A cache resource deployment method and system for internet of vehicles

Abstract

The application relates to a cache resource deployment method and system for a vehicle Internet, the method comprising determining to-be-cached resources and deployment nodes in a vehicle Internet system; predicting real-time cache profits generated when the to-be-cached resources are deployed in the corresponding deployment nodes according to the network state of the vehicle Internet system; calculating historical cache profits generated when the to-be-cached resources deployed in the corresponding deployment nodes exist in history; and calculating target deployment nodes selected by the to-be-cached resources in the deployment nodes according to the real-time cache profits and the historical cache profits. The method can maximize the cache profits by calculating the real-time cache profits of each deployment node and the historical cache profits of the to-be-cached resources in a period of time, and using the total cache profits as a reference to guide how to deploy the to-be-cached resources in the cache deployment nodes, so that the rationality of the to-be-cached resource deployment is improved.

Description

Technical Field

[0001] This application relates to the field of vehicle networking technology, and in particular to a method and system for deploying cache resources in vehicle networking. Background Technology

[0002] In vehicle-to-everything (V2X) systems, when a vehicle initiates a task request, caching resources on appropriate deployment nodes (vehicles, edge systems) can effectively shorten the data transmission chain of vehicle task requests and reduce content delivery latency. Caching deployment is not only limited by the storage space of the deployment nodes but also affected by the available storage space and computing resources. Therefore, designing a high-performance caching deployment strategy is a pressing issue. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail herein. This overview is not intended to limit the scope of the claims.

[0004] The main objective of this invention is to propose a method and system for deploying cached resources in the Internet of Vehicles (IoV) to improve the rationality of cached resource deployment.

[0005] To achieve the above objectives, a first aspect of the present invention provides a method for deploying cache resources in a vehicle-to-everything (V2X) network, the method comprising:

[0006] Identify the resources to be cached and the deployment nodes in the vehicle-to-everything (V2X) system;

[0007] Based on the network status of the vehicle network system, and using the call latency of the cached resources as a standard, predict the real-time caching profit generated when the corresponding cached resources are deployed on the corresponding deployment nodes;

[0008] If the resource to be cached has a historical deployment, the historical caching profit generated during the deployment of the resource to be cached on the corresponding deployment node is calculated using the call latency of the resource to be cached as the standard.

[0009] Based on the real-time cache profit and the historical cache profit, a greedy search algorithm is used to select a target deployment node for the resource to be cached.

[0010] Advantages of this embodiment:

[0011] This embodiment calculates the real-time caching revenue of each deployment node based on the current network status, and also calculates the historical caching revenue of deploying corresponding cacheable resources on the deployment node over a period of time. The two parts constitute the total caching profit. The total caching profit is used as a reference to guide how to deploy cacheable resources on the cache deployment node, thereby improving the rationality of cacheable resource deployment.

[0012] In some implementations, the historical caching profit generated during the process of calculating the historical deployment of the cached resource on the corresponding deployment node includes:

[0013] Retrieve historical tasks that invoke the cached resources on the deployment node;

[0014] Calculate the historical cache profit based on the call latency generated by the historical tasks.

[0015] In some implementations, the step of selecting a target deployment node for the resource to be cached using a greedy search algorithm based on the real-time cache profit and the historical cache profit includes:

[0016] Sort the resources to be cached;

[0017] Each of the resources to be cached is traversed sequentially to determine the target deployment node selected for each resource to be cached, wherein the process of determining the target deployment node selected for any one of the resources to be cached includes:

[0018] Identify multiple deployment nodes capable of deploying the resources to be cached;

[0019] Calculate the sum of the real-time cache profit and the historical cache profit, assuming the resource to be cached is deployed on each of the plurality of deployment nodes;

[0020] The deployment node corresponding to the maximum sum of the real-time cache profit and the historical cache profit is selected as the target deployment node.

[0021] In some implementations, the function for calculating the sum of the real-time cache profit and the historical cache profit includes:

[0022] R(t)=λ j r j,k (t)+(1-λ j )v j,k (t)

[0023] Where R(t) represents the value at time t when deploying node v j Deploy cached resources on q k The sum of real-time cache profit and historical cache profit; r j,k (t) represents the value at time t when deploying node v. j Deploy cached resources on q k Real-time caching profit, v j,k (t) represents the resource q to be cached. k Deployed on deployment node v j The historical cache profit generated above, λj This represents the weighting coefficient.

[0024] In some implementations, r j,k The computational functions for (t) include:

[0025]

[0026] Among them, T i,k (t) represents vehicle v at time t. i For deployment node v j The cached resource q on k The cache call latency, δ represents the discount factor, u k The value is 0 or 1, when the task request is a computation task request, u k =0,F j Indicates the deployment node v j Its computing power.

[0027] In some implementations, the method for deploying cache resources in the vehicle network further includes:

[0028] Preset cycle;

[0029] At the beginning of each cycle, update the historical cached profit v as follows: j,k (t):

[0030]

[0031]

[0032] in, γ represents the learning rate, and γ represents the time discount factor. Indicates t ′ Resources to be cached at any time q k Deployed on deployment node v j Cache profit, T L Indicates the preset period, t ′ The range of values ​​for time is [tT] L +1,t],T i,k (t ′ ) represents t ′ Resources to be cached at any time q k Deployed on deployment node v j Above, by vehicle v i The task completion delay of historical task calls.

[0033] In some implementations, sorting the resources to be cached includes:

[0034] Calculate the popularity and size of the resource to be cached;

[0035] Sort according to the ratio of the popularity of the resource to be cached to the size of the resource.

[0036] To achieve the above objectives, a second aspect of the present invention provides a cache resource deployment system for a vehicle-to-everything (V2X) network, the cache resource deployment system comprising:

[0037] The data determination unit is used to determine the resources to be cached and the deployment nodes in the vehicle-to-everything (V2X) system;

[0038] The real-time cache profit calculation unit is used to predict the real-time cache profit generated when the corresponding cached resource is deployed to the corresponding deployment node, based on the network status of the vehicle network system and using the call latency of the cached resource as a standard.

[0039] The historical cache profit calculation unit is used to calculate the historical cache profit generated during the deployment of the resource to be cached on the corresponding deployment node, using the call latency of the resource to be cached as the standard, if the resource to be cached has a historical deployment.

[0040] The node determination unit is used to select a target deployment node for the resource to be cached based on the real-time cache profit and the historical cache profit using a greedy search algorithm.

[0041] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: at least one control processor and a memory for communicatively connecting to the at least one control processor; the memory stores instructions executable by the at least one control processor, the instructions being executed by the at least one control processor to enable the at least one control processor to execute the above-described vehicle network cache resource deployment method.

[0042] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium storing computer-executable instructions for causing a computer to execute the above-described method for deploying cache resources in a vehicle network. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1This is a flowchart illustrating a method for deploying cache resources in a vehicle network according to an embodiment of this application;

[0045] Figure 2 This is a schematic diagram of the structure of a vehicle network cache resource deployment system according to an embodiment of this application;

[0046] Figure 3 This is a schematic diagram of the structure of an electronic device provided in one embodiment of this application. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0048] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0050] Reference Figure 1 One embodiment of this application provides a method for deploying cache resources in a vehicle-to-everything (V2X) network. The method includes:

[0051] Step S100: Determine the resources to be cached and the deployment nodes in the vehicle networking system.

[0052] Typically, a vehicle-to-everything (V2X) system includes base stations, cloud servers, edge servers, and vehicles, with vehicles being the primary source of task requests. During off-peak hours, the cached resources required for task requests initiated by vehicle users can be deployed on vehicles or edge servers closer to the requesting vehicle. This helps avoid high uplink latency when users make requests. For example, when initiating a computation task, the vehicle needs to offload the task to the corresponding node (vehicle or edge server). If the offloading node has pre-configured cached resources for the task (e.g., video programs, service models), it can directly perform the computation without needing to access the resources.

[0053] In step S100, it is assumed that the task request has multiple types: a computation task request and a content task request. If the task request is a computation task request, the resources to be cached may include, but are not limited to, video processing programs, service models, etc., depending on the specific computation task request. If the task request is a content task request, the resources to be cached may include, but are not limited to, web pages, videos, images, etc.

[0054] Step S200: Based on the network status of the vehicle network system, and using the call latency of the cached resources as the standard, predict the real-time caching profit generated when the corresponding cached resources are deployed to the corresponding deployment nodes.

[0055] In step S200, when calculating the real-time cache profit, the network status of the vehicle network system needs to be considered. The network status includes, but is not limited to, the remaining cache capacity of the deployment node, the distance between the vehicle and the deployment node, and the communication environment (which affects data transmission latency).

[0056] In some embodiments, assuming there are M deployment nodes, v j Let v represent the j-th deployment node, which has L vehicles (which can act as task request initiators or deployment nodes). i This indicates that for the i-th vehicle, there are K resources to be cached, represented by q. k Represents the k-th resource. (Using r) j,k (t) represents the deployment node v at time t. j Deploy cached resources on q k Real-time caching profit:

[0057]

[0058] Among them, T i,k (t) represents the vehicle v at time t. i Treating cached resources q k The cache call latency, δ represents the discount factor, u k The value is 0 or 1, when the task request is a computation task request, u k =0;F j Indicates the deployment node v j The computing power. The cache call latency is related to the communication environment, communication distance, and resource size of both parties. The higher the δ value, the greater the return considering the computational task request portion.

[0059] Compared to existing solutions, this embodiment considers multiple types of task requests, one of which is a computation task request u. k =0, another is content task request u k=1, using the average cache call latency at time t as a reference, and also considering the computing power of the node requesting the computing task, to calculate the current latency at time t on the deployment node v. j Deploy cached resources on q k Real-time caching profits.

[0060] Step S300: If the resource to be cached has a historical deployment, use the call latency of the resource to be cached as the standard to calculate the historical cache profit generated during the deployment of the resource to be cached on the corresponding deployment node.

[0061] Step S300 includes:

[0062] Step S310: Obtain the historical tasks of cached resources on the deployment node.

[0063] Step S320: Calculate the historical cache profit based on the task completion delays generated by historical tasks.

[0064] Since the task requests from future vehicle users cannot be determined, the specific revenue from deploying cached resources cannot be estimated. Therefore, future revenue is predicted based on historical data of task requests initiated by past vehicle users. This embodiment uses the task completion delays requested by past vehicle users as a reference for calculating historical cache profits.

[0065] Step S400: Based on the real-time cache profit and the historical cache profit, a greedy search algorithm is used to select a target deployment node for the resource to be cached.

[0066] In step S400, this embodiment calculates the real-time cache profit of each deployment node based on the network status, and also calculates the historical cache profit of deploying corresponding cacheable resources on the deployment node over a period of time. The two parts constitute the total cache profit. Based on the greedy deployment scheme, the total cache profit can be used as a reference to guide how to deploy cacheable resources on the deployment node, and at the same time improve the rationality of the deployment of cacheable resources.

[0067] In some embodiments of this application, step S400 is specifically as follows:

[0068] Step S410: Sort the resources to be cached.

[0069] Step S420: Iterate through each resource to be cached in sequence to determine the target deployment node selected for each resource to be cached.

[0070] Step S421: Determine multiple deployment nodes that can deploy the resources to be cached.

[0071] Step S422: Calculate the sum of real-time cache profit and historical cache profit for each deployment node in the case where the resource to be cached is deployed in multiple deployment nodes.

[0072] Step S423: Select the deployment node corresponding to the maximum sum of real-time cache profit and historical cache profit as the target deployment node.

[0073] In some embodiments of this application, the function for calculating the sum of real-time cache profit and historical cache profit includes:

[0074] R(t)=λ j r j,k (t)+(1-λ j )v j,k (t)

[0075] Where R(t) represents the value at time t in the deployment node v. j Deploy cached resources on q k The sum of real-time cache profit and historical cache profit; r j,k (t) represents the deployment node v at time t. j Deploy cached resources on q k Real-time caching profit; v j,k (t) represents the resource q to be cached. k Deployed on deployment node v j Historical cache profits generated above; λ j λ represents the weighting coefficient. j It changes depending on the deployment node.

[0076] Because in vehicle-to-everything (V2X) systems, due to the mobility of vehicles, they may leave the communication range of the base station. As a deployment node, maintaining the node's V... j,k (t) is unstable. Therefore, when deploying caching resources on mobile vehicle nodes, it is necessary to pay more attention to the current state of the vehicle, that is, increase the weight of real-time gain when caching vehicles and reduce v. j,k The influence of (t). Therefore, this embodiment introduces a discriminative real-time weighting factor λ. j It can adjust the proportion of real-time cache profit and historical cache profit in real time. j It can be set based on experience.

[0077] In some embodiments of this application, the method for deploying cache resources in the Internet of Vehicles further includes:

[0078] Step S500, preset cycle.

[0079] Step S600: At the beginning of each cycle, update the historical cache profit v according to the following method. j,k (t):

[0080]

[0081]

[0082] in, γ represents the learning rate, and γ represents the time discount factor. Indicates t ′ Resources to be cached at any time q k Deployed on deployment node v j Cache profit, T L Indicates the preset period, t ′ The range of values ​​for time is [tT] L +1,t],T i,k (t ′ ) represents t ′ Resources to be cached at any time q k Deployed on deployment node v j Above, by vehicle v i The task completion delay of historical task calls.

[0083] Because in real-world scenarios, the update frequency of cached resources is often slower compared to changes in the network environment under edge computing, the formulation of caching deployment strategies has a certain time sensitivity. Frequent updates to historical cache profits can lead to heavy computational loads. Therefore, to adapt to changes in the cache popularity of cached resources, the occurrence of task requests from vehicle users, and the differences in change frequency, the above steps establish a periodicity to update historical cache profits on a large timescale.

[0084] Since the task completion delay of a user's task request is known in the past, according to the Bellman update principle, the known task completion delay of the last large time scale is used as a reward for updating the historical cache profit. A multi-step update method can be used to update the historical cache profit.

[0085] This application also provides an embodiment as shown below;

[0086]

[0087]

[0088] Total caching revenue consists of two parts: historical caching profit and real-time caching profit.

[0089] Lines 7 and 8 of the code calculate the total caching benefit. First, based on the p of the resource to be cached... k / y k Sort by popularity ratio; p k / yk Larger resources to be cached are placed at the head of the queue. After the queue is complete, the total caching benefit of each resource deployed on each deployment node is calculated sequentially, and the node with the largest total caching benefit is selected for deployment. After each round, the storage space of the deployment nodes is updated until the last resource to be cached in the queue has been placed.

[0090] Output the corresponding cache deployment results.

[0091] This application also provides an application example of a method for deploying cache resources in the Internet of Vehicles:

[0092] For vehicle-to-everything (V2X) systems, pre-setting cached resources on deployment nodes can save time on unloading computations. Given the limited storage capacity of deployment nodes, an effective caching strategy for cached resources can significantly reduce computational latency for vehicle tasks. In V2X systems, each vehicle and edge system can serve as a deployment node. To maximize caching profits for cached resources, this method calculates the real-time caching revenue of each deployment node based on the current network status, and also calculates the historical caching revenue of deploying corresponding cached resources on the deployment node over a past period. These two parts constitute the total caching profit. Using the total caching profit as a reference guides how to deploy cached resources on caching deployment nodes, maximizing caching profits and improving the rationality of cached resource deployment.

[0093] like Figure 3 This application also provides an electronic device, which includes:

[0094] At least one memory;

[0095] At least one processor;

[0096] At least one program;

[0097] The program is stored in memory, and the processor executes at least one program to implement the super-resolution reconstruction method for sea surface height described above in this disclosure.

[0098] This electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0099] The electronic devices according to embodiments of this application will now be described in detail.

[0100] The processor 1600 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0101] The memory 1700 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1700 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the program code is stored in the memory 1700 and is called and executed by the processor 1600 to execute the super-resolution reconstruction method for sea surface height according to the embodiments of this application.

[0102] The input / output interface 1800 is used to implement information input and output.

[0103] The communication interface 1900 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0104] Bus 2000 transmits information between various components of the device (e.g., processor 1600, memory 1700, input / output interface 1800, and communication interface 1900);

[0105] The processor 1600, memory 1700, input / output interface 1800 and communication interface 1900 are connected to each other within the device via bus 2000.

[0106] This application embodiment also provides a storage medium, which is a computer-readable storage medium storing computer-executable instructions for causing a computer to perform the above-described super-resolution reconstruction method for sea surface height.

[0107] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0108] The embodiments described in this application are intended to more clearly illustrate the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0109] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0110] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0112] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0113] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0114] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0117] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, 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. This computer software product is stored in a storage medium and includes multiple instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0118] The above is a detailed description of the preferred embodiments of this application. However, the embodiments of this application are not limited to the above-described implementation methods. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the embodiments of this application. All such equivalent modifications or substitutions are included within the scope defined by the claims of the embodiments of this application.

Claims

1. A method for deploying cache resources in a vehicle-to-everything (V2X) network, characterized in that, The method for deploying cache resources in the vehicle network includes: Identify the resources to be cached and the deployment nodes in the vehicle-to-everything (V2X) system; Based on the network status of the vehicle network system, and using the call latency of the cached resources as a standard, predict the real-time caching profit generated when the corresponding cached resources are deployed on the corresponding deployment nodes; If the resource to be cached has a historical deployment, the historical caching profit generated during the deployment of the resource to be cached on the corresponding deployment node is calculated using the call latency of the resource to be cached as the standard. Based on the real-time cache profit and the historical cache profit, a greedy search algorithm is used to select a target deployment node for the resource to be cached; the step of selecting a target deployment node for the resource to be cached using a greedy search algorithm based on the real-time cache profit and the historical cache profit includes: Sort the resources to be cached; Each of the resources to be cached is traversed sequentially to determine the target deployment node selected for each resource to be cached, wherein the process of determining the target deployment node selected for any one of the resources to be cached includes: Identify multiple deployment nodes capable of deploying the resources to be cached; Calculate the sum of the real-time cache profit and the historical cache profit, assuming the resource to be cached is deployed on each of the plurality of deployment nodes; The deployment node corresponding to the maximum sum of the real-time cache profit and the historical cache profit is selected as the target deployment node; The function for calculating the sum of the real-time cache profit and the historical cache profit includes: ； in, express Deploying nodes at all times Deploy cached resources on The sum of real-time cache profit and historical cache profit; express Deploying nodes at all times Deploy cached resources on Real-time caching profits Indicates resources to be cached Deployed on deployment node Historical cache profits generated on top of Indicates the weighting coefficient; The computational functions include: ； in, express Vehicles at all times For deployment nodes Resources to be cached Cache call latency, Indicates the discount factor. It can be 0 or 1, when the task request is a computation task request. , Indicates the deployment node Its computing power.

2. The method for deploying cache resources in a vehicle-to-everything (V2X) network according to claim 1, characterized in that, The historical caching profit generated during the process of deploying the cached resource with historical deployments to the corresponding deployment node includes: Retrieve historical tasks that invoke the cached resources on the deployment node; Calculate the historical cache profit based on the call latency generated by the historical tasks.

3. The method for deploying cache resources in a vehicle-to-everything (V2X) network according to claim 1, characterized in that, The method for deploying cache resources in the vehicle network also includes: Preset cycle; At the end of each cycle, update the historical cache profit as follows: : ； ； in, Indicates the learning rate. This represents the time discount factor. express Resources to be cached at all times Deployed on deployment node Cache profit, Indicates the preset period. The range of values ​​for time is , express Resources to be cached at all times Deployed on deployment node On, by vehicle The task completion delay of historical task calls.

4. The method for deploying cache resources in a vehicle-to-everything (V2X) network according to claim 1, characterized in that, Sort the resources to be cached, including: Calculate the popularity and size of the resource to be cached; Sort according to the ratio of the popularity of the resource to be cached to the size of the resource.

5. A cache resource deployment system for vehicle networking, characterized in that, The vehicle network cache resource deployment system includes: The data determination unit is used to determine the resources to be cached and the deployment nodes in the vehicle-to-everything (V2X) system; The real-time cache profit calculation unit is used to predict the real-time cache profit generated when the corresponding cached resource is deployed to the corresponding deployment node, based on the network status of the vehicle network system and using the call latency of the cached resource as a standard. The historical cache profit calculation unit is used to calculate the historical cache profit generated during the deployment of the resource to be cached on the corresponding deployment node, using the call latency of the resource to be cached as the standard, if the resource to be cached has a historical deployment. A node determination unit is configured to select a target deployment node for the resource to be cached using a greedy search algorithm based on the real-time cache profit and the historical cache profit; the step of selecting a target deployment node for the resource to be cached using a greedy search algorithm based on the real-time cache profit and the historical cache profit includes: Sort the resources to be cached; Each of the resources to be cached is traversed sequentially to determine the target deployment node selected for each resource to be cached, wherein the process of determining the target deployment node selected for any one of the resources to be cached includes: Identify multiple deployment nodes capable of deploying the resources to be cached; Calculate the sum of the real-time cache profit and the historical cache profit, assuming the resource to be cached is deployed on each of the plurality of deployment nodes; The deployment node corresponding to the maximum sum of the real-time cache profit and the historical cache profit is selected as the target deployment node; The function for calculating the sum of the real-time cache profit and the historical cache profit includes: ； in, express Deploying nodes at all times Deploy cached resources on The sum of real-time cache profit and historical cache profit; express Deploying nodes at all times Deploy cached resources on Real-time caching profits Indicates resources to be cached Deployed on deployment node Historical cache profits generated on top of Indicates the weighting coefficient; The computational functions include: ； in, express Vehicles at all times For deployment nodes Resources to be cached Cache call latency, Indicates the discount factor. It can be 0 or 1, when the task request is a computation task request. , Indicates the deployment node Its computing power.

6. An electronic device, characterized in that, include: At least one control processor and a memory for communicatively connecting to the at least one control processor; The memory stores instructions that can be executed by the at least one control processor, which, when executed by the at least one control processor, enables the at least one control processor to perform the vehicle network cache resource deployment method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions for causing a computer to execute the vehicle network cache resource deployment method according to any one of claims 1 to 4.

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