Block chain-based edge computing Internet of Vehicles distributed resource allocation method and device

Abstract

The invention relates to an edge computing Internet of Vehicles distributed resource allocation method and device based on a block chain, and the method comprises the steps: constructing a block chain system revenue model based on the parameters of an unloading task; constructing an edge computing system income model based on the time delay of the unloading task; according to the block chain system income model and the edge computing system income model, constructing a resource optimization model by taking income maximization as a target; solving the resource optimization model by adopting an alternating direction multiplier method to obtain an optimal unloading strategy and a resource allocation scheme; and adopting the optimal unloading strategy and the resource allocation scheme to allocate the distributed resources of the Internet of Vehicles. According to the method, the video task unloading efficiency can be effectively improved, and the resource allocation strategy in the unloading process is optimized.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technologies, and in particular to a blockchain-based edge computing vehicle network distributed resource allocation method and device. Background Art

[0002] The network connection of autonomous vehicles is dynamic, the network structure changes frequently, the connection distance is short, but the channel changes rapidly, and the connection interruption often occurs, resulting in relatively low network stability. The promotion of intelligent transportation systems has made video surveillance technology play an important role in traffic management. It can monitor and track vehicles in a specific area and respond to emergencies such as traffic jams, speeding and illegal driving in a timely manner. In order to enhance the safety and efficiency of traffic, a large number of video streams need to be processed and analyzed. Video analysis technology is regarded as a key technology to improve the security and efficiency of autonomous vehicle network transmission. However, the large amount of video data transmission and the complex analysis of the video put a significant pressure on the vehicle network. Due to the limited computing resources of the vehicle, in-vehicle edge computing has been proposed as a promising technology to cope with the sharp rise in the demand for vehicle terminal applications. In-vehicle edge computing combines the advantages of Internet of Vehicles and mobile edge computing to meet the needs of these applications by intelligently utilizing the computing resources at the edge of the vehicle and its surrounding network.

[0003] In addition, the unstable network environment also affects the reliability of video data, which poses a challenge to the security and scalability of data sharing in the network of autonomous vehicles. In recent years, with the integration of the Internet of Things and blockchain technology, the application of blockchain systems in the Internet of Vehicles has received increasing attention. The Internet of Vehicles blockchain system combines the Internet of Vehicles and blockchain technology to achieve information sharing, data security and privacy protection between vehicles, and provides innovative solutions for fields such as intelligent transportation and autonomous driving. Blockchain, with its characteristics of decentralization, openness, transparency, credibility and immutability, is often used to solve security issues and ensure reliable access and control of the Internet of Vehicles. In addition, blockchain and on-board edge computing share a distributed network architecture, which facilitates their integration and enables data storage and computing at the edge, thereby providing secure data processing and improving the end-user experience.

[0004] At present, some studies are exploring the combination of blockchain technology and vehicle-mounted edge computing to achieve data sharing and ensure transaction security. For example, the patent document with application number CN202110557813.0 discloses a vehicle-mounted network application data transmission and billing optimization method based on mobile edge computing and blockchain. By constructing a computing model, a cache model and a blockchain model, the energy consumption and overhead of the transmission and computing process are calculated, the deep neural network is trained, and the parameters such as the vehicle task offloading decision and content cache decision are adjusted to complete the optimal resource allocation within the scene. Therefore, combining blockchain technology with vehicle-mounted edge computing to ensure data security is an important foundation for future technological development.

[0005] The redundancy problem in the video data transmission process cannot be ignored. High-quality video transmission does not require retaining all the original video data. Directly offloading uncompressed video tasks will consume a lot of unnecessary resources. Therefore, proper compression of video tasks is a necessary step before offloading. Current research rarely considers the compression rate of video tasks, offloading decisions, bandwidth and computing resource constraints at the same time. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a blockchain-based edge computing Internet of Vehicles distributed resource allocation method and device, which can effectively improve the efficiency of video task offloading and optimize the resource allocation strategy of the offloading process.

[0007] The technical solution adopted by the present invention to solve the technical problem is: to provide a distributed resource allocation method for edge computing Internet of Vehicles based on blockchain, including the following steps:

[0008] Construct a blockchain system profit model based on the parameters of the offloading task;

[0009] Build an edge computing system revenue model based on the latency of offloaded tasks;

[0010] According to the blockchain system revenue model and edge computing system revenue model, a resource optimization model is constructed with the goal of maximizing revenue;

[0011] The resource optimization model is solved by using an alternating direction multiplier method to obtain an optimal unloading strategy and resource allocation scheme;

[0012] The optimal unloading strategy and resource allocation scheme are used to allocate the distributed resources of the Internet of Vehicles.

[0013] The blockchain system revenue model is expressed as: in, is the blockchain system income, h is the fixed reward, l is the variable reward factor, For user n m The size of the blocks mined, For user n m Computational resources used for mining, is the total number of users, z is the time factor, λ represents the arrival rate of the task, and η is the energy consumption coefficient.

[0014] The edge computing system revenue model is expressed as: in, For edge computing system revenue, is the reward provided to the edge computing system by the user who offloads the task, κ is the unit price of the energy consumption of the edge computing system, For user n m The uninstall decision variables, is the total energy consumption of offloading tasks to roadside units, is the total energy consumption of the task offloaded to the vehicle group.

[0015] The total energy consumption of offloading the task to the roadside unit is expressed as: Among them, P (1,idle) and P (1,act) are the power consumption of the roadside unit in non-working state and working state respectively, is the transmission delay from task offloading to the roadside unit, expressed as: is the size of the task, is the compression ratio of the task, W (1) For available bandwidth, To allocate spectrum to roadside units for processing tasks, is the transmission power density, is the channel gain between the user and the roadside unit, γ 2 is the noise power, M is the number of roadside units; is the queuing delay of the task unloading to the roadside unit, expressed as: in, is the remaining processing time of the task on the mth roadside unit, ρ represents the service intensity, E[] represents the expectation, is the current computing capacity of the mth roadside unit; is the processing delay of offloading the task to the roadside unit, expressed as: The service time of a task follows an exponential distribution with parameter μ. It is the computing resources allocated to the roadside unit for processing tasks.

[0016] The total energy consumption of offloading the task to the vehicle group is expressed as: Among them, P (0 ,idle) and P (0,act) are the power consumption of the vehicle group in non-working state and working state respectively, is the transmission delay of task offloading to the vehicle group, expressed as: is the size of the task, is the compression ratio of the task, is the number of subtasks that the task is divided into, Assigned to vehicles The percentage of spectrum resources, W (0) For vehicle group Total bandwidth, Spectral efficiency for user-to-vehicle groups; is the processing delay of offloading tasks to the vehicle group, expressed as: ξ is the CPU cycle required to process the unit size computing task, For vehicles computing power.

[0017] The objective function of the resource optimization model is: in, Benefits to the blockchain system, Revenue for edge computing systems.

[0018] The constraints of the resource optimization model include: m The restrictions on offloading decision variables, the restrictions on the compression rate of tasks, the restrictions on the spectrum resources allocated to users to process tasks not exceeding the total amount of available spectrum resources of roadside units and vehicle groups, the restrictions on the computing resources required for blockchain systems and task processing not exceeding the limits of total computing resources, and the maximum limit on the total delay of task processing.

[0019] The technical solution adopted by the present invention to solve the technical problem is: to provide an edge computing vehicle network distributed resource allocation device based on blockchain, comprising:

[0020] The first building module is used to build a blockchain system revenue model based on the parameters of the offloading task;

[0021] The second building module is used to build an edge computing system revenue model based on the latency of the offloaded tasks;

[0022] The third construction module is used to construct a resource optimization model with the goal of maximizing revenue according to the blockchain system revenue model and the edge computing system revenue model;

[0023] A solution module, used for solving the resource optimization model by using an alternating direction multiplier method to obtain an optimal unloading strategy and resource allocation solution;

[0024] The allocation module is used to allocate the distributed resources of the Internet of Vehicles using the optimal unloading strategy and resource allocation scheme.

[0025] The technical solution adopted by the present invention to solve its technical problems is: to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of the above-mentioned blockchain-based edge computing Internet of Vehicles distributed resource allocation method are implemented.

[0026] The technical solution adopted by the present invention to solve its technical problem is: providing a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the steps of the above-mentioned blockchain-based edge computing vehicle network distributed resource allocation method are implemented.

[0027] Beneficial Effects

[0028] Due to the adoption of the above technical solution, the present invention has the following advantages and positive effects compared with the prior art: the present invention proposes a distributed computing offloading and resource allocation method based on blockchain in an on-board edge computing environment, which can improve the efficiency of task offloading and optimize resource allocation. The present invention uses blockchain for recording to ensure data security and privacy. The distributed algorithm alternating direction multiplier method used also improves the efficiency of problem handling compared to the centralized algorithm. Compared with other algorithms, the present invention can obtain the maximum system benefit, optimize offloading decisions, and reduce resource consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic diagram of a vehicle-mounted edge computing network framework combined with blockchain in a first embodiment of the present invention;

[0030] Figure 2 This is a flowchart of a distributed resource allocation method for edge computing Internet of Vehicles based on blockchain in the first embodiment of the present invention;

[0031] Figure 3 It is a flow chart of solving the resource optimization model by using the alternating direction multiplier method in the first embodiment of the present invention. DETAILED DESCRIPTION

[0032] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms fall within the scope limited by the appended claims of the application equally.

[0033] The first embodiment of the present invention relates to a distributed resource allocation method for edge computing Internet of Vehicles based on blockchain, which can be applied to Figure 1 In the system framework shown, the system framework includes two parts: blockchain system and mobile edge computing system.

[0034] Consider that the user of the autonomous driving car on the road has a video task to be unloaded, and can choose to unload it to the roadside unit or vehicle group. Assume that there are M roadside units in the system, N m users, and Due to limited computing resources, the video tasks generated by users need to be offloaded to servers with abundant computing power for processing. Therefore, users can transfer video tasks to roadside units or vehicle groups for transmission and processing through wireless channels. The offload records are stored in the blockchain system, and users collaborate to maintain the stable operation of the blockchain by implementing a practical Byzantine fault-tolerant consensus mechanism. Figure 2 As shown, the distributed resource allocation method of edge computing Internet of Vehicles based on blockchain in this embodiment includes the following steps:

[0035] Step 1: Build a blockchain system profit model based on the parameters of the offloading task.

[0036] The user will perform video analysis tasks Offload to the edge computing system and record the offloading behavior through the blockchain system. Can be defined by the following parameters: in, For user n m Uninstall decision variables, is the task size, is the compression ratio of the task, Assign profiles to spectrum, Assign a profile to a compute resource.

[0037] After the transaction information is packaged into blocks and consensus is reached, the user will receive rewards from the blockchain system. The rewards are Among them, h is a fixed reward, is a variable reward, l is a given variable reward factor, For user n m The size of the blocks mined, The probability of successfully mining a block and reaching consensus is:

[0038]

[0039] Where z is the time factor, is the computing resource used for mining, λ represents the arrival rate of the task, is the total number of users. The energy consumption calculation formula for the mining process is Among them, η is the energy consumption coefficient. The income of the blockchain system is:

[0040]

[0041] Step 2: Build an edge computing system revenue model based on the latency of the offloaded tasks.

[0042] In a mobile edge computing system, video tasks can be offloaded to roadside units or groups of vehicles. Indicates unloading to a roadside unit, Indicates unloading to the vehicle group.

[0043] When the video task is offloaded to the roadside unit, its total delay includes transmission delay, queuing delay and processing delay.

[0044] Video Tasks go through After compression, it is transmitted to the roadside unit through the wireless channel, and the transmission delay is defined as:

[0045]

[0046] in, Indicates the task of processing video assigned to roadside units The spectrum, W (1) is the available bandwidth, is the transmission power density, is the channel gain between the user and the roadside unit, γ 2 is the noise power.

[0047] Assume that the arrival of video tasks follows Poisson distribution, the arrival rate is λ, and the service time conforms to the exponential distribution with parameter μ. Under this assumption, the queuing process of video tasks in roadside units can be simulated as an M / M / 1 queuing model, and the queuing delay is defined as

[0048]

[0049] in, represents the remaining processing time of the task on the mth roadside unit, ρ represents the service intensity, E[] represents the expectation, is the current computing capacity of the mth roadside unit. The processing delay is:

[0050]

[0051] in, Refers to the roadside unit assigned to handle the task computing resources.

[0052] The total energy consumption is composed of the energy consumption of three stages: transmission, queuing and processing, which can be expressed as:

[0053]

[0054] Among them, P (1,idle) and P (1,act)They are the power consumption of the roadside unit in non-working state and working state respectively.

[0055] When the video task is offloaded to the vehicle group, in the vehicle group mode, the total delay includes the transmission delay and the processing delay.

[0056] In order to simplify the processing, the original video task The average is subtasks, through After compression, it is sent to the vehicle group. The transmission delay from the user to the vehicle group can be defined as:

[0057]

[0058] in, Assigned to vehicles The percentage of spectrum resources, W (0) For vehicle group Total bandwidth, Spectral efficiency for user-to-vehicle groups.

[0059] Assuming that the vehicle group can process these subtasks in parallel, processing delay It can be expressed as the average value of the processing delay of the entire vehicle group, and the formula is as follows:

[0060]

[0061] Among them, ξ is the CPU cycle required to process the unit size computing task, For vehicles computing power.

[0062] In the vehicle group mode, the total energy consumption consists of transmission energy consumption and processing energy consumption, which can be expressed as:

[0063]

[0064] Among them, P (0,idle) and P (0,act) They are the power consumption of the vehicle group in non-working state and working state respectively.

[0065] In order to encourage heterogeneous roadside units and vehicle groups to participate more in task offloading, when the video task processing is completed, the task submitter will give the edge service node a reward. The total revenue of the edge system is

[0066]

[0067] Among them, κ is the unit price of energy consumption of the edge computing system.

[0068] Step 3: Based on the blockchain system revenue model and the edge computing system revenue model, a resource optimization model is constructed with the goal of maximizing revenue.

[0069] The optimization goal of this implementation is to maximize the system revenue. The system revenue refers to the sum of the rewards obtained by users from the blockchain system and the rewards given to the edge service node by the task submitter after the task is processed in the mobile edge computing system. Therefore, the resource optimization model can be expressed as:

[0070]

[0071] Among them, C1 and C2 ensure the rationality of offloading decisions and compression ratio parameters, C3 and C4 ensure that the spectrum resources allocated to user processing tasks will not exceed the total available spectrum resources of roadside units and vehicle groups, C5 ensures that the computing resources required for blockchain systems and task processing do not exceed the limits of total computing resources, and C6 sets the maximum limit on the total delay of task processing.

[0072] Step 4: Use the alternating direction multiplier method to solve the resource optimization model to obtain the optimal unloading strategy and resource allocation solution.

[0073] The resource optimization model in this embodiment is a non-convex optimization problem. In order to transform it into a convex optimization problem, this embodiment relaxes and replaces its variables. First, the discrete variables a, θ and c are relaxed into continuous variables, that is, Then introduce some new parameters to replace the original product terms, let The original optimization problem is transformed into a convex optimization problem.

[0074] Due to the existence of global variables, the optimization problem cannot be divided. In order to allow each roadside unit to participate in the problem-solving process, this implementation method decomposes the problem so that it can be solved by a distributed method. Therefore, a local copy of the global variable is introduced. In the coverage area of ​​the mth roadside unit, the local copy and the global variable are defined as Then the global version of the resource optimization model can be written as:

[0075]

[0076] Since the alternating direction multiplier method usually minimizes the objective function, the above objective function is transformed into:

[0077]

[0078] After that, the alternating direction multiplier method is used to update local variables, global variables and Lagrange multipliers in sequence until the parameters converge. The specific process is shown in Figure 3 .

[0079] Step 5: Use the optimal unloading strategy and resource allocation scheme to allocate the distributed resources of the Internet of Vehicles.

[0080] It is not difficult to find that the present invention proposes a distributed computing offloading and resource allocation method based on blockchain in an on-board edge computing environment, which can improve the efficiency of task offloading and optimize resource allocation. The present invention uses blockchain for recording to ensure data security and privacy. The distributed algorithm alternating direction multiplier method used also improves the efficiency of problem handling compared to the centralized algorithm. Compared with other algorithms, the present invention can obtain the maximum system benefits, optimize offloading decisions, and reduce resource consumption.

[0081] The second embodiment of the present invention relates to a distributed resource allocation device for edge computing Internet of Vehicles based on blockchain, comprising:

[0082] The first building module is used to build a blockchain system revenue model based on the parameters of the offloading task;

[0083] The second building module is used to build an edge computing system revenue model based on the latency of the offloaded tasks;

[0084] The third construction module is used to construct a resource optimization model with the goal of maximizing revenue according to the blockchain system revenue model and the edge computing system revenue model;

[0085] A solution module, used for solving the resource optimization model by using an alternating direction multiplier method to obtain an optimal unloading strategy and resource allocation solution;

[0086] The allocation module is used to allocate the distributed resources of the Internet of Vehicles using the optimal unloading strategy and resource allocation scheme.

[0087] The blockchain system revenue model constructed by the first building block is expressed as: in, is the blockchain system income, h is the fixed reward, l is the variable reward factor, For user n m The size of the blocks mined, For user n m Computational resources used for mining, is the total number of users, z is the time factor, λ represents the arrival rate of the task, and η is the energy consumption coefficient.

[0088] The revenue model of the edge computing system constructed by the second building block is expressed as: in, For edge computing system revenue, is the reward provided to the edge computing system by the user who offloads the task, κ is the unit price of the energy consumption of the edge computing system, For user n m The uninstall decision variables, is the total energy consumption of offloading tasks to roadside units, is the total energy consumption of the task offloaded to the vehicle group.

[0089] The total energy consumption of offloading the task to the roadside unit is expressed as: Among them, P (1,idle) and P (1,act) are the power consumption of the roadside unit in non-working state and working state respectively, is the transmission delay from task offloading to the roadside unit, expressed as: is the size of the task, is the compression ratio of the task, W (1) For available bandwidth, To allocate spectrum to roadside units for processing tasks, is the transmission power density, is the channel gain between the user and the roadside unit, γ 2 is the noise power, M is the number of roadside units; is the queuing delay of the task unloading to the roadside unit, expressed as: in, is the remaining processing time of the task on the mth roadside unit, ρ represents the service intensity, E[] represents the expectation, is the current computing capacity of the mth roadside unit; is the processing delay of offloading the task to the roadside unit, expressed as: The service time of a task follows an exponential distribution with parameter μ. It is the computing resources allocated to the roadside unit for processing tasks.

[0090] The total energy consumption of offloading the task to the vehicle group is expressed as: Among them, P (0 ,idle) and P (0,act) are the power consumption of the vehicle group in non-working state and working state respectively, is the transmission delay of task offloading to the vehicle group, expressed as: is the size of the task, is the compression ratio of the task, is the number of subtasks that the task is divided into, Assigned to vehicles The percentage of spectrum resources, W (0) For vehicle group Total bandwidth, Spectral efficiency for user-to-vehicle groups; is the processing delay of offloading tasks to the vehicle group, expressed as: ξ is the CPU cycle required to process the unit size computing task, For vehicles computing power.

[0091] The objective function of the resource optimization model constructed by the third building block is: in, Benefits to the blockchain system, Revenue for edge computing systems.

[0092] The constraint conditions of the resource optimization model constructed by the third construction module include: m The restrictions on offloading decision variables, the restrictions on the compression rate of tasks, the restrictions on the spectrum resources allocated to users to process tasks not exceeding the total amount of available spectrum resources of roadside units and vehicle groups, the restrictions on the computing resources required for blockchain systems and task processing not exceeding the limits of total computing resources, and the maximum limit on the total delay of task processing.

[0093] A third embodiment of the present invention relates to an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above-mentioned blockchain-based edge computing Internet of Vehicles distributed resource allocation method are implemented.

[0094] The fourth embodiment of the present invention relates to a computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned blockchain-based edge computing vehicle network distributed resource allocation method are implemented.

[0095] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0096] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction method, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0099] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A distributed resource allocation method for edge computing Internet of Vehicles based on blockchain, characterized in that: The following steps are involved: Construct a blockchain system profit model based on the parameters of the offloading task; Build an edge computing system revenue model based on the latency of offloaded tasks; According to the blockchain system revenue model and edge computing system revenue model, a resource optimization model is constructed with the goal of maximizing revenue; The resource optimization model is solved by using an alternating direction multiplier method to obtain an optimal unloading strategy and resource allocation scheme; The optimal unloading strategy and resource allocation scheme are used to allocate the distributed resources of the Internet of Vehicles.

2. The distributed resource allocation method for edge computing Internet of Vehicles based on blockchain according to claim 1 is characterized in that: The blockchain system revenue model is expressed as: in, is the blockchain system income, h is the fixed reward, l is the variable reward factor, For user n m The size of the blocks mined, For user n m Computational resources used for mining, is the total number of users, z is the time factor, λ represents the arrival rate of the task, and η is the energy consumption coefficient.

3. The distributed resource allocation method for edge computing Internet of Vehicles based on blockchain according to claim 1 is characterized in that: The edge computing system revenue model is expressed as: in, For edge computing system revenue, is the reward provided to the edge computing system by the user who offloads the task, κ is the unit price of the energy consumption of the edge computing system, For user n m The uninstall decision variables, is the total energy consumption of offloading tasks to roadside units, is the total energy consumption of the task offloaded to the vehicle group.

4. The distributed resource allocation method for edge computing Internet of Vehicles based on blockchain according to claim 3 is characterized in that: The total energy consumption of offloading the task to the roadside unit is expressed as: Among them, P (1,idle) and P (1,act) are the power consumption of the roadside unit in non-working state and working state respectively, is the transmission delay from task offloading to the roadside unit, expressed as: is the size of the task, is the compression ratio of the task, W (1) For available bandwidth, To allocate spectrum to roadside units for processing tasks, is the transmission power density, is the channel gain between the user and the roadside unit, γ 2 is the noise power, M is the number of roadside units; is the queuing delay of the task unloading to the roadside unit, expressed as: in, is the remaining processing time of the task on the mth roadside unit, ρ represents the service intensity, E[] represents the expectation, is the current computing capacity of the mth roadside unit; is the processing delay of offloading the task to the roadside unit, expressed as: The service time of a task follows an exponential distribution with parameter μ. It is the computing resources allocated to the roadside unit for processing tasks.

5. The distributed resource allocation method for edge computing Internet of Vehicles based on blockchain according to claim 3 is characterized in that: The total energy consumption of offloading the task to the vehicle group is expressed as: Among them, P (0,idle) and P (0,act) are the power consumption of the vehicle group in non-working state and working state respectively, is the transmission delay of task offloading to the vehicle group, expressed as: is the size of the task, is the compression ratio of the task, is the number of subtasks that the task is divided into, Assigned to vehicles The percentage of spectrum resources, W (0) For vehicle group Total bandwidth, Spectral efficiency for user-to-vehicle groups; is the processing delay of offloading tasks to the vehicle group, expressed as: ξ is the CPU cycle required to process the unit size computing task, For vehicles computing power.

6. The distributed resource allocation method for edge computing Internet of Vehicles based on blockchain according to claim 1 is characterized in that: The objective function of the resource optimization model is: in, Benefits to the blockchain system, Revenue for edge computing systems.

7. The distributed resource allocation method for edge computing Internet of Vehicles based on blockchain according to claim 1 is characterized in that: The constraints of the resource optimization model include: m The restrictions on offloading decision variables, the restrictions on the compression rate of tasks, the restrictions on the spectrum resources allocated to users to process tasks not exceeding the total amount of available spectrum resources of roadside units and vehicle groups, the restrictions on the computing resources required for blockchain systems and task processing not exceeding the limits of total computing resources, and the maximum limit on the total delay of task processing.

8. A distributed resource allocation device for edge computing Internet of Vehicles based on blockchain, characterized in that: include: The first building module is used to build a blockchain system revenue model based on the parameters of the offloading task; The second building module is used to build an edge computing system revenue model based on the latency of the offloaded tasks; The third construction module is used to construct a resource optimization model with the goal of maximizing revenue according to the blockchain system revenue model and the edge computing system revenue model; A solution module, used for solving the resource optimization model by using an alternating direction multiplier method to obtain an optimal unloading strategy and resource allocation solution; The allocation module is used to allocate the distributed resources of the Internet of Vehicles using the optimal unloading strategy and resource allocation scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the blockchain-based edge computing vehicle network distributed resource allocation method are implemented as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, the steps of the blockchain-based edge computing vehicle network distributed resource allocation method are implemented as described in any one of claims 1 to 7.

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