Power control method and device of vehicle, vehicle-mounted controller and vehicle

Abstract

The application relates to a power control method and device of a vehicle, a vehicle-mounted controller and the vehicle. The method comprises the following steps: determining an initial power parameter according to physiological parameters and sitting posture parameters of a driver; obtaining emotional state parameters of the driver through multiple sensors during driving of the vehicle; adjusting the initial power parameter according to the emotional state parameters to obtain real-time power parameters, and performing power control on the vehicle according to the real-time power parameters. The initial power benchmark is established through the physiological parameters and the sitting posture parameters, the power output is preliminarily matched with the physical characteristics and the driving posture of the driver, the power is dynamically corrected through the emotional state parameters obtained through the multiple sensors, the vehicle power response can be adapted to the psychological state change of the driver in real time, a double regulation mechanism from static physical adaptation to dynamic psychological adaptation is formed, and the adaptability of the vehicle power characteristics to the driver is improved, so that the driving experience is optimized while the driving safety is considered.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and in particular to a vehicle power control method, device, on-board controller, and vehicle. Background Technology

[0002] With the rapid development of intelligent driving technology, multiple fixed power modes supported by the vehicle can be selected during the driving process to control the vehicle's power, such as economy mode, standard mode, and sport mode.

[0003] However, the aforementioned fixed power mode is not well-suited to drivers. Summary of the Invention

[0004] Therefore, it is necessary to provide a power control method, device, on-board controller, and vehicle that can improve the adaptability to the driver in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for controlling the power of a vehicle, comprising:

[0006] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0007] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0008] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0009] In one embodiment, initial dynamic parameters are determined based on physiological parameters and sitting posture parameters, including:

[0010] Based on physiological parameters, a health sensitivity coefficient is determined; the health sensitivity coefficient is used to characterize the correction relationship between physiological parameters and acceleration curves.

[0011] Based on the sitting posture parameters, the sitting posture sensitivity coefficient is determined; the sitting posture sensitivity coefficient is used to characterize the correction relationship between the sitting posture parameters and the acceleration curve.

[0012] Initial dynamic parameters were determined based on health sensitivity coefficients and sitting posture sensitivity coefficients.

[0013] In one embodiment, the initial dynamic parameters include an upper limit of acceleration, a torque response range, and pedal sensitivity; the initial dynamic parameters are determined based on health sensitivity coefficients and posture sensitivity coefficients, including:

[0014] Acceleration curves were determined based on health sensitivity coefficients and posture sensitivity coefficients.

[0015] Based on the acceleration curve, the upper limit of acceleration, torque response range, and pedal sensitivity are determined.

[0016] In one embodiment, the physiological parameters include body mass index (BMI); determining a health sensitivity coefficient based on the physiological parameters includes:

[0017] Determine the first difference between your BMI and the preset standard BMI;

[0018] The health sensitivity coefficient is determined based on the first product of the first difference and the first weighting coefficient, and the first preset value.

[0019] In one embodiment, the posture parameters include the seat back angle; determining the posture sensitivity coefficient based on the posture parameters includes:

[0020] Determine the second difference between the seat back angle and the preset reference angle;

[0021] The posture sensitivity coefficient is determined based on the second product of the second difference and the second weighting coefficient, and the second preset value.

[0022] In one embodiment, the emotional state parameters include heart rate data and skin conductance data; adjusting the initial dynamic parameters based on the emotional state parameters to obtain real-time dynamic parameters includes:

[0023] Determine the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data;

[0024] Determine the skin conductance deviation rate based on the current skin conductance and baseline skin conductance data;

[0025] The adjustment coefficients for the dynamic parameters are determined based on the heart rate deviation rate and skin conductance deviation rate.

[0026] The initial power parameters are adjusted according to the power parameter adjustment coefficient to obtain the real-time power parameters.

[0027] In one embodiment, the initial power parameters are adjusted according to the power parameter adjustment coefficient to obtain the real-time power parameters, including:

[0028] The adjustment amount of the power parameters is determined based on the power parameter adjustment coefficient;

[0029] The initial power parameters are adjusted based on the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0030] In one embodiment, determining the dynamic parameter adjustment coefficient based on the heart rate deviation rate and skin conductance deviation rate includes:

[0031] Determine the sum of the heart rate deviation rate and the skin conductance deviation rate;

[0032] The power parameter adjustment coefficient is determined based on the sum and the third product of the third weighting coefficient, as well as the third preset value.

[0033] In one embodiment, the method further includes:

[0034] Obtain driver usage habit information within a historical period;

[0035] Determine the user habit adjustment coefficient based on driver usage habit information;

[0036] The real-time power parameters are adjusted based on user habits and the weighting coefficients corresponding to historical periods, and the vehicle's power is controlled based on the adjusted real-time power parameters.

[0037] Secondly, this application also provides a vehicle power control device, comprising:

[0038] The determination module is used to determine the initial power parameters based on the driver's physiological parameters and seating posture parameters;

[0039] The acquisition module is used to acquire the driver's emotional state parameters through multiple sensors during vehicle operation;

[0040] The control module is used to adjust the initial power parameters based on the emotional state parameters, obtain the real-time power parameters, and control the vehicle's power based on the real-time power parameters.

[0041] Thirdly, this application also provides an on-board controller, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0042] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0043] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0044] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0045] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0046] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0047] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0048] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0049] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0050] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0051] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0052] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0053] The aforementioned vehicle power control method, device, on-board controller, and vehicle determine initial power parameters based on the driver's physiological and posture parameters. During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors. The initial power parameters are adjusted based on these emotional state parameters to obtain real-time power parameters, which are then used to control the vehicle's power. By establishing an initial power benchmark using physiological and posture parameters, the power output is initially matched with the driver's physical characteristics and driving posture. The power is then dynamically corrected using emotional state parameters acquired through multiple sensors, enabling the vehicle's power response to adapt to changes in the driver's psychological state in real time. This forms a dual adjustment mechanism from static physical adaptation to dynamic psychological adaptation, improving the adaptability of vehicle power characteristics to the driver and optimizing the driving experience while ensuring driving safety. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments of this application or related technologies will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a diagram illustrating the application environment of a vehicle power control method in one embodiment.

[0056] Figure 2 This is a flowchart illustrating a vehicle power control method in one embodiment;

[0057] Figure 3 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0058] Figure 4 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0059] Figure 5 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0060] Figure 6 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0061] Figure 7 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0062] Figure 8 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0063] Figure 9 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0064] Figure 10 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0065] Figure 11 This is a flowchart illustrating the vehicle power control method in another embodiment;

[0066] Figure 12 This is a structural block diagram of the vehicle's power control device in one embodiment;

[0067] Figure 13 This is a diagram of the internal structure of the vehicle controller in one embodiment. Detailed Implementation

[0068] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0069] It should be noted that the terms "first," "second," etc., used in this application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "comprising" and "having," and any variations thereof, used in this application, are intended to cover non-exclusive inclusion. The term "multiple" used in this application refers to two or more. The term "and / or" used in this application refers to one of the embodiments, or any combination of multiple embodiments.

[0070] The vehicle power control method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, the vehicle controller 102 communicates with multiple sensors 104 installed on the vehicle. The vehicle controller 102 acquires the driver's physiological and posture parameters through the multiple sensors 104, and then performs vehicle power control based on these parameters.

[0071] In one embodiment, such as Figure 2 As shown, a vehicle power control method is provided, which is applied to... Figure 1 Taking the vehicle-mounted controller as an example, the explanation includes:

[0072] S201 determines the initial power parameters based on the driver's physiological parameters and seating posture parameters.

[0073] Optionally, physiological parameters may include indicators reflecting the driver's physical condition such as heart rate, skin conductance, and electromyography; seating posture parameters may include information reflecting the driver's driving posture such as seat fore-and-aft position, backrest angle, steering wheel distance, and seat cushion height. Initial power parameters may include braking response sensitivity, steering assist characteristics, and energy recovery intensity.

[0074] In this embodiment, the vehicle controller generates initial power parameters that match the current driver by analyzing and weighting physiological parameters and posture parameters, thereby determining the reference power output characteristics of the vehicle during subsequent driving based on the initial power parameters.

[0075] For example, the vehicle controller detects the driver's weight distribution and sitting posture through a pressure sensor array built into the seat, and acquires the driver's heart rate and facial status through a heart rate sensor on the steering wheel and a camera in the driver monitoring system. This information is then matched with a pre-stored driver profile to determine the driver's identity. If the driver is detected to be slender and sitting forward, the initial power parameters are set to a gentler acceleration curve and moderate torque output; if the driver is detected to be robust and sitting backward, the initial power parameters are set to a more aggressive power response characteristic.

[0076] S202 uses multiple sensors to acquire the driver's emotional state parameters while the vehicle is in motion.

[0077] Among them, the emotional state parameter is used to reflect the driver's psychological and physiological emotional response in the current driving environment, and may include stress level, fatigue level, anxiety index, excitement level, etc.

[0078] Optionally, the multiple sensors may include a driver monitoring camera, a steering wheel grip force sensor, a seat vibration sensor, a voice acquisition microphone, and a heart rate detection sensor, etc.

[0079] In this embodiment, each sensor collects information that can characterize emotional state from different dimensions and sends the information to the vehicle controller. Furthermore, the data from each sensor is fused to generate comprehensive emotional state parameters.

[0080] For example, a driver monitoring camera can be used to capture images of the driver's facial expressions, and an image processing algorithm can be used to extract information such as the driver's eye movement features, eyebrow state, mouth corner angle, and pupil diameter to determine whether the driver is in a state of tension, fatigue, or distraction.

[0081] For example, a steering wheel grip force sensor can detect the driver's grip force on the steering wheel and changes in hand temperature. Grip force exceeding a grip force threshold and / or hand temperature exceeding a temperature threshold can reflect that the driver is in a state of tension or anxiety.

[0082] For example, the tone, speed and volume of a driver's voice can be detected by a voice acquisition microphone. A speech speed exceeding a speech speed threshold and / or a tone exceeding a tone threshold can reflect that the driver is in a state of agitation.

[0083] S203 adjusts the initial power parameters based on the emotional state parameters to obtain real-time power parameters, and then performs power control on the vehicle based on the real-time power parameters.

[0084] In this embodiment, when the emotional state parameter indicates that the driver is in a calm or relaxed state, the controller maintains or slightly increases the initial power parameter to make the vehicle handling response smoother; when the emotional state parameter indicates that the driver is in a tense, anxious or fatigued state, the controller reduces the power parameter to make the acceleration process smoother and more linear, thereby improving driving safety.

[0085] For example, when the controller determines that the driver is currently in a state of mild tension based on facial expressions and voice characteristics, it reduces the accelerator pedal response sensitivity in the initial power parameters by 15% and limits the maximum torque output to 85% of the initial value; when the driver is currently in a state of fatigue or high tension, it further reduces the power response and increases the safe following distance from the vehicle in front.

[0086] The aforementioned vehicle power control method determines initial power parameters based on the driver's physiological and posture parameters. During vehicle operation, multiple sensors acquire the driver's emotional state parameters. The initial power parameters are adjusted based on these emotional state parameters to obtain real-time power parameters, which are then used to control the vehicle's power. By establishing an initial power baseline using physiological and posture parameters, the power output is initially matched to the driver's physical characteristics and driving posture. Then, the emotional state parameters acquired by multiple sensors dynamically correct the power output, enabling the vehicle's power response to adapt to changes in the driver's psychological state in real time. This forms a dual adjustment mechanism from static physical adaptation to dynamic psychological adaptation, improving the compatibility between the vehicle's power characteristics and the driver, optimizing the driving experience while ensuring driving safety.

[0087] In one embodiment, one implementation of S201 above is provided, such as... Figure 3 As shown, the above-mentioned "determining initial dynamic parameters based on physiological parameters and sitting posture parameters" includes:

[0088] S301, Based on physiological parameters, determine the health sensitivity coefficient; the health sensitivity coefficient is used to characterize the correction relationship between physiological parameters and acceleration curves.

[0089] In this embodiment, physiological parameters can be obtained from the driver's profile or the in-vehicle input interface, or collected from multiple in-vehicle sensors. The in-vehicle controller determines a health sensitivity coefficient based on the driver's static physiological parameters such as height, weight, and arm length, to reflect the degree to which the driver's current physical condition affects the acceleration curve.

[0090] For example, each combination of physiological parameters is mapped to a corresponding health sensitivity coefficient through a preset mapping relationship. For instance, if the driver's weight is greater than 90kg, the health sensitivity coefficient can be set to 0.6; if the driver's height is less than 160cm, the health sensitivity coefficient can be set to 0.7; if the driver's arm length is greater than 75cm, the health sensitivity coefficient can be set to 0.55; if the driver's height is between 165cm and 180cm and the weight is between 60kg and 80kg, the health sensitivity coefficient can be set to 0.5.

[0091] S302, Determine the posture sensitivity coefficient based on the posture parameters; the posture sensitivity coefficient is used to characterize the correction relationship between the posture parameters and the acceleration curve.

[0092] In this embodiment, the vehicle controller determines the posture sensitivity coefficient based on posture parameters such as seat fore-and-aft position, backrest angle, steering wheel distance, and seat cushion height, in order to reflect the driver's operating habits and control expectations based on the current posture.

[0093] For example, the seat adjustment sensor reads the seat's fore-and-aft position scale, backrest angle, and steering wheel distance. If the sensor detects that the seat position is too far back (e.g., scale ≥ 7) and the backrest angle is too large (e.g., angle ≥ 20°), the posture sensitivity coefficient is determined to be 0.7. If the sensor detects that the seat position is too far forward (e.g., scale ≤ 3) and the backrest angle is too small (e.g., angle ≤ 10°), the posture sensitivity coefficient is determined to be 0.3.

[0094] S303, based on the health sensitivity coefficient and the sitting posture sensitivity coefficient, determines the initial dynamic parameters.

[0095] In this embodiment, the health sensitivity coefficient and posture sensitivity coefficient are calculated according to a preset fusion strategy to generate a comprehensive correction coefficient. Further, based on the baseline dynamic parameters, static adjustments can be made according to the comprehensive correction coefficient to generate initial dynamic parameters that match the driver's current body characteristics and posture; alternatively, the initial dynamic parameters can be determined based on the mapping relationship between the comprehensive correction coefficient and the dynamic parameters. For example, the fusion strategy can be a weighted sum or an average, etc.

[0096] In the above-mentioned embodiments, the health sensitivity coefficient and the posture sensitivity coefficient are determined by the driver's static physiological parameters and fixed sitting posture parameters, respectively, and the initial power parameters are statically adapted before the vehicle is started, so that the vehicle power reference is initially matched with the driver's physical characteristics and driving preferences.

[0097] In one embodiment, an implementation of the above S303 is provided, wherein the initial power parameters include an upper limit of acceleration, a torque response range, and pedal sensitivity, such as... Figure 4 As shown, the above-mentioned "determining initial dynamic parameters based on health sensitivity coefficient and sitting posture sensitivity coefficient" includes:

[0098] S401 determines the acceleration curve based on the health sensitivity coefficient and the sitting posture sensitivity coefficient.

[0099] The acceleration curve is used to characterize the mapping relationship between the accelerator pedal opening and the vehicle's acceleration or driving force. The larger the slope of the curve, the more sensitive the acceleration response, and the smaller the slope of the curve, the smoother the acceleration response.

[0100] In this embodiment, the health sensitivity coefficient reflects the correspondence between the driver's physiological parameters and acceleration characteristics, while the posture sensitivity coefficient reflects the correspondence between the driver's posture preferences and acceleration characteristics. Therefore, the shape characteristics of the acceleration curve are determined based on the health sensitivity coefficient and the posture sensitivity coefficient. The two sensitivity coefficients can be input into a preset curve generation model to output an acceleration curve adapted to the driver.

[0101] For example, several baseline acceleration curve templates can be pre-stored, corresponding to different combinations of sensitivity coefficients. When the health sensitivity coefficient is 0.5 and the sitting posture sensitivity coefficient is 0.5, a medium-sensitivity acceleration curve template can be selected; when the health sensitivity coefficient is 0.8 and the sitting posture sensitivity coefficient is 0.7, a lower-sensitivity acceleration curve template can be selected, so that the acceleration rise is more gradual when the throttle opening is small.

[0102] S402, based on the acceleration curve, determines the upper limit of acceleration, torque response range, and pedal sensitivity.

[0103] In this embodiment, the acceleration curve determines the mapping relationship between accelerator pedal opening and acceleration, from which the upper limit of acceleration, torque response range, and pedal sensitivity can be extracted. The upper limit of acceleration is the maximum acceleration value of the acceleration curve when the accelerator pedal opening is at its maximum; the torque response range is the slope or rate of rise of the acceleration curve in the initial stage; and the pedal sensitivity is the slope of the acceleration curve in the middle region.

[0104] For example, the acceleration value corresponding to the accelerator pedal opening of 100% is read from the acceleration curve as the upper limit of acceleration. If the acceleration value at that point is 3.5 m / s², then the upper limit of acceleration is determined to be 3.5 m / s².

[0105] For example, the torque response range is obtained by calculating the slope of the acceleration curve in the range of 0 to 30% throttle opening. If the slope is 0.12 (m / s²) / %, the torque response speed is determined to be 0.12.

[0106] For example, the pedal sensitivity is obtained by calculating the average slope of the acceleration curve in the range of 30% to 70% throttle opening. If the slope is 0.08 (m / s²) / %, the pedal sensitivity is determined to be 0.08.

[0107] In the above-mentioned embodiments, the acceleration curve is first determined by the health sensitivity coefficient and the sitting posture sensitivity coefficient, and then the upper limit of acceleration, torque response range and pedal sensitivity are extracted based on the acceleration curve, so that the power parameters are constrained by a unified curve, thereby improving the coordination and consistency of the initial power parameters.

[0108] In one embodiment, an implementation of S301 is provided, wherein the physiological parameter includes Body Mass Index (BMI); such as Figure 5 As shown, the above-mentioned "determining the health sensitivity coefficient based on physiological parameters" includes:

[0109] S501, determine the first difference between the BMI and the preset standard BMI.

[0110] In this embodiment, the fore-and-aft position of the seat is statistically correlated with the driver's height, while the backrest angle reflects the driver's torso length and posture preference, and can be used as an auxiliary correction basis for height estimation. The fore-and-aft position information of the seat is obtained through a seat rail position sensor, and the tilt angle information of the seat back is obtained through a seat angle sensor. The mapping relationship between the fore-and-aft position of the seat and the height estimate, as well as the compensation relationship between the backrest angle and the height estimate, can be pre-stored. Thus, the height estimate is calculated based on the mapping between the fore-and-aft position of the seat and the backrest angle.

[0111] In this embodiment of the application, at least one pressure sensing unit can be provided under the seat cushion. The voltage or resistance value of the sensing unit is read, and the voltage or resistance value is converted into a pressure value. The total pressure exerted by the driver on the seat can be obtained by summing the pressure values ​​of all sensing units, which is the estimated value of the driver's weight.

[0112] In this embodiment, the driver's BMI value is calculated according to the standard formula of Body Mass Index based on height and weight, i.e., BMI=W / (H²). Wherein, the normal range of BMI is 18.5-23.9; W is the driver's weight in kg; and H is the driver's height in m.

[0113] In this embodiment of the application, the preset standard BMI can be 22, and the first difference is BMI-22.

[0114] S502, determine the health sensitivity coefficient based on the first product of the first difference and the first weighting coefficient, and the first preset value.

[0115] In this embodiment of the application, the first preset value is 1, and the health sensitivity coefficient S_bmi = 1 + α × (BMI - 22), where α is the first weighting coefficient, which can be 0.08, that is, for every 1 increase in BMI, the health sensitivity coefficient increases by 8%.

[0116] For example, the higher the BMI, the greater the sensitivity coefficient, and the lower the slope of the corresponding acceleration curve; when BMI≥28 (obesity criteria), S_bmi≥1.48, the upper limit of maximum acceleration is reduced by 20%.

[0117] In this embodiment of the application, a health sensitivity coefficient is determined based on BMI, so that the initial dynamic parameters can be adapted to the cardiac load tolerance of people with different weights.

[0118] In one embodiment, an implementation of the above S302 is provided, wherein the sitting posture parameters include the seat back angle; such as Figure 6 As shown, the above "determining the posture sensitivity coefficient based on posture parameters" includes:

[0119] S601, determine the second difference between the seat back angle and the preset reference angle.

[0120] In this embodiment of the application, the second difference is θ-100, where θ is the seat back angle in degrees, with a normal range of 90-160° and a reference angle of 100°.

[0121] S602, determine the posture sensitivity coefficient based on the second product of the second difference and the second weighting coefficient, and the second preset value.

[0122] In this embodiment of the application, the second preset value is 1, then the posture sensitivity coefficient S_posture=1+β×(θ-100) / 10, where β is the second weighting coefficient, which can be 0.15, that is, for every 10° the backrest tilts back, the sensitivity increases by 15%.

[0123] For example, with a backrest angle of 100° as the baseline, for every 10° increase in angle, the sensitivity increases by 15%, and the corresponding acceleration curve slope decreases by 15%; when the backrest angle is ≥120°, S_posture≥1.3, and the acceleration slope decreases by an additional 10%.

[0124] In the above application embodiments, the posture sensitivity coefficient is determined based on the seat back angle to ensure consistent experience under the same posture.

[0125] In one embodiment, an implementation of S203 above is provided, wherein the emotional state parameters include heart rate data and electrodermal data; such as Figure 7 As shown, the above "adjusting the initial dynamic parameters based on the emotional state parameters to obtain the real-time dynamic parameters" includes:

[0126] S701 determines the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data.

[0127] Among them, the heart rate deviation rate is used to quantify the degree of deviation of the driver's current heart rate from its resting heart rate. The greater the deviation, the higher the driver's emotional fluctuation or tension.

[0128] In this embodiment, heart rate data is acquired via a heart rate sensor on the steering wheel or a smart wearable device worn by the driver. The resting heart rate is the driver's baseline heart rate in a relaxed state, which can be pre-collected before vehicle startup or obtained from the driver's historical records. The heart rate deviation rate HR_dev is obtained by dividing the difference between the current heart rate and the resting heart rate by the resting heart rate.

[0129] For example, if the driver's resting heart rate is 70 beats per minute and the current heart rate is 84 beats per minute, then the heart rate deviation rate is (84-70) / 70=20%, that is, the current heart rate has increased by 20% relative to the resting heart rate.

[0130] S702 determines the skin conductance deviation rate based on the current skin conductance and baseline skin conductance in the skin conductance data.

[0131] Among them, the skin conductance deviation rate is used to quantify the degree of deviation of the driver's skin conductance from its baseline value at the current moment. An increase in skin conductance usually reflects enhanced sympathetic nerve activation and is associated with emotional states such as tension and anxiety.

[0132] In this embodiment, the electroskin data is collected via an electroskin sensor on the steering wheel or seat. The baseline electroskin conductance is the driver's basic electroskin conductance value in a relaxed state, which can be pre-collected before vehicle startup or obtained from the driver's history file. The difference between the current electroskin conductance and the baseline electroskin conductance is divided by the baseline electroskin conductance to obtain the electroskin conductance deviation rate GSR_dev.

[0133] For example, if the driver's baseline skin conductance is 2 microsiemens and the skin conductance detected at the current moment is 2.6 microsiemens, then the skin conductance deviation rate is (2.6-2) / 2=0.3, that is, the current skin conductance has increased by 30% relative to the baseline value.

[0134] S703 determines the adjustment coefficients for dynamic parameters based on heart rate deviation rate and skin conductance deviation rate.

[0135] Optionally, the heart rate deviation rate and skin conductance deviation rate are multiplied by their respective weighting coefficients and summed to obtain a comprehensive emotional deviation value. This comprehensive emotional deviation value is then converted into a power parameter adjustment coefficient using a preset mapping function. For example, if the weight of the heart rate deviation rate is 0.4 and the weight of the skin conductance deviation rate is 0.6, and the current heart rate deviation rate is 0.2 and the skin conductance deviation rate is 0.3, then the comprehensive emotional deviation value is 0.4 × 0.2 + 0.6 × 0.3 = 0.26. After conversion using the mapping function, the power parameter adjustment coefficient is 0.2. Increased heart rate and skin conductance indicate that the user is in a state of anxiety, excitement, or road rage, and the adjustment coefficient is reduced, resulting in a smoother power output. The minimum adjustment coefficient is 0.5, which limits the maximum power output to 50% of the normal level. If the driver's state is stable, the adjustment coefficient is 1, maintaining normal power output.

[0136] Optionally, such as Figure 8 As shown, the above-mentioned "determining the dynamic parameter adjustment coefficient based on heart rate deviation rate, skin conductance deviation rate, and third weighting coefficient" includes:

[0137] S801, determine the sum of heart rate deviation rate and skin conductance deviation rate.

[0138] S802, determine the power parameter adjustment coefficient based on the sum and the third product of the third weighting coefficient, and the third preset value.

[0139] In this embodiment of the application, the sum of the heart rate deviation rate HR_dev and the skin conductance deviation rate GSR_dev is determined, and the third preset value is 1. Further, the dynamic parameter adjustment coefficient K_adjust=1-γ×(HR_dev+GSR_dev) / 2, where γ is the third weighting coefficient, which can be 0.6.

[0140] S704 adjusts the initial power parameters according to the power parameter adjustment coefficient to obtain the real-time power parameters.

[0141] Optionally, the adjustment method involves multiplying or adding an adjustment factor to each power control parameter in the initial power parameters. The adjustment factor is 1 - the power parameter adjustment coefficient, so that the power response tends to be smoother and more conservative as the degree of emotional fluctuation increases. When the power parameter adjustment coefficient is large, the controller reduces the upper limit of acceleration, reduces the torque response range, and reduces pedal sensitivity, making the vehicle's power output smoother; when the power parameter adjustment coefficient is small, the controller maintains the initial power parameters unchanged or only makes minor adjustments.

[0142] For example, if the initial acceleration upper limit is 4 m / s² and the dynamic parameter adjustment coefficient is 0.2, then the real-time acceleration upper limit is 4 × (1 - 0.2) = 3.2 m / s². If the initial pedal sensitivity is 0.12, then the real-time pedal sensitivity is adjusted to 0.12 × (1 - 0.2) = 0.096. The controller recalculates the dynamic parameter adjustment coefficient after each round of emotion state parameter updates and periodically updates the real-time dynamic parameters, with the update frequency consistent with the sampling frequency of the emotion state parameters.

[0143] In the above-mentioned embodiments, the initial power parameters can be quantitatively adjusted according to the degree of fluctuation in emotional state, realizing adaptive matching between power control and the driver's real-time emotions, and improving the accuracy of power control and driver comfort during driving.

[0144] In one embodiment, one implementation of the above S704 is provided, such as... Figure 9 As shown, the above-mentioned "adjusting the initial power parameters according to the power parameter adjustment coefficient to obtain the real-time power parameters" includes:

[0145] S901, determine the adjustment amount of power parameters based on the power parameter adjustment coefficient.

[0146] In this embodiment, the power parameter adjustment amount is used to quantify the specific value or proportion that each power control parameter needs to be adjusted. The controller calculates the adjustment amount corresponding to each parameter based on the power parameter adjustment coefficient and the current value of each parameter in the initial power parameters.

[0147] For example, for the upper limit of acceleration, the adjustment amount is expressed in units of acceleration; for the torque response range, the adjustment amount is expressed as a proportion of the response rate; and for pedal sensitivity, the adjustment amount is expressed as the change in the sensitivity coefficient.

[0148] For example, the controller uses the product of the power parameter adjustment coefficient and the initial upper limit of acceleration as the adjustment amount for the upper limit of acceleration. If the initial upper limit of acceleration is 4 m / s² and the power parameter adjustment coefficient is 0.2, then the adjustment amount for the upper limit of acceleration is 0.8 m / s². For the torque response range, if the initial torque response range is 0.15, then the adjustment amount is 0.15 × 0.2 = 0.03. For the pedal sensitivity, if the initial pedal sensitivity is 0.12, then the adjustment amount is 0.12 × 0.2 = 0.024.

[0149] S902 adjusts the initial power parameters according to the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0150] In this embodiment, the power parameter adjustment process can be expressed as P(t) = P0 + (P1 - P0) × [1 - e^(-t / τ)], where P0 is the initial power parameter, P1 is the real-time power parameter, P(t) is the power parameter value at time t, and τ is the time constant, which is required to be ≥2 seconds to ensure that the transition process is sufficiently smooth. P0 and P1 must satisfy ΔP = |P1 - P0| ≤ 5% × P0, that is, the power parameter adjustment amount does not exceed 5% of the initial value. If the power parameter adjustment amount exceeds 5% of the initial value, it can be divided into multiple adjustments.

[0151] In the above-mentioned application embodiments, the specific adjustment amount of each control parameter is determined by the power parameter adjustment coefficient, and the initial power parameters are gradually adjusted according to the preset adjustment speed, so that the change process of power response is smooth, avoiding the jump of power parameters caused by sudden changes in emotional state, and improving the stability of the power adjustment process.

[0152] In one embodiment, such as Figure 10 As shown, the power control method for the aforementioned vehicle also includes:

[0153] S1001, Obtain driver usage habit information within the historical period.

[0154] Among them, usage habit information includes driver driving behavior data over a historical period, such as average accelerator pedal opening, average brake pedal depth, commonly used speed range, acceleration preference level, and frequency of energy recovery usage.

[0155] In this embodiment, the vehicle controller acquires driver usage habit information within a historical period. The historical period refers to a preset time frame prior to the current moment, such as the past 30 days or the past 100 driving trips. The vehicle controller retrieves and analyzes the driver's historical driving records from the vehicle storage unit or a cloud server to obtain the driver usage habit information.

[0156] For example, the controller reads the driver's driving records for the past 30 days from local memory and calculates indicators such as the average accelerator pedal opening, the frequency of maximum acceleration, the average vehicle speed, and the frequency of braking each day. If the statistics show that the driver has exceeded the preset accelerator pedal opening and exceeded the preset number of rapid acceleration operations in the past 30 days, the acceleration preference level in the habit information is marked as high; if the acceleration operation is relatively gentle, it is marked as low.

[0157] S1002, Determine the user habit adjustment coefficient based on the driver's usage habit information.

[0158] In this embodiment, the usage habit information includes the driver's driving behavior data over a historical period, such as average accelerator pedal opening, average brake pedal depth, commonly used speed range, and acceleration preference level. The driver's long-term established control preference characteristics are extracted from the usage habit information, and these preference characteristics are converted into user habit adjustment coefficients using a preset mapping rule or calculation model. For example, a user habit adjustment coefficient greater than 1 indicates a preference for an aggressive power response style, a user habit adjustment coefficient less than 1 indicates a preference for a gentle power response style, and a user habit adjustment coefficient equal to 1 indicates a preference for a neutral style.

[0159] For example, if the acceleration preference level in the user habit information is high, the user habit adjustment coefficient is set to 1.15; if the acceleration preference level is medium, the user habit adjustment coefficient is set to 1.0; and if the acceleration preference level is low, the user habit adjustment coefficient is set to 0.85.

[0160] S1003 adjusts the real-time power parameters based on user habits and the weighting coefficients corresponding to historical periods, and performs power control on the vehicle based on the adjusted real-time power parameters.

[0161] In this embodiment, the expected sensitivity corresponding to the historical period is determined based on the user habit adjustment coefficient and the real-time power parameters. Then, the sensitivity coefficient is adjusted based on the expected sensitivity, the user habit adjustment coefficient, and the weight coefficient corresponding to the historical period, thereby adjusting the real-time power parameters based on the adjusted sensitivity coefficient.

[0162] For example, S_new = S_old + η × (S_feedback - S_old), where S_feedback is the desired sensitivity, S_old is the sensitivity coefficient before adjustment, S_new is the sensitivity coefficient after adjustment, and η is the weighting coefficient corresponding to the historical period. The sensitivity coefficient can be any one of the health sensitivity coefficient, posture sensitivity coefficient, and dynamic parameter adjustment coefficient.

[0163] In the above-mentioned application embodiments, the user habit adjustment coefficient is determined by the driver's usage habit information, and the real-time power parameters are differentiated by combining the historical cycle weight coefficient. This allows the long-term driving preferences to be integrated into the current power control, which retains the historical reference value while focusing on recent habit changes, thereby improving the adaptability of power control to the driver's driving style.

[0164] In one embodiment, a complete vehicle power control method is provided, such as Figure 11 As shown, the method includes:

[0165] S1, determine the first difference between the BMI and the preset standard BMI.

[0166] S2, determine the health sensitivity coefficient based on the first product of the first difference and the first weighting coefficient, and the first preset value.

[0167] S3, determine the second difference between the seat back angle and the preset reference angle.

[0168] S4. Determine the posture sensitivity coefficient based on the second product of the second difference and the second weighting coefficient, and the second preset value.

[0169] S5. Based on the health sensitivity coefficient and the sitting posture sensitivity coefficient, determine the acceleration curve.

[0170] S6, based on the acceleration curve, determines the upper limit of acceleration, torque response range, and pedal sensitivity.

[0171] The S7 uses multiple sensors to acquire the driver's emotional state parameters while the vehicle is in motion.

[0172] S8 determines the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data.

[0173] S9, determine the skin conductance deviation rate based on the current skin conductance and baseline skin conductance in the skin conductance data.

[0174] S10, determine the sum of heart rate deviation rate and skin conductance deviation rate.

[0175] S11, determine the power parameter adjustment coefficient based on the sum and the third product of the third weighting coefficient, and the third preset value.

[0176] S12, determine the adjustment amount of the power parameters based on the power parameter adjustment coefficient.

[0177] S13, adjust the initial power parameters according to the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0178] S14, Obtain driver usage habit information within the historical period.

[0179] S15, determine the user habit adjustment coefficient based on the driver's usage habit information.

[0180] S16 adjusts the real-time power parameters based on user habits and the weighting coefficients corresponding to historical periods, and performs power control on the vehicle based on the adjusted real-time power parameters.

[0181] S17 controls the vehicle's power based on real-time power parameters.

[0182] The aforementioned vehicle power control method determines initial power parameters based on the driver's physiological and posture parameters. During vehicle operation, multiple sensors acquire the driver's emotional state parameters. The initial power parameters are adjusted based on these emotional state parameters to obtain real-time power parameters, which are then used to control the vehicle's power. By establishing an initial power baseline using physiological and posture parameters, the power output is initially matched to the driver's physical characteristics and driving posture. Then, the emotional state parameters acquired by multiple sensors dynamically correct the power output, enabling the vehicle's power response to adapt to changes in the driver's psychological state in real time. This forms a dual adjustment mechanism from static physical adaptation to dynamic psychological adaptation, improving the compatibility between the vehicle's power characteristics and the driver, optimizing the driving experience while ensuring driving safety.

[0183] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps. It is understood that the steps in different embodiments can be freely combined as needed, and all non-contradictory solutions formed by such combinations are within the scope of protection of this application.

[0184] Based on the same inventive concept, this application also provides a vehicle power control device for implementing the above-described vehicle power control method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations of one or more vehicle power control device embodiments provided below can be found in the limitations of the vehicle power control method described above, and will not be repeated here.

[0185] In one embodiment, such as Figure 12 As shown, a vehicle power control device is provided, comprising: a determining module 10, an acquiring module 11, and a controlling module 12, wherein:

[0186] The determination module 10 is used to determine the initial power parameters based on the driver's physiological parameters and sitting posture parameters.

[0187] The acquisition module 11 is used to acquire the driver's emotional state parameters through multiple sensors during the vehicle's operation.

[0188] The control module 12 is used to adjust the initial power parameters according to the emotional state parameters, obtain the real-time power parameters, and perform power control on the vehicle according to the real-time power parameters.

[0189] In one embodiment, the control module 10 includes: a first determining unit, a second determining unit, and a third determining unit, wherein:

[0190] The first determining unit is used to determine the health sensitivity coefficient based on physiological parameters; the health sensitivity coefficient is used to characterize the correction relationship between physiological parameters and acceleration curves.

[0191] The second determining unit is used to determine the posture sensitivity coefficient based on the posture parameters; the posture sensitivity coefficient is used to characterize the correction relationship between the posture parameters and the acceleration curve.

[0192] The third determining unit is used to determine the initial dynamic parameters based on the health sensitivity coefficient and the sitting posture sensitivity coefficient.

[0193] In one embodiment, the third determining unit is specifically used to determine the acceleration curve based on the health sensitivity coefficient and the sitting posture sensitivity coefficient; and based on the acceleration curve, to determine the upper limit of acceleration, the torque response range, and the pedal sensitivity.

[0194] In one embodiment, the first determining unit is specifically used to determine a first difference between the BMI and a preset standard BMI; and to determine a health sensitivity coefficient based on the first product of the first difference and a first weighting coefficient, and a first preset value.

[0195] In one embodiment, the second determining unit is specifically used to determine a second difference between the seat back angle and a preset reference angle; and to determine a posture sensitivity coefficient based on the second product of the second difference and a second weighting coefficient, and a second preset value.

[0196] In one embodiment, the control module includes: a fourth determining unit, a fifth determining unit, a sixth determining unit, and an adjustment unit, wherein:

[0197] The fourth determining unit is used to determine the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data.

[0198] The fifth determining unit is used to determine the skin conductance deviation rate based on the current skin conductance and baseline skin conductance in the skin conductance data.

[0199] The sixth determining unit is used to determine the adjustment coefficient of the dynamic parameters based on the heart rate deviation rate and skin conductance deviation rate.

[0200] The adjustment unit is used to adjust the initial power parameters according to the power parameter adjustment coefficient to obtain the real-time power parameters.

[0201] In one embodiment, the adjustment unit is specifically used to determine the adjustment amount of the power parameters based on the power parameter adjustment coefficient; and to adjust the initial power parameters based on the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0202] In one embodiment, the sixth determining unit is specifically used to determine the sum of the heart rate deviation rate and the skin conductance deviation rate; and to determine the dynamic parameter adjustment coefficient based on the third product of the sum and the third weighting coefficient, and the third preset value.

[0203] In one embodiment, the power control device for the vehicle further includes: a historical information acquisition module, an adjustment coefficient determination module, and an adjustment module, wherein:

[0204] The historical information acquisition module is used to acquire driver usage habit information within a historical period.

[0205] The adjustment coefficient determination module is used to determine the user habit adjustment coefficient based on the driver's usage habit information.

[0206] The adjustment module is used to adjust the real-time power parameters according to the user's habits and the weighting coefficients corresponding to the historical period, and to control the vehicle's power based on the adjusted real-time power parameters.

[0207] The various modules in the power control device of the aforementioned vehicle can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the on-board controller in hardware form or independent of it, or stored in the memory of the on-board controller in software form, so that the processor can call and execute the corresponding operations of each module.

[0208] In one embodiment, an in-vehicle controller is provided, which may be an in-vehicle controller, and its internal structure diagram may be as follows: Figure 13 As shown, the vehicle controller includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, Near Field Communication (NFC), or other technologies. When the computer program is executed by the processor, it implements a vehicle power control method. The display unit of the vehicle controller is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the vehicle controller can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the housing of the vehicle controller, or external keyboards, touchpads, or mice, etc.

[0209] Those skilled in the art will understand that Figure 13 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the vehicle controller to which the present application is applied. A specific vehicle controller may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0210] In one embodiment, an on-board controller is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps:

[0211] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0212] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0213] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0214] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0215] Based on physiological parameters, a health sensitivity coefficient is determined; the health sensitivity coefficient is used to characterize the correction relationship between physiological parameters and acceleration curves.

[0216] Based on the sitting posture parameters, the sitting posture sensitivity coefficient is determined; the sitting posture sensitivity coefficient is used to characterize the correction relationship between the sitting posture parameters and the acceleration curve.

[0217] Initial dynamic parameters were determined based on health sensitivity coefficients and sitting posture sensitivity coefficients.

[0218] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0219] Acceleration curves were determined based on health sensitivity coefficients and posture sensitivity coefficients.

[0220] Based on the acceleration curve, the upper limit of acceleration, torque response range, and pedal sensitivity are determined.

[0221] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0222] Determine the first difference between your BMI and the preset standard BMI;

[0223] The health sensitivity coefficient is determined based on the first product of the first difference and the first weighting coefficient, and the first preset value.

[0224] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0225] Determine the second difference between the seat back angle and the preset reference angle;

[0226] The posture sensitivity coefficient is determined based on the second product of the second difference and the second weighting coefficient, and the second preset value.

[0227] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0228] Determine the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data;

[0229] Determine the skin conductance deviation rate based on the current skin conductance and baseline skin conductance data;

[0230] The adjustment coefficients for the dynamic parameters are determined based on the heart rate deviation rate and skin conductance deviation rate.

[0231] The initial power parameters are adjusted according to the power parameter adjustment coefficient to obtain the real-time power parameters.

[0232] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0233] The adjustment amount of the power parameters is determined based on the power parameter adjustment coefficient;

[0234] The initial power parameters are adjusted based on the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0235] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0236] Determine the sum of the heart rate deviation rate and the skin conductance deviation rate;

[0237] The power parameter adjustment coefficient is determined based on the sum and the third product of the third weighting coefficient, as well as the third preset value.

[0238] In one embodiment, the processor, when executing a computer program, also performs the following steps:

[0239] Obtain driver usage habit information within a historical period;

[0240] Determine the user habit adjustment coefficient based on driver usage habit information;

[0241] The real-time power parameters are adjusted based on user habits and the weighting coefficients corresponding to historical periods, and the vehicle's power is controlled based on the adjusted real-time power parameters.

[0242] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0243] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0244] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0245] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0246] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0247] Based on physiological parameters, a health sensitivity coefficient is determined; the health sensitivity coefficient is used to characterize the correction relationship between physiological parameters and acceleration curves.

[0248] Based on the sitting posture parameters, the sitting posture sensitivity coefficient is determined; the sitting posture sensitivity coefficient is used to characterize the correction relationship between the sitting posture parameters and the acceleration curve.

[0249] Initial dynamic parameters were determined based on health sensitivity coefficients and sitting posture sensitivity coefficients.

[0250] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0251] Acceleration curves were determined based on health sensitivity coefficients and posture sensitivity coefficients.

[0252] Based on the acceleration curve, the upper limit of acceleration, torque response range, and pedal sensitivity are determined.

[0253] In one embodiment, when the computer program is executed by a processor, it further performs the following steps:

[0254] Determine the first difference between your BMI and the preset standard BMI;

[0255] The health sensitivity coefficient is determined based on the first product of the first difference and the first weighting coefficient, and the first preset value.

[0256] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0257] Determine the second difference between the seat back angle and the preset reference angle;

[0258] The posture sensitivity coefficient is determined based on the second product of the second difference and the second weighting coefficient, and the second preset value.

[0259] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0260] Determine the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data;

[0261] Determine the skin conductance deviation rate based on the current skin conductance and baseline skin conductance data;

[0262] The adjustment coefficients for the dynamic parameters are determined based on the heart rate deviation rate and skin conductance deviation rate.

[0263] The initial power parameters are adjusted according to the power parameter adjustment coefficient to obtain the real-time power parameters.

[0264] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0265] The adjustment amount of the power parameters is determined based on the power parameter adjustment coefficient;

[0266] The initial power parameters are adjusted based on the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0267] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0268] Determine the sum of the heart rate deviation rate and the skin conductance deviation rate;

[0269] The power parameter adjustment coefficient is determined based on the sum and the third product of the third weighting coefficient, as well as the third preset value.

[0270] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0271] Obtain driver usage habit information within a historical period;

[0272] Determine the user habit adjustment coefficient based on driver usage habit information;

[0273] The real-time power parameters are adjusted based on user habits and the weighting coefficients corresponding to historical periods, and the vehicle's power is controlled based on the adjusted real-time power parameters.

[0274] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, performs the following steps:

[0275] The initial power parameters are determined based on the driver's physiological and seating posture parameters;

[0276] During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors.

[0277] The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the vehicle's power is controlled based on the real-time power parameters.

[0278] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0279] Based on physiological parameters, a health sensitivity coefficient is determined; the health sensitivity coefficient is used to characterize the correction relationship between physiological parameters and acceleration curves.

[0280] Based on the sitting posture parameters, the sitting posture sensitivity coefficient is determined; the sitting posture sensitivity coefficient is used to characterize the correction relationship between the sitting posture parameters and the acceleration curve.

[0281] Initial dynamic parameters were determined based on health sensitivity coefficients and sitting posture sensitivity coefficients.

[0282] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0283] Acceleration curves were determined based on health sensitivity coefficients and posture sensitivity coefficients.

[0284] Based on the acceleration curve, the upper limit of acceleration, torque response range, and pedal sensitivity are determined.

[0285] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0286] Determine the first difference between your BMI and the preset standard BMI;

[0287] The health sensitivity coefficient is determined based on the first product of the first difference and the first weighting coefficient, and the first preset value.

[0288] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0289] Determine the second difference between the seat back angle and the preset reference angle;

[0290] The posture sensitivity coefficient is determined based on the second product of the second difference and the second weighting coefficient, and the second preset value.

[0291] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0292] Determine the heart rate deviation rate based on the current heart rate and resting heart rate in the heart rate data;

[0293] Determine the skin conductance deviation rate based on the current skin conductance and baseline skin conductance data;

[0294] The adjustment coefficients for the dynamic parameters are determined based on the heart rate deviation rate and skin conductance deviation rate.

[0295] The initial power parameters are adjusted according to the power parameter adjustment coefficient to obtain the real-time power parameters.

[0296] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0297] The adjustment amount of the power parameters is determined based on the power parameter adjustment coefficient;

[0298] The initial power parameters are adjusted based on the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

[0299] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0300] Determine the sum of the heart rate deviation rate and the skin conductance deviation rate;

[0301] The power parameter adjustment coefficient is determined based on the sum and the third product of the third weighting coefficient, as well as the third preset value.

[0302] In one embodiment, when the computer program is executed by a processor, it also performs the following steps:

[0303] Obtain driver usage habit information within a historical period;

[0304] Determine the user habit adjustment coefficient based on driver usage habit information;

[0305] The real-time power parameters are adjusted based on user habits and the weighting coefficients corresponding to historical periods, and the vehicle's power is controlled based on the adjusted real-time power parameters.

[0306] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, artificial intelligence (AI) processors, etc., and are not limited to these.

[0307] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0308] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for controlling the power of a vehicle, characterized in that, The method includes: The initial power parameters are determined based on the driver's physiological and seating posture parameters; During vehicle operation, the driver's emotional state parameters are acquired through multiple sensors. The initial power parameters are adjusted based on the emotional state parameters to obtain real-time power parameters, and the power control of the vehicle is performed based on the real-time power parameters.

2. The method according to claim 1, characterized in that, The step of determining the initial dynamic parameters based on the physiological parameters and sitting posture parameters includes: Based on the physiological parameters, a health sensitivity coefficient is determined; the health sensitivity coefficient is used to characterize the correction relationship between the physiological parameters and the acceleration curve. Based on the sitting posture parameters, a sitting posture sensitivity coefficient is determined; the sitting posture sensitivity coefficient is used to characterize the correction relationship between the sitting posture parameters and the acceleration curve. The initial dynamic parameters are determined based on the health sensitivity coefficient and the sitting posture sensitivity coefficient.

3. The method according to claim 2, characterized in that, The initial dynamic parameters include an upper limit of acceleration, a torque response range, and pedal sensitivity; determining the initial dynamic parameters based on the health sensitivity coefficient and the sitting posture sensitivity coefficient includes: The acceleration curve is determined based on the health sensitivity coefficient and the sitting posture sensitivity coefficient; Based on the acceleration curve, the upper limit of acceleration, the torque response range, and the pedal sensitivity are determined.

4. The method according to claim 2, characterized in that, The physiological parameters include Body Mass Index (BMI); determining the health sensitivity coefficient based on the physiological parameters includes: Determine the first difference between the BMI and the preset standard BMI; The health sensitivity coefficient is determined based on the first product of the first difference and the first weighting coefficient, and the first preset value.

5. The method according to claim 2, characterized in that, The sitting posture parameters include the seat back angle; determining the sitting posture sensitivity coefficient based on the sitting posture parameters includes: Determine a second difference between the seat back angle and a preset reference angle; The posture sensitivity coefficient is determined based on the second product of the second difference and the second weighting coefficient, and the second preset value.

6. The method according to claim 1, characterized in that, The emotional state parameters include heart rate data and skin conductance data; adjusting the initial dynamic parameters based on the emotional state parameters to obtain real-time dynamic parameters includes: Based on the current heart rate and resting heart rate in the heart rate data, determine the heart rate deviation rate; The skin conductance deviation rate is determined based on the current skin conductance and baseline skin conductance in the aforementioned skin conductance data; Based on the heart rate deviation rate and the skin conductance deviation rate, determine the dynamic parameter adjustment coefficient; The initial power parameters are adjusted according to the power parameter adjustment coefficient to obtain the real-time power parameters.

7. The method according to claim 6, characterized in that, The step of adjusting the initial power parameters according to the power parameter adjustment coefficient to obtain the real-time power parameters includes: The adjustment amount of the power parameters is determined based on the aforementioned power parameter adjustment coefficient; The initial power parameters are adjusted according to the power parameter adjustment amount and the preset adjustment speed to obtain the real-time power parameters.

8. The method according to claim 6, characterized in that, The step of determining the dynamic parameter adjustment coefficient based on the heart rate deviation rate and the skin conductance deviation rate includes: Determine the sum of the heart rate deviation rate and the skin conductance deviation rate; The power parameter adjustment coefficient is determined based on the third product of the sum and the third weighting coefficient, and the third preset value.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: Obtain driver usage habit information within a historical period; Based on the driver's usage habit information, determine the user habit adjustment coefficient; The real-time power parameters are adjusted according to the user habit adjustment coefficient and the weight coefficient corresponding to the historical period, and the vehicle power is controlled based on the adjusted real-time power parameters.

10. A power control device for a vehicle, characterized in that, The device includes: The determination module is used to determine the initial power parameters based on the driver's physiological parameters and seating posture parameters; The acquisition module is used to acquire the driver's emotional state parameters through multiple sensors during vehicle operation. The control module is used to adjust the initial power parameters according to the emotional state parameters to obtain real-time power parameters, and to perform power control on the vehicle according to the real-time power parameters.

11. An on-board controller, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 9.

12. A vehicle, characterized in that, The vehicle includes the on-board controller as described in claim 11.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

14. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 9.

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