A scenario mode recommendation method, device, electronic equipment and storage medium

Abstract

The application provides a scenario mode recommendation method and device, electronic equipment and a storage medium. The method comprises the following steps: determining whether a vehicle currently meets preset conditions for recommending a target scenario mode; determining user perception risk data when it is determined that the vehicle meets the preset conditions; wherein the perception risk data is used to describe the experience of the user when the target scenario mode is recommended to the vehicle; determining whether the current time belongs to a target recommendation opportunity based on the perception risk data; and recommending the target scenario mode to the vehicle when it is determined that the current time belongs to the target recommendation opportunity. The method can comprehensively consider various risks, select a recommendation opportunity, and improve the adoption probability and use willingness of the user.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of vehicles, and more particularly, to a method, device, electronic equipment and storage medium for scenario mode recommendation in the field of vehicles. BACKGROUND

[0002] With the upgrading of intelligent experience in users' daily life, people's demand and expectation for car intelligence are also increasing. The vehicle can detect the state of the user in the vehicle and the state of the vehicle in real time, and recommend a suitable scenario mode to the user according to the user state and the state of the vehicle.

[0003] The timing of recommending a scenario mode to a user by the vehicle in the prior art generally avoids the time that causes objective risks, such as reducing the recommendation during the user's driving focus to avoid the user's driving distraction. However, the timing of the recommendation in the prior art is only based on the objective risk, which is not comprehensive enough, and the user still has a relatively high probability of rejecting the recommended scheme after the recommendation, resulting in a low use intention and adoption rate of the recommended scenario mode by the user. SUMMARY

[0004] The present application provides a method, device, electronic equipment and storage medium for scenario mode recommendation, which can more comprehensively consider the risks of all parties to select the timing of the recommendation and improve the user's adoption probability and use intention.

[0005] In a first aspect, a method for scenario mode recommendation is provided, which includes: determining whether a vehicle currently meets a preset condition for recommending a target scenario mode; determining user perception risk data when it is determined that the vehicle meets the preset condition; wherein the perception risk data is used to describe the user experience when the target scenario mode is recommended to the vehicle; determining whether the current time belongs to a target recommendation timing based on the perception risk data; and recommending the target scenario mode to the vehicle when it is determined that the current time belongs to the target recommendation timing.

[0006] In the above technical solution, the perception risk data describing the user experience after the target scenario mode is recommended to the vehicle is determined, and it is determined whether the current time is a suitable recommendation timing based on the perception risk data, so that the determined recommendation timing combines the user's subjective perception risk data, the risk considered by the recommendation timing is more comprehensive, and it is ensured that the recommendation can improve the user's use experience and can improve the user's adoption rate and use intention for the target scenario mode after the recommendation.

[0007] In a possible implementation manner of the first aspect, the determining whether the current time is the target recommendation time based on the perceived risk data comprises: predicting a user adoption probability and / or a use willingness value of the target scenario mode based on the perceived risk data; determining that the current time is the target recommendation time when the user adoption probability is greater than or equal to a preset probability threshold and / or the use willingness value is greater than or equal to a preset value; and determining that the current time is not the target recommendation time when the user adoption probability is less than the preset probability threshold and / or the use willingness value is less than the preset value.

[0008] In a possible implementation manner of the first aspect and the above implementation manner, the perceived risk data comprises a time risk value used to describe the user experience from a time dimension, and the target scenario mode comprises a target function. The determining the perceived risk data of the user comprises: obtaining operation duration data of the user required for the vehicle to execute the target function; and / or determining schedule data of the user; and calculating the time risk value of the user based on the operation duration data and / or the schedule data.

[0009] In the above technical solution, by determining the operation duration data of the user operating the vehicle, the recommendation time determined based on the perceived risk data can improve the user experience in the time dimension, and ensure that the time spent by the user after the target scenario mode is recommended is not too long, so as to avoid a bad experience of the user. The time risk value determined based on the schedule data of the user can avoid a conflict between the recommendation time determined based on the perceived risk data and the schedule of the user, and can improve the adoption willingness and use willingness of the user.

[0010] In a possible implementation manner of the first aspect and the above implementation manner, the operation duration data comprises first operation duration data and second operation duration data. The obtaining the operation duration data consumed by the vehicle to execute the target function comprises: predicting first operation duration data of the user required for the vehicle to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtaining second operation duration data of the user required for the vehicle to execute the target function in a case where the vehicle does not adopt the target scenario mode; and calculating the time risk value based on the operation duration data comprises: calculating the time risk value based on a difference between the first operation duration data and the second operation duration data.

[0011] In the above technical solution, by predicting the first operation duration data of the user after the user adopts the target scenario mode, and obtaining the second operation duration data of the user in a case where the user does not adopt the target scenario mode, the time risk value of the time spent by the user after the target scenario mode is recommended can be accurately determined based on the operation duration data before and after the adoption, so as to determine the recommendation time based on the determined time risk value, and ensure that the user experience in the time dimension can be improved after the target scenario mode is recommended, and the user adoption probability and use willingness can be improved.

[0012] In a possible implementation of the first aspect and the above implementation, in some possible implementation, the perceived risk data includes an operation risk value for describing the user experience from the operation times dimension, the target scenario mode includes a target function, and determining the perceived risk data of the user includes: predicting a first operation times of the user required for performing the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtaining a second operation times of the user required for performing the target function in a case where the vehicle does not adopt the target scenario mode; and calculating the operation risk value based on the first operation times and the second operation times.

[0013] In the above technical solution, by predicting the first operation times of the user based on the target scenario mode and the second operation times of the user in a case where the target scenario mode is not adopted, and calculating the operation risk value based on the first operation times and the second operation times, the recommended time determined based on the perceived risk data can be combined with the experience of the user in the operation times dimension, so as to ensure that the recommended target scenario mode can improve the user experience in the operation times dimension, and avoid the poor user experience caused by the increased operation times after the recommendation.

[0014] In a possible implementation of the first aspect and the above implementation, in some possible implementation, the perceived risk data includes an energy consumption risk value for describing the user experience from the energy consumption dimension, the target scenario mode includes a target function, and determining the perceived risk data of the user includes: obtaining first fuel consumption data and / or first electric consumption data of the target function based on the target scenario mode; obtaining second fuel consumption data and / or second electric consumption data of the target function in a case where the vehicle does not adopt the target scenario mode; calculating the energy consumption risk value of the vehicle according to the first fuel consumption data and the second fuel consumption data; or, calculating the energy consumption risk value of the vehicle according to the first electric consumption data and the second electric consumption data; or, calculating the energy consumption risk value according to the first fuel consumption data, the first electric consumption data, the second fuel consumption data and the second electric consumption data.

[0015] In the above technical solution, by predicting the first fuel consumption data and / or the first electric consumption data of the user based on the target scenario mode, determining the second fuel consumption data and / or the electric consumption data in a case where the target scenario mode is not adopted, and calculating the energy consumption risk value of the target scenario mode based on the above data, the recommended time determined based on the perceived risk data can be combined with the experience of the user in the energy consumption dimension, so as to ensure that the recommended target scenario mode can improve the user experience, and avoid the poor user experience caused by the high energy consumption after the recommendation.

[0016] In combination with the first aspect and the above implementation manners, in some possible implementation manners, the perception risk data includes a traffic risk value used to describe the experience of the user from a traffic consumption dimension, the target scenario mode includes a target function, and determining the perception risk data of the user includes: predicting a first traffic consumed by the vehicle based on the target function executed by the target scenario mode after the vehicle adopts the target scenario mode; obtaining a second traffic consumed by the vehicle based on the target function executed by the vehicle in a case where the vehicle does not adopt the target scenario mode; and calculating the traffic risk value based on the first traffic and the second traffic.

[0017] In the above technical solution, by predicting the first traffic data of the user based on the target scenario mode, determining the second traffic data of the user in a case where the target scenario mode is not adopted, and calculating the traffic risk value of the target scenario mode based on the first traffic data and the second traffic data, the recommended time determined based on the perception risk data can be combined with the experience of the user from the traffic consumption dimension of the vehicle, so that the recommended target scenario mode can improve the use experience of the user, and the use experience of the user after the recommendation is avoided from being poor due to the high traffic consumption of the vehicle.

[0018] In summary, the application determines the perceived risk data describing the user experience after recommending the target scenario mode to the vehicle, determines whether the current time is a suitable recommendation opportunity based on the perceived risk data, so that the determined recommendation opportunity combines the user's subjective perceived risk data, so that the risk considered by the recommendation opportunity is more comprehensive, and the recommendation can improve the user's use experience and can improve the user's adoption rate and use willingness after the recommendation. By determining the time length data spent by the user operating the vehicle, the recommendation opportunity determined based on the perceived risk data can improve the user's experience in the time dimension, ensure that the user spends a reasonable length of time after recommending the target scenario mode, and avoid bringing bad experience to the user; determining the time risk value according to the user's schedule data can avoid the conflict between the user's schedule and the recommendation opportunity determined based on the perceived risk data, and can improve the user's adoption willingness and use willingness. By predicting the first operation time length data of the user after adopting the target scenario mode, obtaining the second operation time length data when the target scenario mode is not adopted, and based on the operation time length data before and after adoption, the time risk value of the user spending more time after recommending the target scenario mode can be accurately determined, so as to determine the recommendation opportunity in the next step based on the determined time risk value, and ensure that the recommended target scenario mode can improve the user's experience in the time dimension, improve the user's adoption probability and use willingness. By predicting the first operation frequency of the user based on the target scenario mode and the second operation frequency when the target scenario mode is not adopted, and calculating the operation risk value based on the first operation frequency and the second operation frequency, the recommendation opportunity determined based on the perceived risk data can combine the user's experience in the operation frequency dimension, and ensure that the recommended target scenario mode can improve the user's use experience in the operation frequency dimension, and avoid the user's use experience being poor due to the increase of operation frequency after the recommendation. By predicting the first fuel consumption data and / or the first electricity consumption data of the user based on the target scenario mode, determining the second fuel consumption data and / or the electricity consumption data when the target scenario mode is not adopted, and calculating the energy consumption risk value of the target scenario mode based on the above data, the recommendation opportunity determined based on the perceived risk data can combine the user's experience in the energy consumption dimension, and ensure that the recommended target scenario mode can improve the user's use experience, and avoid the user's use experience being poor due to high energy consumption after the recommendation. By predicting the first traffic data of the user based on the target scenario mode, determining the second traffic data when the target scenario mode is not adopted, and calculating the traffic risk value of the target scenario mode based on the first traffic data and the second traffic data, the recommendation opportunity determined based on the perceived risk data can combine the user's experience in the vehicle traffic consumption dimension, and ensure that the recommended target scenario mode can improve the user's use experience, and avoid the user's use experience being poor due to high vehicle traffic consumption after the recommendation.

[0019] In a second aspect, an apparatus for scenario mode recommendation is provided, which comprises: a first determining module configured to determine whether a vehicle currently meets preset conditions for recommending a target scenario mode; a second determining module configured to determine perceptual risk data of a user if it is determined that the vehicle meets the preset conditions; wherein the perceptual risk data is used to describe an experience of the user when the target scenario mode is recommended to the vehicle; a judging module configured to determine whether a current time belongs to a target recommendation time based on the perceptual risk data; and a recommending module configured to recommend the target scenario mode to the vehicle if it is determined that the current time belongs to the target recommendation time.

[0020] With reference to the second aspect, in some possible implementation manners, the judging module is specifically configured to predict a probability of adoption and / or a value of willingness to use of the target scenario mode by the user based on the perceptual risk data; it is determined that the current time belongs to the target recommendation time when the probability of adoption is greater than or equal to a preset probability threshold and / or the value of willingness to use is greater than or equal to a preset value; and it is determined that the current time does not belong to the target recommendation time when the probability of adoption is less than the preset probability threshold and / or the value of willingness to use is less than the preset value.

[0021] With reference to the second aspect and the foregoing implementation manners, in some possible implementation manners, the perceptual risk data comprises a time risk value used to describe the experience of the user from a time dimension, and the target scenario mode comprises a target function; and the second determining module is specifically configured to: obtain operation duration data of the user required for the vehicle to execute the target function; and / or determine schedule data of the user; and calculate the time risk value of the user based on the operation duration data and / or the schedule data.

[0022] With reference to the second aspect and the foregoing implementation manners, in some possible implementation manners, the operation duration data comprises first operation duration data and second operation duration data; and the second determining module 502 is specifically configured to: predict first operation duration data of the user required for the vehicle to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtain second operation duration data of the user required for the vehicle to execute the target function in a case where the vehicle does not adopt the target scenario mode; and calculate the time risk value based on a difference between the first operation duration data and the second operation duration data.

[0023] With reference to the second aspect and the foregoing implementation manners, in some possible implementation manners, the perceptual risk data comprises an operation risk value used to describe the experience of the user from an operation frequency dimension, and the target scenario mode comprises a target function; and the second determining module is specifically configured to: predict a first operation frequency of the user required for the vehicle to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtain a second operation frequency of the user required for the vehicle to execute the target function in a case where the vehicle does not adopt the target scenario mode; and calculate the operation risk value based on the first operation frequency and the second operation frequency.

[0024] In some possible implementation manners, in combination with the second aspect and the foregoing implementation manners, the perception risk data comprises an energy consumption risk value for describing the experience of the user from the energy consumption dimension, the target scenario mode comprises a target function, and the second determining module is specifically configured to: predict, after the vehicle adopts the target scenario mode, first fuel consumption data and / or first electricity consumption data of the target function executed based on the target scenario mode; acquire, in a case where the vehicle does not adopt the target scenario mode, second fuel consumption data and / or second electricity consumption data of the target function executed by the vehicle; calculate the energy consumption risk value according to the first fuel consumption data and the second fuel consumption data; or calculate the energy consumption risk value according to the first electricity consumption data and the second electricity consumption data; or calculate the energy consumption risk value according to the first fuel consumption data, the first electricity consumption data, the second fuel consumption data and the second electricity consumption data.

[0025] In some possible implementation manners, in combination with the second aspect and the foregoing implementation manners, the perception risk data comprises a traffic risk value for describing the experience of the user from the traffic consumption dimension, and the target scenario mode comprises a target function, and the second determining module is specifically configured to: predict, after the vehicle adopts the target scenario mode, first traffic consumed by the target function executed based on the target scenario mode; acquire, in a case where the vehicle does not adopt the target scenario mode, second traffic consumed by the target function executed by the vehicle; and calculate the traffic risk value based on the first traffic and the second traffic.

[0026] In a third aspect, an electronic device is provided, which comprises a memory and a processor. The memory is configured to store executable program code, and the processor is configured to invoke and run the executable program code from the memory, so that the vehicle executes the method in the first aspect or any possible implementation manner of the first aspect.

[0027] In a fourth aspect, a computer program product is provided, which comprises computer program code. When the computer program code runs on a computer, the computer program code causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect.

[0028] In a fifth aspect, a computer readable storage medium is provided, which stores computer program code. When the computer program code runs on a computer, the computer program code causes the computer to execute the method in the first aspect or any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a system architecture for implementing an embodiment of the present application.

[0030] Figure 2 is a schematic flowchart of a scenario mode recommendation method provided by an embodiment of the present application.

[0031] Figure 3is a recommended target scenario mode interface schematic diagram provided by an embodiment of the application.

[0032] Figure 4 is a target scenario mode control interface schematic diagram provided by an embodiment of the application.

[0033] Figure 5 is a structure schematic diagram of a scenario mode recommendation device provided by an embodiment of the application.

[0034] Figure 6 is a structure schematic diagram of an electronic device provided by an embodiment of the application. DETAILED DESCRIPTION

[0035] The technical solutions in the application will be described in detail below with reference to the drawings. In the description of the embodiments of the application, unless otherwise specified, " / " represents the meaning of or, for example, A / B can represent A or B: "and / or" in the text is only a description of the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist together, and B exists alone, in addition, in the description of the embodiments of the application, "multiple" means two or more than two.

[0036] Hereinafter, the terms "first", "second" are only used for description purposes, and cannot be understood as implying or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more features.

[0037] Before selling the vehicle, the manufacturer can store scenario modes in the vehicle based on big data and the functions executable by the vehicle, and based on the scenario modes, multi-functional linkage can be conveniently and quickly realized to meet the needs of users.

[0038] The user can also develop personalized scenario modes based on his own needs during the use of the vehicle. The vehicle can upload the developed scenario modes to the cloud, or the manufacturer can set new scenario modes based on newly collected data and upload them to the cloud. At this time, the cloud stores a plurality of scenario modes that are not added to the vehicle's scenario mode list, and the plurality of scenario modes can exist in a target scenario mode similar to the user's habit of using the vehicle in the target scenario. At this time, the cloud can recommend the target scenario mode to the vehicle, so that the user adds the target scenario mode to the vehicle's scenario mode list for use.

[0039] However, in the prior art, only objective risk is considered when recommending a scenario mode to a vehicle, and the timing of recommending a scenario mode generally avoids high-risk periods, such as reducing recommendations during periods when the user is focused on driving to avoid user distraction. However, the risk considered in the prior art is not comprehensive, and the user still has a relatively high probability of rejecting the target scenario mode after the recommendation, resulting in low user intent and adoption rate of the recommended scenario mode.

[0040] The present application provides a scenario mode recommendation method that can select a recommendation timing based on user-perceived risk, improve the user's adoption probability and willingness to use the recommended target scenario, and improve the user's vehicle experience.

[0041] Figure 1 is the implementation system architecture of an embodiment of the present application.

[0042] For example, as shown in Figure 1 The system 100 includes a cloud 10 and a vehicle 20.

[0043] The cloud 10 stores a plurality of scenario modes, including manufacturer-set uploaded and user-set uploaded scenario modes.

[0044] The cloud 10 and the vehicle 20 are connected, and the cloud 10 can obtain historical data of the vehicle 20, recommend a target scenario mode to the user based on the historical data of the vehicle 20, and the target scenario mode can be any one of the plurality of scenario modes stored in the cloud 10.

[0045] The vehicle 20 is a vehicle to be recommended a scenario mode by the user, and the vehicle 20 includes a storage space storing historical data of the vehicle 20 within a certain time period, and the vehicle 20 can upload the historical data to the cloud 10.

[0046] The historical data includes the number of times the vehicle executes a target function, the user's operation duration for controlling the vehicle to execute the target function each time, and the number of operations.

[0047] Figure 2 is a schematic flowchart of a scenario mode recommendation method provided by an embodiment of the present application. The method is applied to a cloud.

[0048] For example, as shown in Figure 2 The method includes:

[0049] Step 201, determine whether the vehicle currently meets the preset conditions for recommending a target scenario mode;

[0050] The vehicle can be provided with a perception device, which can acquire perception data associated with the vehicle in real time, and upload the acquired perception data to the cloud under certain conditions, so that the cloud determines whether the vehicle currently meets the preset conditions of the recommended target scenario mode according to the perception data of the vehicle.

[0051] In some embodiments, the user can start the personalized recommendation service of the vehicle through a touch screen, voice, etc. In response to the user's operation of starting the personalized recommendation service of the vehicle, the vehicle uploads the acquired perception data to the cloud.

[0052] The target scenario mode is a scenario mode that provides certain functions for the user. The target scenario mode can include control instructions for controlling the target function of the vehicle to be in a working state, and the control instructions can also include working parameters of the target function. When the vehicle starts the target scenario mode, the target function of the vehicle can be triggered to be in a working state and maintained at the target working parameter to meet the user's needs.

[0053] It should be understood that the target scenario mode can be a preset scenario mode, or a scenario mode configured by other users according to their own needs when using other vehicles.

[0054] The cloud can divide the user's vehicle use scenarios based on the perception data uploaded by the vehicle. Specifically, the user's vehicle use scenarios can be divided according to the user's driving time, driving distance, and location information within a preset time range (for example, in the past month). For example, in the past month, the user drove the vehicle from home to the company at about 8:00 in the morning, and finally parked the vehicle in the company garage, so this scenario is divided into a work scenario; the user drove the vehicle from the company to home at about 6:00-8:00 in the afternoon, and finally parked the vehicle in the underground garage at home, so this scenario is divided into a commuting scenario. The user's vehicle use scenarios and scenario modes have a certain correlation, and the scenario related to the target scenario mode can be referred to as a target scenario.

[0055] The perception device can include a time perception device, a driving distance perception device, and a location perception device, which can acquire the current time, driving distance, and location information, etc. according to the above-mentioned perception devices, to determine whether the vehicle is in the target scenario.

[0056] For example, assuming that the target scenario is a commuting scenario, when the time is acquired as 6:00-8:00 in the afternoon, the user drives the vehicle from the company to home, and the current location of the vehicle is the underground garage at home, it is determined that the vehicle is in the target scenario.

[0057] The preset condition can be a condition for enabling the target scenario mode in the target scenario. For example, the preset condition can be that it is detected that the user needs to rest in the target scenario. The perception device can further include a user state perception device, and the state information of the user in the target scenario can be obtained according to the perception device. According to the state information of the user, it is determined whether the user needs to rest. It is assumed that the user is determined to be in a tired state according to the obtained state information of the user, and it is determined that the user needs to rest. At this time, it is determined that the vehicle meets the preset condition.

[0058] In some embodiments, the preset condition can also be obtained according to historical data of the user. Specifically, the cloud can obtain historical data of the user on the vehicle in a preset time period, and analyze and process the historical data to obtain user habits of the user in the target scenario. The user habits of the user in the target scenario can be used as the preset condition in the target scenario.

[0059] For example, it is assumed that the obtained historical data is historical data of the user on the vehicle in the past month. After analyzing and processing the historical data, it is obtained that in the past month, the user drives the vehicle from the company to the home 20 times every day from 6:00 to 8:00 in the afternoon, rests on the vehicle 18 times, the average rest time is 15 minutes, the frequency of playing pure music is 95%, the frequency of rhythm of atmosphere light is 95%, the average vehicle external air temperature is 28-30°C, the average temperature of the vehicle air conditioner is 25°C, the frequency of the air volume of the air conditioner is 100%, the frequency of the seat back reclining is 95%, and the frequency of the seat cushion height adjustment is 95%.

[0060] Determining whether the vehicle currently meets the preset condition can include: determining whether the current behavior of the user meets the user habits in the target scenario according to the perception data perceived by the perception device, and determining that the vehicle currently meets the preset condition when it is determined that the current behavior of the user meets the user habits, and determining that the vehicle currently does not meet the preset condition when the current behavior of the user does not meet the user habits.

[0061] For example, when it is perceived according to the perception data that the current time is 6:30 in the afternoon, the user drives the vehicle from the company to the home, opens the music software to play pure music, opens the atmosphere light to select rhythm with music, adjusts the temperature of the vehicle air conditioner to 25°C, the air volume to one gear, the seat back to recline, and the seat cushion to height, it is determined whether the current behavior of the user meets the user habits. At this time, it is determined that the vehicle meets the preset condition of recommending the target scenario mode.

[0062] Step 202, in a case where it is determined that the vehicle meets the preset condition, perception risk data of the user is determined;

[0063] The perception risk data is used to describe the experience of the user when the target scenario mode is recommended to the vehicle.

[0064] The experience of the user can be an experience based on multiple dimensions, which can include a time dimension, an operation frequency dimension, an energy consumption dimension, a traffic consumption dimension, etc. A risk value describing the experience of the user based on the multiple dimensions can be obtained, and the perceived risk data of the user when recommending the target scenario mode to the vehicle is obtained.

[0065] In a possible implementation, the perceived risk data includes a time risk value for describing the experience of the user from the time dimension, and the target scenario mode includes a target function. The perceived risk data of the user is determined, including: obtaining operation duration data of the user required for the vehicle to perform the target function; and / or, determining schedule data of the user; and calculating the time risk value of the user based on the operation duration data and / or the schedule data.

[0066] The target scenario mode includes a target function that can be performed by the vehicle, where the target function can be one or more. The target function included in the target scenario mode can be determined. It is assumed that the target scenario mode includes multiple target functions, such as function A, function B, and function C.

[0067] The user needs to spend a certain duration to control the vehicle based on the recommended target scenario mode to make the vehicle perform the target function. The cloud can obtain the operation duration data based on the duration predicted to be spent by the user.

[0068] Specifically, the cloud can simulate a scenario of recommending the target scenario mode to the vehicle. For example, according to the model of the vehicle, it is determined that a large screen is arranged in front of the vehicle. After the target scenario mode is recommended to the vehicle, the vehicle can display a recommendation interface of the target scenario mode to the user based on the large screen. The user can select to adopt and use the target scenario mode based on the recommendation interface (use the target scenario mode, that is, control the vehicle based on the target scenario mode). When the user controls the vehicle based on the recommended target scenario mode, a certain duration is needed. The cloud can simulate a scenario in which the user controls the vehicle based on the recommended target scenario mode to make the vehicle perform multiple target functions, such as function A, function B, and function C, and predict the operation duration data spent by the user based on the simulated scenario.

[0069] The preset duration can be stored in the cloud. After the operation duration data is predicted, the cloud can compare the operation duration in the operation duration data with the preset duration, and determine the time risk value based on the operation duration and the preset duration.

[0070] For example, it is assumed that the cloud predicts that the operation duration to be spent is 20 seconds, and the preset duration is 15 seconds. After calculation (for example, the operation duration is subtracted from the preset duration, and then divided by the preset duration, and a percentage value is obtained), the time risk value is 33%.

[0071] In some embodiments, when the vehicle displays the target scenario mode to the user based on the recommendation interface, the user also needs to read and understand the recommended target scenario mode based on the recommendation interface, which also takes a certain length of time. The cloud can predict the length of time the user takes to read and understand the recommended target scenario mode based on the simulated scenario of the recommended target scenario mode, and the length of time data can also include the length of time the user takes to read and understand the recommended target scenario mode.

[0072] In some embodiments, the recommendation interface can also include a toolbar for resetting the target scenario mode. After the user reads and understands the recommended target scenario mode based on the recommendation interface, the user may also reset the function of the recommended target scenario mode in the toolbar if the function does not meet the user's expectations. The cloud can also predict the length of time the user takes to reset the function based on the simulated scenario of the recommended target scenario mode, and the length of time data can also include the length of time the user takes to reset.

[0073] Optionally, the vehicle or a terminal (such as a mobile phone, a tablet, a bracelet, etc.) connected with the vehicle is provided with a calendar, and the user can set the next schedule in the calendar in advance. At this time, the vehicle can obtain the schedule set by the user in the calendar to obtain schedule data uploaded to the cloud, and the cloud receives the schedule data.

[0074] The vehicle and the terminal can be connected through, for example, Bluetooth, Wireless Fidelity (WiFi), Near Field Communication (NFC), etc., which are not limited in the embodiments of the present application.

[0075] For example, it is assumed that the user sets the schedule in the calendar in advance: 7:00 pm, family dinner. At this time, the vehicle can obtain the schedule set by the user to obtain schedule data "7:00 pm, family dinner", and upload to the cloud.

[0076] The cloud can obtain the current time when the vehicle is in the target scenario and triggers the recommended target scenario mode, obtain the use length of the target scenario mode, determine the end time based on the current time and the use length. Determine the conflict length between the obtained schedule data and the end time based on the determined conflict length, and determine the time risk value based on the determined conflict length.

[0077] Specifically, a list of preset length ranges and corresponding time risk values can be determined based on the conflict length and the list.

[0078] Table 1

[0079] Time range Time risk value 0 min < T ≤ 10 min 30％ 10 min < T ≤ 30 min 45％ 30 min < T ≤ 60 min 70％ 60 min ≤ T 100％

[0080] As shown in Table 1: where T is the conflict duration, when the conflict duration is greater than 0 minutes and less than or equal to 10 minutes, the time risk value is 30%, when the conflict duration is greater than 10 minutes and less than or equal to 30 minutes, the time risk value is 45%, when the conflict duration is greater than 30 minutes and less than or equal to 60 minutes, the time risk value is 70%, and when the conflict duration is greater than 60 minutes, the time risk value is 100%.

[0081] For example, assuming that the current time is 6:50 pm and the use duration of the target scenario mode is 15 minutes, the end time can be determined as 7:05 pm. At this time, based on the schedule data "dinner at 7:00 pm" and the end time 7:05 pm, it is determined that there is a conflict between the two, the conflict duration is 5 minutes, and Table 1 shows that the risk value corresponding to 5 minutes is 30%. Therefore, the time risk value is determined to be 30%.

[0082] In some embodiments, a camera is arranged in the vehicle interior to capture images of the vehicle interior and identify the objects in the images. The cloud can obtain the in-vehicle object information identified by the current vehicle and determine the user schedule based on the in-vehicle object information. The cloud can store a preset time risk value, and when it is determined that there is a user schedule based on the in-vehicle object information, the preset time risk value can be determined as the time risk value.

[0083] For example, assuming that the preset time risk value is 33%, the in-vehicle object information obtained includes information associated with the celebration of a birthday, such as flowers, cakes, and gifts. The cloud can determine that the user's schedule data is "celebrating a birthday" based on the in-vehicle object information associated with the celebration of a birthday, such as flowers, cakes, and gifts. At this time, it is determined that there is a user schedule based on the in-vehicle object information, and the time risk value is determined to be 33%.

[0084] In some embodiments, the time risk value can also be determined based on the operation duration data and the schedule data. Specifically, the time risk value determined based on the operation duration data can be referred to as a first risk value, and the time risk value determined based on the schedule data can be referred to as a second risk value. The time risk value is the average of the first risk value and the second risk value.

[0085] In the above method, by determining the duration data of the user operating the vehicle, the recommended timing determined based on the perception risk data can improve the user's experience in the time dimension, ensuring that the user spends a reasonable amount of time after the target scenario mode is recommended, and avoiding a poor user experience. By determining the time risk value based on the user's schedule data, the recommended timing determined based on the perception risk data can avoid conflicts with the user's schedule, which can improve the user's adoption probability and willingness to use.

[0086] In a possible implementation, the operation duration data includes first operation duration data and second operation duration data, and the operation duration data consumed by the user for the vehicle to perform the target function is obtained by: predicting the first operation duration data of the user required for the vehicle to perform the target function based on the target scenario mode after the vehicle adopts the target scenario mode; and obtaining the second operation duration data of the user required for the vehicle to perform the target function in a case where the vehicle does not adopt the target scenario mode; and calculating the time risk value based on the operation duration data, including: calculating the time risk value based on a difference between the first operation duration data and the second operation duration data.

[0087] As in the above embodiment, the cloud can simulate a scenario in which the user controls the vehicle to perform the target function based on the recommended target scenario mode after the user adopts the target scenario mode, predict the duration required by the user based on the simulated scenario, and determine the predicted duration as the first operation duration data.

[0088] When the vehicle does not adopt the target scenario mode, the user can also control the vehicle to perform the target function through some operations. Historical data of the vehicle can be obtained, and the second operation duration data of the user required for the vehicle to perform the target function when the vehicle does not adopt the target scenario mode is determined based on the historical data.

[0089] The historical data can be historical data in a preset time period, for example, historical data in the past month. The historical data can include a number of times that the user controls the vehicle to perform each function in the preset time period and a duration corresponding to each operation. The average duration of operations for each function can be calculated based on the historical data, and the average duration of operations for each target function is determined from the average duration of operations for each function. The average duration of operations for each target function is added to obtain the second operation duration data.

[0090] Table 2

[0091] Function Number of times Average time spent / seconds Function A 15 3 Function B 6 7 Function C 9 5 Function D 13 7 Function E 18 4

[0092] As shown in Table 2, the historical data in the past month includes: function A, performed 15 times, and the average duration of operations of the user for the vehicle to perform function A is calculated to be 3 seconds; function B, performed 6 times, and the average duration of operations of the user for the vehicle to perform function B is calculated to be 7 seconds; function C, performed 9 times, and the average duration of operations of the user for the vehicle to perform function C is calculated to be 5 seconds; function D, performed 13 times, and the average duration of operations of the user for the vehicle to perform function D is calculated to be 7 seconds; and function E, performed 18 times, and the average duration of operations of the user for the vehicle to perform function E is calculated to be 4 seconds.

[0093] The average time length of the user operating the vehicle to perform each function is added to obtain the second operation time length data of 15 seconds.

[0094] As in the above embodiment, the predicted time length is 20 seconds, that is, the first operation time length data is 20 seconds. The time risk value can be calculated based on the difference between the first operation time length and the second operation time length. For example, the first operation time length of 20 seconds is subtracted from the second operation time length of 15 seconds to obtain a difference of 5 seconds. Then, the difference of 5 seconds is divided by the second operation time length of 15 seconds, and the percentage value is obtained to obtain a time risk value of 33%.

[0095] In the above method, the first operation time length data of the user after adopting the target scenario mode is predicted, the second operation time length data when the target scenario mode is not adopted is obtained, and the time risk value of the user spending more time after recommending the target scenario mode can be accurately determined based on the operation time length data before and after the adoption, so as to determine the recommendation opportunity in the next step, and improve the user experience in the time dimension after recommending the target scenario mode, and improve the adoption probability and use willingness of the user.

[0096] In a possible implementation manner, the perception risk data includes an operation risk value for describing the experience of the user from the operation frequency dimension, and the target scenario mode includes a target function. The perception risk data of the user is determined, including: predicting the first operation frequency of the user required for performing the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtaining the second operation frequency of the user required for performing the target function in the case that the vehicle does not adopt the target scenario mode; and calculating the operation risk value based on the first operation frequency and the second operation frequency.

[0097] As in the above embodiment, the target scenario mode includes function A, function B, and function C. The user needs to perform a certain operation to control the vehicle to perform the target function based on the recommended target scenario mode. The cloud can predict the operation frequency of the user for operating the vehicle to perform the target function based on the target scenario mode.

[0098] It should be understood that the user can control the vehicle to perform the target function by clicking the mechanical button arranged around the vehicle or the soft button integrated on the large screen of the car machine. One click of the user is recorded as one operation.

[0099] The cloud can simulate a scenario in which the user controls the vehicle to perform the target function based on the recommended target scenario mode after the vehicle is recommended the target scenario mode. The first operation frequency of the user is predicted based on the simulated scenario. Specifically, the cloud can simulate a control interface for controlling the vehicle based on the target scenario mode, and predict the first operation frequency of the user based on the simulated control interface.

[0100] In some embodiments, the user can adopt a target scenario mode based on the recommended interface, and then control the vehicle based on the control interface of the target scenario mode. The first number of operations may also include the number of times the user adopts the target scenario mode. The cloud can simulate the recommended interface before simulating the control interface, predict the number of times the user operates on the recommended interface, and then simulate the control interface to predict the number of times the user operates on the control interface. The sum of the obtained number of operations is the first number of operations.

[0101] Figure 3 This is a schematic diagram of an interface for a recommended target scenario mode provided in an embodiment of this application.

[0102] For example, such as Figure 3 As shown, the interface 300 includes a target scenario mode name display area 301, a recommendation information display area 302, an ignore button 303, an accept button 304, and an immediate enable button 305.

[0103] The recommendation information display area 302 can display recommendation information describing the characteristics of the target scenario mode, which may include the reason for recommending the target scenario mode, recommendation quality information, and usage effect information.

[0104] Users can click the "Activate Now" button (306) to adopt the target scenario mode and open the target scenario mode control interface. This action is recorded as one operation. Users can then control the vehicle to perform the target function through the control interface, and the number of operations can be accumulated to obtain the first operation count.

[0105] The target scenario mode includes three target functions: Function A, Function B, and Function C. The control interface of the target scenario mode can be as follows: Figure 4 As shown.

[0106] Figure 4 This is a schematic diagram of the control interface for a target scenario mode provided in an embodiment of this application.

[0107] For example, such as Figure 4 As shown, the interface 400 includes: an on / off button 401 and an off button 402 for function A, an on / off button 403 and an off button 404 for function B, and an on / off button 405 and an off button 406 for function C. Users can click the on / off button 401 for function A, the on / off button 403 for function B, and the on / off button 405 for function C to control the vehicle to perform the target function based on the target scenario mode. At this time, the operation count is incremented by 3, confirming that the first operation count is 4.

[0108] In some embodiments, such as Figure 3The recommended interface shown can also include a toolbar 306 for resetting the target scenario mode. After reading and understanding the recommended target scenario mode based on the recommended interface, the user may find that a function of the recommended target scenario mode does not meet expectations, and the user can also reset the function in the toolbar 306. The cloud can also predict the number of operations of the user resetting the function based on the simulated scenario of the recommended target scenario mode. The first number of operations can also include the number of operations of the user resetting.

[0109] When the vehicle does not adopt the target scenario mode, the user can also control the vehicle to perform the target function through some operations. The cloud can obtain the number of operations of the user controlling the vehicle to perform function A, the number of operations of the user controlling the vehicle to perform function B, and the number of operations of the user controlling the vehicle to perform function C from the historical data of the vehicle not adopting the target scenario mode. The cloud adds the number of operations to obtain a second number of operations.

[0110] For example, the cloud obtains that the number of operations of the user controlling the vehicle to perform function A, function B and function C is 1 time each, and the second number of operations is 3 times by adding.

[0111] The cloud can calculate the operation risk value based on the first number of operations and the second number of operations obtained. Specifically, the cloud can subtract the second number of operations 3 times from the first number of operations 4 times to obtain a difference value 1 time between the first number of operations and the second number of operations, and then divide the difference value 1 time by the second number of operations 3 times to obtain a percentage value, and the operation risk value is about 33%.

[0112] In the above method, by predicting the first number of operations of the user based on the target scenario mode and the second number of operations of the user not adopting the target scenario mode, and calculating the operation risk value based on the first number of operations and the second number of operations, the recommended timing determined based on the perception risk data can be combined with the experience in the number of operations dimension of the user, so that the recommended target scenario mode can improve the user experience in the number of operations dimension, and avoid the user experience being poor due to the increase in the number of operations after the target scenario mode is recommended.

[0113] In a possible implementation, the perception risk data includes an energy consumption risk value for describing a user experience from an energy consumption dimension, the target scenario mode includes a target function, and determining the perception risk data of the user includes: predicting first fuel consumption data and / or first electricity consumption data of the target function executed by the vehicle based on the target scenario mode after the vehicle adopts the target scenario mode; obtaining second fuel consumption data and / or second electricity consumption data of the target function executed by the vehicle in a case where the vehicle does not adopt the target scenario mode; calculating the energy consumption risk value of the vehicle according to the first fuel consumption data and the second fuel consumption data; or, calculating the energy consumption risk value of the vehicle according to the first electricity consumption data and the second electricity consumption data; or, calculating the energy consumption risk value according to the first fuel consumption data, the first electricity consumption data, the second fuel consumption data, and the second electricity consumption data.

[0114] The vehicle can be a pure electric vehicle, a pure oil vehicle, or a hybrid oil-electric vehicle. When the vehicle is a pure electric vehicle, the target function can include a function that needs to consume electricity. The cloud can first determine the function that needs to consume electricity from the target function, and then predict the first electricity consumption of the vehicle in a scenario in which the user controls the vehicle to execute the target function based on the recommended target scenario mode.

[0115] As in the above embodiment, the target scenario mode includes function A, function B, and function C, and it is assumed that function A needs to consume electricity, where function A can be, for example, a function of controlling the operation of a vehicle-mounted air conditioner or controlling the beat of an atmosphere lamp. In the following embodiment, function A is taken as an example of controlling the operation of a vehicle-mounted air conditioner: the cloud can predict the first electricity consumption of the vehicle in executing function A based on the target scenario mode. The specific steps can be, for example: the cloud can determine the running parameters and running duration of function A after the user turns on the target scenario mode, and determine the first electricity consumption of the vehicle based on the running parameters and the running duration.

[0116] For example, it is assumed that the running parameters are controlling the operation of a vehicle-mounted air conditioner at 24 DEG C, and the running duration is 15 minutes. The vehicle-mounted air conditioner consumes 0.68 degrees of electricity for one minute of operation at 24 DEG C, and consumes 10.05 degrees of electricity for 15 minutes of operation. The first electricity consumption is obtained as 10.2 degrees.

[0117] The cloud can obtain the number of times of executing function A in the target scenario and the electricity consumption of each time within a preset time period (for example, within one month) of the vehicle, and determine the average electricity consumption of executing function A as the second electricity consumption.

[0118] For example, it is assumed that the cloud obtains that the number of times of executing target function A in the target scenario within one month is 8, and the electricity consumption of each time is 10 degrees, 9.7 degrees, 10.5 degrees, 9.3 degrees, 10 degrees, 9.5 degrees, 10.5 degrees, and 10.5 degrees. The average electricity consumption is calculated as 10 degrees, and the second electricity consumption is obtained as 10 degrees.

[0119] The cloud can calculate the energy consumption risk value based on the obtained first energy consumption and second energy consumption. Specifically, the first energy consumption 10.2 degrees can be subtracted from the second energy consumption 10 degrees to obtain a difference value 0.2 degrees between the first energy consumption and the second energy consumption, and then the obtained difference value 0.2 degrees is divided by the second energy consumption 10 degrees to obtain a percentage value, and the energy consumption risk value is about 2%.

[0120] When the vehicle is a pure oil vehicle, the target function can include a function that needs to consume fuel. The cloud can first determine the function that needs to consume fuel from the target function, and predict the first fuel consumption of the vehicle based on the target scenario mode executing the function. As described in the above embodiment, the running time of the function and the fuel consumption per minute can be determined, and the first fuel consumption can be obtained by multiplying the running time by the fuel consumption per minute.

[0121] The cloud can calculate the average fuel consumption based on the number of times the user turns on the function under the target scenario and the fuel consumption per time in the historical data of the vehicle within a preset time period (for example, within a month), and determine the obtained average fuel consumption as the second fuel consumption. Then, the first fuel consumption is subtracted from the second fuel consumption to obtain a fuel consumption difference value, and the fuel consumption difference value is divided by the second fuel consumption to obtain the energy consumption risk value.

[0122] In some embodiments, the vehicle can also be a hybrid vehicle, which can be set as pure electric priority by default. In this case, the energy consumption risk value can be determined as described above for a pure electric vehicle. When the vehicle's electricity is lower than the electricity threshold, there can be functions that consume electricity and functions that consume fuel at the same time. At this time, the cloud can determine the functions that consume electricity and the functions that consume fuel in the target function respectively, determine the first energy consumption risk value based on the functions that consume electricity, determine the second energy consumption risk value based on the functions that consume fuel, and obtain the energy consumption risk value based on the first energy consumption risk value and the second energy consumption risk value, for example, taking the average of the first energy consumption risk value and the second energy consumption risk value to obtain the energy consumption risk value. The process of determining the first energy consumption risk value and the second energy consumption risk value can be as described in the above embodiment, which will not be described here.

[0123] In the above method, by predicting the first fuel consumption data and / or the first electricity consumption data of the user based on the target scenario mode, determining the second fuel consumption data and / or electricity consumption data without adopting the target scenario mode, and calculating the energy consumption risk value of the target scenario mode based on the above data, the recommended time determined based on the perception risk data can be combined with the user's experience in the energy consumption dimension, to ensure that the recommended target scenario mode can improve the user's experience, and avoid the user's poor experience due to high energy consumption after the recommendation.

[0124] In a possible implementation, the perception risk data includes a traffic risk value used to describe the experience of the user from the traffic consumption dimension, the target scenario mode includes a target function, and the determining of the perception risk data of the user includes: predicting a first traffic consumed by the vehicle based on the target function executed by the vehicle based on the target scenario mode; obtaining a second traffic consumed by the vehicle based on the target function executed by the vehicle in a case where the vehicle does not adopt the target scenario mode; and calculating the traffic risk value based on the first traffic and the second traffic.

[0125] The target function can include a function that needs to consume traffic, and the cloud can first determine the function that needs to consume traffic from the target function, and then predict the first traffic consumed by the vehicle in a scenario in which the user controls the vehicle based on the recommended target scenario mode to make the vehicle execute the target function.

[0126] As in the above embodiment, the target scenario mode includes function A, function B and function C, and it is assumed that it is determined that function B needs to consume traffic, where function B may be, for example, a function of playing soothing music, playing a comedy short film, etc. In the following embodiment, function B is taken as an example of playing a comedy short film: the step of predicting the first traffic consumed by the vehicle may be, for example, that the cloud can determine the playing duration and the quality of the comedy short film played when function B is executed based on the target scenario mode, and determine the first traffic consumed by the vehicle based on the playing duration and the quality.

[0127] For example, it is assumed that the cloud determines that the playing duration of the comedy short film played when function B is executed based on the target scenario mode is 15 minutes, and the quality is 1080P, and based on the quality, it is determined that 26.25M traffic is consumed per minute of playing the short film. The first traffic is obtained by multiplying the playing duration 15 by the traffic consumed per minute 26.25M, and the first traffic is 393.75M.

[0128] The cloud can obtain the number of times that the vehicle executes function B in the target scenario and the traffic consumed each time in a preset time period (for example, in the past month), and determine the average traffic consumed by executing function B, and determine the obtained average traffic as the second traffic.

[0129] For example, it is assumed that the cloud obtains that the vehicle executes the target function 5 times in the commuting scenario in the past month, and the traffic used each time is 220M, 216M, 230M, 215M and 219M respectively, and the average traffic consumed is calculated to be 220M, and the second traffic is obtained to be 220M.

[0130] The cloud can calculate a traffic risk value based on the obtained first traffic and second traffic. Specifically, the first traffic 393.75M can be subtracted from the second traffic 220M to obtain a difference value 173.75M between the first traffic and the second traffic, and the obtained difference value 173.75M can be divided by the second traffic 220M to obtain a percentage value, and the traffic risk value is about 79%.

[0131] In some embodiments, the cloud can also obtain the remaining traffic of the vehicle in the current month, and predict the remaining number of times that the user controls the vehicle based on the target scenario mode to make the vehicle perform the target function in the current month according to the use habit of the user, calculate whether the traffic consumed by the vehicle in the current month will exceed the remaining traffic based on the first traffic and the remaining number of times, multiply the calculated traffic risk value by a first preset coefficient when it is determined that the traffic consumed by the vehicle in the current month will not exceed the remaining traffic, to obtain a final traffic risk value, wherein the first preset coefficient is less than 1. Multiply the calculated traffic risk value by a second preset coefficient when it is determined that the traffic consumed by the vehicle in the current month exceeds the remaining traffic, to obtain a final traffic risk value, wherein the second preset coefficient is greater than 1.

[0132] For example, assuming that the remaining traffic of the vehicle in the current month is 5G, the first preset coefficient is 0.6, the second preset coefficient is 1.2, it is determined according to the use habit of the user that the user will be in the off-work scenario on weekdays in the current month, and the remaining weekdays in the current month are 10 days, then the remaining number of times that the user controls the vehicle based on the target scenario mode to make the vehicle perform the target function in the current month is 10 times after the user adopts the target scenario mode, the consumed traffic is about 400M each time, and the remaining consumed traffic is about 4000M, which is less than the remaining traffic 5G in the current month, then the obtained traffic risk value 79% is multiplied by the first preset coefficient 0.6 to obtain a final traffic risk value of 47.4%.

[0133] In some embodiments, a user perception risk evaluation model can be established in advance, and the data predicted by the cloud and the historical data of the vehicle obtained can be input into the perception risk evaluation model to obtain user perception risk data.

[0134] In the above method, by predicting the first traffic data of the user based on the target scenario mode, determining the second traffic data of the user without adopting the target scenario mode, and calculating the traffic risk value of the target scenario mode based on the first traffic data and the second traffic data, the recommended time determined based on the perception risk data can be combined with the experience of the user in the traffic consumption dimension of the vehicle, so as to ensure that the recommended target scenario mode can improve the use experience of the user and avoid the use experience of the user being poor due to the high traffic consumption of the vehicle after the recommendation.

[0135] Step 203, determining whether the current time belongs to a target recommendation time based on the perception risk data;

[0136] The target recommendation opportunity is a suitable recommendation opportunity. Determining whether the current time belongs to the target recommendation opportunity is to determine whether the current time is a suitable recommendation opportunity.

[0137] The current time can be determined to be a suitable recommendation opportunity based on the risk values of the user's experience in multiple dimensions described in the perception risk data when recommending the target scenario mode to the vehicle. Specifically, a plurality of dimension risk thresholds corresponding to the suitable recommendation opportunity (target recommendation opportunity) can be preset. When the risk values in the plurality of dimensions are all less than the corresponding risk thresholds, it is determined that the current time is a suitable recommendation opportunity, i.e., the current time belongs to the target recommendation opportunity.

[0138] For example, assuming that the preset time risk threshold is 33%, the operation risk threshold is 50%, the energy consumption risk threshold is 20%, and the traffic risk threshold is 50%, it can be determined that the current time is a target recommendation opportunity according to the calculated time risk value 30% less than the time risk threshold 33%, the operation risk value 33% less than the operation risk threshold 50%, the energy consumption risk value 2% less than the energy consumption risk threshold 20%, and the traffic risk value 47.4% less than the traffic risk threshold 50%.

[0139] In some embodiments, a total threshold value corresponding to the risk values in multiple dimensions can be preset. The sum of the risk values in multiple dimensions can be compared with the total threshold value. When the sum is less than the preset total threshold value, it is determined that the current time is a suitable recommendation opportunity, i.e., the current time belongs to the target recommendation opportunity.

[0140] In one possible implementation, determining whether the current time belongs to the target recommendation opportunity based on the perception risk data includes: predicting the adoption probability and / or the use willingness value of the user for the target scenario mode based on the perception risk data; determining that the current time belongs to the target recommendation opportunity when the adoption probability is greater than or equal to a preset probability threshold value and / or the use willingness value is greater than or equal to a preset value; and determining that the current time does not belong to the target recommendation opportunity when the adoption probability is less than the preset probability threshold value and / or the use willingness value is less than the preset value.

[0141] The cloud can have a preset prediction model of the use willingness level. The obtained perception risk data can be input into the preset prediction model of the use willingness level to obtain the use willingness value of the user for the target scenario mode. The obtained use willingness value can be compared with a preset value. When the use willingness value is less than the preset value, it is determined that the current time does not belong to the target recommendation opportunity. When the use willingness value is greater than or equal to the preset value, it is determined that the current time belongs to the target recommendation opportunity.

[0142] The cloud-based system can also pre-determine adoption probabilities. The perceived risk data can be input into a pre-defined predictive model based on the user's willingness to use the target scenario, yielding the probability of adoption for the user. This probability can be compared to the pre-defined probability. If the adoption probability is less than the pre-defined probability, the current moment is determined not to be a suitable time for the target recommendation. Conversely, if the willingness to use is greater than or equal to the pre-defined value, the current moment is determined to be a suitable time for the target recommendation.

[0143] In some embodiments, when the user's willingness to use and the probability of adoption are obtained simultaneously, the current moment can be determined to be a target recommendation opportunity if the willingness to use is greater than or equal to a preset value, and otherwise the current moment can be determined not to be a target recommendation opportunity.

[0144] Step 204: When it is determined that the current moment is a target recommendation opportunity, recommend the target scenario mode to the vehicle.

[0145] When the cloud determines that the current moment is an appropriate time to make a recommendation, it can recommend the target scenario mode to the vehicle, so that the user can adopt and use the target scenario mode.

[0146] Specifically, the vehicle is equipped with a remote communication terminal (Telematics BOX, TBOX). The TBOX can establish a connection with the cloud through a vehicle-to-everything (V2X) information service provider (Telematics Service Provider, TSP). The cloud can send the target scenario mode to the TBOX through the TSP, and the TBOX will display the target scenario mode in the vehicle in a preset manner.

[0147] In some embodiments, the vehicle can detect whether the user has adopted or used the target scenario mode based on the displayed interface, and record the user's adoption and usage behaviors. Based on the recorded adoption and usage behaviors, the vehicle can optimize the prediction model for adoption probability and the prediction model for usage intention.

[0148] like Figure 3 As shown, after the vehicle display interface 300, the system detects and responds to user-clicked buttons. Specifically, when the user clicks the "ignore" button 303, the target scenario mode is ignored, confirming that the user has not adopted the target scenario mode, and this is recorded as "user has not adopted the target scenario mode." When the user clicks the "accept" button 304, the target scenario mode is added to the scenario mode list, and this is recorded as "user has adopted the target scenario mode but has not used it." When the user clicks the "enable immediately" button 305, the target scenario mode is added to the scenario mode list, and the vehicle is controlled to execute the target function based on the target scenario mode, and this is recorded as "user has adopted the target scenario mode and has used it."

[0149] When the user does not adopt the target scenario mode, the perceived risk data can be used as a negative sample to optimize the prediction model of the adoption probability, and when the user does not use the target scenario mode, the perceived risk data can be used as a negative sample to optimize the prediction model of the willingness level. When the user adopts the target scenario mode, the perceived risk data can be used as a positive sample to optimize the prediction model of the adoption probability, and when the user uses the target scenario mode, the perceived risk data can be used as a positive sample to optimize the prediction model of the willingness level.

[0150] In the above method, the perceived risk data describing the user experience after the target scenario mode is recommended to the vehicle is determined, and whether the current time is a suitable recommendation opportunity is determined based on the perceived risk data, so that the determined recommendation opportunity combines the user's subjective perceived risk data, so that the risk considered by the recommendation opportunity is more comprehensive, and it is ensured that the recommendation can improve the user's use experience, and can improve the adoption rate and the use willingness of the user after the recommendation.

[0151] In summary, the present application determines the perceived risk data describing the user experience after recommending the target scenario mode to the vehicle, determines whether the current time is a suitable recommendation opportunity based on the perceived risk data, so that the determined recommendation opportunity combines the user's subjective perceived risk data, so that the risk considered by the recommendation opportunity is more comprehensive, and the recommendation can improve the user's use experience and can improve the user's adoption rate and use willingness after the recommendation. By determining the time length data spent by the user operating the vehicle, the recommendation opportunity determined based on the perceived risk data can improve the user's experience in the time dimension, and ensure that the time spent by the user after recommending the target scenario mode is not too long, so that the user has a bad experience; determining the time risk value according to the user's schedule data can avoid the conflict between the user's schedule and the recommendation opportunity determined based on the perceived risk data, and can improve the user's adoption willingness and use willingness. By predicting the first operation time length data of the user after adopting the target scenario mode, obtaining the second operation time length data when the target scenario mode is not adopted, and based on the operation time length data before and after adoption, the time risk value of the user spending more time after recommending the target scenario mode can be accurately determined, so that the next step is to determine the recommendation opportunity based on the determined time risk value, and ensure that the target scenario mode after the recommendation can improve the user's experience in the time dimension, improve the user's adoption probability and use willingness. By predicting the first operation frequency of the user based on the target scenario mode and the second operation frequency when the target scenario mode is not adopted, and calculating the operation risk value based on the first operation frequency and the second operation frequency, the recommendation opportunity determined based on the perceived risk data can combine the user's experience in the operation frequency dimension, and ensure that the recommended target scenario mode can improve the user's use experience in the operation frequency dimension, and avoid the user's use experience caused by the increase of the operation frequency after the recommendation. By predicting the first fuel consumption data and / or the first electricity consumption data of the user based on the target scenario mode, determining the second fuel consumption data and / or the electricity consumption data when the target scenario mode is not adopted, and calculating the energy consumption risk value of the target scenario mode based on the above data, the recommendation opportunity determined based on the perceived risk data can combine the user's experience in the energy consumption dimension, and ensure that the recommended target scenario mode can improve the user's use experience, and avoid the user's use experience caused by the high energy consumption after the recommendation. By predicting the first traffic data of the user based on the target scenario mode, determining the second traffic data when the target scenario mode is not adopted, and calculating the traffic risk value of the target scenario mode based on the first traffic data and the second traffic data, the recommendation opportunity determined based on the perceived risk data can combine the user's experience in the vehicle traffic consumption dimension, and ensure that the recommended target scenario mode can improve the user's use experience, and avoid the user's use experience caused by the high vehicle traffic consumption after the recommendation.

[0152] Figure 5 FIG. 1 is a structural schematic diagram of a scenario mode recommendation device provided by an embodiment of the present application.

[0153] As shown in Figure 5 The apparatus 500 includes the following modules:

[0154] A first determining module 501 is configured to determine whether the vehicle currently meets preset conditions of a recommended target scenario mode.

[0155] A second determining module 502 is configured to determine perceptual risk data of the user, when it is determined that the vehicle meets the preset conditions, wherein the perceptual risk data is used to describe the experience of the user when the target scenario mode is recommended to the vehicle.

[0156] A judging module 503 is configured to determine whether the current time belongs to a target recommendation opportunity based on the perceptual risk data.

[0157] A recommending module 504 is configured to recommend the target scenario mode to the vehicle, when it is determined that the current time belongs to the target recommendation opportunity.

[0158] In a possible implementation, the judging module 503 is specifically configured to predict a probability of adoption and / or a value of willingness to use of the target scenario mode by the user based on the perceptual risk data, and determine that the current time belongs to the target recommendation opportunity when the probability of adoption is greater than or equal to a preset probability threshold and / or the value of willingness to use is greater than or equal to a preset value, and determine that the current time does not belong to the target recommendation opportunity when the probability of adoption is less than the preset probability threshold and / or the value of willingness to use is less than the preset value.

[0159] In a possible implementation, the perceptual risk data includes a time risk value used to describe the experience of the user from a time dimension, and the target scenario mode includes a target function, and the second determining module 502 is specifically configured to: obtain operation time length data of the user required for the vehicle to execute the target function; and / or, determine schedule data of the user; and calculate the time risk value of the user based on the operation time length data and / or the schedule data.

[0160] In a possible implementation, the operation time length data includes first operation time length data and second operation time length data, and the second determining module 502 is specifically configured to: predict the first operation time length data of the user required for the vehicle to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtain the second operation time length data of the user required for the vehicle to execute the target function in a case where the vehicle does not adopt the target scenario mode; and calculate the time risk value based on a difference between the first operation time length data and the second operation time length data.

[0161] In one possible implementation, the perceived risk data includes an operational risk value used to describe the user experience from the dimension of the number of operations, the target scenario mode includes a target function, and the second determining module 502 is specifically used to: predict the first number of user operations required to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtain the second number of user operations required for the vehicle to execute the target function if the vehicle does not adopt the target scenario mode; and calculate the operational risk value based on the first number of operations and the second number of operations.

[0162] In one possible implementation, the perceived risk data includes an energy consumption risk value used to describe the user's experience from an energy consumption perspective, the target scenario mode includes a target function, and the second determining module 502 is specifically used to: predict the first fuel consumption data and / or the first electricity consumption data based on the target scenario mode when the vehicle adopts the target scenario mode; if the vehicle does not adopt the target scenario mode, obtain the second fuel consumption data and / or the second electricity consumption data of the vehicle performing the target function; calculate the energy consumption risk value based on the first fuel consumption data and the second fuel consumption data; or, calculate the energy consumption risk value based on the first electricity consumption data and the second electricity consumption data; or, calculate the energy consumption risk value based on the first fuel consumption data, the first electricity consumption data, the second fuel consumption data, and the second electricity consumption data.

[0163] In one possible implementation, the perceived risk data includes a traffic risk value used to describe the user experience from the perspective of traffic consumption, the target scenario mode includes a target function, and the second determining module 502 is specifically used to: predict the first traffic consumed by the vehicle in executing the target function based on the target scenario mode after the vehicle adopts the target scenario mode; obtain the second traffic consumed by the vehicle in executing the target function if the vehicle does not adopt the target scenario mode; and calculate the traffic risk value based on the first traffic and the second traffic.

[0164] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0165] For example, such as Figure 6 As shown, the electronic device 600 includes a memory 601 and a processor 602. The memory 601 stores executable program code 6011, and the processor 602 is used to call and execute the executable program code 6011 to perform a scenario mode recommendation method.

[0166] Furthermore, embodiments of this application also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a scenario mode recommendation method provided in embodiments of this application.

[0167] The embodiment can divide the functions of the device according to the method examples described above, for example, each function module can be divided, or two or more functions can be integrated into one processing module. The integrated module can be implemented in the form of hardware. It should be noted that the division of the modules in the embodiment is illustrative, and is only a logical function division. In actual implementation, another division mode can be used.

[0168] In the case of dividing each function module according to each function, the device can further include a first determination module, a second determination module, a judgment module, a recommendation module, and the like. It should be noted that all related contents of each step involved in the method embodiments described above can be cited in the function description of the corresponding function module, and will not be described here.

[0169] It should be understood that the device provided by the embodiment is used to execute the method of recommending a scenario mode described above, and thus the same effect as the implementation method described above can be achieved.

[0170] In the case of using an integrated unit, the device can include a processing module and a storage module. When the device is applied to a vehicle, the processing module can be used to control and manage the actions of the vehicle. The storage module can be used to support the vehicle to execute program codes and the like.

[0171] The processing module can be a processor or a controller, which can implement or execute various exemplary logical blocks, modules and circuits described in combination with the disclosure of the present application. The processor can also be a combination of computing functions, such as one or more microprocessor combinations, a combination of digital signal processing (DSP) and microprocessor, and the like. The storage module can be a memory.

[0172] In addition, the device provided by the embodiment of the present application can be a chip, a component or a module. The chip can include a connected processor and a memory. The memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the method of recommending a scenario mode provided by the above embodiment.

[0173] The embodiment also provides a computer readable storage medium, which stores computer program codes. When the computer program codes run on a computer, the computer can execute the related method steps described above to implement the method of recommending a scenario mode provided by the above embodiment.

[0174] The embodiment also provides a computer program product, which makes the computer execute the related steps described above to implement the method of recommending a scenario mode provided by the above embodiment when the computer program product runs on the computer.

[0175] Among them, the device, computer readable storage medium, computer program product or chip provided by the embodiment are used for executing the corresponding method provided above, so the beneficial effects achieved thereby can refer to the beneficial effects in the corresponding method provided above, which will not be repeated here.

[0176] Through the description of the above embodiments, those skilled in the art can understand that, for the convenience and brevity, only the above division of functional modules is taken as an example, and in actual application, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above.

[0177] In the embodiments provided in the present application, it should be understood that the disclosed device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another device, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interface, device or unit, and can be electrical, mechanical or other forms.

[0178] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of context mode recommendation, characterized by, The method comprises: determining whether the vehicle currently meets preset conditions of a recommended target scenario mode; in a case where it is determined that the vehicle meets the preset conditions, determining perceptual risk data of a user; wherein the perceptual risk data is used to describe an experience of the user when the target scenario mode is recommended to the vehicle; judging whether a current time belongs to a target recommendation opportunity based on the perceptual risk data; in a case where it is determined that the current time belongs to the target recommendation opportunity, recommending the target scenario mode to the vehicle.

2. The method of claim 1, wherein, The judging whether the current time belongs to the target recommendation opportunity based on the perceptual risk data comprises: predicting an adoption probability and / or a use willingness value of the target scenario mode of the user based on the perceptual risk data; in a case where the adoption probability is greater than or equal to a preset probability threshold value, and / or the use willingness value is greater than or equal to a preset value, determining that the current time belongs to the target recommendation opportunity; in a case where the adoption probability is less than the preset probability threshold value, and / or the use willingness value is less than the preset value, determining that the current time does not belong to the target recommendation opportunity.

3. The method according to claim 1 or 2, characterized in that, The perceptual risk data comprises a time risk value used to describe the experience of the user from a time dimension, and the target scenario mode comprises a target function, and the determining the perceptual risk data of the user comprises: obtaining operation duration data of the user required for the vehicle to execute the target function; and / or, determining schedule data of the user; calculating the time risk value based on the operation duration data and / or the schedule data.

4. The method of claim 3, wherein, The operation duration data comprises first operation duration data and second operation duration data, and the obtaining operation duration data consumed by the vehicle to execute the target function comprises: predicting first operation duration data of the user required for the vehicle to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; in a case where the vehicle does not adopt the target scenario mode, obtaining second operation duration data of the user required for the vehicle to execute the target function; The calculating the time risk value based on the operation duration data comprises: calculating the time risk value based on the first operation duration data and the second operation duration data.

5. The method according to claim 1 or 2, characterized in that, The perceptual risk data comprises an operation risk value used to describe the experience of the user from an operation frequency dimension, and the target scenario mode comprises a target function, and the determining the perceptual risk data of the user comprises: predicting first operation frequency of the user required for the vehicle to execute the target function based on the target scenario mode after the vehicle adopts the target scenario mode; in a case where the vehicle does not adopt the target scenario mode, obtaining second operation frequency of the user required for the vehicle to execute the target function; calculating the operation risk value based on the first operation frequency and the second operation frequency.

6. The method of claim 1 or 2, wherein, The perceptual risk data comprises an energy consumption risk value used to describe the experience of the user from an energy consumption dimension, and the target scenario mode comprises a target function, and the determining the perceptual risk data of the user comprises: predicting first fuel consumption data and / or first electricity consumption data of the vehicle performing the target function based on the target scenario mode after the vehicle adopts the target scenario mode; acquiring second fuel consumption data and / or second electricity consumption data of the vehicle performing the target function in the case that the vehicle does not adopt the target scenario mode; calculating the energy consumption risk value according to the first fuel consumption data and the second fuel consumption data; or, calculating the energy consumption risk value according to the first electricity consumption data and the second electricity consumption data; or, calculating the energy consumption risk value according to the first fuel consumption data, the first electricity consumption data, the second fuel consumption data and the second electricity consumption data.

7. The method according to claim 1 or 2, characterized in that, The perception risk data includes a traffic risk value for describing the experience of the user from a traffic consumption dimension, and the target scenario mode includes a target function, and the determining the perception risk data of the user includes: predicting first traffic consumed by the vehicle performing the target function based on the target scenario mode after the vehicle adopts the target scenario mode; acquiring second traffic consumed by the vehicle performing the target function in the case that the vehicle does not adopt the target scenario mode; calculating the traffic risk value based on the first traffic and the second traffic.

8. An apparatus for a scenario mode recommendation, the apparatus comprising: The device includes: a first determining module configured to determine whether a vehicle currently meets a preset condition of recommending a target scenario mode; a second determining module configured to determine perception risk data of a user in the case that the vehicle meets the preset condition, wherein the perception risk data is used to describe the experience of the user when the target scenario mode is recommended to the vehicle; a judging module configured to judge whether a current time belongs to a target recommendation time based on the perception risk data; a recommending module configured to recommend the target scenario mode to the vehicle in the case that the current time is determined to belong to the target recommendation time.

9. An electronic device, comprising: The electronic device includes: a memory configured to store executable program codes; a processor configured to call and run the executable program codes from the memory, so that the electronic device performs the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, when the computer program is executed, the method according to any one of claims 1 to 7 is realized. The computer readable storage medium stores a computer program, when the computer program is executed, the method according to any one of claims 1 to 7 is realized.

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