Cabin adjustment method and device, vehicle and computer readable storage medium

Abstract

The invention provides a cabin adjusting method and device, a vehicle and a computer readable storage medium, and relates to the technical field of vehicle control. The method comprises the following steps: acquiring multi-source data acquired in a previous period; wherein the multi-source data comprises a plurality of parameters representing actions, behaviors and states of the user; according to the multi-source data, performing quantitative scoring on a plurality of preset user state types to obtain a scoring result corresponding to each user state type; and determining a cabin adjustment strategy of the current period according to the scoring result corresponding to each user state type, and executing the cabin adjustment strategy. According to the method, intelligent cabin environment adjustment can be realized based on the user state, and the driving and riding comfort and safety are improved.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a cockpit adjustment method, device, vehicle, and computer-readable storage medium. Background Technology

[0002] With the rapid development of automotive technology, intelligent and personalized driving experiences have become important demands for consumers. Among these, cabin environment adjustment is gradually becoming an important means of enhancing the driving and riding experience.

[0003] In practical applications, the impact of the cabin environment on the physical and mental health of passengers is becoming increasingly significant. For example, during long-distance driving or riding, passengers may experience fatigue, tension, stress (such as road rage), motion sickness, and other conditions.

[0004] However, in related technologies, cockpit systems lack the ability to perceive these states in real time and coordinate adjustments across multiple systems, making it impossible to achieve adaptive adjustments to the entire cockpit. Therefore, there is an urgent need for technologies capable of adaptive adjustments to the entire cockpit. Summary of the Invention

[0005] This application provides a cockpit adjustment method, device, vehicle, and computer-readable storage medium that can precisely adjust the cockpit environment to improve driving safety and comfort.

[0006] The technical solution of this application embodiment is implemented as follows: In a first aspect, embodiments of this application provide a cockpit adjustment method, which includes: acquiring multi-source data collected in the previous cycle; wherein the multi-source data includes multiple parameters characterizing the user's actions, behaviors, and states; quantifying and scoring multiple preset user state types based on the multi-source data to obtain a scoring result corresponding to each user state type; determining the cockpit adjustment strategy for the current cycle based on the scoring result corresponding to each user state type, and executing it.

[0007] Based on the aforementioned technical means, by acquiring multi-source data collected in the previous cycle, the user's actions, behaviors, and states can be comprehensively reflected. Combined with a pre-set quantitative scoring mechanism for various user state types, real-time assessment of the user's health status is possible. Finally, based on the scoring results, a cabin adjustment strategy is formulated and executed, thereby achieving intelligent cabin environment adjustment based on user states, improving driving comfort and safety. Compared to existing technologies that lack quantitative analysis and coordinated adjustment of user states, this solution achieves calculable and comparable state recognition and can drive multi-system collaborative responses.

[0008] In some embodiments, quantifying and scoring multiple preset user state types based on multi-source data includes: obtaining a set of data items corresponding to a first user state type; wherein the set of data items includes multiple parameter items, each parameter item representing a user's behavior, action, or state, and the first user state type is one of multiple user state types; obtaining parameter values ​​corresponding to each parameter item in the set of data items from the multi-source data; quantifying and scoring each parameter item based on the parameter values ​​corresponding to each parameter item to obtain a scoring result corresponding to each parameter item; and determining the scoring result corresponding to the first user state type based on the scoring results corresponding to each parameter item.

[0009] Based on the aforementioned technical means, each user status type is refined into specific parameter items. By scoring the parameter values ​​corresponding to each parameter item, the scoring process becomes more targeted and accurate. Finally, the scores corresponding to all parameter items in the data item set corresponding to the user status type are summarized to obtain the total score result corresponding to that user status type, which prepares for the subsequent determination of the cabin adjustment strategy.

[0010] In some embodiments, each parameter item is quantitatively scored based on its corresponding parameter value to obtain a score result for each parameter item, including: obtaining the scoring rules for each parameter item; and quantitatively scoring each parameter item based on its corresponding parameter value and the scoring rules to obtain a score result for each parameter item.

[0011] Based on the aforementioned technical means, the parameter values ​​of the parameter items are scored using preset scoring rules to quantify the user's performance on that parameter item, facilitating subsequent processing and comparison, while ensuring the consistency and repeatability of the scoring process.

[0012] In some embodiments, determining the cabin adjustment strategy for the current period based on the scoring results corresponding to each user state type includes: determining the weight of each user state type based on the scoring results corresponding to each user state type; and determining the cabin adjustment strategy for the current period based on the weight of each user state type.

[0013] Based on the aforementioned technical means, by calculating the weight of each user state type, the mutual influence between different user state types can be reflected, thereby supporting more reasonable adjustment decisions and avoiding imbalance in adjustment strategies due to excessive influence from a certain user state type.

[0014] In some embodiments, determining the cabin adjustment strategy for the current period based on the rating results corresponding to each user status type includes: obtaining the rating results for each user status type in the previous period; determining the cumulative rating result for each user status type based on the rating results for each user status type in the previous period and the rating results corresponding to each user status type; and determining the cabin adjustment strategy for the current period based on the cumulative rating results for each user status type.

[0015] Based on the aforementioned technical means, by accumulating the scoring results of each user state type in each prior period, a cumulative scoring result for each user state type is obtained. The cumulative scoring result can characterize the user's state change process in each user state type, realize the tracking of user state, improve the accuracy of the cockpit adjustment strategy, and make the cockpit adjustment strategy more in line with user needs and make appropriate and flexible adjustments according to changes in user state.

[0016] In some embodiments, determining the cabin adjustment strategy for the current period based on the weight of each user state type includes: determining the cabin adjustment strategy corresponding to the user state type whose weight exceeds a preset weight threshold as the cabin adjustment strategy for the current period.

[0017] Based on the aforementioned technical means, the most significant user state types are selected by pre-setting weight thresholds, and adjustment strategies are formulated accordingly, which can ensure the effectiveness and consistency of the system's adjustment behavior under complex conditions.

[0018] In some embodiments, multi-source data includes at least two of the following: visual image data, audio data, schedule data, driving route data, driving habit data, external device data, and cockpit sensor data.

[0019] Based on the aforementioned technical means, by integrating multimodal signals from vision, hearing, schedule, driving, etc., it is possible to more comprehensively capture the user's physical, psychological, and behavioral characteristics, thereby constructing a more accurate user status model, which has higher discrimination ability and robustness compared to single sensor data.

[0020] In some embodiments, the plurality of parameters include at least two of the following: yawning frequency, eye movement frequency, red-eye count, coughing frequency, voice vitality level, tone of voice, high volume frequency, multimedia switching frequency, laughter frequency, humming along to songs, anniversary events, meeting events, medication events, menstrual period events, path deviation events, heading to the target destination, overtaking frequency, driving events within the target time period, speeding events, vehicle creeping events, heart rate, blood oxygen, exercise volume, body temperature, call volume, call duration, and body twisting frequency.

[0021] Based on the aforementioned technical means, by selecting representative parameters, it is possible to effectively distinguish different user state types, such as fatigue, tension, pleasure, health, and stress, thereby improving the relevance and practicality of the scoring.

[0022] Secondly, embodiments of this application provide a cockpit adjustment device, which includes: an acquisition module for acquiring multi-source data collected in the previous cycle; wherein the multi-source data includes multiple parameters characterizing the user's actions, behaviors, and states; a scoring module for quantifying and scoring multiple preset user state types based on the multi-source data to obtain a scoring result corresponding to each user state type; and an adjustment module for determining and executing a cockpit adjustment strategy for the current cycle based on the scoring results corresponding to each user state type.

[0023] Thirdly, embodiments of this application provide a vehicle including a processor and a memory, the memory storing a program or instructions executable on the processor, the program or instructions being executed by the processor to implement the steps of the cockpit adjustment method described in any one of the first aspects above.

[0024] Fourthly, embodiments of this application provide a computer-readable storage medium storing a program or instructions that, when executed by a processor, implement the steps of the cockpit adjustment method described in any one of the first aspects.

[0025] Fifthly, embodiments of this application provide a computer program product, including a computer program or computer-executable instructions, which, when executed by a processor, implement the steps of the cockpit adjustment method described in any one of the first aspects.

[0026] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and are not intended to limit the technical solutions of this application. Attached Figure Description

[0027] Figure 1 This is a schematic flowchart of a cockpit adjustment method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating a method for quantitatively scoring various user status types, as provided in an embodiment of this application. Figure 3 This is a flowchart illustrating a method for quantifying and scoring various parameter items according to an embodiment of this application. Figure 4 This is a schematic diagram of the overall process of a cockpit adjustment method provided in an embodiment of this application; Figure 5 This is a logic block diagram of a cockpit adjustment device provided in an embodiment of this application; Figure 6 This is a schematic diagram of the hardware structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0028] In order to gain a more detailed understanding of the features and technical content of the embodiments of this application, the implementation of the embodiments of this application will be described in detail below with reference to the accompanying drawings. The accompanying drawings are for reference and illustration only and are not intended to limit the embodiments of this application.

[0029] Unless otherwise defined, the technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit the application. In the following description, references to "some embodiments" describe a subset of all possible embodiments; however, it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0030] It should also be noted that the terms "first, second, and third" used in the embodiments of this application are only used to distinguish similar objects and do not represent a specific order of objects. It can be understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0031] Furthermore, the reference to "embodiment" herein means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0032] As the level of intelligence in new energy vehicles continues to improve, cabin environment adjustment is gradually becoming an important means of enhancing the driving and riding experience. For example, a related technology proposes an intelligent cabin adjustment system that automatically lowers the driver's seat when the user opens the door and automatically raises it after the user sits in the driver's seat. However, this technology can only achieve intelligent adjustment of the seat and cannot achieve adaptive adjustment of the entire cabin.

[0033] In practical applications, the impact of the cabin environment on the physical and mental health of passengers is becoming increasingly significant. For example, during long-distance driving or riding, passengers may experience fatigue, tension, stress (such as road rage), motion sickness, and other conditions.

[0034] However, among related technologies, the cockpit system lacks the ability to perceive these states in real time and coordinate adjustments across multiple systems. Therefore, there is an urgent need for a technical solution that can intelligently adjust the cockpit based on the health status of the occupants in order to improve the safety and comfort of driving and riding.

[0035] To address the aforementioned technical problems, this application provides a cockpit adjustment method. This method collects multi-source user data and quantifies and scores preset user state types based on this data. It quantitatively evaluates the user state from multiple dimensions to accurately understand user needs. Based on this understanding, it dynamically generates and executes corresponding cockpit adjustment strategies. This method achieves quantitative evaluation and accurate identification of user states, and on this basis, performs multi-system coordinated adjustment to make the cockpit environment more suitable for the user's current state, effectively improving the intelligence level of the vehicle cockpit system.

[0036] The technical concept of this solution includes: constructing a health status quantitative assessment system based on multimodal signal fusion, identifying five states of occupants—health, fatigue, stress, tension, and pleasure—through a calculable scoring mechanism, and triggering cross-system linkage control strategies based on the state proportion to achieve a "perception-judgment-adjustment" closed loop.

[0037] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0038] The cockpit adjustment methods provided in the embodiments of this application can be executed by an in-vehicle computing system, which can be an in-vehicle host, a smart cockpit controller, etc. That is, the cockpit adjustment methods in the embodiments of this application can be executed by an in-vehicle host, or by a smart cockpit controller, or by interaction between the in-vehicle host and the smart cockpit controller.

[0039] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating a cockpit adjustment method provided in an embodiment of this application. The following description uses an intelligent cockpit controller as an example to illustrate this cockpit adjustment method. Figure 1 As shown, the method may include steps 101 to 103.

[0040] Step 101: Obtain the multi-source data collected in the previous cycle.

[0041] Multi-source data includes multiple parameters that characterize a user's actions, behaviors, and states.

[0042] In this embodiment, multi-source data refers to a collection of various types of data collected from in-vehicle sensors and external devices. Multi-source data includes at least two of the following: visual image data, audio data, schedule data, driving route data, driving habit data, external device data, and cabin sensor data. These data are collectively used to characterize the user's actions, behaviors, and physical state.

[0043] Multi-source data may originate from in-vehicle visual cameras, microphones, and seat sensors, as well as from external devices such as user terminals (e.g., mobile phones, cameras, laptops, tablets), wearable devices (e.g., smart bracelets, smartwatches), and the vehicle's own records of driving routes and habits. This multi-source data can also be referred to as multimodal data.

[0044] In this embodiment, the cockpit adjustment method provided in this application is triggered after the vehicle is started or the intelligent cockpit controller is started. Furthermore, since the cockpit adjustment method provided in this embodiment dynamically adjusts based on the user's real-time status, and since the user's physical, mental, and health states may change during driving, the cockpit adjustment method provided in this embodiment can cyclically execute the data collection-strategy formulation-cockpit adjustment process according to a fixed period.

[0045] Here, the previous cycle refers to the previous cabin adjustment cycle. To ensure the continuity of cabin adjustment, this application proposes to determine the cabin adjustment strategy for the current cycle based on multi-source data collected in the previous cycle, and to repeat this process. The duration of each cabin adjustment cycle can be set by the user or be a system default value. For example, the cabin adjustment cycle may be 10 minutes or 15 minutes, etc.

[0046] In the initial cycle when the vehicle is first started, due to the lack of effective multi-source data, the cabin environment is typically controlled based on the vehicle's historical state. In the second cycle after the vehicle starts, the multi-source data collected in the first cycle can be acquired.

[0047] In this embodiment of the application, after acquiring the multi-source data collected in the previous cycle, the multi-source data can also be cleaned to remove invalid information.

[0048] Step 102: Quantify and score the preset user status types based on multi-source data to obtain the score results corresponding to each user status type.

[0049] In this context, user state type refers to the classification of a user's current psychological or physiological state. In this embodiment, multiple user state types correspond to different dimensions of a user's state, such as health, fatigue, stress, tension, and pleasure. Each user state type corresponds to a specific set of behavioral characteristic indicators and can be identified through a quantitative scoring mechanism.

[0050] In some implementations, users can choose their desired user state type. For example, the intelligent cockpit controller can interact with the user through a voice interaction system to provide multiple candidate user state types. These candidate user state types are typically categorized into five types: healthy, fatigued, stressed, tense, and happy. Users can choose some or all of these as their vehicle's user state type.

[0051] In some other implementations, multiple user state types are the default setting for the smart cockpit controller and cannot be changed.

[0052] In this embodiment of the application, by analyzing multi-source data, multiple behavioral characteristic indicators can be obtained. Each behavioral characteristic indicator is a statistical result of a user's behavior, action, or state. Based on these statistical results, the user is evaluated from different dimensions.

[0053] For example, the process of analyzing multi-source data to determine multiple behavioral characteristic indicators includes: recognizing image data to obtain yawning frequency, eye movement frequency, and red-eye frequency. Red-eye frequency refers to the number of times the eyes turn red. Red eyes may occur when a user has an eye condition.

[0054] Audio data recognition can identify cough frequency, voice vitality level, tone of voice, frequency of high volume, frequency of multimedia switching, frequency of laughter, and number of times users hum along to songs. Voice vitality level characterizes the volume and speed of a user's speech to determine if there is a decrease in voice vitality. Tone of voice includes, for example, angry or frustrated tones. Frequency of high volume refers to the frequency with which the user speaks at a high volume. Multimedia switching includes, for example, switching songs, videos, or radio programs. Frequent switching may indicate a state of agitation. Frequency of laughter and number of times users hum along to songs typically indicate a state of pleasure.

[0055] The system uses schedule data to determine if there are anniversaries, medication reminders, or menstrual periods within the next week. In this embodiment, the schedule data is provided by a mini-program such as a scheduler. In some implementations, the schedule data may be obtained through an external device or through an in-vehicle schedule mini-program.

[0056] The system analyzes driving route data to determine if route deviations have occurred or if the destination is unusual. Route deviation refers to a departure from the user's usual driving route. For example, a user's commute is generally fixed; a deviation from this route may indicate a change in the user's lifestyle. Unusual destinations include airports, hospitals, and train stations.

[0057] Analyzing driving habit data helps determine if there is frequent overtaking, whether driving times are too early or too late, and whether speeds are too fast or too slow. Driving habits include driving time, such as driving between 8-9 am or 5-6 pm. Other examples include typical speeds of around 40 km / h or 60 km / h.

[0058] Analyze data from external devices to determine if there are abnormal heart rates, abnormal blood oxygen levels, or excessive exercise.

[0059] The system analyzes body movement data to determine if there is frequent body movement, if body temperature exceeds a preset temperature, or if continuous phone calls exceed a preset duration. Body movement refers to any twisting or turning movements the user makes while driving. High frequency of body movement may indicate that the user is currently in an abnormal state.

[0060] It should be noted that the above analysis of multi-source data is only an example and does not constitute a limitation on the embodiments of this application.

[0061] Based on the above analysis, it can be seen that in this embodiment of the application, multiple behavioral feature indicators can be obtained from multi-source data, and then the user status can be quantitatively analyzed from multiple dimensions based on these multiple behavioral feature indicators.

[0062] In one possible implementation, please refer to Figure 2 , Figure 2 This is a flowchart illustrating a method for quantitatively scoring various user status types, as provided in an embodiment of this application. The method includes: Step 201: Obtain the set of data items corresponding to the first user status type.

[0063] The data item set includes multiple parameter items, each of which represents a user's behavior, action, or state.

[0064] The first user state type is one of several user state types. It should be noted that in this embodiment, the data item sets corresponding to each user state type can be acquired simultaneously, without any order of priority.

[0065] A data item set refers to a set of quantifiable behavioral, behavioral, or physiological indicators designed to assess a primary user state type. Different user state types correspond to data item sets containing different parameters. The design of the data item set is based on the user's daily behavioral patterns, in-vehicle environmental monitoring capabilities, and third-party devices or software applications to ensure a comprehensive reflection of the characteristics of the primary user state type.

[0066] In some embodiments, the plurality of parameters include at least two of the following: yawning frequency, eye movement frequency, red-eye count, coughing frequency, voice vitality level, tone of voice, high volume frequency, multimedia switching frequency, laughter frequency, humming along to songs, anniversary events, meeting events, medication events, menstrual period events, path deviation events, heading to the target destination, overtaking frequency, driving events within the target time period, speeding events, vehicle creeping events, heart rate, blood oxygen, exercise volume, body temperature, call volume, call duration, and body twisting frequency.

[0067] For example, when the first user state type is a healthy state, the data item set may include multiple parameter items such as the number of red eyes, cough frequency, medication events, menstrual events, heart rate, and blood oxygen. Each parameter item represents the user's specific performance under the first user state type. As another example, when the first user state type is a fatigued state, the data item set may include behavioral indicators such as yawn frequency, eye movement frequency, and decreased vocal vitality. In this embodiment, the data item set can also be dynamically adjusted to adapt to the needs of different user groups or driving scenarios.

[0068] Step 202: Obtain the parameter values ​​corresponding to each parameter item in the data item set from the multi-source data.

[0069] Multi-source data refers to data streams from various channels, including in-vehicle sensors (such as cameras, microphones, and seat sensors), external devices (such as smartwatches and mobile phones), and user schedule information. These data have different collection frequencies and data formats, requiring standardization processing by the intelligent cockpit controller.

[0070] Parameter values ​​are specific numerical expressions of each parameter item, such as yawning frequency of 7 times / minute and heart rate of 92 beats / minute. The acquisition of parameter values ​​relies on a real-time data acquisition and processing mechanism to ensure accurate recording of changes in user behavior within each cabin adjustment cycle (e.g., 20 minutes). For example, if frequent yawning is detected, the number of yawns is recorded and converted into yawning frequency.

[0071] It should be noted that, in this embodiment of the application, after analyzing multi-source data, multiple behavioral feature indicators can be obtained. Then, for each user state type, the parameter values ​​corresponding to each parameter item in the data item set corresponding to each user state type are extracted from the multiple behavioral feature indicators. There is no sequential order among the various user state types.

[0072] Step 203: Quantify and score each parameter item according to its corresponding parameter value to obtain the score result for each parameter item.

[0073] The quantitative scoring refers to assigning a score to each parameter item based on the degree of matching between the parameter value and a preset threshold. Different parameter items correspond to different preset thresholds. For example, for the parameter item "yawning frequency," if the yawning frequency exceeds 5 times / minute, 1 point is awarded per minute; if the yawning frequency does not exceed 5 times / minute, no point is awarded. The scoring results are used to measure the intensity of the user's performance on the parameter item. For example, if a user yawns more than 5 times / minute a total of 7 times within a 20-minute period, then the score for the "yawning frequency" parameter item is 7 points.

[0074] This application embodiment, by quantifying and scoring each parameter item, can transform unstructured user behavior into comparable and calculable numerical indicators, thereby supporting subsequent state classification and linkage control decisions.

[0075] In one possible implementation, please refer to Figure 3 , Figure 3 This is a flowchart illustrating a method for quantifying and scoring various parameter items according to an embodiment of this application. The method includes: Step 301: Obtain the scoring rules corresponding to each parameter item.

[0076] Please refer to Table 1, which exemplarily shows the scoring rules corresponding to each parameter item obtained from multi-source data, as well as the user status type to which each parameter item belongs.

[0077] Table 1

[0078] It should be noted that the scoring rules shown in Table 1 are only one possible example and do not constitute a limitation on this scheme.

[0079] In this embodiment, the scoring rules refer to the scoring standards and classification logic set for different parameter items, used to score the user's performance on that parameter item based on the parameter value corresponding to that parameter item. In this embodiment, the design of the scoring rules is based on actual application scenarios and user behavior characteristics. For example, for the parameter item of frequent overtaking in driving habits, when designing the scoring rules, a threshold can be set based on historical driving data, and scoring will only begin when the overtaking frequency exceeds a certain benchmark, in order to avoid misjudgment.

[0080] The scoring rules for each parameter are pre-set. It should be noted that the scoring rules may differ for users of different age groups. This application does not exhaustively list the scoring rules for all parameter items.

[0081] By setting clear scoring rules, this method can achieve consistent processing of multi-source heterogeneous data, enabling a unified evaluation basis among different collection items, thereby improving the accuracy and stability of overall health status assessment.

[0082] Step 302: Based on the parameter values ​​and scoring rules corresponding to each parameter item, quantify and score each parameter item to obtain the scoring results corresponding to each parameter item.

[0083] Among them, quantitative scoring refers to scoring a user's performance on a parameter item based on the obtained scoring rules and the parameter values ​​corresponding to the parameter items.

[0084] For example, when the first user state type is fatigued, the data item set may include behavioral indicators such as yawning frequency, eye movement frequency, and decreased vocal activity. Then, from multi-source data, it is found that within a cabin adjustment cycle (20 minutes), a yawning frequency of 3 times / minute occurred 5 times, a yawning frequency of 4 times / minute occurred 2 times, a yawning frequency of 6 times / minute occurred 3 times, and a yawning frequency of 7 times / minute occurred 10 times.

[0085] The scoring rule for yawning frequency is, for example, that 1 point is awarded for yawning more than 5 times per minute. Therefore, according to this scoring rule, the score for the yawning frequency parameter is 13 points (the sum of yawning frequencies of 6 times per minute and yawning frequencies of 7 times per minute).

[0086] For example, from multi-source data, within a cabin adjustment cycle (20 minutes), the number of times the tone of voice was angry was 5, and the number of times the tone of voice was not angry was 15. Referring to Table 1, the scoring rule is "1 point / minute for detecting angry tone of voice", so this parameter item scores 5 points.

[0087] Based on the same principle, we can obtain eye movement frequency scores, speech vitality level scores, and so on, which will not be listed here.

[0088] This application embodiment obtains the scoring rules corresponding to each parameter item and performs quantitative scoring operations, which can convert complex multi-source data into scoring indicators to quantitatively evaluate the user's performance on each parameter item. This enables accurate identification of the user's state and triggers targeted cockpit linkage control, which is beneficial to improving driving comfort and safety.

[0089] Step 204: Determine the score result corresponding to the first user status type based on the score results corresponding to each parameter item.

[0090] The first user state type can be any one of the following: healthy, fatigued, stressed, anxious, or happy. The rating result corresponding to the first user state type can reflect the user's overall performance under the first user state type.

[0091] The process of determining the rating result corresponding to the first user state type is as follows: obtain the rating of all parameter items in the data item set corresponding to the first user state type, sum the ratings of all parameter items corresponding to the first user state type, and obtain the rating result corresponding to the first user state type.

[0092] For example, when the first user state type is fatigue state, its corresponding data item set includes: yawning frequency, decreased vocal vitality, and eye movement frequency. The yawning frequency score is 7 points, the decreased vocal vitality score is 3 points, and the eye movement frequency score is 0 points. Therefore, the total score for the first user state type is 10 points.

[0093] It should be noted that the scores for each parameter item refer to the cumulative scores within the same cabin adjustment cycle.

[0094] In this embodiment, each user state type is refined into specific behavior or state parameter items, and then each parameter item is scored, and the total score result of the state type is obtained by summarizing. This makes the scoring process more targeted and accurate, helps to more finely characterize the user state, and improves the sensitivity and adaptability of the scoring system.

[0095] Step 103: Determine and execute the cabin adjustment strategy for the current period based on the scoring results corresponding to each user status type.

[0096] In this embodiment of the application, each user state type corresponds to a pre-set cabin adjustment strategy. The cabin adjustment strategy for the current period can be one of these pre-set cabin adjustment strategies.

[0097] In this embodiment of the application, the target user status type can be determined based on the scoring results corresponding to each user status type, and the cabin adjustment strategy corresponding to the target user status type can be determined as the cabin adjustment strategy for the current period.

[0098] In some embodiments, the target user state type is, for example, the user state type corresponding to the highest rating result. Alternatively, the target user state type is, for example, the user state type whose rating result exceeds a preset rating threshold.

[0099] In other embodiments, the weight of each user state type can be determined based on the scoring results corresponding to each user state type; and the cabin adjustment strategy for the current period can be determined based on the weight of each user state type.

[0100] Weight refers to the relative importance of a variable among multiple variables or factors. In the embodiments of this application, weight is used to quantify the influence of different user state types on the overall user state assessment, thereby providing a basis for cabin adjustment strategies. By dynamically adjusting the weights of each user state type, the actual needs and state changes of users can be reflected more accurately, improving the rationality and personalization of cabin adjustment strategies.

[0101] In this embodiment of the application, the weight of the user state type can be calculated based on the following formula: The weight of a user status type is calculated as the sum of the scores for all user status types.

[0102] Then, the user state type corresponding to the highest weight can be determined as the target user state type. Alternatively, the user state type whose weight exceeds a preset weight threshold can be determined as the target user state type.

[0103] For example, the preset weight threshold is 50%. If the weight of the health type is greater than 50%, then the health type is the target user status type.

[0104] Based on the above embodiments, this application embodiment also sets priorities for each user status type. For example, the priorities corresponding to each user status type can be represented as follows: Health > Fatigue > Stress > Tension > Pleasure

[0105] In some cases, there may be two or more target user status types. In this case, the two or more target user status types can be sorted according to the priority sequence mentioned above, and the cabin adjustment strategy corresponding to the target user status type that is ranked first can be selected as the cabin adjustment strategy for the current period.

[0106] In this application embodiment, it is also possible that the weight ratio of any user state type does not exceed the preset weight threshold. In this case, the current cockpit adjustment strategy remains unchanged.

[0107] Please refer to Table 2 below, which illustrates the cabin adjustment strategies corresponding to various possible user state types.

[0108] Table 2

[0109] It should be noted that Table 2 is only an exemplary illustration and does not constitute a limitation on the cabin adjustment strategy in this application.

[0110] In this embodiment, the cabin adjustment strategy refers to a set of cabin environment adjustment instructions formulated based on the user state type assessment results. The aim is to improve user experience and ensure driving safety by executing the cabin adjustment strategy. The cabin adjustment strategy can encompass the linkage of multiple subsystems such as air conditioning temperature adjustment, fragrance release, seat function switching, and music playback. The intelligent cabin controller achieves multi-dimensional environmental optimization through these linked operations.

[0111] For example, when a user is fatigued, the cabin adjustment strategy may activate the cooling mode, refreshing aromatherapy, massage function, and upbeat music; while when a user is stressed, the air conditioning will automatically switch to natural wind mode, release a calming lavender aromatherapy, activate gentle massage ventilation, and play soothing instrumental music to help the user relieve stress.

[0112] In practice, the intelligent cockpit controller will also handle situations where multiple user state types simultaneously meet the adjustment conditions, prioritizing them in the order of health > fatigue > stress > tension > pleasure. For example, if a user is simultaneously in both fatigue and tension states during a certain period, and both states exceed the adjustment threshold, the system will prioritize the cockpit adjustment strategy corresponding to the fatigue state. Furthermore, when the system detects a user in a dangerous state (such as a stress state), it will trigger an active safety alert and may even prompt the user to activate intelligent driving assistance functions to ensure driving safety.

[0113] Based on the above embodiments, this application also proposes to determine the cabin adjustment strategy for the current period based on the cumulative score results of each user status type. Wherein: Obtain the rating results for each user status type in the previous period; determine the cumulative rating result for each user status type based on the rating results for the previous period and the corresponding rating results for each user status type; determine the cabin adjustment strategy for the current period based on the cumulative rating results for each user status type.

[0114] In some embodiments, the target user status type can be determined based on the cumulative score results corresponding to each user status type, and the cabin adjustment strategy corresponding to the target user status type can be determined as the cabin adjustment strategy for the current period.

[0115] In some embodiments, the target user status type is, for example, the user status type corresponding to the highest cumulative rating result. Alternatively, the target user status type is, for example, the user status type whose cumulative rating result exceeds a preset rating threshold.

[0116] In other embodiments, the weight of each user state type can be determined based on the cumulative score results corresponding to each user state type; and the cabin adjustment strategy for the current period can be determined based on the weight of each user state type.

[0117] In this embodiment of the application, when the user's state changes, these changes will be reflected in the cumulative score results corresponding to each user state type. Therefore, the cumulative score results can characterize the user's state change process in each user state type, realize the tracking of user state, improve the accuracy of the cockpit adjustment strategy, make the cockpit adjustment strategy more in line with user needs, and make appropriate and flexible adjustments according to changes in user state.

[0118] The cockpit adjustment method provided in this application integrates multimodal signals to construct a computable health status type assessment model, and combines it with a multi-system linkage adjustment mechanism to achieve intelligent adjustment of the cockpit environment, thereby effectively responding to the diverse needs of users in different scenarios.

[0119] Based on the above embodiments, the embodiments of this application will be described below in conjunction with specific application scenarios.

[0120] Please refer to Figure 4 , Figure 4 This is a schematic diagram of the overall process of a cockpit adjustment method provided in an embodiment of this application.

[0121] Step 401: Start the vehicle and initialize the system.

[0122] Step 402: Start the cabin adjustment cycle timing.

[0123] The cabin adjustment cycle can be, for example, 20 minutes, or other durations.

[0124] Step 403: Continue monitoring and repeat the process.

[0125] Step 404: Calculate the score of each collected item in real time and classify the collected items into five status types.

[0126] In this embodiment, during the collection of multi-source data, data cleaning and identification can be performed in real time to obtain multiple behavioral feature indicators, which are the collection items. The collection items corresponding to each of the five states are determined, the values ​​of the collection items corresponding to each state are obtained, and the collection items are scored based on their values.

[0127] The collection items in this application embodiment are the same as the parameter items in the aforementioned embodiments.

[0128] Step 405: Check if the cockpit adjustment cycle has ended.

[0129] If the cabin adjustment cycle timer has ended, proceed to step 406. If the cabin adjustment cycle timer has not ended, return to step 403.

[0130] Step 406: Calculate the percentage of each state type within the cabin adjustment cycle.

[0131] In the embodiments of this application, the percentage of each state type refers to the cumulative result over 20 minutes (cabin adjustment cycle).

[0132] Step 407: Detect whether there is a state type with a proportion greater than or equal to the proportion threshold.

[0133] The percentage threshold is, for example, 50%, and the percentage of state types is, for example, the weight of user state types in the aforementioned embodiment.

[0134] If it exists, proceed to steps 408 to 411.

[0135] If it does not exist, proceed to step 410.

[0136] Step 408: Select the highest priority state to execute the linkage strategy.

[0137] In this embodiment of the application, a priority is set for each state type, and the priority order of each state type can be represented as follows: Health > Fatigue > Stress > Tension > Pleasure

[0138] Step 409, adjust the following settings: air conditioning / fragrance / seat / music / intelligent driving prompts.

[0139] Step 410: Record user feedback.

[0140] Among the user feedback were requests to manually cancel adjustments and to allow users to customize settings for air conditioning, fragrance, seats, music, etc.

[0141] Step 411, update the personalized model.

[0142] Step 412, begin the next cabin adjustment cycle.

[0143] Then return to step 403.

[0144] Step 413: Determine whether to end the trip.

[0145] If the trip ends, proceed to step 414. If the trip does not end, return to step 402 to start the timing for the next cabin adjustment cycle.

[0146] Step 414: System hibernates, statistics are reset.

[0147] The following scenario simulation will illustrate this solution.

[0148] User A's scenario is as follows: Time: 8:00-8:20 AM (first 20-minute cycle) User status: I stayed up all night the night before and have an important meeting this morning, so I drove to the company.

[0149] Data collection: Visual: Yawning frequency 7 times / minute (score 7 points) → Fatigue Hearing: No special findings (0 points) Schedule: Important meeting today (20 minutes) → Tense Driving directions: To the company (not a specific destination, 0 points) Driving habits: Normal driving time (0 points) External device: Heart rate 92 beats / min (normal, 0 points) Physical sensation: 5 movements / minute (normal, 0 points) 20-minute cycle score statistics: Health: 0 points Fatigue: 7 points Emergency response: 0 points Nervousness: 20 points Pleasure: 0 points Total score: 27 points State percentage calculation: Health: 0% Fatigue: 7 / 27 × 100% = 25.9% Emergency: 0% Tension: 20 / 27 × 100% = 74.1% Pleasure: 0% Cockpit adjustment strategy triggered: The stress level was higher than 50% (74.1%), triggering the stress level adjustment strategy. 50% is a preset threshold percentage.

[0150] Execution: Moderate natural wind + Lavender calming fragrance + Gentle massage ventilation + Soothing pure music + Intelligent driving intervention prompt.

[0151] The second 20 - minute cycle (8:20 - 8:40): Accumulate data from the previous cycle: Tension for 20 minutes, Fatigue for 7 minutes.

[0152] New addition: Yawning frequency drops to 3 times per minute (counts 3 points) → Fatigue New addition: Detect singing along voice (counts 20 points) → Pleasure Total score of the cycle: Tension 20 points + Fatigue 10 points + Pleasure 20 points = 50 points.

[0153] State proportion: Tension 40%, Fatigue 20%, Pleasure 40%.

[0154] No single state exceeds 50%. Select the tension state according to the priority to continue executing the adjustment strategy.

[0155] The cockpit adjustment method provided by the embodiments of this application, through multi - source signals and a clear scoring mechanism, realizes the computability and comparability of states, so as to be able to conduct quantifiable health assessments. And, adopting a multi - system collaborative adjustment method, based on the state proportion, triggers the linkage of multiple systems such as air - conditioning, fragrance, seats, music, intelligent driving, etc., so as to be able to adjust the overall cockpit environment. In addition, by setting the priorities of each state type, it ensures the consistency and rationality of the system adjustment behavior in complex states; supports user feedback and model self - updating, and continuously improves the adjustment accuracy.

[0156] It should be noted that although the steps of the method in this application are described in a specific order in the drawings, this does not require or imply that these steps must be executed in that specific order, or that all the shown steps must be executed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step for execution, and / or one step can be decomposed into multiple steps for execution, etc.; or, the steps in different embodiments can be combined into a new technical solution.

[0157] In another embodiment of this application, a cockpit adjustment device is provided. Please refer to Figure 5 , Figure 5This is a logic block diagram of a cockpit adjustment device 500 provided in an embodiment of this application. The cockpit adjustment device 500 may include: an acquisition module 501, a scoring module 502, and an adjustment module 503. Specifically: the acquisition module 501 is used to acquire multi-source data collected in the previous cycle; wherein the multi-source data includes multiple parameters characterizing the user's actions, behaviors, and states; the scoring module 502 is used to quantify and score multiple preset user state types based on the multi-source data, obtaining a score result corresponding to each user state type; the adjustment module 503 is used to determine and execute the cockpit adjustment strategy for the current cycle based on the score results corresponding to each user state type.

[0158] In one embodiment, the scoring module 502 is specifically used for: Obtain the set of data items corresponding to the first user state type; wherein, the set of data items includes multiple parameter items, each parameter item representing a user's behavior, action or state, and the first user state type is one of multiple user state types; Extract the parameter values ​​corresponding to each parameter item in the data item set from multiple sources; Based on the parameter values ​​corresponding to each parameter item, each parameter item is quantitatively scored to obtain the score result corresponding to each parameter item; The score for the first user status type is determined based on the score for each parameter.

[0159] In one embodiment, the scoring module 502 is specifically used to: obtain the scoring rules corresponding to each parameter item; and quantify and score each parameter item according to the parameter value and scoring rules corresponding to each parameter item to obtain the scoring result corresponding to each parameter item.

[0160] In one embodiment, the adjustment module 503 is specifically used for: The weight of each user status type is determined based on the scoring results corresponding to each user status type. The cabin adjustment strategy for the current period is determined based on the weight of each user status type.

[0161] In some embodiments, the adjustment module 503 is specifically used for: Get the rating results for each user status type in the previous period; The cumulative score for each user status type is determined based on the score results of each user status type in the previous period and the score results corresponding to each user status type. The cabin adjustment strategy for the current period is determined based on the cumulative score results of each user status type.

[0162] In one embodiment, the adjustment module 503 is specifically used for: The cabin adjustment strategy corresponding to the user status type whose weight exceeds the preset weight threshold is determined as the cabin adjustment strategy for the current period.

[0163] In one embodiment, the multi-source data includes at least two of the following: visual image data, audio data, schedule data, driving route data, driving habit data, external device data, and cockpit sensor data.

[0164] In one embodiment, the plurality of parameters includes at least two of the following: yawning frequency, eye movement frequency, red-eye count, coughing frequency, voice vitality level, tone of voice, high volume frequency, multimedia switching frequency, laughter frequency, humming along to songs, anniversary events, meeting events, medication events, menstrual period events, path deviation events, heading to the target destination, overtaking frequency, driving events within the target time period, speeding events, vehicle creeping events, heart rate, blood oxygen, exercise volume, body temperature, call volume, call duration, and body twisting frequency.

[0165] Each module in the aforementioned cockpit adjustment device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0166] Please refer to Figure 6 , Figure 6 This is a schematic diagram of the hardware structure of a vehicle provided in an embodiment of this application. The vehicle may include a communication interface 601, a memory 602, and a processor 603; the various components are coupled together through a bus system 604. It is understood that the bus system 604 is used to implement communication between these components. In addition to a data bus, the bus system 604 also includes a power bus, a control bus, and a status signal bus. However, for clarity, in… Figure 6 The general designated all buses as Bus System 604.

[0167] In this embodiment, the communication interface 601 is used to send and receive information with other external devices; the memory 602 is used to store a computer program that can run on the processor 603; the processor 603 is used to execute the steps of the cockpit adjustment method described in any of the foregoing embodiments when running the computer program.

[0168] It is understood that the memory 602 in this embodiment can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), Synchronous DRAM (SDRAM), Double Data Rate Synchronous DRAM (DDRSDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DRRAM). The memory 602 of the systems and methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0169] The processor 603 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of the processor 603 or by instructions in software form. The processor 603 can be a general-purpose processor, 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. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software modules can be located in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. This storage medium is located in memory 602, and the processor 603 reads the information in memory 602 and, in conjunction with its hardware, completes the steps of the above method.

[0170] It is also understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described herein, or combinations thereof.

[0171] For software implementation, the techniques described herein can be implemented through modules (e.g., procedures, functions, etc.) that perform the functions described herein. Software code can be stored in memory and executed by a processor. The memory can be implemented in the processor or externally. Wherein, if implemented as a software functional module and not sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0172] In another embodiment of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the steps of the cockpit adjustment method described in the foregoing embodiments.

[0173] In another embodiment of this application, a computer program product is also provided, including a computer program or instructions that, when executed by a processor, implement the steps of the cockpit adjustment method as described in the foregoing embodiments.

[0174] Those skilled in the art will understand that embodiments of this application can be provided as methods, apparatus, devices, or computer program products. Therefore, this application can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage and optical storage) containing computer-usable program code.

[0175] It should be noted that, in this application, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0176] The sequence numbers of the embodiments in this application are merely for descriptive purposes and do not represent the superiority or inferiority of the embodiments. The features disclosed in the several product embodiments provided in this application can be arbitrarily combined to obtain new product embodiments without conflict. Similarly, the features disclosed in the several method or device embodiments provided in this application can be arbitrarily combined to obtain new method or device embodiments without conflict. The above descriptions are merely specific implementations of this application, but the protection scope of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the protection scope of this application.

Claims

1. A cockpit adjustment method, characterized in that, The method includes: Acquire multi-source data collected in the previous period; wherein, the multi-source data includes multiple parameters characterizing the user's actions, behaviors, and states; The preset user status types are quantitatively scored based on the multi-source data to obtain the score results corresponding to each user status type. The cabin adjustment strategy for the current period is determined and executed based on the scoring results corresponding to each user status type.

2. The method according to claim 1, characterized in that, The step of quantifying and scoring multiple preset user status types based on the multi-source data includes: Obtain a set of data items corresponding to a first user state type; wherein, the set of data items includes multiple parameter items, each parameter item representing a user's behavior, action, or state, and the first user state type is one of the multiple user state types; Obtain the parameter values ​​corresponding to each parameter item in the data item set from the multi-source data; Based on the parameter values ​​corresponding to each parameter item, each parameter item is quantitatively scored to obtain the scoring results corresponding to each parameter item. The score result corresponding to the first user status type is determined based on the score result corresponding to each of the parameter items.

3. The method according to claim 2, characterized in that, The step of quantifying and scoring each parameter item based on its corresponding parameter value to obtain a score result for each parameter item includes: Obtain the scoring rules corresponding to each of the aforementioned parameter items; Based on the parameter values ​​and scoring rules corresponding to each parameter item, each parameter item is quantitatively scored to obtain the scoring result corresponding to each parameter item.

4. The method according to claim 1, characterized in that, The step of determining the cabin adjustment strategy for the current period based on the scoring results corresponding to each user status type includes: The weight of each user status type is determined based on the scoring results corresponding to each user status type. The cabin adjustment strategy for the current period is determined based on the weight of each user state type.

5. The method according to claim 1, characterized in that, The step of determining the cabin adjustment strategy for the current period based on the scoring results corresponding to each user status type includes: Obtain the rating results for each user status type in the previous period; The cumulative score for each user status type is determined based on the score results of each user status type in the previous period and the score results corresponding to each user status type. The cabin adjustment strategy for the current period is determined based on the cumulative score results of each user status type.

6. The method according to claim 4, characterized in that, The step of determining the cabin adjustment strategy for the current period based on the weights of each user state type includes: The cabin adjustment strategy corresponding to the user state type whose weight exceeds the preset weight threshold is determined as the cabin adjustment strategy for the current period.

7. The method according to any one of claims 1-5, characterized in that, The multiple parameters include at least two of the following: yawning frequency, eye movement frequency, red-eye frequency, coughing frequency, voice vitality level, tone of voice, high volume frequency, multimedia switching frequency, laughter frequency, humming along to songs, anniversary events, meeting events, medication events, menstrual period events, route deviation events, heading to the target destination, overtaking frequency, driving events within the target time period, speeding events, vehicle creeping events, heart rate, blood oxygen, exercise volume, body temperature, call volume, call duration, and body twisting frequency.

8. A cockpit adjustment device, characterized in that, The device includes: The acquisition module is used to acquire multi-source data collected in the previous period; wherein, the multi-source data includes multiple parameters characterizing the user's actions, behaviors, and states; The scoring module is used to quantify and score multiple preset user status types based on the multi-source data, and obtain the scoring results corresponding to each user status type. The adjustment module is used to determine and execute the cabin adjustment strategy for the current period based on the scoring results corresponding to each user status type.

9. A vehicle, characterized in that, It includes a processor and a memory, the memory storing programs or instructions that can run on the processor, the programs or instructions being executed by the processor to implement the steps of the cockpit adjustment method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program or instructions that, when executed by a processor, implement the steps of the cockpit adjustment method as described in any one of claims 1 to 7.

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