is c(N a,b ), which is represented as:

[0085]

[0086] The loss of data privacy in the transaction process is defined as:

[0087] PL = pc a ·N a,b

[0088] Where pc a represents the privacy protection coefficient of the sharing party a, and the total cost h(N a,b ) of the sharing party a sharing data to the receiving party b is:

[0089] h(N a,b ) = c(N a,b ) + PL

[0090] The utility function of the sharing side is:

[0091]

[0092] wherein, PS a,b is the income obtained by the sharing side from selling data.

[0093] Further, in step S4, the benefit w of the agent node and the overall utility function SW of the agent node are calculated in the following manner:

[0094]

[0095] wherein, PD b,a is the cost of receiving data by the receiving side, PS a,b is the income obtained by the sharing side from sharing data, p is the data quality purchased by the receiving side b from the sharing side a, q a is the transmission quality of the sharing side a;

[0096] The overall utility maximization problem Objective1 is in the form of:

[0097]

[0098]

[0099] wherein, S is the demand matrix of all receiving sides, B is the sharing matrix of all sharing sides, BD b is the data demand of the receiving side bd, SD a is the data sharing amount of the sharing side a, c1-c4 are constraints that should be satisfied by the optimization problem, the c1 constraint indicates that the data demand range of each receiving side b is between the minimum value and the maximum value ; the c2 constraint indicates that the sharing amount of the sharing side is less than the maximum data amount owned by itself the c3 constraint indicates that when the market reaches equilibrium, the demand and supply of the data sharing sides are equal; and the c4 constraint indicates that the sharing amount of the sharing side is not negative.

[0100] Further, in step S5, Objective1 is converted into the overall utility function maximization problem Objective2:

[0101]

[0102]

[0103] wherein, x ba is the bid of the receiving side, y abObjective1 and Objective2 are different, but they have the same constraint condition, and the data sharing rules of both parties are solved by using the Lagrange multiplier method to maximize the overall utility function, when the data sharing rules meet the following conditions, solving Objective2 is equivalent to solving Objective1:

[0104]

[0105] Further, according to the data sharing rules of both parties, a utility maximization problem BMP for the receiver is designed as follows:

[0106]

[0107] Where, x b is the bid vector submitted by the receiver b, U b is the utility function of the receiver b, P b (x b ) is the service cost paid by the receiver to the agent;

[0108] The utility maximization problem SMP of the sharing party is as follows:

[0109]

[0110] Where, y a is the bid vector submitted by the sharing party a, U a is the utility function of the sharing party a, R a (y a ) is the remuneration given by the agent to the sharing party.

[0111] In addition, the application proposes a blockchain-based data sharing system in a vehicular Internet of Things in an edge cloud computing environment, which comprises the following modules:

[0112] A scene sharing construction module is assumed that the data sharing scene involves P users, wherein the number of sharing parties participating in sharing is represented by P A , the number of receivers is represented by P B , P=P A +P B , each user has an index value, wherein the index value of the sharing party is represented by a, a∈{1,2,...,P A}; the index value of the receiver is represented by b, b∈{1,2,...,P B};

[0113] The data updating module calculates the data quality shared by the receiving party from the sharing party according to the retention time and the data amount of the data, determines the update of the data of the sharing party based on the obsolescence of the data, establishes a data updating equation, and determines the purchase intention W of the user according to the data updating equation b ;

[0114] The utility function construction module calculates the loss of privacy of the data according to the available data amount and the collection cost collected by the system, further calculates the total cost of the sharing party sharing the data to the receiving party, and obtains the utility function U of the sharing party a ;

[0115] The maximization target module calculates the overall utility function of the agent node according to the utility functions of the data sharing parties, and establishes an optimization equation Objective1 based on the constraint conditions that should be met by the data sharing system between the data sharing parties, and the optimization target is to maximize the overall utility function while meeting the demand constraint conditions;

[0116] The solving module converts the optimization equation Objective1 into an overall utility function maximization problem Objective2 by using Taylor expansion, and solves the data sharing rules of the data sharing parties when maximizing the overall utility function by using the Lagrange multiplier method;

[0117] The data sharing module designs an utility maximization problem for the sharing party and the receiving party according to the data sharing rules of the data sharing parties, so that each participant performs data sharing in a manner of maximizing the overall utility function.

[0118] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit it, although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the present application can be modified or replaced by the same, without departing from the spirit and scope of the present application. Any modification or equivalent replacement, which should be covered within the protection scope of the claims of the present application.

Claims

1.A method for sharing data based on a blockchain in a vehicle Internet of Things (V-IoT) in an edge cloud computing environment, the method comprising: The method comprises the following steps: S1. Assume that the data sharing scenario involves P users, where the number of participants is P. A Indicates the number of receivers as P B It means that P=P A +P B , each user has an index value, where the shared party index value is represented by a, a∈{1,2,...,P A }; The receiver index value is represented by b, b∈{1,2,...,P B }; S2, calculating the data quality shared by the receiver from the sharer according to the retention time and the data amount of the data, deciding the update of the sharer data based on the obsolescence degree of the data, establishing a data update equation, deciding the purchase intention W of the user according to the data update equation, and calculating the utility function U of the receiver b according to the data quality and the data transmission quality of each sharer and the purchase data cost of the receiver b ; S3, calculating the privacy loss of data according to the amount of available data collected by the system and the cost of collection, calculating the total cost of the sharing party sharing data to the receiving party, obtaining the utility function U of the sharing party a ; S4, calculating the overall utility function of the agent node according to the utility functions of the two parties of data sharing, establishing an optimization equation Objective1 based on the constraint conditions that the data sharing system should meet for the two parties of data sharing, and the optimization target is to maximize the overall utility function under the condition of meeting the demand constraint conditions; S5, converting the optimization equation Objective1 into an overall utility function maximization problem Objective2 by using Taylor expansion, and solving the data sharing rules of the two parties of data sharing when maximizing the overall utility function by using the Lagrange multiplier method; S6, designing an utility maximization problem for the sharing party and the receiving party respectively according to the data sharing rules of the two parties of data sharing, so that each participant performs data sharing in a manner of maximizing the overall utility function. 2.The data sharing method based on blockchain in Internet of Vehicles in edge cloud computing environment according to claim 1, characterized in that, The data quality purchased by the receiving party b from the sharing party a in the step S2 is expressed as: where τ1 and τ2 are data quality parameters for the purchase data, f(t) represents the instantaneous value of the data at a retention time of t, is the total value obtained for holding the data for t time, b,a is the amount of data that the recipient b needs the provider a to share; When the data retention time t meets the preset condition, the data is outdated and should be updated, the sharing party re-collects data and updates the available data amount and the data collection cost, and the sharing party data updating equation is as follows: wherein C is the cost of updating the given data, W represents the user's willingness to buy, and when W = 1; W = 0. The transmission quality of the sharing party a is denoted by q a which is related to the transmission cost of the sharing party a: q a = W log2(1 + ηk a ) where k a denotes the transmission cost of the sharing party a, and η is a predefined parameter. The utility function of the receiving party is defined as: where PD b,a is the cost of receiving data for the receiver. 3.The data sharing method based on blockchain in Internet of Vehicles in edge cloud computing environment according to claim 2, characterized in that, The specific method of the step S3 is as follows: Let the amount of data available to the provider a to provide to the receiver b be N a,b , and l1 and l2 are two cost factors with data collection cost c(N a,b ), denoted as: The loss of data privacy in the transaction process is defined as: PL = pc a • N a,b where pc a represents the privacy protection coefficient of the sharing party a, and the total cost h(N a,b ) of the sharing party a sharing data to the receiving party b is: h(N a,b ) = c(N a,b )+ PL The utility function of the sharing party is: where PS is the revenue obtained for selling the data to the sharing party. a,b the revenue obtained for selling the data to the sharing party. 4.The method of claim 3, wherein, In the step S4, the income w of the agent node and the overall utility function SW of the agent node are calculated in the following manner: wherein P Db,a is the cost of receiving data for the receiver, P a,b is the income obtained by the sharer for sharing data, p is the quality of data purchased by the receiver b from the sharer a, q a is the transmission quality of the sharer a; The overall utility maximization problem Objective1 is in the form of: Objective 1: c1: c2: c3: N b,a = N a,b , a e {1, 2,..., P A}, b e {1, 2,..., P B} c4: N a,b ≥ 0, a e {1, 2,..., P A}, b e {1, 2,..., P B} Among them, S is the demand matrix of all receivers, B is the sharing matrix of all sharers, BD b The data requirements of the receiver bd, SD a is the data sharing amount of sharing party a, c1~c4 are the constraints that the optimization problem should satisfy, and the c1 constraint indicates that the data demand range of each receiving party b is between the minimum value and maximum value c2 constraint means that the amount of data shared by the sharing party is less than the maximum amount of data it owns. The c3 constraint means that when the market reaches equilibrium, the demand and supply of both parties in data sharing are equal; the c4 constraint means that the sharing amount of the sharing party is not negative. 5.The method of claim 4, wherein, In the step S5, Objective1 is converted into the overall utility function maximization problem Objective2: Objective2: c1: c2: c3: N b,a = N a,b , a e {1, 2,..., P A}, b e {1, 2,..., P B} c4: N a，b ≥ 0, a e {1, 2,..., P A}, b e {1, 2,..., P B} where x ba is the bid of the receiver, y ab is the bid of the sharer, Objective1 and Objective2 are different, they have the same constraint condition, and the data sharing rules of both parties are solved by using the Lagrange multiplier method to maximize the overall utility function. When the data sharing rules meet the following conditions, solving Objective2 is equivalent to solving Objective1: 6.The method of claim 5, wherein, According to the data sharing rules of the two parties of data sharing, an utility maximization problem BMP for the receiving party is designed as follows: where x b is the bid vector submitted by the receiver b, U b is the utility function of the receiver b, P b (x b ) is the service fee paid by the receiver to the agent. The utility maximization problem SMP of the sharing party is as follows: where y a is the bid vector submitted by the sharing party a, U a is the utility function of the sharing party a, R a (y a ) is the reward given by the agent to the sharing party. 7.A data sharing system based on a blockchain in a vehicle Internet of Things (V-IoT) in an edge cloud computing environment, characterized by, The system comprises the following modules: The scene sharing construction module assumes that the data sharing scene involves P users, where the number of sharing parties participating in sharing is denoted by P A The number of receiving parties is denoted by P B The number of receiving parties is denoted by P A +P B Each user has an index value, where the index value of a sharing party is denoted by a, and a∈{1,2,...,P A The index value of a receiving party is denoted by b, and b∈{1,2,...,P B} The data updating module calculates the data quality shared by the receiving party from the sharing party according to the retention time and the data amount of the data, decides the update of the data of the sharing party based on the obsolescence degree of the data, establishes a data updating equation, and decides the purchase intention W of the user according to the data updating equation, and calculates the utility function U of the receiving party b according to the data quality of each sharing party, the data transmission quality and the purchase data cost of the receiving party b ; The utility function construction module calculates the privacy loss of the data according to the amount of available data collected by the system and the collection cost, further calculates the total cost of the sharing party sharing the data to the receiving party, and obtains the utility function U of the sharing party a ; The maximization target module calculates the overall utility function of the agent node according to the utility functions of the two parties of data sharing, establishes an optimization equation Objective1 based on the constraint conditions that the data sharing system should meet for the two parties of data sharing, and the optimization target is to maximize the overall utility function under the condition of meeting the demand constraint conditions; The solving module converts the optimization equation Objective1 into an overall utility function maximization problem Objective2 by using Taylor expansion, and solves the data sharing rules of the two parties of data sharing when maximizing the overall utility function by using the Lagrange multiplier method; The data sharing module designs an utility maximization problem for the sharing party and the receiving party respectively according to the data sharing rules of the two parties of data sharing, so that each participant performs data sharing in a manner of maximizing the overall utility function.

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