Vehicle computing resource allocation method, device and equipment and vehicle

Abstract

The invention discloses a computing resource allocation method and device for a vehicle, equipment and the vehicle, and relates to the technical field of vehicles. The computing resource allocation method for the vehicle comprises the steps that sensor information associated with at least one function module in the vehicle running process is obtained, each function module is provided with an edge computing node, and the edge computing nodes are used for processing data of the corresponding function modules; predicting the driving condition of the vehicle based on the sensor information and a preset prediction model to obtain predicted condition information of the vehicle; determining required computing resources of at least one edge computing node according to the predicted working condition information; and finally, allocating computing resources to at least one edge computing node according to the required computing resources, so that the at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources, thereby improving the timeliness of computing resource allocation of the vehicle edge computing node.

Description

Technical Field

[0001] The present application relates to the field of vehicle technology, and in particular to a method, device, equipment and vehicle for allocating computing resources for a vehicle. Background Art

[0002] With the development of vehicle intelligence and interconnection technology, the edge computing node technology in vehicles has also developed rapidly. The edge computing nodes in vehicles refer to computing resources deployed inside or near the vehicle. They are responsible for processing the data generated by the vehicle and performing data analysis, processing and decision-making tasks before the data is sent to the cloud or data center.

[0003] Edge computing nodes often need to have sufficient computing power to process the complex data generated by vehicles. At the same time, edge computing nodes are usually limited by computing resources, storage space and energy.

[0004] In the related art, the resource allocation of edge computing nodes during vehicle driving mainly relies on the monitoring of computing resources in the edge computing nodes and the execution of a fixed computing resource allocation strategy based on the monitoring results, which may lead to the problem of insufficiently timely resource allocation of edge computing nodes. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to provide a method, device, equipment and vehicle for allocating computing resources for a vehicle, aiming to improve the timeliness of computing resource allocation of vehicle edge computing nodes.

[0006] To achieve the above-mentioned purpose, one aspect of an embodiment of the present application proposes a method for allocating computing resources of a vehicle, comprising the following steps: obtaining sensor information associated with at least one functional module during vehicle driving, each functional module being configured with an edge computing node, and the edge computing node being used to process data of the corresponding functional module; predicting the driving condition of the vehicle based on the sensor information and a preset prediction model to obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with the at least one functional module and historical operating condition information corresponding to the historical sensor information; determining the required computing resources of at least one edge computing node based on the predicted operating condition information; and allocating the computing resources to the at least one edge computing node based on the required computing resources, so that the at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources.

[0007] In some embodiments, determining the required computing resources of at least one of the edge computing nodes based on the predicted operating condition information includes: determining a key edge computing node based on the predicted operating condition information, the key edge computing node being used to characterize an edge computing node whose demand for computing resources in the predicted operating condition is greater than a preset resource threshold; and determining the required computing resources of the key edge computing node based on a computing resource demand model corresponding to the key edge computing node.

[0008] In some embodiments, allocating the computing resources to the at least one edge computing node according to the required computing resources includes: allocating the set computing resources to the edge computing node according to the required computing resources; and dynamically adjusting the computing resources of the edge computing node according to current operating condition information.

[0009] In some embodiments, dynamically adjusting the computing resources of the edge computing node according to the current operating condition information includes: obtaining a preset resource threshold of the edge computing node under the current operating condition information; monitoring the computing resource usage of the edge computing node under the current operating condition information; and dynamically adjusting the computing resources of the edge computing node based on the preset resource threshold and the computing resource usage.

[0010] In some embodiments, dynamically adjusting the computing resources of the edge computing node according to the current operating condition information also includes: determining the priority information of the functional module according to the current operating condition information; and dynamically adjusting the computing resources of the edge computing node based on the priority information of the functional module.

[0011] In some embodiments, the dynamically adjusting the computing resources of the edge computing node according to the current operating condition information also includes: obtaining the computing resource usage of the edge computing node under the current operating condition information; dynamically adjusting the computing resources of the edge computing node through a preset scheduling model according to the predicted operating condition information and the computing resource usage; wherein the preset scheduling model is constructed based on historical resource data, and the historical resource data includes: historical computing resource usage information associated with the edge computing node and historical operating condition information corresponding to the historical computing resource usage information.

[0012] In some embodiments, dynamically adjusting the computing resources of the edge computing nodes according to the current operating condition information also includes: obtaining the computing resource usage of multiple edge computing nodes under the current operating condition information; and dynamically allocating the computing resources among the multiple edge computing nodes according to the computing resource usage.

[0013] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a computing resource allocation device for a vehicle, the computing resource allocation device for the vehicle comprising: an acquisition module, used to acquire sensor information associated with at least one functional module during vehicle driving, each functional module being configured with an edge computing node, the edge computing node being used to process data of the corresponding functional module; a prediction module, used to predict the driving condition of the vehicle based on the sensor information and a preset prediction model, and obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information comprises: historical sensor information associated with the at least one functional module and historical operating condition information corresponding to the historical sensor information; a determination module, used to determine the required computing resources of at least one edge computing node based on the predicted operating condition information; an allocation module, used to allocate the computing resources to the at least one edge computing node based on the required computing resources, so that the at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources.

[0014] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the computing resource allocation method of the above-mentioned vehicle when executing the computer program.

[0015] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides a vehicle, which includes a computing resource allocation device for a vehicle according to the above-mentioned embodiment or an electronic device according to the above-mentioned embodiment.

[0016] The embodiments of the present application include at least the following beneficial effects:

[0017] The present application provides a method, device, equipment and vehicle for allocating computing resources for a vehicle. In an embodiment of the present application, first, sensor information associated with at least one functional module during vehicle driving is obtained, and each functional module is configured with an edge computing node, which is used to process data of the corresponding functional module; secondly, the driving condition of the vehicle is predicted based on the sensor information and a preset prediction model to obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with at least one functional module and historical operating condition information corresponding to the historical sensor information; based on the predicted operating condition information, the required computing resources of at least one edge computing node are determined; finally, computing resources are allocated to at least one edge computing node based on the required computing resources, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources. Therefore, the driving condition of the vehicle is predicted through sensor information and a preset prediction model to obtain the predicted operating condition information of the vehicle. The driving condition information of the vehicle can be known in advance, and the required computing resources of the edge computing node can be determined through the predicted operating condition information. The computing resources of the edge node can be estimated in advance, and computing resources can be allocated to at least one edge computing node based on the required computing resources, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources. The required computing resources are determined through the predicted operating condition information, and computing resources are allocated to the edge computing nodes based on the required computing resources. This can improve the timeliness of computing resource allocation of the vehicle edge computing nodes.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0020] Figure 1 is a flowchart of a method for allocating computing resources of a vehicle provided by some embodiments of the present application;

[0021] Figure 2 is another flow chart of a method for allocating computing resources of a vehicle provided by some embodiments of the present application;

[0022] Figure 3 is a schematic block diagram of modules of a computing resource allocation device for a vehicle provided in some embodiments of the present application;

[0023] Figure 4 It is a schematic diagram of the hardware structure of an electronic device provided in some embodiments of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0025] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0026] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0028] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0029] In order to make the inventive concept of the present application easy to understand, before describing the embodiments of the present application in detail, the English abbreviations (terms) / related concepts involved in the embodiments of the present application are first described. The English abbreviations (terms) / related concepts involved in the embodiments of the present application are subject to the following explanations.

[0030] DMS: The full name of DMS is Driver monitoring system, which refers to the driver monitoring system.

[0031] HUD: The full name of HUD is head up display, which refers to head-up display.

[0032] AVAS: AVAS stands for Acoustic vehicle alerting system, which refers to the vehicle acoustic warning system.

[0033] With the development of vehicle intelligence and interconnection technology, the edge computing node technology in vehicles has also developed rapidly. The edge computing nodes in vehicles refer to computing resources deployed inside or near the vehicle. They are responsible for processing the data generated by the vehicle and performing data analysis, processing and decision-making tasks before the data is sent to the cloud or data center.

[0034] Edge computing nodes often need to have sufficient computing power to process the complex data generated by the vehicle. At the same time, edge computing nodes are usually limited by computing resources, storage space, and energy. For example, in a real car, the domain controller is equipped with a variety of application modules that require computing power, including HUD, AVAS, DMS, etc. These application modules may run at the same time, generating complex computing power requirements, so the computing power of each module needs to be considered comprehensively.

[0035] During the actual vehicle evaluation process, a variety of actual working conditions need to be considered, including urban roads, highways, complex traffic scenes, etc. The inventors found that different driving conditions will affect the computing power requirements of each application module. For example, on highways, more attention may be paid to the display effect of HUD and the safety warning of AVAS, while on urban roads, more attention may be paid to the driver monitoring function of DMS. Therefore, it is necessary to comprehensively consider the computing power requirements of multiple application modules under different working conditions.

[0036] In the related art, the resource allocation of edge computing nodes during vehicle driving mainly relies on the monitoring of computing resources in the edge computing nodes and the execution of a fixed computing resource allocation strategy based on the monitoring results, which may lead to the problem of insufficiently timely resource allocation of edge computing nodes.

[0037] In view of this, the present application proposes a method, device, equipment and vehicle for allocating computing resources of a vehicle. The scheme obtains sensor information associated with at least one functional module during driving of the vehicle, and each functional module is configured with an edge computing node, and the edge computing node is used to process the data of the corresponding functional module; the driving condition of the vehicle is predicted based on the sensor information and a preset prediction model to obtain the predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with at least one functional module and historical operating condition information corresponding to the historical sensor information; according to the predicted operating condition information, the required computing resources of at least one edge computing node are determined; according to the required computing resources, computing resources are allocated to at least one edge computing node, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources. Therefore, the driving condition of the vehicle is predicted through sensor information and a preset prediction model to obtain the predicted operating condition information of the vehicle. The driving condition information of the vehicle can be known in advance, and the required computing resources of the edge computing node can be determined through the predicted operating condition information. The computing resources of the edge node can be estimated in advance, and computing resources can be allocated to at least one edge computing node based on the required computing resources, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources. The required computing resources are determined through the predicted operating condition information, and computing resources are allocated to the edge computing nodes based on the required computing resources. This can improve the timeliness of computing resource allocation of the vehicle edge computing nodes.

[0038] The method provided in the embodiment of the present application can be applied to the electronic device provided in the embodiment of the present application, wherein the electronic device can be a terminal, or a central processing unit (ECU) or multiple computing units distributed in a vehicle.

[0039] The terminal may be a vehicle-mounted computer, a tablet computer, a laptop computer, etc., but is not limited thereto. It should be understood that when the method in the embodiment of the present application is run in the terminal, the terminal needs to establish a communication connection with the vehicle system and can transmit information to each other.

[0040] The following will describe in detail the implementation steps of a vehicle computing resource allocation method provided by an embodiment of the present application in conjunction with the accompanying drawings.

[0041] Please refer to Figure 1 , Figure 1 A flowchart of a method for allocating computing resources of a vehicle provided for some embodiments of the present application. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and, although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0042] The method of the embodiment of the present application comprises the following steps:

[0043] Step 101: Acquire sensor information associated with at least one functional module during vehicle driving, each functional module is configured with an edge computing node, and the edge computing node is used to process data of the corresponding functional module;

[0044] Step 102: predicting the driving condition of the vehicle based on the sensor information and a preset prediction model to obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with at least one functional module and historical operating condition information corresponding to the historical sensor information;

[0045] Step 103: Determine required computing resources of at least one edge computing node according to the predicted operating condition information;

[0046] Step 104: Allocate computing resources to at least one edge computing node according to the required computing resources, so that at least one edge computing node processes data of a corresponding functional module based on the allocated computing resources.

[0047] In steps 101 to 104 shown in the embodiment of the present application, the driving condition of the vehicle is predicted through sensor information and a preset prediction model to obtain the predicted operating condition information of the vehicle, so that the driving condition information of the vehicle can be known in advance; the required computing resources of the edge computing node are determined through the predicted operating condition information, and the computing resources of the edge node can be estimated in advance; computing resources are allocated to at least one edge computing node according to the required computing resources, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources, the required computing resources are determined through the predicted operating condition information, and computing resources are allocated to the edge computing nodes according to the required computing resources, so as to improve the timeliness of computing resource allocation of the vehicle edge computing nodes.

[0048] The specific implementation methods of the above steps are introduced below.

[0049] In step 101, sensor information associated with at least one functional module during vehicle driving is obtained, and each functional module is configured with an edge computing node, which is used to process data of the corresponding functional module.

[0050] Among them, the functional modules may be HUD, AVAS, DMS, etc., which is not limited in this application.

[0051] These functional modules usually correspond to associated sensors. For example, HUD is a technology that projects important information into the driver's field of view to reduce the possibility of driver distraction. HUD can display a variety of information, including speed, navigation instructions, warning information, etc. In order to provide this information, HUD is usually connected to the following vehicle sensors: speed sensor, steering angle sensor, acceleration sensor, lane keeping assist system sensor (such as camera and radar), and light sensor, etc.

[0052] By deploying edge computing nodes in these functional modules, these nodes can collect and process local data in real time, such as CPU and GPU utilization, memory usage, network communication data volume, etc. The operating status of each module can be monitored in real time through edge computing nodes, and the processed key data can be aggregated to the central control unit to reduce the bandwidth consumption of the network in the vehicle.

[0053] By acquiring sensor information associated with at least one functional module during vehicle driving, data support can be provided for subsequent prediction of the vehicle's driving conditions through sensor information. By configuring edge computing nodes in each functional module, the data of the corresponding functional module can be processed through the edge computing nodes, reducing data transmission delays and bandwidth consumption and improving the timeliness of data processing.

[0054] In step 102, the driving condition of the vehicle is predicted based on the sensor information and a preset prediction model to obtain the predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with at least one functional module and historical operating condition information corresponding to the historical sensor information.

[0055] The preset prediction model can collect data (historical information) of each functional module under different vehicle working conditions and use big data technology to build a training set. After that, a deep learning algorithm is used to train to obtain a prediction model, which can predict the computing resource requirements and system performance (predicted working condition information) of each module under different working conditions.

[0056] Among them, the deep learning algorithm can be a deep neural network (DNN), a long short-term memory network (LSTM), a deep belief network (DBN), a Transformer or BERT, etc., and this application does not impose any restrictions on this.

[0057] The vehicle's driving condition can be predicted through the acquired sensor information and the preset prediction model. For example, during the vehicle's driving process, the condition prediction can be performed based on the sensor information (such as vehicle speed, steering angle, acceleration, environmental conditions, etc.). For example, the acquired sensor information can be input into the preset prediction model to predict the vehicle's driving condition in the next few seconds or minutes, as well as the computing resource requirements and system performance of each module under the corresponding vehicle driving condition. For example, it is predicted that the vehicle is about to enter a high-speed driving state, complex urban roads or driving in bad weather, etc., and includes the computing resource requirements and system performance of modules such as HUD, AVAS and DMS corresponding to the high-speed driving state, complex urban roads or bad weather.

[0058] The vehicle's driving conditions include conditions related to vehicle speed, conditions related to road type and environment, conditions related to weather and environmental conditions, conditions related to driving behavior, and conditions related to the power system.

[0059] Among them, the working conditions related to vehicle speed include: low-speed driving: the vehicle is driving on urban roads and in congested traffic, and the speed is usually between 0-30km / h; medium-speed driving: the vehicle is driving on urban expressways or suburban roads, and the speed is usually between 30-60km / h; high-speed driving: the vehicle is driving on highways, and the speed is usually above 60km / h.

[0060] The working conditions related to road type and environment include: Urban roads: including frequent starting, stopping, turning, and dealing with complex environments such as traffic lights and pedestrians; Expressways: vehicles travel continuously at a higher speed with fewer turning and stopping needs; Rural roads: generally narrow roads, road conditions may be poor, accompanied by ups and downs and sharp turns; Mud / gravel roads: driving on unpaved roads, dealing with low adhesion and unstable road conditions; Mountain roads: including steep ups and downs, a large number of sharp turns and steering operations.

[0061] The operating conditions of weather and environmental conditions include: Sunny days: under normal lighting conditions, the vehicle travels on dry roads; Rainy days: under slippery road conditions, the vehicle needs to deal with lower adhesion and possible water accumulation; Snowy days: on snowy or icy roads, tire grip is significantly reduced and braking distance is extended; Foggy days: visibility is low, sensors may be significantly interfered with, and the system needs to cope with the challenge of shortened sight range; Night driving: driving in low-light environments, especially on unlit roads in rural or mountainous areas.

[0062] The operating conditions related to driving behavior include: normal driving: stable acceleration, deceleration and steering operations, which are in line with the normal habits of most drivers; sudden acceleration / sudden deceleration: the vehicle accelerates quickly or brakes suddenly; sharp turns: the vehicle makes sharp turns at a higher speed; following / merging: the vehicle is driving in dense traffic and needs to follow the vehicle and change lanes frequently.

[0063] The operating conditions related to the power system include: idling: various operations when the engine is in idle state, such as waiting for a stop or moving slowly; cruising: the vehicle travels at a fixed speed for a long time; acceleration: the vehicle accelerates from a stationary state to the target speed; braking: the vehicle decelerates from a high speed state to a complete stop.

[0064] The embodiment provided in the present application predicts the driving condition of the vehicle through the acquired sensor information and the preset prediction model to obtain the predicted working condition information of the vehicle. The predicted working condition information can be used to know in advance the working condition that the vehicle is about to enter, the computing resource requirements and system performance of each module under different working conditions, and the computing resource allocation can be made in advance for the driving condition that the vehicle is about to enter, thereby improving the timeliness of the computing resource allocation of the vehicle edge computing node.

[0065] In step 103, the required computing resources of at least one edge computing node are determined based on the predicted operating condition information.

[0066] In some embodiments, key edge computing nodes can be determined based on predicted operating condition information, and the key edge computing nodes are used to characterize edge computing nodes whose demand for computing resources in the predicted operating condition is greater than a preset resource threshold; based on the computing resource demand model corresponding to the key edge computing nodes, the required computing resources of the key edge computing nodes are determined.

[0067] A key edge computing node may be an edge computing node that consumes more computing resources during the vehicle's driving conditions. It should be understood that, generally, the vehicle sets a preset resource threshold for each edge computing node, and this preset resource threshold is used to represent the computing resources that need to be consumed as counted by the corresponding edge computing node. However, under some driving conditions, some edge computing nodes often need to consume more computing resources, exceeding the preset resource threshold set for them. At this time, these edge computing nodes can be considered key edge computing nodes.

[0068] It can be understood that being determined as a key edge computing node means that the key edge computing node will play a more important role in the predicted working condition information. For example, when the predicted working condition is night driving, the night vision module and the sensor fusion module may require more computing resources. Therefore, the edge computing nodes corresponding to the night vision module and the sensor fusion module can be determined as key edge computing nodes.

[0069] In some embodiments, a computing resource requirement model corresponding to the specific working condition of the vehicle can be established for the edge computing node. For example, the operating data of the vehicle under various working conditions can be collected, including but not limited to engine speed, vehicle speed, temperature, oil pressure, etc. Afterwards, performance indicators such as response time, calculation accuracy, throughput, etc. are defined for each functional module (edge ​​computing node), and a computing resource requirement model for each module under different working conditions is established based on historical data and performance indicators.

[0070] According to the predicted working condition information, the computing resource demand model corresponding to the key edge computing node is found, and then the required computing resources of the key edge computing node are determined based on the computing resource demand model corresponding to the key edge computing node.

[0071] After determining the required computing resources of the key edge computing nodes, the required computing resources of the remaining edge computing nodes are determined.

[0072] The embodiments provided in the present application can first ensure the required computing resources of key edge computing nodes by determining the required computing resources of at least one edge computing node based on predicted operating condition information, thereby ensuring the resource allocation of important and priority functions under the current operating conditions of the vehicle and improving the safety of vehicle driving.

[0073] In step 104, computing resources are allocated to at least one edge computing node according to the required computing resources, so that the at least one edge computing node processes data of the corresponding functional module based on the allocated computing resources.

[0074] In some implementations, set computing resources may be allocated to edge computing nodes based on required computing resources, and then the computing resources of the edge computing nodes may be dynamically adjusted based on current operating condition information.

[0075] After obtaining the required computing resources of the edge computing node, the set computing resources can be allocated to the corresponding edge computing node according to the required computing resources. It should be understood that the set computing resources are related to the required computing resources. In some cases, the set computing resources can be equal to the required computing resources.

[0076] Afterwards, the computing resources of the edge computing nodes are further dynamically adjusted according to the current working condition information.

[0077] It should be understood that the required computing resources are determined by predicting the operating condition information. Therefore, after the set computing resources are allocated to the edge computing node, the current operating condition information may be the same as or different from the predicted operating condition information; if the current operating condition information is the same as the predicted operating condition information, the computing resources of the edge computing node can be started to be further dynamically adjusted according to the current operating condition information; if the current operating condition information is different from the predicted operating condition information, that is, the predicted operating condition information will occur after the current operating condition information, then the computing resources of the edge computing node can be further dynamically adjusted according to the current operating condition information after a delay of a preset time. It can be understood that the preset time can be flexibly set according to the advance amount of the predicted operating condition information. For example, if the vehicle driving condition is predicted to be 30 seconds later, the preset time can be set to be less than or equal to 30 seconds.

[0078] In some embodiments, a preset resource threshold of an edge computing node under current operating condition information can be obtained; computing resource usage of the edge computing node under current operating condition information can be monitored; and computing resources of the edge computing node can be dynamically adjusted based on the preset resource threshold and computing resource usage.

[0079] It should be understood that the vehicle system needs to monitor the resource usage, performance indicators and current operating status of each functional module in real time. To this end, the resource usage threshold of each functional module can be set based on the historical data and engineering experience of each functional module. This resource usage threshold should be the statistical result of historical data under the current working conditions. After that, monitor the computing resource usage of the edge computing node under the current working condition information. The monitoring indicators can be CPU usage, memory usage, sensor data processing delay, module response time, etc. If a functional module approaches or exceeds the set resource usage threshold, it should be allocated more computing resources first. This process is continuous and dynamic.

[0080] In some implementations, priority information of the functional modules may be determined according to current operating condition information, and then computing resources of the edge computing nodes may be dynamically adjusted based on the priority information of the functional modules.

[0081] For example, priorities can be set for each functional module, such as the safety-related ADAS (Advanced Driver Assistance System) module has the highest priority, and non-critical modules such as the entertainment system have a lower priority; priorities can also be set for each module based on the current working condition information. For example, on highways, more attention may be paid to the display effect of HUD and the safety warning of AVAS, so HUD and AVAS have a higher priority, while DMS has a lower priority. On urban roads, more attention may be paid to the driver monitoring function of DMS, so DMS has a higher priority, while AVAS has a slightly lower priority.

[0082] When system resources are insufficient, the needs of high-priority modules are met first. For example, in complex working conditions, the computing resource allocation of ADAS modules is increased and the resource allocation of non-critical modules is reduced.

[0083] In some embodiments, the computing resource usage of the edge computing node under the current operating condition information can be obtained; the computing resources of the edge computing node are dynamically adjusted through a preset scheduling model according to the predicted operating condition information and the computing resource usage; wherein the preset scheduling model is constructed based on historical resource data, and the historical resource data includes: historical computing resource usage information associated with the edge computing node and historical operating condition information corresponding to the historical computing resource usage information.

[0084] For example, a reinforcement learning algorithm (such as Q-learning, DQN) can be used to train an intelligent agent (preset scheduling model) to dynamically adjust the allocation of computing resources of edge computing nodes according to the current system status (including operating condition prediction and real-time monitoring data).

[0085] Optionally, a reward function can be set to optimize system performance. For example, a higher reward is given when the system maximizes overall efficiency while ensuring stable operation of all high-priority modules.

[0086] In the actual operation process, the scheduling strategy can be gradually optimized by exploring different resource allocation schemes.

[0087] In some implementations, the computing resource usage of multiple edge computing nodes under current operating condition information may also be obtained; and computing resources may be dynamically allocated among the multiple edge computing nodes based on the computing resource usage.

[0088] For example, tasks can be dynamically allocated among multiple edge computing nodes. For example, in high-speed driving conditions, the allocation of computing resources to the lane keeping assist system is increased, while the resource usage of other non-critical tasks is reduced.

[0089] If the computing resources of a functional module are insufficient, part of the computing tasks can be migrated to other edge computing nodes or central processing units for processing to avoid single-point overload.

[0090] Please refer to Figure 2 , Figure 2 This is another flow chart of a method for allocating computing resources for a vehicle provided in some embodiments of the present application. Figure 2 In the process, the working condition prediction and resource demand assessment are first carried out, and then the functional modules are monitored in real time and resource allocation decisions are made. After the resources are allocated, the resources are dynamically scheduled, and finally, the system is evaluated and feedback is given.

[0091] Specifically, during vehicle driving, operating condition prediction can be performed based on historical data and sensor information (such as vehicle speed, steering angle, acceleration, environmental conditions, etc.). For example, the system can predict driving conditions in the next few seconds or minutes, such as about to enter a high-speed driving state, complex urban roads, or driving in bad weather. Among them, time series analysis or machine learning models (such as LSTM, GRU) can be used to predict upcoming conditions. Based on the prediction results, the computing resource requirements of each module under specific conditions are evaluated. For example, when driving at night, the night vision system and sensor fusion module may require more computing resources.

[0092] The resource usage, performance indicators and current operating status of each module can be monitored in real time. Among them, the monitoring indicators can include CPU usage, memory usage, sensor data processing delay, module response time, etc. After that, based on historical data and engineering experience, the resource usage thresholds of different modules are set. If a module approaches or exceeds the set threshold, the system should give priority to allocating more computing resources to it.

[0093] In order to achieve dynamic scheduling of resources, the following algorithms can be used:

[0094] 1. Priority-based scheduling algorithm:

[0095] Priorities can be set for each module, for example, safety-related ADAS (Advanced Driver Assistance System) modules have the highest priority, while non-critical modules such as entertainment systems have lower priorities.

[0096] When system resources are insufficient, the needs of high-priority modules are met first. For example, in complex working conditions, the computing resources of the ADAS module are increased and the resource allocation of non-critical modules is reduced.

[0097] 2. Scheduling algorithm based on reinforcement learning:

[0098] Reinforcement learning algorithms (such as Q-learning, DQN) can be used to train an intelligent agent to dynamically adjust resource allocation strategies based on the current system status (including operating condition predictions and real-time monitoring data).

[0099] At the same time, a reward function is set to optimize system performance. For example, when the system maximizes overall efficiency while ensuring the stable operation of all high-priority modules, a higher reward is given.

[0100] In the actual operation process, the scheduling strategy can be gradually optimized by exploring different resource allocation schemes.

[0101] 3. Distributed scheduling based on edge computing:

[0102] Tasks can be dynamically allocated between multiple edge computing nodes. For example, in high-speed driving conditions, more computing resources are allocated to the lane keeping assist system, while reducing the resource usage of other non-critical tasks.

[0103] If the computing resources of a certain module are insufficient, part of the computing tasks can be migrated to other edge nodes or central processing units for processing to avoid single-point overload.

[0104] Regularly conduct system performance evaluation to verify the accuracy of the prediction model and the effectiveness of the resource allocation strategy, including: model prediction accuracy, resource allocation strategy effectiveness, system stability, and real-time and response speed.

[0105] The prediction accuracy of the model can be measured by prediction error, prediction stability, and response time. The prediction error can measure the difference between the model prediction result and the actual working condition. For example, the mean square error (MSE) or mean absolute error (MAE) can be used to quantify the prediction accuracy; prediction stability can evaluate the stability of the model under different working conditions, that is, whether the model can continuously output accurate prediction results under various environmental conditions; response time can evaluate the time from the input data to the output prediction result of the model, and evaluate the real-time performance of the prediction model.

[0106] The effectiveness of resource allocation strategy can be evaluated by resource utilization, resource allocation flexibility and task completion rate. Among them, resource utilization is used to calculate the utilization of resources, including the use of CPU, GPU, memory, etc. It evaluates whether the resource allocation strategy can avoid resource waste while meeting system requirements; resource allocation flexibility is used to measure whether the system can flexibly adjust resource allocation under different working conditions to ensure that high-priority tasks obtain sufficient computing resources; task completion rate is used to measure whether the completion rate of key tasks remains at a high level under working conditions after resource allocation, especially for safety-related tasks such as ADAS (Advanced Driver Assistance System).

[0107] System stability can be evaluated by system failure rate, failure recovery time and stable operation time. Among them, the system failure rate is used to measure the frequency of failures during system operation and evaluate the stability of the system in dynamic resource adjustment; the failure recovery time is used to measure the time required for the system to recover to normal operation after a failure and examine the effectiveness of the self-recovery mechanism; the stable operation time is used to measure the length of time the system can continue to operate without major failures.

[0108] Real-time performance and response speed can be evaluated by task response time and system latency. Task response time refers to the time from when a task request is issued to when the task starts to execute. For tasks with high real-time requirements, such as driver assistance systems, response speed is particularly important. System latency refers to the total delay time for each module to respond after the resource allocation strategy is executed. Low latency is the key to the efficient operation of the smart cockpit system.

[0109] Afterwards, the configuration of AI models and edge computing nodes can be continuously optimized based on the evaluation results.

[0110] Specifically, it can be achieved through collecting evaluation data, analyzing evaluation results, model optimization, edge computing node configuration optimization, real-time adjustment and adaptive optimization, and the specific manifestation of evaluation results.

[0111] Among them, collecting evaluation data can collect evaluation data from various dimensions through system performance evaluation, such as model prediction accuracy, resource utilization, system stability, response time, user experience, etc., and store the evaluation data in a structured manner in a data warehouse for subsequent analysis and modeling.

[0112] The analysis and evaluation results can be achieved through performance bottleneck identification, that is, by analyzing the evaluation data, the performance bottleneck of the system can be identified. For example, it is found that the model prediction error under certain working conditions is large, or the resource utilization of certain modules is too high, resulting in a decrease in system performance. After that, the relationship between the resource allocation strategy and system performance under different working conditions is analyzed to determine which configuration optimizations can improve the overall system performance.

[0113] Model optimization can be achieved by retraining the AI ​​model. Specifically, the training data can be enhanced based on the evaluation results. For example, for working conditions with large prediction errors, more relevant data can be collected or synthetic data can be generated to enhance the performance of the model in specific scenarios. According to the model defects found in the evaluation, adjust the architecture or parameters of the model, such as increasing the number of neural network layers, changing the activation function, or optimizing the loss function. Perform online fine-tuning based on the real-time data of the system so that the model can adapt to dynamically changing working conditions. And verify the model, including: cross-validation, using cross-validation technology to evaluate the performance of the optimized model on different data sets to ensure that the model has good prediction capabilities under various working conditions; online A / B testing, in actual operation, use A / B testing to compare the performance of the new and old models, and select the best model version for deployment.

[0114] Edge computing node configuration optimization can be achieved through resource reallocation; dynamic load balancing, adjusting the load balancing strategy of each edge computing node based on the evaluation results, so that computing tasks are more reasonably distributed among the nodes to avoid single-point overload; node expansion or reduction, increasing or decreasing the number of edge computing nodes according to actual working conditions. For example, in high-load situations, standby nodes can be started to increase computing power; in low-load situations, some nodes can be shut down to save resources.

[0115] Hardware upgrades and configuration adjustments include hardware configuration optimization, which involves upgrading the hardware configuration of edge computing nodes based on hardware bottlenecks found in the assessment (such as insufficient memory, storage IO bottlenecks, etc.); firmware and driver updates, which involve updating the firmware and drivers of edge computing nodes to improve hardware performance and compatibility and ensure that resource allocation strategies can be effectively executed; and distributed computing optimization, which involves optimizing the communication and collaboration methods between edge nodes and improving the efficiency of task migration and data sharing.

[0116] Real-time adjustment and adaptive optimization include: adaptive scheduling, developing adaptive scheduling algorithms based on evaluation results, so that the system can automatically adjust the configuration of AI models and edge computing nodes according to real-time working conditions; machine learning driven, using reinforcement learning or adaptive control technology to adjust resource allocation strategies in real time and optimize system performance; closed-loop feedback mechanism, feeding back the results of each optimization to the system to form a closed-loop optimization process. Through continuous feedback and adjustment, the system gradually approaches the optimal configuration state.

[0117] The specific manifestations of the evaluation results include: performance improvement indicators. After optimization, the key performance indicators of the system (such as prediction accuracy, response time, stability, etc.) should be significantly improved. The differences before and after the evaluation can be quantified and compared and analyzed; user experience improvement. Subjective indicators such as user satisfaction, system fluency, and response time perception should also reflect obvious improvements; cost-effectiveness improvement. The cost and benefits of system operation before and after optimization are evaluated, such as reduced energy consumption and improved computing resource utilization, to ensure that the system improves performance while reducing operating costs.

[0118] In some implementations, edge computing nodes may be equipped with high-performance processors and necessary sensors to independently run and process data, and also have data caching and compression capabilities to reduce the amount of data transmission.

[0119] Algorithms such as deep neural networks (DNN) or long short-term memory networks (LSTM) can be used to predict computing resource requirements under multiple working conditions. During the model training process, the mutual influence between different working conditions and different modules can be considered to improve the prediction accuracy. Based on the prediction results, reinforcement learning algorithms are used to adjust the computing resource allocation strategy in real time, and efficient resource scheduling is achieved through the collaborative work of edge computing nodes and central control units.

[0120] The above is an introduction to an embodiment of the method for allocating computing resources for a vehicle of the present application.

[0121] The implementation of the vehicle computing resource allocation device provided in the embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0122] For the vehicle computing resource allocation method provided in the above embodiment, the present application embodiment also provides a vehicle computing resource allocation device for implementing the above method, such as Figure 3 As shown, Figure 3 This is a schematic block diagram of a module of a computing resource allocation device for a vehicle according to an embodiment of the present application. The computing resource allocation device 300 for a vehicle includes:

[0123] An acquisition module 301 is used to acquire sensor information associated with at least one functional module during vehicle driving. Each functional module is configured with an edge computing node, and the edge computing node is used to process data of the corresponding functional module;

[0124] A prediction module 302 is used to predict the driving condition of the vehicle based on the sensor information and a preset prediction model to obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with at least one functional module and historical operating condition information corresponding to the historical sensor information;

[0125] A determination module 303 is used to determine the required computing resources of at least one edge computing node according to the predicted working condition information;

[0126] The allocation module 304 is used to allocate computing resources to at least one edge computing node according to the required computing resources, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources.

[0127] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0128] like Figure 4 As shown, the embodiment of the present application further provides an electronic device 400, the electronic device 400 includes a memory 401, one or more processors 402 ( Figure 4Only one is shown) and a computer program stored in the memory 401 and executable on the processor 402. Among them: the memory 401 is used to store software programs and units, and the processor 402 executes various functional applications and data processing by running the software programs and units stored in the memory 401 to obtain the resources corresponding to the above-mentioned preset events. Optionally, the processor 402 implements the above-mentioned vehicle computing resource allocation method by running the above-mentioned computer program stored in the memory 401.

[0129] The memory 401, as a non-transitory computer-readable medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory 401 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 401 may optionally include a memory remotely arranged relative to the processor 402, and these remote memories may be connected to the processor 402 via a network.

[0130] It can be understood that the contents of the above method embodiments are all applicable to the electronic device embodiment, the functions specifically implemented by the electronic device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0131] The embodiment of the present application also provides a vehicle, which includes the computing resource allocation device of the vehicle or the electric drive assembly of the electronic device. Specifically, the vehicle can be a private car, such as a sedan, SUV, MPV or pickup truck. The vehicle can also be an operating vehicle, such as a van, bus, small truck or large trailer. The vehicle can be a gasoline vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle.

[0132] It can be understood that the contents of the above method embodiments are all applicable to the present vehicle embodiment, the functions specifically implemented by the present vehicle embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0133] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by one or more processors, it can implement the steps of the computing resource allocation method for the vehicle as described above.

[0134] It can be understood that the contents of the above method embodiments are all applicable to the present computer program product, the functions specifically implemented by the present computer program product embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0135] The vehicle computing resource allocation method, device, electronic device, vehicle and computer program product provided in the embodiments of the present application can determine the required computing resources of the edge computing nodes through predicted operating condition information, and make an estimate of the computing resources of the edge nodes in advance; allocate computing resources to at least one edge computing node based on the required computing resources, so that at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources, determine the required computing resources through predicted operating condition information, and allocate computing resources to the edge computing nodes based on the required computing resources, which can improve the timeliness of computing resource allocation of the vehicle edge computing nodes.

[0136] The vehicle computing resource allocation method, device, electronic device, vehicle and computer program product provided in the embodiments of the present application can dynamically allocate resources according to the real-time needs of each functional module, optimize resource utilization, improve the overall system performance, and effectively control the computing power allocation in the system to avoid the situation where the performance of other modules is reduced due to the high computing power demand of a certain module. This helps to improve the stability and reliability of the system and reduce system failures caused by resource competition; it can also give priority to the computing power requirements of the application modules that the user is using according to the current usage, thereby ensuring the smoothness and response speed of the user experience. This can improve user satisfaction and enhance the user's trust in the vehicle system.

[0137] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0138] Although specific embodiments are described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are also within the scope of the present disclosure. For example, any of the functions and / or processing capabilities described in conjunction with a particular device or component may be performed by any other device or component. In addition, although various exemplary implementations and architectures have been described according to embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of the present disclosure.

[0139] Some aspects of the present disclosure are described above with reference to the block diagram and flow chart of the system, method, system and / or computer program product according to the exemplary embodiments.It should be understood that the combination of one or more blocks in the block diagram and the flow chart and the blocks in the block diagram and the flow chart can be realized by executing computer executable program instructions respectively.Equally, according to some embodiments, some blocks in the block diagram and the flow chart may not need to be executed in the order shown, or may not need to be executed in full.In addition, additional components and / or operations beyond those components and / or operations shown in the blocks in the block diagram and the flow chart may be present in certain embodiments.

[0140] Therefore, the blocks in the block diagrams and flow charts support the combination of devices for performing the specified functions, the combination of elements or steps for performing the specified functions, and the program instruction devices for performing the specified functions. It should also be understood that each block in the block diagrams and flow charts and the combination of blocks in the block diagrams and flow charts can be implemented by a special-purpose hardware computer system or a combination of special-purpose hardware and computer instructions that performs a specific function, element or step.

[0141] The program modules, applications, etc. described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0142] Software components can be encoded with any of various programming languages. An exemplary programming language can be a low-level programming language, such as an assembly language associated with a specific hardware architecture and / or an operating system platform. The software component comprising assembly language instructions may need to be converted to executable machine code by an assembler before being executed by hardware architecture and / or platform. Another exemplary programming language can be a higher-level programming language, which can be transplanted across multiple architectures. The software component comprising a higher-level programming language may need to be converted to an intermediate representation by an interpreter or a compiler before execution. Other examples of programming languages ​​include but are not limited to macro languages, shells or command languages, job control languages, scripting languages, database queries or search languages ​​or report writing languages. In one or more exemplary embodiments, the software component comprising the instruction of one of the above-mentioned programming language examples can be directly executed by an operating system or other software components, without first being converted into another form.

[0143] Software components may be stored as files or other data storage structures. Software components of similar type or related functions may be stored together, such as in a specific directory, folder, or library. Software components may be static (e.g., preset or fixed) or dynamic (e.g., created or modified at execution time).

[0144] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A method for allocating computing resources of a vehicle, characterized in that: The following steps are involved: Acquire sensor information associated with at least one functional module during vehicle travel, each functional module is configured with an edge computing node, and the edge computing node is used to process data of the corresponding functional module; Predicting the driving condition of the vehicle based on the sensor information and a preset prediction model to obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with the at least one functional module and historical operating condition information corresponding to the historical sensor information; Determining required computing resources of at least one of the edge computing nodes according to the predicted operating condition information; The computing resources are allocated to the at least one edge computing node according to the required computing resources, so that the at least one edge computing node processes data of the corresponding functional module based on the allocated computing resources.

2. The method for allocating computing resources of a vehicle according to claim 1, characterized in that: The determining, according to the predicted operating condition information, required computing resources of at least one of the edge computing nodes includes: Determine a key edge computing node according to the predicted operating condition information, where the key edge computing node is used to characterize an edge computing node whose demand for computing resources in the predicted operating condition is greater than a preset resource threshold; Based on the computing resource requirement model corresponding to the key edge computing node, the required computing resources of the key edge computing node are determined.

3. The method for allocating computing resources of a vehicle according to claim 1, characterized in that: The allocating the computing resources to the at least one edge computing node according to the required computing resources includes: Allocate the set computing resources to the edge computing node according to the required computing resources; The computing resources of the edge computing node are dynamically adjusted according to the current operating condition information.

4. The method for allocating computing resources of a vehicle according to claim 3, characterized in that: The dynamically adjusting the computing resources of the edge computing node according to the current working condition information includes: Obtaining a preset resource threshold of the edge computing node under the current working condition information; Monitoring the computing resource usage of the edge computing node under the current working condition information; The computing resources of the edge computing node are dynamically adjusted based on the preset resource threshold and the computing resource usage.

5. The method for allocating computing resources of a vehicle according to claim 3, characterized in that: The dynamically adjusting the computing resources of the edge computing node according to the current working condition information also includes: Determine the priority information of the functional module according to the current working condition information; The computing resources of the edge computing node are dynamically adjusted based on the priority information of the functional modules.

6. The method for allocating computing resources of a vehicle according to claim 3, characterized in that: The dynamically adjusting the computing resources of the edge computing node according to the current working condition information also includes: Obtaining computing resource usage of the edge computing node under the current working condition information; Dynamically adjusting the computing resources of the edge computing node through a preset scheduling model according to the predicted operating condition information and the computing resource usage; Among them, the preset scheduling model is constructed based on historical resource data, and the historical resource data includes: historical computing resource usage information associated with the edge computing node and historical operating condition information corresponding to the historical computing resource usage information.

7. The method for allocating computing resources of a vehicle according to claim 3, characterized in that: The dynamically adjusting the computing resources of the edge computing node according to the current working condition information also includes: Obtain computing resource usage of the plurality of edge computing nodes under the current working condition information; The computing resources are dynamically allocated among the plurality of edge computing nodes according to usage of the computing resources.

8. A computing resource allocation device for a vehicle, characterized in that: The device comprises: An acquisition module, used to acquire sensor information associated with at least one functional module during vehicle driving, each functional module is configured with an edge computing node, and the edge computing node is used to process data of the corresponding functional module; A prediction module, configured to predict the driving condition of the vehicle based on the sensor information and a preset prediction model to obtain predicted operating condition information of the vehicle, wherein the prediction model is constructed based on historical information, and the historical information includes: historical sensor information associated with the at least one functional module and historical operating condition information corresponding to the historical sensor information; A determination module, configured to determine required computing resources of at least one of the edge computing nodes according to the predicted operating condition information; An allocation module is used to allocate the computing resources to the at least one edge computing node according to the required computing resources, so that the at least one edge computing node processes the data of the corresponding functional module based on the allocated computing resources.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the vehicle computing resource allocation method as described in any one of claims 1 to 7 when executing the computer program.

10. A vehicle, characterized in that: The vehicle comprises the computing resource allocation device for a vehicle according to claim 8 or the electronic device according to claim 9 .

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