Computing resource sharing verification method, device and equipment and readable storage medium

Abstract

The application provides a kind of computing resource sharing verification method, device, equipment and readable storage medium, the method comprises: determining the request computing resource task generated by user node, the service node matched by the user node and the transaction information corresponding to the request computing resource task;Based on the transaction information and the service node, determine the computing result of the request computing resource task;In the case where the user node rejects the computing result, the computing result is verified by the verification node connected to the user node;Determine the verification fee based on the verification result, the fee of the user node and the fee of the service node.The application guarantees the timeliness of information while guaranteeing the quality of service through the vehicle networking blockchain computing resource sharing and transaction verification process.

Description

Technical Field

[0001] This invention relates to the field of blockchain system verification technology, and in particular to a method, apparatus, device, and readable storage medium for verifying shared computing resources. Background Technology

[0002] Current research on vehicle-to-everything (V2X) (blockchain) system verification focuses primarily on detecting erroneous events or information. These studies assess the authenticity and security of information, initiating a verification mechanism when the authenticity and security fall outside acceptable limits. However, these studies lack specific verification process designs. Existing solutions merely mention randomly selecting nearby vehicles for verification or submitting the questionable information to higher-level or authoritative nodes for verification, failing to guarantee both service quality and timeliness. Summary of the Invention

[0003] This invention provides a computing resource sharing verification method, apparatus, device, and readable storage medium to solve the technical problem that the information verification process of existing vehicle network (blockchain) systems cannot guarantee both service quality and information timeliness.

[0004] This invention provides a method for verifying shared computing resources, comprising:

[0005] Determine the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task;

[0006] Based on the transaction information and the service node, determine the calculation result of the requested computing resource task;

[0007] If the user node rejects the calculation result, the calculation result is verified by the verification node connected to the user node.

[0008] The verification fee, the user node fee, and the service node fee are determined based on the verification results.

[0009] According to a computing resource sharing verification method provided by the present invention, determining the service node matched by the user node includes:

[0010] The user node connects to the drive test unit to obtain the service queue information, service quality and integrity assessment value of each service node.

[0011] Based on the service queue information, service quality, and integrity assessment value of each service node, as well as the service queue information of the drive test unit, the service node that matches the user node is determined among the service nodes.

[0012] According to a computing resource sharing verification method provided by the present invention, the step of determining the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task includes:

[0013] Determine the task data for the request computing resource task generated by the user node;

[0014] Based on the task data and the service node matched with the user node, the transaction information corresponding to the requested computing resource task is determined.

[0015] According to a computing resource sharing verification method provided by the present invention, determining the computation result of the requested computing resource task based on the transaction information and the service node includes:

[0016] The service node performs calculations on the requested computing resource task to obtain the task result and the calculation completion time.

[0017] The computation cost is determined based on the task data, and the computation result of the requested computation resource task is obtained. The computation result includes the task result, the computation completion time, and the computation cost.

[0018] According to a computing resource sharing verification method provided by the present invention, after determining the computation result of the requested computing resource task based on the transaction information and the service node, the method further includes:

[0019] Determine the historical reputation value of the service node;

[0020] If a calculation timeout is determined based on the calculation completion time, the user node's account and the service node's account are updated based on the service node's compensation fee.

[0021] If the user node confirms the calculation result based on the historical reputation value, the user node's account and the service node's account are updated based on the calculation cost;

[0022] The reputation values ​​of the user node and the service node are updated based on the calculation results.

[0023] According to a computing resource sharing verification method provided by the present invention, the step of determining the verification fee, the fee of the user node, and the fee of the service node based on the verification result includes:

[0024] If the verification result confirms that the calculation result is accurate, the compensation fee and verification fee for the user node are determined.

[0025] If the verification result indicates that the calculation result is inaccurate, determine the compensation fee and verification fee for the service node;

[0026] The reputation values ​​of the user node and the service node are updated based on the verification results.

[0027] The present invention also provides a computing resource sharing verification device, comprising:

[0028] The transaction information determination module is used to determine the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task.

[0029] The calculation result determination module is used to determine the calculation result of the requested computing resource task based on the transaction information and the service node;

[0030] The verification module is used to verify the calculation result through a verification node connected to the user node if the user node rejects the calculation result.

[0031] The cost determination module is used to determine the verification cost, the cost of the user node, and the cost of the service node based on the verification results.

[0032] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the computing resource sharing verification method as described above.

[0033] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the computing resource sharing verification method as described above.

[0034] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the computing resource sharing verification method as described above.

[0035] The present invention provides a computing resource sharing verification method, apparatus, device, and readable storage medium. A user node generates a task requesting computing resources. A verification node connected to the user node determines a service node matching the user node, along with transaction information corresponding to the requested computing resource task. The user node sends the transaction information to the service node, which performs calculations on the task and obtains the result. The user node confirms the result. If the user node rejects the result, the verification node connected to the user node verifies the result. Finally, based on the verification result, the verification fee, the user node's fee, and the service node's fee are determined. This vehicle-to-everything (V2X) computing resource sharing and transaction verification process ensures both service quality and information timeliness. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is one of the flowcharts of the computing resource sharing verification method provided by the present invention;

[0038] Figure 2 This is a schematic diagram of the smart contract design in the computing resource sharing verification method provided by the present invention;

[0039] Figure 3 This is a schematic diagram of the network structure in the computing resource sharing verification method provided by the present invention;

[0040] Figure 4 This is the second flowchart of the computing resource sharing verification method provided by the present invention;

[0041] Figure 5 This is a schematic diagram of the structure of the computing resource sharing verification device provided by the present invention;

[0042] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0044] The following is combined with Figures 1-4 The present invention describes a method for verifying shared computing resources.

[0045] Please refer to Figure 1 This invention provides a method for verifying shared computing resources, comprising:

[0046] Step 100: Determine the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task;

[0047] Specifically, we define sets B-UEs, B-SNs, and B-VNs as user nodes, service nodes, and authentication nodes, respectively. Under generalization, we assume that each user node generates tasks independently according to a Poisson process. Therefore, the task generation process of the entire system remains a Poisson process. We define the task generation rate, noting that different tasks have different data sizes and computational resource requirements. To reflect the differences in computing capabilities among different vehicles, the computing capabilities of all vehicles as service nodes follow a normal distribution with parameters X kilobytes per second.

[0048] Although the wireless channel quality varies greatly between user nodes and verification nodes, the average transmission rate that each service node can provide can be calculated or simulated through ergonomic capacity analysis and simulation based on spatial Poisson point processes. When the vehicle-to-everything (blockchain) road test unit is configured as a verification node with the same bandwidth resources, its average transmission and average computation time can be quantified from a statistical analysis perspective.

[0049] For diverse services, each service type is defined as follows: intelligent service definition includes the request service data packet size, the service type calculation content size, the service type system average service latency, the latency tolerance factor, and the service type tolerable latency. Therefore, the intelligent service service latency is related to the transmission rate from intelligent vehicle u (user vehicle) to intelligent vehicle s (service vehicle), and the computing power of service vehicle s in providing services for service type e. The intelligent vehicle transmission rate is affected by channel bandwidth, path loss, shadowing fading, signal power, interference signal power, and noise power.

[0050] In practice, V2V direct links may fail to connect due to distance, service vehicle s may be unable to provide type e services, and either user vehicle u or service vehicle s may be unable to provide the required service deposit, thus preventing service delivery. Therefore, optimizing service latency is also related to the communication association between vehicle u and vehicle s, the association between vehicle s and service type e content, and whether the deposits of vehicle u and vehicle s are sufficient.

[0051] Step 200: Based on the transaction information and the service node, determine the calculation result of the requested computing resource task;

[0052] Specifically, the user node sends the raw data requesting the computing resource task to the service node matched with it via a direct communication interface (e.g., PC5). If the first transmission fails, it is retransmitted. The service node then calculates the requested computing resource task and obtains the result. A transaction records the calculation result, completion time, and cost of the requested computing resource task, generates the service node's historical reputation, and sends it to the user node. This transaction is also packaged and added to the contract.

[0053] Step 300: If the user node rejects the calculation result, the calculation result is verified through the verification node connected to the user node.

[0054] Specifically, when the user node's verification process is automatically activated, a transaction recording the task index will be generated and sent to the vehicle service center serving the area. The road test unit corresponding to this transaction management serves as the verification node. Since road test units can share various types of information such as road conditions, any road test unit can serve as a verification node to complete the verification of the calculation results of various types of business.

[0055] Step 400: Determine the verification fee, the fee of the user node, and the fee of the service node based on the verification results.

[0056] Then, the validator node uses the request in the transaction recording the task index to calculate the resource task index, queries the blockchain for the calculation result of the resource task, the task verification rules, and the task payment, and calculates the final judgment. If the final judgment is correct, the user node must pay the compensation fee defined by the service node system, and the user node must pay the verification fee defined by the validator node system; if the final judgment is incorrect, the service node must pay the compensation fee defined by the user node system, and the service node must pay the verification fee defined by the validator node system. The reputation of the service node and the user node is updated based on the final judgment. Transactions recording the latest reputation and account balance of the user node and the service node, as well as transactions recording the final judgment of the task and the account balance of the validator node, are also recorded in the contract. Finally, all transaction data is packaged and uploaded to the blockchain through the road test unit.

[0057] Therefore, smart contracts designed based on the transaction information that needs to be recorded in the transaction process, such as Figure 2 As shown, Figure 2 In this agreement, Party A and Party B represent the service node and user node, respectively. For diverse business types, the service phase considers factors such as the type of business requested by the user node, the service node's service capabilities, quality and reputation, and the adequacy of the security deposits held by both the service node and user node, all of which affect the total service latency. Subsequently, based on the service results, relevant vehicles are rewarded or penalized according to regulations, with corresponding credit scores and monetary amounts.

[0058] Regarding the security deposit, in order to avoid breach of trust, both parties are required to pay a certain amount of security deposit before the business service commences. Figure 2 If neither party commits fraud during the business transaction, only the service fees will be transferred and the remaining deposit will be refunded; if fraud occurs during the transaction, the amount remaining after deducting the transaction fees from the fraudulent party's deposit will be used to compensate other related parties for their losses.

[0059] In this embodiment, a user node generates a task requesting computing resources. A verification node connected to the user node determines a service node that matches the user node, as well as the transaction information corresponding to the task requesting computing resources. The user node sends the transaction information corresponding to the task requesting computing resources to the service node. The service node performs calculations on the task requesting computing resources and obtains the calculation result. The user node confirms the calculation result. If the user node rejects the calculation result, the verification node connected to the user node verifies the calculation result. Finally, based on the verification result, the verification fee, the user node's fee, and the service node's fee are determined. This vehicle-to-everything (V2X) computing resource sharing and transaction verification process ensures both service quality and information timeliness.

[0060] In one embodiment, the computing resource sharing verification method provided in this application may further include:

[0061] Step 110: Obtain the service queue information, service quality and integrity assessment value of each service node through the drive test unit connected to the user node;

[0062] Step 120: Based on the service queue information, service quality, and integrity assessment value of each service node, as well as the service queue information of the drive test unit, determine the service node that matches the user node among the service nodes.

[0063] Based on the above system model design, to further improve system service efficiency during the service phase, it is necessary to optimize the matching scheme between business requesters and computing resource providers. Therefore, we also propose an intelligent solution design for the service node selection problem of intelligent business data offloading processing in the Internet of Vehicles (Blockchain). We adopt a class of intelligent algorithms, Deep Reinforcement Learning (DRL), to determine the optimal intelligent business data offloading node based on the current dynamically changing scenario. Taking the system state as input, we fully consider the estimated total service latency of vehicles in the business state, the maximum tolerable latency of the business, the number of queued businesses QR,u,s in the business transmission waiting queue of the roadside unit state, the number of queued businesses QS,s in the vehicle service waiting queue of the vehicle state, the service quality of the vehicle for this type of business (i.e., the service quality in this embodiment), and the current business integrity assessment value of the vehicle (i.e., the business integrity assessment value in this embodiment). The output of the policy network is the decided service vehicle node.

[0064] The Double DQN algorithm is chosen to train the network. During training, the overall optimization objective function serves as the basis for network training. The reward value is the business revenue from successful service delivery, minus the verification time cost incurred due to malicious user denial, or the service deception penalty for service failure plus the verification time cost, or a timeout penalty. In the Double DQN algorithm, the environment state S serves as the system input in the Q-network, yielding the Q-value output corresponding to all actions of the Q-network. A greedy algorithm selects the corresponding action A from the current Q-value output (e.g., B-UE1 selects the nearest vehicle as the service node to provide shared computing resources for the user vehicle). Then, by executing the current action A in state S, a new state S', reward R, and whether to terminate the state is_end are obtained. {S, A, R, S', is_end} are stored in the experience replay set, updating the current state S = S'. m samples are sampled from the experience replay set to calculate the current target Q-value, and the mean squared error loss function is calculated. All parameters of the Q-network are updated through gradient backpropagation of the neural network.

[0065] The state space is represented by the total latency required for J vehicles to act as service providers, predicted through vehicle perception; the maximum tolerable latency of the current service e; the number of queued services in the RSUs that the covering user vehicle u and service vehicle s may manage this transaction; the number of services currently waiting for service vehicle s; the perceived quality of service provided by service vehicle s for the current type e service; and the perceived integrity assessment value of service vehicle s for the current service. For the action space, since J is determined, the action space is also determined. When the action is j, it represents selecting the j-th vehicle node as the service node for intelligent service data offloading processing, which is the service node matched with the user node in this embodiment.

[0066] In Double DQN, there are essentially two types of Q-networks: Q-evaluation network and Q-target network. The Q-evaluation network is used to select actions, and the Q-target network is used to generate target Q-values. According to the MDP scheme in this paper, in each time step, the roadside unit inputs the system state into Double DQN and obtains an action, which selects a serving vehicle for data offloading for each B-UE.

[0067] Taking the selection of the nth business data unloading node in a time step as an example, the management requests the roadside unit of the nth business vehicle to collect the system status and put it into the Q-evaluation network. Then, the Q-evaluation will output a Q-value based on the system status. According to the ∈-greedy algorithm, the service vehicle corresponding to the maximum Q-value is selected with a probability of 1-∈, and a vehicle is randomly selected with a probability of ∈. Simultaneously, the corresponding reward is calculated based on the business service situation, and the system state is transitioned. The system state, action space, transitioned system state, and reward are stored as training samples in the experience pool. Finally, random samples are taken from the experience pool to calculate the current target Q-value. The parameters of the Q-network are updated through the mean squared error loss function and the gradient backpropagation of the neural network. The network parameters are continuously updated, enabling better decisions based on real-time changes in the environmental state.

[0068] The trained network will be directly applied to real-world scenarios. Based on testing, the trained network model will make the best decision after processing in the test scenario, and determine the appropriate service node for data offloading for the B-UE request's service n.

[0069] This embodiment uses a deep reinforcement learning algorithm to determine the optimal intelligent business data offloading node, i.e., the service node that matches the user node, based on the current dynamically changing scenario.

[0070] In one embodiment, the computing resource sharing verification method provided in this application may further include:

[0071] Step 130: Determine the task data for the request computing resource task generated by the user node;

[0072] Step 140: Based on the task data and the service node matched with the user node, determine the transaction information corresponding to the requested computing resource task.

[0073] Specifically, such as Figure 3 As shown, in the sharing and trading of computing resources in the Internet of Vehicles (Blockchain), there are two basic network entities: Vehicle User Equipment (UE) and Roadside Units (RSUs). User terminals generate tasks and requests for computing resources, while vehicles and RSUs with service capabilities are the providers of these computing resources. Since blockchain is used to build incentive and billing mechanisms in the Internet of Vehicles (Blockchain), user terminals and roadside units also act as participating nodes in the blockchain; therefore, user terminals and roadside units are also blockchain user terminals (B-UEs) and blockchain roadside units (B-RSUs).

[0074] In the process of vehicle-to-everything (V2X) (blockchain), road vehicles can act as user nodes to submit business requests, or as blockchain service nodes (B-SNs) to provide services for the requested business. To ensure the fairness of the final judgment, blockchain verification nodes (B-VNs) will verify the results when there are objections to the business results. All nodes with high credibility can act as B-VNs. At the same time, because RSUs have a high degree of awareness of surrounding road conditions and other information, they can ensure the authenticity and reliability of the calculation results. Therefore, B-RSUs will act as blockchain verification nodes (B-VNs).

[0075] The B-UE generates a task requesting computing resources, generates a transaction, and records the data size, time limit, task verification rules and task payment, the historical reputation of the B-UEs, and other information required for wireless channel quality testing, which is the transaction information in this embodiment. This transaction is sent to the B-RSU connected to the B-UE. The transaction is also packaged and added to the smart contract. Finally, all transaction data is packaged and uploaded to the blockchain through the B-RSU to determine the service types existing in the intelligent vehicle network (blockchain). Each service type is defined as the request service data packet size, the service type computing content size, the service type system average service latency, the latency tolerance factor, and the service type tolerable latency, which is the task data in this embodiment.

[0076] This embodiment determines the task data of the request computing resource task generated by the user node, and based on the task data and the service node matched with the user node, determines the transaction information corresponding to the request computing resource task, thus laying the data foundation for determining the calculation result.

[0077] In one embodiment, the computing resource sharing verification method provided in this application may further include:

[0078] Step 201: The service node performs calculations on the requested computing resource task to obtain the task result and the calculation completion time;

[0079] Step 202: Determine the computation cost based on the task data, and obtain the computation result of the requested computation resource task. The computation result includes the task result, the computation completion time, and the computation cost.

[0080] Specifically, the B-UE sends the raw data requesting the computational resource task to the service node via the direct communication interface, retransmitting if the first transmission fails. The service node then computes the requested computational resource task and obtains the result. A transaction records the task's result, completion time, and cost, generates the B-SN's historical reputation, and sends it to the B-UE. This transaction is also packaged and added to the contract.

[0081] This embodiment calculates the computational resource task requested by the service node and obtains the result, and ensures service quality through the sharing of computing resources in the Internet of Vehicles (Blockchain).

[0082] Please refer to Figure 4 In one embodiment, the computing resource sharing verification method provided in this application may further include:

[0083] Step 210: Determine the historical reputation value of the service node;

[0084] Step 220: If a calculation timeout is determined based on the calculation completion time, update the user node's account and the service node's account based on the service node's compensation fee.

[0085] Step 230: If the user node confirms the calculation result based on the historical reputation value, update the user node's account and the service node's account based on the calculation cost;

[0086] Step 240: Update the reputation value of the user node and the reputation value of the service node based on the calculation results.

[0087] If the calculation times out, the service node must pay the user node's system-defined compensation fee. If the B-UE confirms the result, a predefined payment is deducted from the user node's account, and the payment is added accordingly to the service node's account. Based on the task response, the reputations of the service node and user node are updated, a transaction recording the updated reputations and account balances of the B-UE and B-SN is generated, and packaged into the contract. If the B-UE rejects the result, the verification process will be automatically activated by the B-UE.

[0088] This embodiment updates the account and reputation value by having the user node confirm the calculation results, thus ensuring the timeliness of the information.

[0089] In one embodiment, the computing resource sharing verification method provided in this application may further include:

[0090] Step 410: If the verification result confirms that the calculation result is accurate, determine the compensation fee and verification fee for the user node;

[0091] Step 420: If the verification result indicates that the calculation result is inaccurate, determine the compensation fee and verification fee for the service node;

[0092] Step 430: Update the reputation value of the user node and the reputation value of the service node based on the verification result.

[0093] Specifically, when the verification process B-UE is automatically activated, a transaction recording the task index is generated and sent to the vehicle room serving that area. The B-RSU corresponding to this transaction is used as the B-VN. Because various types of information, such as road conditions, can be shared among B-RSUs, any B-RSU can act as a B-VN to complete the verification of various types of services.

[0094] Then, B-VN uses the transaction recording the task index to request the index of the computational resource task, queries the blockchain for the result of the computational resource task, the task verification rules, and the task payment, and calculates the final judgment. If the final judgment is correct, the user node must pay the compensation fee defined by the service node system, and the user node must pay the verification fee defined by the verification node system. If the final judgment is incorrect, the service node must pay the compensation fee defined by the user node system, and the service node must pay the verification fee defined by the verification node system. The reputation of the service node and user node is updated based on the final judgment. Transactions that generate records of the latest reputation and account balance of B-UE and B-SN, as well as transactions that generate records of the final judgment of the task and the account balance of B-VN, are also recorded in the contract. Finally, all transaction data is packaged and uploaded to the blockchain via B-RSU.

[0095] This embodiment verifies the calculation results through verification nodes, accurately identifying dishonest transaction behavior.

[0096] The computing resource sharing verification device provided by the present invention is described below. The computing resource sharing verification device described below can be referred to in correspondence with the computing resource sharing verification method described above.

[0097] Please refer to Figure 5 The present invention also provides a computing resource sharing verification device, comprising:

[0098] The transaction information determination module 501 is used to determine the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task.

[0099] The calculation result determination module 502 is used to determine the calculation result of the requested computing resource task based on the transaction information and the service node;

[0100] Verification module 503 is used to verify the calculation result through a verification node connected to the user node when the user node rejects the calculation result;

[0101] The cost determination module 504 is used to determine the verification cost, the cost of the user node, and the cost of the service node based on the verification results.

[0102] Optionally, the transaction information determination module includes:

[0103] The information acquisition unit is used to acquire the service queue information, service quality and integrity assessment value of each service node through the drive test unit connected to the user node.

[0104] The service node determination unit is used to determine the service node that matches the user node among the service nodes based on the service queue information, service quality, service integrity assessment value of each service node and the service queue information of the drive test unit.

[0105] Optionally, the transaction information determination module further includes:

[0106] The task data determination unit is used to determine the task data of the request computing resource task generated by the user node.

[0107] The transaction information determination unit is used to determine the transaction information corresponding to the requested computing resource task based on the task data and the service node matched with the user node.

[0108] Optionally, the calculation result determination module includes:

[0109] The task calculation unit is used to calculate the requested computing resource task through the service node to obtain the task result and the calculation completion time;

[0110] The calculation result determination unit is used to determine the calculation cost based on the task data and obtain the calculation result of the requested computing resource task. The calculation result includes the task result, the calculation completion time, and the calculation cost.

[0111] Optionally, the computing resource sharing verification device further includes:

[0112] The historical reputation value determination module is used to determine the historical reputation value of the service node;

[0113] The first account update module is used to update the user node's account and the service node's account based on the service node's compensation fee when a calculation timeout is determined based on the calculation completion time.

[0114] The second account update module is used to update the account of the user node and the account of the service node based on the calculation fee when the user node confirms the calculation result based on the historical reputation value.

[0115] The first reputation value update module is used to update the reputation value of the user node and the reputation value of the service node based on the calculation results.

[0116] Optionally, the cost determination module includes:

[0117] The first cost determination unit is used to determine the compensation cost and verification cost of the user node if the verification result is accurate.

[0118] The second cost determination unit is used to determine the compensation cost and verification cost of the service node when the verification result indicates that the calculation result is inaccurate.

[0119] The second reputation value update unit is used to update the reputation value of the user node and the reputation value of the service node based on the verification result.

[0120] Figure 6 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions from the memory 630 to execute a computing resource sharing verification method.

[0121] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0122] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the computing resource sharing verification method provided by the above methods.

[0123] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the computing resource sharing verification method provided by the above methods.

[0124] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0125] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0126] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for verifying shared computing resources, characterized in that, include: Determine the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task; The process of determining the service node matching the user node includes: obtaining the service queue information, service quality, and integrity assessment value of each service node through the road test unit connected to the user node; based on the service queue information, service quality, and integrity assessment value of each service node, as well as the service queue information of the road test unit, using a deep reinforcement learning algorithm to determine the service node matching the user node; wherein, the policy network of the deep reinforcement learning algorithm takes the system state as input, including the estimated total vehicle service latency, the maximum tolerable service latency, the number of queued services in the service transmission waiting queue of the roadside unit, the number of queued services in the vehicle service waiting queue of the service node, the service quality, and the integrity assessment value, and the output of the policy network is the decided service node; the deep reinforcement learning algorithm is the Double DQN algorithm, and the Double DQN algorithm is selected to train the network; during the training phase, the overall optimization objective function is used as the training basis for the network, and the reward value is the service revenue of successful service, minus the verification time consumption caused by malicious denial by the user, or the service deception penalty for service failure plus the verification time consumption, or the timeout penalty; Based on the transaction information and the service node, determine the calculation result of the requested computing resource task; If the user node rejects the calculation result, the calculation result is verified by the verification node connected to the user node. When the user node's verification process is automatically activated, a transaction recording the task index is generated and sent to the vehicle serving the region. The road test unit corresponding to this transaction management acts as the verification node. The region is the region where the user node is located. The verification fee, the user node fee, and the service node fee are determined based on the verification results.

2. The computing resource sharing verification method according to claim 1, characterized in that, The process of determining the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task includes: Determine the task data for the request computing resource task generated by the user node; Based on the task data and the service node matched with the user node, the transaction information corresponding to the requested computing resource task is determined.

3. The computing resource sharing verification method according to claim 2, characterized in that, The step of determining the computation result of the requested computing resource task based on the transaction information and the service node includes: The service node performs calculations on the requested computing resource task to obtain the task result and the calculation completion time. The computation cost is determined based on the task data, and the computation result of the requested computation resource task is obtained. The computation result includes the task result, the computation completion time, and the computation cost.

4. The computing resource sharing verification method according to claim 3, characterized in that, After determining the calculation result of the requested computing resource task based on the transaction information and the service node, the process includes: Determine the historical reputation value of the service node; If a calculation timeout is determined based on the calculation completion time, the user node's account and the service node's account are updated based on the service node's compensation fee. If the user node confirms the calculation result based on the historical reputation value, the user node's account and the service node's account are updated based on the calculation cost; The reputation values ​​of the user node and the service node are updated based on the calculation results.

5. The computing resource sharing verification method according to claim 4, characterized in that, The process of determining the verification fee, the user node fee, and the service node fee based on the verification results includes: If the verification result confirms that the calculation result is accurate, the compensation fee and verification fee for the user node are determined. If the verification result indicates that the calculation result is inaccurate, determine the compensation fee and verification fee for the service node; The reputation values ​​of the user node and the service node are updated based on the verification results.

6. A computing resource sharing verification device, characterized in that, include: The transaction information determination module is used to determine the request computing resource task generated by the user node, the service node matched by the user node, and the transaction information corresponding to the request computing resource task. The process of determining the service node matching the user node includes: obtaining the service queue information, service quality, and integrity assessment value of each service node through the road test unit connected to the user node; based on the service queue information, service quality, and integrity assessment value of each service node, as well as the service queue information of the road test unit, using a deep reinforcement learning algorithm to determine the service node matching the user node; wherein, the policy network of the deep reinforcement learning algorithm takes the system state as input, including the estimated total vehicle service latency, the maximum tolerable service latency, the number of queued services in the service transmission waiting queue of the roadside unit, the number of queued services in the vehicle service waiting queue of the service node, the service quality, and the integrity assessment value, and the output of the policy network is the decided service node; the deep reinforcement learning algorithm is the Double DQN algorithm, and the Double DQN algorithm is selected to train the network; during the training phase, the overall optimization objective function is used as the training basis for the network, and the reward value is the service revenue of successful service, minus the verification time consumption caused by malicious denial by the user, or the service deception penalty for service failure plus the verification time consumption, or the timeout penalty; The calculation result determination module is used to determine the calculation result of the requested computing resource task based on the transaction information and the service node; The verification module is used to verify the calculation result through a verification node connected to the user node when the user node rejects the calculation result; wherein, when the user node's verification process is automatically activated, a transaction recording the task index is generated and sent to the vehicle serving the region, and the road test unit corresponding to this transaction management serves as the verification node; the region is the region where the user node is located. The cost determination module is used to determine the verification cost, the cost of the user node, and the cost of the service node based on the verification results.

7. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the computing resource sharing verification method as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the computing resource sharing verification method as described in any one of claims 1 to 5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the computing resource sharing verification method as described in any one of claims 1 to 5.

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