CN115643294A - Vehicle control method, apparatus, system, and storage medium - Google Patents

Abstract

The application provides a vehicle control method, device, system and storage medium, and relates to the technical field of vehicles. The vehicle control method is applied to an intelligent computing engine deployed in a cloud, and comprises the following steps: acquiring relevant data of a vehicle end, wherein the relevant data comprises vehicle end data and user behavior data; performing data analysis on the user behavior data based on the vehicle end data to obtain a scene strategy matched with the user behavior data, wherein the scene strategy comprises execution operation in a corresponding scene; and responding to the normal communication connection with the vehicle end, performing dynamic resource adjustment and/or information push on the vehicle end according to the scene strategy, intelligently identifying the user behavior according to the user behavior data, and making the scene strategy matched with the user behavior so as to meet the application requirement of the user.

Description

Vehicle control method, device, system and storage medium

technical field

The present application relates to the technical field of vehicles, and in particular to a vehicle control method, device, system and storage medium.

Background technique

With the development of science and technology, cloud computing has become more and more widely used due to its advantages of high flexibility, scalability and high performance ratio. For example, cloud computing is applied to the vehicle-end system to make full use of cloud resources to realize massive calculation of vehicle-end data. However, when cloud computing is applied to the vehicle-end system, it cannot meet the application requirements of users.

Contents of the invention

Embodiments of the present application provide a vehicle control method, device, system, and storage medium to solve the problem that application requirements of users cannot be met when cloud computing is applied to a vehicle end system.

In the first aspect, the embodiment of the present application provides a vehicle control method, which is applied to an intelligent computing engine deployed in the cloud, including: obtaining relevant data on the vehicle end, the relevant data including vehicle end data and user behavior data; based on the vehicle end data, Perform data analysis on user behavior data to obtain a scenario strategy adapted to the user behavior data. The scenario strategy includes the execution operations in the corresponding scenario; in response to the normal communication connection with the vehicle end, adjust the dynamic resources of the vehicle end according to the scenario strategy and/or information push.

In a possible implementation mode, a cloud virtual machine and an application container engine are also deployed in the cloud, and dynamic resource adjustment and/or information push is performed on the car end according to the scenario strategy, including: according to the scenario strategy, the corresponding application on the car end The container engine performs dynamic resource adjustment; and/or, according to the scenario strategy, pushes information to the virtual machine corresponding to the vehicle end.

In a possible implementation, the vehicle control method further includes: sending the scene policy to the vehicle end, so that the vehicle end switches the policy according to the scene policy through the vehicle end engine when the communication connection between the vehicle end and the cloud is abnormal.

In a possible implementation manner, the vehicle control method further includes: in response to an abnormal communication connection between the cloud and the vehicle end, stop executing the step of dynamically adjusting resources on the vehicle end according to the scenario strategy.

In the second aspect, the embodiment of the present application provides a vehicle control method, which is applied to the vehicle end, including: in response to detecting an abnormal communication connection with the cloud, obtaining the latest received scenario strategy, the scenario strategy is an intelligent computing in the cloud The engine analyzes the user behavior data of the car based on the data of the car, and obtains a scenario strategy adapted to the user behavior data. The scenario strategy includes the execution operations in the corresponding scenario; the strategy is switched according to the scenario strategy through the car engine .

In a possible implementation manner, the vehicle control method further includes: when the communication connection with the cloud is normal, receiving and storing the scenario strategy sent by the cloud through the intelligent computing engine.

In a third aspect, the embodiment of the present application provides a vehicle control device, which is applied to an intelligent computing engine deployed in the cloud, including:

The obtaining module is used to obtain the relevant data of the vehicle end, the relevant data includes the vehicle terminal data and user behavior data;

The data analysis module is used to perform data analysis on the user behavior data based on the vehicle end data, and obtain a scenario strategy adapted to the user behavior data, and the scenario strategy includes execution operations in the corresponding scenario;

The processing module is configured to perform dynamic resource adjustment and/or information push to the vehicle end according to the scenario policy in response to the normal communication connection with the vehicle end.

In a possible implementation, a cloud virtual machine and an application container engine are also deployed in the cloud, and the processing module is specifically used to: adjust the dynamic resources of the application container engine corresponding to the vehicle end according to the scenario strategy; and/or, according to the scenario strategy to push information to the virtual machine corresponding to the car.

In a possible implementation manner, the vehicle control device further includes a sending module, which is used to: send the scenario strategy to the vehicle end, so that when the communication connection between the vehicle end and the cloud is abnormal Policy switch.

In a possible implementation manner, the vehicle control method further includes an execution module configured to: respond to an abnormal communication connection between the cloud and the vehicle end, stop executing the step of dynamically adjusting resources on the vehicle end according to the scenario policy.

In the fourth aspect, the embodiment of the present application provides a vehicle control device, which is applied to the vehicle end, including:

The acquisition module is used to obtain the latest received scenario strategy in response to the detection of an abnormal communication connection with the cloud. The scenario strategy is that the intelligent computing engine in the cloud performs data analysis on the user behavior data of the vehicle terminal based on the vehicle terminal data. , to obtain a scenario strategy adapted to the user behavior data, the scenario strategy includes execution operations in the corresponding scenario;

The processing module is used to switch the policy according to the scene policy through the vehicle engine.

In a possible implementation manner, the vehicle control device further includes a receiving module, and the receiving module is configured to receive and store the scenario policy sent by the cloud through the intelligent computing engine when the communication connection with the cloud is normal.

In the fifth aspect, the present application provides an intelligent computing engine system, including: an intelligent computing engine deployed in the cloud, for executing the vehicle control method provided in the first aspect; The engine at the vehicle end is used to execute the vehicle control method provided in the second aspect.

In a possible implementation manner, a cloud virtual machine and an application container engine are also deployed on the cloud, wherein the cloud virtual machine is installed with a vehicle-end application.

In a possible implementation, the cloud virtual machine includes a data collection unit, which is used to collect relevant data on the vehicle end and send the relevant data to the intelligent computing engine. The relevant data includes vehicle end data and user behavior data.

In a sixth aspect, the present application provides an electronic device, including: at least one processor; and a memory connected to the at least one processor; wherein the memory is used to store instructions executable by at least one processor, and the instructions are processed by at least one The controller executes, so that at least one processor can execute the vehicle control method provided in the first aspect, and/or, enable at least one processor to execute the vehicle control method provided in the second aspect.

In a seventh aspect, the present application provides a computer-readable storage medium, the computer-readable storage medium stores computer-executable instructions, and when executed, the computer-readable instructions are used to implement the vehicle control method provided in the first aspect, and/or, When executed, the computer-executed instructions are used to implement the vehicle control method provided in the second aspect.

In a fifth aspect, the present application provides a program product, where the program product includes computer-executable instructions. When the computer-executed instructions are executed, the vehicle control method provided by the first aspect is realized, and/or the vehicle control method provided by the second aspect is realized.

This application provides a vehicle control method, device, system, and storage medium. The vehicle control method is applied to an intelligent computing engine deployed in the cloud, by obtaining vehicle-side data and user behavior data at the vehicle-side; and based on the vehicle-side data, the user Behavior data is analyzed to obtain a scenario strategy adapted to the user behavior data, wherein the scenario strategy includes execution operations in the corresponding scenario; when the communication connection with the vehicle terminal is normal, the vehicle terminal is dynamically implemented according to the scenario strategy. Resource adjustment and/or information push, by intelligently identifying user behavior based on user behavior data, and formulating scenario strategies that match user behavior to meet user application needs.

Description of drawings

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

Fig. 1 is an architecture diagram of the intelligent computing engine system provided by the embodiment of the present application;

FIG. 2 is a flowchart of a vehicle control method provided by an embodiment of the present application;

FIG. 3 is a flowchart of a vehicle control method provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a vehicle control device provided by an implementation of the present application;

FIG. 5 is a schematic structural diagram of a vehicle control device provided by an implementation of the present application;

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

By means of the above drawings, specific embodiments of the present application have been shown, which will be described in more detail hereinafter. These drawings and text descriptions are not intended to limit the scope of the concept of the application in any way, but to illustrate the concept of the application for those skilled in the art by referring to specific embodiments.

Detailed ways

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with aspects of the present application as recited in the appended claims.

Based on the above-mentioned problem that when cloud computing is applied to the vehicle end system, it cannot meet the user's application needs, the embodiment of the present application uses the intelligent computing engine deployed in the cloud together with the vehicle end engine to intelligently identify the corresponding vehicle end data and user behavior. Combined with data analysis methods such as big data and artificial intelligence, a scenario strategy adapted to user behavior data is obtained, and further according to the scenario strategy, dynamic resource adjustments and/or information pushes are made to the vehicle end, and information is sent to the vehicle end Scenario strategies to meet user application requirements.

For ease of understanding, an application scenario of the embodiment of the present application is firstly introduced.

The application scenarios of the embodiments of the present application include vehicles and cloud servers. Among them, the vehicle and the cloud server are connected through communication. Specifically, the vehicle uploads the vehicle data and corresponding user behavior data to the cloud server through a communication connection. Behavioral data is analyzed and processed to obtain scenario strategies that are compatible with user behavior data, and based on the scenario strategies, dynamic resource adjustments and/or information pushes and strategy delivery are performed on vehicles to meet user application needs.

FIG. 1 is a structural diagram of an intelligent computing engine system provided by an embodiment of the present application. As shown in FIG. 1 , the intelligent computing engine system 10 includes a cloud 11 and a vehicle end 12 . Optionally, the intelligent computing engine is deployed in the cloud 11 , and the vehicle-end engine is deployed in the vehicle-end 12 . Specifically, the intelligent computing engine and the vehicle-end engine are used to execute specific steps in the vehicle control method provided in the embodiment of the present application.

Optionally, the cloud 11 is also deployed with a cloud virtual machine and an application container engine. Wherein, the vehicle end application is installed in the cloud virtual machine. Exemplarily, the in-vehicle applications may be application 1, application 2, and cooperative applications as shown in FIG. 1 . Specifically, application 1 and application 2 may be audio and video software, map navigation software, and gallery software, etc., and the cooperative application is an application obtained after point-burying processing is performed on the basis of application 1 and/or application 2 and other software. Exemplarily, the application engine container may be a Docker container.

Optionally, the cloud virtual machine may also include a data collection unit, specifically, the data collection unit is used to collect relevant data at the vehicle end and send the relevant data to the intelligent computing engine. Among them, the relevant data may include car end data and user behavior data.

Optionally, the framework (Framework) in the cloud virtual machine is similar to the Framework in the vehicle operating system (Operating System, OS for short), and is used to respond to information pushed by the intelligent computing engine.

The vehicle control method provided by the embodiment of the present application will be described in detail below with the intelligent computing engine in the intelligent computing engine system as the execution subject through specific embodiments.

Fig. 2 is a flowchart of a vehicle control method provided by an embodiment of the present application. As shown in Figure 2, the vehicle control method includes the following steps:

S201. Obtain relevant data on the vehicle end, where the relevant data includes vehicle end data and user behavior data.

Optionally, the data acquisition unit in the intelligent computing engine system can obtain relevant data from the vehicle end based on the communication connection with the vehicle end, and send the relevant data to the intelligent computing engine for corresponding data analysis. It is understandable that when the data acquisition unit acquires relevant data from the vehicle, the communication connection between the cloud and the vehicle is in a normal connection state. When the communication connection between the cloud and the vehicle is abnormal, the data acquisition unit cannot obtain relevant data from the vehicle.

Exemplarily, the vehicle-side data may include but not limited to resource data of the vehicle-machine system, application installation data of the vehicle-side, operating data of the vehicle processor, and the like.

Exemplarily, the user behavior data may include, but not limited to, the data of the user using the application in the cloud, the user voice input data, and the like. Specifically, the data of the user using the application in the cloud may be the user's stay time in the application interface, and the like.

It should be noted that user behavior data may include common user behavior data and in-depth user behavior data, wherein the common user behavior data refers to the data obtained from the application 1 or application 2 shown in Figure 1 above, and the in-depth user behavior data refers to the data obtained from the cooperative application shown in Figure 1 above. Exemplarily, when application 1 or application 2 is an audio and video application, the corresponding common user behavior data may be the length of time the user stays on the application interface; when the cooperative application is an audio and video application, the corresponding in-depth user behavior data may be The name of the audio the user is listening to, and so on. The embodiment of the present application does not limit the number and types of applications and cooperative applications.

Optionally, the corresponding in-depth user behavior data of the cooperative application can be uploaded to the intelligent computing engine through the network.

S202. Based on the vehicle-side data, perform data analysis on the user behavior data to obtain a scenario strategy adapted to the user behavior data, where the scenario strategy includes execution operations in corresponding scenarios.

Optionally, when performing data analysis on user behavior data, artificial intelligence analysis methods such as big data analysis and machine learning as shown in FIG. 1 may be used.

In a possible implementation manner, the user behavior data may be classified into scenarios first, and then the user behavior data in different scenarios may be further analyzed to obtain a scenario strategy adapted to the user behavior data. Exemplarily, the scene policy may be an audio play policy, a video play policy, a map navigation policy, a policy for adjusting the brightness of the vehicle center control screen, and the like.

Exemplarily, the execution operation in the corresponding scenario may be pushing some applications, advertisements and specific messages. In some embodiments, the execution operation can be used to send the corresponding scene to the framework (Framework) shown in the cloud in FIG. 1 .

S203, in response to the normal communication connection with the vehicle end, perform dynamic resource adjustment and/or information push to the vehicle end according to the scenario strategy.

In some embodiments, according to the scenario policy, the dynamic resource adjustment of the vehicle end can be performed through the application container engine shown in FIG. 1 . Exemplarily, when the scenario strategy is an audio playback strategy, the container engine may be used to perform dynamic resource adjustments on the vehicle side, so as to achieve the best audio playback effect. The dynamic resource adjustment may also be the dynamic adjustment of frame rate and code rate as shown in FIG. 1 .

In other embodiments, information can be pushed to the vehicle terminal according to the scenario strategy. Exemplarily, the pushed information may be applications, advertisements and so on. Optionally, information push between the vehicle end and the cloud can be realized through the connection service shown in FIG. 1 .

It should be noted that for the applications pushed from the cloud to the car end, the car end can be used normally without installation.

In the embodiment of the present application, by obtaining the related data of the vehicle end, the relevant data includes the data of the vehicle end and the user behavior data; based on the data of the vehicle end, the user behavior data is analyzed to obtain the scenario strategy adapted to the user behavior data. The scenario strategy includes execution operations in the corresponding scenario; in response to the normal communication connection with the vehicle end, according to the scenario strategy, dynamic resource adjustment and/or information push is performed on the vehicle end to meet the user's application needs.

In some embodiments, performing dynamic resource adjustment and/or information push on the car end according to the scenario strategy may specifically include the following steps: performing dynamic resource adjustment on the corresponding application container engine of the car end according to the scenario strategy; and/or, According to the scenario strategy, information is pushed to the virtual machine corresponding to the car. Among them, the application container engine and virtual machine corresponding to the car end are not shown in Figure 1, and their specific functions are similar to those of the corresponding application container engine and virtual machine on the cloud, so they will not be repeated here.

In some other embodiments, the above vehicle control method may further include the following steps: sending the scene policy to the car end, so that the car end switches the policy according to the scene policy through the car end engine when the communication connection between the car end and the cloud is abnormal. Exemplarily, when the communication connection between the car end and the cloud is abnormal, in order not to affect the user's application requirements, the car end switches the policy through the car end engine according to the scenario policy sent by the cloud. Exemplarily, the scene policy sent to the vehicle end may be a specific policy, or a friendly prompt as shown in FIG. 1 . The strategies that can be used for switching at the car end can be map navigation, audio and video, and adaptable applications as shown in Figure 1. It can be understood that only when the communication connection between the vehicle terminal and the cloud is normal, the cloud can send the scenario strategy to the vehicle terminal, and the sending of the scenario strategy is real-time. In response to an abnormal communication connection between the car end and the cloud, the car end engine performs policy switching according to the latest received scene policy.

Optionally, the above vehicle control method may further include the following step: in response to an abnormal communication connection between the cloud and the vehicle end, stop executing the step of dynamically adjusting resources on the vehicle end according to the scenario strategy.

In the embodiment of the present application, by using the cloud intelligent computing engine in conjunction with the car-side engine, it can meet the needs of users when the communication connection between the cloud and the car-side is normal or abnormal. In addition, it solves the problem of applying cloud computing to the car. Stability issues faced by end systems.

In the following, the vehicle control method provided by the embodiment of the present application will be described in detail through specific embodiments, taking the vehicle-end engine in the intelligent computing engine system as the execution subject.

Fig. 3 is a flowchart of a vehicle control method provided by an embodiment of the present application. As shown in Figure 3, the vehicle control method includes the following steps:

S301, in response to detecting an abnormal communication connection with the cloud, obtain the latest received scenario strategy, the scenario strategy is that the intelligent computing engine in the cloud analyzes the user behavior data of the vehicle based on the vehicle-side data, and obtains the corresponding Scenario strategy for user behavior data adaptation, the scenario strategy includes execution operations in the corresponding scenario.

The specific implementation method in this step is similar to the method described above, and will not be repeated here.

S302, switch the policy according to the scene policy through the vehicle engine.

The specific implementation method in this step is similar to the method described above, and will not be repeated here.

In the embodiment of the present application, by responding to the detection of an abnormal communication connection with the cloud, the latest received scenario policy is obtained. Based on data analysis, a scenario strategy adapted to user behavior data is obtained. The scenario strategy includes execution operations in the corresponding scenario, and the vehicle engine performs policy switching according to the scenario strategy to ensure that when the communication connection between the vehicle and the cloud is abnormal, it can also To meet the needs of users, in addition, it solves the stability problem faced when applying cloud computing to the vehicle-end system.

In some embodiments, the vehicle control method with the vehicle engine as the execution subject may further include the following steps: when the communication connection with the cloud is normal, receiving and storing the scenario strategy sent by the cloud through the intelligent computing engine.

The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

Fig. 4 is a schematic structural diagram of a vehicle control device provided by an embodiment of the present application. As shown in Figure 4, the vehicle control device 40 includes: an acquisition module 410, a data analysis module 420 and a processing module 430, wherein:

The obtaining module 410 is used to obtain relevant data of the vehicle end, and the relevant data includes the vehicle terminal data and user behavior data; the data analysis module 420 is used to perform data analysis on the user behavior data based on the vehicle terminal data, and obtain data suitable for the user behavior data. The scene policy is configured, and the scene policy includes the execution operations in the corresponding scene; the processing module 430 is configured to perform dynamic resource adjustment and/or information push on the car end according to the scene policy in response to the normal communication connection with the car end.

In a possible implementation manner, a cloud virtual machine and an application container engine are also deployed in the cloud, and the processing module 430 is specifically used to: perform dynamic resource adjustment on the application container engine corresponding to the vehicle end according to the scenario policy; and/or, according to Scenario strategy, which pushes information to the virtual machine corresponding to the car.

In a possible implementation manner, the vehicle control device further includes a sending module (not shown), the sending module is used to: send the scene policy to the vehicle end, so that the vehicle end passes the vehicle end when the communication connection with the cloud is abnormal. The engine performs policy switching according to the scene policy.

In a possible implementation, the vehicle control method further includes an execution module (not shown), the execution module is used for: in response to an abnormal communication connection between the cloud and the vehicle end, stop executing dynamic resources for the vehicle end according to the scenario policy Adjustment steps.

The device provided in the embodiment of the present application can be used to execute the method in the embodiment shown in FIG. 2 , and its implementation principle and technical effect are similar, and will not be repeated here.

Fig. 5 is a schematic structural diagram of a vehicle control device provided by an embodiment of the present application. As shown in Figure 5, the vehicle control device 50 includes: an acquisition module 510 and a processing module 520, wherein:

The obtaining module 510 is used to obtain the latest received scenario policy in response to detecting an abnormal communication connection with the cloud. After analysis, a scenario strategy adapted to the user behavior data is obtained, and the scenario strategy includes the execution operations in the corresponding scenario; the processing module 520 is used to switch the strategy through the vehicle engine according to the scenario strategy.

In a possible implementation manner, the vehicle control device further includes a receiving module (not shown), the receiving module is configured to receive and store the scenario strategy sent by the cloud through the intelligent computing engine when the communication connection with the cloud is normal.

The device provided in the embodiment of the present application can be used to execute the method in the embodiment shown in FIG. 3 , and its implementation principle and technical effect are similar, and details are not repeated here.

It should be noted that it should be understood that the division of each module of the above device is only a division of logical functions, and may be fully or partially integrated into one physical entity or physically separated during actual implementation. And these modules can all be implemented in the form of calling software through processing elements; they can also be implemented in the form of hardware; some modules can also be implemented in the form of calling software through processing elements, and some modules can be implemented in the form of hardware. For example, the processing module can be a separate processing element, or it can be integrated in a chip of the above-mentioned device. In addition, it can also be stored in the memory of the above-mentioned device in the form of program code, and a certain processing element of the above-mentioned device can Call and execute the functions of the above processing modules. The implementation of other modules is similar. In addition, all or part of these modules can be integrated together, and can also be implemented independently. The processing element here may be an integrated circuit, which has a signal processing capability. In the implementation process, each step of the above method or each module above can be completed by an integrated logic circuit of hardware in the processor element or an instruction in the form of software.

For example, the above modules may be one or more integrated circuits configured to implement the above method, for example: one or more specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), or, one or more microprocessors ( Digital Signal Processor (DSP for short), or one or more Field Programmable Gate Arrays (Field Programmable Gate Array, FPGA for short), etc. For another example, when one of the above modules is implemented in the form of a processing element scheduling program code, the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, CPU for short) or other processors that can call program codes. For another example, these modules can be integrated together and implemented in the form of a System-On-a-Chip (SOC for short).

In the above embodiments, all or part of them may be implemented by software, hardware, firmware or any combination thereof. When implemented using software, it may be implemented in whole or in part in the form of a computer program product. A computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on the computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. A computer can be a general purpose computer, special purpose computer, computer network, or other programmable device. Computer instructions may be stored in or transmitted from one computer-readable storage medium to another computer-readable storage medium, e.g. Coaxial cable, optical fiber, Digital Subscriber Line (Digital Subscriber Line, DSL for short)) or wireless (such as infrared, wireless, microwave, etc.) to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that can be accessed by a computer, or a data storage device such as a server, a data center, etc. integrated with one or more available media. Available media can be magnetic media (eg, floppy disk, hard disk, magnetic tape), optical media (eg, Digital Video Disc, (Digital Video Disc, DVD for short)), or semiconductor media (eg, solid state disk (SSD)) wait.

FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application. As shown in FIG. 6 , the electronic device 60 includes: at least one processor 610 , a memory 620 , a communication interface 630 and a system bus 640 . Among them, the memory 620 and the communication interface 630 are connected to the processor 610 through the system bus 640 and complete mutual communication, the memory 620 is used to store instructions, the communication interface 630 is used to communicate with other devices, and the processor 610 is used to call the instructions to execute the solution of the above-mentioned embodiment of the vehicle control method, the specific implementation and technical effect are similar, and will not be repeated here.

The system bus 640 mentioned in FIG. 6 may be a Peripheral Component Interconnect (PCI for short) bus or an Extended Industry Standard Architecture (EISA for short) bus or the like. The system bus 640 can be divided into address bus, data bus, control bus and so on. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

The communication interface 630 is used to realize the communication between the database access device and other devices (such as client, read-write library and read-only library).

The memory 620 may include a random access memory (Random Access Memory, RAM for short), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

The processor 610 may be a general-purpose processor, including a central processing unit, a network processor (Network Processor, NP for short), etc.; a digital signal processor (Digital Signal Processor, DSP for short), an application specific integrated circuit (Application Specific Integrated Circuit, ASIC for short) , Field Programmable Gate Array (Field Programmable Gate Array, FPGA for short) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

The embodiment of the present application also provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, they are used to implement the method steps in the above-mentioned method embodiments, and specific implementation methods It is similar to the technical effect and will not be repeated here.

The embodiment of the present application also provides a program product, where the program product includes computer execution instructions. When the computer-executed instructions are executed to implement the method steps in the above method embodiments, the specific implementation manner and technical effect are similar, and will not be repeated here.

Other embodiments of the invention will be readily apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the application, these modifications, uses or adaptations follow the general principles of the application and include common knowledge or conventional technical means in the technical field not disclosed in the application . The specification and examples are to be considered exemplary only, with a true scope and spirit of the application indicated by the following claims.

It should be understood that the present application is not limited to the precise constructions which have been described above and shown in the accompanying drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (13)

1. A vehicle control method is applied to an intelligent computing engine deployed in a cloud, and comprises the following steps:

obtaining relevant data of a vehicle end, wherein the relevant data comprises vehicle end data and user behavior data;

performing data analysis on the user behavior data based on the vehicle end data to obtain a scene strategy matched with the user behavior data, wherein the scene strategy comprises execution operation in a corresponding scene;

and responding to the normal communication connection with the vehicle end, and performing dynamic resource adjustment and/or information pushing on the vehicle end according to the scene strategy.

2. The method of claim 1, wherein a cloud virtual machine and an application container engine are further deployed in the cloud, and performing dynamic resource adjustment and/or information pushing on the vehicle end according to the scenario policy comprises:

according to the scene strategy, performing dynamic resource adjustment on an application container engine corresponding to the vehicle end;

and/or carrying out information push on the virtual machine corresponding to the vehicle end according to the scene strategy.

3. The method of claim 1 or 2, further comprising:

and sending the scene strategy to the vehicle end so as to switch the strategy through a vehicle end engine according to the scene strategy when the communication connection between the vehicle end and the cloud end is abnormal.

4. The method of claim 3, further comprising:

and responding to the abnormal communication connection between the cloud end and the vehicle end, and stopping executing the step of performing dynamic resource adjustment on the vehicle end according to the scene strategy.

5. A vehicle control method, characterized by being applied to a vehicle end, the vehicle control method comprising:

in response to the detection of abnormal communication connection with a cloud end, acquiring a latest received scene strategy, wherein the scene strategy is a scene strategy which is obtained by performing data analysis on user behavior data of the vehicle end by an intelligent computing engine in the cloud end based on vehicle end data of the vehicle end and is adaptive to the user behavior data, and the scene strategy comprises execution operation in a corresponding scene;

and carrying out strategy switching through the vehicle-end engine according to the scene strategy.

6. The method of claim 5, further comprising:

and when the communication connection with the cloud is normal, receiving and storing the scene strategy sent by the cloud through the intelligent computing engine.

7. A vehicle control apparatus applied to a smart computing engine deployed in a cloud, the vehicle control apparatus comprising:

the acquisition module is used for acquiring relevant data of a vehicle end, wherein the relevant data comprises vehicle end data and user behavior data;

the data analysis module is used for carrying out data analysis on the user behavior data based on the vehicle end data to obtain a scene strategy matched with the user behavior data, and the scene strategy comprises execution operation in a corresponding scene;

and the processing module is used for responding to the normal communication connection with the vehicle end and performing dynamic resource adjustment and/or information pushing on the vehicle end according to the scene strategy.

8. A vehicle control apparatus, characterized by being applied to a vehicle end, comprising:

the acquisition module is used for responding to the detection of communication connection abnormity with a cloud end and acquiring a latest received scene strategy, wherein the scene strategy is that an intelligent calculation engine in the cloud end carries out data analysis on user behavior data of the vehicle end based on vehicle end data of the vehicle end to obtain a scene strategy matched with the user behavior data, and the scene strategy comprises execution operation in a corresponding scene;

and the processing module is used for carrying out strategy switching according to the scene strategy through a vehicle-end engine.

9. An intelligent computing engine system, comprising:

a cloud-deployed smart compute engine configured to perform the vehicle control method of any of claims 1-4;

a vehicle end having a vehicle end engine deployed therein for performing the vehicle control method of claim 5 or 6.

10. The smart computing engine system of claim 9, wherein the cloud is further deployed with a cloud virtual machine and an application container engine, wherein the cloud virtual machine is installed with a vehicle-end application.

11. The intelligent computing engine system according to claim 10, wherein the cloud virtual machine includes a data collection unit, the data collection unit is configured to collect relevant data of the vehicle end and send the relevant data to the intelligent computing engine, and the relevant data includes vehicle end data and user behavior data.

12. An electronic device, comprising:

at least one processor;

and a memory coupled to the at least one processor;

wherein the memory is configured to store instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 4 and/or to enable the at least one processor to perform the method of claim 5 or 6.

13. A computer-readable storage medium having stored therein computer-executable instructions for performing the method of any one of claims 1-4, and/or for performing the method of claim 5 or 6 when executed.

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