Block chain-based data sharing method and system in Internet of Vehicles in edge cloud computing environment

Abstract

The invention discloses a block chain-based data sharing method and system in Internet of Vehicles in an edge cloud computing environment. The method comprises the steps of collecting user information and data requirements of a data sharing party and a data receiving party; calculating a receiver utility function, a sharer utility function and an agent node overall utility function according to the user information; setting constraints, determining a target equation, and establishing an optimization equation under constraint conditions; solving an extreme value of the optimization equation by using a Lagrange multiplier method; designing an iterative two-way auction algorithm based on the game theory to carry out problem conversion; and the direction and the result of data sharing are determined according to the bidding price of the user. The data sharing enthusiasm of the user can be improved, it is guaranteed that the user safely and efficiently shares the data, private information of the user is protected, and the overall utility function can be maximized.

Description

Technical Field

[0001] The present invention proposes a data sharing method and system based on blockchain in an Internet of Vehicles under an edge cloud computing environment, which belongs to the field of data sharing in the Internet of Vehicles. Background Art

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

[0003] With the surge in the scale of vehicle data, the high-speed mobility of connected vehicles, and the insecurity of communication networks, data cannot be effectively transmitted to vehicles. In addition, some sensor data has its spatial range and limited lifespan, such as current traffic information, intersection traffic congestion information, etc., and the realization of vehicle data transactions requires a low waiting time. Traditional data is transmitted through the remote cloud, and the transmission quality is difficult to guarantee. Edge computing can greatly reduce the cost of data transmission and reduce the burden on data centers by processing data at the edge close to the user instead of uploading all data to the cloud, but edge computing still faces many risks in terms of security. For example, edge devices are placed on the edge side, which is physically closer to attackers, and the data that needs to be transmitted generally contains very sensitive information of the creator, so car users may be reluctant to store their content in untrusted cache storage space. Considering the extreme importance of security issues, blockchain technology is introduced into the sharing of connected vehicle data as a powerful method that can solve the trust problem of the Internet of Things and provide secure data storage. In addition, vehicles or infrastructure in the connected vehicle system 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. Since vehicles travel at high speeds, the timeliness of data transmission needs to be guaranteed. For example, if the road condition data transmission is delayed by 5 minutes, it will be meaningless to the driver. Therefore, ensuring the timeliness of data transmission is the key, that is, the data of the Internet of Vehicles system must be transmitted in a short time.

[0005] Privacy: The data generated by vehicles contains a large amount of private information about vehicle users, such as places they frequently visit, work and rest schedules, their hobbies, the location of their homes and workplaces, and the routes they usually take can be easily inferred by attackers. The data shared in the Internet of Vehicles system must not leak the privacy of users. Reliability: The quality of data collected by vehicles varies. 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 user's driving experience and may also cause traffic accidents. Therefore, it is very important to ensure the reliability of data. Overall utility: In the traditional data sharing market, the selfishness of data agents who only pursue the maximization of their own utility rather than the maximization of overall utility and the problem of easy leakage of user privacy have hindered the development of data transactions. Summary of the invention

[0006] Purpose of the invention: The present invention proposes a data sharing method and system based on blockchain in the Internet of Vehicles under edge cloud computing environment, aiming to improve the user's enthusiasm for trading data. The proposed scheme can ensure that the user's private information is protected, and the security of data sharing can be guaranteed by smart contracts and encryption technology in the blockchain. This scheme can also improve the user's enthusiasm for sharing data and maximize the overall utility function.

[0007] Technical solution: In order to achieve the above invention objectives, the present invention proposes a data sharing method based on blockchain in the Internet of Vehicles under the edge cloud computing environment, which method includes the following steps:

[0008] S1. Assume that the data sharing scenario involves P users, where the number of participants is P. A The number of receivers is represented by P B It means that P=P A +P B , each user has an index value, where the sharing 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};

[0009] S2. Calculate the quality of the data shared by the receiver from the sharing party based on the retention time and data volume of the data, determine the update of the sharing party's data based on the obsolescence of the data, establish a data update equation, determine the user's purchase intention W based on the data update equation, and calculate the receiver b's utility function U based on the data quality and data transmission quality of each sharing party and the receiver's purchase data 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 utility function U of the sharing party. a ;

[0011] S4. Calculate the overall utility function of the proxy node based on the utility functions of both parties of data sharing, and establish the optimization equation Objective 1 based on the constraints that the data sharing system should meet on both parties of data sharing. The optimization goal is to maximize the overall utility function under the conditions of meeting the demand constraints.

[0012] S5. Use Taylor expansion to transform the optimization equation Objective 1 into the overall utility function maximization problem Objective 2, and 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. According to 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 can share data in a way that maximizes the overall utility function.

[0014] Furthermore, the quality of 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 the data again and update the available data volume and data collection cost. The sharing party data update equation is as follows:

[0018]

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

[0020] The transmission quality of sharing party a is q a It is related to the transmission cost of sharing party a:

[0021] q a =Wlog2(1+ηk a )

[0022] Among them, k a represents the transmission cost of sharing party a, and η is a predefined parameter.

[0023] The receiver's utility function is defined as:

[0024]

[0025] Among them, PD b,a The cost of receiving data for the receiver.

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

[0027] Define the amount of available data provided by sharing party a to receiving party b as N a,b , l1 and l2 are two cost factors, and their data collection cost is c(N a,b ), expressed as:

[0028]

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

[0030] PL = pc a ·N a,b

[0031] Among them, pc a represents the privacy protection coefficient of the sharing party a, and the total cost h(N a,b )for:

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

[0033] The utility function of the sharing party is:

[0034]

[0035] Among them, PS a,b The income obtained from selling data to the sharing party.

[0036] Furthermore, in step S4, the revenue w of the proxy node and the overall utility function SW of the proxy node are calculated as follows:

[0037]

[0038] Among them, PD b,a is the cost of receiving data at the receiver, PS a,b is the income obtained by the sharing party from sharing data, ρ is the data quality purchased by the receiving party b from the sharing party a, and q a is the transmission quality of sharing party a;

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

[0040]

[0041]

[0042] Among them, S is the demand matrix of all receivers, B is the sharing matrix of all sharers, BD b The data requirement 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.

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

[0044]

[0045]

[0046] Among them, x ba is the bid of the recipient, y ab is the bid of the sharing party. Objective1 and Objective2 are different, but they have the same constraints. The Lagrange multiplier method is used to solve the data sharing rules of the two data sharing parties when the overall utility function is maximized. When the data sharing rules meet 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 an Internet of Vehicles under an edge cloud computing environment, the system comprising the following modules:

[0055] Scenario sharing building module, assuming that the data sharing scenario involves P users, where the number of sharing parties involved is P A The number of receivers is represented by P B It means that P=P A +P B , each user has an index value, where the sharing 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 sharer based on the retention time and data volume of the data, determines the update of the sharer's data based on the obsolescence 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 sharer and the data purchase cost of the receiver. b ;

[0057] The utility function building module calculates the privacy loss of data according to 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 utility function U of the sharing party. a;

[0058] The maximization objective module calculates the overall utility function of the proxy node based on the utility functions of both parties in the data sharing process, and establishes the optimization equation Objective1 based on the constraints that the data sharing system should meet on both parties in the data sharing process. 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 two 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 can share 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] For the problem of Internet of Vehicles data sharing in edge cloud computing environment, in order to ensure the security and credibility of both parties, protect the privacy of both parties in the sharing process and maximize the system utility, a data sharing algorithm based on iterative double auction is proposed. The proposed blockchain-based data sharing algorithm can reduce the data transmission cost. The integrated blockchain and edge cloud computing solutions can make up for each other's limitations, eliminate the scalability barriers often encountered by traditional blockchain frameworks, and the decentralized management, security, coordination and computing-related problems often encountered by cloud-edge computing systems. Therefore, the cloud-edge solution supporting blockchain can provide reliable access and control, as well as efficient storage and computing in large-scale networks, and maximize the utility function while meeting security requirements. 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 an Internet of Vehicles under an edge cloud computing environment, the method comprising the following steps:

[0065] S1. Assume that the data sharing scenario involves P users, where the number of participants is P. A The number of receivers is represented by P B It means that P=P A +P B, each user has an index value, where the sharing 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};

[0066] S2. Calculate the quality of the data shared by the receiver from the sharing party based on the retention time and data volume of the data, determine the update of the sharing party's data based on the obsolescence of the data, establish a data update equation, determine the user's purchase intention W based on the data update equation, and calculate the receiver b's utility function U based on the data quality and data transmission quality of each sharing party and the receiver's purchase data cost b ;

[0067] 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 utility function U of the sharing party. a ;

[0068] S4. Calculate the overall utility function of the proxy node based on the utility functions of both parties of data sharing, and establish the optimization equation Objective 1 based on the constraints that the data sharing system should meet on both parties of data sharing. The optimization goal is to maximize the overall utility function under the conditions of meeting the demand constraints.

[0069] S5. Use Taylor expansion to transform the optimization equation Objective 1 into the overall utility function maximization problem Objective 2, and use the Lagrange multiplier method to solve the data sharing rules of the data sharing parties when the overall utility function is maximized;

[0070] S6. According to 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 can share data in a way that maximizes the overall utility function.

[0071] Furthermore, the quality of data purchased by the receiver b from the sharing party a in step S2 is expressed as:

[0072]

[0073] 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;

[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 the data again and update the available data volume and data collection cost. The sharing party data update equation is as follows:

[0075]

[0076] Among them, C is the cost of updating given data, and W represents the user's purchase intention. When W=1; When W=0;

[0077] The transmission quality of sharing party a is q a It is related to the transmission cost of sharing party a:

[0078] q a =Wlog2(1+ηk a )

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

[0080] The receiver's utility function is defined as:

[0081]

[0082] Among them, PD b,a The cost of receiving data for the receiver.

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

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

[0085]

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

[0087] PL = pc a ·N a,b

[0088] Among them, pc a represents the privacy protection coefficient of the sharing party a, and the total cost h(N a,b )for:

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

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

[0091]

[0092] Among them, PS a,b The income obtained from selling data to the sharing party.

[0093] Furthermore, in step S4, the revenue w of the proxy node and the overall utility function SW of the proxy node are calculated as follows:

[0094]

[0095] Among them, PD b,a is the cost of receiving data at the receiver, PS a,b is the income obtained by the sharing party from sharing data, ρ is the data quality purchased by the receiving party b from the sharing party a, and q a is the transmission quality of sharing party a;

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

[0097]

[0098]

[0099] Among them, S is the demand matrix of all receivers, B is the sharing matrix of all sharers, BD b The data requirement 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.

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

[0101]

[0102]

[0103] Among them, x ba is the bid of the recipient, y abis the bid of the sharing party. Objective1 and Objective2 are different, but they have the same constraints. The Lagrange multiplier method is used to solve the data sharing rules of the two data sharing parties when the overall utility function is maximized. When the data sharing rules meet the following conditions, solving Objective2 is equivalent to solving Objective1:

[0104]

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

[0106]

[0107] 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;

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

[0109]

[0110] 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.

[0111] In addition, the present invention proposes a data sharing system based on blockchain in an Internet of Vehicles under an edge cloud computing environment, the system comprising the following modules:

[0112] Scenario sharing building module, assuming that the data sharing scenario involves P users, where the number of sharing parties involved is P A The number of receivers is represented by P B It means that P=P A +P B , each user has an index value, where the sharing 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};

[0113] The data update module calculates the quality of the data shared by the receiver from the sharer based on the retention time and data volume of the data, determines the update of the sharer's data based on the obsolescence 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 sharer and the data purchase cost of the receiver. b ;

[0114] The utility function building module calculates the privacy loss of data according to 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 utility function U of the sharing party. a ;

[0115] The maximization objective module calculates the overall utility function of the proxy node based on the utility functions of both parties in the data sharing process, and establishes the optimization equation Objective1 based on the constraints that the data sharing system should meet on both parties in the data sharing process. The optimization goal is to maximize the overall utility function while meeting the demand constraints.

[0116] 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 two data sharing parties when the overall utility function is maximized;

[0117] 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 can share data in a way that maximizes 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 invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A data sharing method based on blockchain in the Internet of Vehicles under edge cloud computing environment, characterized in that: The method comprises the following steps: S1. Assume that the data sharing scenario involves P users, where the number of participants is P. A The number of receivers is represented by P B It means that P=P A +P B , each user has an index value, where the sharing 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. Calculate the quality of the data shared by the receiver from the sharing party based on the retention time and data volume of the data, determine the update of the sharing party's data based on the obsolescence of the data, establish a data update equation, determine the user's purchase intention W based on the data update equation, and calculate the receiver b's utility function U based on the data quality and data transmission quality of each sharing party and the receiver's purchase data cost b ; S3. Calculate the privacy loss of the data based on the amount of available data collected by the system and the collection cost, calculate the total cost of the sharing party sharing the data with the receiving party, and obtain the utility function U of the sharing party. a ; S4. Calculate the overall utility function of the proxy node based on the utility functions of both parties of data sharing, and establish the optimization equation Objective 1 based on the constraints that the data sharing system should meet on both parties of data sharing. The optimization goal is to maximize the overall utility function under the conditions of meeting the demand constraints. S5. Use Taylor expansion to transform the optimization equation Objective 1 into the overall utility function maximization problem Objective 2, and use the Lagrange multiplier method to solve the data sharing rules of the data sharing parties when the overall utility function is maximized; S6. According to 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 can share data in a way that maximizes the overall utility function.

2. According to the data sharing method based on blockchain in the Internet of Vehicles under the edge cloud computing environment of claim 1, it is characterized in that: The quality of data purchased by the receiver b from the sharing party a in step S2 is expressed as: 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; When the data retention time t meets the preset conditions, the data is outdated and should be updated. The sharing party re-collects the data and updates the available data volume and data collection cost. The sharing party data update equation is as follows: Among them, C is the cost of updating given data, and W represents the user's purchase intention. When W=1; When W=0; The transmission quality of sharing party a is q a It is related to the transmission cost of sharing party a: q a =Wlog2(1+ηk a ) Among them, k a represents the transmission cost of sharing party a, η is a predefined parameter; The receiver's utility function is defined as: Among them, PD b,a The cost of receiving data for the receiver.

3. According to the data sharing method based on blockchain in the Internet of Vehicles under the edge cloud computing environment of claim 2, it is characterized in that: The specific method of step S3 is as follows: Define the amount of available data provided by sharing party a to receiving party b as N a,b , l1 and l2 are two cost factors, and their data collection cost is c(N a,b ), expressed as: The possible loss of data privacy during the transaction process is defined as: PL=pc a ·N a,b Among them, pc a represents the privacy protection coefficient of the sharing party a, and the total cost h(N a,b )for: h(N a,b )=c(N a,b )+PL The utility function of the sharing party is: Among them, PS a,b The income obtained from selling data to the sharing party.

4. According to the data sharing method based on blockchain in the Internet of Vehicles under the edge cloud computing environment of claim 3, it is characterized in that: In step S4, the revenue w of the proxy node and the overall utility function SW of the proxy node are calculated as follows: Among them, P Db,a is the cost of receiving data at the receiver, PS a,b is the income obtained by the sharing party from sharing data, ρ is the data quality purchased by the receiving party b from the sharing party a, and q a is the transmission quality of sharing party a; The overall utility maximization problem Objective1 is in the form of: Objective1: c1: c2: c3:N b,a =N a,b ,a∈{1,2,...,P A },b∈{1,2,...,P B } c4:N a,b ≥0,a∈{1,2,...,P A },b∈{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 requirement 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. According to the data sharing method based on blockchain in the Internet of Vehicles under the edge cloud computing environment of claim 4, it is characterized in that: In step S5, Objective1 is transformed into the overall utility function maximization problem Objective2: Objective2: c1: c2: c3:N b,a =N a,b ,a∈{1,2,...,P A },b∈{1,2,...,P B } c4:N a，b ≥0,a∈{1,2,...,P A },b∈{1,2,...,P B } Among them, x ba is the recipient's bid, y ab For the bid of the sharing party, Objective1 and Objective2 are different, but they have the same constraints. The Lagrange multiplier method is used to solve the data sharing rules of the two data sharing parties when the overall utility function is maximized. When the data sharing rules meet the following conditions, solving Objective2 is equivalent to solving Objective1:

6. According to claim 5, a data sharing method based on blockchain in the Internet of Vehicles under edge cloud computing environment is characterized in that: According to the data sharing rules of both parties, a utility maximization problem BMP is designed for the receiver as follows: 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; The utility maximization problem SMP of the sharing party is as follows: 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.

7. A data sharing system based on blockchain in the Internet of Vehicles under edge cloud computing environment, characterized in that: The system includes the following modules: Scenario sharing building module, assuming that the data sharing scenario involves P users, where the number of sharing parties involved is P A The number of receivers is represented by P B It means that P=P A +P B , each user has an index value, where the sharing 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 }; The data update module calculates the quality of the data shared by the receiver from the sharer based on the retention time and data volume of the data, determines the update of the sharer's data based on the obsolescence 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 sharer and the data purchase cost of the receiver. b ; The utility function building module calculates the privacy loss of data according to 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 utility function U of the sharing party. a ; The maximization objective module calculates the overall utility function of the proxy node based on the utility functions of both parties in the data sharing process, and establishes the optimization equation Objective1 based on the constraints that the data sharing system should meet on both parties in the data sharing process. The optimization goal is to maximize the overall utility function while meeting the demand constraints. 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 two data sharing parties when the overall utility function is maximized; 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 can share data in a way that maximizes the overall utility function.

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