Cloud vehicle machine control method and device and readable storage medium

Abstract

The invention discloses a cloud vehicle machine control method and device and a readable storage medium, and relates to the technical field of cloud services, the cloud vehicle machine control method and device are applied to a server side, the server side is connected with a target vehicle and a cloud vehicle machine management platform, and the method comprises the steps that target information of the target vehicle is acquired, the target information comprises first information which is sent by the target vehicle and is used for representing the start-stop state change of the target vehicle, or second information which is used for representing the vehicle state data of the target vehicle; determining a use demand of a target cloud vehicle machine based on the target information, the target cloud vehicle machine being matched with the target vehicle identity; and controlling the target cloud vehicle machine to start or stop based on the use demand. The target cloud vehicle machine can be controlled to be started or stopped according to the use requirement, the target cloud vehicle machine does not need to be started or stopped by a user, and the convenience of controlling the target cloud vehicle machine is improved.

Description

Technical Field

[0001] The present application relates to the field of cloud service technology, and more specifically, to a cloud vehicle control method, device and readable storage medium. Background Art

[0002] At present, with the continuous increase in the number of car machine applications and the complexity of their functions, the computing power of the chips installed in the car machines is insufficient, and the storage space of the car machines is also small, which leads to the lag of the car machine system and affects the user experience. Although, by establishing a cloud car machine corresponding to the vehicle on the cloud car machine platform, users can control the vehicle to interact with the cloud car machine, thereby reducing the lag of the car machine system. However, the current control of starting or stopping the cloud car machine is not very convenient. Summary of the invention

[0003] The present application proposes a cloud vehicle control method, device and readable storage medium.

[0004] In the first aspect, an embodiment of the present application provides a cloud vehicle control method, which is applied to a server, and the server is connected to a target vehicle and a cloud vehicle management platform respectively, and the method includes: obtaining target information of the target vehicle, wherein the target information includes first information sent by the target vehicle to characterize the start / stop state change of the target vehicle, or second information characterizing the vehicle status data of the target vehicle; determining the usage requirements of the target cloud vehicle based on the target information, wherein the target cloud vehicle matches the identity of the target vehicle; and controlling the start or stop of the target cloud vehicle based on the usage requirements.

[0005] Optionally, for a possible implementation, the target information includes second information, and determining the usage requirement of the target cloud car machine based on the target information includes: determining the demand probability for the target cloud car machine through a pre-acquired target prediction model based on the second information; when the demand probability is less than a probability threshold, determining that the target cloud car machine does not need to be used; when the demand probability is greater than or equal to the probability threshold, determining that the target cloud car machine needs to be used.

[0006] Optionally, for a possible implementation, the target information includes first information, and determining the usage requirement of the target cloud car machine based on the target information includes: when the first information changes from a start state to a stop state, determining that the target cloud car machine does not need to be used; when the first information changes from a stop state to a start state, determining that the target cloud car machine needs to be used.

[0007] Optionally, for a possible implementation, the target information also includes identity information of the user of the target vehicle, and determining the usage requirements of the target cloud car machine based on the target information includes: among multiple cloud car machines in the cloud car machine management platform, determining a cloud car machine that matches the identity information based on the identity information as the target cloud car machine; and determining the usage requirements of the target cloud car machine based on the target information.

[0008] Optionally, for a possible implementation, before obtaining the target information of the target vehicle, it also includes: obtaining multiple behavior data of users in the target vehicle and alternative state data of the target vehicle, each of the behavior data includes first time information of the user performing an operation, and each of the alternative state data corresponds to second time information; obtaining third time information of the user requesting to start or stop the target cloud car machine; based on the first time information, determining the behavior data matching the third time information as the target behavior data; based on the second time information, determining the alternative state data matching the third time information as the target state data; training an initial model based on the target behavior data and the target state data to obtain the target prediction model.

[0009] Optionally, for a possible implementation, determining the behavior data matching the third time information based on the first time information as the target behavior data includes: constructing a time range based on the third time information and a specified time period before the third time information; searching for the first time information matching the time range as the first target time information; determining the behavior data corresponding to the first target time information as the target behavior data; determining the alternative state data matching the third time information based on the second time information as the target state data includes: searching for the second time information matching the time range as the second target time information; and determining the alternative state data corresponding to the second target time information as the target state data.

[0010] In the second aspect, an embodiment of the present application also provides a cloud vehicle control method, which is applied to a target vehicle, and the target vehicle is respectively connected to a server and a cloud vehicle management platform, and the method includes: sending target information to the server, wherein the target information includes first information representing a change in the start / stop state of the target vehicle, or second information representing vehicle status data of the target vehicle; when it is detected that the target cloud vehicle in the cloud vehicle management platform switches to the start state, the target task is executed through the target cloud vehicle, wherein the target cloud vehicle matches the identity of the target vehicle.

[0011] In the third aspect, the embodiment of the present application also provides a cloud vehicle control device, which is applied to the server side, and the server side is connected to the target vehicle and the cloud vehicle management platform respectively, and the device includes: an acquisition unit, a determination unit and a control unit. Among them, the acquisition unit is used to obtain the target information of the target vehicle, wherein the target information includes the first information sent by the target vehicle to characterize the start-stop state change of the target vehicle, or the second information characterizing the vehicle status data of the target vehicle; the determination unit is used to determine the use requirements of the target cloud vehicle based on the target information, wherein the target cloud vehicle matches the identity of the target vehicle; the control unit is used to control the start or stop of the target cloud vehicle based on the use requirements.

[0012] In the fourth aspect, the embodiment of the present application also provides a cloud vehicle control device, which is applied to a target vehicle, and the target vehicle is connected to a service end and a cloud vehicle management platform respectively, and the device includes: a sending unit and a task execution unit. Among them, the sending unit is used to send target information to the service end, wherein the target information includes first information representing the start / stop state change of the target vehicle, or second information representing the vehicle status data of the target vehicle; the task execution unit is used to execute the target task through the target cloud vehicle when it is detected that the target cloud vehicle in the cloud vehicle management platform switches to the start state, wherein the target cloud vehicle matches the identity of the target vehicle.

[0013] In a fifth aspect, an embodiment of the present application further provides a computer-readable storage medium, in which a program code is stored. The program code can be called by a processor to execute the method described in the first aspect above.

[0014] The cloud car machine control method, device and readable storage medium provided in the embodiment of the present application first obtain the target information of the target vehicle, wherein the target information includes the first information sent by the target vehicle to characterize the start-stop state change of the target vehicle, or the second information characterizing the vehicle status data of the target vehicle; then determine the use requirements of the target cloud car machine based on the target information, wherein the target cloud car machine matches the identity of the target vehicle; and then control the start or stop of the target cloud car machine based on the use requirements. If the user actively controls the start or stop of the cloud car machine according to the demand, the convenience is poor and the user's use experience is also poor. In the present application, the start or stop of the target cloud car machine can be controlled according to the use requirements, and the use requirements are determined based on the first information sent by the target vehicle to characterize the start-stop state change of the target vehicle, or the second information characterizing the vehicle status data of the target vehicle. Therefore, the use requirements can better characterize whether the user needs to use the target cloud car machine, and further control the start or stop of the target cloud car machine, without the user operating to start or stop the target cloud car machine, thereby improving the convenience of controlling the target cloud car machine.

[0015] Other features and advantages of the embodiments of the present application will be described in the subsequent description, and partly become apparent from the description, or can be understood by practicing the embodiments of the present application. The purposes and other advantages of the embodiments of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 An application scenario diagram of the cloud vehicle control method provided in an embodiment of the present application is shown;

[0018] Figure 2 A method flow chart of a cloud vehicle control method provided by an embodiment of the present application is shown;

[0019] Figure 3 A method flow chart of a cloud vehicle control method provided by another embodiment of the present application is shown;

[0020] Figure 4 A method flow chart of a cloud vehicle control method provided by another embodiment of the present application is shown;

[0021] Figure 5A method flow chart of a cloud vehicle control method provided by another embodiment of the present application is shown;

[0022] Figure 6 A schematic diagram of a cloud vehicle control system provided in an embodiment of the present application is shown;

[0023] Figure 7 The structure block diagram of the cloud vehicle control device provided in the embodiment of the present application is shown;

[0024] Figure 8 A structural block diagram of a cloud vehicle control device provided by another embodiment of the present application is shown;

[0025] Fig. 9 A structural block diagram of a computer-readable storage medium provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] In order to make those skilled in the art better understand the present application scheme, the technical scheme in the present application embodiment will be clearly and completely described below in conjunction with the drawings in the present application embodiment. Obviously, the described embodiment is only a part of the present application embodiment, rather than all the embodiments. The components of the present application embodiment usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the present application for protection, but merely represents the selected embodiment of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0027] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0028] At present, with the continuous increase in the number of car computer applications and the complexity of their functions, the computing power of the chips installed in the car computers is insufficient, and the storage space of the car computers is also small, which leads to the lag of the car computer system and affects the user experience. Although, by establishing a cloud car computer corresponding to the vehicle on the cloud car computer platform, users can control the vehicle to interact with the cloud car computer, thereby reducing the lag of the car computer system. However, the current control of the cloud car computer to start or stop is not convenient. How to improve the convenience of controlling the start or stop of the cloud car computer is an urgent problem to be solved.

[0029] At present, a cloud car machine corresponding to the vehicle can be established on the server side. Specifically, operating resources can be allocated to the cloud car machine based on hardware virtualization technology. For example, the processing resources of the server side can be time-division multiplexed, and the storage resources of the server side can be elastically expanded. Then, the system interface running in the cloud is transmitted to the terminal, that is, the vehicle, through picture and sound interception and encoding compression technology. Thus, a communication connection with the cloud car machine can be established in the vehicle to use the cloud car machine service, so that the cloud car machine can be responsible for at least part of the computing tasks, storage tasks, etc., thereby reducing the pressure of task operation of the vehicle-mounted car system and alleviating the jamming of the vehicle-mounted car system.

[0030] Among them, since the use of cloud car machine is reflected in the vehicle, users generally only use cloud car machine during the use of the vehicle, so cloud car machine is generally started or stopped manually by the user. For example, the user can manually click the application icon corresponding to the cloud car machine in the car machine system installed in the vehicle to start the cloud car machine service, thereby controlling the start of the cloud car machine corresponding to the vehicle on the server side; the user can also manually click the application icon corresponding to the cloud car machine in the car machine system installed in the vehicle to close the cloud car machine service.

[0031] However, the inventors found in their research that manually starting or stopping the cloud car machine is cumbersome and inconvenient.

[0032] Therefore, the present application provides a cloud vehicle control method, device and readable storage medium to solve or partially solve the above problems.

[0033] See also Figure 1 , Figure 1 The application scenario diagram of the cloud vehicle control method provided in the embodiment of the present application is shown, namely, the cloud vehicle control scenario 100. The cloud vehicle control scenario 100 includes a server 110, a target vehicle 120, and a cloud vehicle management platform 130. The server 110 is connected to the target vehicle 120 and the cloud vehicle management platform 130 respectively.

[0034] For some implementations, the server 110 may be a telemetry service provider (TSP), which can collect, process, analyze, and provide feedback on vehicle information.

[0035] The target vehicle 120 can send target information to the server 110, so that the server 110 can analyze the received target information to determine the usage requirements of the target cloud car machine, and then control the start or stop of the target cloud car machine based on the usage requirements. For a detailed description of the method, please refer to the subsequent embodiments. Among them, the target vehicle 120 can establish a communication connection with the server 110 through 4G, 5G mobile networks, wireless communication technologies Wi-Fi, Bluetooth, etc.

[0036] The cloud vehicle machine management platform 130 can determine the cloud vehicle machine that matches the target vehicle identity, and then start or stop the target cloud vehicle machine in response to the control of the server 110. The cloud vehicle machine management platform 130 can be a cloud server with high computing power and large storage space, so that the resources of the cloud vehicle machine management platform can be allocated to establish various cloud vehicle machines. In some embodiments, the cloud vehicle machine can also be called a cloud vehicle machine instance.

[0037] It should be noted that after the target cloud vehicle machine is started, the target vehicle 120 can directly communicate with the target cloud vehicle machine in the cloud vehicle machine management platform 130 .

[0038] See also Figure 2 , Figure 2 A method flow chart of a cloud vehicle control method provided by an embodiment of the present application is shown. The cloud vehicle control method can be applied to Figure 1 The server shown in the figure may specifically use the processor of the server as the execution subject. The server is connected to the target vehicle and the cloud vehicle management platform respectively. The method may specifically include steps S110 to S130.

[0039] Step S110: Acquire target information of the target vehicle, wherein the target information includes first information sent by the target vehicle for characterizing a start / stop state change of the target vehicle, or second information characterizing vehicle state data of the target vehicle.

[0040] The target vehicle is the vehicle corresponding to the target cloud vehicle controlled by the cloud vehicle control method. The target vehicle can establish a communication connection with the server. Thus, the target vehicle can send target information to the server, and after obtaining the target information, the server can analyze the target information and subsequently control the target cloud vehicle to start or stop.

[0041] The target information may include first information sent by the target vehicle to characterize the start / stop state change of the target vehicle, or second information characterizing the vehicle state data of the target vehicle. The server can be used to obtain the target information sent by the target vehicle, so that the target cloud vehicle can be controlled to start or stop through the target information. For a detailed description, please refer to the subsequent steps.

[0042] For some implementations, the vehicle system carried by the target vehicle can monitor the start state and stop state of the target vehicle, so that when the start state changes, the state before and after the change is used as the first information. For example, the target vehicle changes from the stop state to the start state, and the first information here may include "the start state of the target vehicle changes from the stop state to the start state".

[0043] For some other implementations, information representing the start-stop state change of the target vehicle can also be captured as the first information through the log text in the vehicle system carried by the target vehicle. The log text can record information about the start-stop state change of the target vehicle, and can also record information about important notifications of the target vehicle. For example, "the start-up state of the target vehicle has changed from the stop state to the start state" is captured in the log text as the first information.

[0044] The target vehicle can also obtain vehicle status data through the onboard system. The vehicle status data can be used to characterize the motion state of the target vehicle, for example, it may include the target vehicle's driving speed, gear position, acceleration, pitch angle, steering angle, position information, etc. In some embodiments, the target vehicle may be configured with a sensor module, so that the motion state of the target vehicle can be collected through the sensor module and then sent to the onboard system. The onboard system organizes and summarizes the acquired motion state to generate second information characterizing the vehicle status data of the target vehicle.

[0045] In some implementations, event tracking may be pre-set in the vehicle system of the target vehicle, so that the user's operation and the vehicle status data of the target vehicle may be acquired through the event tracking.

[0046] Optionally, the target vehicle may also be configured with an enterprise standard data collection module, so that the vehicle status data can be acquired through the enterprise standard data collection module.

[0047] It should be noted that the target vehicle changes to a stopped state, which can indicate that the target vehicle is in a powered-off state. At this time, some parts of the vehicle system of the target vehicle are powered off, while other parts can remain powered on. For example, the part responsible for intelligent driving can be powered off, and the parts responsible for driving speed monitoring, gear position monitoring, etc. can also be powered off; while the part responsible for communicating with the server and the part responsible for recording log text in the vehicle system can remain powered on. Among them, after the parts responsible for driving speed monitoring, gear position monitoring, etc. are powered off, the corresponding motion state can be set to the default value. For example, 0km / h can be used as the default value of the driving speed. After the driving speed monitoring part is powered off, 0km / h is sent to the vehicle system as the driving speed; for another example, the parking gear can be used as the default value of the gear, so that after the gear monitoring part is powered off, the parking gear is sent to the vehicle system as the gear.

[0048] Optionally, the target information sent by the target vehicle to the server is specifically the first information or the second information, which can be determined according to the control mode for the target cloud car machine that is pre-set. Exemplarily, the user can pre-set the control mode of the target cloud car machine in the car machine system of the target vehicle through the application corresponding to the cloud car machine. For example, the user can set "the target cloud car machine and the target vehicle start and stop at the same time", then it can be determined that the target information sent to the server by the subsequent target vehicle is the first information; for another example, the user can set "start and stop the target cloud car machine according to usage habits", then it can be determined that the target information sent to the server by the subsequent target vehicle is the second information. Among them, usage habits are the user's habits of using the target car machine.

[0049] Step S120: Determine the usage requirements of the target cloud vehicle machine based on the target information, wherein the target cloud vehicle machine matches the identity of the target vehicle.

[0050] After obtaining the target information, the server can determine the usage requirements of the target cloud vehicle machine based on the target information, wherein the target cloud vehicle machine matches the identity of the target vehicle.

[0051] It is understandable that a plurality of cloud vehicle machines can be configured in the cloud vehicle machine management platform, and the cloud vehicle machine that matches the identity of the target vehicle is the target cloud vehicle machine. In one exemplary embodiment, the identity of the target vehicle can be the identity information of the target vehicle; in another exemplary embodiment, the identity of the target vehicle can also be the identity information of the user who has performed a login operation in the vehicle machine system of the target vehicle.

[0052] The usage requirement of the target cloud vehicle machine may be a prediction result of the server end on whether the target vehicle may need to use the target cloud vehicle machine in the future according to the target information. For example, the usage requirement may include not needing to use the target cloud vehicle machine or needing to use the target cloud vehicle machine.

[0053] It is understandable that users generally only use the cloud car machine during vehicle use. Therefore, in some embodiments, the server can subsequently control the target cloud car machine to start or stop according to the first information obtained that represents the start / stop state change of the target vehicle. In other embodiments, the server can also pre-train a target prediction model, which can be used to analyze the input vehicle status data to output the probability of demand for the target cloud car machine.

[0054] Therefore, when the target information includes the first information, it is possible to determine whether the target cloud vehicle machine needs to be used according to the changes in the start state and the stop state; when the target information includes the second information, it is possible to determine the demand probability for the target cloud vehicle machine according to the target prediction model, and then determine whether the target cloud vehicle machine needs to be used based on the demand probability. For a detailed description, please refer to the description of the subsequent embodiments.

[0055] Step S130: Control the target cloud vehicle machine to start or stop based on the usage demand.

[0056] As can be seen from the above introduction, the usage requirement may include not needing to use the target cloud vehicle machine or needing to use the target cloud vehicle machine. Therefore, when the usage requirement is not needing to use the target cloud vehicle machine, the target cloud vehicle machine can be controlled to stop; and when the usage requirement is needing to use the target cloud vehicle machine, the target cloud vehicle machine can be controlled to start.

[0057] For some implementations, each cloud car machine in the cloud car machine management platform is created based on a request from a vehicle, so each cloud car machine matches the identity of the vehicle that requested the creation of the cloud car machine. Therefore, when the server controls the start or stop of the target cloud car machine, it can send a control signal for characterizing the start or stop, as well as identity information for characterizing the identity of the target vehicle. The cloud car machine management platform can then determine the target cloud car machine from multiple cloud car machines based on the acquired identity information. Furthermore, the target cloud car machine can be controlled to start or stop based on the control signal.

[0058] The cloud car machine control method provided in the embodiment of the present application first obtains the target information of the target vehicle, wherein the target information includes the first information sent by the target vehicle to characterize the start-stop state change of the target vehicle, or the second information characterizing the vehicle status data of the target vehicle; then the use demand of the target cloud car machine is determined based on the target information, wherein the target cloud car machine matches the identity of the target vehicle; and then the target cloud car machine is controlled to start or stop based on the use demand. If the user actively controls the start or stop of the cloud car machine according to the demand, the convenience is poor and the user's use experience is also poor. In the present application, the start or stop of the target cloud car machine can be controlled according to the use demand, and the use demand is determined based on the first information sent by the target vehicle to characterize the start-stop state change of the target vehicle, or the second information characterizing the vehicle status data of the target vehicle. Therefore, the use demand can better characterize whether the user needs to use the target cloud car machine, and further control the start or stop of the target cloud car machine, without the user operating to start or stop the target cloud car machine, thereby improving the convenience of controlling the target cloud car machine.

[0059] See also Figure 3 , Figure 3A method flow chart of a cloud vehicle control method provided by an embodiment of the present application is shown. The cloud vehicle control method can be applied to Figure 1 The server shown in the figure may specifically use the processor of the server as the execution subject. The server is connected to the target vehicle and the cloud vehicle management platform respectively. The method may specifically include steps S210 to S260.

[0060] Step S210: Acquire target information of the target vehicle, wherein the target information includes first information sent by the target vehicle to characterize a start / stop state change of the target vehicle, or second information characterizing vehicle state data of the target vehicle.

[0061] Among them, step S210 has been introduced in detail in the above embodiment and will not be repeated here.

[0062] Step S220: Determine, among the multiple cloud vehicle machines in the cloud vehicle machine management platform, a cloud vehicle machine matching the identity information based on the identity information as a target cloud vehicle machine.

[0063] In some embodiments, the target information sent by the target vehicle to the server may also include the identity information of the target vehicle, so that the server can determine a cloud car machine that matches the identity information from multiple cloud car machines in the cloud car machine management platform based on the identity information as the target cloud car machine.

[0064] Exemplarily, each cloud car machine in the cloud car machine platform corresponds to an identity, so that each identity can be matched with the identity information, and the cloud car machine corresponding to the identity with the same identity information is used as the target cloud car machine.

[0065] Step S230: Whether the first information is changed from the start state to the stop state.

[0066] For some implementations, the target information may be the first information. Thus, it may be determined based on the first information whether the target cloud vehicle machine needs to be used. Specifically, it may be determined whether the first information changes from a start state to a stop state. If so, the process may jump to step S240; if not, the process may jump to step S250.

[0067] Step S240: When the first information indicates that the state has changed from the start state to the stop state, it is determined that the target cloud vehicle machine does not need to be used.

[0068] Step S250: When the first information indicates a change from a stopped state to a started state, it is determined that the target cloud vehicle machine needs to be used.

[0069] From the above introduction, it can be known that users generally only use the cloud car machine during the use of the vehicle. Therefore, when it is detected that the target vehicle changes from a stopped state to a started state, the target cloud car machine can be further controlled to start; and when it is detected that the target vehicle changes from a started state to a stopped state, the target cloud car machine can be further controlled to stop. Therefore, when the first information is a change from a started state to a stopped state, it can be determined that the target cloud car machine does not need to be used, that is, it is determined that the use requirement is not needed; when the first information is a change from a stopped state to a started state, it can be determined that the target cloud car machine needs to be used, that is, it is determined that the use requirement is needed.

[0070] The target cloud car machine can then be started or stopped based on demand.

[0071] Step S260: Control the target cloud vehicle machine to start or stop based on the usage demand.

[0072] Specifically, when the usage requirement is that the target cloud vehicle machine is not needed, the target cloud vehicle machine can be controlled to stop; and when the usage requirement is that the target cloud vehicle machine is needed, the target cloud vehicle machine can be controlled to start.

[0073] Among them, step S260 has been introduced in detail in the above embodiment and will not be repeated here.

[0074] It is understandable that if the target vehicle needs to use the target cloud car machine, it is necessary to first control the start of the target cloud car machine. During the startup process, the target cloud car machine needs to perform initialization, and the specific initialization may include system startup, data mounting, etc. It takes a certain amount of time to perform initialization, such as 30 seconds, 60 seconds, etc. After the target cloud car machine is initialized, the communication connection between the target cloud car machine and the target vehicle can be further established, so that the target vehicle can use the target cloud car machine. Therefore, if the user starts the target cloud car machine when he needs to use the target cloud car machine, the user needs to wait for a period of time for the target cloud car machine to perform initialization, and the user's experience is poor. In the embodiment of the present application, when the change from the stop state to the start state is monitored, the start of the target cloud car machine can be automatically executed, that is, the target vehicle and the target cloud car machine can be approximately started and stopped at the same time. Then, when the subsequent user needs to use the target cloud car machine, he can directly establish a communication connection between the target vehicle and the target cloud car machine, avoiding the time waiting for the target cloud car machine to initialize, and improving the user's experience.

[0075] The cloud car machine control method provided in the embodiment of the present application determines the cloud car machine matching the identity information among the multiple cloud car machines in the cloud car machine management platform based on the identity information as the target cloud car machine. Determine whether the first information is a change from the start state to the stop state; if the first information is a change from the start state to the stop state, determine that the target cloud car machine does not need to be used; if the first information is a change from the stop state to the start state, determine that the target cloud car machine needs to be used; thereby controlling the start or stop of the target cloud car machine based on the use demand. In the embodiment provided in the present application, the server can automatically control the start or stop of the target cloud car machine based on the first information of the start-stop state change of the target vehicle, without user operation, thereby improving the convenience of controlling the target cloud car machine. At the same time, since the control of the stop of the target cloud car machine is executed when the first information is a change from the start state to the stop state, the user has a high probability of not using the target cloud car machine at this time, so stopping the target cloud car machine at this time will not affect the user, and on the other hand, it can also reduce the time of the target cloud car machine running without load, reducing the energy consumption of the cloud car machine platform.

[0076] See also Figure 4 , Figure 4 A method flow chart of a cloud vehicle control method provided by an embodiment of the present application is shown. The cloud vehicle control method can be applied to Figure 1 The server shown in the figure may specifically use the processor of the server as the execution subject. The server is connected to the target vehicle and the cloud vehicle management platform respectively. The method may specifically include steps S310 to S3110.

[0077] Step S310: Acquire multiple behavior data of the user in the target vehicle and alternative state data of the target vehicle, each of the behavior data includes first time information when the user performs an operation, and each of the alternative state data corresponds to second time information.

[0078] As can be seen from the above introduction, the server can pre-train the model to obtain the target prediction model. Specifically, the server can first obtain multiple behavior data of the user in the target vehicle, wherein each behavior data includes the first time information of the user performing an operation.

[0079] Exemplarily, the behavior data may be operations performed by the user, such as operations on the accelerator pedal, operations on the brake pedal, and so on. The behavior data may also include the first time information when the user performs each operation. For example, the behavior data may include that the user performed the operation of stepping on the accelerator pedal at 8 a.m. It should be noted that the first time information may also include more detailed time information, such as year, month, and day information. Continuing with the above example, the behavior data may include that the user performed the operation of stepping on the accelerator pedal at 8 a.m. on October 1, 2020.

[0080] The server can also obtain alternative state data of the target vehicle, wherein each of the alternative state data corresponds to the second time information. The alternative state data can be the state data of the target vehicle obtained, which is similar to the vehicle state data of the target vehicle obtained in the aforementioned embodiment. The state data can also be collected by the sensor module carried by the target vehicle, or by obtaining the state data through a pre-set buried point, or by obtaining the state data through an enterprise standard data collection module.

[0081] Exemplarily, the alternative state data may include the target vehicle's driving speed, gear, acceleration, pitch angle, steering angle, position information, etc. Each alternative state data may also correspond to the second time information, for example, the target vehicle's driving speed is 50 km / h at 9 am. Similar to the first time information, the second time information may also include more detailed time information, which will not be described in detail here.

[0082] Optionally, the acquired behavior data and alternative status data may be privacy processed, thereby shielding or deleting part of the behavior data and alternative status data that may involve user privacy, so as to improve the user's privacy security.

[0083] Step S320: Obtain the third time information of the user requesting to start or stop the target cloud vehicle machine.

[0084] Furthermore, the server can also obtain the third time information of the user's request to start or stop the target cloud car machine. For example, if the user performs the operation of starting the target cloud car machine at 8 am, the third time information corresponding to starting the target cloud car machine is 8 am. Similar to the first time information, the third time information can also include more detailed time information, which will not be repeated here.

[0085] Step S330: Based on the first time information, determine the behavior data matching the third time information as the target behavior data.

[0086] It is understandable that there may be a large number of user behavior data, and in order to predict whether the target cloud car machine needs to be used through the trained target prediction model, the behavior data can be screened and the behavior data corresponding to the first time information near the time when the user requests to start or stop the target cloud car machine can be selected. Specifically, based on the first time information, the behavior data matching the third time information can be determined as the target behavior data. Step S330 can include steps S331 to S333.

[0087] Step S331: construct a time range based on the third time information and a specified time period before the third time information.

[0088] Step S332: searching for first time information matching the time range as first target time information.

[0089] Step S333: Determine that the behavior data corresponding to the first target time information is the target behavior data.

[0090] In some embodiments, a time range may be constructed first. Specifically, the time range is constructed based on the third time information and the specified time period before the third time information. Exemplarily, taking the third time information as 9:00 a.m. and the specified time period as 1 minute as an example, 8:59 a.m. to 9:00 a.m. may be constructed as the time range.

[0091] It should be noted that the specified time period shown above is only an example and can be flexibly set as needed in actual applications, and the embodiments of the present application do not make specific limitations.

[0092] Further, the first time information matching the time range may be searched as the first target time information. Specifically, the first time information within the time range may be searched as the first time information matching the time range. Then, the behavior data corresponding to the first target time information may be determined as the target behavior data.

[0093] Step S340: Based on the second time information, determine candidate state data matching the third time information as target state data.

[0094] Similar to the behavior data, there may be a large number of alternative state data, and in order to predict whether the target cloud car machine needs to be used through the trained target prediction model, the alternative state data can be screened and the alternative state data corresponding to the second time information near the time when the user requests to start or stop the target cloud car machine can be selected. Specifically, based on the second time information, the alternative state data that matches the third time information can be determined as the target state data. Step S340 may include step S341 and step S342.

[0095] Step S341: searching for second time information matching the time range as second target time information.

[0096] Step S342: Determine the candidate state data corresponding to the second target time information as the target state data.

[0097] Among them, the method for determining the time range can refer to the above steps and will not be repeated here. Then, the second time information matching the time range can be searched as the second target time information. Specifically, the second time information within the time range can be searched as the second time information matching the time range. Then, the alternative state data corresponding to the second target time information can be further determined as the target state data.

[0098] Step S350: training an initial model based on the target behavior data and the target state data to obtain the target prediction model.

[0099] From the above introduction, it can be known that the first time information corresponding to the target behavior data and the second time information corresponding to the target state data obtained in the embodiment of the present application are both near the time when the user requests to start or stop the target cloud car machine. Then, the initial model is subsequently trained with the target behavior data and the target state data to obtain the target prediction model. The target prediction model can be used to predict whether the target cloud car machine needs to be used.

[0100] For some implementations, the initial model can be constructed based on association rule algorithms, time series analysis algorithms, machine learning algorithms, deep learning algorithms, etc. Exemplarily, the association rule algorithm can be an association rule mining algorithm Apriori; the time series analysis can be an autoregressive integrated moving average model (ARIMA); the machine learning algorithm can be a support vector machine algorithm (SVM); and the deep learning algorithm can be a recurrent neural network algorithm (RNN).

[0101] It should be noted that the target prediction model trained by the server is for the target vehicle, that is, the target prediction model can be used to predict whether the target vehicle needs to use the target cloud vehicle machine. Therefore, in the embodiment provided by the present application, the target prediction model trained can predict whether the target vehicle needs to use the target cloud vehicle machine based on the user's usage habits.

[0102] Among them, the target prediction model can be matched with the target vehicle or with the user of the target vehicle. For example, if the user performs a login operation in the vehicle system of the target vehicle, the target vehicle can obtain the identity information of the user who performed the login operation, and then the target prediction model can be matched with the user's identity information.

[0103] Step S360: Acquire target information of the target vehicle, wherein the target information includes first information sent by the target vehicle to characterize a start / stop state change of the target vehicle, or second information characterizing vehicle state data of the target vehicle.

[0104] Among them, step S360 has been introduced in detail in the above embodiment and will not be repeated here.

[0105] Step S370: Determine the demand probability for the target cloud vehicle machine based on the second information through a pre-acquired target prediction model.

[0106] For some implementations, the target information may be second information, and the demand probability for the target cloud vehicle machine may be determined based on the second information through a pre-acquired target prediction model. Specifically, the second information may be input as an input to the target prediction model, thereby obtaining an output of the target model as the demand probability for the target cloud vehicle machine.

[0107] For example, if the user is accustomed to not using the target cloud car machine when driving for a long time at a highway, and uses the target cloud car machine when resting at a service area, then if the second information includes the location information of a highway, the demand probability determined by the target prediction model is low; if the second information includes the location information of a service area, the demand probability determined by the target prediction model is high.

[0108] Optionally, in some embodiments, the second information may also include the current time information corresponding to the vehicle status data. Thus, the demand probability for the target cloud car machine can be determined by combining the current time information with the pre-acquired target prediction model. For example, if the user is accustomed to not using the target cloud car machine for commuting on weekdays, and uses the target cloud car machine on weekends, at this time, if the current time information included in the second information is Monday, the demand probability determined by the target prediction model is low; if the current time information included in the second information is Sunday, the demand probability determined by the target prediction model is high.

[0109] It should be noted that the above specific example of determining the demand probability for the target cloud vehicle machine based on the second information through a pre-acquired target prediction model is only an explanation of this step and does not constitute a limitation on the embodiments of the present application.

[0110] Among them, among the multiple cloud car machines in the cloud car machine management platform, a cloud car machine matching the identity information can be determined based on the identity information as the target cloud car machine. For a detailed introduction, please refer to the aforementioned embodiment and will not be repeated here.

[0111] Step S380: Whether the demand probability is less than the probability threshold.

[0112] Therefore, it is possible to determine whether the target cloud vehicle machine needs to be used based on the acquired demand probability. Specifically, it is possible to determine whether the demand probability is less than the probability threshold. If so, the process can jump to step S390; if not, the process can jump to step S3100.

[0113] Step S390: When the demand probability is less than the probability threshold, it is determined that the target cloud vehicle machine does not need to be used.

[0114] Step S3100: When the demand probability is greater than or equal to the probability threshold, determine that the target cloud vehicle machine needs to be used.

[0115] Among them, the probability threshold can be a pre-set value, and the setting of the specific value can be flexibly set as needed, which is not limited in the embodiments of the present application. Thus, when the demand probability is less than the probability threshold, it is determined that the target cloud car machine does not need to be used, that is, the use demand is determined to be unnecessary. When the demand probability is greater than or equal to the probability threshold, it is determined that the target cloud car machine needs to be used, that is, the use demand is determined to be necessary.

[0116] It can be understood that the larger the probability threshold is set, the easier it is to determine that the target cloud car machine does not need to be used, and the easier it is to control the target cloud car machine to stop subsequently, thereby further reducing the power consumption of the cloud car machine platform; and if the probability threshold is set to a smaller value, it is easier to determine that the target cloud car machine needs to be used, and the easier it is to control the target cloud car machine to start subsequently, thereby reducing the time the user may wait for the target cloud car machine to initialize, thereby improving the user experience.

[0117] Step S3110: Control the target cloud vehicle machine to start or stop based on the usage demand.

[0118] Among them, step S3110 has been introduced in detail in the above embodiment and will not be repeated here.

[0119] In the embodiment of the present application, when the demand probability is greater than or equal to the probability threshold, the target cloud car machine can be automatically started. Then, when the subsequent user needs to use the target cloud car machine, the communication connection between the target vehicle and the target cloud car machine can be directly established, avoiding the time waiting for the target cloud car machine to initialize, and improving the user experience. In addition, the initial model can be trained to obtain the target prediction model, so as to predict whether the target vehicle needs to use the target cloud car machine through the target prediction model. Among them, the target prediction model can predict whether the target vehicle needs to use the target cloud car machine based on the user's usage habits. That is to say, in the embodiment provided by the present application, the start or stop of the target cloud car machine can be controlled according to the user's usage habits.

[0120] See also Figure 5 , Figure 5 A method flow chart of a cloud vehicle control method provided by an embodiment of the present application is shown. The cloud vehicle control method can be applied to Figure 1 The target vehicle shown in the figure may specifically use the processor of the target vehicle as the execution subject. The target vehicle is connected to the server and the cloud vehicle management platform respectively. The method may specifically include steps S410 to S420.

[0121] Step S410: Send target information to the server, wherein the target information includes first information representing a start / stop state change of the target vehicle, or second information representing vehicle state data of the target vehicle.

[0122] Step S420: When it is detected that the target cloud vehicle machine in the cloud vehicle machine management platform is switched to the startup state, the target task is executed by the target cloud vehicle machine, wherein the target cloud vehicle machine matches the identity of the target vehicle.

[0123] The target vehicle can obtain target information and send the target information to the server, wherein the target information includes first information representing the start / stop state change of the target vehicle or second information representing the vehicle state data of the target vehicle.

[0124] Thus, when the target vehicle detects that the target cloud vehicle in the cloud vehicle management platform switches to the startup state, the target vehicle executes the target task through the target cloud vehicle, wherein the target cloud vehicle matches the identity of the target vehicle. It is understandable that after detecting that the target cloud vehicle switches to the startup state, it is also necessary to establish a communication connection with the target cloud vehicle after the target cloud vehicle completes initialization, so as to execute the target task through the target cloud vehicle.

[0125] Among them, the target task may include at least part of the computing tasks, storage tasks, etc., so that the target task is executed through the target cloud vehicle system to reduce the resource usage of the vehicle system of the target vehicle and alleviate the jamming situation.

[0126] Among them, for the detailed introduction of the above steps, please refer to the step introduction in the method embodiments corresponding to the aforementioned servers, which will not be repeated here.

[0127] See also Figure 6 , Figure 6 A schematic diagram of a cloud vehicle control system provided in an embodiment of the present application is shown. The cloud vehicle control system 600 can be used to execute the cloud vehicle control method shown in the aforementioned embodiments. Specifically, the cloud vehicle control system 600 includes a server 610, a target vehicle 620, and a cloud vehicle platform 630. Among them, the server 620 is connected to the target vehicle 610 and the cloud vehicle platform 630 respectively.

[0128] The target vehicle 620 includes a vehicle power-on and power-off module 621, a vehicle system module 622, and a data acquisition system 623. Among them, the power-on and power-off module 621 can be used to power on the target vehicle 620 to control the target vehicle 620 to enter a start state; it can also be used to power off the target vehicle 620 to control the target vehicle 620 to enter a stop state. The vehicle system module 622 can be used to establish a communication connection with the server 610, and can also be used to set a tracking point, and obtain the user's operation and the vehicle status data of the target vehicle through the tracking point. The data acquisition system 623 can be an enterprise standard data acquisition module, so that the vehicle status data can be obtained through the enterprise standard data acquisition module.

[0129] The server 610 includes a target prediction model 611 and a control signal sending module 612. The target prediction model 611 is connected to the control signal sending module 612, and the target prediction model 611 is also connected to the vehicle system module 622 and the data acquisition system 623 respectively, and the control signal sending module 612 is also connected to the power module 621. The target prediction model 611 is used to predict whether the target vehicle needs to use the target cloud vehicle 631. The control signal sending module 612 is used to send a control signal to the cloud vehicle platform 630 to control the target cloud vehicle 631 in the cloud vehicle platform 630. Specifically, the control signal may include a signal to control the start of the target cloud vehicle 631 or a signal to control the stop of the target cloud vehicle 631.

[0130] The target cloud car machine 630 includes a target cloud car machine 631 and a start-stop module 632. Among them, the start-stop module 632 is connected to the target cloud car machine 631 and the control signal sending module 612 respectively. The start module 632 is used to control the target cloud car machine 631 to start or stop according to the control signal after receiving the control signal sent by the control signal sending module 612. Specifically, if a signal to control the target cloud car machine 631 to start is obtained, the target cloud car machine 631 can be controlled to start; if a signal to control the target cloud car machine 631 to stop is obtained, the target cloud car machine 631 can be controlled to stop.

[0131] Among them, the detailed introduction of the methods executed by each module in the cloud vehicle control system 600 can be found in the aforementioned method embodiments, which will not be repeated here.

[0132] See also Figure 7 , Figure 7 A structural block diagram of a cloud vehicle machine control device 700 provided in an embodiment of the present application is shown. The cloud vehicle machine control device 700 is applied to a server end, and the server end is connected to a target vehicle and a cloud vehicle machine management platform respectively. The cloud vehicle machine control device 700 includes: an acquisition unit 710, a determination unit 720 and a control unit 730.

[0133] The acquisition unit 710 is used to acquire the target information of the target vehicle, wherein the target information includes first information sent by the target vehicle to characterize the start / stop state change of the target vehicle, or second information characterizing the vehicle state data of the target vehicle.

[0134] Optionally, the acquisition unit 710 can also be used to obtain multiple behavior data of users in the target vehicle and alternative state data of the target vehicle, each behavior data including first time information of the user performing an operation, and each alternative state data corresponding to second time information; obtain third time information of the user requesting to start or stop the target cloud car machine; based on the first time information, determine the behavior data matching the third time information as target behavior data; based on the second time information, determine the alternative state data matching the third time information as target state data; train an initial model based on the target behavior data and the target state data to obtain the target prediction model.

[0135] The acquisition unit 710 can also be used to construct a time range based on the third time information and the specified time period before the third time information; search for the first time information that matches the time range as the first target time information; determine the behavior data corresponding to the first target time information as the target behavior data; search for the second time information that matches the time range as the second target time information; and determine the alternative state data corresponding to the second target time information as the target state data.

[0136] The determination unit 720 is used to determine the usage requirements of the target cloud vehicle machine based on the target information, wherein the target cloud vehicle machine matches the identity of the target vehicle.

[0137] Optionally, the determination unit 720 can also be used to determine the demand probability for the target cloud car machine based on the second information through a pre-acquired target prediction model; when the demand probability is less than a probability threshold, it is determined that the target cloud car machine does not need to be used; when the demand probability is greater than or equal to the probability threshold, it is determined that the target cloud car machine needs to be used.

[0138] Optionally, the determination unit 720 can also be used to determine that the target cloud car machine does not need to be used when the first information changes from a start state to a stop state; and to determine that the target cloud car machine needs to be used when the first information changes from a stop state to a start state.

[0139] Optionally, the determination unit 720 can also be used to determine a cloud car machine matching the identity information among multiple cloud car machines in the cloud car machine management platform based on the identity information as a target cloud car machine; and determine the usage requirements of the target cloud car machine based on the target information.

[0140] The control unit 730 is used to control the start or stop of the target cloud vehicle machine based on the usage demand.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0142] In several embodiments provided in the present application, the coupling between the units may be electrical, mechanical or other forms of coupling. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0143] See also Figure 8 , Figure 8 A structural block diagram of a cloud vehicle control device 800 provided in an embodiment of the present application is shown. The cloud vehicle control device 800 is applied to a target vehicle, and the target vehicle is connected to a server and a cloud vehicle management platform respectively. The cloud vehicle control device 800 includes: a sending unit 810 and a task execution unit 820.

[0144] The sending unit 810 is used to send target information to the server, wherein the target information includes first information representing a start / stop state change of the target vehicle, or second information representing vehicle state data of the target vehicle.

[0145] The task execution unit 820 is used to execute the target task through the target cloud vehicle machine when it is detected that the target cloud vehicle machine in the cloud vehicle machine management platform is switched to the startup state, wherein the target cloud vehicle machine matches the identity of the target vehicle.

[0146] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and units can refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0147] In several embodiments provided in the present application, the coupling between the units may be electrical, mechanical or other forms of coupling. In addition, each functional unit in each embodiment of the present application may be integrated into a processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0148] See also Fig. 9, which shows a structural block diagram of a computer-readable storage medium provided in an embodiment of the present application. The computer-readable medium 900 stores program codes, which can be called by a processor to execute the method described in the above method embodiment.

[0149] The computer readable storage medium 900 can be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read-only memory), an EPROM, a hard disk, or a ROM. Optionally, the computer readable storage medium 900 includes a non-transitory computer-readable storage medium. The computer readable storage medium 900 has storage space for program code 910 that performs any method steps in the above method. These program codes can be read from or written to one or more computer program products. The program code 910 can be compressed, for example, in an appropriate form.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A cloud vehicle control method, characterized in that: Applied to a server, the server is connected to a target vehicle and a cloud vehicle management platform respectively, and the method includes: Acquire target information of the target vehicle, wherein the target information includes first information sent by the target vehicle and used to characterize a start / stop state change of the target vehicle, or second information characterizing vehicle state data of the target vehicle; Determine the usage requirements of the target cloud vehicle machine based on the target information, wherein the target cloud vehicle machine matches the identity of the target vehicle; The target cloud vehicle machine is controlled to start or stop based on the usage demand.

2. The method according to claim 1, characterized in that The target information includes second information, and determining the usage requirements of the target cloud vehicle machine based on the target information includes: Determine the demand probability for the target cloud vehicle machine through a pre-acquired target prediction model based on the second information; When the demand probability is less than the probability threshold, determining that the target cloud vehicle machine does not need to be used; When the demand probability is greater than or equal to the probability threshold, it is determined that the target cloud vehicle machine needs to be used.

3. The method according to claim 1, characterized in that The target information includes first information, and determining the usage requirements of the target cloud vehicle machine based on the target information includes: When the first information indicates that the state changes from the start state to the stop state, it is determined that the target cloud vehicle machine does not need to be used; When the first information indicates that the state has changed from the stopped state to the started state, it is determined that the target cloud vehicle machine needs to be used.

4. The method according to claim 1, characterized in that: The target information also includes identity information of the user of the target vehicle, and determining the usage requirements of the target cloud vehicle based on the target information includes: Among the multiple cloud vehicle machines in the cloud vehicle machine management platform, determining a cloud vehicle machine matching the identity information based on the identity information as a target cloud vehicle machine; The usage requirements of the target cloud vehicle machine are determined based on the target information.

5. The method according to claim 1, characterized in that Before acquiring the target information of the target vehicle, the method further includes: Acquire multiple behavior data of the user in the target vehicle and alternative state data of the target vehicle, each of the behavior data includes first time information of the user performing an operation, and each of the alternative state data corresponds to second time information; Obtaining third time information of the user requesting to start or stop the target cloud vehicle machine; Based on the first time information, determining behavior data matching the third time information as target behavior data; Based on the second time information, determining candidate state data matching the third time information as target state data; An initial model is trained based on the target behavior data and the target state data to obtain the target prediction model.

6. The method according to claim 5, characterized in that The determining, based on the first time information, behavior data matching the third time information as target behavior data includes: Constructing a time range based on the third time information and a specified time period before the third time information; Searching for first time information matching the time range as first target time information; Determining the behavior data corresponding to the first target time information as the target behavior data; The determining, based on the second time information, candidate state data matching the third time information as target state data includes: Searching for second time information matching the time range as second target time information; Determine the candidate state data corresponding to the second target time information as the target state data.

7. A cloud vehicle control method, characterized in that: Applied to a target vehicle, the target vehicle is connected to a service end and a cloud vehicle management platform respectively, and the method includes: Sending target information to the server, wherein the target information includes first information representing a start / stop state change of the target vehicle, or second information representing vehicle state data of the target vehicle; When it is detected that the target cloud vehicle machine in the cloud vehicle machine management platform is switched to the startup state, the target task is executed by the target cloud vehicle machine, wherein the target cloud vehicle machine matches the identity of the target vehicle.

8. A cloud vehicle control device, characterized in that: Applied to the server, the server is connected to the target vehicle and the cloud vehicle management platform respectively, and the device includes: An acquisition unit, used for acquiring target information of the target vehicle, wherein the target information includes first information sent by the target vehicle for characterizing a start / stop state change of the target vehicle, or second information characterizing vehicle state data of the target vehicle; A determination unit, configured to determine a usage requirement of a target cloud vehicle machine based on the target information, wherein the target cloud vehicle machine matches an identity of the target vehicle; A control unit is used to control the start or stop of the target cloud vehicle machine based on the usage demand.

9. A cloud vehicle control device, characterized in that: Applied to a target vehicle, the target vehicle is connected to a service end and a cloud vehicle management platform respectively, and the device comprises: A sending unit, used to send target information to the server, wherein the target information includes first information representing a start / stop state change of the target vehicle, or second information representing vehicle state data of the target vehicle; A task execution unit is used to execute a target task through the target cloud vehicle machine when detecting that the target cloud vehicle machine in the cloud vehicle machine management platform is switched to a startup state, wherein the target cloud vehicle machine matches the identity of the target vehicle.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program codes, which can be called by a processor to execute the method according to any one of claims 1 to 7.

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