Vehicle auxiliary edge computing offloading method and device, electronic equipment and medium

Abstract

The application discloses a vehicle-assisted edge computing offloading method and device, electronic equipment and medium, which is applied to a road side unit and a vehicle. In response to the fact that the self computing resource of the road side unit is less than a preset threshold, a scoring function of a computing offloading task is published. A bidding strategy sent by the vehicle is received, and the vehicle is sorted based on the bidding strategy. An offloading object is selected from the vehicle according to the sorting. In response to receiving the scoring function of the computing offloading task published by the road side unit, a bidding strategy is formulated according to the scoring function and self parameters. The bidding strategy is sent to the road side unit. The vehicle-assisted edge computing offloading method provided by the application can recruit surrounding vehicles to perform a computing offloading task when the self computing resource of the road side unit is insufficient, so that the computing load pressure of the road side unit can be relieved, and the safety and efficiency of edge computing can be ensured.

Description

Technical Field

[0001] This invention relates to the field of vehicle networking technology, and in particular to edge computing offloading methods, devices, electronic devices and media in vehicle networking scenarios. Background Technology

[0002] The development of connected vehicle applications and services has led to an explosive growth in data. Deploying infrastructure such as Roadside Units (RSUs) can support the massive computing and storage resource demands arising from this data growth. When vehicle users enter the communication range of a Roadside Unit, they can establish communication and transmit content through their onboard units (OBUs). Users expect to experience ubiquitous connected vehicle services, but this expectation cannot be met due to the prohibitively high cost of densely deploying Roadside Units. Furthermore, due to hardware limitations, the computing and storage resources of Roadside Units are limited. When providing services to vehicle users, the availability of Roadside Unit resources directly impacts the Quality of Experience (QoE), and insufficient Roadside Unit resources directly threaten the efficiency of edge computing.

[0003] Because roadside units (LSUs) cannot be densely deployed, vehicle users establish communication with them opportunistically during driving, potentially leading to vehicles entering areas without LSU coverage. Furthermore, the computing and storage resources of LSUs themselves may be insufficient to meet the surge in service requests from users, especially in traffic hotspots. During peak hours, LSUs may face excessive pressure from edge computing tasks. In addition, the dynamic changes in the vehicle-to-everything (V2X) topology caused by vehicle movement can lead to uneven resource allocation. Therefore, resource sharing strategies are being considered, and how to utilize idle resources within the V2X has become a pressing issue. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to overcome the defects in the prior art and provide a vehicle-assisted edge computing unloading method, device, electronic device and medium based on a multi-attribute reverse auction mechanism.

[0005] This invention solves the above-mentioned technical problems through the following technical solution: a vehicle-assisted edge computing unloading method, characterized in that it is applied to a roadside unit and includes,

[0006] In response to the roadside unit's own computing resources being less than a preset threshold, a scoring function for the computing offloading task is published. The scoring function is used to characterize the roadside unit's preference for various attributes when performing the computing offloading task.

[0007] Receive the bidding strategy sent by the vehicle, and sort the vehicle based on the bidding strategy;

[0008] The unloading target is selected from the vehicles according to the sorting.

[0009] Preferably, before publishing the scoring function for the computational offloading task in response to the roadside unit's own computing resources being less than a preset threshold, the method further includes:

[0010] Construct a system model based on the roadside unit and surrounding roads;

[0011] The scoring function for the computational unloading task is determined based on the system model.

[0012] Preferably, determining the scoring function for the computational unloading task based on the system model includes:

[0013] The minimum requirement for non-price attributes in the scoring function is determined based on the system model.

[0014] Preferably, before receiving the bidding strategy sent by the receiving vehicle, the method further includes:

[0015] Obtain the reputation value of the vehicle that sends the bidding strategy to the roadside unit;

[0016] The vehicles that send the bidding strategy are filtered based on the reputation value.

[0017] Preferably, the method further includes,

[0018] A task data packet is sent to the uninstallation object. The task data packet is obtained by dividing the total task packet into the number of uninstallation objects and is randomly sent to the uninstallation object.

[0019] Receive the task result data packet sent by the uninstallation object;

[0020] Both the task data packet and the task result data packet are encrypted.

[0021] Preferably, the attributes include price attributes and non-price attributes, and the rating function includes a satisfaction function for representing the preference for non-price attributes, wherein the rating function is the satisfaction function minus the function representing the price attributes.

[0022] In another aspect, the present invention provides a vehicle-assisted edge computing unloading method, characterized in that it is applied to a vehicle and includes,

[0023] In response to receiving the scoring function for the calculation and unloading task published by the roadside unit, determine whether the vehicle meets the conditions for participating in the calculation and unloading task;

[0024] In response to the vehicle meeting the conditions for participating in the unloading task calculation, a bidding strategy is formulated based on the scoring function and its own parameters; the bidding strategy is a strategy that maximizes the value of the scoring function.

[0025] The bidding strategy is sent to the roadside unit.

[0026] Preferably, the rating function includes a satisfaction function for characterizing preferences for non-price attributes;

[0027] The bidding strategy is determined based on the scoring function and its own parameters; specifically, this involves...

[0028] The optimal bidding strategy for each non-price attribute in the satisfaction function is determined based on the satisfaction function and the vehicle's own cost function in the scoring function.

[0029] The optimal vehicle pricing strategy is determined based on the optimal bidding strategy for the aforementioned non-price attributes.

[0030] Preferably, the method further includes receiving a task data packet sent from the roadside unit, performing a calculation unloading task and generating a task result data packet, and sending the task result data packet to the roadside unit.

[0031] Preferably, both the task data packet and the task result data packet are encrypted.

[0032] In another aspect, the present invention provides a vehicle-assisted edge computing unloading device, characterized in that it is located in a roadside unit and includes,

[0033] The publishing unit is used to publish a scoring function for the computational offloading task in response to the roadside unit's own computing resources being less than a preset threshold. The scoring function is used to characterize the roadside unit's preference for various attributes when performing the computational offloading task.

[0034] The receiving unit is used to receive the bidding strategy sent by the vehicle;

[0035] A sorting unit is used to sort the vehicles based on the bidding strategy;

[0036] The selection unit is used to select an unloading object from the vehicles according to the sorting.

[0037] In another aspect, the present invention provides a vehicle-assisted edge computing unloading device, characterized in that it is located in the vehicle and includes,

[0038] A response unit is used to determine whether the vehicle meets the conditions for participating in the calculation and unloading task in response to receiving a scoring function for the calculation and unloading task published by the roadside unit.

[0039] The strategy formulation unit is used to formulate a bidding strategy based on the scoring function and its own parameters in response to the vehicle meeting the conditions for participating in the calculation of the unloading task; the bidding strategy is a strategy that maximizes the value of the scoring function.

[0040] A sending unit is used to send the bidding strategy to the roadside unit.

[0041] In another aspect, the present invention provides an electronic device comprising:

[0042] At least one processor; and

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions for execution by the at least one processor, which, when executed, enable the at least one processor to perform any of the methods described above.

[0045] In another aspect, the present invention provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in any of the preceding claims.

[0046] The positive and progressive effects of this invention are as follows: It provides a vehicle-assisted edge computing offloading method based on multi-attribute auction, which takes into account the situation that the computing resources of roadside units cannot cope with the explosive edge computing requests of vehicle users, and provides a computing offloading method for vehicle-assisted edge computing, which can alleviate the computing load pressure of roadside units and ensure the efficiency of edge computing.

[0047] The vehicle-assisted edge computing offloading method provided by this invention considers the reputation value of vehicles to screen unreliable vehicles during the auction process and encrypts data transmission to avoid data security problems caused by malicious attacks. Attached Figure Description

[0048] Figure 1 This is a schematic flowchart of the vehicle-assisted edge computing unloading method provided in Embodiment 1 of the present invention;

[0049] Figure 2 This is a schematic flowchart of the vehicle-assisted edge computing unloading method provided in Embodiment 2 of the present invention;

[0050] Figure 3 This is a schematic diagram of the system model used in the vehicle-assisted edge computing unloading method provided in Embodiment 2 of the present invention;

[0051] Figure 4 This is a schematic flowchart of the vehicle-assisted edge computing unloading method provided in Embodiment 3 of the present invention;

[0052] Figure 5 This is a schematic flowchart of the vehicle-assisted edge computing unloading method provided in Embodiment 4 of the present invention;

[0053] Figure 6 This is a schematic diagram of the specific process of step S402 of the vehicle-assisted edge computing unloading method provided in Embodiment 4 of the present invention;

[0054] Figure 7 This is a flowchart illustrating the vehicle-assisted edge computing unloading method provided in Embodiment 5 of the present invention;

[0055] Figure 8 This is a schematic diagram of the vehicle-assisted edge computing unloading device provided in Embodiment 6 of the present invention;

[0056] Figure 9 This is a schematic diagram of another vehicle-assisted edge computing unloading device provided in Embodiment 6 of the present invention;

[0057] Figure 10 This is a schematic diagram of the vehicle-assisted edge computing unloading device provided in Embodiment 7 of the present invention;

[0058] Figure 11 This is a schematic diagram of the vehicle-assisted edge computing unloading device provided in Embodiment 8 of the present invention. Detailed Implementation

[0059] The present invention will be further illustrated by way of embodiments below, but the present invention is not limited to the scope of the embodiments described herein.

[0060] Example 1

[0061] like Figure 1 The diagram shown is a schematic flowchart of the vehicle-assisted edge computing unloading method provided in Embodiment 1 of the present invention. The method is applied to a roadside unit (RSU) and includes the following steps:

[0062] S101, in response to the roadside unit's own computing resources being less than a preset threshold, a scoring function for the computing offloading task is published, the scoring function being used to characterize the roadside unit's preference for various attributes when performing the computing offloading task;

[0063] S102, Receive the bidding strategy sent by the vehicle, and sort the vehicle according to the bidding strategy;

[0064] S103, Select the unloading object from the vehicles according to the sorting.

[0065] During peak hours, roadside units can recruit surrounding vehicles to participate in edge computing services to alleviate their own computing load. The roadside unit checks its own computing resources in real time, with a preset threshold. Once its computing resources fall below the preset threshold, the roadside unit publishes the computing offloading task to the cloud service platform and simultaneously publishes the scoring function for the computing offloading task. Vehicles with idle resources can participate in the task bidding and execute the computing offloading task.

[0066] The scoring function for calculating the unloading task can be published by the road test unit to all vehicles within its coverage area, or it can be selectively published to only certain vehicles, such as only to certain parked vehicles, or for example, to vehicles in certain specific locations, etc.

[0067] The scoring function characterizes the roadside unit's preference for various attributes when performing the computational unloading task. A higher scoring function value represents a higher level of utility obtained by the roadside unit. The scoring function includes various attributes considered by the roadside unit when performing edge computational unloading to the vehicle, including non-price attributes and price attributes.

[0068] The non-price attributes include bandwidth, computing resources, and service time. They may also include various attributes related to the cost of providing computing offloading services by the vehicle but not to price, such as reliability, security, energy efficiency, network connectivity, and privacy protection—in other words, the resources that the vehicle can provide for computing offloading services. The price attribute is the vehicle's bidding price. In the scoring function, the part describing the non-price attributes has a separate definition: a satisfaction function. This satisfaction function characterizes the preference for non-price attributes in the scoring function. The value of the satisfaction function indicates the level of utility provided by the vehicle's computing offloading services.

[0069] Typically, in the usage scenario of this embodiment, the scoring function includes a satisfaction function minus a function representing the price attribute. That is, the total utility that the roadside unit can obtain by selecting the corresponding vehicle to perform the unloading task is reduced by the price it needs to pay. The scoring function value is used to characterize the utility level of the roadside unit. Therefore, maximizing the scoring function value indicates that the utility of the roadside unit is maximized.

[0070] In step S102, after receiving bidding strategies from multiple vehicles, the roadside unit sorts the vehicles based on the bidding strategies.

[0071] Specifically, the vehicles are ranked based on the bidding strategy, including:

[0072] S1021, determine the scoring function value of the vehicle based on the bidding strategy; to determine the utility that the roadside unit can obtain by selecting the corresponding vehicle to perform the calculation unloading task.

[0073] S1022, Sort the vehicles based on the scoring function value.

[0074] The vehicle is one that receives the scoring function published by the roadside unit and determines whether its own conditions meet the requirements for participating in the unloading task calculation. After determining that it can participate in the unloading task calculation, the vehicle uses a bidding strategy based on the scoring function and its own parameters to maximize the utility of both the roadside unit and itself.

[0075] Step S103: Based on the sorting, select the top R vehicles in the sorting as unloading targets as needed, and perform the calculation unloading task.

[0076] This embodiment provides a vehicle-assisted edge computing offloading method based on multi-attribute auctions. It considers the situation where roadside unit computing resources cannot cope with the explosive edge computing requests from vehicle users, and provides a vehicle-assisted edge computing offloading method to alleviate the computational load pressure on roadside units. Roadside units can meet edge computing needs by scheduling idle computing resources from neighboring vehicles. By analyzing the utility of roadside units and vehicles, a multi-attribute reverse auction mechanism is applied to simulate the collaboration process between roadside units and vehicles. Vehicles will choose a bidding strategy that maximizes the utility of the roadside unit to meet its needs. After the vehicles participating in the auction decide on their bidding strategies, the roadside unit can choose the set of vehicles that maximizes its own utility to participate in edge computing.

[0077] Example 2

[0078] The vehicle-assisted edge computing unloading method provided in this embodiment can also be applied to roadside units, such as... Figure 2 As shown, the method includes,

[0079] S2001, Construct a system model based on the roadside unit and surrounding roads;

[0080] S2002, Determine the scoring function for the computational unloading task based on the system model;

[0081] S201, in response to the roadside unit's own computing resources being less than a preset threshold, a scoring function for the computing offloading task is published, the scoring function being used to characterize the roadside unit's preference for various attributes when performing the computing offloading task;

[0082] S202, Receive the bidding strategy sent by the vehicle, and sort the vehicle according to the bidding strategy;

[0083] S203, Select the unloading object from the vehicles according to the sorting.

[0084] In step S2001, a system model is constructed based on the roadside unit and its surrounding roads;

[0085] Specifically, determining the scoring function for the computational unloading task based on the system model includes determining the minimum requirements for each non-price attribute in the scoring function based on the system model.

[0086] As an alternative implementation method, such as Figure 3 As shown, the system model includes a set of RSUs (Resource Units) providing edge computing services, denoted as J = {1,…,j,…,J}. During peak hours, RSUs can recruit vehicles to participate in edge computing services to alleviate computing load pressure.

[0087] For any RSUj, j∈J, the set of roads within its communication range is represented as L. j = {1,…l,…L}. The probability that a vehicle entering the coverage area of ​​RSUj requests RSUj's assistance in completing a computational task is denoted by P. re express.

[0088] For roads l∈L within the coverage area of ​​RSUj j In other words, the vehicle arrival rate over a future period, i.e., the number of vehicles arriving per unit time, can be represented by λ. l Indicates. λ l It follows a Poisson distribution. Vehicle arrival rate is related to vehicle density and the average speed of vehicles within the road. λ l It remains basically stable over a period of time, which is represented as

[0089] λ l =ρ l v l (2.1)

[0090] Where, ρ l v represents the traffic density within road l. l This represents the average speed of all vehicles within road l.

[0091] For RSU j, the vehicle arrival rate over a future period is determined by the vehicle arrival rate of each road within its coverage area, denoted as:

[0092]

[0093] From λ j This allows us to obtain the number of vehicles arriving within the communication range of RSUj per unit time. The total number of vehicles entering the range of RSUj directly affects the number of edge computing requests received by RSUj.

[0094] The set of candidate free vehicles near the RSU is denoted as I = {1,…,i,…,I}. RSUj checks its remaining computing resources a. j This determines whether to issue a scoring function to surrounding vehicles to recruit cars to perform the computational unloading task. When a... j When the resource consumption falls below a preset threshold, RSUj automatically requests a computation offloading task to handle the received edge computing tasks. RSU publishes these offloading tasks to a cloud service platform, where vehicles with idle resources can participate in the bidding. RSUj publishes a scoring function and selects the set of vehicles that maximize their utility from the candidate vehicles through an auction process. The scoring function includes both price and non-price attributes.

[0095] To ensure that the vehicles undergoing computational unloading meet the basic expectations of RSUj, it is necessary to define the minimum requirements of RSUj for its non-price attributes. In this embodiment, the selected non-price attributes are bandwidth, computational resources, and service time. Using b j This represents the minimum bandwidth requirement of RSUj for candidate vehicles, c j t represents the minimum computational resource requirements of RSUj for candidate vehicles. j RSUj represents the minimum service time requirement for candidate vehicles. Based on historical traffic conditions during the same period, RSUj can roughly estimate the received edge computing workload and determine the minimum service time requirement t. j .

[0096] The bandwidth provided by vehicle i participating in the bidding needs to meet the expectations of RSUj to support the offloading of various computational tasks and the uploading of computation results. Therefore, the minimum bandwidth required by each candidate vehicle is the minimum bandwidth requirement b of RSUj. j , represented as

[0097]

[0098] Among them, Z in Z represents the average size of the computation task. out The average size of the calculated results is represented by R. R represents the number of vehicles that RSUj chooses to cooperate with. The set R = {1, ..., r, ..., R} is introduced to represent the final set of vehicles that RSUj chooses to cooperate with.

[0099] RSUj's minimum computing resource requirements c j c j It is obtained from the following formula,

[0100]

[0101] Where Q represents the average computing resources required by vehicles submitting edge computing requests to this RSUj, and Pre The probability of requesting RSUj's assistance in completing the computation task for each vehicle within the coverage area of ​​RSUj.

[0102] Based on this, it can be determined that the scoring function for calculating the unloading task by the roadside unit RSUj is:

[0103]

[0104] in The rating function is a satisfaction function related to non-price attributes and used to characterize the preference for non-price attributes. The rating function is the satisfaction function characterizing non-price attributes minus the function characterizing price attributes.

[0105]

[0106] κ i ξ i These are the bidding strategies for vehicle i, i∈I, in terms of bandwidth, computing resources, and service time; that is, the bidding strategies for each non-price attribute, p. i This is the bidding price for vehicle i. ω b ω c and ω t Let α be the weighting coefficients of each non-price attribute in the satisfaction function. The three terms on the right-hand side of the equation represent RSUj's satisfaction with the bidding strategies submitted by vehicle i regarding bandwidth, computing resources, and service time. b α c and α t The adjustment parameter is greater than 1, used to ensure that the satisfaction level of RSUj is greater than 0.

[0107] As another alternative implementation, more non-price attributes can be selected to determine the scoring function, or the function for the price attribute part can be modified, such as adding a corresponding fixed parameter before the bidding price of the vehicle, etc.

[0108] Steps S201, S202, and S203 in this embodiment are basically the same as in Embodiment 1.

[0109] In this embodiment, by using a pre-built model to accurately determine the scoring function for computational unloading tasks, roadside units can publish computational unloading tasks more efficiently and select vehicles that maximize their own utility to cooperate in computational unloading tasks more accurately. This not only reduces the computational and network burden on roadside units but also better provides services to other devices in the vehicle network, thereby improving the resource utilization efficiency in the vehicle network.

[0110] Example 3

[0111] like Figure 4As shown, the vehicle-assisted edge computing unloading method provided in this embodiment includes the following steps.

[0112] S301, in response to the roadside unit's own computing resources being less than a preset threshold, a scoring function for computing the unloading task is published, the scoring function being used to characterize the roadside unit's preference for various attributes when computing the unloading task;

[0113] S3001, Obtain the reputation value of the vehicle that sent the bidding strategy to the roadside unit;

[0114] S3002, Based on the reputation value, filter the vehicles that send the bidding strategy;

[0115] S302, Receive the bidding strategy sent by the vehicle, and sort the vehicle according to the bidding strategy;

[0116] S303, Select the unloading object from the vehicles according to the sorting;

[0117] S304, a task data packet is sent to the unloading object. The task data packet is obtained by dividing the total task into the number of unloading objects and is randomly sent to the unloading object.

[0118] S305, Receive the task result data packet sent by the unloading object;

[0119] Both the task data packet and the task result data packet are encrypted.

[0120] In step S3001, the reputation value of the vehicle that sends the bidding strategy to the roadside unit is obtained; specifically, the reputation value of vehicle i, i∈I, that sends the bidding strategy to RSUj is χ. ij .

[0121] χ j ,χ j ∈[0,1] represents the initial reputation threshold when RSUj publishes the computational unloading task, and the minimum reputation threshold is a preset parameter. Relying solely on the minimum reputation value to filter the credibility of bidding vehicles cannot accurately prevent unreliable vehicles from maliciously bidding. Moreover, considering that the number of candidate vehicles meeting the conditions within the scope covered by RSUj may be insufficient, the vehicle reputation threshold of candidate vehicles in RSUj should change accordingly.

[0122] Therefore, the following real-time reputation threshold χ should be used. j (t) is used as the threshold for filtering vehicles.

[0123]

[0124] The time decay factor set for RSU j reflects the urgency of the task and affects χ. j The decreasing trend of (t). To calculate the unloading safety of the task, χ² j (t) should satisfy χ j (t)≥ x j .

[0125] The reputation value of vehicle i that sends the bidding strategy to the roadside unit needs to satisfy χ. ij ≥χ j (t). The time t represents the time difference between the RSUj issuing the computational unloading task and the vehicle sending the bidding strategy to the RSUj.

[0126] Therefore, as described above, as an optional implementation, in step S3002, the vehicles sending bidding strategies are screened based on the reputation threshold, specifically as follows:

[0127] Obtain the initial reputation threshold when RSUj publishes the scoring function for the computational unloading task;

[0128] A real-time reputation threshold is determined based on the initial reputation threshold, wherein the real-time reputation threshold is a threshold that changes in real time from the start of the RSU release computation unloading task;

[0129] Determine if the vehicle's reputation value is greater than the real-time reputation threshold. If yes, continue with the subsequent steps; otherwise, refuse to receive the bidding strategy sent by the vehicle.

[0130] This embodiment only illustrates one optional implementation method. Other methods can also be used to screen vehicles that send bidding strategies, or vehicles can be screened by judging their reputation value.

[0131] Using reputation scores to screen vehicles participating in the bidding process can prevent vehicles from threatening the security of edge computing tasks due to their own malicious intent or vulnerability. Reputation score screening can also prevent malicious behavior by vehicles from jeopardizing the task unloading process.

[0132] The vehicle's credit score can be obtained through the following methods:

[0133] Historical evaluation and feedback: RSUs can collect past vehicle behavior and performance data and evaluate vehicles based on this data. For example, a vehicle's reputation score can be assessed based on its driving behavior, cooperation history, violations, etc.

[0134] Certificate Mechanism: Vehicles can prove their identity and trustworthiness through digital certificates. These certificates are issued by authorized authorities and contain the vehicle's identity information and trust level. The RSU can verify the vehicle's certificates to obtain its reputation information and thus determine its reputation value.

[0135] Monitoring and detection: RSU can monitor and detect vehicle behavior through the network, such as detecting malicious attacks or abnormal data transmission, thereby assessing the vehicle's reputation level and obtaining a reputation score.

[0136] Recommended partner vehicles: RSUs can exchange information with other RSUs or vehicles to obtain other RSUs' or vehicles' reputation ratings or recommendations for a specific vehicle, thereby determining its reputation score.

[0137] The method for obtaining vehicle reputation value described in this embodiment is merely an example and is not intended to limit the specific method. The method for obtaining vehicle reputation value may include, but is not limited to, any of the methods described above, and the vehicle's reputation value may change over time.

[0138] By screening vehicles based on their reputation scores, it can be ensured that the unloading targets selected by the roadside unit have sufficient reliability to perform the unloading task calculation, thereby improving the safety performance of the unloading task calculation and avoiding malicious bidding by unreliable vehicles.

[0139] In step S304, a task data packet is sent to the unloading object. The task data packet is obtained by dividing the total task into the number of unloading objects and is randomly sent to the unloading object.

[0140] Step S305: Receive the task result data packet sent by the unloading object;

[0141] Both the task data packet and the task result data packet are encrypted.

[0142] Some computational tasks, such as vehicle navigation, pose a risk of interfering with traffic rules and endangering road safety in the connected vehicle network. If a vehicle user's navigation route is tampered with by attackers, it could lead to the emergence of unconventional traffic hotspots, disrupting the normal operation of the connected vehicle network. Furthermore, vehicle routes involve the driver's private information. If leaked, malicious users could potentially deduce the driver's detailed location at specific times during their journey. Therefore, measures are needed to ensure the security of the computation and transmission processes.

[0143] The original task is transmitted to the roadside unit via vehicles requiring edge computing services. After determining the unloading targets, the roadside unit divides the original task into R task data packets based on the number of unloading targets (i.e., the number of vehicles, R ≥ 2), and randomly transmits these packets to the designated vehicles as the unloading targets via R routes. Upon receiving the task data packets, the designated vehicles decrypt them before performing the unloading calculations and need to transmit the task result data packets back to the roadside unit. These task result data packets can also be transmitted encrypted.

[0144] Both the original task and the corresponding final task result can be encrypted in the following way: S = E k (D), where D represents the ciphertext S encrypted using the symmetric encryption algorithm E and the session key k. The key k is obtained in the initial session using asymmetric encryption; the key k is randomly generated, and different keys are generated for different sessions, resulting in high security. D represents the original task and the corresponding final task result.

[0145] The ciphertext S can be divided into R parts, S = {s1, ..., s2} R There exist R data packets containing sub-ciphertext, denoted as...

[0146] d r ={s r ,seq,r,T r}, r∈1,...,R(3.2)

[0147] Here, seq and r represent the session number and block ID, respectively. The session number and block ID help the receiver reassemble the received information. r It is a timestamp used to defend against replay attacks.

[0148] The final data packet sent to the receiver when the above R data packets are sent is represented as follows:

[0149] m r ={s r ,seq,r,T r H k (s r ,seq,r,T r )}, r∈1,...,R(3.3)

[0150] Among them, H k (s r ,seq,r,T r H represents the message verification code. Based on the session key k and the message digest, H can be obtained. k (s r ,seq,r,T r Before forwarding information, a message verification code needs to be added to each data packet containing the sub-ciphertext. The message verification code is considered a unique signature identifying the data sender. Simultaneously, the message verification code can also be used for data integrity verification. After the receiver receives the task information via the R-path route, it can reassemble and decrypt the information to obtain the original message content.

[0151] In the roadside unit, the task data packets need to be encrypted and the task result data packets need to be decrypted.

[0152] The encryption method described above is merely one optional method exemplified in this embodiment.

[0153] In this embodiment, selecting R unloading objects to execute the computational unloading task and splitting a corresponding number of data packets are intended to improve the transmission reliability and efficiency of the computational unloading task, balance the load, and enhance security, enabling the edge computing system to better cope with unstable network conditions and provide better services. Furthermore, encrypting the data further ensures the security of both the computational unloading process and the data transmission process.

[0154] During the transmission of the original task and the return of the calculation results, due to the broadcast characteristics of the wireless link, the information transmission process may be attacked by malicious third parties, resulting in the loss or even tampering of the original task or calculation results.

[0155] Therefore, to ensure data security, a certificate mechanism can be introduced to verify the identity of the RSU. The certificate is managed and authenticated by the CA (Certificate Authority). Before accessing the network, the RSU requests a certificate from the CA; the certificate contains a key pair. Before transmitting information between the RSU and the vehicle, the RSU information is encrypted using the private key from the key pair before being sent to the vehicle. The vehicle authenticates the identity information from the target RSU using the electronic certificate issued by the CA. The introduction of the certificate mechanism can prevent the RSU identity from being impersonated, ensure the confidentiality of the session key negotiation process, and guarantee communication security.

[0156] After authentication, the vehicle and RSU use a key pair in the initial session and negotiate subsequent session keys using asymmetric encryption. Because the session key negotiation process is encrypted using asymmetric encryption, the key leakage issues associated with symmetric encryption are avoided. After the winning vehicle completes its assigned edge computing task, it uses the session key to symmetrically encrypt the computation results.

[0157] To ensure data integrity during task transmission, hash functions can be used to generate digests of the original information. Since different messages generate different digests using hash functions, malicious third parties cannot forge message digests. During data transmission, a multiplexed return mechanism is used to divide the ciphertext into multiple segments and send them to the destination via multiple routes. This way, even if some routes are attacked and the transmitted ciphertext is intercepted, the attacker only obtains a small encrypted fragment of the original computation task or result. Due to the protection of the session key and ciphertext segmentation, the security of the computation task is further guaranteed.

[0158] Example 4

[0159] This embodiment provides a vehicle-assisted edge computing offloading method, applied to vehicles, such as... Figure 5 As shown,

[0160] S401, in response to receiving the scoring function for calculating the unloading task published by the roadside unit, determine whether the vehicle meets the conditions for participating in the calculation of the unloading task;

[0161] S402, in response to the vehicle meeting the conditions for participating in the calculation of the unloading task, a bidding strategy is formulated based on the scoring function and its own parameters;

[0162] S403, send the bidding strategy to the roadside unit;

[0163] After receiving the scoring function for the unloading task published by the roadside unit, the vehicle needs to first determine whether it meets the conditions for participating in the unloading task. These conditions could be whether its idle time is sufficient for participation, or other conditions, such as the vehicle's own definition to refuse participation in such tasks, or other conditions attached to the unloading task. If the conditions are met, a bidding strategy is formulated based on the scoring function and the vehicle's own parameters; otherwise, it can choose not to participate in the bidding.

[0164] The bidding strategy formulated by the vehicle is a strategy that maximizes the utility of the roadside unit based on the scoring function and its own parameters, that is, a strategy that maximizes the value of the scoring function.

[0165] The vehicle formulates a bidding strategy based on its own parameters and a scoring function. The bidding strategy must maximize the utility of the roadside unit, thereby increasing the vehicle's chances of winning the bid and maximizing the vehicle's own utility.

[0166] The vehicle's own parameters, including the vehicle's own cost parameter θ i For various non-price attributes such as fixed cost parameters and unit power consumption, different vehicles have different parameters, which leads to different bidding strategies and varying scores that can be provided to roadside units.

[0167] As shown in Example 2, when the non-price attributes are selected as bandwidth, computing resources, and service time, the scoring function for the roadside unit RSUj to calculate the offloading task can be expressed as:

[0168]

[0169] The scoring function is used to characterize the utility of the roadside unit RSUj, where The rating function is a satisfaction function related to non-price attributes and used to characterize the preference for non-price attributes. The rating function is the satisfaction function characterizing non-price attributes minus the function characterizing price attributes.

[0170]

[0171] k i ξ i These are the bidding strategies for vehicle i, i∈I, in terms of bandwidth, computing resources, and service time; that is, the bidding strategies for each non-price attribute, p. i This is the bidding price for vehicle i. ω b ω c and ω t These are the weighting coefficients for each non-price attribute in the satisfaction function.

[0172] Therefore, when a vehicle formulates a bidding strategy based on the above scoring function and its own parameters, it should pursue the maximization of RSU utility.

[0173] The optimization objective can be defined as:

[0174]

[0175]

[0176] Meanwhile, the self-utility function of vehicle i can be expressed as,

[0177]

[0178] in, The cost function representing vehicle i, i.e.

[0179]

[0180] Among them, the cost parameter θ i These are private parameters of vehicle i. The vehicle can be accessed via θ. i The probability distribution is used to estimate the private cost parameters of other competing vehicles. Typically, θ is assumed to be... i obey A uniform distribution. i β represents the power consumption of vehicle i per unit time. b β c and β t It is the fixed cost parameter of vehicle i for each non-price attribute, namely bandwidth, computing resources and service time.

[0181] In step S402, the bidding strategy is determined based on the scoring function and its own parameters; specifically, as follows: Figure 6 As shown,

[0182] S4021, Determine the optimal bidding strategy for each non-price attribute in the satisfaction function based on the satisfaction function and the vehicle cost function in the scoring function;

[0183] S4022, Determine the optimal pricing strategy for the vehicle based on the optimal bidding strategy for the non-price attribute.

[0184] Specifically, determining the optimal bidding strategy for each non-price attribute in the satisfaction function based on the satisfaction function and the vehicle cost function in the scoring function involves maximizing the satisfaction function minus the vehicle cost function in the scoring function to determine the optimal bidding strategy for each non-price attribute in the satisfaction function.

[0185] Specifically, this can be expressed as follows:

[0186]

[0187] Furthermore, the optimal bandwidth bidding strategy for vehicle i can be obtained by solving the problem. for:

[0188]

[0189] Similarly, the optimal bidding strategy for computational resources for vehicle i can be expressed as:

[0190]

[0191] The optimal bidding strategy for the service time of vehicle i is:

[0192]

[0193] Based on the optimal bidding strategy for the non-price attributes already determined above, the optimal pricing strategy for vehicle i is determined by maximizing RSU utility.

[0194]

[0195] The optimal bidding strategy and optimal pricing strategy for the aforementioned non-price attributes constitute the bidding strategy for the vehicle. By bidding for the vehicle according to this strategy, the RSU can maximize its utility, thereby maximizing its chances of winning the bid and ultimately maximizing the vehicle's own utility.

[0196] Example 5

[0197] like Figure 7 The diagram shown is a flowchart of the vehicle-assisted edge computing unloading method provided in this embodiment. In this embodiment, based on the above-described embodiment 4, the following steps may be further included:

[0198] S501, Receive the task data packet sent from the roadside unit.

[0199] S502, execute the calculation unloading task and generate a task result data packet, and send the task result data packet to the roadside unit.

[0200] The above steps are based on the fact that the bidding strategy sent by the vehicle meets the needs of the roadside unit. The roadside unit determines to select the vehicle to perform the calculation and unloading task. At this time, the roadside unit will send the task data packet to the vehicle. After the vehicle performs the calculation and unloading task, it will send the obtained task result data packet to the roadside unit.

[0201] As an optional implementation, both the task data packet and the task result data packet are encrypted. The encryption method can refer to the encryption method in Embodiment 2, or other encryption methods can be used. Therefore, on the vehicle side, the task data packet needs to be decrypted, and the task result data packet needs to be encrypted.

[0202] Encrypting data can improve the reliability, efficiency, and security of data transmission, enabling edge computing systems to better cope with unstable network conditions and provide better services.

[0203] Example 6

[0204] like Figure 8 As shown, this embodiment provides a vehicle-assisted edge computing unloading device. The device, located in a roadside unit, includes...

[0205] The publishing unit 601 is used to publish a scoring function for the computational offloading task in response to the roadside unit's own computing resources being less than a preset threshold. The scoring function is used to characterize the roadside unit's preference for various attributes when performing the computational offloading task.

[0206] The attributes include price attributes and non-price attributes, and the rating function includes a satisfaction function for representing the preference for non-price attributes. The rating function is the satisfaction function minus the function representing the price attributes.

[0207] The receiving unit 602 is used to receive the bidding strategy sent by the vehicle;

[0208] The sorting unit 603 is used to sort the vehicles based on the bidding strategy;

[0209] The selection unit 604 is used to select an unloading object from the vehicles according to the sorting.

[0210] Preferably, the sorting unit 603 includes a determining unit 6031, which is used to determine the evaluation function value of the vehicle based on the vehicle's bidding strategy; the sorting unit 603 is used to sort the vehicles according to the evaluation function value.

[0211] As an option, such as Figure 9 As shown, the device also includes,

[0212] Construction unit 605 constructs a system model based on the roadside unit and surrounding roads;

[0213] The scoring function determination unit 606 is used to determine the scoring function of the computational unloading task based on the system model.

[0214] The scoring function determination unit 606 determines the minimum requirement for non-price attributes in the scoring function based at least on the system model.

[0215] In some alternative embodiments, the device further includes,

[0216] The reputation screening unit 607 is used to obtain the reputation value of the vehicle that sends the bidding strategy to the roadside unit; and to screen the vehicle that sends the bidding strategy based on the reputation value.

[0217] In some alternative embodiments, the filtering of vehicles sending bidding strategies based on the reputation threshold specifically involves:

[0218] Obtain the initial reputation threshold when RSUj publishes the scoring function for the computational unloading task;

[0219] A real-time reputation threshold is determined based on the initial reputation threshold, wherein the real-time reputation threshold is a threshold that changes in real time from the start of the RSU release computation unloading task;

[0220] Determine if the vehicle's reputation value is greater than the real-time reputation threshold. If yes, continue with the subsequent steps; otherwise, refuse to receive the bidding strategy sent by the vehicle.

[0221] By screening vehicles based on their reputation scores, it can be ensured that the unloading targets selected by the roadside unit have sufficient reliability to perform the unloading task calculation, thereby improving the safety performance of the unloading task calculation and avoiding malicious bidding by unreliable vehicles.

[0222] The device also includes,

[0223] The task sending unit 608 is used to send a task data packet to the unloading object. The task data packet is obtained by dividing the total task packet into the number of unloading objects and is randomly sent to the unloading object.

[0224] The result receiving unit 609 is used to receive the task result data packet sent by the unloading object;

[0225] The encryption processing unit 610 is used to encrypt and decrypt both the task data packet and the task result data packet.

[0226] Some computational tasks, such as vehicle navigation, pose a risk of interfering with traffic rules and endangering road safety in the connected vehicle network. If a vehicle user's navigation route is tampered with by attackers, it could lead to the emergence of unconventional traffic hotspots, disrupting the normal operation of the connected vehicle network. Furthermore, vehicle routes involve the driver's private information. If leaked, malicious users could potentially deduce the driver's detailed location at specific times during their journey. Therefore, measures are needed to ensure the security of the computation and transmission processes.

[0227] After determining the unloading targets, the roadside unit divides the original task requiring unloading calculation into R task data packets based on the number of unloading targets, i.e., the number of vehicles R (R≥2). These packets are then randomly transmitted to the designated vehicles as unloading targets via R routes. Upon receiving the task data packets, each designated vehicle performs a calculation and then sends the task result data packet back to the roadside unit.

[0228] Selecting R unloading objects for computational unloading tasks and splitting them into a corresponding number of data packets aims to improve the reliability and efficiency of computational unloading task transmission, balance the load, and enhance security, enabling the edge computing system to better cope with unstable network conditions and provide better services. Furthermore, encrypting the data further ensures the security of both the computational unloading process and the data transmission process.

[0229] This embodiment provides a vehicle-assisted edge computing offloading device based on multi-attribute auctions. It addresses the situation where roadside unit computing resources cannot handle the explosive edge computing requests from vehicle users, providing a computational offloading method for vehicle-assisted edge computing to alleviate the computational load pressure on roadside units. Roadside units can meet edge computing needs by scheduling idle computing resources from neighboring vehicles. By analyzing the utility of roadside units and vehicles, a multi-attribute reverse auction mechanism is applied to simulate the collaboration process between them. Vehicles choose bidding strategies that maximize the utility of the roadside unit to meet its needs. After the participating vehicles determine their bidding strategies, the roadside unit can select the set of vehicles that maximizes its own utility to participate in edge computing.

[0230] Example 7

[0231] like Figure 10 As shown, this embodiment provides a vehicle-assisted edge computing unloading device. The device is located in the vehicle and includes...

[0232] The response unit 701 is used to determine whether the vehicle meets the conditions for participating in the calculation and unloading task in response to receiving the scoring function of the calculation and unloading task published by the roadside unit.

[0233] The strategy formulation unit 702 is used to formulate a bidding strategy based on the scoring function and its own parameters in response to the vehicle meeting the conditions for participating in the calculation of the unloading task; the bidding strategy is a strategy that maximizes the value of the scoring function.

[0234] The sending unit 703 is used to send the bidding strategy to the roadside unit.

[0235] Preferably, the strategy formulation unit 702 includes a non-price attribute formulation unit 7021, which is used to determine the optimal bidding strategy for each non-price attribute in the satisfaction function based on the satisfaction function and the vehicle's own cost function in the scoring function;

[0236] The price attribute determination unit 7022 is used to determine the optimal pricing strategy for the vehicle based on the optimal bidding strategy of the non-price attribute.

[0237] Specifically, the optimal bidding strategy for each non-price attribute in the satisfaction function is determined based on the satisfaction function and the vehicle cost function in the rating function. More specifically, the optimal bidding strategy for each non-price attribute in the satisfaction function is determined by maximizing the difference between the satisfaction function and the vehicle cost function in the rating function. Based on the determined optimal bidding strategies for the non-price attributes, the optimal pricing strategy for vehicle i is then determined.

[0238] The optimal bidding strategy and optimal pricing strategy for each of the aforementioned non-price attributes constitute the bidding strategy for the vehicle. The vehicle can be auctioned according to this strategy, maximizing the utility of the RSU and thus maximizing its chances of winning the bid, while also maximizing the vehicle's own utility.

[0239] The device further includes a task receiving unit 704 for receiving task data packets sent from the roadside unit.

[0240] The task execution unit 705 is used to execute the computational unloading task and generate a task result data packet.

[0241] The task result sending unit 706 is used to send the task result data packet to the roadside unit.

[0242] The above steps are based on the fact that the bidding strategy sent by the vehicle meets the needs of the roadside unit. The roadside unit determines to select the vehicle to perform the calculation and unloading task. At this time, the roadside unit will send the task data packet to the vehicle. After the vehicle performs the calculation and unloading task, it will send the obtained task result data packet to the roadside unit.

[0243] As an optional implementation, the device further includes an encryption unit 704 for decrypting the received task data packets and encrypting the task result data packets.

[0244] The device in this embodiment uses encryption to improve the reliability, efficiency, and security of transmitted data, enabling the edge computing system to better cope with unstable network conditions and provide better services.

[0245] Example 8

[0246] Figure 11 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present invention is shown. Device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in ROM (Read-Only Memory) 502 or loaded from storage unit 508 into RAM (Random Access Memory) 503. Various programs and data required for the operation of device 500 may also be stored in RAM 503. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. An I / O (Input / Output) interface 505 is also connected to bus 504.

[0247] Multiple components in device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0248] The computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the vehicle-assisted edge computing offloading method. For example, in some embodiments, the vehicle-assisted edge computing offloading method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program can be loaded and / or installed on device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the methods described above can be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the aforementioned vehicle-assisted edge computing offloading method by any other suitable means (e.g., by means of firmware).

[0249] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0250] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0251] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0252] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A vehicle-assisted edge computing unloading method, characterized in that, Applied to roadside units, including, Construct a system model based on the roadside unit and surrounding roads; The scoring function for calculating the unloading task is determined based on the system model; specifically, the minimum requirement for non-price attributes in the scoring function is determined based on the system model. In response to the roadside unit's own computing resources being less than a preset threshold, a scoring function for the computing offloading task is published. The scoring function is used to characterize the roadside unit's preference for various attributes when performing the computing offloading task. Receive the bidding strategy sent by the vehicle, and sort the vehicle based on the bidding strategy; The unloading target is selected from the vehicles according to the sorting; The attributes include price attributes and non-price attributes, and the rating function includes a satisfaction function for representing the preference for non-price attributes. The rating function is the satisfaction function minus the function representing the price attributes.

2. The vehicle-assisted edge computing unloading method as described in claim 1, characterized in that, Before receiving the bidding strategy sent by the vehicle, the following steps are also included: Obtain the reputation value of the vehicle that sends the bidding strategy to the roadside unit; The vehicles that send the bidding strategy are filtered based on the reputation value.

3. The vehicle-assisted edge computing unloading method as described in claim 1, characterized in that, The method also includes, A task data packet is sent to the uninstallation object. The task data packet is obtained by dividing the total task packet into the number of uninstallation objects and is randomly sent to the uninstallation object. Receive the task result data packet sent by the uninstallation object; Both the task data packet and the task result data packet are encrypted.

4. A vehicle-assisted edge computing unloading method, characterized in that, Applied to vehicles, including, In response to receiving the scoring function for the calculation and unloading task published by the roadside unit, determine whether the vehicle meets the conditions for participating in the calculation and unloading task; In response to the vehicle meeting the conditions for participating in the unloading task calculation, a bidding strategy is formulated based on the scoring function and its own parameters; the bidding strategy is a strategy that maximizes the value of the scoring function. The bidding strategy is sent to the roadside unit; The scoring function includes a satisfaction function to characterize preferences for non-price attributes; the bidding strategy is determined based on the scoring function and its own parameters; specifically, The optimal bidding strategy for each non-price attribute in the satisfaction function is determined based on the satisfaction function and the vehicle's own cost function in the scoring function. The optimal vehicle pricing strategy is determined based on the optimal bidding strategy for the aforementioned non-price attributes.

5. The vehicle-assisted edge computing unloading method as described in claim 4, characterized in that, The method further includes receiving a task data packet sent from the roadside unit, performing a calculation unloading task and generating a task result data packet, and sending the task result data packet to the roadside unit.

6. The vehicle-assisted edge computing unloading method as described in claim 5, characterized in that, Both the task data packet and the task result data packet are encrypted.

7. A vehicle-assisted edge computing unloading device, characterized in that, Located in the roadside unit, including, The publishing unit is used to publish a scoring function for the computational offloading task in response to the roadside unit's own computing resources being less than a preset threshold. The scoring function is used to characterize the roadside unit's preference for various attributes when performing the computational offloading task. The construction unit is used to construct a system model based on the roadside unit and surrounding roads; The scoring function determination unit is used to determine the scoring function of the computational unloading task based on the system model, specifically including determining the minimum requirement of non-price attributes in the scoring function based on the system model; The receiving unit is used to receive the bidding strategy sent by the vehicle; A sorting unit is used to sort the vehicles based on the bidding strategy; A selection unit is used to select unloading objects from the vehicles according to the sorting; The attributes include price attributes and non-price attributes, and the rating function includes a satisfaction function for representing the preference for non-price attributes. The rating function is the satisfaction function minus the function representing the price attributes.

8. A vehicle-assisted edge computing unloading device, characterized in that, Located in the vehicle, including, A response unit is used to determine whether the vehicle meets the conditions for participating in the calculation and unloading task in response to receiving a scoring function for the calculation and unloading task published by the roadside unit. The rating function includes a satisfaction function used to characterize preferences for non-price attributes; The strategy formulation unit is used to formulate a bidding strategy based on the scoring function and its own parameters in response to the vehicle meeting the conditions for participating in the calculation of the unloading task. The bidding strategy is one that maximizes the value of the scoring function. Specifically, it includes: The non-price attribute determination unit is used to determine the optimal bidding strategy for each non-price attribute in the satisfaction function based on the satisfaction function and the vehicle's own cost function in the scoring function. The price attribute determination unit is used to determine the optimal pricing strategy for the vehicle based on the optimal bidding strategy of the non-price attribute. A sending unit is used to send the bidding strategy to the roadside unit.

9. An electronic device, comprising: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions for execution by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-3 or 4-6.

10. A non-transitory computer-readable storage medium storing computer instructions, wherein, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-3 or 4-6.

Images