In-vehicle rest mode control method and device, storage medium and vehicle

Abstract

The invention provides an in-vehicle rest mode control method and device, a storage medium, electronic equipment and a vehicle, and relates to the technical field of vehicle control, and the method comprises the steps: obtaining interaction input information of a user; according to the interactive input information and the historical use habit information of the user for the in-vehicle rest mode, obtaining a target rest mode habitually used by the user in a historical scene through a preset recognition model, and determining the target rest mode as a target estimated rest mode corresponding to the intention information of the user, the preset recognition model is used for recognizing estimated rest modes corresponding to intention information of different interactive input information according to the use habit information; and starting an in-vehicle rest mode for the user according to the target estimated rest mode. The control process of the in-vehicle rest mode can be simplified, the adjustment efficiency of the in-vehicle rest mode is improved, and then high-quality riding experience is provided for a user.

Description

Technical Field

[0001] The present application relates to the field of vehicle control technology, and in particular to a control method, device, storage medium and vehicle for an in-vehicle rest mode. Background Art

[0002] With the rapid development of vehicle intelligent technology, more and more intelligent functions are installed in vehicles. In order to meet the needs of users to rest in the car, a variety of in-car rest modes have been developed accordingly. When the in-car rest mode is turned on, the vehicle will automatically control the seats and other equipment to make the rest environment in the car comfortable.

[0003] Currently, each time a user experiences the car, the user is required to set the desired in-car rest mode, such as setting specific parameters of the in-car rest mode to be turned on. However, such setting operation is cumbersome and time-consuming, resulting in a poor user experience. Summary of the invention

[0004] In view of this, the present application provides a control method, device, storage medium, electronic device and vehicle for an in-vehicle rest mode, which can realize intelligent adjustment of the seat mode, simplify the control process of the in-vehicle rest mode, improve the adjustment efficiency of the seat mode, and thus provide users with a high-quality riding experience.

[0005] According to a first aspect of the present application, a method for controlling an in-vehicle rest mode is provided, comprising:

[0006] Get user's interactive input information;

[0007] According to the interactive input information and the user's historical usage habit information for the in-vehicle rest mode, a target rest mode that the user is accustomed to using in historical scenarios is obtained through a preset recognition model, and the target rest mode is determined as a target estimated rest mode corresponding to the user's intention information, wherein the preset recognition model is used to identify the estimated rest modes corresponding to the intention information of different interactive input information respectively with reference to the usage habit information;

[0008] According to the target estimated rest mode, the in-vehicle rest mode is turned on for the user.

[0009] According to a second aspect of the present application, a control device for a rest mode in a vehicle is provided, comprising:

[0010] An acquisition module is used to obtain user's interactive input information;

[0011] an identification module, configured to obtain, based on the interactive input information and the user's historical usage habit information for the in-vehicle rest mode, a target rest mode that the user is accustomed to using in historical scenarios through a preset identification model, and determine the target rest mode as a target estimated rest mode corresponding to the user's intention information, wherein the preset identification model is used to identify the estimated rest modes corresponding to the intention information of different interactive input information, respectively, with reference to the usage habit information;

[0012] The activation module is used to activate the in-vehicle rest mode for the user according to the target estimated rest mode.

[0013] According to a third aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the control method of the in-vehicle rest mode described in the first aspect is implemented.

[0014] According to the fourth aspect of the present application, an electronic device is provided, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, wherein when the processor executes the computer program, the control method for the in-vehicle rest mode described in the first aspect is implemented.

[0015] According to a fifth aspect of the present application, a vehicle is provided, comprising: the electronic device as described in the fourth aspect.

[0016] By means of the above technical scheme, the present application provides a control method, device, storage medium, electronic device and vehicle for an in-car rest mode. Compared with the current control method for the in-car rest mode, the present application can obtain the user's interactive input information; based on the interactive input information and the user's historical usage habit information for the in-car rest mode, the target rest mode that the user is accustomed to using in historical scenarios is obtained through a preset recognition model, and the target rest mode is determined as the target estimated rest mode corresponding to the user's intention information, wherein the preset recognition model is used to identify the estimated rest modes corresponding to the intention information of different interactive input information with reference to the usage habit information; based on the target estimated rest mode, the in-car rest mode is turned on for the user. The present application can simplify the control process of the in-car rest mode, improve the adjustment efficiency of the in-car rest mode, and thus provide users with a high-quality riding experience.

[0017] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A flow chart of a method for controlling a rest mode in a vehicle provided in an embodiment of the present application;

[0021] Figure 2 A flow chart of a method for controlling a rest mode in a vehicle provided in an embodiment of the present application;

[0022] Figure 3 A flow chart of controlling a rest mode in a vehicle provided by an embodiment of the present application;

[0023] Figure 4 A schematic diagram of the structure of a control device for an in-vehicle rest mode provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following is a description of exemplary embodiments of the present application in conjunction with the accompanying drawings, including various details of the embodiments of the present application to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0025] The following describes a control method, device, storage medium, electronic device, and vehicle for an in-vehicle rest mode according to an embodiment of the present application with reference to the accompanying drawings.

[0026] In the related art, each time a user experiences the vehicle, the user is required to set the desired in-car rest mode, such as setting specific parameters of the in-car rest mode that needs to be turned on.

[0027] In order to solve the above technical problems, the present application provides a control method, device, storage medium, electronic device and vehicle for the in-vehicle rest mode, which can simplify the control process of the in-vehicle rest mode, improve the adjustment efficiency of the in-vehicle rest mode, and thus provide users with a high-quality riding experience.

[0028] like Figure 1 As shown, an embodiment of the present application provides a method for controlling an in-vehicle rest mode, comprising:

[0029] Step 101: Obtain user interactive input information.

[0030] In specific application scenarios, the user's interactive input information can be obtained through environmental perception systems, such as infotainment domain controller (Head Unit, HU), driving recorder, camera, sensor, etc. The interactive input information can be interactive information of the user expressing the intention to rest, which can include but is not limited to body behavior input information, gesture input information, voice input information, etc.

[0031] The execution subject of this embodiment can be a control device or equipment for the in-car rest mode, which can be configured on the vehicle computer side, the domain controller side, etc., and can obtain the user's interactive input information; identify the target estimated rest mode corresponding to the user's intention information based on the interactive input information and the user's historical usage habit information for the in-car rest mode; and turn on the in-car rest mode for the user based on the target estimated rest mode. This embodiment can simplify the control process of the in-car rest mode, improve the adjustment efficiency of the in-car rest mode, and thus provide users with a high-quality riding experience.

[0032] Step 102: Based on the interactive input information and the user's historical usage habit information for the in-car rest mode, a preset recognition model is used to obtain a target rest mode that the user is accustomed to using in historical scenarios, and the target rest mode is determined as a target estimated rest mode corresponding to the user's intention information.

[0033] The preset recognition model may be a large language model or other machine learning model, etc. The preset recognition model may be used to refer to the user's historical usage habit information for the in-car rest mode, and identify the estimated rest modes corresponding to the intention information of different interactive input information.

[0034] In this embodiment, the rest mode adjustment can be triggered based on the user's interactive input information, and the target estimated rest mode corresponding to the current user's intention can be identified through the preset recognition model in combination with the user's historical usage habit information. Among them, the usage habit information can be used to record the interactive input information of different users when the rest mode adjustment is triggered and the corresponding in-car rest mode, which helps to detect the user's intention and provide the user with a high-quality interactive experience.

[0035] Step 103: Based on the target estimated rest mode, enable the in-car rest mode for the user.

[0036] As a possible implementation method, it is possible to determine whether the current vehicle supports the target estimated rest mode based on the vehicle condition parameters. If the vehicle supports the target estimated rest mode, the target estimated rest mode can be adjusted to the in-vehicle rest mode; if the vehicle does not support the target estimated rest mode, the default rest mode can be selected according to the vehicle condition parameters, and then the default rest mode can be adjusted to the in-vehicle rest mode. Among them, the default rest mode is the default rest mode set for different vehicle condition parameters, which can be used for the initial rest scenario where no interactive input information is received, or the rest scenario where the current vehicle does not support the target estimated rest mode.

[0037] In summary, according to a control method of an in-car rest mode of this embodiment, compared with the current control method of the in-car rest mode, this application can identify the target estimated rest mode corresponding to the user's intention information based on the interactive input information and the user's historical usage habit information of the in-car rest mode; according to the target estimated rest mode, the in-car rest mode is turned on for the user. This application can simplify the control process of the in-car rest mode, improve the adjustment efficiency of the in-car rest mode, and thus provide users with a high-quality riding experience.

[0038] Further, as a refinement and extension of the above embodiment, in order to fully illustrate the specific implementation process of the method of this embodiment, this embodiment provides the following Figure 2 The specific method shown includes:

[0039] Step 201: Obtain user interactive input information.

[0040] For this embodiment, the specific implementation process can refer to the relevant description in step 101 of the embodiment, which will not be repeated here.

[0041] Step 202: Based on the interactive input information and the user's historical usage habit information for the in-car rest mode, a preset recognition model is used to obtain a target rest mode that the user is accustomed to using in historical scenarios, and the target rest mode is determined as a target estimated rest mode corresponding to the user's intention information.

[0042] Optionally, the interactive input information may include voice input information. Accordingly, the implementation steps of step 202 may include: obtaining the user's seat position according to the sound source collection position of the voice input information; then taking the receiving time of the voice input information, the semantic recognition information of the voice input information, the user's seat position and the vehicle condition parameters corresponding to the receiving time as the current scene features, and obtaining the target rest mode that the user is accustomed to using in historical scenes that are the same or matching the current scene features through a preset recognition model, such as obtaining the target rest mode that the user is accustomed to selecting in historical scenes that are the same or matching the current scene features, and determining the target rest mode as the target estimated rest mode.

[0043] For example, Figure 3 As shown, after obtaining the user's voice input information, the user's intention can be extracted through the processor of the preset recognition model (such as the large language model), and the target rest mode that the user is accustomed to selecting in the same or matching historical scene can be obtained based on the current scene characteristics, and the target rest mode is determined as the target estimated rest mode, and then the condition detection is performed to determine whether the target estimated rest mode is supported. If the target estimated rest mode is supported, the parameter processor can be used to set the corresponding parameters, and the mode business interface is called through the intelligent decision engine, and the business processor is further used to adjust the in-vehicle rest mode. Among them, the voice input information can be the natural language of the user expressing the intention to rest, including but not limited to voice text (such as "I want to sleep for a while", "I want to rest"), voice request time period, seat position of the voice source, etc.; the large language mode processor can use the large language artificial intelligence algorithm to perform semantic recognition on the voice input information, and recognize the user's intention information according to the language meaning and context; the current scene characteristics can include but not limited to the receiving time of the voice input information, the semantic recognition information of the voice input information, the user's seat position and the vehicle condition parameters corresponding to the receiving time, etc.

[0044] Based on the above, the target estimated rest mode can be calculated by a preset recognition model. Furthermore, the training process of the preset recognition model may include: obtaining a training set corresponding to the preset recognition model, the training set includes historical record information of the user's selection of the in-vehicle rest mode, and the historical record information includes environmental parameters, vehicle condition parameters and control parameters; wherein the environmental parameters include at least one of the time period of voice input and the seat position of the voice source, the vehicle condition parameters include at least one of the vehicle charging status, battery power and seat occupancy information, and the control parameters include at least one of the rest mode type selected by the user, the seat position selected to enter the rest mode and the rest duration; based on the training set, the preset recognition model is trained to fill in parameter data based on the default data link in the preset recognition model to obtain the estimated rest modes corresponding to different intention information, and the default data link is a data link created according to the default rest mode.

[0045] For this embodiment, a preset recognition model can be trained for different users. During the training process of the preset recognition model, historical record information of different users' selection of in-vehicle rest modes can be collected as a training set, and the corresponding historical in-vehicle rest modes can be used as training labels. Parameter data can be filled based on the default data link in the preset recognition model to obtain estimated rest modes corresponding to different intention information.

[0046] Among them, the historical record information may include but is not limited to environmental parameters, vehicle condition parameters and control parameters, etc. The environmental parameters may include but are not limited to the time period of voice input (such as 00:00-00:59, 01:00-01:59), the seat position of the voice source (such as driver's seat, co-driver's seat, second row left, second row right, third row left, third row right), etc.; the vehicle condition parameters may include but are not limited to the vehicle charging status (such as charging, not charging), battery power (such as power less than 30%, power greater than or equal to 30%), seat occupancy information (such as driver's seat position, co-driver's seat position, second row left seat position, second row right seat position, third row left seat position, third row right seat position, front row seat position, second row seat position, third row seat position, left seat position, right seat position, and all vehicle seat positions), etc.; the control parameters may include but are not limited to the type of rest mode selected by the user (such as nap mode, big bed mode), the seat position selected to enter the rest mode (such as driver's seat position, co-driver's seat position, second row left seat position, second row right seat position, third row left seat position, third row right seat position The default data link DefaultDataSequence is a data link created according to the default rest mode, which is the default data without training. Some parameters can be empty and filled after multiple trainings to gradually detect the user's intention. The historical data link HistoryDataSequence is a data link created during the user's historical use. For example, user 1 "I want to take a nap", the data link is environmental parameters E (request time period 01:00-01:59T1, voice source main driver S1)-vehicle condition parameters (charging C1, power less than 30% P1)-control parameters (target seat position main driver L1, rest time less than or equal to 120 minutes T1), which can be simplified to E(T1+S1)-V(C1+P1)-B(L1+T1), and the output is a main driver's nap of 30(X) minutes.

[0047] Optionally, step 202 may specifically include: inputting the current scene features into a preset recognition model for calculation, so as to determine the target rest mode based on the parameter data with the largest parameter weight and corresponding change frequency in historical scenes that are the same as or match the current scene features, and use the target rest mode as the target estimated rest mode corresponding to the user's intention information.

[0048] Exemplarily, determining the target rest mode based on the parameter data with the largest change frequency and parameter weight corresponding to the user in historical scenes that are identical or matching the current scene characteristics may specifically include: obtaining the rest mode that the user has selected most times within a preset time period from the parameter data with the largest change frequency and parameter weight corresponding to the user in historical scenes that are identical or matching the current scene characteristics, as the target rest mode.

[0049] The preset time period can be set according to the current time. For example, if the current time is around 12 noon, which is the lunch break time, the time period between 12 and 13 o'clock every day in the last month can be selected. This embodiment can accurately select the rest mode that the user has selected the most times in the preset time period from the parameter data with the largest change frequency and parameter weight corresponding to the historical scenes with the same or matching characteristics as the current scene, as the target rest mode, thereby improving the accuracy of determining the target rest mode.

[0050] When the current scene features are input for calculation in this embodiment, the target estimated rest mode corresponding to the user's intention information can be determined based on the corresponding change frequency and parameter data with the largest parameter weight in the historical scenes that are the same or matching the current scene features. Among them, the frequency of each parameter change Frequency can be used to detect the user's intention; the weight of each parameter Weight can be set for the parameter in the recorded information according to the user's historical record information. For the same type of parameters, the parameters can be weighted during the model training process, and the weight of the parameter that is selected more times by the user will be set higher, and the in-car rest mode corresponding to the highest parameter weight will be recommended to the user as the target estimated rest mode.

[0051] Optionally, step 202 may specifically include: regularly updating the training set according to the latest record information selected by the user for the in-vehicle rest mode; using the updated training set to train the preset recognition model to update the change frequency and parameter weight of each parameter data in the default data link.

[0052] During the training process of the preset recognition model, a training cycle can also be set to regularly collect the latest user mode usage data, update the training set for iterative training, and update the change frequency and parameter weight of each parameter data in the default data link, thereby reducing user interaction time and providing personalized services that meet user usage habits. Among them, the training cycle can be a user-defined cycle for updating the preset recognition model, for example: one week.

[0053] Specifically, it can collect user voice input information within the training cycle and input it into the preset recognition model in real time for training. It can determine the driving position and interaction time in the environmental parameters based on the voice source, and detect the battery status and the position of the passengers in the car to determine the vehicle condition parameters. At the same time, it can receive user voice input information in real time for conversation interaction, and gradually determine the user's rest intention during the conversation. After training, it can perform business processing based on the user's historical usage habits, quickly recommend estimated rest modes to users, reduce conversation interaction time, and provide wake-up services at the end of the estimated rest mode to provide a good car experience.

[0054] Exemplarily, the training scenario of the preset recognition model is as follows:

[0055] 1. Voice interaction scenario before training

[0056] Environmental parameters: At 12 noon, the driver initiated a voice request;

[0057] Vehicle condition parameters: Charging, battery charge 20%, seat occupancy 07 (i.e. both the driver and the front passenger seats are occupied);

[0058] Conversational interaction:

[0059] User: I want to take a nap;

[0060] Voice Assistant: How long do you want to rest?

[0061] User: 30 minutes;

[0062] Voice Assistant: Does your co-pilot also need to take a rest?

[0063] User: Need;

[0064] Voice Assistant: OK, nap mode will be turned on for you. The driver and passenger seats will move back and fold down.

[0065] Business processing: Turn on the nap mode, fold down the front passenger and driver seats, turn on the nap linkage, and the alarm will wake you up after the nap lasts for 30 minutes.

[0066] Through this training process, a preset recognition model is obtained, and the preset recognition model can be used in the subsequent interactive scene to obtain the estimated rest mode. For details, please refer to the following example:

[0067] 2. Voice interaction scenario after training 1

[0068] Environmental parameters: At 12 noon, the driver initiated a voice request;

[0069] Vehicle status parameters: Charging, battery charge 20%, seat occupancy O7 (i.e. both the driver and the front passenger are occupied);

[0070] Conversational interaction:

[0071] User: I want to take a nap;

[0072] Voice Assistant: Do you want to rest for 30 minutes together with the passenger as usual?

[0073] User: Yes;

[0074] [Specifically, environmental parameters (such as the main driver's seat, 12 noon, etc.), vehicle condition parameters (such as charging, battery power 20%, seat occupancy O7, etc.), and semantic recognition information such as needing to rest for 30 minutes can be input as current scene features into the preset recognition model for calculation, and then the preset recognition model is used to obtain the nap mode that the user is accustomed to choosing in historical scenes that are the same or matching with the current scene features, and then the voice assistant outputs the following content according to the nap mode, and performs corresponding business processing. ]

[0075] Voice Assistant: OK, nap mode will be turned on for you. The driver and passenger seats will move back and fold down.

[0076] Business processing: Turn on the nap mode, fold down the front passenger and driver seats, turn on the nap linkage, and the alarm will wake you up after the nap lasts for 30 minutes.

[0077] 3. Post-training voice interaction scenario 2

[0078] Environmental parameters: At 12 noon, the driver initiated a voice request;

[0079] Vehicle status parameters: Charging, battery charge 20%, seat occupancy O1 (only the driver is occupied);

[0080] Conversational interaction:

[0081] User: I want to take a nap;

[0082] Voice Assistant: Would you like to take a 30-minute break as usual?

[0083] User: Yes;

[0084] [Specifically, environmental parameters (such as the main driver's seat, 12 noon, etc.), vehicle condition parameters (such as charging, battery power 20%, seat occupancy O1, etc.), and semantic recognition information such as needing to rest for 30 minutes can be input as current scene features into the preset recognition model for calculation, and then the preset recognition model is used to obtain the nap mode that the user is accustomed to selecting in historical scenes that are the same or matching with the current scene features, and then the voice assistant outputs the following content according to the nap mode, and performs corresponding business processing. ]

[0085] Voice Assistant: OK, nap mode will be turned on for you, and the driver's seat will move back and fall down;

[0086] Business processing: Turn on the nap mode, fold down the front driver's seat, turn on the nap linkage, and the alarm will wake you up after the nap lasts for 30 minutes.

[0087] 4. Post-training voice interaction scenario 3

[0088] Environmental parameters: At 12 noon, the driver initiated a voice request;

[0089] Vehicle status parameters: Charging, battery charge 20%, seat occupancy O1 (only the driver is occupied);

[0090] Conversational interaction:

[0091] User: I want to take a nap;

[0092] Voice Assistant: Would you like to take a 30-minute break as usual?

[0093] User: No, I want to sleep more today;

[0094] Voice Assistant: OK, can I take a break for an hour?

[0095] User: Yes;

[0096] [Specifically, environmental parameters (such as the main driver's seat, 12 noon, etc.), vehicle condition parameters (such as charging, battery power 20%, seat occupancy O1, etc.), and semantic recognition information such as needing to rest for 1 hour can be input as current scene features into the preset recognition model for calculation, and then the preset recognition model is used to obtain the nap mode that the user is accustomed to choosing in historical scenes that are the same or matching with the current scene features, and then the voice assistant outputs the following content according to the nap mode, and performs corresponding business processing. ]

[0097] Voice Assistant: Nap mode will be turned on for you, and the driver's seat will move back and fall down;

[0098] Business processing: Turn on the nap mode, fold down the front driver's seat, turn on the nap linkage, and the alarm will wake you up after the nap lasts for 1 minute.

[0099] Step 203: Based on the real-time vehicle condition parameters of the vehicle, determine whether the vehicle supports turning on the target estimated rest mode.

[0100] For this embodiment, condition detection can be performed based on the current vehicle condition parameters to determine whether the current vehicle state supports the activation of the target estimated rest mode, wherein the condition detection may include vehicle gear position (such as parking gear, non-parking gear), charging status (such as charging), battery power (such as power less than 30%), range extender status (such as on, not on), etc. For example, when the vehicle gear position is parking gear, the battery power is higher than 50%, and the vehicle range extender is not started, the big bed mode can be activated for the user.

[0101] Step 204a: If the vehicle supports turning on the target estimated rest mode, turning on the in-vehicle rest mode for the user according to the target estimated rest mode.

[0102] As a possible implementation method, if the vehicle supports turning on the target estimated rest mode, that is, the current vehicle state meets the execution conditions of the target estimated rest mode, the in-car rest mode can be turned on for the user according to the target estimated rest mode. In addition, the turning-on result can be fed back to the user in the form of preset prompts. If the mode fails to turn on, the user can be prompted to turn on the target estimated rest mode again; if the mode is turned on successfully, the user can be prompted to have entered the target estimated rest mode, and a wake-up service can be provided at the end of the rest. Among them, the preset prompt form may include but is not limited to the car machine page prompt, voice broadcast prompt, vibration prompt, etc.

[0103] For this embodiment, the target estimated rest mode may include: target rest mode type, target rest duration and target seat position for entering the rest mode; the target rest mode type may be the target estimated rest mode for the user to rest, including but not limited to nap mode, big bed mode, etc. The nap mode can provide a series of linked and combined in-car services when the user is tired or sleepy, to meet the user's needs for a short and quick rest in the car; the big bed mode can adjust the in-car seat position in outdoor and camping scenarios to provide a space for lying flat, to meet the user's needs for long-term sleep, overnight and entertainment in the car; the target rest duration may be the estimated duration of the user's rest; the target seat position for entering the rest mode may be the estimated seat position for the user to rest.

[0104] Optionally, step 203 may include: adjusting the in-vehicle seat corresponding to the target seat position to a seat mode corresponding to the target rest mode type, and setting the operating duration of the seat mode according to the target rest duration.

[0105] In a specific application scenario, after determining that the target estimated rest mode is available, the vehicle computer can send adjustment instructions to the target adjustment seat and set the target rest duration. The seat in the vehicle corresponding to the target seat position can be adjusted to the seat mode corresponding to the target rest mode type, and the target rest duration can be set as the operating duration of the seat mode.

[0106] Optionally, step 203 may also include: adjusting the target device in the vehicle to a working mode corresponding to the target rest mode type, and setting the operating duration of the working mode according to the target rest duration, wherein the target device includes at least one of the following: temperature management equipment; multimedia equipment; air conditioning equipment.

[0107] For this embodiment, the target device in the vehicle can also be adjusted to a working mode corresponding to the target rest mode type, and the operating time of the working mode can be set according to the target rest time to create a more comfortable rest environment and provide a good rest experience. For example, during the user's rest, the in-vehicle multimedia can be controlled to play soothing music, the sunshade curtains of the windows can be controlled to be closed, and the in-vehicle aromatherapy can be controlled to emit a sleeping fragrance. Among them, the target device may include but is not limited to temperature management equipment (such as air conditioners, fans, etc.), multimedia equipment (such as speakers, audio, displays, etc.), air conditioning equipment (such as aromatherapy, air purification devices, etc.), etc.

[0108] In step 204b of the embodiment parallel to step 204a of the embodiment, if the vehicle does not support the activation of the target estimated rest mode, the in-vehicle rest mode is activated for the user according to the default rest mode.

[0109] As a possible implementation method, if the vehicle does not support the activation of the target estimated rest mode, that is, the current vehicle state does not meet the execution conditions of the target estimated rest mode, at least one vehicle condition parameter information can be extracted from the vehicle condition parameters, and the default rest mode matching the at least one vehicle condition parameter information can be determined based on the decision conditions of the default rest mode. The default rest mode is then activated for the user, and a prompt message indicating that the mode adjustment is completed is output to the user.

[0110] Exemplarily, the default rest mode and its decision conditions are as follows:

[0111] Charging status:

[0112] Charging - nap mode;

[0113] Battery level:

[0114] Battery level below 30% — nap mode;

[0115] Battery level greater than or equal to 30% - Double bed mode;

[0116] Target seat position:

[0117] Driver seat position, front passenger seat position, second row left seat position, second row right seat position, front row seat position, second row seat position - nap mode;

[0118] Three-row left seat position, three-row right seat position, three-row seat position, left seat position, right seat position, full vehicle seat position - king bed mode;

[0119] Rest time:

[0120] Less than or equal to 120 minutes - nap mode;

[0121] More than 120 minutes - big bed mode.

[0122] In summary, according to a control method of an in-car rest mode of this embodiment, compared with the current control method of the in-car rest mode, this application can identify the target estimated rest mode corresponding to the user's intention information based on the interactive input information and the user's historical usage habit information of the in-car rest mode; according to the target estimated rest mode, the in-car rest mode is turned on for the user. This application can simplify the control process of the in-car rest mode, improve the adjustment efficiency of the in-car rest mode, and thus provide users with a high-quality riding experience.

[0123] Based on the above Figure 1 and Figure 2 The specific implementation of the method shown in the embodiment provides a control device for the rest mode in the vehicle, such as Figure 4 , the device includes: an acquisition module 31, an identification module 32, and an opening module 33;

[0124] The acquisition module 31 can be used to obtain the user's interactive input information;

[0125] The recognition module 32 may be used to obtain the target rest mode that the user is accustomed to using in historical scenarios through a preset recognition model according to the interactive input information and the user's historical usage habit information for the in-vehicle rest mode, and determine the target rest mode as the target estimated rest mode corresponding to the user's intention information, wherein the preset recognition model is used to identify the estimated rest modes corresponding to the intention information of different interactive input information respectively with reference to the usage habit information;

[0126] The activation module 33 can be used to activate the in-vehicle rest mode for the user based on the target estimated rest mode.

[0127] In a specific application scenario, the activation module 33 can be used to determine whether the vehicle supports the activation of the target estimated rest mode based on the real-time vehicle condition parameters of the vehicle; if the vehicle supports the activation of the target estimated rest mode, the in-vehicle rest mode is activated for the user based on the target estimated rest mode; if the vehicle does not support the activation of the target estimated rest mode, the in-vehicle rest mode is activated for the user based on the default rest mode.

[0128] In a specific application scenario, the recognition module 32 can be specifically used to obtain the seat position of the user according to the sound source collection position of the voice input information; use the receiving time of the voice input information, the semantic recognition information of the voice input information, the seat position of the user and the vehicle condition parameters corresponding to the receiving time as the current scene features, and obtain the target rest mode that the user is accustomed to using in historical scenes that are the same or matching the current scene features through a preset recognition model, and determine the target rest mode as the target estimated rest mode.

[0129] In a specific application scenario, the recognition module 32 can be specifically used to obtain a training set corresponding to a preset recognition model, the training set including historical record information of the user's selection of the in-vehicle rest mode, the historical record information including environmental parameters, vehicle condition parameters and control parameters; wherein, the environmental parameters include at least one of the time period of voice input and the seat position of the voice source, the vehicle condition parameters include at least one of the vehicle charging status, the battery power and the seat occupancy information, and the control parameters include at least one of the rest mode type selected by the user, the seat position selected to enter the rest mode and the rest duration; the preset recognition model is trained based on the training set, and parameter data is filled based on the default data link in the preset recognition model to obtain estimated rest modes corresponding to different intention information, and the default data link is a data link created according to the default rest mode.

[0130] In a specific application scenario, the identification module 32 can also be used to regularly update the training set according to the latest record information selected by the user for the in-vehicle rest mode; use the updated training set to train the preset recognition model to update the change frequency and parameter weight of each parameter data in the default data link.

[0131] In a specific application scenario, the recognition module 32 can be specifically used to input the current scene features into a preset recognition model for calculation, so as to determine the target rest mode based on the parameter data with the largest change frequency and parameter weight corresponding to the user in historical scenes that are the same or matching the current scene features.

[0132] In a specific application scenario, the identification module 32 may be specifically configured to obtain the rest mode selected most times by the user within a preset time period from the parameter data with the largest frequency of change and parameter weight, as the target rest mode.

[0133] In a specific application scenario, the module 33 is turned on, which can be used to adjust the in-vehicle seat corresponding to the target seat position to a seat mode corresponding to the target rest mode type, and set the operating time of the seat mode according to the target rest time.

[0134] In a specific application scenario, module 33 is turned on, which can be used to adjust the target device in the vehicle to a working mode corresponding to the target rest mode type, and set the operating time of the working mode according to the target rest time, wherein the target device includes at least one of the following: temperature management equipment; multimedia equipment; air conditioning equipment.

[0135] It should be noted that for other corresponding descriptions of the functional units involved in the control device for the in-vehicle rest mode provided in this embodiment, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0136] Based on the above Figure 1 and Figure 2 The method shown in the embodiment also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The method shown.

[0137] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, USB flash drive, mobile hard disk, etc.), including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods of various implementation scenarios of the present application.

[0138] Based on the above Figure 1 and Figure 2 The method shown, and Figure 4 In order to achieve the above-mentioned purpose, the embodiment of the present application further provides an electronic device which can be configured on the vehicle (such as an electric vehicle) side, and the device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The method shown.

[0139] Optionally, the above-mentioned physical device may also include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a WI-FI interface), etc.

[0140] Those skilled in the art will appreciate that the above-mentioned physical device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different arrangements of components.

[0141] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of the above-mentioned physical device, and supports the operation of the information processing program and other software and / or programs. The network communication module is used to realize the communication between the components inside the storage medium, and the communication with other hardware and software in the information processing physical device.

[0142] Based on the above electronic device, the embodiment of the present application further provides a vehicle, which may specifically include: the above electronic device. The vehicle may specifically be an electric car, etc.

[0143] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. The technical solution in this embodiment can identify the target estimated rest mode corresponding to the user's intention information based on the interactive input information and the user's historical usage habit information for the in-car rest mode; based on the target estimated rest mode, turn on the in-car rest mode for the user. The present application can simplify the control process of the in-car rest mode, improve the adjustment efficiency of the in-car rest mode, and thus provide users with a high-quality riding experience.

[0144] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0145] The above is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. A method for controlling a rest mode in a vehicle, It is characterized in that include: Get user's interactive input information; According to the interactive input information and the user's historical usage habit information for the in-vehicle rest mode, a target rest mode that the user is accustomed to using in historical scenarios is obtained through a preset recognition model, and the target rest mode is determined as a target estimated rest mode corresponding to the user's intention information, wherein the preset recognition model is used to identify the estimated rest modes corresponding to the intention information of different interactive input information respectively with reference to the usage habit information; According to the target estimated rest mode, the in-vehicle rest mode is turned on for the user.

2. The method according to claim 1, It is characterized in that The step of starting the in-vehicle rest mode for the user according to the target estimated rest mode includes: Based on the real-time vehicle condition parameters of the vehicle, determining whether the vehicle supports turning on the target estimated rest mode; If the vehicle supports turning on the target estimated rest mode, turning on the in-vehicle rest mode for the user according to the target estimated rest mode; If the vehicle does not support turning on the target estimated rest mode, the in-vehicle rest mode is turned on for the user according to the default rest mode.

3. The method according to claim 1, It is characterized in that The interactive input information includes voice input information; The step of obtaining the target rest mode that the user is accustomed to using in historical scenarios by using a preset recognition model according to the interactive input information and the user's historical usage habit information for the in-vehicle rest mode includes: Acquiring the seat position of the user according to the sound source collection position of the voice input information; The receiving time of the voice input information, the semantic recognition information of the voice input information, the seat position of the user and the vehicle condition parameters corresponding to the receiving time are taken as current scene features, and the target rest mode that the user is accustomed to using in historical scenes that are the same as or matching the current scene features is obtained through the preset recognition model.

4. The method according to claim 3, It is characterized in that The training process of the preset recognition model includes: Acquire a training set corresponding to the preset recognition model, the training set including historical record information of the user's selection and use of the in-vehicle rest mode, the historical record information including environmental parameters, vehicle condition parameters and control parameters; wherein the environmental parameters include at least one of a time period of voice input and a seat position of a voice source, the vehicle condition parameters include at least one of a vehicle charging state, a battery charge and seat occupancy information, and the control parameters include at least one of a rest mode type selected by the user, a seat position selected to enter the rest mode and a rest duration; The preset recognition model is trained based on the training set to fill parameter data based on the default data link in the preset recognition model to obtain estimated rest modes corresponding to different intention information, wherein the default data link is a data link created according to the default rest mode.

5. The method according to claim 4, It is characterized in that The method further comprises: Regularly updating the training set according to the latest record information of the user's selection of the in-car rest mode; The preset recognition model is trained using the updated training set to update the change frequency and parameter weight of each parameter data in the default data link.

6. The method according to claim 5, It is characterized in that The method of using the reception time of the voice input information, the semantic recognition information of the voice input information, the seat position of the user, and the vehicle condition parameter corresponding to the reception time as the current scene feature, and obtaining the target rest mode that the user is accustomed to using in the historical scene that is the same as or matches the current scene feature through a preset recognition model includes: The current scene feature is input into the preset recognition model for calculation, so as to determine the target rest mode according to the parameter data with the largest change frequency and parameter weight corresponding to the user in the historical scene that is the same as or matches the current scene feature.

7. The method according to claim 6, It is characterized in that The determining of the target rest mode according to the parameter data with the largest change frequency and parameter weight corresponding to the user in the historical scenes having the same or matching features as the current scene includes: From the parameter data with the largest frequency of change and parameter weight, the rest mode selected most times by the user within a preset time period is obtained as the target rest mode.

8. A control device for a rest mode in a vehicle, It is characterized in that include: An acquisition module is used to obtain user's interactive input information; an identification module, configured to obtain, based on the interactive input information and the user's historical usage habit information for the in-vehicle rest mode, a target rest mode that the user is accustomed to using in historical scenarios through a preset identification model, and determine the target rest mode as a target estimated rest mode corresponding to the user's intention information, wherein the preset identification model is used to identify the estimated rest modes corresponding to the intention information of different interactive input information, respectively, with reference to the usage habit information; The activation module is used to activate the in-vehicle rest mode for the user according to the target estimated rest mode.

9. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, It is characterized in that When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

11. A vehicle, It is characterized in that include: The electronic device as claimed in claim 10.

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