MBS-assisted non-uniform vehicle task unloading method for complex road conditions

Abstract

The invention relates to an MBS assistance-based non-uniform vehicle task unloading method for complex road conditions, and belongs to the technical field of communication. The invention provides a non-uniform vehicle task unloading method based on assistance of a macro base station MBS, and aims at solving the problem of task unloading failure caused by instability of a vehicle communication link due to high time-varying characteristics of a vehicle running speed and a track in scenes such as a large transportation hub and a complex interchange. According to the method, an MBS searches a candidate unloading vehicle set for each sub-task according to an unloading request of a task vehicle and computing resource distribution of a vehicle network, selects a local or V2V or V2I2V unloading mode for each sub-task according to a vehicle link state change rule, establishes a corresponding candidate unloading path set, constructs a feasible unloading decision, and performs unloading on each sub-task according to the feasible unloading decision. The unloading decision is iteratively optimized through the simulated annealing algorithm, so that the task completion rate is effectively improved, and the unit task time delay of the system is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication networks, and in particular relates to a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance. Background Art

[0002] In recent years, cellular vehicle to everything (C-V2X) technology has been widely used in the field of intelligent connected vehicles due to its excellent performance in communication range and anti-interference. C-V2X supports vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication through direct communication and cellular communication, enabling vehicles to offload tasks to other vehicles through direct communication or forwarding by macro base stations.

[0003] Existing research on vehicle edge network computing offloading is mainly based on scenarios where vehicle speed and direction are constant. The scheduling and resource allocation strategies of offloading tasks are difficult to meet the needs of actual application scenarios. For example, in scenarios such as large transportation hubs and complex interchanges, changes in vehicle speed and trajectory may cause rapid and disordered changes in network connectivity, resulting in a decrease in network resource utilization and a deterioration in user service experience.

[0004] In view of the above problems, the present invention proposes a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance. In this method, the macro base station (MBS) searches for a set of candidate unloading vehicles for each subtask according to the unloading request of the task vehicle and the distribution of available computing resources in the vehicle network. According to the change law of the link state between the task vehicle and the candidate unloading vehicle, a local (Local) or vehicle-to-vehicle (V2V) or vehicle-to-infrastructure-to-vehicle (V2I2V) unloading mode is selected for each subtask, and a corresponding set of candidate unloading paths is established, a feasible unloading decision is constructed, and a simulated annealing algorithm is used for iterative optimization, thereby effectively improving the task completion rate and reducing the system unit task delay. Summary of the invention

[0005] The present invention aims to solve the above problems of the prior art. A method for unloading tasks of vehicles with non-uniform speed in complex road conditions based on MBS assistance is proposed. The technical solution of the present invention is as follows:

[0006] A method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance comprises the following steps:

[0007] 101. Use simulated annealing algorithm to establish vehicle network task offloading optimization model. Suppose task vehicle i* The offloading request J = {j}, where j is a parallel subtask, the offloading decision vector and the optimal objective function value are ψ = (ψ j | j∈J ), t * , where ψ j For the unloading path of subtask j, initialize the current temperature T cur , termination temperature T end , temperature cooling coefficient ε and maximum number of iterations of unloading decision vector η max , let the iteration count variable η = 0, and the temporary set J′ = J;

[0008] 102. If Take the first element j from the set J′, and according to the amount of data a of subtask j j , computational complexity c j , establish a candidate unloading path set P j ={p j,k}, where p j,k If it is a candidate offloading path coded as k for subtask j, jump to step 102; otherwise, jump to step 103;

[0009] 103. From each set P j | j∈J Choose any path p j,k Initialize the offload decision vector Calculate the objective function value t and initialize the objective function value t of the current solution cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, let the temporary set

[0010] 104. Let the iteration count variable η=η+1, if η≤η max , let the temporary set J″=JJ′, Count variables Wherein, ω is the proportionality coefficient, jump to step 105, otherwise, jump to step 108;

[0011] 105. Let n = n-1. If n ≥ 0, randomly select subtask j from set J″ and add it to temporary set J′, and select subtask j from candidate offloading path set P. j Randomly select path p j,k , let the offloading path ψ of subtask j be j =p j,k , jump to step 105, otherwise, jump to step 106;

[0012] 106. Calculate the objective function value t. If t<t* , let the objective function value of the current solution be t cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, jump to step 104, otherwise, jump to step 107;

[0013] 107. Calculate the receiving probability of the offloading decision vector ψ And generate random numbers if Let the objective function value of the current solution be t cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, jump to step 104, otherwise, jump to step 104;

[0014] 108. Update current temperature T cur =ε·T cur , if T cur >T end , set the iteration count variable η=0, jump to step 104, otherwise, jump to step 109;

[0015] 109. Output the optimal unloading decision ψ best , the step ends.

[0016] Furthermore, in step 102, a candidate offloading path set P is established for subtask j. j ={p j,k}, specifically including the following steps:

[0017] 1) According to the vehicle network status, initialize the set of all vehicles I = {i}, where i is any vehicle, and the candidate unloading vehicle set The set of candidate offloading paths for subtask j The maximum tolerable delay of task offloading t max ;

[0018] 2) If Take the first element i from the set I, according to the amount of data a of subtask j j , computational complexity c j , calculated at t max The minimum computing resources that vehicle i needs to allocate for subtask j within time Jump to step 3), otherwise, jump to step 4);

[0019] 3) If the remaining available computing resources f of vehicle i i r Greater than or equal to Add vehicle i to the candidate unloading vehicle set I′ and jump to step 2); otherwise, jump to step 2);

[0020] 4) If Take the first vehicle i from the set I′ and calculate the computing resources f that the candidate unloading vehicle i needs to allocate for subtask j under fair allocation of onboard resources Fr , let candidate unloading vehicle i be the computing resource f allocated to subtask j i,j =∞, jump to step 5), otherwise, jump to step 11);

[0021] 5) If i=i * , let unloading mode model = Local, candidate unloading vehicle i is the computing resource allocated to subtask j Jump to step 10), otherwise, jump to step 6);

[0022] 6) If the mission vehicle i * The distance d between candidate unloading vehicle i i*,i Less than or equal to the effective communication distance D of the vehicle, let the unloading mode model = V2V, calculate the task vehicle i * The link duration τ with candidate unloading vehicle i i*,i , according to the upload delay of subtask j to candidate unloading vehicle i in V2V unloading mode The expected return delay of candidate unloading vehicle i along the original route Where r is the link transmission rate, σ j is the ratio of the uploaded and returned data volumes of subtask j, calculated in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time Jump to step 7), otherwise, jump to step 8);

[0023] 7) If Let candidate unloading vehicle i be the computing resource allocated to subtask j Jump to step 8), otherwise, jump to step 8);

[0024] 8) Let unloading mode model = V2I2V, according to the upload delay of subtask j to candidate unloading vehicle i in V2I2V unloading mode The expected return delay of candidate unloading vehicle i along the original route Calculated at t max The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time if Jump to step 9), otherwise, jump to step 10);

[0025] 9) If Let candidate unloading vehicle i be the computing resource allocated to subtask j Jump to step 10); otherwise, jump to step 10);

[0026] 10) If f i,j ≠∞, f i,j The corresponding candidate offloading path p j,k Add candidate offloading path set P j , jump to step 4), otherwise, jump to step 4);

[0027] 11) Output candidate offloading path set P j , the step ends.

[0028] Further, in step 2), at t max The minimum computing resources that vehicle i needs to allocate for subtask j within time The calculation method is shown in formula (1):

[0029]

[0030] In formula (1), a j represents the amount of data for subtask j, c j represents the computational complexity of subtask j, t max Indicates the maximum tolerated delay of task offloading.

[0031] Furthermore, in the step 4), the computing resources f that the candidate unloading vehicle i needs to allocate for subtask j under fair allocation of onboard resources is Fr The calculation method is shown in formula (2):

[0032]

[0033] In formula (2), f i represents the total computing resources of vehicle i, |I * | represents the total number of mission vehicles, |J max represents the maximum number of subtasks contained in each unloading request, and |I| represents the total number of vehicles.

[0034] Furthermore, in step 6), the task vehicle i * The distance between candidate unloading vehicle i Link duration τ i*,i and in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The values ​​of are calculated by formula (3), formula (4) and formula (5) respectively:

[0035]

[0036] In formula (3), (x i ,y i ) represent the current task vehicle i * and the position coordinates of candidate unloading vehicle i. In formula (4), D represents the effective communication distance of the vehicle, μ max Indicates the maximum speed of the vehicle. γ i They represent the direction of the task vehicle i after the change of direction. * The angle between the driving direction of candidate unloading vehicle i and the positive direction of the x-axis is calculated as shown in formula (6):

[0037]

[0038] In formula (6), α i Represents the mission vehicle i * The angle between the current driving direction of candidate unloading vehicle i and the positive direction of the x-axis, β represents the angle between the task vehicle i * The angle change when the candidate unloading vehicle i changes from the current driving direction to the reverse driving direction is calculated as shown in formula (7):

[0039]

[0040] In formula (7), θ represents the instantaneous maximum steering angle of the vehicle.

[0041] Furthermore, in step 8), in the V2I2V unloading mode, t max The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The calculation method of is shown in formula (8):

[0042]

[0043] Furthermore, the calculation method of the objective function value t in step 103 is shown in formula (9):

[0044]

[0045] In formula (9), represents subtask j along path p j,k The unloading completion delay to candidate unloading vehicle i is calculated as shown in formula (10): is a binary variable, indicating whether subtask j is along path p j,k Uninstall to p j,k The last node vehicle i, if yes, let Otherwise, let The calculation method is shown in formula (11):

[0046]

[0047] In formula (9), represents subtask j along path p j,k The upload delay to candidate unloading vehicle i, represents subtask j along path p j,k The return delay of the original return result.

[0048] Furthermore, the reception probability of the offloading decision vector ψ in step 107 is The calculation method of is shown in formula (12):

[0049]

[0050] The advantages and beneficial effects of the present invention are as follows:

[0051] The present invention discloses a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance. Existing research on the problem of unloading tasks in vehicle networks usually only considers vehicles traveling at a constant speed and direction, and does not consider the problem of real-time changes in vehicle speed and direction in large transportation hubs or complex interchange environments. In response to the above problems, the present invention proposes a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance. In this method, MBS searches for a set of candidate unloading vehicles for each subtask based on the unloading request of the task vehicle and the distribution of available computing resources in the vehicle network, selects a local or V2V or V2I2V unloading mode for each subtask based on the link connection status between the task vehicle and the candidate unloading vehicle, establishes a corresponding set of candidate unloading paths, constructs a feasible unloading decision, and iteratively optimizes the unloading decision through a simulated annealing algorithm, thereby effectively improving the task completion rate and reducing the system unit task delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 The present invention provides a flowchart of a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance in a preferred embodiment. DETAILED DESCRIPTION

[0053] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0054] The technical solution of the present invention to solve the above technical problems is:

[0055] The concepts and models involved in the present invention are as follows:

[0056] The present invention assumes that in a vehicle network with complex road conditions, each vehicle travels at any speed and direction. Based on the effective communication distance constraint between vehicles, tasks can be offloaded through V2V direct links or MBS-assisted forwarding. Each vehicle is equipped with a server with a certain computing power. Vehicle tasks are composed of multiple different types of subtasks, which can be offloaded to local vehicles in parallel or to other vehicles through direct links or macro base station-assisted forwarding. Among them, the same task cannot be offloaded to two or more offloading vehicles.

[0057] The technical solution of the present invention is described as follows:

[0058] 1. In t max The minimum computing resources that vehicle i needs to allocate for subtask j within time

[0059] The calculation method is shown in formula (1):

[0060]

[0061] In formula (1), a j represents the amount of data for subtask j, c j represents the computational complexity of subtask j, t max Indicates the maximum tolerable delay of task offloading;

[0062] 2. Computational resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of on-board resources Fr

[0063] The calculation method is shown in formula (2):

[0064]

[0065] In formula (2), f i represents the total computing resources of vehicle i, |I * | represents the total number of mission vehicles, |J max represents the maximum number of subtasks contained in each unloading request, and |I| represents the total number of vehicles;

[0066] 3. Mission Vehicle i * The distance d between candidate unloading vehicle i i*,i , link duration and in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time

[0067] The calculation methods are obtained by formula (3), formula (4) and formula (5) respectively:

[0068]

[0069] In formula (3), (x i ,y i ) represent the current task vehicle i * and the position coordinates of candidate unloading vehicle i. In formula (4), D represents the effective communication distance of the vehicle, μ max Indicates the maximum speed of the vehicle. γ i They represent the direction of the task vehicle i after the change of direction. * The angle between the driving direction of candidate unloading vehicle i and the positive direction of the x-axis is calculated as shown in formula (6). In formula (5), represents the upload delay of subtask j to candidate unloading vehicle i in V2V unloading mode, represents the estimated return delay of the candidate unloading vehicle i along the original route in the V2V unloading mode;

[0070]

[0071] In formula (6), α i Represents the mission vehicle i * The angle between the current driving direction of candidate unloading vehicle i and the positive direction of the x-axis, β represents the angle between the task vehicle i * The angle change when the candidate unloading vehicle i changes from the current driving direction to the reverse driving direction is calculated as shown in formula (7):

[0072]

[0073] In formula (7), θ represents the instantaneous maximum steering angle of the vehicle;

[0074] 4. In V2I2V unloading mode, t max The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time

[0075] The calculation method is shown in formula (8):

[0076]

[0077] In formula (8), represents the upload delay of subtask j to candidate unloading vehicle i in V2I2V unloading mode, represents the expected return delay of the candidate unloading vehicle i along the original route in the V2I2V unloading mode;

[0078] 5. Objective function value t

[0079] The calculation method is shown in formula (9):

[0080]

[0081] In formula (9), f i,j represents the computing resources allocated to subtask j by candidate unloading vehicle i, p j,k represents f i,j The corresponding candidate offloading path, represents subtask j along path p j,k The unloading completion delay to candidate unloading vehicle i is calculated as shown in formula (10): is a binary variable, indicating whether subtask j is along path p j,k Uninstall to p j,k The last node vehicle i, if yes, let Otherwise, let The calculation method is shown in formula (11):

[0082]

[0083] In formula (10), represents subtask j along path p j,k The upload delay to candidate unloading vehicle i, represents subtask j along path p j,k The return delay of the original return result, in formula (11), ψ j represents the offloading path of subtask j;

[0084] 6. Receiving probability of offloading decision vector ψ

[0085] The calculation method is shown in formula (12):

[0086]

[0087] In formula (12), t cur represents the objective function value of the current solution, T cur Indicates the current temperature;

[0088] 7. Sub-algorithm 1: Establish a set of candidate offloading paths P for subtask j j ={p j,k}

[0089] Step 1: According to the vehicle network status, initialize the set of all vehicles I = {i}, where i is any vehicle, and the candidate unloading vehicle set The set of candidate offloading paths for subtask j The maximum tolerable delay of task offloading t max ;

[0090] Step 2: If Take the first element i from the set I, according to the amount of data a of subtask j j , computational complexity c j , calculated at t max The minimum computing resources that vehicle i needs to allocate for subtask j within time Jump to step 3, otherwise, jump to step 4;

[0091] Step 3: If the remaining available computing resources f of vehicle i i r Greater than or equal to Add vehicle i to the candidate unloading vehicle set I′ and jump to step 2; otherwise, jump to step 2;

[0092] Step 4: If Take the first vehicle i from the set I′ and calculate the computing resources f that the candidate unloading vehicle i needs to allocate for subtask j under fair allocation of onboard resources Fr , let candidate unloading vehicle i be the computing resource f allocated to subtask j i,j =∞, jump to step 5, otherwise, jump to step 11;

[0093] Step 5: If i=i * , let unloading mode model = Local, candidate unloading vehicle i is the computing resource allocated to subtask j Jump to step 10, otherwise, jump to step 6;

[0094] Step 6: If the mission vehicle i * The distance d between candidate unloading vehicle i i*,i Less than or equal to the effective communication distance D of the vehicle, let the unloading mode model = V2V, calculate the task vehicle i * The link duration τ with candidate unloading vehicle i i*,i , according to the upload delay of subtask j to candidate unloading vehicle i in V2V unloading mode The expected return delay of candidate unloading vehicle i along the original route Where r is the link transmission rate, σ j is the ratio of the uploaded and returned data volumes of subtask j, calculated in min{t max ,τ i*,i The minimum computing resources that candidate unloading vehicle i needs to allocate for subtask j within the time Jump to step 7, otherwise, jump to step 8;

[0095] Step 7: If Let candidate unloading vehicle i be the computing resource allocated to subtask j Jump to step 8, otherwise, jump to step 8;

[0096] Step 8: Let unloading mode model = V2I2V, and according to the upload delay of subtask j to candidate unloading vehicle i under V2I2V unloading mode The expected return delay of candidate unloading vehicle i along the original route Calculated at t max The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time if Jump to step 9, otherwise, jump to step 10;

[0097] Step 9: If Let candidate unloading vehicle i be the computing resource allocated to subtask j Jump to step 10, otherwise, jump to step 10;

[0098] Step 10: If f i,j ≠∞, f i,j The corresponding candidate offloading path p j,k Add candidate offloading path set P j , jump to step 4, otherwise, jump to step 4;

[0099] Step 11: Output candidate offloading path set P j , the step ends.

[0100] A method for unloading tasks of vehicles with non-uniform speed in complex road conditions based on MBS assistance specifically comprises the following steps:

[0101] Step 1: Use simulated annealing algorithm to establish vehicle network task offloading optimization model. Suppose task vehicle i * The offloading request J = {j}, where j is a parallel subtask, the offloading decision vector and the optimal objective function value are ψ = (ψ j | j∈J ), t * , where ψ j For the unloading path of subtask j, initialize the current temperature T cur , termination temperature T end , temperature cooling coefficient ε and maximum number of iterations of unloading decision vector η max , let the iteration count variable η = 0, and the temporary set J′ = J;

[0102] Step 2: If Take the first element j from the set J′, and according to the amount of data a of subtask j j , computational complexity c j , call sub-algorithm 1 to establish a candidate offloading path set P j={p j,k}, where p j,k is a candidate offloading path coded as k for subtask j, jump to step 2, otherwise, jump to step 3;

[0103] Step 3: From each set P j | j∈J Choose any path p j,k Initialize the offload decision vector Calculate the objective function value t and initialize the objective function value t of the current solution cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, let the temporary set

[0104] Step 4: Set the iteration count variable η = η + 1, if η ≤ η max , let the temporary set J″=JJ′, Count variables Where ω is the proportional coefficient, jump to step 5, otherwise, jump to step 8;

[0105] Step 5: Let n = n-1. If n ≥ 0, randomly select subtask j from set J″ and add it to temporary set J′, and select subtask j from candidate offloading path set P. j Randomly select path p j,k , let the offloading path ψ of subtask j be j =p j,k , jump to step 5, otherwise, jump to step 6;

[0106] Step 6: Calculate the objective function value t. If t<t * , let the objective function value of the current solution be t cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, jump to step 4, otherwise, jump to step 7;

[0107] Step 7: Calculate the receiving probability of the offloading decision vector ψ And generate random numbers if Let the objective function value of the current solution be t cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, jump to step 4, otherwise, jump to step 4;

[0108] Step 8: Update the current temperature T cur =ε·T cur , if Tcur >T end , set the iteration count variable η = 0, jump to step 4, otherwise, jump to step 9;

[0109] Step 9: Output the optimal offloading decision ψ best , the step ends.

[0110] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0111] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0112] The above embodiments should be understood to be only used to illustrate the present invention and not to limit the protection scope of the present invention. After reading the contents of the present invention, technicians can make various changes or modifications to the present invention, and these equivalent changes and modifications also fall within the scope defined by the claims of the present invention.

Claims

1. A method for unloading tasks of vehicles with non-uniform speed in complex road conditions based on MBS assistance, characterized in that: The following steps are involved:

101. Use simulated annealing algorithm to establish vehicle network task offloading optimization model. Suppose task vehicle i * The offloading request J = {j}, where j is a parallel subtask, the offloading decision vector and the optimal objective function value are ψ = (ψ j | j∈J ), t * , where ψ j For the unloading path of subtask j, initialize the current temperature T cur , termination temperature T end , temperature cooling coefficient ε and maximum number of iterations of unloading decision vector η max , let the iteration count variable η = 0, and the temporary set J′ = J; 102. If Take the first element j from the set J′, and according to the amount of data a of subtask j j , computational complexity c j , establish a candidate unloading path set P j ={p j,k }, where p j,k If it is a candidate offloading path coded as k for subtask j, jump to step 102; otherwise, jump to step 103; 103. From each set P j | j∈J Choose any path p j,k Initialize the offload decision vector Calculate the objective function value t and initialize the objective function value t of the current solution cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, let the temporary set 104. Let the iteration count variable η=η+1, if η≤η max , let the temporary set J″=JJ′, Count variables Wherein, ω is the proportionality coefficient, jump to step 105, otherwise, jump to step 108; 105. Let n = n-1. If n ≥ 0, randomly select subtask j from set J″ and add it to temporary set J′, and select subtask j from candidate offloading path set P. j Randomly select path p j,k , let the offloading path ψ of subtask j be j =p j,k , jump to step 105, otherwise, jump to step 106; 106. Calculate the objective function value t. If t<t * , let the objective function value of the current solution be t cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, jump to step 104, otherwise, jump to step 107; 107. Calculate the receiving probability of the offloading decision vector ψ And generate random numbers if Let the objective function value of the current solution be t cur =t, the objective function value of the optimal solution t * =t, optimal unloading decision ψ best =ψ, jump to step 104, otherwise, jump to step 104; 108. Update current temperature T cur =ε·T cur , if T cur >T end , set the iteration count variable η=0, jump to step 104, otherwise, jump to step 109; 109. Output optimal unloading decision ψ best , the step ends.

2. According to claim 1, a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance, characterized in that: In step 102, a candidate offloading path set P is established for subtask j. j ={p j,k }, specifically including the following steps: 1) According to the vehicle network status, initialize the set of all vehicles I = {i}, where i is any vehicle, and the candidate unloading vehicle set The set of candidate offloading paths for subtask j The maximum tolerable delay of task offloading t max ; 2) If Take the first element i from the set I, according to the amount of data a of subtask j j , computational complexity c j , calculated at t max The minimum computing resources that vehicle i needs to allocate for subtask j within time Jump to step 3), otherwise, jump to step 4); 3) If the remaining available computing resources of vehicle i Greater than or equal to Add vehicle i to the candidate unloading vehicle set I′ and jump to step 2); otherwise, jump to step 2); 4) If Take the first vehicle i from the set I′ and calculate the computing resources f that the candidate unloading vehicle i needs to allocate for subtask j under fair allocation of onboard resources Fr , let candidate unloading vehicle i be the computing resource f allocated to subtask j i,j =∞, jump to step 5), otherwise, jump to step 11); 5) If i=i * , let unloading mode model = Local, candidate unloading vehicle i is the computing resource allocated to subtask j Jump to step 10), otherwise, jump to step 6); 6) If the mission vehicle i * The distance between candidate unloading vehicle i Less than or equal to the effective communication distance D of the vehicle, let the unloading mode model = V2V, calculate the task vehicle i * Duration of the link with candidate unloading vehicle i According to the upload delay of subtask j to candidate unloading vehicle i in V2V unloading mode The expected return delay of candidate unloading vehicle i along the original route Where r is the link transmission rate, σ j is the ratio of the uploaded and returned data volumes of subtask j, calculated in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time Jump to step 7), otherwise, jump to step 8); 7) If Let candidate unloading vehicle i be the computing resource allocated to subtask j Jump to step 8), otherwise, jump to step 8); 8) Let unloading mode model = V2I2V, according to the upload delay of subtask j to candidate unloading vehicle i in V2I2V unloading mode The expected return delay of candidate unloading vehicle i along the original route Calculated at t max The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time if Jump to step 9), otherwise, jump to step 10); 9) If Let candidate unloading vehicle i be the computing resource allocated to subtask j Jump to step 10); otherwise, jump to step 10); 10) If f i,j ≠∞, f i,j The corresponding candidate offloading path p j,k Add candidate offloading path set P j , jump to step 4), otherwise, jump to step 4); 11) Output candidate offloading path set P j , the step ends.

3. The MBS-assisted method for unloading tasks of vehicles with non-uniform speed in complex road conditions according to claim 2 is characterized in that: In the step 2), at t max The minimum computing resources that vehicle i needs to allocate for subtask j within time The calculation method is shown in formula (1): In formula (1), a j represents the amount of data for subtask j, c j represents the computational complexity of subtask j, t max Indicates the maximum tolerated delay of task offloading.

4. According to claim 2, a method for unloading tasks of vehicles with non-uniform speed under complex road conditions based on MBS assistance is characterized in that: The computing resources f that candidate unloading vehicle i needs to allocate for subtask j under fair allocation of onboard resources in step 4) Fr The calculation method is shown in formula (2): In formula (2), f i represents the total computing resources of vehicle i, |I * | represents the total number of mission vehicles, |J| max represents the maximum number of subtasks contained in each unloading request, and |I| represents the total number of vehicles.

5. The MBS-assisted method for unloading tasks of vehicles with non-uniform speed in complex road conditions according to claim 2, characterized in that: In step 6), the task vehicle i * The distance between candidate unloading vehicle i Link duration and in The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The values ​​of are calculated by formula (3), formula (4) and formula (5) respectively: In formula (3), (x i ,y i ) represent the current task vehicle i * and the position coordinates of candidate unloading vehicle i. In formula (4), D represents the effective communication distance of the vehicle, μ max Indicates the maximum speed of the vehicle. γ i They represent the direction of the task vehicle i after the change of direction. * The angle between the driving direction of candidate unloading vehicle i and the positive direction of the x-axis is calculated as shown in formula (6): In formula (6), α i Represents the mission vehicle i * The angle between the current driving direction of candidate unloading vehicle i and the positive direction of the x-axis, β represents the angle between the task vehicle i * The angle change when the candidate unloading vehicle i changes from the current driving direction to the reverse driving direction is calculated as shown in formula (7): In formula (7), θ represents the instantaneous maximum steering angle of the vehicle.

6. The MBS-assisted method for unloading tasks of vehicles with non-uniform speed in complex road conditions according to claim 2, characterized in that: In step 8), in the V2I2V unloading mode, t max The minimum computing resources required for candidate unloading vehicle i to allocate for subtask j within time The calculation method of is shown in formula (8):

7. The MBS-assisted method for unloading tasks of vehicles with non-uniform speed in complex road conditions according to claim 1, characterized in that: The calculation method of the objective function value t in step 103 is shown in formula (9): In formula (9), represents subtask j along path p j,k The unloading completion delay to candidate unloading vehicle i is calculated as shown in formula (10): is a binary variable, indicating whether subtask j is along path p j,k Uninstall to p j,k The last node vehicle i, if yes, let Otherwise, let The calculation method is shown in formula (11): In formula (9), represents subtask j along path p j,k The upload delay to candidate unloading vehicle i, represents subtask j along path p j,k The return delay of the original return result.

8. The MBS-assisted method for unloading tasks of vehicles with non-uniform speed in complex road conditions according to claim 1, characterized in that: The reception probability of the offloading decision vector ψ in step 107 The calculation method of is shown in formula (12):

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