CN118046912A - Recommendation method for vehicle scene service - Google Patents

Abstract

The disclosure provides a recommendation method of a vehicle scene service, comprising the following steps: selecting a target component matched with the target vehicle type from the component library according to the target vehicle type to be configured; determining candidate actions of the target vehicle type according to the functions indicated by the target components; responding to configuration operation of a first recommended scene, determining a first target action to be executed in the first recommended scene from candidate actions, and a first trigger condition to be met when the first target action is executed; generating first configuration information of a recommended scene according to a first target action and a first trigger condition, and pushing the first configuration information to a control terminal associated with a vehicle of a target vehicle type, wherein the first configuration information is used for controlling the vehicle to execute the first target action under the condition that the vehicle meets the first trigger condition, so that accurate recommendation of scene service under the recommended scene is realized, driving safety is improved, and user experience is improved by automatically executing the corresponding target action when specific trigger conditions are met.

Description

Recommendation method for vehicle scene service

Technical Field

The disclosure relates to the technical field of vehicles, in particular to a recommendation method of vehicle scene service.

Background

With the continuous development of technologies such as the Internet, the Internet of things and big data, and the continuous promotion of consumers on intelligent, personalized and efficient travel demands. The development of the technologies provides possibility for the generation of vehicle scene services, the vehicle scene services can improve the travel safety and comfort through intelligent and personalized services, the requirements of consumers on efficient travel are met, the requirements of the consumers can be better met through recommending the vehicle scene services, meanwhile, the travel efficiency can be improved, the intelligent level and user experience of vehicles are improved, and therefore, the recommendation of the vehicle scene services is very important.

Disclosure of Invention

The present disclosure aims to solve, at least to some extent, one of the technical problems in the related art.

An embodiment of a first aspect of the present disclosure provides a recommendation method for a vehicle scene service, applied to a cloud server, including: selecting a target component matched with the target vehicle type from the component library according to the target vehicle type to be configured; determining candidate actions of the target vehicle type according to the functions indicated by the target components; responding to configuration operation of a first recommended scene, determining a first target action to be executed in the first recommended scene from the candidate actions, and executing a first trigger condition to be met by the first target action; generating first configuration information of the recommended scene according to the first target action and the first trigger condition, and pushing the first configuration information to a control terminal associated with a vehicle of the target vehicle type, wherein the first configuration information is used for controlling the vehicle to execute the first target action of the first recommended scene when the vehicle meets the first trigger condition of the first recommended scene.

An embodiment of a second aspect of the present disclosure proposes another recommendation method for a vehicle scene service, applied to a control terminal, including: receiving first configuration information of a recommended scene sent by a cloud server; the cloud server selects a target component adapted to a target vehicle type from a component library according to the target vehicle type to be configured, determines a candidate action of the target vehicle type according to the function indicated by the target component, determines a target action to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and generates and transmits a trigger condition required to be met by executing the target action according to the target action and the trigger condition; and controlling the vehicle to execute a first target action of the first recommended scene under the condition that the vehicle of the target vehicle type meets a first trigger condition of the first recommended scene.

An embodiment of a third aspect of the present disclosure provides a recommendation device for a vehicle scene service, which is applied to a cloud server, and includes: the selecting module is used for selecting a target component matched with the target vehicle type from the component library according to the target vehicle type to be configured; the first determining module is used for determining candidate actions of the target vehicle type according to the functions indicated by the target components; the second determining module is used for responding to the configuration operation of the first recommended scene, determining a first target action to be executed in the first recommended scene from the candidate actions, and a first trigger condition to be met when the first target action is executed; the pushing module is configured to generate first configuration information of the recommended scene according to the first target action and the first trigger condition, and push the first configuration information to a control terminal associated with a vehicle of the target vehicle type, where the first configuration information is used to control the vehicle to execute the first target action of the first recommended scene when the vehicle meets the first trigger condition of the first recommended scene.

An embodiment of a fourth aspect of the present disclosure provides another recommendation device for a vehicle scene service, applied to a control terminal, including: the receiving module is used for receiving first configuration information of the recommended scene sent by the cloud server; the cloud server selects a target component adapted to a target vehicle type from a component library according to the target vehicle type to be configured, determines a candidate action of the target vehicle type according to the function indicated by the target component, determines a target action to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and generates and transmits a trigger condition required to be met by executing the target action according to the target action and the trigger condition; and the control module is used for controlling the vehicle to execute a first target action of the first recommended scene under the condition that the vehicle of the target vehicle type meets the first trigger condition of the first recommended scene.

An embodiment of a fifth aspect of the present disclosure proposes an electronic device, including: a processor; a memory for storing the processor-executable instructions; wherein the processor is configured to execute the instructions to implement a recommendation method of a vehicle scene service according to an embodiment of the first aspect of the disclosure or to implement a recommendation method of a vehicle scene service according to an embodiment of the second aspect of the disclosure.

An embodiment of a sixth aspect of the present disclosure proposes a computer readable storage medium, which when executed by a processor of an electronic device, enables the electronic device to perform the recommendation method of the vehicle scene service according to the embodiment of the first aspect of the present disclosure, or to implement the recommendation method of the vehicle scene service according to the embodiment of the second aspect of the present disclosure.

Embodiments of a seventh aspect of the present disclosure provide a computer program product comprising: a computer program, which when executed by a processor, implements a method of recommending a vehicle scene service according to an embodiment of the first aspect of the present disclosure, or implements a method of recommending a vehicle scene service according to an embodiment of the second aspect of the present disclosure.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

1. The components of the matched target vehicle type are selected from the component library, so that the matching of the selected components with the hardware and software requirements of the target vehicle type can be ensured, candidate actions are determined according to the functions indicated by the target components, driving requirements in different scenes can be accurately met, first configuration information of a recommended scene is generated according to the first target actions and first trigger conditions and is pushed to the control terminal, intelligent pushing of the first configuration information can be realized, the accuracy of information pushing is improved, accurate recommendation of scene service in the recommended scene is realized, and driving safety is improved and user experience is improved by automatically executing the corresponding target actions when specific trigger conditions are met;

2. The method comprises the steps of receiving driving data of a vehicle sent by a control terminal, determining a second target action of a second recommended scene matched with the driving data through an algorithm platform based on a digital twin platform, pushing the second target action to a vehicle-mounted terminal, and controlling the vehicle to execute the second target action, so that when the driving data generated by the vehicle in the driving process does not meet the triggering condition of a user scene, the driving data is analyzed, the state of the vehicle and the user requirement can be known more accurately, corresponding actions are recommended more accurately, the accuracy of recommended scene service is improved, and the user experience is improved;

3. The control terminal responds to the copying operation, copies the recommended scene as the user scene into the personalized scene library, and responds to the configuration operation of the user scene in the personalized scene library, and modifies the first configuration information to obtain the second configuration information of the user scene, so that the user can better manage and realize personalized setting of the recommended scene, and the convenience and the comfort of driving are improved.

Additional aspects and advantages of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.

Drawings

The foregoing and/or additional aspects and advantages of the present disclosure will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:

Fig. 1 is a flowchart of a recommendation method for a vehicle scene service according to an embodiment of the disclosure;

Fig. 2 is a flowchart illustrating a recommendation method for a vehicle scene service according to an embodiment of the disclosure;

FIG. 3 is a flowchart illustrating another method for recommending vehicle scene services according to an embodiment of the present disclosure;

FIG. 4 is a schematic illustration of a scene layout provided by an embodiment of the present disclosure;

FIG. 5 is a flowchart illustrating another method for recommending vehicle scene services according to an embodiment of the present disclosure;

fig. 6 is a schematic diagram of a recommendation method for a vehicle scene service according to an embodiment of the disclosure;

fig. 7 is a schematic structural diagram of driving data according to an embodiment of the present disclosure;

fig. 8 is a flowchart illustrating a recommendation method for a vehicle scene service according to an embodiment of the disclosure;

Fig. 9 is a schematic structural diagram of a recommendation device for vehicle scene service according to an embodiment of the disclosure;

fig. 10 is a schematic structural diagram of a recommendation device for vehicle scene service according to an embodiment of the present disclosure;

Fig. 11 is a schematic structural view of an electronic device according to an exemplary embodiment of the present disclosure.

Detailed Description

Embodiments of the present disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present disclosure and are not to be construed as limiting the present disclosure.

It should be noted that, in the technical solution of the present disclosure, the related processes of collecting, storing, using, processing, transmitting, providing, disclosing, etc. of the personal information of the user are all performed on the premise of proving the consent of the user, and all conform to the rules of the related laws and regulations, and do not violate the popular regulations of the public order.

In the related art, a user can subscribe a scene service mode to a cloud end at a terminal (a vehicle-mounted terminal or a mobile terminal) or self-arrange the scene mode, so that under the condition of meeting a certain specific scene, multiple services can be executed in a combined mode through the vehicle end capability, and the situation that the user manually and frequently operates multiple vehicle control functions or application capabilities to influence driving safety is avoided.

However, in the above technology, the trigger condition and execution action of the scene need to be defined manually, which requires the user to understand the capability and arrangement mode of the vehicle model deeply, and the use cost is high.

In view of the above, the present disclosure proposes a recommendation method for a vehicle scene service.

A recommendation method of a vehicle scene service according to an embodiment of the present disclosure is described below with reference to the accompanying drawings.

Fig. 1 is a flowchart illustrating a recommendation method for a vehicle scene service according to an embodiment of the disclosure. The embodiment of the disclosure is applicable to a cloud server.

As shown in fig. 1, the recommendation method of the vehicle scene service may include the steps of:

Step 101, selecting a target component adapted to the target vehicle type from the component library according to the target vehicle type to be configured.

It should be appreciated that each model has its own unique hardware and software requirements, so selecting the adapted components for the target model to be configured from a component library, which may include a plurality of components, which may be specific parts of the vehicle, such as windows, doors, fans on the vehicle, air conditioners on the vehicle, etc., may ensure that these components match the requirements of the target model.

Step 102, determining candidate actions of the target vehicle type according to the functions indicated by the target components.

In order to accurately meet driving requirements in different scenarios, as a possible implementation manner, candidate actions are determined according to functions indicated by the target component, where it is to be noted that each function indicated by the target component may correspond to at least one candidate action.

For example, taking the target component as an air conditioner on a vehicle as an example, the functions indicated by the target component are: the temperature in the vehicle is reduced, and the candidate actions may include: closing a skylight, opening an air conditioner external circulation, opening a fan, adjusting an air outlet of the air conditioner and the like.

Step 103, in response to the configuration operation of the first recommended scene, determining a first target action to be executed in the first recommended scene from the candidate actions, and a first trigger condition to be satisfied when the first target action is executed.

As an example, in the visual interface of the cloud server, the user may perform a configuration operation of the first recommended scenario, for example, determine, from the candidate actions, a first target action to be performed in the first recommended scenario, and a first trigger condition to be met for performing the first target action. For example, the first trigger condition is that the battery power percentage is smaller than a set threshold, the first target action is a charging prompt, a charging service area is marked on a map line, a recommended vehicle speed or a recommended air conditioner temperature is displayed on a central control, and the like.

Step 104, generating first configuration information of the recommended scene according to the first target action and the first trigger condition, and pushing the first configuration information to a control terminal associated with a vehicle of the target vehicle type.

The first configuration information is used for controlling the vehicle to execute a first target action of the first recommended scene under the condition that the vehicle meets a first trigger condition of the first recommended scene.

As an example, first configuration information of the first recommended scene is generated according to the first target action and the first trigger condition, wherein the first configuration information may include, but is not limited to, the first target action and the first trigger condition, and the first configuration information is sent to a control terminal associated with a vehicle of a target vehicle type, so that the control terminal can determine whether the vehicle meets the first trigger condition of the first recommended scene in the running process of the vehicle after receiving the first configuration information, and can control the vehicle to execute the first target action of the first recommended scene if the vehicle meets the first trigger condition.

It should be noted that, in order to improve accuracy of the first configuration information of the first recommended scenario, before the first configuration information of the first recommended scenario is generated according to the first target action and the first trigger condition, the matching property of the first target action and the first trigger condition may be tested to determine whether the first target action and the first trigger condition match, and after the first target action and the first trigger condition pass the test, the first configuration information of the first recommended scenario may be generated according to the first target action and the first trigger condition.

In addition, in order to achieve accurate recommendation of services in a vehicle scene, before pushing first configuration information to a control terminal associated with a vehicle of a target vehicle type, as an example, the first configuration information is tested to obtain a test result of the first configuration information, and the first configuration information is pushed to the control terminal associated with the target vehicle type in response to the test result indicating that the first configuration information passes the test.

In summary, selecting a target component adapted to the target vehicle type from the component library according to the target vehicle type to be configured; determining candidate actions of the target vehicle type according to the functions indicated by the target components; responding to configuration operation of a first recommended scene, determining a first target action to be executed in the first recommended scene from candidate actions, and a first trigger condition to be met when the first target action is executed; generating first configuration information of the recommended scene according to the first target action and the first trigger condition, and pushing the first configuration information to a control terminal associated with a vehicle of a target vehicle type, wherein the first configuration information is used for controlling the vehicle to execute the first target action of the first recommended scene under the condition that the vehicle meets the first trigger condition of the first recommended scene, therefore, by selecting an adaptive component of the target vehicle type from a component library, matching of the selected component and hardware and software requirements of the target vehicle type can be ensured, candidate actions are determined according to functions indicated by the target component, driving requirements under different scenes can be accurately met, first configuration information of the recommended scene is generated according to the first target action and the first trigger condition, intelligent pushing of the first configuration information can be realized, accuracy of information pushing is improved, accurate recommendation of scene service under the recommended scene is realized, driving safety is improved by automatically executing the corresponding target action when the specific trigger condition is met, and user experience is improved.

To clearly illustrate how the above embodiments determine candidate actions of a target vehicle model according to functions indicated by target components, the present disclosure proposes another recommendation method for a vehicle scene service.

Fig. 2 is a flowchart illustrating a recommendation method for a vehicle scene service according to an embodiment of the disclosure. The embodiment of the disclosure is applicable to a cloud server.

As shown in fig. 2, the recommendation method of the vehicle scene service may include the steps of:

Step 201, selecting a target component adapted to the target vehicle type from the component library according to the target vehicle type to be configured.

Step 202, at least one function indicated by the target component is obtained.

As one possible implementation, each target component may indicate at least one function, for example, the target component is an air conditioner on a vehicle, and the at least one function indicated by the target component includes: controlling the temperature in the compartment, exhausting the moisture in the air in the vehicle, ventilating the vehicle, etc.

Step 203, determining at least one candidate action adapted to the target vehicle model under any function for any function of the at least one function.

It should be appreciated that each function indicated by one target component may correspond to at least one candidate action, and in order to accurately determine at least one candidate action adapted to the target vehicle model under any function and more accurately satisfy the user requirement, as an example, for any function of the at least one function, a history action performed by the vehicle of the target vehicle model under any function may be obtained; and taking the historical action as at least one candidate action matched with the target vehicle model under any function.

Step 204, determining the candidate action of the target vehicle type according to at least one candidate action matched with the target vehicle type under each function.

As an example, at least one candidate action adapted to the target vehicle model under each function indicated by one target component is taken as a candidate action of the target vehicle model.

In step 205, in response to the configuration operation of the first recommended scenario, a first target action to be executed in the first recommended scenario and a first trigger condition to be satisfied for executing the first target action are determined from the candidate actions.

And 206, generating first configuration information of the recommended scene according to the first target action and the first trigger condition, and pushing the first configuration information to a control terminal associated with the vehicle of the target vehicle type.

The first configuration information is used for controlling the vehicle to execute a first target action of the first recommended scene under the condition that the vehicle meets a first trigger condition of the first recommended scene.

To sum up, by obtaining at least one function indicated by the target component; at least one candidate action matched with the target vehicle type under any function is determined according to any function of the at least one function, and the candidate action of the target vehicle type is determined according to the at least one candidate action matched with the target vehicle type under each function, so that the candidate action is determined according to the function indicated by the target component, the candidate action is determined based on the function indicated by the target component, and driving requirements under different scenes can be accurately met.

On the basis of any of the above embodiments, as an example, in a case where the driving data of the vehicle does not satisfy the trigger condition of the user scene, the second target action of the second recommended scene matching the driving data may also be determined based on the driving data of the vehicle, which will be described in detail below with reference to the embodiment shown in fig. 3.

Fig. 3 is a flowchart illustrating another recommendation method for a vehicle scene service according to an embodiment of the disclosure. The embodiment of the disclosure is applicable to a cloud server.

As shown in fig. 3, the recommendation method of the vehicle scene service may include the steps of:

step 301, receiving driving data of a vehicle sent by a control terminal.

The driving data are obtained by synchronizing second configuration information of the user scene between the vehicle-mounted terminal and the mobile terminal, and the second configuration information is obtained by copying the recommended scene as the user scene into the personality scene library by the control terminal in response to the copying operation and modifying the configuration operation of the user scene in the personality scene library based on the first configuration information under the condition that the driving data of the vehicle do not meet the triggering condition of the user scene.

In order to achieve personalized customization of the scene service, as a possible implementation manner, as shown in fig. 4, the control terminal copies the recommended scene as the user scene into the personalized scene library in response to the copy operation, and obtains the second configuration information after modifying the configuration operation of the user scene in the personalized scene library based on the first configuration information.

In order to ensure the consistency of the user scene between the vehicle-mounted terminal and the mobile terminal so as to improve the safety of the vehicle, as an example, the second configuration information of the user scene is synchronized between the vehicle-mounted terminal and the mobile terminal, in order to more accurately recommend the vehicle scene service, the vehicle-mounted terminal can acquire the running data in the running process of the vehicle, and the vehicle-mounted terminal sends the running data to the cloud server under the condition that the running data of the vehicle does not meet the triggering condition of the user scene, so that the cloud server can receive the running data of the vehicle sent by the vehicle-mounted terminal.

Step 302, determining, by the algorithm platform, a second target action of a second recommended scenario matching the driving data based on the digital twin platform.

As an example, a digital twin platform is used to map travel data to obtain vehicle data in multiple target dimensions; and inputting the vehicle data in the multiple target dimensions into a scene service identification model of the algorithm platform to obtain a second target action of a second recommended scene.

That is, in order to realize accurate recommendation of services in a recommendation scenario, a digital twin platform may be used to perform twin mapping on driving data to obtain real vehicle data in multiple target dimensions, where the vehicle data in multiple target dimensions may include: driving data, vehicle state data, driving behavior data, environment data and the like, and further, inputting the vehicle data in a plurality of target dimensions into a scene service identification model of an algorithm platform, so that a second target action of a second recommended scene output by the scene service identification model can be obtained.

The scene service identification model is obtained by training the following steps: acquiring sample running data, and performing action labeling on the sample running data to obtain labeling actions corresponding to the sample running data; inputting the sample driving data into the scene service identification model to obtain a prediction action output by the scene service identification model; and training the scene service identification model according to the difference between the labeling action and the predicting action, wherein the sample driving data can be the historical driving data of the vehicle of the target vehicle type.

Step 303, pushing the second target action to the vehicle-mounted terminal so as to control the vehicle to execute the second target action.

Further, to implement the recommendation of the vehicle scenario service, as an example, a second target action implementing the vehicle scenario service is pushed to the vehicle-mounted terminal, which may control the vehicle to perform the second target action.

In summary, the running data of the vehicle sent by the control terminal is received; the method comprises the steps that driving data are obtained by synchronizing second configuration information of a user scene between a vehicle-mounted terminal and a mobile terminal, and the second configuration information is obtained by enabling a control terminal to copy a recommended scene as the user scene into a personalized scene library in response to a copying operation and to modify the configuration operation of the user scene in the personalized scene library based on the first configuration information when the driving data of a vehicle do not meet the triggering condition of the user scene; determining a second target action of a second recommended scene matched with the driving data based on the digital twin platform through the algorithm platform; the second target action is pushed to the vehicle-mounted terminal so as to control the vehicle to execute the second target action, so that when the running data generated in the running process of the vehicle does not meet the triggering condition of a user scene, the running data is analyzed, the state of the vehicle and the user requirement can be known more accurately, the corresponding action is recommended more accurately, accurate recommendation of scene service under the recommended scene is realized, and the user experience is improved.

In order to implement the above embodiment, the present disclosure also proposes another recommendation method for a vehicle scene service.

Fig. 5 is a flowchart illustrating another recommendation method for a vehicle scene service according to an embodiment of the disclosure. It should be noted that the embodiments of the present disclosure may be applied to a control terminal associated with a vehicle of a target vehicle type.

As shown in fig. 5, the recommendation method of the vehicle scene service may include the steps of:

Step 501, receiving first configuration information of a recommended scene sent by a cloud server.

The cloud server selects a target component adapted to the target vehicle type from the component library according to the target vehicle type to be configured, determines candidate actions of the target vehicle type according to the function indicated by the target component, determines target actions to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and trigger conditions to be met for executing the target actions, and generates and transmits the target actions and the trigger conditions.

Step 502, controlling the vehicle to execute a first target action of the first recommended scene when the vehicle of the target vehicle type meets a first trigger condition of the first recommended scene.

It should be noted that the execution of steps 501 and 502 may be implemented in any manner in each embodiment of the disclosure, which is not limited to this embodiment, and is not repeated herein.

In summary, first configuration information of a recommended scene sent by a cloud server is received; the cloud server selects a target component adapted to the target vehicle type from a component library according to the target vehicle type to be configured, determines candidate actions of the target vehicle type according to the function indicated by the target component, determines target actions to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and trigger conditions required to be met for executing the target actions, and generates and transmits the target actions and the trigger conditions; under the condition that the vehicle of the target vehicle type meets the first trigger condition of the first recommended scene, the vehicle is controlled to execute the first target action of the first recommended scene, therefore, the cloud server can ensure that the selected component is matched with the hardware and software requirements of the target vehicle type by selecting the component of the adaptive target vehicle type from the component library, the candidate actions are determined according to the functions indicated by the target component, the driving requirements under different scenes can be accurately met, the first configuration information of the recommended scene is generated according to the first target action and the first trigger condition and is pushed to the control terminal, the intelligent pushing of the first configuration information can be realized, the accuracy of information pushing is improved, the accurate recommendation of scene service under the recommended scene is realized, and the driving safety is improved and the user experience is improved by automatically executing the corresponding target action when the specific trigger condition is met.

As a possible implementation manner, the control terminal includes a vehicle-mounted terminal and a mobile terminal, and the recommendation method of the vehicle scene service further includes: copying the recommended scene as the user scene into a personalized scene library in response to the copying operation, and modifying the first configuration information to obtain second configuration information of the user scene in response to the configuration operation of the user scene in the personalized scene library; synchronizing second configuration information of the user scene between the vehicle-mounted terminal and the mobile terminal; when the traveling data of the vehicle satisfies the trigger condition of the user scene, the in-vehicle terminal controls the vehicle to execute an action corresponding to the trigger condition of the user scene.

As one possible implementation manner, the recommendation method of the vehicle scene service further includes: under the condition that the running data of the vehicle does not meet the triggering condition of the user scene, the running data are sent to a cloud server, wherein the running data are used for determining a second target action of a second recommended scene matched with the running data through an algorithm platform based on a digital twin platform by the cloud server, and pushing the second target action to a vehicle-mounted terminal; and the vehicle-mounted terminal receives the second target action and controls the vehicle to execute the second target action.

As a possible implementation manner, before receiving the first configuration information of the recommended scene sent by the cloud server, the recommendation method of the vehicle scene service further includes: the vehicle-mounted terminal responds to a first login operation and logs in according to an input first account number bound with the vehicle-mounted terminal; and the mobile terminal responds to a second login operation and logs in according to the input second account number bound with the mobile terminal.

On the basis of any of the above embodiments, the implementation principle of the recommendation method for vehicle scene service according to the embodiment of the present disclosure may be as shown in fig. 6, and mainly includes the following steps:

1. after a component is newly added in cloud platform scene engine center (cloud server) -component management, setting an atomic function which can be included in the component and adding an associated event corresponding to the atomic function;

2. Selecting a vehicle type (target vehicle type) to be issued in a cloud platform scene engine center-execution action & trigger condition, selecting a version number of a required component atomic function, and submitting a test;

3. in the cloud platform scene engine center-test, according to the vehicle model under the execution action & trigger condition selected in the step 2, selecting the trigger condition which is just submitted to the test and the component of the execution action, and combining whether the scene engine model can pass the test;

4. After the test is passed, a recommended scene is newly added in a cloud platform scene engine center-recommended scene, a vehicle type is selected, and a scene input scene library possibly encountered by a user during driving is arranged, wherein the scene input scene library comprises but is not limited to: searching a charging station, searching a parking lot, searching landmarks, avoiding special vehicles, driving fatigue, inputting basic information such as scene name description, selecting successful execution actions and triggering conditions tested in the step 3, and sorting required scene services of each scene according to scene definition, wherein the scene services comprise GUI (GRAPHICAL USER INTERFACE ) interaction, TTS (Text to Speech) broadcasting, vehicle control service and application service;

5. Selecting a vehicle type train to be released from cloud platform scene engine center-release task management, selecting a recommended scene to be released, and releasing the scene to a vehicle owner user account configuring a brand train;

6. The recommended scene can be issued to a vehicle-mounted terminal (a vehicle machine) and a mobile terminal (a mobile phone end) according to the selected vehicle type;

For example, the mobile phone terminal: a vehicle owner user of the vehicle with the vehicle type sees a recommendation scene issued immediately in a scene engine recommendation scene in a vehicle owner APP, selects the recommendation scene to be stored in a My scene, and can modify scene names, execution action triggering conditions, execution modes and other configuration information according to personal preference on the basis of recommendation scene parameter configuration to finally generate a special personal scene mode, wherein information data in the My scene is synchronized in a vehicle account;

Vehicle machine end: a vehicle machine user of a vehicle with the vehicle type receives a recommended scene sent from a cloud, copies the recommended scene into a My scene, modifies and stores the recommended scene based on scene information in the copied recommended scene, executing actions, triggering conditions and the like, and synchronizes information data in the My scene in an APP account of a vehicle owner;

step 7: in the running process of the vehicle-mounted terminal, according to the triggering conditions set in the my scene, when the conditions are met, the vehicle-mounted terminal automatically executes corresponding actions;

Step 8: when the driving data does not meet the trigger conditions set in my scene, as shown in fig. 7, the collected driving data (user driving data, vehicle state data, driving behavior data, environmental data) may be sent to the cloud server, where the driving data may include, but is not limited to: target POI, current vehicle speed, average vehicle speed, peak vehicle speed, vehicle mileage, remaining vehicle mileage, battery power percentage, driving mode, foot brake state, gear state, air conditioning state, tire pressure state, weather, etc.;

Step 9: as shown in fig. 8, the cloud server maps real vehicle data through a digital twin platform, and matches the driving dataset vehicle state data with scene service through an AI algorithm platform;

Step 10: releasing the matching result to the vehicle-mounted terminal application;

Step 11: in the running process of the vehicle, if each parameter meets the triggering condition of the scene recommendation service, corresponding operation is actively executed according to a preset mode, and the user does not need to trigger or edit by voice in the process. Is a more intelligent active scene service recommendation;

step 12: and returning the scene recommendation service accepted by the user to the cloud and AI algorithm platform, and continuously optimizing the algorithm model.

As one example, a long-distance driving charging scenario: and judging the remaining mileage which can be travelled by the current battery power according to the current travelling mode of the user, and calculating and marking the optimal charging service area position of the user on the established travelling line so as to relieve the anxiety of the mileage of the user and reduce the traffic flow pressure of charging the service area.

Scene service: when meeting long-distance driving charging scene, automatic marking out the service area that charges on the map route, usable fills electric pile quantity, well accuse and go up to show: speed is recommended, and air conditioning, seat ventilation and the like are set by one key.

In order to implement the embodiments of fig. 1 to 3 described above, the present disclosure proposes a recommendation device for a vehicle scene service.

Fig. 9 is a schematic structural diagram of a recommendation device for vehicle scene service according to an embodiment of the present disclosure, where it is to be noted that the recommendation device for vehicle scene service according to the embodiment of the present disclosure may be applied to a cloud server.

As shown in fig. 9, the recommendation 900 of the vehicle scene service includes: a selection module 910, a first determination module 920, a second determination module 930, and a push module 940.

The selecting module 910 is configured to select, according to a target vehicle type to be configured, a target component adapted to the target vehicle type from the component library; a first determining module 920, configured to determine a candidate action of the target vehicle type according to the function indicated by the target component; a second determining module 930, configured to determine, from the candidate actions, a first target action to be executed in the first recommended scenario and a first trigger condition to be satisfied when the first target action is executed, in response to a configuration operation of the first recommended scenario; the pushing module 940 is configured to generate first configuration information of the recommended scene according to the first target action and the first trigger condition, and push the first configuration information to a control terminal associated with a vehicle of the target vehicle type, where the first configuration information is used to control the vehicle to execute the first target action of the first recommended scene when the vehicle meets the first trigger condition of the first recommended scene.

As a possible implementation manner, the first determining module 920 is configured to obtain at least one function indicated by the target component; determining at least one candidate action adapted to the target vehicle model under any function aiming at any function of the at least one function; and determining the candidate action of the target vehicle type according to at least one candidate action matched with the target vehicle type under each function.

As a possible implementation manner, the first determining module 920 is configured to obtain, for any one of the at least one function, a historical action performed by a vehicle of the target vehicle type under any one function; and taking the historical action as at least one candidate action matched with the target vehicle model under any function.

As a possible implementation manner, the control terminal includes a vehicle-mounted terminal and a mobile terminal, and the recommendation 900 of the vehicle scene service further includes: a receiving module and a third determining module.

The receiving module is used for receiving the driving data of the vehicle sent by the control terminal; the method comprises the steps that driving data are obtained by synchronizing second configuration information of a user scene between a vehicle-mounted terminal and a mobile terminal, and the second configuration information is obtained by enabling a control terminal to copy a recommended scene as the user scene into a personalized scene library in response to copy operation and to modify the configuration operation of the user scene in the personalized scene library based on the first configuration information when the driving data of a vehicle do not meet triggering conditions of the user scene; the third determining module is used for determining a second target action of a second recommended scene matched with the driving data based on the digital twin platform through the algorithm platform; the pushing module 840 is further configured to push the second target action to the vehicle-mounted terminal, so as to control the vehicle to execute the second target action.

As a possible implementation manner, the third determining module is configured to map the driving data by using a digital twin platform to obtain vehicle data in multiple target dimensions; and inputting the vehicle data in the multiple target dimensions into a scene service identification model of the algorithm platform to obtain a second target action of a second recommended scene.

As one possible implementation, the scene service identification model is trained using the following modules: the device comprises an acquisition module, a processing module and a training module.

The system comprises an acquisition module, a labeling module and a control module, wherein the acquisition module is used for acquiring sample running data and labeling the sample running data to obtain labeling actions corresponding to the sample running data; the processing module is used for inputting the sample driving data into the scene service identification model to obtain a prediction action output by the scene service identification model; and the training module is used for training the scene service identification model according to the difference between the labeling action and the prediction action.

As a possible implementation manner, the pushing module 940 is configured to test the first configuration information to obtain a test result of the first configuration information; and responding to the test result to indicate that the first configuration information passes the test, and pushing the first configuration information to a control terminal associated with the target vehicle type.

According to the recommendation device for the vehicle scene service, according to the target vehicle type to be configured, a target component matched with the target vehicle type is selected from a component library; determining candidate actions of the target vehicle type according to the functions indicated by the target components; responding to configuration operation of a first recommended scene, determining a first target action to be executed in the first recommended scene from candidate actions, and a first trigger condition to be met when the first target action is executed; generating first configuration information of the recommended scene according to the first target action and the first trigger condition, and pushing the first configuration information to a control terminal associated with a vehicle of a target vehicle type, wherein the first configuration information is used for controlling the vehicle to execute the first target action of the first recommended scene under the condition that the vehicle meets the first trigger condition of the first recommended scene.

In order to implement the method for recommending the vehicle scene service according to the embodiment of fig. 5, the disclosure proposes another device for recommending the vehicle scene service, where the device for recommending the vehicle scene service is applicable to the control terminal.

Fig. 10 is a schematic structural diagram of a recommendation device for vehicle scene service according to an embodiment of the present disclosure, where it is to be noted that the recommendation device for vehicle scene service according to the embodiment of the present disclosure may be applied to a control terminal.

As shown in fig. 10, the recommendation device 1000 of the vehicle scene service may include: a receiving module 1010 and a control module 1020.

The receiving module 1010 is configured to receive first configuration information of a recommended scene sent by the cloud server; the cloud server selects a target component adapted to the target vehicle type from a component library according to the target vehicle type to be configured, determines candidate actions of the target vehicle type according to the function indicated by the target component, determines target actions to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and trigger conditions required to be met for executing the target actions, and generates and transmits the target actions and the trigger conditions; the control module 1020 is configured to control the vehicle to execute a first target action of the first recommended scenario when the vehicle of the target vehicle type meets a first trigger condition of the first recommended scenario.

As a possible implementation manner, the control terminal includes a vehicle-mounted terminal and a mobile terminal, and the recommendation device 1000 for a vehicle scene service further includes: the device comprises a copying module, a synchronizing module and an executing module.

The copying module is used for copying the recommended scene as the user scene into the personalized scene library in response to the copying operation, and modifying the first configuration information to obtain second configuration information of the user scene in response to the configuration operation of the user scene in the personalized scene library; the synchronization module is used for synchronizing the second configuration information of the user scene between the vehicle-mounted terminal and the mobile terminal; and the execution module is used for controlling the vehicle to execute the action corresponding to the triggering condition of the user scene by the vehicle-mounted terminal under the condition that the driving data of the vehicle meets the triggering condition of the user scene.

As one possible implementation, the recommendation device 1000 for a vehicle scene service further includes: a transmitting module and a control module.

The vehicle-mounted terminal comprises a transmission module, a cloud server and a digital twin platform, wherein the transmission module is used for transmitting running data of a vehicle to the cloud server under the condition that the running data do not meet the triggering condition of a user scene, and the running data are used for the cloud server to determine a second target action of a second recommended scene matched with the running data through the algorithm platform based on the digital twin platform and pushing the second target action to the vehicle-mounted terminal; and the control module is used for receiving the second target action by the vehicle-mounted terminal and controlling the vehicle to execute the second target action.

As one possible implementation, the recommendation device 1000 for a vehicle scene service further includes: and (5) logging in a module.

The login module is used for responding to a first login operation by the vehicle-mounted terminal and logging in according to an input first account number bound with the vehicle-mounted terminal; and the mobile terminal responds to a second login operation and logs in according to the input second account number bound with the mobile terminal.

The recommendation device of the vehicle scene service receives first configuration information of a recommended scene sent by a cloud server; the cloud server selects a target component adapted to the target vehicle type from a component library according to the target vehicle type to be configured, determines candidate actions of the target vehicle type according to the function indicated by the target component, determines target actions to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and trigger conditions required to be met for executing the target actions, and generates and transmits the target actions and the trigger conditions; under the condition that the vehicle of the target vehicle type meets the first trigger condition of the first recommended scene, the vehicle is controlled to execute the first target action of the first recommended scene, therefore, the cloud server can ensure that the selected component is matched with the hardware and software requirements of the target vehicle type by selecting the component of the adaptive target vehicle type from the component library, the candidate actions are determined according to the functions indicated by the target component, the driving requirements under different scenes can be accurately met, the first configuration information of the recommended scene is generated according to the first target action and the first trigger condition, the accuracy and the reliability of the recommended action under the vehicle scene are ensured by defining the first trigger condition, the control terminal can accurately control the vehicle according to the recommended action, and the user experience is improved.

In an exemplary embodiment, an electronic device is also presented.

Wherein, electronic equipment includes:

A processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to execute instructions to implement a recommendation method for a vehicle scene service as proposed by any of the foregoing embodiments.

As an example, fig. 11 is a schematic structural diagram of an electronic device 1000 according to an exemplary embodiment of the disclosure, and as shown in fig. 11, the electronic device 1100 may further include:

The system comprises a memory 1110 and a processor 1120, a bus 1130 connecting different components (including the memory 1110 and the processor 1120), wherein the memory 1110 stores a computer program, and the processor 1120 executes the program to implement the recommended method of the vehicle scene service according to the embodiments of the present disclosure.

Bus 1130 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.

The electronic device 1100 typically includes a variety of electronic device readable media. Such media can be any available media that can be accessed by the electronic device 1100 and includes both volatile and nonvolatile media, removable and non-removable media.

Memory 1110 may also include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 1140 and/or cache memory 1150. The server 1100 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 1160 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 11, commonly referred to as a "hard disk drive"). Although not shown in fig. 11, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In such cases, each drive may be coupled to bus 1130 through one or more data medium interfaces. Memory 1110 may include at least one program product having a set (e.g., at least one) of program modules configured to carry out the functions of the various embodiments of the disclosure.

A program/utility 1180 having a set (at least one) of program modules 1170 may be stored in, for example, memory 1110, such program modules 1170 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 1170 generally perform the functions and/or methods in the embodiments described in this disclosure.

The electronic device 1100 may also communicate with one or more external devices 1190 (e.g., keyboard, pointing device, display 1191, etc.), one or more devices that enable a user to interact with the electronic device 1100, and/or any device (e.g., network card, modem, etc.) that enables the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 1192. Also, the electronic device 1100 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through a network adapter 1193. As shown, the network adapter 1193 communicates with other modules of the electronic device 1100 via the bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 1100, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.

The processor 1120 executes various functional applications and data processing by running programs stored in the memory 1110.

It should be noted that, the implementation process and the technical principle of the electronic device in this embodiment refer to the foregoing explanation of the recommendation method for the vehicle scene service in the embodiment of the disclosure, and are not repeated herein.

In an exemplary embodiment, a computer readable storage medium is also provided, such as a memory, comprising instructions executable by a processor of an electronic device to perform the recommendation method for a vehicle scene service proposed by any of the embodiments described above. Alternatively, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

In an exemplary embodiment, a computer program product is also provided, comprising a computer program/instruction, characterized in that the computer program/instruction, when executed by a processor, implements the recommendation method for a vehicle scene service according to any of the embodiments described above.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims (10)

1. The recommendation method of the vehicle scene service is characterized by being applied to a cloud server and comprising the following steps of:

Selecting a target component matched with the target vehicle type from the component library according to the target vehicle type to be configured;

Determining candidate actions of the target vehicle type according to the functions indicated by the target components;

Responding to configuration operation of a first recommended scene, determining a first target action to be executed in the first recommended scene from the candidate actions, and executing a first trigger condition to be met by the first target action;

Generating first configuration information of the recommended scene according to the first target action and the first trigger condition, and pushing the first configuration information to a control terminal associated with a vehicle of the target vehicle type, wherein the first configuration information is used for controlling the vehicle to execute the first target action of the first recommended scene when the vehicle meets the first trigger condition of the first recommended scene.

2. The method of claim 1, wherein the determining the candidate action for the target vehicle model based on the function indicated by the target component comprises:

acquiring at least one function indicated by the target component;

determining at least one candidate action adapted to the target vehicle model under any one function of the at least one function;

And determining the candidate action of the target vehicle type according to at least one candidate action matched with the target vehicle type under each function.

3. The method of claim 2, wherein the determining at least one candidate action for any one of the at least one function that is adapted to the target vehicle model for the any one function comprises:

For any one of the at least one function, acquiring a history action executed by the vehicle of the target vehicle type under the any one function;

And taking the historical action as at least one candidate action matched with the target vehicle model under any function.

4. The method of claim 1, wherein the control terminal comprises a vehicle-mounted terminal and a mobile terminal, the method further comprising:

Receiving the driving data of the vehicle sent by the control terminal; the driving data are sent when the driving data of the vehicle do not meet the triggering condition of the user scene, the control terminal copies the recommended scene as the user scene into a personalized scene library in response to copy operation, and the control terminal obtains the driving data after modification based on the first configuration information in response to the configuration operation of the user scene in the personalized scene library;

Determining a second target action of a second recommended scene matched with the driving data based on a digital twin platform through an algorithm platform;

pushing the second target action to the vehicle-mounted terminal so as to control the vehicle to execute the second target action.

5. The method of claim 4, wherein the determining, by the algorithmic platform, a second target action for a second recommended scenario matching the travel data based on a digital twin platform comprises:

Mapping the driving data by adopting the digital twin platform to obtain vehicle data in multiple target dimensions;

And inputting the vehicle data in the multiple target dimensions into a scene service identification model of the algorithm platform to obtain a second target action of the second recommended scene.

6. The method of claim 5, wherein the scene service identification model is trained by:

acquiring sample running data, and performing action labeling on the sample running data to obtain labeling actions corresponding to the sample running data;

Inputting the sample driving data into a scene service identification model to obtain a prediction action output by the scene service identification model;

and training the scene service identification model according to the difference between the labeling action and the predicted action.

7. The method of claim 1, wherein pushing the first configuration information to a control terminal associated with a vehicle of the target vehicle type comprises:

Testing the first configuration information to obtain a test result of the first configuration information;

And responding to the test result to indicate that the first configuration information passes the test, and pushing the first configuration information to a control terminal associated with the target vehicle type.

8. A recommendation method for a vehicle scene service, which is applied to a control terminal, comprising:

Receiving first configuration information of a recommended scene sent by a cloud server; the cloud server selects a target component adapted to a target vehicle type from a component library according to the target vehicle type to be configured, determines a candidate action of the target vehicle type according to the function indicated by the target component, determines a target action to be executed in a recommended scene from the candidate actions in response to configuration operation of the recommended scene, and generates and transmits a trigger condition required to be met by executing the target action according to the target action and the trigger condition;

And controlling the vehicle to execute a first target action of the first recommended scene under the condition that the vehicle of the target vehicle type meets a first trigger condition of the first recommended scene.

9. The method of claim 8, wherein the control terminal comprises a vehicle terminal and a mobile terminal, the method further comprising:

Copying the recommended scene as a user scene into a personalized scene library in response to a copying operation, and modifying the first configuration information to obtain second configuration information of the user scene in response to a configuration operation of the user scene in the personalized scene library;

synchronizing second configuration information of the user scene between the vehicle-mounted terminal and the mobile terminal;

And under the condition that the running data of the vehicle meets the triggering condition of the user scene, the vehicle-mounted terminal controls the vehicle to execute the action corresponding to the triggering condition of the user scene.

10. The method of claim 9, wherein prior to receiving the first configuration information of the recommended scene sent by the cloud server, the method further comprises:

the vehicle-mounted terminal responds to a first login operation, and logs in according to an input first account number bound with the vehicle-mounted terminal;

And the mobile terminal responds to a second login operation and logs in according to the input second account number bound with the mobile terminal.