Vehicle control method, electronic equipment, storage medium, product and vehicle

Abstract

The invention relates to a vehicle control method, electronic equipment, a storage medium, a product and a vehicle. The method comprises the following steps: acquiring first sensing data corresponding to a target seat of the vehicle; based on the first sensing data, target control information corresponding to the target seat is determined through a target model; and adjusting the target seat according to the target control information. The seat can be finely adjusted.

Description

Technical Field

[0001] This application relates to the technical field of vehicle control, and in particular, to a vehicle control method, an electronic device, a storage medium, a product, and a vehicle. Background Art

[0002] With the improvement of people's living standards and the development of intelligent electric vehicle technology, automotive seats not only pursue diversified functionality, but also pay more attention to comfort and user-friendly design.

[0003] In the related art, generally, the data collected by sensors is directly used as a reference to adjust the posture of the seat. Such an adjustment method is very rough, resulting in poor comfort of the seat. Summary of the Invention

[0004] Embodiments of this application provide a vehicle control method, an electronic device, a computer-readable storage medium, a computer program product, and a vehicle, which improve the comfort of the seat and thus improve the user's vehicle use experience, so as to at least partially solve the above technical problems.

[0005] To achieve the above object, according to the first aspect of this application, a vehicle control method is provided, including: Obtaining first perception data corresponding to a target seat of a vehicle; Based on the first perception data, determining target control information corresponding to the target seat through a target model; Adjusting the target seat according to the target control information.

[0006] Optionally, the determining the target control information corresponding to the target seat through the target model based on the perception data includes: Processing the first perception data to obtain first target data corresponding to the first perception data; Processing the first target data through a target model to obtain target control information for controlling the target seat.

[0007] Optionally, the first perception data includes perception data collected by at least one first sensor disposed on the target seat.

[0008] Optionally, the first sensor includes at least one of a pressure sensor, a pulse sensor, an accelerometer, and a gyroscope.

[0009] Optionally, when the first perception data is perception data collected by a pressure sensor, the first perception data includes multiple sets of perception data collected by the pressure sensor multiple times.

[0010] Optionally, processing the first perception data to obtain first target data corresponding to the first perception data includes: Processing at least one set of perception data among multiple sets of perception data to obtain first target data corresponding to the first perception data.

[0011] Optionally, the target control information is control information for causing the target seat to be in a target seat mode after adjustment.

[0012] Optionally, the multiple sets of perception data include at least two sets of first perception data collected before the target seat mode is turned on and at least one set of first perception data collected after the target seat mode is turned on.

[0013] Optionally, processing at least one set of perception data among multiple sets of perception data to obtain first target data corresponding to the first perception data includes: Determining at least one set of first group of perception data from at least two sets of first perception data collected before the target seat mode is turned on, and determining at least one set of second group of perception data from at least one set of first perception data collected after the target seat mode is turned on; Processing the first group of perception data and / or the second group of perception data to obtain first target data corresponding to the first perception data.

[0014] Optionally, the first target data includes pressure characteristics information of the area where the pressure sensor is located.

[0015] Optionally, the pressure characteristics information includes at least one of total pressure, peak pressure, pressure area, and average pressure.

[0016] Optionally, the method further includes: When the target control information meets a preset evaluation criterion, adjusting the target seat according to the target control information.

[0017] Optionally, the method further includes: When the target control information does not meet the preset evaluation criterion, re-determining the target control information corresponding to the target seat through the target model.

[0018] Optionally, the preset evaluation criterion includes that the deviation between the target control information and the preset standard control information is not greater than a preset deviation threshold.

[0019] Optionally, the target model includes models corresponding to at least two occupant sitting postures.

[0020] Optionally, the target model includes a first model corresponding to a standard occupant sitting posture and a second model corresponding to a non-standard occupant sitting posture.

[0021] Optionally, the method further includes: When the occupant body pressure distribution corresponding to the first target data meets a preset distribution condition, the target model is the first model.

[0022] Optionally, when the occupant body pressure distribution corresponding to the first target data does not meet the preset distribution condition, the target model is the second model.

[0023] Optionally, after adjusting the target seat according to the target control information, the method further includes: Obtaining an evaluation result corresponding to the adjusted target seat; Adjusting the target seat based on the evaluation result.

[0024] Optionally, the evaluation result includes at least one of a subjective evaluation of the adjusted target seat by the occupant of the target seat, physiological information of the occupant, posture information of the occupant, and body pressure distribution information exerted by the occupant on the target seat.

[0025] Optionally, before adjusting the target seat according to the target control information, the method further includes: Obtaining second perception data corresponding to the target seat of the vehicle; Adjusting the target control information based on the second perception data.

[0026] Optionally, the second perception data includes perception data collected by at least one second sensor.

[0027] Optionally, the second sensor includes at least one of a biomedical sensor, an accelerometer, and a gyroscope.

[0028] Optionally, adjusting the target control information based on the second perception data includes: Processing the second perception data to obtain second target data; Adjusting the target control information according to the second target data.

[0029] Optionally, processing the second perception data to obtain second target data includes: When the second perception data is perception data collected by a biomedical sensor, extracting signal features of the second perception data collected by the biomedical sensor; Determining second target data of the occupant on the target seat according to the signal features, where the second target data includes physiological characteristic data of the occupant.

[0030] Optionally, the biomedical sensor includes a pulse sensor.

[0031] Optionally, the processing of the second sensed data to obtain second target data includes: When the second sensed data includes first sub-sensed data collected by a gyroscope and second sub-sensed data collected by an accelerometer, extracting a first signal feature corresponding to the first sub-sensed data and a second signal feature corresponding to the second sub-sensed data; Determining second target data according to the first signal feature and the second signal feature.

[0032] Optionally, the determining second target data according to the first signal feature and the second signal feature includes: Fusing the first signal feature and the second signal feature to obtain a fused feature; Obtaining second target data according to the fused feature.

[0033] Optionally, the second target data includes user posture change features of the occupant and motion information corresponding to the vehicle.

[0034] Optionally, the method further includes: Obtaining configuration parameters associated with the target model; Sending the configuration parameters to a cloud server so that the cloud server configures the target model according to the configuration parameters.

[0035] Optionally, the configuration parameters include at least one of an evaluation criterion corresponding to the target model and model parameters of the target model.

[0036] Optionally, the method further includes: When the zero-gravity mode of the target seat is turned on, performing the step of determining target control information corresponding to the target seat based on the first sensed data; and adjusting the target seat according to the target control information.

[0037] Optionally, the method further includes: When the target seat is not in the zero-gravity mode, adjusting the target seat based on the posture information of the occupant of the target seat and the motion information of the vehicle.

[0038] Optionally, the method further includes: When the vehicle is in a non-safe scenario corresponding to the zero-gravity mode, controlling the zero-gravity mode to be in a closed state.

[0039] Optionally, the non - safety scenario includes: the driving speed of the vehicle is greater than a preset speed threshold; and / or, the occupant on the target seat meets the corresponding health index.

[0040] In a second aspect, this embodiment also provides an electronic device, which includes a processor and a memory. Among them, the memory stores a computer program. When the computer program is executed by the processor, the processor is caused to execute the steps of the above - mentioned method.

[0041] In a third aspect, this embodiment also provides a computer - readable storage medium, which includes a computer program. When the computer program runs on an electronic device, the computer program is used to cause the electronic device to execute the steps of the above - mentioned method.

[0042] In a fourth aspect, this embodiment also provides a computer program product, including a computer program. The computer program is stored in a computer - readable storage medium; when the processor of an electronic device reads the computer program from the computer - readable storage medium, the processor executes the computer program, causing the electronic device to execute the steps of the above - mentioned method.

[0043] In a fifth aspect, this embodiment also provides a vehicle, on which at least one of the above - mentioned electronic device, computer - readable storage medium, and computer program product is provided.

[0044] In summary, in the embodiment of the present application, through the above - mentioned technical solution, the first perception data corresponding to the target seat of the vehicle is obtained, and based on this first perception data, the target control information corresponding to the target seat is determined through a target model. Then, the target seat is adjusted according to the target control information. Compared with directly using the data collected by sensors to regulate the posture of the seat in the prior art, in the present application, after obtaining the perception data, the target control information is determined through the target model, and the seat is adjusted using this target control information, realizing a more refined adjustment of the seat, and thus effectively improving the comfort of the seat and enhancing the user experience.

[0045] Other features and advantages of the present application will be described in detail in the subsequent specific implementation part. BRIEF DESCRIPTION OF THE DRAWINGS In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following - described drawings are only some embodiments of the present application. For those skilled in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] To more fully understand the present application and its beneficial effects, the following description will be made in conjunction with the accompanying drawings, where the same reference numerals in the following description denote the same parts.

[0047] Figure 1 is the first schematic diagram of the related art provided by the present application; Figure 2 is the second schematic diagram of the related art provided by the present application; Figure 3 is the first schematic diagram of seat adjustment provided in the exemplary embodiment provided by the present application; Figure 4 is the first schematic diagram of vehicle control flow provided in the exemplary embodiment provided by the present application; Figure 5 is the second schematic diagram of seat adjustment provided in the exemplary embodiment provided by the present application; Figure 6 is the third schematic diagram of seat adjustment provided in the exemplary embodiment provided by the present application; Figure 7 is the fourth schematic diagram of seat adjustment provided in the exemplary embodiment provided by the present application; Figure 8 is the architecture schematic diagram of the electronic device provided in the exemplary embodiment of the present application. Detailed Embodiment

[0048] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0049] With the improvement of people's living standards and the development of intelligent electric vehicle technology, car seats not only pursue diversified functionality, but also pay more attention to comfort and user-friendly design. From the ability to only adjust the front-back position of the seat to the multi-component freedom adjustment of the backrest angle, seat height, footrest position, and armrests, and from one or more fixed adjustment angles to the design of multiple angle adjusters for fine-tuning the angle. In the pursuit of the development of seat comfort, safety, and intelligent design, the seat posture of an astronaut in a space capsule is applied to the car driving experience. In this posture, the human body pressure distribution is uniform, greatly reducing the pressure on muscles and bones and providing an extremely comfortable feeling. The zero-gravity seat design has now been widely studied and applied, providing a more comfortable riding experience for passengers.

[0050] For example, a zero-gravity seat safety control method, system, vehicle, and storage medium are disclosed in the related art, such as Figure 1As shown, by obtaining the vehicle gear and vehicle speed signals, it is determined whether the driver's zero-gravity function is turned off, and the backrest angle of the zero-gravity seat at the co-driver position is adjusted to the collision safety angle. Manual adjustment is detected prior to automatic adjustment, and the seat assembly is driven to adjust through the seat controller signal. This invention only combines manual and electronic control signals in terms of the zero-gravity posture, adjusting to a fixed zero-gravity seat tilt angle, lacking consideration for the uses of zero-gravity seats in various scenarios and intelligent control.

[0051] For another example, a zero-gravity seat motor control method and system are disclosed in the related art, as Figure 2 shown. It is determined whether the positions of the respective motors of the seat need to be adjusted according to the corresponding motor ranges, and the information instructions can be sourced from the touch screen controller, button control, or the magnitude and direction of the pressure sensor, to adjust the specific motor rotation range. Adjusting the positions of these respective motors to adjust the corresponding parts of the seat reduces the manual adjustment operations of the zero-gravity seat. This zero-gravity seat motor control method and system also only consider a fixed posture mode in terms of the zero-gravity posture. The added pressure sensor only checks whether its magnitude and aspect trigger a set threshold, not meeting the user's personalized best zero-gravity seat experience and the pursuit of high intelligence that vary from person to person.

[0052] To solve the above problems, in view of the existing automotive seats, this application addresses the problems that affect driving and riding comfort, such as ergonomic design, limited personalized seat postures, and adjustment functions, and emulates the zero-gravity state of space science to provide a more superior body position support for the seat. At the same time, considering that zero-gravity currently focuses on seat posture research and each team has a different understanding of its posture, there is a lack of a seat posture standard suitable for everyone.

[0053] This application can combine sensor technology, AI large models, cloud interaction technology, and intelligent control systems to automatically adjust the seat posture according to the user's sitting posture habits, body shape characteristics, and vehicle status, realizing an intelligent adaptive zero-gravity seat evaluation, control method, and system.

[0054] Specifically, this application proposes a vehicle control method, electronic device, computer-readable storage medium, computer program product, and vehicle, which can adaptively adjust the seat posture according to the user's sitting posture habits, body shape characteristics, and personal preferences, improving the comfort of the seat.

[0055] As Figure 3 shown, the adaptive zero-gravity seat evaluation and control system in this embodiment can include a sensor integration module, a data preprocessing module, a model algorithm module, a motor control module, and a cloud interaction module.

[0056] The sensor module is used to configure different types of sensors in multiple areas of the seat to detect the physical signs of the user. For example, the pressure sensor detects the real-time pressure exerted by the user on the seat. Each sensor sends a pressure test signal based on points and forms a body pressure distribution numerical matrix according to the divided areas. The pulse wave sensor monitors the heart rate characteristics of the user. Combining the pressure sensor and the temperature sensor can provide more comprehensive health monitoring, fatigue warning, and comfort adjustment, etc. In addition, the gyroscope monitors the sitting posture change of the user and combines with the pressure sensor for global posture adjustment.

[0057] In this application, whether the seat is in the Zero Gravity Mode (ZGM) or not, the sensors can collect data to provide the best comfort experience for the user.

[0058] The data preprocessing module is mainly used to obtain, process, and analyze the numerical matrix. It optimizes the detection signal scheme of each sensor to ensure data accuracy, performs filtering, noise reduction, and data compensation processing on the data to reduce deviations; extracts corresponding features through algorithms, standardizes all influencing variables and the numerical matrix, and then uses the methods of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA) to analyze the factor effects between the numerical matrices, so as to obtain the contribution of each influencing factor to the system variation and extract the principal components, in order to predict the seat adjustment rule. On this basis, this application can also refine the characteristic elements such as the total pressure, peak pressure, contact area, and average pressure of different areas divided by the seat, and can train the large model of the adaptive zero-gravity seat based on the new characteristic variables, contribution weights, and regional characteristic elements obtained by the ANOVA and PCA methods.

[0059] The algorithm and evaluation module will receive the processed numerical matrix set and dynamically adjust the adaptive zero-gravity posture through algorithms and machine learning large model training.

[0060] Among them, the adaptive zero-gravity seat attitude evaluation criteria in this application can at least include: Evaluation criterion 1, the motor Hall position corresponding to the zero-gravity attitude output by the machine learning model, ensuring that the seat attitude greatly reduces the pressure on muscles and bones, and the body pressure distribution value detected by the sensor at the contact part is less than the threshold and evenly distributed; Evaluation criterion 2, the subjective evaluation of the user's riding experience in this attitude is used as an auxiliary reference. The comfort evaluation form and the heart rate value change trend chart at different positions can further fine-tune the local motor to improve the rationality of the seat attitude and provide health monitoring; Evaluation criterion 3, the gyroscope and accelerometer detect the user's posture change, and the overall seat attitude is adjusted through the algorithm according to the feedback value of the sensor of the contact area at this time. The uniform error of the body pressure distribution value is less than 5%, solving the problem of excessive local pressure caused by posture change; Evaluation criterion 4, minimum pressure thresholds are set for the vehicle driving speed and seat pressure detection respectively to ensure that the zero-gravity seat mode will not be activated during vehicle driving and for infants, and the seat and vehicle states are detected to give a safety warning when the vehicle jolts, and the collision protection measures activated by the seat when the vehicle collides are emergency stops for zero-gravity attitude adjustment and rapid return to the damaged state, etc.

[0061] The motor control module receives control instructions sent by the vehicle central control screen controller, armrest screen controller, physical buttons, model algorithm module, etc., and adjusts the rotation position of the corresponding motor through the Hall position of each Hall motor. Among them, the priority of the control instruction signal for manual adjustment is higher than that of automatic adjustment, and the priority of the warning unit is the highest to ensure the safety of the occupants to the greatest extent.

[0062] The cloud interaction module can realize the personalized customization of the zero-gravity seat solution. The large model in the cloud can learn the body shape, sitting posture habits of each user and the best sitting postures in different scenarios, and download and associate models according to the user's preferences through cloud interaction. Similarly, this application is not limited to the existing comfort evaluation criteria of zero-gravity seats. Users can store custom settings and update and iterate the model pre-configuration with the rapid development of technology.

[0063] On this basis, as Figure 4 shown, the vehicle control method in this application can at least include the following steps: S10, obtain the first perception data corresponding to the target seat of the vehicle; In this embodiment, the target seat can be the seat to be adjusted.

[0064] It should be noted that the seat designed based on the ergonomic principle in this embodiment, wherein the skeleton can be an adjustable frame structure, and lightweight and high-strength materials such as aluminum alloy are used; the seat fabric is selected from materials with good breathability and comfort, such as leather, fabric or synthetic fiber, etc., and the user experience is improved with delicate craftsmanship; the filler pursues comfort and support, and high-density sponge, memory foam, etc. are used. The multi-layer partition structure design can provide comfort and support according to the needs of different body parts, and ensure that the seat hardware conditions meet the safety and comfort experience of users through unique design and material matching.

[0065] The first perception data can be data collected by sensors arranged on the target seat.

[0066] In one embodiment, the first perception data includes perception data collected by at least one first sensor arranged on the target seat.

[0067] The first sensor includes at least one of a pressure sensor, a pulse sensor, an accelerometer, and a gyroscope.

[0068] Specifically, for example, in this embodiment, the sensor integration module is composed of a pressure sensor, a pulse sensor, an accelerometer, and a gyroscope, which real-time monitors the driver's body posture and pressure distribution, so as to adjust the posture of the seat support point; in order to study the influence of the seat posture on the body pressure distribution, heart rate, and blood pressure of the human body, the position layout of the sensors can be analyzed to obtain an accurate data set of users.

[0069] For example, pressure sensors are evenly arranged in the contact area between the human body and the seat, and the pressure values collected by the pressure sensors arranged in each area form a pressure value matrix; the pulse sensor is installed in the neck area of the seat and inside the hidden armrest for monitoring the pulse to obtain the user's real-time heart rate; the three-axis accelerometer is installed at the center of the seat bottom near the skeleton position for monitoring the overall motion state of the seat; the three-axis gyroscopes are respectively installed at the top of the seat backrest and the seat bottom position for detecting the user's posture changes.

[0070] Specifically, for example, as Figure 3As shown in the figure, the data preprocessing module includes a data acquisition unit, a data processing unit, and an analysis unit, aiming to obtain sensor data and process and analyze the numerical matrix. The data acquisition unit adopts different strategies when acquiring data from different sensors: when acquiring human body pressure distribution data, the user tries to fit the seat as closely as possible, and after the pressure stabilizes, three different seat positions are preset for acquisition (illustrated in subsequent embodiments); the heart rate monitoring data is collected from the handguard and neck areas using real-time monitoring; the accelerometer and gyroscope respond in a timely manner to obtain data when the data changes. The collected data is processed by the data processing unit for filtering and noise reduction to remove interference signals and improve data quality, and then the corresponding features are calculated through the numerical matrix: for the data detected by the pressure sensor, the total pressure, peak pressure, contact area, and average pressure of each area are calculated; for the data detected by the pulse sensor, relevant indicators such as heart rate (Beats Per Minute, BPM), heart rate variability (Heart Rate Variability, HRV), heart rate trend, and heart rate intensity are extracted; the gyroscope extracts the change in the local posture tilt angle of the user through its angular velocity data to construct the overall posture change of the user; the accelerometer extracts the acceleration magnitude and acceleration direction information through the acceleration change data to obtain the motion information of the seat and the vehicle. There are two types of analysis for the processed pressure data, namely influence factor analysis and prediction analysis, which jointly calibrate the zero-gravity seat attitude evaluation. The data analysis unit analyzes the factor effects between the data matrices through variance analysis and principal component analysis to obtain the contribution of each influence factor to the system variation and extract the principal components through dimensionality reduction. This data analysis method can intuitively reflect the influence of the influence factors on the body pressure distribution evaluation result of the seat attitude and summarize the seat attitude adjustment rules. In addition, prediction analysis can directly predict the motor position of the optimal zero-gravity seat through an adaptive algorithm large model based on the collected data.

[0071] S20. Based on the first perception data, determine the target control information corresponding to the target seat through the target model. It should be noted that in this embodiment, the target model can be an AI large model. This embodiment does not specifically limit the type, structure, etc. of the model. For example, the model can be a machine learning model or a neural network model, etc. The role of the target model is to predict the control information of the seat based on the perception data to control the seat to enter the zero-gravity mode, where the human body pressure distribution on the seat is uniform, greatly reducing the pressure on muscles and bones and providing extreme comfort.

[0072] S30. Adjust the target seat according to the target control information.

[0073] In this embodiment, after the vehicle determines the target control information, it can use the target control information to adjust the target seat.

[0074] Among them, the above control information in this embodiment may include information for controlling the motor of the seat, such as the Hall position of the Hall motor of the seat, and the rotation position of the corresponding motor is adjusted using this Hall position to adjust the posture of the seat.

[0075] Therefore, in this application, the first perception data corresponding to the target seat of the vehicle is obtained, and based on this first perception data, the target control information corresponding to the target seat is determined through the target model, and then the target seat is adjusted according to the target control information. Compared with directly using the data collected by the sensor to control the posture of the seat in the prior art, in this application, after obtaining the perception data, the target control information is determined through the target model, and the seat is adjusted using this target control information, realizing a more refined adjustment of the seat, and thus effectively improving the comfort of the seat and enhancing the user experience.

[0076] In one embodiment, in the above S20, "based on the first perception data, determining the target control information corresponding to the target seat through the target model" may include: S201, processing the first perception data to obtain first target data corresponding to the first perception data; S202, processing the first target data through the target model to obtain target control information for controlling the target seat.

[0077] In this embodiment, the perception data collected by the sensor may be processed first, such as feature extraction, data fusion, etc., to obtain first target data, and then the first target data is input into the target model to obtain the target control information of the target seat.

[0078] In one embodiment, when the first perception data is the perception data collected by the pressure sensor, the first perception data includes multiple groups of perception data collected by the pressure sensor multiple times.

[0079] It should be noted that in this embodiment, as Figure 5 shown in the adaptive zero-gravity seat surface body pressure distribution control and posture adjustment method, it is ensured that the total body pressure of the occupant is minimized and evenly distributed in the zero-gravity mode. As Figure 5 shown in the pressure sensor of the sensor integration module, there is a trigger instruction, and each sensor sends a signal based on a point to obtain the pressure information on the surface of the zero-gravity seat.

[0080] In this embodiment, the position arrangement of the sensors is studied to obtain accurate user data sets. For example, according to the contact parts of the user with the seat surface, the seat is divided into six areas: headrest, shoulder, back, waist, seat cushion, and leg rest. The body pressure values in the back, waist, and seat cushion areas account for a relatively large proportion, and adjusting the seat posture mainly relieves the pressure on this area. The body pressure distribution in the back and waist is mainly affected by the backrest angle and less affected by the seat cushion height; the pressure distribution in the hip and leg areas is mainly affected by the seat cushion height and the backrest angle and less affected by the seat position. Therefore, in this embodiment, the backrest angle and the seat cushion height are the main adjustment objects, and a larger number of pressure sensors are distributed to form an array with an interval of 3.6 cm on the backrest and the seat cushion to restore the user's body pressure distribution information with the optimal cost-effective sensor arrangement.

[0081] In this embodiment, when obtaining the body pressure distribution data on the seat surface, multiple data acquisitions are performed to ensure accuracy, and multiple groups of perception data are obtained.

[0082] In one embodiment, in the above S201, "processing the first perception data to obtain the first target data corresponding to the first perception data" may include: S2011, processing at least one group of perception data in multiple groups of perception data to obtain the first target data corresponding to the first perception data.

[0083] In this embodiment, the vehicle can obtain at least one group of perception data from multiple groups of perception data for processing to obtain the first target data corresponding to the first perception data.

[0084] In one embodiment, the target control information is control information for making the target seat be in the target seat mode after adjustment.

[0085] It can be understood that the mode of the target seat in this embodiment can be divided into a target seat mode and a non-target seat mode, and the target seat mode can specifically be a zero-gravity mode. Referring to the above embodiment, it will not be elaborated here.

[0086] In one embodiment, the multiple groups of perception data include at least two groups of first perception data collected before the target seat mode is turned on and at least one group of first perception data collected after the target seat mode is turned on.

[0087] In this embodiment, taking the pressure sensor collecting data three times as an example, for the first collection, when the user is sitting normally on the seat in the standard upright position, the real-time pressure sensor data exerted by the user on the seat for 10 s is collected every 3 minutes, and the average value of each sensor within the collection time is stored. This state of collection automatically ends after repeating 5 times (n > 3, n is an integer). Optionally, since there may be situations where pressure information exists in some areas and the number of collections is insufficient, the data of the latter two collections shall prevail.

[0088] Second collection: It is triggered when the user sitting on the seat sends an instruction to enter the zero-gravity mode through a button, an armrest screen or a central control screen. At this time, keep a proper sitting posture according to the instruction manual or voice prompt, and quickly collect the sensor data at a sampling frequency of 100 Hz (m > 10, m is a positive integer). The standard sitting posture makes the user's body fit the seat as much as possible to ensure the accuracy of the detected body pressure distribution values.

[0089] It can be understood that the perception data collected in the first two collections are at least two groups of first perception data collected before the target seat mode is turned on in this embodiment.

[0090] Third collection: The seat enters the preset default standard zero-gravity posture, and quickly collects the data in the current state at 100 Hz as at least one group of first perception data collected after the target seat mode is turned on.

[0091] In one embodiment, in the above S2011, "processing at least one group of perception data among multiple groups of perception data to obtain the first target data corresponding to the first perception data" may include: Determining at least one group of first-group perception data from at least two groups of first perception data collected before the target seat mode is turned on, and determining at least one group of second-group perception data from at least one group of first perception data collected after the target seat mode is turned on; Processing the first-group perception data and / or the second-group perception data to obtain the first target data corresponding to the first perception data.

[0092] It should be noted that in this embodiment, the vehicle can calculate the variance between the 5 times of data obtained for the first time, and select the 3 groups of data with the smallest mean square error, which reflects the body pressure distribution in the normal posture, and compare them with the data obtained for the second time. The best data is selected to represent the surface body pressure distribution before the zero-gravity seat is adjusted (i.e., the first-group perception data in this embodiment).

[0093] The third data obtained in the zero-gravity mode (i.e., the second-group perception data in this embodiment) can already ensure the accuracy of the data.

[0094] The processing of the pressure sensor data in this embodiment effectively avoids the interference caused by factors such as random touches.

[0095] In one embodiment, the first target data includes the pressure characteristics information of the area where the pressure sensor is located.

[0096] Specifically, the pressure characteristics information includes at least one of total pressure, peak pressure, pressure area, and average pressure.

[0097] Combined with the above description, in this embodiment, the sensed data can be processed to obtain the sensed data corresponding before and after the zero-gravity mode is turned on.

[0098] Furthermore, according to the sensed data (which can be the first set of sensed data before the zero-gravity mode is turned on, or the second set of sensed data after the zero-gravity mode is turned on, or the sensed data after the fusion of the first set of sensed data and the second set of sensed data), the sum of the pressures in each area where the pressure sensor is located can be calculated = , the peak pressure in each area , the average pressure and the actual pressure area ( is the number of sensors in each area i), so that the above data can be input into the target model subsequently.

[0099] In addition, as Figure 5 shown, this embodiment can also decompose the data matrix (including the sum of the pressures in each area = , the peak pressure in each area , the average pressure and the actual pressure area ) through the data analysis methods of variance and principal components to explore the influence factor effects between each area , where is the effect of a certain factor (the factor here can be the seat back angle, the footrest angle, the seat cushion angle, the seat height, etc. Here, it is mainly to analyze the influence law of other factors on the body pressure and other data by variance analysis), is the observed variable (such as the factor to be adjusted), is the average offset of all variables, so that the percentage of the sum of squares of each factor in the total change of the data can be used to explain the influence degree of each seat adjustment operation on the measurement variable, to evaluate the contribution of each variable to the system result, summarize the attitude adjustment law, and the attitude adjustment law can be output and displayed to the occupant, so that the occupant can manually adjust the seat, etc.

[0100] In one embodiment, the vehicle control method in the present application may further include: S40. When the target control information meets the preset evaluation criteria, adjust the target seat according to the target control information.

[0101] It should be noted that, in this embodiment, as Figure 3 shown, the model algorithm module (i.e., the target model in this embodiment) may include a large model prediction, model evaluation, and feedback adjustment unit. First, model training is required. A large amount of volunteer test data is obtained on a zero-gravity seat equipped with a data acquisition system. The input data in model training needs to be standardized, and then the large model is trained according to the zero-gravity evaluation target.

[0102] Specifically, first, a suitable large model needs to be selected. A large amount of volunteer test data sets are used as the input for training the model, and the algorithm optimizes the training speed, generalization ability, and system stability of the large model. Optionally, judging whether the data is sensor data collected in the case of the same body type with different standard sitting postures or different body types with the same standard sitting posture is also beneficial to optimizing the deviation between the prediction result and the actual situation and improving the generalization of the model. The output of model training is the motor positions corresponding to the standard zero-gravity posture and the occupant body type data. Among them, the evaluation criteria for the zero-gravity standard posture can be the minimum total pressure and uniform distribution of body pressure data sizes, the body trunk blood pressure horizontal line, heart rate indicators, and subjective evaluations, etc.

[0103] The trained large model (i.e., the target model in this embodiment) takes the data of the data preprocessing module as input to predict the adaptive zero-gravity seat posture, and the output is the Hall values of the positions of each motor of the zero-gravity seat and the body type data of the user.

[0104] In this embodiment, the evaluation unit sets two schemes to be implemented simultaneously (i.e., the preset evaluation criteria in this embodiment). The first is to perform error analysis on the Hall positions of the motors of the zero-gravity seat in the prediction result. If the result error is greater than 5%, it enters the feedback unit; the second is the subjective evaluation signal sent by the user in this zero-gravity seat posture and whether the real-time sensor data of the seat at this time deviates from the evaluation criteria. When the subjective evaluation is uncomfortable or the sensor data deviation evaluation criteria are too large at this time (heart rate value, sitting posture change), it enters the feedback unit. The feedback unit receives the information from the evaluation unit and performs feedback adjustment for the adaptive zero-gravity seat adjustment. At this time, the sensor preprocessing data can be re-obtained and the user body type parameters can be added as the input for model prediction. In addition, when abnormal user health-related data, abnormal seat adjustment movements, and vehicle emergencies are detected, etc., the warning unit can be triggered to activate the safety measures of the zero-gravity seat, such as buzzers, health warning upload systems, and rapid return of the seat safety angle, etc., to ensure safety.

[0105] On this basis, the preset evaluation criteria in this embodiment may include that the deviation between the target control information and the preset standard control information is not greater than the preset deviation threshold.

[0106] Specifically, for example, the deviation between the Hall position corresponding to the Hall motor in the target control information and the preset standard control information (such as the standard Hall position, which can be obtained during model training) is not greater than the preset deviation threshold.

[0107] In one embodiment, the target model includes models corresponding to at least two occupant sitting postures.

[0108] It should be noted that in this embodiment, as Figure 5 shown, the first target data (i.e., the data obtained by processing the perception data collected by the pressure sensor in the above embodiment) can be subjected to numerical matrix standardization processing first, and then it is judged whether the formed pressure distribution is symmetric and whether the force applicator is a child or other object.

[0109] On this basis, the at least two occupant sitting postures in this embodiment can include at least a standardized sitting posture and a non-standardized sitting posture.

[0110] On this basis, when the occupant body pressure distribution corresponding to the first target data meets the preset distribution conditions, the target model is the first model.

[0111] When the occupant body pressure distribution corresponding to the first target data does not meet the preset distribution conditions, the target model is the second model.

[0112] The preset distribution conditions can include that there are values in each area corresponding to the sensor and they are approximately symmetric.

[0113] In this embodiment, if there are values in each area of the body pressure distribution and they are approximately symmetric, the standard sitting posture model (i.e., the first model in this embodiment) is used for prediction. If there are no pressure values in some areas of the body pressure distribution or the distribution is asymmetric, the non-standard sitting posture model (i.e., the second model in this embodiment) is used for prediction. If the force applicator is a child or other object, the zero-gravity mode is not turned on.

[0114] Among them, the occupant body pressure distribution in this embodiment can be obtained by analyzing the first target data, which will not be elaborated here.

[0115] In one embodiment, after S30, "adjust the target seat according to the target control information", the following can also be included: S50, obtain the evaluation result corresponding to the adjusted target seat; S60, adjust the target seat based on the evaluation result.

[0116] In this embodiment, the vehicle can obtain the evaluation result corresponding to the adjusted target seat and adjust the target seat based on this evaluation result.

[0117] In one embodiment, the evaluation result includes at least one of the subjective evaluation of the occupant of the target seat on the adjusted target seat, the physiological information of the occupant, the posture information of the occupant, and the body pressure distribution information exerted by the occupant on the target seat.

[0118] It can be understood that in this embodiment, the vehicle can determine whether the evaluation result meets its corresponding evaluation criteria. For example, if the prediction result deviation is less than 5% and the subjective evaluation is a comfort evaluation, then it passes the evaluation; if the prediction result deviation is more than 5% or the subjective evaluation is uncomfortable, a feedback signal is sent to the data preprocessing unit to re-collect and process the perception data to obtain new target control information, so as to optimize and adjust the target seat using the new target control information.

[0119] On this basis, if the evaluation result corresponding to the adjusted target seat meets the corresponding evaluation criteria, the target control information can be transmitted to the motor control module to control the motor to adjust the seat to the zero-gravity posture. In addition, the motor control instruction can also be sent through the armrest screen, the central control screen, the buttons, and the warning unit, etc. (the signal priority and effectiveness need to be processed here, for example, the warning unit has the highest priority to ensure user safety), and the received instruction signal adjusts the Hall positions of the corresponding multi-angle adjuster and the fine-tuning motor to achieve more-dimensional adjustment, and all sensor data is comprehensively processed for collision protection and seat posture adjustment analysis. In the zero-gravity seat posture at this time, the average pressure of the pressure sensors between regions is the smallest, and the uniformity of the region can also be characterized by the static seat pressure distribution within each region The smaller the value, the more uniform the distribution. Among them, is the pressure detected by each sensor, is the maximum pressure detected by the sensor.

[0120] In addition, in this embodiment, when the seat fails or the vehicle encounters an emergency, the motor can take safety measures such as emergency stop and quickly returning to the seat protection position, and the user can perform personalized customization according to their own preferences, adjust the evaluation indicators of the model for adaptive adjustment, and update and iterate the large model.

[0121] In one embodiment, before the above S30, "adjust the target seat according to the target control information", it may further include: S70, obtain the second perception data corresponding to the target seat of the vehicle; S80, adjust the target control information based on the second perception data.

[0122] In this embodiment, the vehicle can also obtain the second sensing data corresponding to the target seat, and use the second sensing data to adjust the target control information to optimize the zero-gravity posture of the seat and further improve user comfort.

[0123] In one embodiment, the second sensing data includes: sensing data collected by at least one second sensor.

[0124] Specifically, the second sensor includes at least one of a biomedical sensor, an accelerometer, and a gyroscope.

[0125] It can be understood that when the first sensor is specifically a pressure sensor, the second sensor can include at least one of a biomedical sensor, an accelerometer, and a gyroscope to optimize the result predicted by the target model using the data collected by other sensors. For example, the second sensing data can be combined with the zero-gravity seat posture standard predicted by the above model, that is, the surface pressure distribution and the pulse signal characteristics are used together to control the seat posture, and the comfort level in the zero-gravity mode is evaluated through data fusion in multiple dimensions, and then the motor module is adjusted.

[0126] In one embodiment, the biomedical sensor includes a pulse sensor.

[0127] In one embodiment, in S80 above, "adjusting the target control information based on the second sensing data" may include: S801, processing the second sensing data to obtain second target data; S802, adjusting the target control information according to the second target data.

[0128] It can be understood that the processing methods of the second sensing data collected by different types of sensors in this embodiment are different, and corresponding second target data is obtained.

[0129] When adjusting the target control information according to the second target data, the second target data can be used to finely adjust the target control information to obtain the final control information to control the motor of the seat. Refer to the subsequent embodiments and details will not be elaborated here.

[0130] In one embodiment, in S801 above, "processing the second sensing data to obtain second target data" may include: S8011, when the second sensing data is the sensing data collected by the biomedical sensor, extracting the signal characteristics of the second sensing data collected by the biomedical sensor; S8012, determining the second target data of the occupant on the target seat according to the signal characteristics, where the second target data includes the physiological characteristic data of the occupant.

[0131] It should be noted that in this embodiment, as Figure 6 shown in the health assessment and cloud interaction system of the adaptive zero-gravity seat, it is used to monitor the health of the user and improve the comfort of the adaptive zero-gravity seat. Among them, the pulse sensor of the sensor integration module is triggered by an instruction to obtain an optoelectronic signal and convert it into a digital signal.

[0132] In this embodiment, the pulse sensor can be installed in the neck area of the seat and inside the hidden armrest to monitor the pulse and obtain the real-time heart rate of the user. Among them, the neck area of the seat back is installed with an adjustable hidden design, which can be adjusted within a preset range to avoid affecting the user experience; the area near the wrist inside the armrest screen is an embedded installation design, hidden inside the armrest screen to maintain beauty.

[0133] For the adaptive zero-gravity seats of the driver and front passenger, the pulse sensor in the neck area collects data in real time when the user leans against the seat; for the adaptive zero-gravity seats with armrest screens in the back row, the wrist and neck areas can be collected.

[0134] Considering that high acquisition frequencies may capture more noise, the raw data collected needs to be filtered and denoised to remove interference signals and improve data quality. In this embodiment, the algorithm can extract the physiological characteristic data of the occupant such as BPM, HRV, heart rate trend, and heart rate intensity according to the signal characteristics of the pulse sensor (i.e., the signal characteristics of the second perception data collected by the pulse sensor in this embodiment).

[0135] On this basis, the physiological characteristic data in this embodiment can be combined with the zero-gravity seat attitude standard predicted by the large model, that is, the surface pressure distribution and pulse signal characteristics are used together to regulate the seat attitude, and the comfort level in the zero-gravity mode is evaluated through data fusion and then the motor module is adjusted. In addition, the evaluation index corresponding to the best position of the zero-gravity seat mode can be updated and iterated through the cloud interaction module and the user's personalized settings are recorded, and then re-imported into the large model for prediction.

[0136] It can be seen that the standard of the adaptive zero-gravity seat can be updated in this embodiment: by integrating the seat surface pressure distribution and heart rate characteristics, and mainly judging the best regulation of the adaptive zero-gravity seat based on the human blood pressure horizontal line, the comfort level is further improved.

[0137] In addition, the pulse characteristic data extracted by the algorithm can also be used for seat posture adjustment and health monitoring. It is fused and adjusted with other data in the zero-gravity mode, and can be adjusted according to the heart rate in the non-zero-gravity mode. For example, when the heart rate increases, the reclining angle is automatically adjusted to make the user relax, and the analyzed pulse data helps the user understand their own situation, such as safety warnings like fatigue driving reminders.

[0138] In one embodiment, in S801 above, "processing the second sensing data to obtain second target data" may include: S8013. When the second sensing data includes first sub-sensing data collected by a gyroscope and second sub-sensing data collected by an accelerometer, extracting a first signal feature corresponding to the first sub-sensing data and a second signal feature corresponding to the second sub-sensing data; S8014. Determining second target data according to the first signal feature and the second signal feature.

[0139] It should be noted that in this embodiment, as Figure 7 shown in the overall adaptive adjustment and safety system of the adaptive zero-gravity seat, it includes several main modules such as data acquisition, data processing, the zero-gravity seat pressure unit control system, and result prediction and analysis. It can select high-precision three-axis gyroscopes and install them at the top of the seat back and the bottom of the seat respectively to detect the posture changes of the user. When the user is sitting, the posture change data of the backrest area and the bottom area of the seat will be collected. After algorithm noise reduction and zero-point drift compensation, the data is stored and sent to the data processing unit. The offset angle, angular velocity, and angular acceleration data (i.e., the first signal feature in this embodiment) are extracted through signal features, and the overall posture change of the user is constructed by using the local posture inclination angle change of the user.

[0140] Similarly, the three-axis accelerometer can be installed at the center of the bottom of the seat near the skeleton with the same coordinate system to monitor the overall motion state of the seat. The collected acceleration data is subjected to filtering compensation processing, and relevant indicators of the motion characteristics are extracted, such as the magnitude and direction of acceleration and the motion direction, etc. (i.e., the second signal feature in this embodiment).

[0141] Furthermore, second target data can be determined according to the first signal feature and the second signal feature.

[0142] In a specific embodiment, in S8014 above, "determining second target data according to the first signal feature and the second signal feature" may include: Fusing the first signal feature and the second signal feature to obtain a fused feature; Obtaining second target data according to the fused feature.

[0143] In this embodiment, the data collected by the accelerometer and the data collected by the gyroscope can be fused to obtain a more comprehensive user posture change feature.

[0144] In one embodiment, the second target data includes the user posture change feature of the occupant and the motion information corresponding to the vehicle.

[0145] Specifically, for example, in this embodiment, a motion algorithm that combines data fusion and machine learning can be used to identify motion, which helps predict the complete motion information of the seat and the vehicle based on changes in the user's posture offset angle, angular velocity magnitude, and the magnitude and direction of the seat's acceleration.

[0146] In addition, as Figure 7 shown, in this embodiment, the user's sitting posture habits can also be analyzed based on the sitting posture, and the head offset angle and side lying of the driver's seat can be monitored to determine whether the user is fatigued. Safety aids and safety measures for emergencies can be set according to the motion of the seat, such as reminders on bumpy roads, emergency stops during seat adjustment, and rapid return to the best protection angle position.

[0147] In addition, the seat can be automatically adjusted according to the user's posture changes and vehicle motion information in the non-zero gravity mode to improve comfort and safety; in the zero-gravity seat mode, more data information can be provided for predicting the seat posture by the zero-gravity seat pressure unit control system, and global posture adjustment can be performed according to the monitored seat motion state.

[0148] In one embodiment, the vehicle control method in this application may further include: Obtaining configuration parameters associated with the target model; Sending the configuration parameters to the cloud server so that the cloud server configures the target model according to the configuration parameters.

[0149] Specifically, the configuration parameters include at least one of the evaluation criteria corresponding to the target model and the model parameters of the target model.

[0150] It can be understood that, as Figure 6 shown and Figure 7 shown, the cloud interaction control system in this embodiment supports users to perform personalized parameter settings according to their preferences, pre-configure a new zero-gravity seat prediction large model, and give full play to the network cloud interaction ability, which not only increases the flexibility of the adaptive zero-gravity seat evaluation and control system but also adapts to the update and iteration of technological development.

[0151] Moreover, to adapt to the rapid development of AI large models, a large model pre-configuration function is added. The evaluation criteria for different zero-gravity modes can be updated to perform the latest large model pre-configuration, such as jointly optimizing the user's posture in the zero-gravity mode with pulse information and body pressure distribution, and the human blood pressure horizontal line and body pressure distribution.

[0152] In one embodiment, when the zero-gravity mode of the target seat is turned on, perform the steps of determining the target control information corresponding to the target seat through the target model based on the first perception data; and adjusting the target seat according to the target control information.

[0153] When the target seat is not in the zero - gravity mode, the target seat is adjusted based on the posture information of the occupant of the target seat and the motion information of the vehicle.

[0154] In this embodiment, if it is detected that the zero - gravity mode of the target seat is turned on, the target seat can be adjusted according to the perception data through the target model.

[0155] If it is detected that the zero - gravity mode of the target seat is not turned on, the seat can be directly adjusted using the perception data without prediction through the target model.

[0156] In one embodiment, when the vehicle is in a non - safe scenario corresponding to the zero - gravity mode, the zero - gravity mode is controlled to be in the off state.

[0157] Specifically, the non - safe scenario includes: the driving speed of the vehicle is greater than a preset speed threshold; and / or, the occupant on the target seat meets the corresponding health indicators.

[0158] In this embodiment, the driving speed of the vehicle and the minimum pressure threshold for seat pressure detection can be preset to ensure that the zero - gravity seat mode is not turned on during vehicle driving and for young children.

[0159] Among them, the occupant meeting the corresponding health indicators can specifically refer to the weight of the occupant. Considering that the weight of young children is relatively light, if it is detected through seat pressure detection that the pressure exerted by the occupant on the seat is less than the set minimum pressure threshold, it can be determined that the occupant does not meet the health indicators, and at this time, the zero - gravity mode of the seat will not be turned on.

[0160] In addition, this application can also detect the operating states of the seat and the vehicle to give a safety warning when the vehicle jolts, and when there is a collision, the seat starts collision protection measures such as emergency stop for attitude adjustment and rapid return to the injury state.

[0161] Generally speaking, the solution adopted by this application for the problem of the riding comfort of car seats is a zero - gravity seat designed based on ergonomic principles. Its seat frame, fabric, process, and filling pursue optimal performance, which is different from traditional car seats and can provide a sense of comfort and support for the parts of the user's body in contact, meeting their aesthetic sense, safety, and comfort experience.

[0162] The adaptive zero-gravity seat evaluation and control system proposed in this application is an intelligent control system that combines sensor technology, machine learning algorithms, AI large models, and cloud interaction technology. Different from the single mode of manual adjustment or fixed posture of traditional seats, it can not only adaptively adjust the seat posture according to different user body types based on technologies such as sensors and prediction large models, but also identify dynamic postures for compensation adjustment under the same body type. In addition, the combination of multiple zero-gravity mode standard evaluations makes the prediction results of the adaptive zero-gravity seat more generalizable and provides a more targeted comfortable and safe experience for different users in different scenarios.

[0163] The sensor interaction evaluation and health monitoring control system proposed in this application conducts adaptive research and adjustment on different sensors in terms of sensor position layout, data collection, and data processing methods to ensure the accuracy of preprocessed data and make the adaptive zero-gravity seat control system more robust. The evaluation system for sensor data fusion and interaction effectively evaluates the posture of the zero-gravity seat mode and can dynamically adjust the seat posture according to changes in the user's sitting posture, heart rate characteristics, and vehicle status. Compared with zero-gravity seats based only on pressure sensors, this application can realize health monitoring of users with different characteristics, safety warnings in different scenarios, and adjustment of adaptive zero-gravity seats.

[0164] The cloud interaction control system proposed in this application allows users to perform personalized parameter settings according to their preferences and pre-configure a new zero-gravity seat prediction large model, giving full play to the network cloud interaction ability. This not only increases the flexibility of the adaptive zero-gravity seat evaluation and control system but also adapts to the update and iteration of technological development.

[0165] The safety warning module proposed in this application conducts safety monitoring on the entire adaptive zero-gravity seat evaluation and control system and sets a pressure threshold to ensure that the zero-gravity seat mode will not be accidentally touched or activated by children. Different from the safety measures of traditional seats that only rely on seat belts for protection, it can start safety warnings and measures through health abnormalities detected by sensors, seat failures, and sudden vehicle conditions, resulting in better safety.

[0166] Correspondingly, an embodiment of this application further provides a vehicle control device, which may include: An acquisition module for acquiring first perception data corresponding to a target seat of a vehicle; A determination module for determining target control information corresponding to the target seat based on the first perception data through a target model; A first adjustment module for adjusting the target seat according to the target control information.

[0167] Optionally, the determination module is further configured to: Process the first perception data to obtain first target data corresponding to the first perception data; Process the first target data through the target model to obtain target control information for controlling the target seat.

[0168] Optionally, the first sensing data includes sensing data collected by at least one first sensor provided on the target seat.

[0169] Optionally, the first sensor includes at least one of a pressure sensor, a pulse sensor, an accelerometer, and a gyroscope.

[0170] Optionally, when the first sensing data is the sensing data collected by the pressure sensor, the first sensing data includes multiple sets of sensing data collected by the pressure sensor multiple times.

[0171] Optionally, the determining module is further configured to: Process at least one set of sensing data among the multiple sets of sensing data to obtain first target data corresponding to the first sensing data.

[0172] Optionally, the target control information is control information for causing the target seat to be in a target seat mode after adjustment.

[0173] Optionally, the multiple sets of sensing data include at least two sets of first sensing data collected before the target seat mode is turned on and at least one set of first sensing data collected after the target seat mode is turned on.

[0174] Optionally, the determining module is further configured to: Determine at least one set of first set of sensing data from at least two sets of first sensing data collected before the target seat mode is turned on, and determine at least one set of second set of sensing data from at least one set of first sensing data collected after the target seat mode is turned on; Process the first set of sensing data and / or the second set of sensing data to obtain first target data corresponding to the first sensing data.

[0175] Optionally, the first target data includes pressure characteristics information of the area where the pressure sensor is located.

[0176] Optionally, the pressure characteristics information includes at least one of total pressure, peak pressure, pressure area, and average pressure.

[0177] Optionally, the vehicle control device in this application further includes: A second adjustment module, configured to adjust the target seat according to the target control information when the target control information meets a preset evaluation criterion. When the target control information does not meet the preset evaluation criterion, re-determine the target control information corresponding to the target seat through the target model.

[0178] Optionally, the preset evaluation criterion includes that the deviation between the target control information and the preset standard control information is not greater than a preset deviation threshold.

[0179] Optionally, the target model includes models corresponding to at least two occupant sitting postures.

[0180] Optionally, the target model includes a first model corresponding to a standard occupant sitting posture and a second model corresponding to a non-standard occupant sitting posture.

[0181] Optionally, when the occupant body pressure distribution corresponding to the first target data meets the preset distribution condition, the target model is the first model.

[0182] Optionally, when the occupant body pressure distribution corresponding to the first target data does not meet the preset distribution condition, the target model is the second model.

[0183] Optionally, the vehicle control device in this application further includes: An evaluation result acquisition module, configured to acquire an evaluation result corresponding to the adjusted target seat; A third adjustment module, configured to adjust the target seat based on the evaluation result.

[0184] Optionally, the evaluation result includes at least one of: a subjective evaluation of the adjusted target seat by the occupant of the target seat, the physiological information of the occupant, the posture information of the occupant, and the body pressure distribution information exerted by the occupant on the target seat.

[0185] Optionally, the vehicle control device in this application further includes: A fourth adjustment module, configured to acquire second perception data corresponding to the target seat of the vehicle; and adjust the target control information based on the second perception data.

[0186] Optionally, the second perception data includes perception data collected by at least one second sensor.

[0187] Optionally, the second sensor includes at least one of a biomedical sensor, an accelerometer, and a gyroscope.

[0188] Optionally, the fourth adjustment module is further configured to: Process the second perception data to obtain second target data; Adjust the target control information according to the second target data.

[0189] Optionally, the fourth adjustment module is further configured to: When the second sensed data is the sensed data collected by the biomedical sensor, extract the signal features of the second sensed data collected by the biomedical sensor; Determine the second target data of the occupant on the target seat according to the signal features, where the second target data includes the physiological characteristic data of the occupant.

[0190] Optionally, the biomedical sensor includes a pulse sensor.

[0191] Optionally, the fourth adjustment module is further configured to: When the second sensed data includes the first sub-sensed data collected by the gyroscope and the second sub-sensed data collected by the accelerometer, extract the first signal feature corresponding to the first sub-sensed data and the second signal feature corresponding to the second sub-sensed data; Determine the second target data according to the first signal feature and the second signal feature.

[0192] Optionally, the fourth adjustment module is further configured to: Fuse the first signal feature and the second signal feature to obtain a fused feature; Obtain the second target data according to the fused feature.

[0193] Optionally, the second target data includes the user posture change feature of the occupant and the motion information corresponding to the vehicle.

[0194] Optionally, the vehicle control device in this application further includes: A configuration parameter acquisition module, configured to acquire configuration parameters associated with the target model; A sending module, configured to send the configuration parameters to the cloud server so that the cloud server configures the target model according to the configuration parameters.

[0195] Optionally, the configuration parameters include at least one of the evaluation criteria corresponding to the target model and the model parameters of the target model.

[0196] Optionally, the vehicle control device in this application further includes: An execution module, configured to, when the zero-gravity mode of the target seat is turned on, execute the step of determining the target control information corresponding to the target seat based on the first sensed data through the target model; and adjusting the target seat according to the target control information.

[0197] Optionally, the vehicle control device in this application further includes: The fifth adjustment module is further configured to: when the target seat is not in the zero - gravity mode, adjust the target seat based on the posture information of the occupant of the target seat and the motion information of the vehicle.

[0198] Optionally, the vehicle control device in this application further includes: A shutdown module, configured to control the zero - gravity mode to be in a shutdown state when the vehicle is in a non - safe scenario corresponding to the zero - gravity mode.

[0199] Optionally, the non - safe scenario includes: the driving speed of the vehicle is greater than a preset speed threshold; and / or, the occupant on the target seat meets the corresponding health indicators.

[0200] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0201] Correspondingly, an embodiment of this application further provides an electronic device, as Figure 8 shown, Figure 8 is a schematic structural diagram of the electronic device provided by the embodiment of this application. The electronic device 1100 includes a processor 1101 with one or more processing cores, a memory 1102 with one or more computer - readable storage media, and a computer program stored on the memory 1102 and executable on the processor. Among them, the processor 1101 is electrically connected to the memory 1102. Those skilled in the art can understand that the vehicle structure shown in the figure does not constitute a limitation on the vehicle, and it may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0202] The processor 1101 is the control center of the electronic device 1100, connecting various parts of the entire electronic device 1100 through various interfaces and lines. By running or loading software programs and / or units stored in the memory 1102, and by calling data stored in the memory 1102, it executes various functions of the electronic device 1100 and processes data, thereby monitoring the entire electronic device 1100. The processor 1101 may be a central processing unit (CPU), a graphics processing unit (GPU), a network processor (NP), etc., and can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0203] In an embodiment of the present application, the processor 1101 in the electronic device 1100 will load the instructions corresponding to the processes of one or more application programs into the memory 1102 according to the following steps, and the processor 1101 will run the application programs stored in the memory 1102 to implement various functions, such as: Obtain first sensing data corresponding to a target seat of a vehicle; Based on the first sensing data, determine target control information corresponding to the target seat through a target model; Adjust the target seat according to the target control information.

[0204] For the specific implementation of each of the above operations, reference may be made to the previous embodiments and will not be elaborated herein.

[0205] Optionally, as Figure 8 shown, the electronic device 1100 further includes: a touch display screen 1103, a radio frequency circuit 1104, an audio circuit 1105, an input unit 1106, and a power supply 1107. Among them, the processor 1101 is electrically connected to the touch display screen 1103, the radio frequency circuit 1104, the audio circuit 1105, the input unit 1106, and the power supply 1107 respectively. Those skilled in the art can understand that Figure 8 the vehicle structure shown in

[0206] The touch display screen 1103 can be used to display a graphical user interface and receive operation instructions generated by a user's interaction with the graphical user interface. The touch display screen 1103 may include a display panel and a touch panel. Among them, the display panel can be used to display information input by the user or information provided to the user, as well as various graphical user interfaces of the vehicle. These graphical user interfaces can be composed of graphics, text, icons, videos, and any combination thereof. Optionally, the display panel can be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. The touch panel can be used to collect touch operations of the user on or near it (such as operations of the user using a finger, a stylus, or any suitable object or accessory on or near the touch panel), and generate corresponding operation instructions, and the operation instructions execute the corresponding program. Optionally, the touch panel may include two parts: a touch display system and a touch controller. Among them, the touch display system detects the touch position of the user and detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch display system, converts it into contact coordinates, and then sends it to the processor 1101, and can receive and execute commands sent by the processor 1101. The touch panel can cover the display panel. When the touch panel detects a touch operation on or near it, it is transmitted to the processor 1101 to determine the type of touch event. Subsequently, the processor 1101 provides a corresponding visual output on the display panel according to the type of touch event. In the embodiments of the present application, the touch panel and the display panel can be integrated into the touch display screen 1103 to implement input and output functions. However, in some embodiments, the touch panel and the touch panel can be implemented as two independent components to implement input and output functions. That is, the touch display screen 1103 can also be used as part of the input unit 1106 to implement the input function.

[0207] The radio frequency circuit 1104 can be used to transmit and receive radio frequency signals to establish wireless communication with a network device or another vehicle through wireless communication, and transmit and receive signals with the network device or another vehicle.

[0208] The audio circuit 1105 can be used to provide an audio interface between the user and the vehicle through a speaker and a microphone. The audio circuit 1105 can transmit the electrical signal converted from the received audio data to the speaker, and the speaker converts it into a sound signal for output; on the other hand, the microphone converts the collected sound signal into an electrical signal, which is received by the audio circuit 1105 and then converted into audio data. After the audio data is output to the processor 1101 for processing, it is transmitted through the radio frequency circuit 1104 to, for example, another vehicle, or the audio data is output to the memory 1102 for further processing. The audio circuit 1105 may also include an earphone jack to provide communication between a peripheral earphone and the vehicle.

[0209] The input unit 1106 can be used to receive input digital, character information or user characteristic information (such as fingerprint, iris, face information, etc.), and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0210] The power supply 1107 is used to supply power to each component of the electronic device 1100. Optionally, the power supply 1107 can be logically connected to the processor 1101 through a power management device, so as to realize functions such as management of charging, discharging, and power consumption management through the power management device. The power supply 1107 can also include any components such as one or more DC or AC power supplies, recharge power failure detection circuits, power converters or inverters, and power status indicators.

[0211] Although Figure 8 not shown in the figure, the electronic device 1100 may further include a camera, a sensor, a Wi-Fi module, a Bluetooth module, etc., which will not be elaborated here.

[0212] In the above embodiments, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0213] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling relevant hardware through instructions. The instructions can be stored in a computer-readable storage medium and loaded and executed by a processor.

[0214] Therefore, an embodiment of the present application provides a computer-readable storage medium, in which multiple computer programs are stored. The computer programs can be loaded by a processor to execute any vehicle control method provided by the embodiments of the present application. The computer programs can execute the following steps of the vehicle control method: Obtain first perception data corresponding to a target seat of a vehicle; Based on the first perception data, determine target control information corresponding to the target seat through a target model; Adjust the target seat according to the target control information.

[0215] For the specific implementation of each of the above operations, reference can be made to the previous embodiments, which will not be elaborated here.

[0216] Among them, the computer-readable storage medium may include: read-only memory (ROM, Read Only Memory), random access memory (RAM, Random Access Memory), magnetic disk or optical disc, etc.

[0217] Since the computer program stored in the computer-readable storage medium can execute any one of the vehicle control methods provided by the embodiments of the present application, the beneficial effects achievable by any one of the vehicle control methods provided by the embodiments of the present application can be realized. For details, refer to the previous embodiments and will not be elaborated here.

[0218] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0219] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of flows and / or blocks in the flowchart and / or block diagram can also be implemented. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0220] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0221] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the specified functions in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0222] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0223] The memory may include non-permanent memory in the form of computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. The memory is an example of computer-readable media.

[0224] Computer-readable media includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transitory media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated communication signals and carrier waves.

[0225] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of this application, "a plurality of" means two or more, unless otherwise specifically defined.

[0226] In the above embodiments, the descriptions of the various embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0227] Among the embodiments, embodiments, and related technical features of this application, they can be combined and replaced with each other without conflict.

[0228] The above are only the preferred embodiments of this application and do not impose any form of limitation on this application. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of this application without departing from the content of the technical solution of this application still fall within the scope of the technical solution of this application.

Claims

1. A vehicle control method, characterized in that: The method comprises: Acquire first perception data corresponding to a target seat of the vehicle; Based on the first perception data, determining target control information corresponding to the target seat through a target model; The target seat is adjusted according to the target control information.

2. The vehicle control method according to claim 1, characterized in that: The determining, based on the perception data, target control information corresponding to the target seat through a target model includes: Processing the first perception data to obtain first target data corresponding to the first perception data; The first target data is processed by a target model to obtain target control information for controlling the target seat.

3. The vehicle control method according to claim 2, characterized in that: The first perception data includes perception data collected by at least one first sensor arranged at the target seat.

4. The vehicle control method according to claim 3, characterized in that: The first sensor includes at least one of a pressure sensor, a pulse sensor, an accelerometer and a gyroscope.

5. The vehicle control method according to claim 4, characterized in that: In the case where the first perception data is perception data collected by a pressure sensor, the first perception data includes multiple groups of perception data collected multiple times by the pressure sensor.

6. The vehicle control method according to claim 5, characterized in that: The processing of the first perception data to obtain first target data corresponding to the first perception data includes: At least one set of perception data among the multiple sets of perception data is processed to obtain first target data corresponding to the first perception data.

7. The vehicle control method according to claim 6, characterized in that: The target control information is control information for causing the target seat to be in a target seat mode after adjustment.

8. The vehicle control method according to claim 7, characterized in that: The multiple sets of perception data include at least two sets of first perception data collected before the target seat mode is turned on and at least one set of first perception data collected after the target seat mode is turned on.

9. The vehicle control method according to claim 8, characterized in that: The processing of at least one set of perception data among the multiple sets of perception data to obtain first target data corresponding to the first perception data includes: Determining at least one set of first sense data from at least two sets of first sense data collected before the target seat mode is turned on, and determining at least one set of second sense data from at least one set of first sense data collected after the target seat mode is turned on; The first group of perception data and / or the second group of perception data are processed to obtain first target data corresponding to the first perception data.

10. The vehicle control method according to claim 6, characterized in that: The first target data includes pressure characteristic information of the area where the pressure sensor is located.

11. The vehicle control method according to claim 10, characterized in that: The pressure characteristic information includes at least one of total pressure, peak pressure, pressure area and average pressure.

12. The vehicle control method according to claim 2, characterized in that: The method further comprises: In a case where the target control information meets a preset evaluation standard, the target seat is adjusted according to the target control information.

13. The vehicle control method according to claim 12, characterized in that: The method further comprises: When the target control information does not meet the preset evaluation criteria, the target control information corresponding to the target seat is re-determined by using the target model.

14. The vehicle control method according to claim 13, characterized in that: The preset evaluation standard includes that the deviation between the target control information and the preset standard control information is not greater than a preset deviation threshold.

15. The vehicle control method according to claim 2, characterized in that: The target model includes at least two models corresponding to the sitting postures of the occupants.

16. The vehicle control method according to claim 15, characterized in that: The target model includes a first model corresponding to a standard passenger sitting posture and a second model corresponding to a non-standard passenger sitting posture.

17. The vehicle control method according to claim 16, characterized in that: The method further comprises: When the occupant body pressure distribution corresponding to the first target data satisfies a preset distribution condition, the target model is the first model.

18. The vehicle control method according to claim 16, characterized in that: When the occupant body pressure distribution corresponding to the first target data does not satisfy a preset distribution condition, the target model is the second model.

19. The vehicle control method according to claim 1, characterized in that: After adjusting the target seat according to the target control information, the method further includes: Obtaining an evaluation result corresponding to the adjusted target seat; Based on the evaluation result, the target seat is adjusted.

20. The vehicle control method according to claim 19, characterized in that: The evaluation result includes at least one of: a subjective evaluation of the adjusted target seat by an occupant of the target seat, physiological information of the occupant, posture information of the occupant, and body pressure distribution information applied by the occupant to the target seat.

21. The vehicle control method according to claim 1, characterized in that: Before adjusting the target seat according to the target control information, the method further includes: Acquire second perception data corresponding to a target seat of the vehicle; The target control information is adjusted based on the second perception data.

22. The vehicle control method according to claim 21, characterized in that: The second perception data includes: perception data collected by at least one second sensor.

23. The vehicle control method according to claim 22, characterized in that: The second sensor includes at least one of a biomedical sensor, an accelerometer, and a gyroscope.

24. The vehicle control method according to claim 21, characterized in that: Adjusting the target control information based on the second perception data includes: Processing the second perception data to obtain second target data; The target control information is adjusted according to the second target data.

25. The vehicle control method according to claim 24, characterized in that: The processing of the second perception data to obtain second target data includes: In the case where the second perception data is perception data collected by a biomedical sensor, extracting signal features of the second perception data collected by the biomedical sensor; Second target data of the occupant in the target seat is determined according to the signal characteristics, wherein the second target data includes physiological characteristic data of the occupant.

26. The vehicle control method according to claim 25, characterized in that: The biomedical sensor includes a pulse sensor.

27. The vehicle control method according to claim 21, characterized in that: The processing of the second perception data to obtain second target data includes: In the case where the second perception data includes first sub-perception data collected by a gyroscope and second sub-perception data collected by an accelerometer, extracting a first signal feature corresponding to the first sub-perception data and a second signal feature corresponding to the second sub-perception data; Second target data is determined according to the first signal feature and the second signal feature.

28. The vehicle control method according to claim 27, characterized in that: The determining the second target data according to the first signal feature and the second signal feature includes: Fusing the first signal feature and the second signal feature to obtain a fused feature; According to the fusion feature, second target data is obtained.

29. The vehicle control method according to claim 28, characterized in that: The second target data includes a user posture change feature of the occupant and corresponding motion information of the vehicle.

30. The vehicle control method according to claim 1, characterized in that: The method further comprises: Get the configuration parameters associated with the target model; The configuration parameters are sent to a cloud server so that the cloud server configures the target model according to the configuration parameters.

31. The vehicle control method according to claim 30, characterized in that: The configuration parameters include at least one of an evaluation criterion corresponding to the target model and a model parameter of the target model.

32. The vehicle control method according to claim 1, characterized in that: The method further comprises: When the zero gravity mode of the target seat is turned on, the steps of determining target control information corresponding to the target seat through a target model based on the first perception data; and adjusting the target seat according to the target control information are performed.

33. The vehicle control method according to claim 32, characterized in that: The method further comprises: When the target seat is not in the zero gravity mode, the target seat is adjusted based on posture information of an occupant of the target seat and motion information of the vehicle.

34. The vehicle control method according to claim 32, characterized in that: The method further comprises: When the vehicle is in a non-safe scenario corresponding to the zero-gravity mode, the zero-gravity mode is controlled to be in an off state.

35. The vehicle control method according to claim 34, characterized in that: The unsafe scenario includes: the driving speed of the vehicle is greater than a preset speed threshold; and / or the occupant in the target seat meets the corresponding health index.

36. An electronic device, characterized in that: It includes a processor and a memory, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the method according to any one of claims 1 to 35.

37. A computer-readable storage medium, characterized in that: It includes a computer program, which is used to make the electronic device execute any one of the methods described in claims 1 to 35 when the computer program is run on the electronic device.

38. A computer program product, characterized in that It includes a computer program, which is stored in a computer-readable storage medium; when a processor of an electronic device reads the computer program from the computer-readable storage medium, the processor executes the computer program, so that the electronic device executes any one of the methods described in claims 1 to 35.

39. A vehicle, characterized in that: The vehicle is provided with at least one of the electronic device according to claim 36, the computer-readable storage medium according to claim 37, and the computer program product according to claim 38.

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