Optimization Method, Device, Equipment and Medium for Resource Allocation of Vehicular Network Computing Offloading

Abstract

An embodiment of the present invention discloses a method, device, equipment and medium for optimizing the allocation of computing offloading resources in a vehicle network. The method includes: obtaining the computing power of the vehicle itself; judging whether the vehicle itself can complete the computing task corresponding to the demand according to the computing power of the vehicle itself; if the vehicle can complete the computing task corresponding to the demand, the computing task is processed by the vehicle itself; if the vehicle cannot complete the computing task corresponding to the demand, the computing task is offloaded to the MEC server for processing. By offloading the computing tasks that the vehicle itself cannot complete to the MEC server according to the proximity principle, then allocating resources to the MEC server by the Lagrange multiplier method, and finally sending the calculation result back to the vehicle, the present invention realizes the optimal allocation of computing resources and avoids the problems of incorrect resource offloading and insufficient resources.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle networking, and more specifically to a method, device, equipment and medium for optimizing the allocation of computing offloading resources in vehicle networking. Background Art

[0002] With the rapid development of intelligent vehicles, a large number of in-vehicle applications that are delay-sensitive and computationally intensive, such as in-vehicle speech recognition and autonomous driving, have emerged. However, these in-vehicle applications also pose severe challenges to the limited computing resources and energy reserves of the vehicle itself. How to complete high-density tasks within the delay constraint has become a hot issue in current vehicle networking.

[0003] The existing task offloading method for vehicle clusters in vehicle networking based on edge computing is to offload tasks to nearby vehicles and perform task calculations through the resources of the collection of other vehicles. This method requires collecting the number of surrounding vehicles, the computing resources of the vehicle itself, and whether the computing resources of the vehicle itself are occupied. Moreover, the computing resources of the vehicle itself are not very abundant, so multiple vehicles are required, and a large amount of data needs to be collected. In addition, the vehicles are moving at high speed, and the increase and decrease of the vehicle set are relatively fast, which easily leads to problems such as resource offloading errors and insufficient resources. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method, device, equipment and medium for optimizing the allocation of computing offloading resources in vehicle networking.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] In a first aspect, a method for optimizing the allocation of computing offloading resources in vehicle networking includes:

[0007] Obtain the computing ability of the vehicle itself;

[0008] Judge whether the vehicle itself can complete the computing task corresponding to the requirement according to the computing ability of the vehicle itself;

[0009] If the vehicle can complete the computing task corresponding to the requirement, the computing task is processed by the vehicle itself;

[0010] If the vehicle cannot complete the computing task corresponding to the requirement, the computing task is offloaded to the MEC server for processing.

[0011] A further technical solution thereof is that: the step of judging whether the vehicle itself can complete the computing task corresponding to the requirement according to the computing ability of the vehicle itself includes:

[0012] Calculate the computing time required for the vehicle itself to complete the computing task corresponding to the requirement;

[0013] If the computing time required for the vehicle itself to complete the computing tasks corresponding to the requirements does not reach the maximum tolerable time delay for the vehicle to complete the computing tasks, it is determined that the vehicle itself can complete the computing tasks corresponding to the requirements;

[0014] If the computing time required for the vehicle itself to complete the computing tasks corresponding to the requirements reaches the maximum tolerable time delay for the vehicle to complete the computing tasks, it is determined that the vehicle itself cannot complete the computing tasks corresponding to the requirements.

[0015] Its further technical solution is: The offloading of the computing tasks to the MEC server for processing includes:

[0016] Obtain the maximum utility function of the MEC server;

[0017] Set constraint conditions for the maximum utility function;

[0018] Represent the maximum utility function through the constructed Lagrangian function;

[0019] Calculate the optimal computing resources allocated by the MEC server to the vehicle according to the constructed Lagrangian function.

[0020] Its further technical solution is: Before determining whether the vehicle itself can complete the computing tasks corresponding to the requirements according to the computing ability of the vehicle itself, it further includes:

[0021] Judge whether the computing tasks corresponding to the vehicle requirements can be divided;

[0022] If it can be divided, execute the determination of whether the vehicle itself can complete the computing tasks corresponding to the requirements according to the computing ability of the vehicle itself;

[0023] If it cannot be divided, allocate the computing tasks to the vehicle itself for processing or to the MEC server for processing.

[0024] In the second aspect, an optimization device for computing offloading resource allocation in a vehicle-to-everything network includes an acquisition unit, a first judgment unit, a local processing unit, and an MEC processing unit;

[0025] The acquisition unit is used to acquire the computing ability of the vehicle itself;

[0026] The first judgment unit is used to judge whether the vehicle itself can complete the computing tasks corresponding to the requirements according to the computing ability of the vehicle itself;

[0027] The local processing unit is used to, if the computing tasks corresponding to the requirements can be completed, process the computing tasks by the vehicle itself;

[0028] The MEC processing unit is used to, if the computing tasks corresponding to the requirements cannot be completed, offload the computing tasks to the MEC server for processing.

[0029] Its further technical solution is: the first judgment unit includes a first calculation module, a first determination module, and a second determination module;

[0030] The first calculation module is used to calculate the calculation time required for the vehicle itself to complete the corresponding required calculation tasks;

[0031] The first determination module is used to determine that the vehicle itself can complete the corresponding required calculation tasks if the calculation time required for the vehicle itself to complete the corresponding required calculation tasks does not reach the maximum tolerable time delay for the vehicle to complete the calculation tasks;

[0032] The second determination module is used to determine that the vehicle itself cannot complete the corresponding required calculation tasks if the calculation time required for the vehicle itself to complete the corresponding required calculation tasks reaches the maximum tolerable time delay for the vehicle to complete the calculation tasks.

[0033] Its further technical solution is: the MEC processing unit includes an acquisition module, a setting module, a construction module, and a second calculation module;

[0034] The acquisition module is used to acquire the maximum utility function of the MEC server;

[0035] The setting module is used to set constraint conditions for the maximum utility function;

[0036] The construction module is used to represent the maximum utility function through the constructed Lagrangian function;

[0037] The second calculation module is used to calculate the optimal computing resources allocated by the MEC server to the vehicle according to the constructed Lagrangian function.

[0038] Its further technical solution is: it further includes a second judgment unit and an allocation unit;

[0039] The second judgment unit is used to judge whether the calculation tasks corresponding to the vehicle's requirements can be split;

[0040] The allocation unit is used to, if it cannot be split, allocate the calculation tasks to be processed by the vehicle itself or the MEC server.

[0041] In a third aspect, a computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the vehicle networking computing offloading resource allocation optimization method as described above.

[0042] Fourthly, a computer-readable storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to execute the vehicle networking computing offloading resource allocation optimization method as described above.

[0043] The beneficial effects of the present invention compared with the prior art are as follows: by offloading the computing tasks that the vehicle itself cannot complete to the MEC server according to the proximity principle, then allocating resources to the MEC server by the Lagrange multiplier method, and finally transmitting the calculation results back to the vehicle, the optimal allocation of computing resources is achieved, avoiding the problems of resource offloading errors and insufficient resources.

[0044] The above description is only an overview of the technical solution of the present invention. In order to understand the technical means of the present invention more clearly, it can be implemented according to the content of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and understandable, the following preferred embodiments are specifically described in detail as follows. Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 Schematic diagram of the application scenario provided for the specific embodiment of the present invention;

[0047] Figure 2 Flowchart of the vehicle networking computing offloading resource allocation optimization method provided for the specific embodiment of the present invention;

[0048] Figure 3 Schematic block diagram of the vehicle networking computing offloading resource allocation optimization device provided for the specific embodiment of the present invention;

[0049] Figure 4 Schematic block diagram of a computer device provided for the specific embodiment of the present invention. Detailed Embodiments

[0050] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0051] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0052] It should also be understood that the terms used in the specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly dictates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0053] It should be further understood that the term " / and" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0054] Figure 1 Provide a schematic diagram of the application scenario for the specific embodiments of the present invention, as Figure 1 shown, the resources of the MEC server are allocated through the core network, the vehicles are randomly allocated on the road, and the resource allocation range of each MEC server is limited. Only the vehicles within the range can choose to offload the computing task or not. The present invention will be introduced below through specific embodiments.

[0055] As Figure 2 shown, the resource allocation optimization method for vehicle networking computing offloading includes the following steps: S10 - S40.

[0056] S10. Obtain the computing power of the vehicle itself.

[0057] The computing power of each vehicle itself can be clearly known, but each vehicle is different at different times or in different working states. For example, in the current state, a part of the computing resources of the vehicle itself is occupied, and only some resources can be used. Therefore, what needs to be obtained is the computing power of the vehicle itself in the current state.

[0058] In one embodiment, after step S10, the following steps are further included: S15 - S17.

[0059] S15. Determine whether the computing task corresponding to the vehicle's requirements can be divided.

[0060] S16. If it can be divided, then execute to determine whether it can complete the computing task corresponding to the requirements according to the computing power of the vehicle itself.

[0061] S17. If it cannot be split, allocate the computing task to the vehicle itself or the MEC server for processing.

[0062] The relationships between some computing tasks are tight and cannot be split, with part for the vehicle to calculate itself and part for the MEC to calculate. Therefore, it is necessary to determine whether it can be split. Only if it can be split can it enter the subsequent offloading decision-making process. If it cannot be split, it can be allocated to the vehicle itself or the MEC server for processing. It should be noted that the present invention mainly focuses on the case where the computing task can be split.

[0063] S20. Determine whether the vehicle itself can complete the computing task corresponding to the requirement according to the vehicle's own computing power.

[0064] In one embodiment, step S20 specifically includes the following steps:

[0065] S201. Calculate the computing time required for the vehicle itself to complete the computing task corresponding to the requirement.

[0066] S202. If the computing time required for the vehicle itself to complete the computing task corresponding to the requirement does not reach the maximum tolerable time delay for the vehicle to complete the computing task, it is determined that the vehicle itself can complete the computing task corresponding to the requirement.

[0067] S203. If the computing time required for the vehicle itself to complete the computing task corresponding to the requirement reaches the maximum tolerable time delay for the vehicle to complete the computing task, it is determined that the vehicle itself cannot complete the computing task corresponding to the requirement.

[0068] In this embodiment, it is assumed that all vehicles are driving on a straight highway, using a network that includes N vehicles, M MEC servers, and the total computing resources of each server is F, a cloud server, and a core network. The coverage range of each MEC server is set to R, and the set of MECs is defined as M = {1, 2,...., M}. Then the highway can be divided into M sections, and all vehicles are randomly allocated on these highway sections. This vehicle set can be expressed as: N = {1, 2,..., N}, and the computing tasks carried by each vehicle are expressed as where Y i represents the computing resources required to complete the task, Z i is the size of the input data for computing, and t max is the maximum tolerable time delay for completing the task.

[0069] When vehicle i processes its computing task locally. Then, the local computing time can be expressed as:

[0070] where, is the task processing time delay of the vehicle itself, and Yi The computing resources required for the computing task are the computing resources of the vehicle itself.

[0071] If the computing task is completed by offloading to the MEC server at close range, the computing time of the MEC server can be expressed as:

[0072] Among them, is the MEC task processing delay, is the computing resources allocated by the MEC to the vehicle, Z i is the size of the input data for the calculation, γ i is the uplink transmission rate of the vehicle.

[0073] Make an offloading decision through the principle of proximity and comparison with the maximum tolerable delay:

[0074]

[0075] S30. If the computing task corresponding to the requirements can be completed, the computing task is processed by the vehicle itself.

[0076] S40. If the computing task corresponding to the requirements cannot be completed, the computing task is offloaded to the MEC server for processing.

[0077] Determine the offloading strategy through the principle of proximity and comparison with the maximum tolerable delay, that is, whether the computing task corresponding to the vehicle is processed by the vehicle itself or offloaded to the MEC server for processing.

[0078] In one embodiment, step S40 specifically includes the following steps: S401 - S404.

[0079] S401. Obtain the maximum utility function of the MEC server.

[0080] When the vehicle offloads its computing task to the MEC server, the optimal computing resources to be allocated are:

[0081] Among them, β i is the weight coefficient, ρ m is the transmission power of the vehicle, is the current actual delay.

[0082] S402. Set constraint conditions for the maximum utility function.

[0083] The set constraint conditions are: Among them, N m is the set of vehicles on the mth edge server.

[0084] S403. Represent the maximum utility function through the constructed Lagrangian function.

[0085] The maximum utility function is expressed as a Lagrangian function as follows:

[0086] where λ is the Lagrange multiplier related to the computing resource constraint of the MEC server, and F is the total computing resource of the edge server.

[0087] S404. Calculate the optimal computing resource allocated by the MEC server to the vehicle according to the constructed Lagrangian function.

[0088] The following system of equations can be established through the above constraint relationships:

[0089]

[0090] Through the constructed system of equations, the optimal computing resource allocation F can be obtained, and the following formula can be derived:

[0091]

[0092] In the present invention, the computing tasks that the vehicle itself cannot complete are offloaded to the MEC server according to the proximity principle, and then the Lagrange multiplier method is used to allocate resources to the MEC server. Finally, the calculation results are sent back to the vehicle, realizing the optimal allocation of computing resources and avoiding the problems of resource offloading errors and insufficient resources.

[0093] Figure 3 FIG. is a schematic block diagram of a vehicle-to-internet computing offloading resource allocation optimization device provided by an embodiment of the present invention; corresponding to the above vehicle-to-internet computing offloading resource allocation optimization method, an embodiment of the present invention further provides a vehicle-to-internet computing offloading resource allocation optimization device 100.

[0094] As Figure 3 shown, the vehicle-to-internet computing offloading resource allocation optimization device 100 includes an acquisition unit 110, a first judgment unit 120, a local processing unit 130, and an MEC processing unit 140.

[0095] The acquisition unit 110 is configured to acquire the computing power of the vehicle itself.

[0096] The computing power of each vehicle itself can be clearly known, but the computing power of each vehicle is different at different times or in different working states. For example, in the current state, a part of the computing resources of the vehicle itself are occupied, and only some resources can be used. Therefore, it is necessary to acquire the computing power of the vehicle itself in the current state.

[0097] In an embodiment, the vehicle-to-internet computing offloading resource allocation optimization device 100 further includes a second judgment unit and an allocation unit.

[0098] A second judgment unit, configured to judge whether the computing task corresponding to the vehicle's requirement can be split.

[0099] An allocation unit, configured to, if it cannot be split, allocate the computing task to the vehicle itself for processing or to the MEC server for processing.

[0100] The relationships between some computing tasks are tight and cannot be split, with a part for the vehicle itself to calculate and a part for the MEC to calculate. Therefore, it is necessary to judge whether it can be split. Only if it can be split can it enter the subsequent offloading decision-making process. If it cannot be split, it can be allocated to the vehicle itself for processing or to the MEC server for processing. It should be noted that the present invention mainly focuses on the case where the computing task can be split.

[0101] A first judgment unit 120, configured to judge whether the vehicle itself can complete the computing task corresponding to the requirement according to the vehicle's own computing power.

[0102] In an embodiment, the first judgment unit 120 includes a first calculation module, a first determination module, and a second determination module.

[0103] The first calculation module is configured to calculate the computing time required for the vehicle itself to complete the computing task corresponding to the requirement.

[0104] The first determination module is configured to, if the computing time required for the vehicle itself to complete the computing task corresponding to the requirement does not reach the maximum tolerable time delay for the vehicle to complete the computing task, determine that the vehicle itself can complete the computing task corresponding to the requirement.

[0105] The second determination module is configured to, if the computing time required for the vehicle itself to complete the computing task corresponding to the requirement reaches the maximum tolerable time delay for the vehicle to complete the computing task, determine that the vehicle itself cannot complete the computing task corresponding to the requirement.

[0106] In this embodiment, it is assumed that all vehicles are driving on a straight road, using a network that includes N vehicles, M MEC servers, and the total computing resources of each server are F, a cloud server, and a core network. The coverage range of each MEC server is set as R, and the set of MECs is defined as M = {1, 2,...., M}. Then the road can be divided into M sections, and all vehicles are randomly allocated on these highway sections. This vehicle set can be expressed as: N = {1, 2,..., N}, and the computing tasks carried by each vehicle are expressed as where Y i represents the computing resources required to complete the task, Z i is the size of the input data for calculation, and t max is the maximum tolerable time delay to complete the task.

[0107] When vehicle i processes its computing tasks locally. Then, the local computing time can be expressed as:

[0108] Where, is the task processing delay of the vehicle itself, and Y i is the computing resource required for the computing task, is the computing resource of the vehicle itself.

[0109] If the computing task is completed by offloading it to the MEC server through short-range communication, the MEC server computing time can be expressed as:

[0110] Where, is the MEC task processing delay, is the computing resource allocated by the MEC to the vehicle, and Z i is the size of the input data for the computing, and γ i is the uplink transmission rate of the vehicle.

[0111] Make an offloading decision based on the principle of proximity and comparison with the maximum tolerable delay:

[0112]

[0113] The local processing unit 130 is used to process the computing task by the vehicle itself if it can complete the computing task corresponding to the requirements.

[0114] The MEC processing unit 140 is used to offload the computing task to the MEC server for processing if it cannot complete the computing task corresponding to the requirements.

[0115] Determine the offloading strategy by the principle of proximity and comparison with the maximum tolerable delay, that is, whether the computing task corresponding to the vehicle is processed by the vehicle itself or offloaded to the MEC server for processing.

[0116] In an embodiment, the MEC processing unit 140 includes an acquisition module, a setting module, a construction module, and a second computing module.

[0117] The acquisition module is used to acquire the maximum utility function of the MEC server.

[0118] When the vehicle offloads its computing task to the MEC server, the optimal computing resource to be allocated is:

[0119] Where, β i is the weight coefficient, ρ m is the transmission power of the vehicle, is the current actual delay.

[0120] The setting module is used to set constraint conditions for the maximum utility function.

[0121] Set the constraint conditions as follows: where N m is the set of vehicles on the m-th edge server.

[0122] A construction module for representing the maximum utility function through the constructed Lagrangian function.

[0123] The maximum utility function is represented by the Lagrangian function as follows:

[0124] where λ is the Lagrange multiplier related to the computing resource constraint of the MEC server, and F is the total computing resource of the edge server.

[0125] A second computing module for calculating the optimal computing resources allocated by the MEC server to the vehicles according to the constructed Lagrangian function.

[0126] The following system of equations can be established through the above constraint relationships:

[0127]

[0128] The optimal computing resource allocation F can be obtained through the constructed system of equations, and the following formula can be obtained:

[0129]

[0130] The above vehicle networking computing offloading resource allocation optimization device can be implemented in the form of a computer program, and this computer program can run on a computer device as shown in Figure 4 shown.

[0131] Please refer to Figure 4 , Figure 4 which is a schematic block diagram of a computer device provided by an embodiment of the present application. The computer device 700 can be a server. Among them, the server can be an independent server or a server cluster composed of multiple servers.

[0132] As shown in Figure 4 shown, this computer device includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, the steps of the vehicle networking computing offloading resource allocation optimization method as described above are implemented.

[0133] The computer device 700 can be a terminal or a server. The computer device 700 includes a processor 720, a memory, and a network interface 750 connected through a system bus 710. Among them, the memory can include a non-volatile storage medium 730 and an internal memory 740.

[0134] The non-volatile storage medium 730 can store an operating system 731 and a computer program 732. When the computer program 732 is executed, it can cause the processor 720 to execute any one of the vehicle networking computing offloading resource allocation optimization methods.

[0135] The processor 720 is used to provide computing and control capabilities to support the operation of the entire computer device 700.

[0136] The internal memory 740 provides an environment for the operation of the computer program 732 in the non-volatile storage medium 730. When the computer program 732 is executed by the processor 720, it can cause the processor 720 to execute any one of the vehicle networking computing offloading resource allocation optimization methods.

[0137] The network interface 750 is used for network communication, such as sending assigned tasks, etc. Those skilled in the art can understand that Figure 4 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device 700 to which the solution of this application is applied. The specific computer device 700 may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements. Among them, the processor 720 is used to run the program code stored in the memory to implement the following steps:

[0138] The vehicle networking computing offloading resource allocation optimization method includes:

[0139] Obtain the computing power of the vehicle itself;

[0140] Judge whether the vehicle itself can complete the computing task corresponding to the requirement according to the computing power of the vehicle itself;

[0141] If the vehicle itself can complete the computing task corresponding to the requirement, the computing task is processed by the vehicle itself;

[0142] If the vehicle itself cannot complete the computing task corresponding to the requirement, the computing task is offloaded to the MEC server for processing.

[0143] In an embodiment: the step of judging whether the vehicle itself can complete the computing task corresponding to the requirement according to the computing power of the vehicle itself includes:

[0144] Calculate the computing time required for the vehicle itself to complete the computing task corresponding to the requirement;

[0145] If the computing time required for the vehicle itself to complete the computing task corresponding to the requirement does not reach the maximum tolerance delay for the vehicle to complete the computing task, it is determined that the vehicle itself can complete the computing task corresponding to the requirement;

[0146] If the computing time required for the vehicle to complete the computing tasks corresponding to the requirements reaches the maximum tolerable delay for the vehicle to complete the computing tasks, it is determined that the vehicle itself cannot complete the computing tasks corresponding to the requirements.

[0147] In one embodiment: The offloading the computing tasks to the MEC server for processing includes:

[0148] Obtain the maximum utility function of the MEC server;

[0149] Set constraint conditions for the maximum utility function;

[0150] Represent the maximum utility function through the constructed Lagrangian function;

[0151] Calculate the optimal computing resources allocated by the MEC server to the vehicle according to the constructed Lagrangian function.

[0152] In one embodiment: Before determining whether the vehicle can complete the computing tasks corresponding to the requirements according to the computing ability of the vehicle itself, it further includes:

[0153] Judge whether the computing tasks corresponding to the vehicle requirements can be divided;

[0154] If it can be divided, execute determining whether the vehicle can complete the computing tasks corresponding to the requirements according to the computing ability of the vehicle itself;

[0155] If it cannot be divided, allocate the computing tasks to the vehicle itself for processing or to the MEC server for processing.

[0156] It should be understood that in the embodiments of the present application, the processor 720 may be a central processing unit (Central Processing Unit, CPU), and this processor 720 may also be other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc.

[0157] Those skilled in the art can understand that Figure 4 the structure of the computer device 700 shown in does not constitute a limitation on the computer device 700, and it may include more or fewer components than shown in the figure, or combine some components, or arrange different components.

[0158] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the vehicle networking computing offloading resource allocation optimization method disclosed in the embodiments of the present invention.

[0159] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described devices, apparatuses, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0160] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, or units with the same function can be aggregated into one unit. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling, direct coupling, or communication connection may be an indirect coupling or communication connection through some interfaces, devices, or units, and can also be in the form of electrical, mechanical, or other connections.

[0161] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0162] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0163] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), magnetic disks, or optical discs.

[0164] As described above, the above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. Optimization method for resource allocation of vehicle networking computing offloading, characterized in that, Including: Obtain the computing power of the vehicle itself; Judge whether the vehicle itself can complete the computing tasks corresponding to the requirements according to the computing power of the vehicle itself; If the computing tasks corresponding to the requirements can be completed, the computing tasks are processed by the vehicle itself; If the computing tasks corresponding to the requirements cannot be completed, the computing tasks are offloaded to the MEC server for processing; The offloading of the computing tasks to the MEC server for processing includes: Obtain the maximum utility function of the MEC server. Specifically, when the vehicle offloads its computing tasks to the MEC server, the optimal computing resource allocation required is: , where is the weight coefficient, is the transmission power of the vehicle, is the current actual time delay, is the MEC task processing time delay, is the computing resource allocated by MEC to the vehicle; Set constraints on the maximum utility function. Specifically, the constraints are set as follows: , , , where is the set of vehicles on the m-th edge server, is the computing resource required for the computing task, is the size of the input data for computing, is the uplink transmission rate of the vehicle, and N represents the set of vehicles; Represent the maximum utility function by the constructed Lagrangian function. Specifically: The maximum utility function is expressed by the Lagrangian function as follows: , where is the Lagrange multiplier related to the computing resource constraint of the MEC server, and F is the total computing resource of the edge server; Calculate the optimal computing resources allocated by the MEC server to the vehicle according to the constructed Lagrangian function. Specifically: The following equations can be established through the constraint relationship of the Lagrangian function: ； The resources allocated to each vehicle are solved by the constructed system of equations , and the following formula can be obtained: 。 2. The optimization method for computing offloading resource allocation in an Internet of Vehicles according to claim 1, wherein, The judging whether the vehicle itself can complete the computing tasks corresponding to the requirements according to the computing power of the vehicle itself includes: Calculate the computing time required for the vehicle itself to complete the computing tasks corresponding to the requirements; If the computing time required for the vehicle itself to complete the computing tasks corresponding to the requirements does not reach the maximum tolerable delay for the vehicle to complete the computing tasks, it is determined that the vehicle itself can complete the computing tasks corresponding to the requirements; If the computing time required for the vehicle itself to complete the computing tasks corresponding to the requirements reaches the maximum tolerable delay for the vehicle to complete the computing tasks, it is determined that the vehicle itself cannot complete the computing tasks corresponding to the requirements.

3. The vehicle networking computing offloading resource allocation optimization method according to claim 1, characterized in that The offloading of the computing tasks to the MEC server for processing includes: Obtain the maximum utility function of the MEC server; Set constraint conditions for the maximum utility function; Represent the maximum utility function by the constructed Lagrangian function; Calculate the optimal computing resources allocated by the MEC server to the vehicle according to the constructed Lagrangian function.

4. An optimized device for resource allocation of vehicular network computing offloading, when running, executes the method according to any one of claims 1 to 3, characterized in that Including an acquisition unit, a first judgment unit, a local processing unit, and an MEC processing unit; The acquisition unit is used to obtain the computing power of the vehicle itself; The first judgment unit is used to judge whether the vehicle itself can complete the computing tasks corresponding to the requirements according to the computing power of the vehicle itself; The local processing unit is used to process the computing tasks by the vehicle itself if the computing tasks corresponding to the requirements can be completed; The MEC processing unit is used to offload the computing tasks to the MEC server for processing if the computing tasks corresponding to the requirements cannot be completed; The MEC processing unit includes an acquisition module, a setting module, a construction module, and a second calculation module; The acquisition module is used to obtain the maximum utility function of the MEC server; The setting module is used to set constraint conditions for the maximum utility function; The construction module is used to represent the maximum utility function by the constructed Lagrangian function; The second calculation module is used to calculate the optimal computing resources allocated by the MEC server to the vehicle according to the constructed Lagrangian function.

5. The vehicle networking computing offloading resource allocation optimization device according to claim 4, wherein The first judgment unit includes a first calculation module, a first determination module, and a second determination module; The first calculation module is used to calculate the computing time required for the vehicle itself to complete the computing tasks corresponding to the requirements; The first determination module is configured to determine that the vehicle itself can complete the computing task corresponding to the requirement if the computing time required for the vehicle itself to complete the computing task corresponding to the requirement does not reach the maximum tolerable time delay for the vehicle to complete the computing task; The second determination module is configured to determine that the vehicle itself cannot complete the computing task corresponding to the requirement if the computing time required for the vehicle itself to complete the computing task corresponding to the requirement reaches the maximum tolerable time delay for the vehicle to complete the computing task.

6. The vehicle networking computing offloading resource allocation optimization device according to claim 4, wherein It further includes a second judgment unit and an allocation unit; The second judgment unit is configured to judge whether the computing task corresponding to the vehicle requirement can be divided; The allocation unit is configured to, if it cannot be divided, allocate the computing task to be processed by the vehicle itself or the MEC server.

7. A computer device, characterized in that, It includes a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements the vehicle networking computing offloading resource allocation optimization method described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program includes program instructions. When the program instructions are executed by the processor, the processor is caused to execute the vehicle networking computing offloading resource allocation optimization method described in any one of claims 1 to 3.

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