Seat adjusting method and device, storage medium and terminal

Abstract

The invention provides a seat adjusting method and device, a storage medium and a terminal. The method comprises the steps that biological perception data and historical seat adjusting records of a user are acquired; determining a first adjustment parameter corresponding to a preset adjustment target according to the biological perception data and a historical seat adjustment record; acquiring a feedback signal of the first adjustment parameter; determining a feedback optimization weight vector and a fatigue accumulation simulation result of the first adjustment parameter according to the first adjustment parameter and the feedback signal; determining a second adjustment parameter according to the feedback optimization weight vector and a fatigue accumulation simulation result; and adjusting the seat by adopting the second adjusting parameter. The first adjustment parameter is determined through the biological sensing data and the historical seat adjustment record, and the first adjustment parameter is subjected to feedback adjustment based on the feedback signal corresponding to the first adjustment parameter and the fatigue accumulation simulation result, so that the seat adjustment effect is continuously monitored and optimized, and the differential support requirement of the seat is met.

Description

Technical Field

[0001] The present application relates to the technical field of automobile intelligent cockpits, and in particular to a seat adjustment method, device, storage medium, and terminal. Background Art

[0002] As car ownership continues to rise, road congestion intensifies, and driving time generally increases. Since seat adjustment is a key factor affecting driving experience and safety, insufficient seat support can exacerbate back fatigue in congested traffic with frequent starts and stops. Proper backrest angle and lumbar support adjustment can alleviate muscle strain and reduce the risk of distracted driving due to physical discomfort, making seat adjustment increasingly important.

[0003] Currently, existing seat adjustment methods use a single sensor type and only adjust the position based on preset parameters such as the user's height or weight. This results in users being unable to obtain support that fits their body shape, cannot effectively relieve fatigue from long-term driving, and is difficult to meet users' driving experience and personalized needs. Summary of the Invention

[0004] In view of the above problems, the embodiments of the present application are proposed to provide a seat adjustment method, device, storage medium and vehicle-mounted terminal that overcome the above problems or at least partially solve the above problems.

[0005] According to a first aspect of the present application, a seat adjustment method is provided, the method comprising: Obtain the user's biometric data and historical seat adjustment records; Determining a first adjustment parameter corresponding to a preset adjustment target based on the biometric sensing data and the historical seat adjustment record; obtaining a feedback signal of the first adjustment parameter; Determining a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal; determining a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result; The seat is adjusted using the second adjustment parameter.

[0006] Optionally, the preset adjustment targets include: comfort target, energy consumption target and temperature target; Determining a first adjustment parameter corresponding to a preset adjustment target according to the biometric sensing data and the historical seat adjustment record includes: Determine an optimal solution set that meets the comfort target, the regulation energy consumption target, and the temperature target through a multi-objective optimization function; determining a first weight vector based on the historical seat adjustment record and the biometric sensing data; A first adjustment parameter matching the first weight vector is obtained by matching the first weight vector in the optimal solution set.

[0007] Optionally, determining a first weight vector according to the historical seat adjustment record and the biometric sensing data includes: constructing a seat adjustment preference model based on the historical seat adjustment records and the user's physiological characteristics; The biometric sensing data is input into the seat adjustment preference model, and a first weight vector matching the biometric sensing data of the user is determined.

[0008] Optionally, the feedback signal includes a comfort score and physiological index feedback data; Obtaining a feedback signal of the first adjustment parameter includes: The user's comfort score for the first adjustment parameter is collected and the user's physiological indicator feedback data for the first adjustment parameter is confirmed based on the bio-perception data.

[0009] Optionally, determining a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal includes: determining a reward value of the first adjustment parameter according to the first adjustment parameter and a feedback signal corresponding to the first adjustment parameter; determining the fatigue accumulation simulation result according to the first adjustment parameter and the biological perception data; A feedback optimization weight vector that matches the first adjustment parameter under a preset feedback strategy is determined according to the reward value.

[0010] Optionally, the biosensing data includes at least electromyographic signal data and data acquisition time; Determining the fatigue accumulation simulation result according to the first adjustment parameter and the biological perception data includes: The fatigue accumulation simulation result of the user under the first adjustment parameter is determined according to the electromyographic signal data and the data acquisition time.

[0011] Optionally, when the fatigue cumulative simulation result is greater than or equal to a fatigue cumulative simulation threshold, determining a fatigue adjustment strategy according to the fatigue cumulative simulation result; Determining a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result includes: When the fatigue accumulation simulation result is less than the fatigue accumulation simulation threshold, determining a second adjustment parameter matching the feedback optimization weight vector in the optimal solution set; When the fatigue accumulation simulation result is greater than or equal to the fatigue accumulation simulation threshold, the comfort weight coefficient in the feedback optimization weight vector is adjusted according to the fatigue adjustment strategy, a second weight vector is determined, and a second adjustment parameter matching the second weight vector is determined in the optimal solution set.

[0012] According to a second aspect of the present application, a seat adjustment device is provided, comprising: A data acquisition module is used to obtain the user's biometric data and historical seat adjustment records; a first adjustment parameter determination module, configured to determine a first adjustment parameter corresponding to a preset adjustment target based on the biometric sensing data and the historical seat adjustment record; A feedback signal acquisition module, configured to acquire a feedback signal of the first adjustment parameter; a data processing module, configured to determine a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal; A second adjustment parameter determination module is used to determine a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result; The seat adjustment module is configured to adjust the seat using the second adjustment parameter.

[0013] According to a third aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned seat adjustment method are implemented.

[0014] According to the fourth aspect of the present application, a vehicle-mounted terminal is provided, comprising a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory to implement the steps of the above-mentioned seat adjustment method.

[0015] The embodiments of the present application include the following advantages: In this application, the user's biometric data and historical seat adjustment records are obtained; a first adjustment parameter corresponding to a preset adjustment target is determined based on the biometric data and historical seat adjustment records; a feedback signal of the first adjustment parameter is obtained; a feedback optimization weight vector and fatigue accumulation simulation results of the first adjustment parameter are determined based on the first adjustment parameter and the feedback signal; a second adjustment parameter is determined based on the feedback optimization weight vector and fatigue accumulation simulation results; and the seat is adjusted using the second adjustment parameter. The first adjustment parameter is determined using biometric data and historical seat adjustment records, and feedback adjustment is performed on the first adjustment parameter based on the feedback signal corresponding to the first adjustment parameter and the fatigue accumulation simulation results, thereby achieving continuous monitoring and optimization of the seat adjustment effect and meeting the differentiated support requirements of the seat.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0018] Figure 1 is a flowchart of steps of an embodiment of a seat adjustment method of the present application; Figure 2 This is a structural block diagram of an embodiment of a seat adjustment device of the present application. DETAILED DESCRIPTION

[0019] The following describes exemplary embodiments of the present application in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0020] The terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects, and are not used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of this application can be implemented in an order other than that illustrated or described herein, and that the objects distinguished by "first," "second," and the like are generally of the same type, and do not limit the number of objects; for example, the first object can be one or more. In addition, the term "and / or" in the specification and claims refers to at least one of the connected objects, and the character " / " generally indicates that the objects connected are in an "or" relationship.

[0021] Below, in conjunction with the accompanying drawings, a seat adjustment method, device, storage medium and vehicle-mounted terminal provided by the embodiments of the present application are described in detail through specific embodiments and their application scenarios.

[0022] As car ownership continues to rise, road congestion intensifies, and driving time generally increases. Since seat adjustment is a key factor affecting driving experience and safety, insufficient seat support can exacerbate back fatigue in congested traffic with frequent starts and stops. Proper backrest angle and lumbar support adjustment can alleviate muscle strain and reduce the risk of distracted driving due to physical discomfort, making seat adjustment increasingly important.

[0023] Currently, existing seat adjustment methods rely on a single sensor type, adjusting the seat position based solely on pre-set parameters such as the user's height or weight. However, this approach, based solely on pre-set height / weight parameters, fails to consider dynamic changes in physical signs (such as accumulated fatigue and muscle stress distribution). Furthermore, this single sensor lacks continuous monitoring and optimization of the adjustment effect, resulting in reduced comfort during long rides and an inability to adapt to the differentiated support needs of individuals with specific body types (such as pregnant women and obese people).

[0024] Therefore, in order to solve the problems of single adjustment dimension, lack of feedback mechanism and insufficient personalization in related technologies, the present application provides a seat adjustment method, which obtains the user's biometric data and historical seat adjustment records; determines a first adjustment parameter corresponding to a preset adjustment target based on the biometric data and historical seat adjustment records; obtains a feedback signal of the first adjustment parameter; determines a feedback optimization weight vector and fatigue accumulation simulation result of the first adjustment parameter based on the first adjustment parameter and the feedback signal; determines a second adjustment parameter based on the feedback optimization weight vector and fatigue accumulation simulation result; and adjusts the seat using the second adjustment parameter. The first adjustment parameter is determined through biometric data and historical seat adjustment records, and the first adjustment parameter is feedback-adjusted based on the feedback signal and fatigue accumulation simulation result corresponding to the first adjustment parameter, thereby achieving continuous monitoring and optimization of the seat adjustment effect and meeting the differentiated support requirements of the seat.

[0025] Reference Figure 1 , shows a flowchart of a seat adjustment method embodiment of the present application, which may specifically include the following steps: Step 101: Obtain the user's biometric data and historical seat adjustment records; In step 101, biosensing data can be acquired by arranging a high-density flexible piezoresistive array, a three-dimensional electromyographic sensor strip, and a millimeter-wave vital sign radar on the seat to record the user's historical seat adjustment records.

[0026] Specifically, biosensing data may include pressure distribution entropy, electromyographic signal data, and physiological indicators. The pressure distribution entropy can be detected by embedding a high-density flexible piezoresistive array with a 24×36 dot matrix on the seat surface. Alternatively, an interlaced comb electrode structure can be used to infer the pressure distribution through the capacitance change ΔC. Alternatively, a fiber Bragg grating (FBG) can be arranged on the seat surface to calculate the pressure distribution entropy of the local pressure through the wavelength drift Δλ. The RMS value of the electromyographic signal data can be monitored through a three-dimensional electromyographic sensor belt integrated into the seat back (for example, an EMG sensor array), and the physiological indicators of the occupants (such as heart rate (HR) and breathing rate (BR)) can be detected through a 60GHz radar that penetrates the seat.

[0027] Optional, pressure distribution entropy value It can be determined according to the following formula:

[0028] in: is the pressure distribution entropy; n is the total number of sensor nodes. If the deployment is a 30×40 array, then n=1200; i is the sensor node index; is the ratio of the pressure value of the i-th node to the total pressure of all nodes.

[0029] Step 102: determining a first adjustment parameter corresponding to a preset adjustment target based on the biometric sensing data and the historical seat adjustment record; In step 102, after the biometric sensing data is acquired, the biometric sensing data and the historical seat adjustment records are combined, and a first adjustment parameter that meets the user's current state and usage habits is determined based on the user's biometric sensing data and the historical seat adjustment records.

[0030] Step 103: obtaining a feedback signal of the first adjustment parameter; In this embodiment, after the first adjustment parameter is determined, the seat will be adjusted according to the first adjustment parameter. After that, the on-board HMI can be set to prompt the user to make a comfort score through active acquisition. It can also be passively acquired through a high-density flexible piezoresistive array, a three-dimensional electromyographic sensor belt, and a millimeter-wave vital sign radar to obtain in real time the user's feedback signal after the seat is adjusted using the first adjustment parameter, continuously monitor the adjustment effect, and provide a data basis for the subsequent adjustment of the first adjustment parameter.

[0031] Step 104: determining a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal; Since the first adjustment parameter reflects the seat state that is consistent with the user's current state and usage habits, and the user's fatigue state and perception of the current seat change in real time, in step 103, the feedback optimization weight vector of the first adjustment parameter and the fatigue accumulation simulation result will be determined based on the first adjustment parameter and the feedback signal.

[0032] In practical applications, a Chinese human body mechanics model can be constructed based on AnyBody Biomechanics, and digital twin verification can be performed based on the human body mechanics model to determine the driver's fatigue accumulation simulation results.

[0033] Step 105: determining a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result; In step 105, the feedback optimization weight vector reflects the user's feedback on the first adjustment parameter, and the fatigue accumulation simulation result reflects the user's accumulated fatigue state during driving. The second adjustment parameter re-determined based on the feedback optimization weight vector and the fatigue accumulation simulation result is more in line with the user's current state, thereby realizing continuous monitoring and optimization of the adjustment effect and meeting the differentiated support requirements of the seat.

[0034] Specifically, when the feedback optimization weight vector and the fatigue accumulation simulation result do not require re-determination of the second adjustment parameter for optimization, the first adjustment parameter may be used as the second adjustment parameter to maintain the current seat state.

[0035] Step 106: Use the second adjustment parameter to adjust the seat.

[0036] In step 106, the second adjustment parameter is determined on the basis of the first adjustment parameter according to the user's feedback signal for the first adjustment parameter, the feedback optimization weight vector, and the fatigue accumulation simulation result after the seat is adjusted using the first adjustment parameter. Therefore, adjusting the seat according to the second adjustment parameter can adjust the seat according to changes in the user's dynamic physical signs (such as fatigue accumulation, muscle stress distribution), thereby realizing continuous monitoring and optimization of seat adjustment and meeting the differentiated support needs of various groups of people.

[0037] This embodiment obtains the user's biometric data and historical seat adjustment records; determines a first adjustment parameter corresponding to a preset adjustment target based on the biometric data and historical seat adjustment records; obtains a feedback signal for the first adjustment parameter; determines a feedback optimization weight vector and fatigue accumulation simulation results for the first adjustment parameter based on the first adjustment parameter and the feedback signal; determines a second adjustment parameter based on the feedback optimization weight vector and fatigue accumulation simulation results; and adjusts the seat using the second adjustment parameter. The first adjustment parameter is determined using biometric data and historical seat adjustment records, and feedback adjustment is performed on the first adjustment parameter based on the feedback signal and fatigue accumulation simulation results corresponding to the first adjustment parameter, thereby achieving continuous monitoring and optimization of the seat adjustment effect and meeting the differentiated support requirements of the seat.

[0038] In one embodiment of the present application, the preset adjustment targets include: a comfort target, an adjustment energy consumption target, and a temperature target; Determining a first adjustment parameter corresponding to a preset adjustment target according to the biometric sensing data and the historical seat adjustment record includes: Determine an optimal solution set that meets the comfort target, the regulation energy consumption target, and the temperature target through a multi-objective optimization function; determining a first weight vector based on the historical seat adjustment record and the biometric sensing data; A first adjustment parameter matching the first weight vector is obtained by matching the first weight vector in the optimal solution set.

[0039] In this embodiment, the preset adjustment targets include: comfort target, adjustment energy consumption target and temperature target, among which the comfort target is to improve the comfort of the seat, the adjustment energy consumption target is to avoid excessive adjustment of the seat and avoid wasting vehicle energy consumption, and the temperature target is to prevent the seat from overheating; then, a multi-objective optimization function is set according to the comfort target, the adjustment energy consumption target and the temperature target, and the optimal solution set is determined under the conditions of the multi-objective optimization function that meets the comfort target, the adjustment energy consumption target and the temperature target through a decomposition strategy; then, a first weight vector is determined according to the historical seat adjustment records and biosensing data; and a first adjustment parameter matching the first weight vector is matched in the optimal solution set according to the first weight vector.

[0040] In a specific implementation, the first weight vector is (w1, w2), where w1 and w2 are weight coefficients of the first weight vector. The multi-objective optimization function set according to the comfort target, the energy consumption target, and the temperature target may include a comfort target function Max Ccomfort, an energy consumption target function Min Ecost, and a temperature target function st Tj; the comfort target function Max Ccomfort, the energy consumption target function Min Ecost, and the temperature target function st Tj include the following contents:

[0041] The comfort objective function, Max Ccomfort, quantifies human biomechanical comfort through a weighted combination of minimizing pressure entropy (1 − Hp) and maximizing muscle relaxation, Smuscle. The energy consumption objective function, Min Ecost, constrains system energy consumption using a quadratic penalty term on actuator power consumption to avoid over-regulation. The temperature objective function, st Tj, sets a thermal pain threshold according to the ISO 13732-1 standard to ensure safety and prevent seat overheating. Ccomfort is a comprehensive comfort index; a larger value indicates higher comfort. w1 and w2 are weight coefficients of the first weight vector, which can be dynamically assigned based on user type (e.g., for a driver: w1 = 0.7, w2 = 0.3). Hp is the pressure distribution entropy, calculated by the piezoresistive array, reflecting the degree of pressure concentration. Smuscle is the muscle relaxation score, obtained by mapping the normalized RMS value of the electromyographic signal data. Ecost is the system energy consumption. Pi is the seat cushion pressure, which is adjusted to provide support for the lumbar spine or leg rest. kp is the pressure loss weight. Tj is the heating element temperature. kt is the temperature loss weight.

[0042] In practical applications, the MOEA / D algorithm can be used to generate the Pareto frontier, where the Pareto frontier represents the set of all optimal solutions that cannot be improved, that is, the improvement of any target indicator will inevitably lead to the degradation of other indicators. Since the weight coefficients of different first weight vectors will affect the optimal solution of the multi-objective optimization function, that is, there is a mapping relationship between the first weight vector and the optimal solution of the multi-objective optimization function, the first adjustment parameter that matches the weight coefficient of the first weight vector can be matched in the optimal solution set according to the mapping relationship between the first weight vector and the optimal solution of the multi-objective optimization function.

[0043] Optionally, this embodiment may also introduce a reference point mechanism to improve the distribution of solution sets in the high-dimensional target space, or rule-based fuzzy reasoning to determine the optimal solution set by defining fuzzy sets (for example: stress "high / medium / low", fatigue "severe / medium / none").

[0044] After determining the first weight vector based on historical seat adjustment records and biometric sensing data, a first adjustment parameter matching the first weight vector is obtained in the optimal solution set based on the first weight vector, and multi-dimensional seat adjustment is achieved through the first adjustment parameter.

[0045] In a specific implementation, the first adjustment parameter may include: air pressure P, temperature T and voltage V. The dimension of lumbar support can achieve different degrees of support by adjusting the air pressure P of the seat air cushion; the dimension of seat cushion partition can be achieved by adjusting the temperature T of the seat cushion to meet the needs of different users in different states; the dimension of side wing wrapping can be adjusted by adjusting the voltage V of the electrostrictive polymer of the side wing wrapping to adjust different degrees of wrapping.

[0046] In this embodiment, a multi-objective problem is converted into multiple single-objective sub-problems through a decomposition strategy to achieve collaborative optimization. A first adjustment parameter matching the first weight vector is obtained by matching the first weight vector in the optimal solution set. According to the first adjustment parameter, multi-dimensional seat adjustment is achieved by controlling different execution components to meet the differentiated support requirements of the seat.

[0047] In one embodiment of the present application, determining a first weight vector according to the historical seat adjustment record and the biometric sensing data includes: constructing a seat adjustment preference model based on the historical seat adjustment records and the user's physiological characteristics; The biometric perception data is input into the seat adjustment preference model to determine a first weight vector that matches the biometric perception data of the current user.

[0048] In this embodiment, the user's physiological characteristics include their BMI index; the biometric data may also include the user's height and weight data, which are used to determine the user's BMI (Body Mass Index). A seat adjustment preference model is constructed based on the user's BMI and their historical seat adjustment records. The biometric data is then input into the seat adjustment preference model to determine a BMI index that matches the current user's biometric data. Based on this BMI index, a first weight vector matching the current user is determined.

[0049] Specifically, if the user frequently heated the seat in historical seat adjustment records, the weight coefficient w2 in the first weight vector can be increased. If the BMI (Body Mass Index) determined based on the current user's biometric data is greater than 25, the pressure distribution entropy is of greater concern, and the weight coefficient w1 in the first weight vector can be increased. Ultimately, the seat adjustment preference model determines a first weight vector (w1, w2) that matches the current user. This first weight vector (w1, w2) is then matched within the optimal solution set to obtain a first adjustment parameter that matches the first weight vector.

[0050] By building a seat adjustment preference model based on historical seat adjustment records and the user's physiological characteristics, we can deeply explore the correlation between historical user adjustment data and physiological characteristics, forming a personalized parameter matching mechanism. The seat adjustment preference model then outputs a first weight vector that matches the current user. The resulting first adjustment parameters significantly improve the seat's adaptive capabilities and reduce the burden of repeated manual adjustments. Furthermore, this model in this embodiment features continuous iteration, updating the user's seat adjustment history in real time to meet the differentiated needs of different user groups.

[0051] In one embodiment of the present application, determining a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal includes: determining a reward value of the first adjustment parameter according to the first adjustment parameter and a feedback signal corresponding to the first adjustment parameter; determining the fatigue accumulation simulation result according to the first adjustment parameter and the biological perception data; A feedback optimization weight vector that matches the first adjustment parameter under a preset feedback strategy is determined according to the reward value.

[0052] In this embodiment, a reward value r_t of the first adjustment parameter is determined based on the first adjustment parameter and the feedback signal corresponding to the first adjustment parameter; a feedback optimization weight vector that matches the first adjustment parameter under a preset feedback strategy is determined based on the reward value r_t; and the fatigue accumulation state of the user during driving, that is, the fatigue accumulation simulation result, is determined based on the first adjustment parameter and the biosensory data.

[0053] Specifically, the reward value r_t=w1Δscore+w2Δenergy consumption of the first adjustment parameter can be calculated through the first adjustment parameter and the corresponding feedback signal, wherein: the reward value r_t is used to balance the comfort and energy consumption of the seat, and the optimal solution set is adjusted through the reward value r_t, Δscore is the change value of the comfort score, and Δenergy consumption is the energy consumption used by adjusting the seat based on the first adjustment parameter during the adjacent feedback signal acquisition period.

[0054] In practical applications, the preset feedback strategy can adjust the weight coefficient of the first weight vector according to the Δ score and Δ energy consumption to obtain the feedback optimization weight vector (w1`, w2`). For example: when the Δ score ≥ 1, the weight coefficient w1 in the first weight vector is increased by 0.1, that is, w1`=w1+0.1; when the Δ energy consumption ≥ 10W, the weight coefficient w2 in the first weight vector is reduced by 0.1, that is, w2`=w2-0.1, where: the weight coefficients w1 and w2 in the first weight vector need to satisfy w1+w2=1; the w1` and w2` in the feedback optimization weight vector also need to satisfy w1`+w2`=1.

[0055] This embodiment quantifies the adjustment effect of the first adjustment parameter by calculating the reward value of the feedback signal corresponding to the first adjustment parameter and the fatigue accumulation simulation result, determines the actual effect of the first adjustment parameter on fatigue relief, and optimizes the first adjustment parameter in multiple dimensions through the feedback optimization weight vector matching the first adjustment parameter, thereby realizing continuous monitoring and optimization of the adjustment effect.

[0056] In one embodiment of the present application, the biosensing data includes at least electromyographic signal data and data acquisition time; Determining the fatigue accumulation simulation result according to the first adjustment parameter and the biological perception data includes: The fatigue accumulation simulation result of the user under the first adjustment parameter is determined according to the electromyographic signal data and the data acquisition time.

[0057] In this embodiment, the biosensing data includes at least the RMS value of the electromyographic signal data and the data acquisition time t, where the data acquisition time t represents the time from the user using the seat to the present; then, the current fatigue accumulation simulation result of the user under the first adjustment parameter is determined based on the RMS value of the electromyographic signal data and the data acquisition time t.

[0058] In this embodiment, the current fatigue accumulation simulation of the user under the first adjustment parameter is determined by the RMS value of the electromyographic signal data and the data acquisition time t, thereby realizing quantified dynamic fatigue. The instantaneous electromyographic activity RMS value is combined with the time decay effect, which is more in line with the accumulation-clearance dynamics of muscle metabolites (for example: Hill-Type muscle model).

[0059] Specifically, the current fatigue accumulation simulation result of the user under the first adjustment parameter is determined according to the RMS value of the electromyographic signal data and the data acquisition time t The following formula can be used:

[0060] Where: MVC is the maximum voluntary contraction (MVC), which is measured by pre-calibration experiments; is the root mean square value of myoelectricity over time, which is collected in real time by the EMG sensor; β is the fatigue attenuation coefficient, which controls the attenuation rate of the historical fatigue effect; t is the data collection time; is the integral variable, which represents the time delay integral of fatigue effect.

[0061] The fatigue accumulation simulation results can reflect the biological characteristic that "muscle fatigue has a memory effect, but gradually recovers over time", and then construct a fatigue accumulation simulation mechanism that conforms to the metabolic law. Fatigue prediction based on the fatigue accumulation simulation results can effectively delay the fatigue process and prevent the problem of reduced seat comfort caused by muscle stiffness caused by sitting for a long time. Compared with the traditional method with a single adjustment dimension, not only a multi-objective optimization function is set based on the comfort target, adjustment energy consumption target and temperature target to determine the first adjustment parameter, but also feedback and optimization are performed based on the fatigue accumulation simulation results corresponding to the first adjustment parameter, and the adjustment parameters of the seat are adjusted from multiple dimensions, so that the adjusted seat is more in line with biological characteristics.

[0062] In one embodiment of the present application, the feedback signal includes a comfort score and physiological index feedback data; Obtaining a feedback signal of the first adjustment parameter includes: The user's comfort score for the first adjustment parameter is collected and the user's physiological indicator feedback data for the first adjustment parameter is confirmed based on the bio-perception data.

[0063] In this embodiment, the feedback signal includes a comfort score and physiological index feedback data.

[0064] In actual applications, the in-vehicle HMI can be set to prompt the user to rate their comfort every 20 minutes based on a 5-level Likert scale through active acquisition. Through passive acquisition, after the seat is adjusted according to the first adjustment parameter, the biosensing data acquired in real time by the high-density flexible piezoresistive array, three-dimensional electromyographic sensor belt, and millimeter-wave vital sign radar is used to determine the user's physiological indicator feedback data on the first adjustment parameter, that is, the user's pressure change frequency, occupant heart rate HR, and breathing rate BR after the seat is adjusted according to the first adjustment parameter.

[0065] Through the feedback mechanism of active and passive collection, feedback signals can be obtained through passive collection when the user is concentrating on driving. The combination of active and passive methods can more accurately identify the feedback signals corresponding to the current adjustment parameters, and then optimize the first seat adjustment parameters according to the feedback signals, thereby improving the continuous monitoring and optimization effects of the adjustment effects, thereby improving seat comfort.

[0066] In one embodiment of the present application, when the fatigue accumulation simulation result is greater than or equal to a fatigue accumulation simulation threshold, a fatigue adjustment strategy is determined according to the fatigue accumulation simulation result; Determining a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result includes: When the fatigue accumulation simulation result is less than the fatigue accumulation simulation threshold, determining a second adjustment parameter matching the feedback optimization weight vector in the optimal solution set; When the fatigue accumulation simulation result is greater than or equal to the fatigue accumulation simulation threshold, the comfort weight coefficient in the feedback optimization weight vector is adjusted according to the fatigue adjustment strategy, a second weight vector is determined, and a second adjustment parameter matching the second weight vector is determined in the optimal solution set.

[0067] In this embodiment, when the fatigue accumulation simulation result is greater than the fatigue accumulation simulation threshold, a fatigue adjustment strategy is determined according to the fatigue accumulation simulation result, and different optimization methods are set.

[0068] When the fatigue accumulation simulation result is less than the fatigue accumulation simulation threshold, determining a second adjustment parameter that matches the feedback optimization weight vector in the optimal solution set; When the fatigue cumulative simulation result is greater than or equal to the fatigue cumulative simulation threshold, the comfort weight coefficient in the feedback optimization weight vector is adjusted according to the fatigue adjustment strategy, a second weight vector is determined, and a second adjustment parameter matching the second weight vector is determined in the optimal solution set.

[0069] In practical applications, the fatigue accumulation simulation threshold can be set to 1.5. When it is less than 1.5, the weight vector (w1`, w2`) is directly optimized based on the feedback to match the corresponding second adjustment parameter in the optimal solution set; in the fatigue accumulation simulation results When it is greater than or equal to 1.5, the fatigue adjustment strategy is triggered, and the fatigue accumulation simulation results are used. Adjust the comfort weight coefficient w1` in the feedback optimization weight vector (w1`, w2`) to obtain the second weight vector (W1`, W2`).

[0070] For example, in fatigue accumulation simulation results When it is greater than 1.5, the comfort weight coefficient w1` is increased by 0.2, that is, W1`=w1`+0.2, and the energy consumption weight coefficient W2` is adaptively adjusted so that W1`+W2`=0, so as to accelerate the relief of the user's fatigue.

[0071] Optionally, you can add adjustment parameters to relieve user fatigue, for example: When it is greater than 1.5, user fatigue is relieved by adjusting the seat belt preload, airbag deployment force and seat support stiffness.

[0072] By establishing a multi-dimensional, coordinated control mechanism, when poor user feedback on a first adjustment parameter is detected, an optimized weight vector for the feedback on the first adjustment parameter of the current seat is determined. Simultaneously, a corresponding fatigue adjustment strategy derived from fatigue simulation results is employed to effectively interrupt fatigue accumulation without disrupting normal riding. This achieves a dynamic balance between seat comfort support and fatigue accumulation, breaking the limitation of traditional seat systems that prioritize adjustment over feedback.

[0073] In an embodiment of the present application, the user's biometric data and historical seat adjustment records are obtained; a first adjustment parameter corresponding to a preset adjustment target is determined based on the biometric data and historical seat adjustment records; a feedback signal for the first adjustment parameter is obtained; a feedback optimization weight vector and fatigue accumulation simulation results for the first adjustment parameter are determined based on the first adjustment parameter and the feedback signal; a second adjustment parameter is determined based on the feedback optimization weight vector and the fatigue accumulation simulation results; and the seat is adjusted using the second adjustment parameter. The first adjustment parameter is determined using the biometric data and historical seat adjustment records, and feedback adjustment is performed on the first adjustment parameter based on the feedback signal corresponding to the first adjustment parameter and the fatigue accumulation simulation results, thereby achieving continuous monitoring and optimization of the seat adjustment effect and meeting the differentiated support requirements of the seat.

[0074] Reference Figure 2 , shows a structural block diagram of a seat adjustment device of the present application, which may specifically include the following modules: Data acquisition module 201, used to obtain the user's biometric perception data and historical seat adjustment records; A first adjustment parameter determination module 202 is configured to determine a first adjustment parameter corresponding to a preset adjustment target based on the biometric sensing data and the historical seat adjustment records; A feedback signal acquisition module 203 is configured to acquire a feedback signal of the first adjustment parameter; A data processing module 204 is configured to determine a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal; A second adjustment parameter determination module 205 is configured to determine a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result; The seat adjustment module 206 is configured to adjust the seat using the second adjustment parameter.

[0075] In an embodiment of the present application, the seat adjustment device provided in the embodiment of the present application obtains a user's biometric data and historical seat adjustment records; determines a first adjustment parameter corresponding to a preset adjustment target based on the biometric data and historical seat adjustment records; obtains a feedback signal for the first adjustment parameter; determines a feedback optimization weight vector and fatigue accumulation simulation results for the first adjustment parameter based on the first adjustment parameter and the feedback signal; determines a second adjustment parameter based on the feedback optimization weight vector and the fatigue accumulation simulation results; and adjusts the seat using the second adjustment parameter. The first adjustment parameter is determined using the biometric data and historical seat adjustment records, and feedback adjustment is performed on the first adjustment parameter based on the feedback signal corresponding to the first adjustment parameter and the fatigue accumulation simulation results, thereby achieving continuous monitoring and optimization of the seat adjustment effect and meeting the differentiated support requirements of the seat.

[0076] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0077] The present application also provides a computer-readable storage medium having a computer program stored thereon. When executed by a processor, the computer program implements the various processes of the seat adjustment method embodiment described above and achieves the same technical effects. To avoid repetition, the details are omitted here. The computer-readable storage medium may be, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0078] An embodiment of the present application also provides a vehicle-mounted terminal, comprising: a processor, a memory, and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute a computer program stored in the memory, and when the computer program is executed by the processor, the various processes of the above-mentioned seat adjustment method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0079] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0080] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, apparatuses, or computer program products. Therefore, the embodiments of the present application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware. Furthermore, the embodiments of 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 magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0081] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0082] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0083] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0084] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0085] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0086] It should also be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0087] The above is a detailed introduction to a seat adjustment method, device, storage medium and vehicle-mounted terminal provided by the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A seat adjustment method, characterized in that: The method comprises: Obtain the user's biometric data and historical seat adjustment records; Determining a first adjustment parameter corresponding to a preset adjustment target based on the biometric sensing data and the historical seat adjustment record; obtaining a feedback signal of the first adjustment parameter; Determining a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal; determining a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result; The seat is adjusted using the second adjustment parameter.

2. The method according to claim 1, characterized in that The preset adjustment targets include: comfort target, energy consumption target and temperature target; Determining a first adjustment parameter corresponding to a preset adjustment target according to the biometric sensing data and the historical seat adjustment record includes: Determine an optimal solution set that meets the comfort target, the regulation energy consumption target, and the temperature target through a multi-objective optimization function; determining a first weight vector based on the historical seat adjustment record and the biometric sensing data; A first adjustment parameter matching the first weight vector is obtained by matching the first weight vector in the optimal solution set.

3. The method according to claim 2, characterized in that Determining a first weight vector according to the historical seat adjustment record and the biometric sensing data includes: constructing a seat adjustment preference model based on the historical seat adjustment records and the user's physiological characteristics; The biometric sensing data is input into the seat adjustment preference model, and a first weight vector matching the biometric sensing data of the user is determined.

4. The method according to claim 1, wherein The feedback signal includes a comfort score and physiological index feedback data; Obtaining a feedback signal of the first adjustment parameter includes: The user's comfort score for the first adjustment parameter is collected and the user's physiological indicator feedback data for the first adjustment parameter is confirmed based on the bio-perception data.

5. The method according to claim 1, wherein Determining a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal includes: determining a reward value of the first adjustment parameter according to the first adjustment parameter and a feedback signal corresponding to the first adjustment parameter; determining the fatigue accumulation simulation result according to the first adjustment parameter and the biological perception data; A feedback optimization weight vector that matches the first adjustment parameter under a preset feedback strategy is determined according to the reward value.

6. The method according to claim 5, characterized in that The biosensing data at least includes electromyographic signal data and data acquisition time; Determining the fatigue accumulation simulation result according to the first adjustment parameter and the biological perception data includes: The fatigue accumulation simulation result of the user under the first adjustment parameter is determined according to the electromyographic signal data and the data acquisition time.

7. The method according to claim 2, characterized in that When the fatigue accumulation simulation result is greater than or equal to the fatigue accumulation simulation threshold, determining a fatigue adjustment strategy according to the fatigue accumulation simulation result; Determining a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result includes: When the fatigue accumulation simulation result is less than the fatigue accumulation simulation threshold, determining a second adjustment parameter matching the feedback optimization weight vector in the optimal solution set; When the fatigue accumulation simulation result is greater than or equal to the fatigue accumulation simulation threshold, the comfort weight coefficient in the feedback optimization weight vector is adjusted according to the fatigue adjustment strategy, a second weight vector is determined, and a second adjustment parameter matching the second weight vector is determined in the optimal solution set.

8. A seat adjustment device, characterized in that: The device comprises: A data acquisition module is used to obtain the user's biometric data and historical seat adjustment records; a first adjustment parameter determination module, configured to determine a first adjustment parameter corresponding to a preset adjustment target based on the biometric sensing data and the historical seat adjustment record; A feedback signal acquisition module, configured to acquire a feedback signal of the first adjustment parameter; a data processing module, configured to determine a feedback optimization weight vector of the first adjustment parameter and a fatigue accumulation simulation result according to the first adjustment parameter and the feedback signal; A second adjustment parameter determination module is used to determine a second adjustment parameter according to the feedback optimization weight vector and the fatigue accumulation simulation result; The seat adjustment module is configured to adjust the seat using the second adjustment parameter.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the seat adjustment method according to any one of claims 1 to 7 is implemented.

10. A vehicle-mounted terminal, characterized in that: The invention comprises a processor, a memory and a communication bus; the communication bus is used to connect the processor and the memory; the processor is used to execute the computer program stored in the memory to implement the seat adjustment method according to any one of claims 1 to 7.

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