Vehicle cloud computing power scheduling methods, devices, electronic equipment and storage media

Abstract

This application provides a vehicle cloud computing power scheduling method, apparatus, electronic device, and storage medium. It involves receiving a computing power request from a target vehicle for a target function, the request including the vehicle's first operating state; determining the target computing power based on the request; and sending a first computing power scheduling instruction to a target cloud device based on the target computing power. This instruction instructs the target cloud device to configure a digital twin of the target vehicle, which possesses target computing power resources matching the target computing power. The digital twin is used to run the target function. This allows the vehicle to obtain matching computing power resources to run the target function under different operating states, improving the utilization efficiency of computing power resources on the cloud device side, reducing ineffective use of computing power resources, and thus improving the operational stability of autonomous driving functions.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a vehicle cloud computing power scheduling method, device, electronic device and storage medium. Background Technology

[0002] Vehicle cloud computing refers to a working mode in which target functions run in the cloud, or through collaborative work between the cloud and the vehicle. The interaction between the vehicle, the cloud, and the target function can effectively improve the execution efficiency and operational stability of various autonomous driving functions.

[0003] Currently, in the application scenarios of vehicle cloud computing, the corresponding target functions are usually run by allocating fixed computing resources within the cloud server. Existing solutions suffer from unreasonable allocation of computing resources, leading to waste or insufficient computing resources, which affects the operational stability of autonomous driving functions and reduces vehicle driving safety. Summary of the Invention

[0004] This application provides a vehicle cloud computing power scheduling method, device, electronic device, and storage medium to solve the problem of unreasonable allocation of computing power resources.

[0005] Firstly, this application provides a vehicle cloud computing power scheduling method, including:

[0006] The system receives a computing power request from a target vehicle for a target function, the computing power request including a first operating state of the target vehicle; determines a target computing power based on the computing power request; and sends a first computing power scheduling instruction to a target cloud device based on the target computing power, the first computing power scheduling instruction being used to instruct the target cloud device to configure a digital twin of the target vehicle, the digital twin of the target vehicle having target computing power resources matching the target computing power, wherein the digital twin of the target vehicle is used to run the target function.

[0007] In one possible implementation, determining the target computing power based on the computing power request includes: determining a detection level based on the first operating state, wherein the detection level characterizes the accuracy of the target vehicle when performing environmental detection; and determining the corresponding target computing power based on the detection level.

[0008] In one possible implementation, determining the detection level based on the first operating state includes: determining the target sensor and / or target detection algorithm to be run based on the first operating state; determining the detection level based on at least one of the number of target sensors, the type of target sensors, and the target detection algorithm; wherein the first operating state includes at least one of the following: vehicle speed, road type, and environmental obstacle density.

[0009] In one possible implementation, the computing power request includes a cloud device identifier, which indicates a cloud device currently capable of forming a logical bicycle with the target vehicle; before sending the first computing power scheduling instruction to the target cloud device based on the target computing power, the method further includes:

[0010] Based on the cloud device identifier, at least one target cloud device is identified; sending a first computing power scheduling instruction to the target cloud device based on the target computing power includes: determining the sub-computing power corresponding to each target cloud device based on the target computing power; and sending a first computing power scheduling instruction to the corresponding target cloud device based on each sub-computing power.

[0011] In one possible implementation, the target cloud device is communicatively connected to a collaborative device, and the method further includes: sending a second computing power scheduling instruction to the collaborative device, wherein the second computing power scheduling instruction is used to instruct the collaborative device to provide a first collaborative resource, the first collaborative resource being used to run at least one sub-function of the target function.

[0012] In one possible implementation, the second computing power scheduling instruction includes a computing power release instruction and a computing power allocation instruction. The computing power release instruction is used to instruct the collaborative device to transfer the currently running collaborative function to the corresponding collaborative vehicle side and release the computing power resources corresponding to the collaborative device side. The computing power allocation instruction is used to instruct the collaborative device to provide the first collaborative resource based on the current computing power resources on the collaborative device side after releasing the computing power resources.

[0013] In one possible implementation, the target cloud device is communicatively connected to the collaborative vehicle, and the method further includes: obtaining a second operating state of the collaborative vehicle; and sending a third computing power scheduling instruction to the collaborative vehicle based on the second operating state of the collaborative vehicle, wherein the third computing power scheduling instruction is used to instruct the collaborative vehicle to provide a second collaborative computing power resource, and the second collaborative computing power resource is used to run at least one sub-function of the target function.

[0014] In one possible implementation, the first operating state includes first permission information, which represents the vehicle permission level of the target vehicle; the second operating state includes second permission information, which represents the vehicle permission level of the cooperating vehicle; the step of sending a third computing power scheduling instruction to the cooperating vehicle according to the second operating state of the cooperating vehicle includes: comparing the first permission information and the second permission information; if the vehicle permission level of the target vehicle is greater than the vehicle permission level of the cooperating vehicle, then sending the third computing power scheduling instruction to the cooperating vehicle.

[0015] Secondly, this application provides a vehicle cloud computing power scheduling device, comprising:

[0016] A receiving module is used to receive a computing power request sent by a target vehicle for a target function, wherein the computing power request includes the first operating state of the target vehicle;

[0017] The determination module is used to determine the target computing power based on the computing power request;

[0018] The scheduling module is used to send a first computing power scheduling instruction to the target cloud device based on the target computing power. The first computing power scheduling instruction is used to instruct the target cloud device to configure a target vehicle digital twin. The target vehicle digital twin has target computing power resources that match the target computing power. The target vehicle digital twin is used to run the target function.

[0019] In one possible implementation, the determining module is specifically used to: determine a detection level based on the first operating state, wherein the detection level characterizes the accuracy of the target vehicle when performing environmental detection; and determine the corresponding target computing power based on the detection level.

[0020] In one possible implementation, when the determining module determines the detection level based on the first operating state, it is specifically configured to: determine the target sensor and / or target detection algorithm to be run based on the first operating state; and determine the detection level based on at least one of the number of target sensors, the type of target sensors, and the target detection algorithm; wherein the first operating state includes at least one of the following: vehicle speed, road type, and environmental obstacle density.

[0021] In one possible implementation, the computing power request includes a cloud device identifier, which indicates a cloud device that can currently form a logical bicycle with the target vehicle. Before sending a first computing power scheduling instruction to the target cloud device based on the target computing power, the determining module is further configured to: determine at least one target cloud device based on the cloud device identifier; the scheduling module is specifically configured to: determine the sub-computing power corresponding to each target cloud device based on the target computing power; and send a first computing power scheduling instruction to the corresponding target cloud device based on each sub-computing power.

[0022] In one possible implementation, the target cloud device is communicatively connected to the collaborative device, and the scheduling module is further configured to: send a second computing power scheduling instruction to the collaborative device, wherein the second computing power scheduling instruction is used to instruct the collaborative device to provide a first collaborative resource, the first collaborative resource being used to run at least one sub-function of the target function.

[0023] In one possible implementation, the second computing power scheduling instruction includes a computing power release instruction and a computing power allocation instruction. The computing power release instruction is used to instruct the collaborative device to transfer the currently running collaborative function to the corresponding collaborative vehicle side and release the computing power resources corresponding to the collaborative device side. The computing power allocation instruction is used to instruct the collaborative device to provide the first collaborative resource based on the current computing power resources on the collaborative device side after releasing the computing power resources.

[0024] In one possible implementation, the target cloud device is communicatively connected to the collaborative vehicle, and the scheduling module is further configured to: obtain a second operating state of the collaborative vehicle; and, based on the second operating state of the collaborative vehicle, send a third computing power scheduling instruction to the collaborative vehicle, the third computing power scheduling instruction being used to instruct the collaborative vehicle to provide a second collaborative computing power resource, the second collaborative computing power resource being used to run at least one sub-function of the target function.

[0025] In one possible implementation, the first operating state includes first permission information, which represents the vehicle permission level of the target vehicle; the second operating state includes second permission information, which represents the vehicle permission level of the cooperating vehicle; when the scheduling module sends a third computing power scheduling instruction to the cooperating vehicle according to the second operating state of the cooperating vehicle, it is specifically used to: compare the first permission information and the second permission information; if the vehicle permission level of the target vehicle is greater than the vehicle permission level of the cooperating vehicle, then send the third computing power scheduling instruction to the cooperating vehicle.

[0026] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0027] The memory stores computer-executed instructions;

[0028] The processor executes computer execution instructions stored in the memory to implement the vehicle cloud computing power scheduling method as described in any of the first aspects of the embodiments of this application.

[0029] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the vehicle cloud computing power scheduling method as described in any of the first aspects of the embodiments of this application.

[0030] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the vehicle cloud computing power scheduling method as described in any of the first aspects above.

[0031] The vehicle cloud computing power scheduling method, apparatus, electronic device, and storage medium provided in this application receive a computing power request for a target function sent by a target vehicle, the computing power request including a first operating state of the target vehicle; determine a target computing power based on the computing power request; and send a first computing power scheduling instruction to a target cloud device based on the target computing power. The first computing power scheduling instruction instructs the target cloud device to configure a digital twin of the target vehicle, the target vehicle digital twin having target computing power resources matching the target computing power, wherein the target vehicle digital twin is used to run the target function. Since, after receiving the computing power request, the central server determines the target computing power matching the first operating state in the computing power request, and then sends the first computing power scheduling instruction to the target cloud device, configuring a target vehicle digital twin with corresponding computing power resources on the target cloud device side to run the target function, the vehicle can obtain matching computing power resources to run the target function under different operating states, improving the utilization efficiency of computing power resources on the cloud device side, reducing the ineffective use of computing power resources, and thus improving the operational stability of autonomous driving functions. Attached Figure Description

[0032] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0033] Figure 1 This is an application scenario diagram of the vehicle cloud computing power scheduling method provided in the embodiments of this application;

[0034] Figure 2 A flowchart illustrating a vehicle cloud computing power scheduling method provided in one embodiment of this application;

[0035] Figure 3 for Figure 2 The flowchart of the specific implementation steps of step S102 in the embodiment shown is as follows;

[0036] Figure 4 This is a schematic diagram of a detection level mapping information provided in an embodiment of this application;

[0037] Figure 5 A flowchart of a vehicle cloud computing power scheduling method provided in another embodiment of this application;

[0038] Figure 6 This application provides a schematic diagram of a communication link for a target cloud device.

[0039] Figure 7 This is a schematic diagram of a communication link for a collaborative device provided in an embodiment of this application;

[0040] Figure 8 A signaling interaction diagram for providing a first collaborative resource to a collaborative device, as provided in an embodiment of this application;

[0041] Figure 9 for Figure 5 The flowchart of the specific implementation steps of step S210 in the embodiment shown is as follows;

[0042] Figure 10 This is a schematic diagram of the structure of a vehicle cloud computing power scheduling device provided in one embodiment of this application;

[0043] Figure 11 A schematic diagram of an electronic device provided according to one embodiment of this application;

[0044] Figure 12 This is a block diagram illustrating a terminal device in an exemplary embodiment of this application.

[0045] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0047] The collection, storage, use, processing, transmission, provision, and disclosure of user personal information involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0048] The application scenarios of the embodiments of this application are explained below:

[0049] Figure 1 This diagram illustrates an application scenario of the vehicle cloud computing power scheduling method provided in this application embodiment. The vehicle cloud computing power scheduling method provided in this application embodiment can be applied to vehicle cloud computing scenarios. For example, as shown... Figure 1As shown in the figure, the execution entity of the method provided in this application embodiment can be a terminal device or a server, more specifically, such as the central server shown in the figure. The central server communicates with the intelligent vehicle on one hand and with the edge cloud server on the other. The central server can receive request information sent by the intelligent vehicle, thereby creating a vehicle digital twin corresponding to the intelligent vehicle on the edge cloud server side. The vehicle digital twin and the intelligent vehicle (computing unit) communicate via a high-speed wireless network to exchange data, forming a logical vehicle. The vehicle digital twin has corresponding computing resources. By creating the vehicle digital twin on the edge cloud server side, the purpose of providing the required computing power for the target application to be run is achieved. The vehicle digital twin can run the target application independently or collaboratively with the computing unit within the intelligent vehicle, thereby realizing the vehicle-to-cloud computing working mode.

[0050] Currently, in the application scenarios of vehicle cloud computing, the corresponding target functions are usually run by allocating fixed computing resources within edge cloud servers. For example, if the computing resources allocated to each smart car connected to the edge cloud server are fixed at 1 (preset unit), then if the total computing resources of the edge cloud server are 10 (preset units), then a maximum of 10 smart cars can be provided with vehicle cloud computing services simultaneously. However, in actual applications, different smart cars require different computing resources to run autonomous driving functions under different operating conditions. For example, taking adaptive cruise control and automatic braking as examples, when a smart vehicle is operating in complex road conditions, such as a crowded market, in order to maintain vehicle driving safety, the smart vehicle may activate all detection units and run multiple different environmental detection algorithms to meet the autonomous driving requirements under complex road conditions. However, when a smart vehicle is operating in simple and stable road conditions, such as a highway with few vehicles, in order to save computing resources, the smart car may only activate some detection units, such as only activating the camera unit used for mid-to-long-range detection, while (when appropriate) turning off the LiDAR unit, and no longer processing the LiDAR data.

[0051] Therefore, in the vehicle-to-cloud computing model, even for the same target function, the computing resources required to run the target function will change due to changes in operating conditions and environment. Existing technologies that use fixed computing resources cannot meet the dynamic changes in the vehicle's computing power requirements, thus leading to problems of wasted or insufficient computing resources, affecting the operational stability of autonomous driving functions and reducing vehicle driving safety.

[0052] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0053] Figure 2 This is a flowchart illustrating a vehicle cloud computing power scheduling method according to an embodiment of this application. Exemplarily, the vehicle cloud computing power scheduling method provided in this embodiment can be applied to a server, which is capable of communicating with both the target vehicle and cloud devices. For details, please refer to [link to relevant documentation]. Figure 1 The application scenario diagram shown here, in this embodiment, the server is equivalent to Figure 1 The central server and cloud devices are equivalent to Figure 1 Edge cloud servers in the middle.

[0054] like Figure 2 As shown, the vehicle cloud computing power scheduling method provided in this embodiment includes the following steps:

[0055] Step S101: Receive a computing power request for the target function sent by the target vehicle. The computing power request includes the first operating state of the target vehicle.

[0056] For example, the target function can be a function related to autonomous driving that needs to run in vehicle cloud computing mode, such as automatic navigation, route planning, adaptive cruise control, etc., without limiting the specific function. After the instruction to run the target function is triggered on the target vehicle side (such as the instruction for the user to manually start the automatic navigation function), the target vehicle will send a computing power request to the central server to request the central server to allocate computing power resources to execute vehicle cloud computing for the target application. The computing power resources can include storage resources (such as hard disk, memory space), network resources, computing resources (such as CPU cores, GPU cores, thread resources), etc., that is, the relevant resources required to run the target function in the vehicle cloud computing process.

[0057] The computing power request includes not only the necessary computing power request information but also information characterizing the current operating state of the target vehicle, i.e., the first operating state. The first operating state can be determined through internal and external sensing data during vehicle operation. Internal sensing data includes, for example, vehicle speed data and engine speed data, while external sensing data includes, for example, lidar data and camera data. In one possible implementation, the first operating state includes at least one of the following: vehicle speed, road type, and environmental obstacle density. The road type characterizes the type of road the target vehicle is currently traveling on, such as highways, municipal roads, and country roads. The environmental obstacle density characterizes the number of obstacles within a preset range centered on the target vehicle's current location, including, for example, pedestrians, non-motorized vehicles, and motorized vehicles. The road type and environmental obstacle density can be determined through radar data and / or camera data; the vehicle speed can be determined through vehicle speed data.

[0058] Step S102: Determine the target computing power based on the computing power request.

[0059] When the target vehicle is running in the first operating state, the first operating state is related to the current operating condition of the target vehicle. The operating condition affects the computing power required to run the target function. Therefore, based on this mapping relationship, the target computing power can be determined based on the first operating state in the computing power request.

[0060] In one possible implementation, such as Figure 3 As shown, the specific implementation steps of step S102 include:

[0061] Step S1021: Determine the detection level based on the first operating state. The detection level characterizes the accuracy of the target vehicle when performing environmental detection.

[0062] Step S1022: Determine the corresponding target computing power based on the detection level.

[0063] For example, the first operating state includes a first state identifier and a second state identifier. More specifically, for example, the first state identifier represents the type of road being driven, and the second state identifier represents the density of environmental obstacles. Based on the vehicle state represented by the first and second state identifiers, different detection levels can be assigned, i.e., the accuracy of environmental detection. For example, based on the first and second state identifiers, if the target vehicle is currently driving on a highway and the environmental obstacle density is low (i.e., the number of obstacles within a preset range is less than a first quantity threshold), then the detection level is C, representing relatively low environmental detection accuracy; if the target vehicle is currently driving on a country road and the environmental obstacle density is high (i.e., the number of obstacles within a preset range is greater than a second quantity threshold), then the detection level is A, representing relatively high environmental detection accuracy. The mapping relationship between the first operating state and the detection level can be determined through preset detection level mapping information. Figure 4 This is a schematic diagram of a detection level mapping information provided in an embodiment of this application, such as... Figure 4 As shown, the detection levels include four levels: A, B, C, and D. Correspondingly, the first operating state includes the road type and the density of environmental obstacles. There are three road types: Class I, Class II, and Class III, corresponding to highways, municipal roads, and rural roads, respectively. The environmental obstacle density includes three levels: Level 1, Level 2, and Level 3, representing low density, medium density, and high density, respectively. Based on the different combinations of road types and environmental obstacle densities, the corresponding detection level can be determined. See [link to documentation] for details. Figure 4 As shown.

[0064] In another possible implementation, the vehicle speed, road type, and environmental obstacle density can be quantified and weighted to obtain a weighted evaluation value. The corresponding detection level can then be determined by comparing the weighted evaluation value with a preset detection threshold. This will not be elaborated on here.

[0065] Furthermore, after determining the detection level, the corresponding computing power, i.e., the target computing power, is determined based on the detection accuracy it represents. Generally speaking, the higher the detection accuracy, i.e., the higher the detection level, the greater the amount and accuracy of the required data and algorithms, and therefore, the higher the corresponding target computing power. For example, there is a preset mapping relationship between the detection level and the target computing power, which can be set according to specific needs, and no specific restrictions are imposed here.

[0066] Step S103: Send a first computing power scheduling instruction to the target cloud device based on the target computing power. The first computing power scheduling instruction is used to instruct the target cloud device to configure a target vehicle digital twin. The target vehicle digital twin has target computing power resources that match the target computing power. The target vehicle digital twin is used to run the target function.

[0067] For example, after obtaining the target computing power, the central server sends a first computing power scheduling instruction to the target cloud device based on the target computing power, thereby enabling the target cloud device to provide corresponding computing power resources, thus realizing vehicle cloud computing for the target application. The target cloud device is a cloud device capable of communicating and interacting with the target vehicle. For example, the cloud device can be an edge cloud server, or other devices that communicate with the target vehicle via a wireless network, such as a regional server. The computing power resources in the target cloud device and the computing power resources within the target vehicle together constitute a logical vehicle. For example, the target cloud device is equipped with a vehicle digital twin controller. The first computing power scheduling instruction includes the target computing power. After the central server sends the first computing power scheduling instruction to the twin controller in the target cloud device, the twin controller configures a target vehicle digital twin with corresponding computing power resources (i.e., the target computing power resources) in the target cloud device based on the target computing power in the first computing power scheduling. For example, the process of configuring a target vehicle digital twin on a target cloud device includes both creating a new vehicle digital twin with target computing power resources based on the target computing power, and setting up an existing vehicle digital twin with target computing power resources based on the target computing power.

[0068] Furthermore, after creating a digital twin of the target vehicle on the target cloud device, a logical vehicle is formed based on the computing units (e.g., domain controllers) within the target vehicle digital twin on the target cloud device to run the target function, thereby realizing vehicle cloud computing for the target function. The process of performing vehicle cloud computing based on the logical vehicle composed of the vehicle digital twin and the vehicle-side domain controller is prior art known to those skilled in the art and will not be elaborated here.

[0069] In this embodiment, a computing power request for a target function is received from the target vehicle, the request including the target vehicle's first operating state. Based on the computing power request, a target computing power is determined. A first computing power scheduling instruction is sent to the target cloud device based on the target computing power. This instruction instructs the target cloud device to configure a digital twin of the target vehicle, which possesses target computing power resources matching the target computing power. The target vehicle digital twin is used to run the target function. Since the server determines the matching target computing power based on the first operating state in the computing power request after receiving the request, and then configures a target vehicle digital twin with corresponding computing power resources on the target cloud device side to run the target function by sending the first computing power scheduling instruction, the vehicle can obtain matching computing power resources to run the target function under different operating states. This improves the utilization efficiency of computing power resources on the cloud device side, reduces ineffective use of computing power resources, and thus improves the operational stability of the autonomous driving function.

[0070] Figure 5 A flowchart of a vehicle cloud computing power scheduling method provided in another embodiment of this application is shown below. Figure 5 As shown, the vehicle cloud computing power scheduling method provided in this embodiment is... Figure 2 Based on the vehicle cloud computing power scheduling method provided in the illustrated embodiment, steps S102-S103 are further refined, and steps for interacting with cooperating vehicles and cooperating devices are added. Therefore, the vehicle cloud computing power scheduling method provided in this embodiment includes the following steps:

[0071] Step S201: Receive a computing power request for the target function sent by the target vehicle. The computing power request includes the first operating state of the target vehicle and the cloud device identifier. The cloud device identifier is used to indicate the cloud device that can currently form a logical bicycle with the target vehicle.

[0072] Step S202: Based on the first operating state, determine the target sensor and / or target detection algorithm to be run.

[0073] Step S203: Determine the detection level based on at least one of the following: the number of target sensors, the type of target sensors, and the target detection algorithm.

[0074] For example, the first operating state is information characterizing the current operating state of the target vehicle, including at least one of the following: vehicle speed, road type, and environmental obstacle density. The specific implementation of the first operating state is described in... Figure 2The embodiments shown have been described in detail and will not be repeated here. In one possible implementation, the number and / or type of target sensors to be operated can be determined for the specific implementation of the first operating state. For example, if the target function is adaptive cruise control, according to the first operating state, when the vehicle is traveling on a highway with few vehicles, only the cameras positioned in front of and behind the vehicle are activated to collect image data from the front and rear of the target vehicle. When the corresponding image processing algorithm is run, because the amount of data to be processed is small, and / or the algorithm complexity is low, the required computing power is also low, thus the corresponding detection level is C, corresponding to a low computing power requirement. However, according to the first operating state, when the vehicle is traveling on a municipal road with many vehicles, cameras positioned in the front, rear, left, and right directions of the vehicle need to be activated, along with the LiDAR, to collect image data from the four directions of the target vehicle, as well as LiDAR data. When the corresponding image processing algorithm and LiDAR data processing algorithm are run, because the amount of data to be processed is large, and / or the algorithm complexity is high and the number of algorithms to be run is large, the required computing power is also high, thus the corresponding detection level is A, corresponding to a high computing power requirement.

[0075] For example, step S203 can be implemented in various ways based on a combination of target sensor type, quantity, and algorithm. For instance, the detection level can be determined based on the target sensor type; or based on the number of target sensors; or based on both the number and type of target sensors; or based on the number of target sensors and the target algorithm used; or based on both the target sensor type and the target algorithm used; or based on both the target sensor type, quantity, and target algorithm used. The specific methods for determining the detection level described above can be set as needed and will not be detailed here. The mapping relationship between the target sensor type, quantity, and detection level can be preset.

[0076] In this embodiment, the number and / or type of target sensors and / or target detection algorithm are determined by the first operating state of the target vehicle. Then, the required computing power is determined from the data and algorithm dimensions to achieve a more accurate detection level assessment. This makes the determination of target computing power more accurate in the subsequent step of determining target computing power based on detection level, and improves the matching degree between target computing power and the first operating state.

[0077] Step S204: Determine the corresponding target computing power based on the detection level.

[0078] The steps in this embodiment have been... Figure 3 The embodiments shown are described in detail here. For specific implementation details, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0079] Step S205: Identify at least one target cloud device based on the cloud device identifier.

[0080] Step S206: Based on the target computing power, determine the corresponding computing power of each target cloud device.

[0081] Step S207: Based on each computing power, send the first computing power scheduling instruction to the corresponding target cloud device.

[0082] Figure 6 This is a schematic diagram of a communication link for a target cloud device provided in an embodiment of this application, such as... Figure 6 As shown, the target cloud devices capable of communicating directly or indirectly with the target vehicle include edge cloud server A, edge cloud server B, and edge cloud server C. Edge cloud server A and edge cloud server B are directly connected to the target vehicle, while edge cloud server C is indirectly connected to the target vehicle through edge cloud server B. Edge cloud server A, edge cloud server B, and edge cloud server C are respectively connected to the central cloud server.

[0083] For example, the central server identifies edge cloud servers A, B, and C using the obtained cloud device identifiers. Then, by communicating with edge cloud servers A, B, and C, it obtains their current available computing power resources and allocates the target computing power according to the available computing power resources of each target cloud device, thereby determining the corresponding sub-computing power for edge cloud servers A, B, and C. Subsequently, based on each sub-computing power, the central cloud server sends corresponding first computing power scheduling instructions (shown as scheduling instructions a, b, and c in the diagram) to edge cloud servers A, B, and C. This enables edge cloud servers A, B, and C to provide computing power resources based on their respective sub-computing power, for example, establishing a vehicle digital twin or computing unit with corresponding sub-computing power. Then, based on communication negotiations between multiple target cloud devices and the target vehicle, multiple target cloud devices work collaboratively to jointly provide computing power resources for the target function, realizing vehicle cloud computing for the target function.

[0084] In this embodiment, by acquiring multiple target cloud devices that can participate in vehicle cloud computing and allocating computing power to each target cloud device, the multiple target cloud devices can work collaboratively to achieve vehicle cloud computing for the target function, thereby further improving the rationality of computing power resource allocation, increasing the utilization rate of computing power resources, and avoiding the problem of unstable operation of functions caused by computing power depletion.

[0085] Step S208: Send a second computing power scheduling instruction to the collaborative device, wherein the second computing power scheduling instruction is used to instruct the collaborative device to provide a first collaborative resource, and the first collaborative resource is used to run at least one sub-function of the target function.

[0086] For example, the collaborative device is a device capable of communicating with the target vehicle and providing computing resources to it, such as roadside equipment or intelligent traffic lights. While sending a first computing power scheduling instruction to the target cloud device, the central server can also send a second computing power scheduling instruction to the collaborative device based on specific circumstances. This allows the collaborative device to provide additional computing resources, i.e., first collaborative resources, to run one or more sub-functions within the target function. For instance, when the central server detects a shortage of computing resources on the target cloud device, it sends a second computing power scheduling instruction to the collaborative device, instructing it to provide first collaborative resources. More specifically, this could involve creating a vehicle digital twin or computing unit. Then, through negotiation among the target vehicle, the target cloud device, and the collaborative device, some sub-functions of the target function running on the target cloud device are deployed to the collaborative device for operation. This improves the redundancy of computing resources on the target cloud device and ensures the stability of the target function's operation.

[0087] Optionally, the second computing power scheduling instruction includes a computing power release instruction and a computing power allocation instruction. The computing power release instruction is used to instruct the collaborative device to transfer the currently running collaborative function to the corresponding first collaborative vehicle side and release the computing power resources corresponding to the collaborative device side. The computing power allocation instruction is used to instruct the collaborative device to provide the first collaborative resources based on the current computing power resources on the collaborative device side after releasing the computing power resources.

[0088] Figure 7 This is a schematic diagram of a communication link for a collaborative device provided in an embodiment of this application, such as... Figure 7 As shown, the collaborative device communicates with the target cloud device and the target vehicle, as well as with the first collaborative vehicle. The first collaborative vehicle communicates with the collaborative device and forms a logical vehicle with it using a vehicle-to-cloud computing operating mode. The collaborative device can provide certain computing resources to the first collaborative vehicle to execute the corresponding vehicle-side functions.

[0089] Figure 8 A signaling interaction diagram for providing a first collaborative resource to a collaborative device, as provided in an embodiment of this application, is shown below. Figure 8 As shown, the steps for the collaborative device to provide the first collaborative resource include:

[0090] In step S2081, the central server sends a second computing power scheduling instruction to the collaborative device. The second computing power scheduling instruction includes a computing power release instruction and a computing power allocation instruction.

[0091] In step S2082, the collaborative device responds to the computing power release command and deploys the collaborative function to the computing unit of the first collaborative vehicle;

[0092] Step S2083: The coordinating device releases the corresponding computing resources.

[0093] In step S2084, the collaborative device responds to the computing power allocation instruction and creates a computing unit with the first collaborative resource based on the current computing power resources.

[0094] In this embodiment, by sending a second computing power scheduling command to the collaborative device, the collaborative device first transfers the collaborative application via a computing power release command, releasing the computing power resources within the collaborative device. Then, via a computing power allocation command, based on the current computing power resources, it provides maximum computing power support for the operation of the target function. Since the communication distance between the collaborative device (roadside equipment, intelligent traffic lights, etc.) and the target vehicle is shorter and the communication reliability is better, the solution provided in this embodiment can, when the target cloud device malfunctions, release unnecessary computing power resources within the collaborative device to provide reliable computing power resources for the operation of the target function, ensuring the stable operation of the target function and improving vehicle driving reliability.

[0095] Optionally, after step S208, the target cloud device and the second collaborative vehicle are connected in communication, and the process further includes:

[0096] Step S209: Obtain the second operating status of the second cooperative vehicle.

[0097] Step S210: Based on the second operating state of the second collaborative vehicle, a third computing power scheduling instruction is sent to the collaborative vehicle. The third computing power scheduling instruction is used to instruct the second collaborative vehicle to provide second collaborative computing power resources. The second collaborative computing power resources are used to run at least one sub-function of the target function.

[0098] For example, similar to the first collaborative vehicle, the second collaborative vehicle is an intelligent car that communicates with and collaborates with the target cloud device in a vehicle-to-cloud computing mode. The target cloud device can provide certain computing resources to the first collaborative vehicle to execute the corresponding vehicle-side functions. The central cloud server can obtain the operating status of the second collaborative vehicle, i.e., the second operating status, through communication with the target cloud device. The second operating status includes at least one of the following: vehicle speed, vehicle driving mode, road type, and environmental obstacle density. Based on the second operating status, the computing resources currently required by the second collaborative vehicle can be determined. Taking the second operating status as the vehicle driving mode as an example, the vehicle driving mode includes autonomous driving mode, semi-autonomous driving mode, and assisted driving mode. The autonomous driving mode requires the highest computing resources, the assisted driving mode requires the lowest computing resources, and the semi-autonomous driving mode requires computing resources in between. Based on the second operating status of the second collaborative vehicle, the currently available resources of the collaborative vehicle can be determined. Then, by directly or indirectly sending a third computing power scheduling instruction to the collaborative vehicle, the collaborative vehicle is instructed to provide matching second collaborative computing resources based on the currently available resources. Subsequently, based on communication negotiations between the collaborative vehicle, the target vehicle, and the target cloud device, some sub-functions of the target function running on the target vehicle and / or the target cloud device will be deployed to run on the collaborative vehicle side.

[0099] In this embodiment, by acquiring the second operating state of the second collaborative vehicle and sending a third computing power scheduling instruction to the collaborative vehicle based on the second collaborative vehicle, some sub-functions of the target function running on the target vehicle and / or the target cloud device are deployed to the collaborative vehicle side for operation. This fully utilizes the computing power of the collaborative vehicle to improve the redundancy of computing power resources of the target vehicle and / or the target cloud device, thereby improving the operational stability of the target function and the target vehicle.

[0100] In one possible implementation, the first operating state includes first permission information, which represents the vehicle permission level of the target vehicle; the second operating state includes second permission information, which represents the vehicle permission level of the cooperating vehicle. Figure 9 As shown, the specific implementation steps of step S210 include:

[0101] Step S2101: Compare the first permission information and the second permission information to obtain the permission comparison result.

[0102] Step S2102: Based on the permission comparison result, if the vehicle permission level of the target vehicle is greater than that of the cooperating vehicle, then send a third computing power scheduling instruction to the cooperating vehicle.

[0103] For example, the central server includes permission information representing the vehicle's permission level in both the first operating state of the target vehicle and the second operating state of the cooperating vehicle; that is, the first operating state includes first permission information, and the second operating state includes second permission information. The vehicle permission level represents the priority of the vehicle in using computing resources.

[0104] In a specific application scenario, both the target vehicle and the collaborating vehicle are capable of high-level autonomous driving. The central server has management authority and can schedule the computing resources of the target vehicle and the collaborating vehicle based on their vehicle permission levels. Specifically, a vehicle with a high vehicle permission level, such as the target vehicle, can use a vehicle with a low vehicle permission level, such as a collaborating vehicle. The central server compares first and second permission information to obtain a comparison result. If the vehicle permission level of the target vehicle is higher than that of the collaborating vehicle, a third computing power scheduling instruction is sent to the collaborating vehicle to control it to create a computing unit for running the target function based on its own computing resources. This achieves computing power sharing with the target cloud device or provides additional computing resources to improve the execution speed of the target function. More specifically, the collaborating vehicle may include, for example, a low-speed autonomous freight vehicle, and the target vehicle may be, for example, a high-speed autonomous passenger vehicle. In this embodiment, by detecting the vehicle permission level of the collaborating vehicle, additional computing resources are dynamically acquired to run the target function, ensuring redundancy of computing resources for running the target function and improving the stability and reliability of running the target function.

[0105] Optionally, if the vehicle permission level of the target vehicle is less than or equal to the vehicle permission level of the cooperating vehicle, for example, if the cooperating vehicle is an unmanned passenger vehicle, then no third computing power scheduling instruction will be sent to the cooperating vehicle to avoid affecting the cooperating vehicle and ensure the operational stability of the cooperating vehicle.

[0106] In this embodiment, the implementation of step S201 is the same as that in this application. Figure 2 The implementation of step S101 in the illustrated embodiment is the same, and will not be described in detail here.

[0107] Figure 10 This is a schematic diagram of the structure of a vehicle cloud computing power scheduling device provided in one embodiment of this application, as shown below. Figure 10 As shown, the vehicle cloud computing power scheduling device 3 provided in this embodiment includes:

[0108] The receiving module 31 is used to receive a computing power request sent by the target vehicle for a target function, the computing power request including the first operating state of the target vehicle;

[0109] The determination module 32 is used to determine the target computing power based on the computing power request;

[0110] The scheduling module 33 is used to send a first computing power scheduling instruction to the target cloud device based on the target computing power. The first computing power scheduling instruction is used to instruct the target cloud device to configure a target vehicle digital twin. The target vehicle digital twin has target computing power resources that match the target computing power. The target vehicle digital twin is used to run the target function.

[0111] In one possible implementation, the determining module 32 is specifically used to: determine the detection level based on the first operating state, wherein the detection level characterizes the accuracy of the target vehicle when performing environmental detection; and determine the corresponding target computing power based on the detection level.

[0112] In one possible implementation, when determining the detection level based on the first operating state, the determining module 32 is specifically used to: determine the target sensor and / or target detection algorithm to be run based on the first operating state; and determine the detection level based on at least one of the number of target sensors, the type of target sensors, and the target detection algorithm; wherein the first operating state includes at least one of the following: vehicle speed, road type, and environmental obstacle density.

[0113] In one possible implementation, the computing power request includes a cloud device identifier, which indicates the cloud device that can currently form a logical bicycle with the target vehicle; before sending the first computing power scheduling instruction to the target cloud device based on the target computing power, the determining module 32 is further configured to: determine at least one target cloud device based on the cloud device identifier; the scheduling module 33 is specifically configured to: determine the sub-computing power corresponding to each target cloud device based on the target computing power; and send the first computing power scheduling instruction to the corresponding target cloud device based on each sub-computing power.

[0114] In one possible implementation, the target cloud device and the cooperating device are communicatively connected, and the scheduling module 33 is further configured to: send a second computing power scheduling instruction to the cooperating device, wherein the second computing power scheduling instruction is used to instruct the cooperating device to provide a first cooperating resource, and the first cooperating resource is used to run at least one sub-function of the target function.

[0115] In one possible implementation, the second computing power scheduling instruction includes a computing power release instruction and a computing power allocation instruction. The computing power release instruction is used to instruct the collaborative device to transfer the currently running collaborative function to the corresponding collaborative vehicle side and release the corresponding computing power resources on the collaborative device side. The computing power allocation instruction is used to instruct the collaborative device to provide the first collaborative resource based on the current computing power resources on the collaborative device side after releasing the computing power resources.

[0116] In one possible implementation, the target cloud device is communicatively connected to the collaborative vehicle, and the scheduling module 33 is further configured to: obtain the second operating state of the collaborative vehicle; and send a third computing power scheduling instruction to the collaborative vehicle based on the second operating state of the collaborative vehicle. The third computing power scheduling instruction is used to instruct the collaborative vehicle to provide second collaborative computing power resources, and the second collaborative computing power resources are used to run at least one sub-function of the target function.

[0117] In one possible implementation, the first operating state includes first permission information, which represents the vehicle permission level of the target vehicle; the second operating state includes second permission information, which represents the vehicle permission level of the cooperating vehicle; when the scheduling module 33 sends a third computing power scheduling instruction to the cooperating vehicle according to the second operating state of the cooperating vehicle, it is specifically used to: compare the first permission information and the second permission information; if the vehicle permission level of the target vehicle is greater than the vehicle permission level of the cooperating vehicle, then send a third computing power scheduling instruction to the cooperating vehicle.

[0118] The receiving module 31, the determining module 32, and the scheduling module 33 are connected sequentially. The vehicle cloud computing power scheduling device provided in this embodiment can perform the following... Figures 2-9 The technical solutions of any of the method embodiments shown are similar in implementation principle and technical effect, and will not be described again here.

[0119] Figure 11 A schematic diagram of an electronic device provided in one embodiment of this application, as shown below. Figure 11 As shown, the electronic device 4 provided in this embodiment includes: a processor 41, and a memory 42 communicatively connected to the processor 41.

[0120] Among them, memory 42 stores computer-executed instructions;

[0121] The processor 41 executes computer execution instructions stored in the memory 42 to implement this application. Figures 2-9 The vehicle cloud computing power scheduling method provided in any of the corresponding embodiments.

[0122] The memory 42 and the processor 41 are connected via a bus 43.

[0123] For relevant instructions, please refer to the corresponding text. Figures 2-9 The relevant descriptions and effects of the steps in the corresponding embodiments are understood, and will not be elaborated on here.

[0124] One embodiment of this application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executed by a processor to implement this application. Figures 2-9 The vehicle cloud computing power scheduling method provided in any of the corresponding embodiments.

[0125] The computer-readable storage medium may be ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0126] One embodiment of this application provides a computer program product, including a computer program that, when executed by a processor, implements this application. Figures 2-9 The vehicle cloud computing power scheduling method provided in any of the corresponding embodiments.

[0127] Figure 12 This is a block diagram illustrating a terminal device 800, which may be a domain controller, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness device, personal digital assistant, etc.

[0128] The terminal device 800 may include one or more of the following components: processing component 802, memory 804, power supply component 806, multimedia component 808, audio component 810, input / output (I / O) interface 812, sensor component 814, and communication component 816.

[0129] Processing component 802 typically controls the overall operation of terminal device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0130] Memory 804 is configured to store various types of data to support the operation of terminal device 800. Examples of this data include instructions for any application or method operating on terminal device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0131] Power supply component 806 provides power to various components of terminal device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to terminal device 800.

[0132] Multimedia component 808 includes a screen that provides an output interface between terminal device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When terminal device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0133] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when terminal device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0134] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0135] Sensor assembly 814 includes one or more sensors for providing status assessments of various aspects of terminal device 800. For example, sensor assembly 814 can detect the on / off state of terminal device 800, the relative positioning of components such as the display and keypad of terminal device 800, changes in the position of terminal device 800 or a component of terminal device 800, the presence or absence of user contact with terminal device 800, the orientation or acceleration / deceleration of terminal device 800, and temperature changes of terminal device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0136] Communication component 816 is configured to facilitate wired or wireless communication between terminal device 800 and other devices. Terminal device 800 can access wireless networks based on communication standards, such as WiFi, 3G, 4G, 5G, or other standard communication networks, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0137] In an exemplary embodiment, the terminal device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the functions described in this application. Figures 2-9 The method provided in any of the corresponding embodiments.

[0138] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, which can be executed by a processor 820 of a terminal device 800 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0139] This application also provides a non-transitory computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor of a terminal device, enables the terminal device 800 to perform the above-described embodiments of this application. Figures 2-9 The method provided in any of the corresponding embodiments.

[0140] 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 modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules 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 modules may be electrical, mechanical, or other forms.

[0141] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0142] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A vehicle cloud computing power scheduling method, characterized in that, The method includes: Receive a computing power request for a target function sent by a target vehicle. The computing power request includes a first operating state of the target vehicle. The first operating state includes first permission information, which represents the vehicle permission level of the target vehicle. Determine the target computing power based on the computing power request; Based on the target computing power, a first computing power scheduling instruction is sent to the target cloud device. The first computing power scheduling instruction is used to instruct the target cloud device to configure a target vehicle digital twin. The target vehicle digital twin has target computing power resources that match the target computing power. The target vehicle digital twin is used to run the target function. The target cloud device is communicatively connected to the collaborative vehicle, and the method further includes: The second operating state of the collaborative vehicle is obtained, the second operating state includes second permission information, and the second permission information represents the vehicle permission level of the collaborative vehicle; Comparing the first permission information and the second permission information, if the vehicle permission level of the target vehicle is greater than the vehicle permission level of the cooperating vehicle, a third computing power scheduling instruction is sent to the cooperating vehicle. The third computing power scheduling instruction is used to instruct the cooperating vehicle to provide a second collaborative computing power resource, which is used to run at least one sub-function of the target function.

2. The method according to claim 1, characterized in that, Determining the target computing power based on the computing power request includes: Based on the first operating state, a detection level is determined, wherein the detection level characterizes the accuracy of the target vehicle when performing environmental detection; Based on the detection level, the corresponding target computing power is determined.

3. The method according to claim 2, characterized in that, Determining the detection level based on the first operating state includes: Based on the first operating state, determine the target sensor and / or target detection algorithm that needs to be run; The detection level is determined based on at least one of the following: the number of target sensors, the type of target sensors, and the target detection algorithm. The first operating state includes at least one of the following: vehicle speed, road type, and environmental obstacle density.

4. The method according to claim 1, characterized in that, The computing power request includes a cloud device identifier, which is used to indicate the cloud device that can currently form a logical bicycle with the target vehicle. Before sending the first computing power scheduling instruction to the target cloud device based on the target computing power, the method further includes: Based on the cloud device identifier, at least one of the target cloud devices is identified; Sending the first computing power scheduling instruction to the target cloud device based on the target computing power includes: Based on the target computing power, determine the sub-computing power corresponding to each of the target cloud devices; Based on the computing power of each component, a first computing power scheduling instruction is sent to the corresponding target cloud device.

5. The method according to claim 1, characterized in that, The target cloud device and the collaborating device are connected for communication, and the method further includes: A second computing power scheduling instruction is sent to the collaborative device, wherein the second computing power scheduling instruction is used to instruct the collaborative device to provide a first collaborative resource, the first collaborative resource being used to run at least one sub-function of the target function.

6. The method according to claim 5, characterized in that, The second computing power scheduling instruction includes a computing power release instruction and a computing power allocation instruction. The computing power release instruction is used to instruct the collaborative device to transfer the currently running collaborative function to the corresponding collaborative vehicle side and release the computing power resources corresponding to the collaborative device side. The computing power allocation instruction is used to instruct the collaborative device to provide the first collaborative resource based on the current computing power resources on the collaborative device side after releasing the computing power resources.

7. A vehicle cloud computing power scheduling device, characterized in that, include: A receiving module is used to receive a computing power request for a target function sent by a target vehicle. The computing power request includes a first operating state of the target vehicle. The first operating state includes first permission information, which represents the vehicle permission level of the target vehicle. The target cloud device and the cooperating vehicle are connected in communication. The determination module is used to determine the target computing power based on the computing power request; The scheduling module is used to send a first computing power scheduling instruction to the target cloud device based on the target computing power. The first computing power scheduling instruction is used to instruct the target cloud device to configure a target vehicle digital twin. The target vehicle digital twin has target computing power resources that match the target computing power. The target vehicle digital twin is used to run the target function. The scheduling module is further configured to: obtain a second operating state of the collaborative vehicle, the second operating state including second permission information, the second permission information representing the vehicle permission level of the collaborative vehicle; compare the first permission information and the second permission information, and if the vehicle permission level of the target vehicle is greater than the vehicle permission level of the collaborative vehicle, then send a third computing power scheduling instruction to the collaborative vehicle, the third computing power scheduling instruction being used to instruct the collaborative vehicle to provide second collaborative computing power resources, the second collaborative computing power resources being used to run at least one sub-function of the target function.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the vehicle cloud computing power scheduling method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the vehicle cloud computing power scheduling method as described in any one of claims 1 to 6.

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