Recommended methods, devices, equipment, and readable storage media for intelligent cockpit functions

Abstract

This application discloses a method, apparatus, device, and readable storage medium for recommending intelligent cockpit functions. The method includes the following steps: acquiring passenger information; matching recommendation data from a preset database based on the passenger information, and generating a recommendation scheme based on the recommendation data; outputting the recommendation scheme to a display unit for the current passenger to select the appropriate recommendation scheme according to their own wishes; if the current passenger does not select a recommendation scheme, acquiring the current passenger's operation actions; adjusting the intelligent cockpit based on the operation actions, and updating the operation actions and adjustment actions to the preset database to update the specific content of the recommendation scheme. This application enables the recommendation of different content schemes based on different passengers, thereby allowing different passengers to enjoy the intelligent services of the intelligent cockpit. Furthermore, by updating the database in real time based on the passenger's operation actions, the accuracy of the recommended schemes is improved, thus providing passengers with a more comfortable riding experience.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and in particular to a method, apparatus, device, and readable storage medium for recommending intelligent cockpit functions. Background Technology

[0002] In the existing market, all parties are limited to a single-seat, single-account experience solution, where the driver can log in to their account via camera and directly control the smart cockpit.

[0003] In multi-user scenarios, while the driver logs into their account to control the smart cockpit and experience its services, other passengers may also have service needs. However, since the smart cockpit is controlled by the driver's account, the smart cockpit system will prioritize providing services based on the driver's habits when passengers enjoy the services offered by the smart cockpit.

[0004] Therefore, the intelligent cockpit system cannot make recommendations based on individual passenger circumstances, nor can it provide adaptive service functions for different passengers, resulting in a poor passenger experience when riding in the intelligent cockpit. Summary of the Invention

[0005] In view of this, this application provides a method, apparatus, device and readable storage medium for recommending smart cockpit functions, aiming to improve the passenger experience when riding in a smart cockpit.

[0006] To achieve the above objectives, this application provides a method for recommending intelligent cockpit functions, which includes the following steps:

[0007] Obtain passenger information;

[0008] Based on the passenger information, recommended data is obtained by matching from a preset database, and a recommended scheme is generated based on the recommended data;

[0009] The recommended options are output to the display unit so that the current passenger can select the appropriate recommended option according to their own wishes;

[0010] If the current passenger does not select the recommended option, then obtain the current passenger's action.

[0011] Based on the operation actions, the intelligent cockpit is adjusted, and the operation actions and adjustment actions are updated to the preset database.

[0012] For example, the step of matching recommended data from a preset database based on the passenger information and generating a recommended solution based on the recommended data includes:

[0013] Determine whether a passenger account exists in a preset database that is mapped to the passenger information; wherein, the passenger account contains passenger data of passengers who applied for the account, and when the similarity between the passenger information and the passenger data is greater than a preset threshold, it is determined that the passenger information and the passenger account are mapped.

[0014] If it does not exist, then the passenger data of all passenger accounts in the preset database will be matched with the passenger information to obtain the passenger data with the highest similarity to the passenger information, and the historical operation data of the smart cockpit will be used as the recommended data for the corresponding passenger account.

[0015] Alternatively, if it does not exist, the passenger type of the current passenger is determined based on the passenger information, and the service data provided by the smart cockpit in the preset database is used as the recommended data based on the passenger type.

[0016] A recommendation scheme is generated based on the recommendation data and the passenger information.

[0017] For example, the step of generating a recommendation scheme based on the recommendation data and the passenger information includes:

[0018] The passenger information includes body shape information, action information, and facial expression information;

[0019] Based on the body shape information, determine the current sitting posture of the passenger;

[0020] Based on the sitting posture, movement information, and facial expression information, the current passenger's service needs are determined;

[0021] A recommendation scheme is generated based on the recommended data and the service requirements.

[0022] For example, the step of determining the current passenger's service needs based on the sitting posture, movement information, and facial expression information includes:

[0023] Based on the action information, determine the frequency with which the current passenger performs the same type of action;

[0024] Determine the corresponding position of actions whose frequency is greater than a preset frequency, and determine the adjustable information of the corresponding position;

[0025] The facial expression information is input into a preset analysis model, and the changes in facial expression at the eyebrows, corners of the mouth, and corners of the eyes of the current passenger are determined based on the analysis model.

[0026] The service needs of the current passenger are determined based on the sitting posture, the adjustable information, and the changes in facial expression.

[0027] For example, the step of generating a recommendation scheme based on the recommendation data and the service requirements includes:

[0028] Determine the service data provided by the smart cockpit in the recommended data;

[0029] The service content in the service data that corresponds to the service requirement is taken as the first recommended solution;

[0030] Determine the usage frequency of each service content in the service data, and select the service content whose frequency is greater than the average usage frequency as the second recommended option;

[0031] Based on the first and second recommendation schemes, duplicate items are filtered out, and a recommendation scheme is generated.

[0032] For example, prior to the step of determining whether a passenger account with a mapping relationship to the passenger information exists in a preset database, the process includes:

[0033] Obtain account registration information;

[0034] Based on the account registration information, the registered account passengers are identified, and the passenger data of the registered account passengers is determined; wherein, the passenger data includes at least the passenger's body shape information;

[0035] Collect the operation actions of the registered account passengers and determine the usage habits of the registered account passengers when using the smart cockpit, and use the operation actions and usage habits as historical operation data;

[0036] A passenger account is generated based on the passenger data and the historical operation data.

[0037] For example, before the step of obtaining recommended data from a preset database based on the passenger information, the following steps are included:

[0038] Collect passenger information for unregistered accounts and service data when such unregistered accounts use the smart cockpit;

[0039] Based on the passenger information, determine the passenger type of the current passenger;

[0040] According to the passenger type, the service data is categorized and stored, and a mapping relationship is established between the passenger type and the service data.

[0041] For example, to achieve the above objectives, this application also provides a smart cockpit function recommendation device, the device comprising:

[0042] The first acquisition module is used to acquire passenger information;

[0043] The generation module is used to match recommended data from a preset database based on the passenger information, and generate a recommendation scheme based on the recommended data;

[0044] The output module is used to output the recommended solution to the display unit so that the current passenger can select the corresponding recommended solution according to his own wishes;

[0045] The second acquisition module is used to acquire the current passenger's operation action if the current passenger has not selected the recommended option;

[0046] An adjustment module is used to adjust the smart cockpit according to the operation actions and update the operation actions and adjustment actions to the preset database.

[0047] For example, to achieve the above objectives, this application also provides an intelligent cockpit function recommendation device, the device comprising: a memory, a processor, and an intelligent cockpit function recommendation program stored in the memory and executable on the processor, the intelligent cockpit function recommendation program being configured to implement the steps of the intelligent cockpit function recommendation method as described above.

[0048] For example, to achieve the above objectives, this application also provides a computer-readable storage medium storing a smart cockpit function recommendation program, which, when executed by a processor, implements the steps of the smart cockpit function recommendation method as described above.

[0049] Compared to related technologies where intelligent cockpit systems cannot provide recommendations based on individual passenger circumstances or offer tailored services to different passengers, resulting in a poor passenger experience, this application addresses the following: First, passenger information is acquired. Based on this information, recommendation data is matched from a preset database, and a recommendation scheme is generated. The recommendation scheme is then output to a display unit for the current passenger to select according to their preference. If the current passenger does not select a recommendation scheme, the current passenger's actions are acquired. Based on these actions, the intelligent cockpit is adjusted, and the actions and adjustments are updated in the preset database. In this application, a preset database is established, and recommended data is obtained by matching the acquired passenger information from the preset database. Corresponding recommended solutions are then generated, enabling different recommended solutions to be output for different passengers. This allows passengers to directly select the corresponding service functions provided by the smart cockpit from the recommended solutions, improving the convenience of using the smart cockpit. At the same time, even when the passenger does not select a recommended solution, the smart cockpit can still be adjusted according to the passenger's operation, and the operation record can be updated to the preset database. This allows for the recommendation of more accurate solutions to the passenger or similar passenger groups, comprehensively improving the passenger experience when using the smart cockpit. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the first embodiment of the intelligent cockpit function recommendation method of this application;

[0051] Figure 2 This is a flowchart illustrating the second embodiment of the intelligent cockpit function recommendation method of this application;

[0052] Figure 3 This is a schematic diagram of the hardware operating environment involved in the embodiments of this application.

[0053] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0054] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0055] This application provides a method for recommending intelligent cockpit functions, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the intelligent cockpit function recommendation method of this application.

[0056] This application provides an embodiment of a smart cockpit function recommendation method. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order. For ease of description, the following omits the execution entity describing the various steps of the smart cockpit function recommendation method, which includes:

[0057] Step S110: Obtain passenger information;

[0058] Passenger information refers to information about passengers in seats other than the cockpit, including: passenger weight, posture, age, and current actions in the smart cockpit. This information may also include other information used to distinguish different passengers, such as facial recognition, fingerprint recognition, or identifying the passenger's body position while seated and calculating the passenger's height.

[0059] Based on passenger information, passengers can be categorized into children, adults, and seniors. This facilitates the addition of child safety status detection for children, or the provision of corresponding display units with enlarged fonts and voice prompts for seniors.

[0060] Passengers can also be categorized by weight based on their information, and different cabin shock absorption services can be provided according to the weight of different passengers.

[0061] Step S120: Based on the passenger information, obtain recommended data from a preset database, and generate a recommended plan based on the recommended data;

[0062] The default database is a pre-established mapping database of different passengers and the service functions of the smart cockpit they use. Different passengers may include passengers with registered accounts and passengers without registered accounts.

[0063] Passengers with registered accounts can manually pre-set the corresponding service functions. For example, after detecting that passenger A has entered the cabin, passenger A's account can be retrieved, and the corresponding service functions can be read and provided directly without passenger A's operation.

[0064] For passengers without registered accounts, who are random passengers, the appropriate service function needs to be selected based on the passenger information. For example, if passenger B is a child without a registered account, the historical data of children using the smart cockpit stored in the preset database can be directly retrieved. Based on this historical data, services can be provided to the current passenger B, such as playing animated videos on the display unit or providing simple puzzle games to passenger B.

[0065] In summary, the recommended data is data that is related to different passengers and retrieved from a pre-set database.

[0066] Therefore, this recommendation data can be used to generate a recommendation scheme. For example, for passenger B, the recommendation scheme could be: currently playable animated videos.

[0067] Step S130: Output the recommended solution to the display unit so that the current passenger can select the corresponding recommended solution according to his / her own wishes;

[0068] After generating the recommended solution, the solution can be output to the display unit of the smart cockpit system. The display unit can be a touch screen, in-vehicle TV, or other devices. At the same time, the smart cockpit system can also control other devices inside the vehicle, which can be achieved through the Internet of Things.

[0069] Through the content displayed on this display unit, passengers can choose a recommended option that suits them, or they can actively control the smart cockpit according to their own wishes without using the service functions in the recommended option.

[0070] When displaying recommended options on the display unit, they can be displayed in a list format, with the most recommended options marked (for example, they can be divided into numerical levels such as 50, 30, and 10 recommendation levels to help passengers make better choices).

[0071] Step S140: If the current passenger has not selected the recommended option, then obtain the current passenger's operation action;

[0072] When a recommended option is displayed on the display unit, passengers may not necessarily select it. Instead, they may choose to select service functions and set relevant data independently. Therefore, certain actions will be required, including but not limited to operating through the UI interface of the display unit and manually operating various control buttons of the smart cockpit.

[0073] Step S150: Adjust the smart cockpit according to the operation action, and update the operation action and adjustment action to the preset database.

[0074] Based on the passenger's actions, the smart cockpit will respond to the corresponding adjustment requests, such as turning on the smart cockpit's heating function, adjusting the tightness of the smart cockpit's seat belts, or adjusting the smart cockpit's shock absorption function (increasing or decreasing the shock absorption effect).

[0075] After the passenger operates and adjusts the smart cockpit, the operation data is recorded and matched with the current passenger's passenger information, thereby adding data to the preset database.

[0076] Compared to related technologies where intelligent cockpit systems cannot provide recommendations based on individual passenger circumstances or offer tailored services to different passengers, resulting in a poor passenger experience, this application addresses the following: First, passenger information is acquired. Based on this information, recommendation data is matched from a preset database, and a recommendation scheme is generated. The recommendation scheme is then output to a display unit for the current passenger to select according to their preference. If the current passenger does not select a recommendation scheme, the current passenger's actions are acquired. Based on these actions, the intelligent cockpit is adjusted, and the actions and adjustments are updated in the preset database. In this application, a preset database is established, and recommended data is obtained by matching the acquired passenger information from the preset database. Corresponding recommended solutions are then generated, enabling different recommended solutions to be output for different passengers. This allows passengers to directly select the corresponding service functions provided by the smart cockpit from the recommended solutions, improving the convenience of using the smart cockpit. At the same time, even when the passenger does not select a recommended solution, the smart cockpit can still be adjusted according to the passenger's operation, and the operation record can be updated to the preset database. This allows for the recommendation of more accurate solutions to the passenger or similar passenger groups, comprehensively improving the passenger experience when using the smart cockpit.

[0077] For example, refer to Figure 2 , Figure 2 This is a flowchart illustrating the second embodiment of the intelligent cockpit function recommendation method of this application. Based on the first embodiment of the computing power service credibility assessment method of this application described above, a second embodiment is proposed, wherein the method further includes:

[0078] Step S210: Determine whether there is a passenger account in the preset database that has a mapping relationship with the passenger information; wherein, the passenger account contains passenger data of passengers who applied for the account, and when the similarity between the passenger information and the passenger data is greater than a preset threshold, it is determined that there is a mapping relationship between the passenger information and the passenger account;

[0079] After identifying passenger information, it is necessary to first check in the preset database whether a passenger account corresponding to that passenger information exists. The passenger account contains passenger data of the passenger who applied for the account. This passenger data contains information that is the same as that in the passenger information. For example, if the passenger information includes body shape information or facial recognition data, the passenger data will also contain the corresponding body shape information and facial recognition data. Therefore, when determining whether the passenger and the account correspond, the common parts in the passenger data and the passenger information are extracted respectively, and the similarity between the two is matched. If the similarity is greater than a preset threshold, it is determined that there is a corresponding mapping relationship between the passenger and the passenger account corresponding to the passenger information.

[0080] This is essentially a verification process to determine if a passenger belongs to a specific passenger account. Simultaneously, this process is a kind of traversal, matching passenger information with passenger accounts in a pre-defined database to determine if a mapping relationship exists. When calculating the similarity between them, the passenger account can also be used as a standard. If the similarity between passenger information and passenger data is high but not exceeding a preset threshold, the historical service usage data of the account corresponding to the highly similar passenger data can be used as a reference to generate a recommendation scheme.

[0081] The preset threshold is the ratio, which can be 90%. This means that the similarity needs to be greater than 90% to confirm that there is a mapping relationship between passenger information and passenger account; otherwise, there is no mapping relationship.

[0082] If it exists, the relevant data of the passenger's account can be directly accessed to provide services to that passenger.

[0083] Step S221: If it does not exist, then perform similarity matching between the passenger data of all passenger accounts in the preset database and the passenger information to obtain the passenger data with the highest similarity to the passenger information, and use the historical operation data of the smart cockpit as the recommended data for the corresponding passenger account;

[0084] If, when performing similarity matching based on passenger information and passenger data, it is determined that the current passenger has not registered an account, then the data of the corresponding passenger account cannot be directly accessed as the recommended solution for the passenger to operate the smart cockpit.

[0085] At this point, it is necessary to generate corresponding passenger data based on additional matching and prediction of passenger information, and with reference to previous passenger records when using the smart cockpit.

[0086] Since different passengers have different preferences, we can first match the passenger information with the passenger data of the registered passenger accounts based on similarity. Passenger data with high similarity can be used as reference recommendation data.

[0087] That is, after calculating the similarity between passenger information and passenger data of each passenger account, the passenger data with the highest similarity is used as reference data, and the passenger account corresponding to the passenger data and the historical operation data of the passenger account using the smart cockpit are retrieved as recommended data. The recommended data includes at least the number, type, and frequency of passenger use of smart cockpit service functions, as well as the corresponding adjustment parameters when using service functions.

[0088] Step S222: Or, if it does not exist, determine the passenger type of the current passenger based on the passenger information, and use the service data provided by the smart cockpit in the preset database as recommended data based on the passenger type;

[0089] In addition to matching passenger data, it is also necessary to match data from passengers who have not registered their accounts using the smart cockpit to improve the accuracy of the recommended solutions.

[0090] When matching data with unregistered passenger accounts, it is necessary to first determine the passenger type corresponding to the passenger information, such as the aforementioned child passenger, adult passenger, etc., and then match the data of passengers of this type using the smart cockpit from a preset database, and use this as the recommendation data.

[0091] Step S230: Generate a recommendation scheme based on the recommendation data and the passenger information.

[0092] Furthermore, based on the recommendation data and passenger information, corresponding recommendation schemes can be generated. When generating recommendation schemes, a comprehensive approach is taken, and appropriate adjustments are made based on the recommendation data. For example, if the recommendation data shows that a passenger has used three service functions in the past, the recommendation scheme can generate a scheme involving two to four service functions.

[0093] For example, the step of generating a recommendation scheme based on the recommendation data and the passenger information includes: the passenger information includes body shape information, action information and facial expression information;

[0094] Step a: Determine the current passenger's sitting posture based on the body shape information;

[0095] Body information includes the passenger's current posture, weight, height, and leg length. This information can be obtained by adding corresponding sensors or cameras to the smart cockpit to capture the passenger's appearance and body shape.

[0096] Based on this body shape information, the passenger's current sitting posture can be determined, such as leaning to the side in the cabin, sitting upright or lying down, or leaning forward. The passenger's needs can be analyzed based on the current sitting posture. For example, if the passenger is leaning forward, they want to operate the display unit or watch the in-vehicle TV. The height of the in-vehicle TV and the distance between it and the passenger can be adjusted accordingly. Or, if the passenger is leaning to the side on the backrest and has their eyes closed, it can be determined that the passenger wants to rest.

[0097] Step b: Determine the current passenger's service needs based on the sitting posture, movement information, and facial expression information;

[0098] Therefore, by combining the sitting posture, movement information, and facial expression information, further analysis can be conducted to determine the passenger's service needs.

[0099] Among them, motion information can be a certain action that passengers perform repeatedly. For example, if a passenger frequently straightens their body and wants to find a comfortable sitting posture, services such as adjusting the backrest angle and headrest position can be provided accordingly.

[0100] Among them, facial expression information can be the passenger's micro-expressions. For example, a passenger frowning may mean that the passenger is uncomfortable, and a passenger closing their eyes may mean that they need to rest. Services such as voice prompts or video playback should be turned off.

[0101] For example, the step of determining the current passenger's service needs based on the sitting posture, movement information, and facial expression information includes:

[0102] Step c: Based on the action information, determine the frequency with which the current passenger performs the same type of action;

[0103] When generating corresponding recommended services based on action information, the frequency of the same type of action generated by the passenger in the action information can be determined. For example, if the passenger frequently changes the place where their head is resting, it may mean that the passenger may want to adjust the headrest; or if the passenger frequently adjusts their sitting posture and the position of their back, it may mean that the passenger may want to adjust the backrest; or if the passenger frequently looks at the window, it may mean that the passenger may want to open the window.

[0104] Step d: Determine the corresponding position of the action whose frequency is greater than the preset frequency, and determine the adjustable information of the corresponding position;

[0105] When the frequency exceeds the preset frequency, the corresponding position of the action is determined, such as the headrest, backrest, or car window mentioned above, and the adjustable information of the corresponding position is determined. The preset frequency can be set according to the actual situation, such as three or five times. The adjustable information includes the adjustable functions or services of the corresponding position. For example, the backrest can provide massage, heating, and angle tilt functions, which are the adjustable information.

[0106] Step e: Input the facial expression information into a preset analysis model, and determine the facial expression changes of the current passenger at the eyebrows, corners of the mouth and corners of the eyes based on the analysis model;

[0107] The facial expression information is input into a pre-trained facial expression analysis model. This model can be used by adjusting the relevant parameters of an existing trained model to focus on capturing changes in the passenger's eyebrows, corners of the mouth, and corners of the eyes. Specifically, when the analysis model reads the passenger's facial expression information, it extracts feature information or variables from the eyebrows, corners of the mouth, and corners of the eyes in the passenger's facial image and assigns certain weights to these feature variables. By combining these feature variables, the model comprehensively analyzes the current passenger's expression to determine the passenger's mood, which mainly includes a pleasant state and an uncomfortable state. When the passenger is in an uncomfortable state, corresponding service functions can be recommended to alleviate the passenger's discomfort. For example, if a passenger repeatedly moves their body to adjust their posture and shows signs of discomfort, then the service function of adjusting the backrest can be recommended to the passenger.

[0108] Step f: Determine the current passenger's service needs based on the sitting posture, the adjustable information, and the facial expression changes.

[0109] In summary, based on the passenger's posture, adjustable information, and facial expressions, we can identify areas where the passenger is dissatisfied with the smart cockpit or areas where they have needs. By adjusting the cockpit or other services, we can determine the passenger's service requirements based on these three pieces of information. These service requirements include any service that the smart cockpit can provide.

[0110] Step g: Generate a recommendation scheme based on the recommended data and the service requirements.

[0111] By combining recommended data and service requirements, the system can find relevant data such as operation records and processes of the smart cockpit in the recommended data to provide services, and thus generate further recommended solutions.

[0112] For example, the step of generating a recommendation scheme based on the recommendation data and the service requirements includes:

[0113] Step h: Determine the service data provided by the smart cockpit in the recommended data;

[0114] Step i: Select the service content in the service data that corresponds to the service requirement as the first recommended solution;

[0115] The recommendation data includes data records of all service functions used by passengers of the same type. At this time, the service needs of passengers determined based on passenger information will inevitably overlap with the service data records in the recommendation data. This part of the content will be used as the first recommendation.

[0116] Step j: Determine the usage frequency of each service content in the service data, and select the service content whose frequency is greater than the average usage frequency as the second recommended option;

[0117] In addition to determining the first recommended option, a second recommended option can be determined based on the recommendation data. The second recommended option is to select the service content in the service data whose usage frequency is greater than the average usage frequency as the second recommended option. In other words, the service functions that passengers usually use are selected as the second recommended option.

[0118] Step k: Based on the first recommendation scheme and the second recommendation scheme, filter out duplicate items and generate a recommendation scheme.

[0119] There will inevitably be duplicates between the first and second recommended schemes. After removing the duplicates, the corresponding recommended scheme can be generated.

[0120] In addition, corresponding recommended plans can be generated based on the usage of each service. The relevance of each service can be determined. For example, most passengers use function A and then use function B. Specifically, most passengers use the function to adjust the headrest position after adjusting the backrest tilt angle, or in winter, most passengers use the smart cabin heating function while adjusting the backrest tilt angle.

[0121] In this embodiment, it is determined whether a passenger account exists in a preset database that is mapped to the passenger information. The passenger account contains passenger data of passengers who applied for the account. If the similarity between the passenger information and the passenger data is greater than a preset threshold, it is determined that the passenger information and the passenger account are mapped. If not, the passenger data of all passenger accounts in the preset database are matched with the passenger information to obtain the passenger data with the highest similarity. The corresponding passenger account is then used as recommended data based on the historical operation data of the smart cockpit. Alternatively, if not, the passenger type of the current passenger is determined based on the passenger information, and the service data provided by the smart cockpit in the preset database is used as recommended data based on the passenger type. A recommendation scheme is generated based on the recommended data and the passenger information. That is, the corresponding data is retrieved from the preset database, and the corresponding recommended data is accurately extracted to generate a recommendation scheme, ensuring the accuracy of the generated recommendation scheme.

[0122] For example, based on the first and second embodiments of the intelligent cockpit function recommendation method of this application described above, a third embodiment is proposed, wherein the method further includes:

[0123] Step 1: Obtain account registration information;

[0124] When registering an account, passengers need to be verified through the main control system of the smart cockpit. This main control system can be used to control any smart cockpit inside the vehicle, including the passenger cockpit and the driver cockpit. The main control system has only one main control account, which is logged in by the driver. The driver can control the number of passenger accounts and the content they control. At the same time, the driver can directly control the smart cockpit and other devices, bypassing the passengers. For example, the driver can assist the passenger in making adjustments, or the driver can force adjustments for safety reasons.

[0125] Account registration information includes a registration request and basic passenger information, which can be manually entered by the passenger, such as account nickname, gender of the account owner, and the location where the registration was initiated (corresponding to the smart cockpit at that location).

[0126] Step m: Based on the account registration information, determine the registered account passengers and the passenger data of the registered account passengers; wherein, the passenger data includes at least the passenger's body shape information;

[0127] Based on the account registration information, it is necessary to first identify the registered account of the passenger who applied for registration. For example, if there are four smart cockpits in the car, it is necessary to identify which smart cockpit's passenger initiated the registration request in order to determine that passenger's passenger data.

[0128] Some of this passenger data overlaps with passenger information, such as passenger body information, which requires recording passenger weight and sitting posture habits.

[0129] In addition, passenger data should also include parameters set by the passenger and data on reserved service functions. For example, when registering an account, the passenger will set the functions they need and the parameters for enabling the corresponding functions. Taking the passenger setting to adjust the backrest tilt angle as an example, the passenger data includes the adjusted backrest tilt angle and the corresponding angle parameters.

[0130] Step n: Collect the operation actions of the registered account passenger, determine the usage habits of the registered account passenger when using the smart cockpit, and use the operation actions and usage habits as historical operation data;

[0131] When passengers use the smart cockpit, their actions are collected and their usage habits are recorded. When a passenger needs to register an account, the data of the passenger's actions and usage habits are retrieved and used as historical operation data.

[0132] Step o: Generate a passenger account based on the passenger data and the historical operation data.

[0133] When generating a passenger account based on passenger data and historical operation data, it is necessary to use passenger data as data in a preset database to detect mapping relationships, and to record the passenger's data on using the smart cockpit in order to more accurately recommend options. For example, if the passenger always uses the smart cockpit's backrest tilt function when using it, but the passenger has not enabled the preset settings for this service, then based on their historical operation data, it can be determined that the passenger is likely to use the function to adjust the backrest tilt angle. Therefore, this function option can be directly recommended to the passenger.

[0134] In this embodiment, account registration information is obtained; based on the account registration information, registered account passengers are identified, and passenger data of the registered account passengers is determined; wherein, the passenger data includes at least the passenger's physical information; the operational actions of the registered account passengers are collected, and their usage habits when using the smart cockpit are determined, with the operational actions and usage habits used as historical operation data; a passenger account is generated based on the passenger data and the historical operation data. That is, by collecting information and registration account information of passengers who frequently use the smart cockpit, the corresponding passenger accounts can be directly added to a preset database to facilitate quick use of the smart cockpit by subsequent passengers.

[0135] For example, based on the first and second embodiments of the intelligent cockpit function recommendation method of this application described above, a fourth embodiment is proposed, wherein the method further includes:

[0136] Step p: Collect passenger information for unregistered accounts and service data when the unregistered accounts use the smart cockpit;

[0137] Besides frequent users of the smart cockpit who register account information, other random passengers do not use the smart cockpit by creating an account. In this case, big data collection and analysis are needed to make different recommendations for random passengers. Therefore, the data collection process for these passengers is equally important.

[0138] Step q: Determine the passenger type of the current passenger based on the passenger information;

[0139] When passengers without registered accounts use the smart cockpit, the system analyzes and matches corresponding data from a preset database based on passenger information to generate recommended solutions. Therefore, when collecting data from random passengers, the system also starts with passenger information, using it as a tag to classify data for different types, age groups, and body types of passengers.

[0140] Step r: Based on the passenger type, classify and store the service data, and establish a mapping relationship between the passenger type and the service data.

[0141] Meanwhile, these random passengers will select services based on the recommended options, and there are also cases where they independently select service functions. Therefore, after collecting the passenger information, it is also necessary to collect the service data of the passenger when using the smart cockpit, which is equivalent to establishing a data mapping relationship between passenger information and service data.

[0142] In summary, service data refers to the data records related to the service functions used by the random passenger, while passenger type refers to the types obtained after summarizing and classifying passenger information. These can be classified according to the passenger's body shape (weight, height, etc., to adjust the height and angle of the backrest and headrest accordingly), or according to the passenger's age group, such as children, adults, and the elderly, or according to the passenger's preferences, such as some passengers choosing to watch the news or read books and newspapers, while others choose to watch movies or videos.

[0143] In this embodiment, once the mapping relationship between passenger type and the service data used by the passenger is established, the above data can be stored in a preset database, thereby updating the content of the database and increasing the data capacity of the database. This provides more options for different passengers and improves the accuracy of generating recommendation schemes based on the increase in data volume.

[0144] In addition, this application also provides a smart cockpit function recommendation device, which includes:

[0145] The first acquisition module is used to acquire passenger information;

[0146] The generation module is used to match recommended data from a preset database based on the passenger information, and generate a recommendation scheme based on the recommended data;

[0147] The output module is used to output the recommended solution to the display unit so that the current passenger can select the corresponding recommended solution according to his own wishes;

[0148] The second acquisition module is used to acquire the current passenger's operation action if the current passenger has not selected the recommended option;

[0149] An adjustment module is used to adjust the smart cockpit according to the operation actions and update the operation actions and adjustment actions to the preset database.

[0150] For example, the generation module includes:

[0151] The first determining submodule is used to determine whether there is a passenger account in the preset database that has a mapping relationship with the passenger information; wherein, the passenger account contains passenger data of passengers who applied for the account, and when the similarity between the passenger information and the passenger data is greater than a preset threshold, it is determined that there is a mapping relationship between the passenger information and the passenger account;

[0152] The first judgment submodule is used to perform similarity matching between the passenger data of all passenger accounts in the preset database and the passenger information if the information does not exist, to obtain the passenger data with the highest similarity to the passenger information, and to use the historical operation data of the smart cockpit as the recommended data for the corresponding passenger account.

[0153] The second judgment submodule is used to determine the passenger type of the current passenger based on the passenger information if the passenger type does not exist, and to use the service data provided by the smart cockpit in the preset database as the recommended data based on the passenger type.

[0154] The generation submodule is used to generate a recommendation scheme based on the recommendation data and the passenger information.

[0155] For example, the generation submodule includes:

[0156] The first determining unit is used to determine the current sitting posture of the passenger based on the body information.

[0157] The second determining unit is used to determine the current passenger's service needs based on the sitting posture, action information, and facial expression information.

[0158] The generation unit is used to generate a recommendation scheme based on the recommendation data and the service requirements.

[0159] For example, the second determining unit includes:

[0160] The first determining subunit is used to determine the frequency of the current passenger performing the same type of action based on the action information;

[0161] The second determining subunit is used to determine the corresponding position of the action whose frequency is greater than the preset frequency, and to determine the adjustable information of the corresponding position;

[0162] The third determining subunit is used to input the facial expression information into a preset analysis model, and determine the facial expression changes of the current passenger's eyebrows, corners of the mouth and corners of the eyes based on the analysis model;

[0163] The fourth determining subunit is used to determine the current passenger's service needs based on the sitting posture, the adjustable information, and the facial expression changes.

[0164] For example, the generation unit includes:

[0165] The fifth determining subunit is used to determine the service data provided by the smart cockpit in the recommended data;

[0166] The sixth determining subunit is used to select the service content in the service data that corresponds to the service requirement as the first recommended solution;

[0167] The seventh determining subunit is used to determine the usage frequency of each service content in the service data, and to take the service content whose frequency is greater than the average usage frequency as the second recommended solution;

[0168] A generation subunit is used to filter out duplicate items and generate a recommendation scheme based on the first recommendation scheme and the second recommendation scheme.

[0169] For example, the generation module further includes:

[0170] The `get` submodule is used to retrieve account registration information;

[0171] The second determining submodule is used to determine registered account passengers and passenger data of the registered account passengers based on the account registration information; wherein the passenger data includes at least the passenger's body shape information;

[0172] The data collection submodule is used to collect the operation actions of the registered account passengers and determine the usage habits of the registered account passengers when using the smart cockpit, and to use the operation actions and usage habits as historical operation data.

[0173] The generation submodule is used to generate passenger accounts based on the passenger data and the historical operation data.

[0174] For example, the device further includes:

[0175] The data collection module is used to collect passenger information for unregistered accounts and service data when the unregistered accounts use the smart cockpit;

[0176] The determination module is used to determine the passenger type of the current passenger based on the passenger information;

[0177] A module is established to classify and store the service data according to the passenger type, and to establish a mapping relationship between the passenger type and the service data.

[0178] The specific implementation of the intelligent cockpit function recommendation device in this application is basically the same as the embodiments of the intelligent cockpit function recommendation method described above, and will not be repeated here.

[0179] In addition, this application also provides a smart cockpit function recommendation device. For example...Figure 3 As shown, Figure 3 This is a schematic diagram of the hardware operating environment involved in the embodiments of this application.

[0180] For example, Figure 3 This is a schematic diagram of the hardware operating environment for recommending devices for intelligent cockpit functions.

[0181] like Figure 3 As shown, the intelligent cockpit function recommendation device may include a processor 301, a communication interface 302, a memory 303, and a communication bus 304. The processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304. The memory 303 is used to store computer programs. When the processor 301 executes the program stored in the memory 303, it implements the steps of the intelligent cockpit function recommendation method.

[0182] The communication bus 304 mentioned in the recommended smart cockpit features can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 304 can be divided into an address bus, a data bus, and a control bus, etc. For ease of illustration, it is represented by only one thick line in the figure, but this does not indicate that there is only one bus or one type of bus.

[0183] Communication interface 302 is used for communication between the aforementioned smart cockpit function recommendation device and other devices.

[0184] The memory 303 may include random access memory (RMD) or non-volatile memory (NM), such as at least one disk storage device. Optionally, the memory 303 may also be at least one storage device located remotely from the aforementioned processor 301.

[0185] The processor 301 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0186] The specific implementation method of the intelligent cockpit function recommendation device in this application is basically the same as the embodiments of the intelligent cockpit function recommendation method described above, and will not be repeated here.

[0187] Furthermore, embodiments of this application also propose a computer-readable storage medium storing a smart cockpit function recommendation program, which, when executed by a processor, implements the steps of the smart cockpit function recommendation method as described above.

[0188] The specific implementation of the computer-readable storage medium in this application is basically the same as the embodiments of the above-mentioned recommended methods for intelligent cockpit functions, and will not be described again here.

[0189] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0190] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0191] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0192] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for recommending intelligent cockpit functions, characterized in that, The main control unit applied to the intelligent cockpit system, the intelligent cockpit function recommendation method includes the following steps: Obtain passenger information; Based on the passenger information, recommended data is obtained by matching from a preset database, and a recommended scheme is generated based on the recommended data; The recommended options are output to the display unit so that the current passenger can select the appropriate recommended option according to their own wishes; If the current passenger does not select the recommended option, then obtain the current passenger's action. Based on the operation actions, adjust the smart cockpit, and update the operation actions and adjustment actions to the preset database; The step of matching recommended data from a preset database based on the passenger information and generating a recommended solution based on the recommended data includes: Determine whether a passenger account exists in a preset database that is mapped to the passenger information; wherein, the passenger account contains passenger data of passengers who applied for the account, and when the similarity between the passenger information and the passenger data is greater than a preset threshold, it is determined that the passenger information and the passenger account are mapped. If it does not exist, the passenger data of all passenger accounts in the preset database will be matched with the passenger information to obtain the passenger data with the highest similarity to the passenger information, and the corresponding passenger account will be recommended using the historical operation data of the smart cockpit. Alternatively, if it does not exist, the passenger type of the current passenger is determined based on the passenger information, and the service data provided by the smart cockpit in the preset database is used as the recommended data based on the passenger type. A recommendation scheme is generated based on the recommendation data and the passenger information.

2. The intelligent cockpit function recommendation method as described in claim 1, characterized in that, The step of generating a recommendation scheme based on the recommendation data and the passenger information includes: The passenger information includes body shape information, action information, and facial expression information; Based on the body shape information, determine the current sitting posture of the passenger; Based on the sitting posture, movement information, and facial expression information, the current passenger's service needs are determined; A recommendation scheme is generated based on the recommended data and the service requirements.

3. The intelligent cockpit function recommendation method as described in claim 2, characterized in that, The step of determining the current passenger's service needs based on the sitting posture, movement information, and facial expression information includes: Based on the action information, determine the frequency with which the current passenger performs the same type of action; Determine the corresponding position of actions whose frequency is greater than a preset frequency, and determine the adjustable information of the corresponding position; The facial expression information is input into a preset analysis model, and the changes in facial expression at the eyebrows, corners of the mouth, and corners of the eyes of the current passenger are determined based on the analysis model. The service needs of the current passenger are determined based on the sitting posture, the adjustable information, and the changes in facial expression.

4. The intelligent cockpit function recommendation method as described in claim 2, characterized in that, The step of generating a recommendation scheme based on the recommendation data and the service requirements includes: Determine the service data provided by the smart cockpit in the recommended data; The service content in the service data that corresponds to the service requirement is taken as the first recommended solution; Determine the usage frequency of each service content in the service data, and select the service content whose frequency is greater than the average usage frequency as the second recommended option; Based on the first and second recommendation schemes, duplicate items are filtered out, and a recommendation scheme is generated.

5. The intelligent cockpit function recommendation method as described in claim 1, characterized in that, Before the step of determining whether a passenger account with a mapping relationship to the passenger information exists in the preset database, the following steps are included: Obtain account registration information; Based on the account registration information, the registered account passengers are identified, and the passenger data of the registered account passengers is determined; wherein, the passenger data includes at least the passenger's body shape information; Collect the operation actions of the registered account passengers and determine the usage habits of the registered account passengers when using the smart cockpit, and use the operation actions and usage habits as historical operation data; A passenger account is generated based on the passenger data and the historical operation data.

6. The intelligent cockpit function recommendation method as described in claim 1, characterized in that, Before the step of obtaining recommended data from a preset database based on the passenger information, the following steps are included: Collect passenger information for unregistered accounts and service data when such unregistered accounts use the smart cockpit; Based on the passenger information, determine the passenger type of the current passenger; According to the passenger type, the service data is categorized and stored, and a mapping relationship is established between the passenger type and the service data.

7. A smart cockpit function recommendation device, applied to the smart cockpit function recommendation method as described in any one of claims 1-6, characterized in that, The intelligent cockpit function recommendation device includes: The first acquisition module is used to acquire passenger information; The generation module is used to match recommended data from a preset database based on the passenger information, and generate a recommendation scheme based on the recommended data; The output module is used to output the recommended solution to the display unit so that the current passenger can select the corresponding recommended solution according to his own wishes; The second acquisition module is used to acquire the current passenger's operation action if the current passenger has not selected the recommended option; An adjustment module is used to adjust the smart cockpit according to the operation actions, and update the operation actions and adjustment actions to the preset database; The step of matching recommended data from a preset database based on the passenger information and generating a recommended solution based on the recommended data includes: Determine whether a passenger account exists in a preset database that is mapped to the passenger information; wherein, the passenger account contains passenger data of passengers who applied for the account, and when the similarity between the passenger information and the passenger data is greater than a preset threshold, it is determined that the passenger information and the passenger account are mapped. If it does not exist, the passenger data of all passenger accounts in the preset database will be matched with the passenger information to obtain the passenger data with the highest similarity to the passenger information, and the corresponding passenger account will be recommended using the historical operation data of the smart cockpit. Alternatively, if it does not exist, the passenger type of the current passenger is determined based on the passenger information, and the service data provided by the smart cockpit in the preset database is used as the recommended data based on the passenger type. A recommendation scheme is generated based on the recommendation data and the passenger information.

8. A smart cockpit function recommendation device, characterized in that, The device includes: a memory, a processor, and a smart cockpit function recommendation program stored in the memory and executable on the processor, the smart cockpit function recommendation program being configured to implement the steps of the smart cockpit function recommendation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a smart cockpit function recommendation program, which, when executed by a processor, implements the steps of the smart cockpit function recommendation method as described in any one of claims 1 to 6.

Images