Vehicle-mounted wireless charging method and vehicle

Abstract

The application provides a vehicle-mounted wireless charging method and a vehicle, and relates to the technical field of intelligent cockpits. The method comprises the following steps: detecting a user state of a user in the vehicle and driving environment perception data; obtaining a first parameter based on the user state and the driving environment perception data, the first parameter being used for representing a noise tolerance degree of the user; adjusting a first charging parameter of wireless charging based on the first parameter to obtain a target charging parameter of the wireless charging; and performing wireless charging on a terminal device based on the target charging parameter. The method can reduce the influence of the vehicle-mounted wireless charging function on the concentration of the user, thereby improving the driving experience of the user in the vehicle.

Description

Technical Field

[0001] This application relates to the field of smart cockpit technology, and more specifically, to an in-vehicle wireless charging method and vehicle in the field of smart cockpit technology. Background Technology

[0002] Vehicle-mounted wireless charging can wirelessly charge terminal devices via wireless charging protocols, providing users with a convenient charging experience. However, the noise generated by the vehicle's wireless charging device during operation can easily interfere with the user's auditory perception, affecting their concentration and thus reducing the driving and riding experience for users inside the vehicle.

[0003] Therefore, how to reduce the impact of in-vehicle wireless charging on users' concentration in order to improve the driving experience of users in the vehicle is a technical problem that needs to be solved. Summary of the Invention

[0004] This application provides an in-vehicle wireless charging method and a vehicle. The method can reduce the impact of in-vehicle wireless charging function on the user's concentration and reduce the noise of the wireless charging device from interfering with the user's auditory perception, thereby improving the driving experience of the user in the vehicle.

[0005] Firstly, a method for in-vehicle wireless charging is provided, the method comprising: In response to wireless charging commands to terminal devices, the system detects the user status of users inside the vehicle and driving environment perception data. Based on user status and driving environment perception data, a first parameter is obtained, which represents the user's tolerance for noise. The first charging parameter of wireless charging is adjusted based on the first parameter to obtain the target charging parameter of wireless charging. Wireless charging of terminal devices is performed based on target charging parameters.

[0006] In the embodiments of this application, in response to a wireless charging command for a terminal device, a first parameter is determined based on the user's status and driving environment perception data; and the first charging parameter of wireless charging is adjusted according to the first parameter to obtain the target charging parameter for wireless charging of the terminal device. Compared to related technologies that charge the terminal device according to fixed charging parameters, resulting in noise from the wireless charging device interfering with the user's concentration and thus affecting the driving experience of the user in the vehicle, in the solution of this application, the first parameter is determined in real time based on the user's status and driving environment perception data, and the first parameter is used to measure the user's tolerance to noise; the target charging parameter of wireless charging is dynamically adjusted by combining the first parameter and the first charging parameter to ensure that the obtained target charging parameter is adapted to the current user's noise tolerance. Furthermore, by adjusting the charging parameters of wireless charging, the noise of the wireless charging device is adjusted to ensure that the noise of the wireless charging device matches the current user's noise tolerance, avoiding the wireless charging device generating noise exceeding the user's noise tolerance, thereby reducing the impact of noise during wireless charging on the user's concentration and ensuring an improved driving experience for the user in the vehicle.

[0007] In conjunction with the first aspect, in some implementations of the first aspect, the first parameter is obtained based on user state and driving environment perception data, including: Based on the user's state, a second parameter is obtained, which is negatively correlated with the fatigue level corresponding to the user's state. Based on driving environment perception data, a third parameter is obtained. The driving environment perception data includes at least one of light intensity, cabin voice volume, road type and vehicle speed. The third parameter is positively correlated with light intensity, positively correlated with cabin voice volume, negatively correlated with vehicle speed, and negatively correlated with the noise level corresponding to the road type. The first parameter is obtained based on the second and third parameters.

[0008] In the embodiments of this application, the first parameter is calculated step by step based on the user status and driving environment perception data. By incorporating multi-dimensional information such as user fatigue level, light intensity, cabin voice volume, vehicle speed, and road noise level into the parameter calculation process, the combined impact of the user's real-time status and the vehicle's driving environment on noise tolerance can be comprehensively reflected. This ensures that the user's tolerance to noise under the current user status and driving environment perception data can be accurately measured, thereby obtaining a more accurate first parameter.

[0009] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the first parameter is obtained based on user state and driving environment perception data, including: Based on user status and driving environment perception data, the current driving scenario of the vehicle is obtained; Based on the current driving scenario and the first mapping relationship, the first parameter under the current driving scenario is determined. The first mapping relationship is used to represent the mapping relationship between the vehicle's driving scenario and the first parameter.

[0010] In the embodiments of this application, the current driving scenario is determined based on user status and driving environment perception data; and a first parameter is determined based on the current driving scenario and a first mapping relationship. Using user status and driving environment perception data ensures accurate identification of the vehicle's current driving scenario; determining the first parameter using a pre-established first mapping relationship between different driving scenarios and the first parameter allows for rapid and accurate matching of actual operating conditions under different driving scenarios, improving parameter acquisition efficiency and judgment accuracy. Adjusting charging parameters based on this effectively adapts to noise tolerance requirements in various scenarios, balancing charging efficiency and device operational stability.

[0011] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, adjusting the first charging parameter of wireless charging based on the first parameter to obtain the target charging parameter of wireless charging includes: Based on the first parameter, the adjustment amount of the first charging parameter is determined, and the adjustment amount of the first charging parameter is negatively correlated with the first parameter; The first charging parameter is adjusted based on the adjustment amount of the first charging parameter to obtain the target charging parameter.

[0012] In the embodiments of this application, the adjustment amount of the first charging parameter is determined based on the first parameter to adjust the first charging parameter and obtain the target charging parameter. Since there is a negative correlation between the first parameter and the adjustment amount of the first charging parameter, correcting the charging parameter according to the adjustment amount to obtain the target charging parameter can achieve precise adjustment of the charging parameter, ensuring that the charging output state matches the current noise tolerance level. When the user's noise tolerance is low, the downward adjustment of the charging parameter is increased to reduce the noise of the charging module and improve the cabin acoustic environment; when the user's noise tolerance is high, the downward adjustment of the charging parameter is decreased to ensure charging efficiency, thereby balancing driving comfort and charging efficiency.

[0013] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the charging parameters include charging power, driving frequency of the charging coil, current fluctuation amplitude and magnetic core vibration amplitude; Based on the first parameter, determine the adjustment amount of the first charging parameter, including: Based on the first parameter and the charging power in the first charging parameter, determine the adjustment amount of the charging power; If the first parameter is less than the preset threshold, the adjustment amount of the driving frequency, the adjustment amount of the current fluctuation amplitude, and the adjustment amount of the magnetic core vibration amplitude are determined based on the difference between the first parameter and the preset threshold.

[0014] In the embodiments of this application, the adjustment amount of the charging power is determined based on the first parameter and the charging power in the first charging parameter; when the first parameter is less than a preset threshold, the adjustment amounts of the driving frequency, current fluctuation amplitude, and magnetic core vibration amplitude are determined based on the difference between the first parameter and the preset threshold. This ensures that multiple charging parameters in the charging parameters can be adjusted, so that the noise generated by the charging parameters matches the current noise tolerance level in real time.

[0015] In conjunction with the first aspect and the above implementation methods, in some implementation methods of the first aspect, the adjustment amount of the charging power is determined based on the first parameter and the charging power in the first charging parameter, including: Based on the first parameter and the charging power in the first charging parameter, determine the first adjustment amount of the charging power; Determine the temperature difference between the terminal device's temperature and the preset safe temperature, and based on the temperature difference, determine a second adjustment amount for the charging power; The sum of the first adjustment amount and the second adjustment amount is determined as the adjustment amount of the charging power.

[0016] In the embodiments of this application, an adjustment amount of charging power is determined based on the first parameter and the charging power in the first charging parameter; a second adjustment amount is determined based on the temperature difference between the temperature of the terminal device and the preset safe temperature; the adjustment amount of charging power is obtained by combining the first adjustment amount and the second adjustment amount, which can take into account both the user's tolerance for noise and the safety requirements for temperature control of the terminal device. On the one hand, the charging noise performance is dynamically optimized according to the driving scenario to ensure that the noise generated during wireless charging is adapted to the current user's tolerance for noise, taking into account both driving comfort and charging efficiency; on the other hand, the second adjustment amount of power is determined based on the temperature difference to manage the risk of overheating in real time and avoid high temperature triggering protection or device damage; ensuring that the adjustment of charging power is more comprehensive, achieving a balance between driving comfort and charging efficiency while ensuring charging safety and the lifespan of the terminal device.

[0017] In combination with the first aspect and the above implementation methods, in some implementation methods of the first aspect, the driving environment perception data includes at least one of light intensity, cabin voice volume, road type and vehicle speed; Based on user status and driving environment perception data, the current driving scenario of the vehicle is obtained, including: If the user is in a fatigued state, the light intensity is less than the first light intensity, and the vehicle speed is greater than the first preset speed, the current driving scenario is determined to be the first driving scenario. If the user is not fatigued, the light intensity is less than the second light intensity, the cabin voice volume is less than the first preset volume, and the vehicle speed is less than the second preset speed, then the current driving scenario is determined to be the second driving scenario, the second preset speed is greater than the first preset speed, and the second light intensity is less than the first light intensity. If the user is not fatigued, the vehicle speed is less than the third preset speed and the cabin voice volume is greater than the second preset volume, the current driving scenario is determined to be the third driving scenario, and the third preset speed is less than the first preset speed. If the user is not fatigued, the vehicle speed is greater than the first preset speed, and the light intensity is greater than the third light intensity, the current driving scenario is determined to be the fourth driving scenario, where the third light intensity is greater than the first light intensity. Among them, the first parameter of the first driving scenario is greater than the first parameter of the second driving scenario, the first parameter of the second driving scenario is greater than the first parameter of the third driving scenario, and the first parameter of the third driving scenario is greater than the first parameter of the fourth driving scenario.

[0018] In the embodiments of this application, the driving scenario is divided according to the user status and various driving environment perception data points to determine the current driving scenario; the recognition accuracy of the driving scenario is improved to ensure that different first parameters are obtained under different driving scenarios, and the target charging parameters adapted to the current driving scenario are calculated based on the first parameters.

[0019] In combination with the first aspect and the above implementation methods, some implementation methods of the first aspect further include: Detect user adjustments to target charging parameters in different driving scenarios; Based on the adjustment operation, the first mapping relationship is updated.

[0020] In the embodiments of this application, the first mapping relationship is updated based on the user's adjustment operations of the target charging parameters under different driving scenarios. The first mapping relationship is a pre-set mapping relationship between different driving scenarios and different first parameters. The first mapping relationship is updated according to the user's adjustment parameters, so that the updated first mapping relationship is more adapted to the user's personalized needs. This ensures that when determining the first parameters based on the driving scenario and the first mapping relationship, the first parameters that better meet the user's personalized needs can be obtained, thereby improving the intelligence level of charging parameter adjustment.

[0021] Combining the first aspect and the above implementation methods, in some implementation methods of the first aspect, the user state includes the driver state and the passenger state. Detecting the user state of a user inside the vehicle includes: Based on the driver's blinking frequency, gaze deviation angle, and head tilt angle inside the vehicle, it is determined whether the driver is fatigued. The gaze deviation angle represents the angle by which the driver's gaze deviates from the direction of the center of the lane. Based on the blinking frequency and head tilt angle of passengers inside the vehicle, it can be determined whether the passengers are fatigued.

[0022] In the embodiments of this application, the fatigue state of the driver and passengers is detected based on different detection indicators to ensure that the user status of different users in the vehicle can be identified. Compared with single indicator detection, this effectively improves the accuracy of user status identification and reduces the probability of false positives and false negatives.

[0023] Secondly, an in-vehicle wireless charging device is provided, the device comprising: The detection module is used to detect the user status of the user inside the vehicle and the driving environment perception data; The processing module is used to obtain a first parameter based on user status and driving environment perception data, the first parameter representing the user's tolerance to noise; adjust the first charging parameter of wireless charging based on the first parameter to obtain the target charging parameter of wireless charging; and perform wireless charging on the terminal device based on the target charging parameter.

[0024] Thirdly, a vehicle is provided, the vehicle including a memory and a processor, the memory for storing executable program code, and the processor for calling and running the executable program code from the memory, causing the vehicle to perform the methods of the first aspect or any possible implementation thereof.

[0025] Fourthly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0026] Fifthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the system architecture of an in-vehicle wireless charging parameter control system provided in an embodiment of this application; Figure 2 This is a schematic flowchart of an in-vehicle wireless charging method provided in an embodiment of this application; Figure 3 This is a schematic flowchart of another vehicle-mounted wireless charging method provided in the embodiments of this application; Figure 4 This is a schematic flowchart illustrating another vehicle-mounted wireless charging method provided in the embodiments of this application; Figure 5 This is a schematic flowchart illustrating a method for adjusting wireless charging parameters provided in an embodiment of this application; Figure 6This is a schematic diagram of the structure of an in-vehicle wireless charging device provided in an embodiment of this application; Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions in this application will be clearly and thoroughly described below with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more than two.

[0029] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0030] Vehicle-mounted wireless charging can wirelessly charge terminal devices via wireless charging protocols, providing users with a convenient charging experience. However, the noise generated by the vehicle's wireless charging device during operation can easily interfere with the user's auditory perception, affecting their concentration and thus reducing the driving and riding experience for users inside the vehicle.

[0031] The following examples illustrate the technical problems existing in the relevant technologies.

[0032] For example, related technologies typically use fixed charging parameters to wirelessly charge terminal devices, or match charging parameters to the device type and model. However, this approach ignores the impact of wireless charging device noise on the user's auditory perception. For instance, when a wireless charging device operates at a high power of 80W, it generates electromagnetic oscillation noise and coil mechanical resonance sound ranging from 20 to 100kHz. If the user's concentration is low and attention is easily distracted, forcing the device to maintain a high power of 80W will interfere with the user's auditory perception, increase cognitive load, and affect the user's driving experience.

[0033] Therefore, how to reduce the impact of in-vehicle wireless charging on users' concentration and thus improve their driving experience is a technical problem that needs to be solved.

[0034] In view of this, this application provides an in-vehicle wireless charging method and vehicle. Through the embodiments of this application, a first parameter is determined in real time based on user status and driving environment perception data. The first parameter is used to measure the user's tolerance to noise. Combined with the first parameter and the first charging parameter, the target charging parameter of wireless charging is dynamically adjusted to ensure that the obtained target charging parameter is adapted to the current user's noise tolerance. Under different user statuses and different driving environment perception data, the noise of the wireless charging device is adjusted by adjusting the charging parameters of wireless charging to ensure that the noise of the wireless charging device matches the current user's noise tolerance, thereby reducing the impact of in-vehicle wireless charging function on the user's concentration and improving the user's driving experience.

[0035] Figure 1 This is a schematic diagram of the system architecture of an in-vehicle wireless charging parameter control system provided in an embodiment of this application.

[0036] like Figure 1 As shown in the system architecture 100, the vehicle-mounted wireless charging parameter control system includes a multimodal data sensing module, a driving scene recognition engine, a control algorithm module, and a wireless charging device. The functions of each module are further explained below.

[0037] For example, the multimodal data perception module is used to acquire perception data in multiple dimensions, including user eye movement data, user head posture data, ambient light intensity, road type, vehicle speed, and cabin voice volume. Specifically, user eye movement data includes the user's blink frequency and gaze deviation angle. The gaze deviation angle represents the angle by which the user's gaze point deviates from the reference direction, with the center of the vehicle's forward driving path as the reference direction. User head posture data includes the user's head tilt angle.

[0038] For example, a high-precision infrared eye-tracking camera integrated inside the steering wheel can collect user eye movement data; a high-precision six-axis head posture sensor deployed on the vehicle's dashboard or rearview mirror area can detect the user's head tilt angle; an ambient light sensor integrated in the center console can detect ambient light intensity; vehicle speed, road type, and other user status data can be obtained in real time via the Controller Area Network (CAN); and a microphone array installed on the A-pillars (the two pillars at the front of the vehicle connecting the roof and dashboard) or the vehicle's roof can collect cabin voice volume to determine whether the user is talking or in a silent state. This location can be used to collect cabin acoustic signals, and by leveraging the advantages of the array structure, the accuracy and reliability of voice and noise data collection can be improved.

[0039] For example, the driving scene recognition engine maps multi-dimensional perception data to multiple driving scenarios. For instance, a fatigue driving scenario is identified when blink frequency > 8 times / minute, head tilt angle > 15°, light intensity < 50 lux (lux, a unit of light intensity representing the luminous flux of visible light per unit area), and vehicle speed > 80 km / h. A nighttime quiet scenario is identified when light intensity < 30 lux, cabin voice volume < 35 dB (decibels), and vehicle speed < 60 km / h. A city congestion mode is identified when vehicle speed < 30 km / h and cabin voice volume > 40 dB. A highway cruising mode is identified when vehicle speed > 80 km / h and light intensity > 100 lux. Multi-dimensional perception data can also include the temperature of the terminal device. A high-temperature emergency mode is identified when the temperature of the terminal device > 40°C and there are no fatigue characteristics (e.g., blink frequency ≤ 8 times / minute, head tilt angle ≤ 15°).

[0040] Driving scene recognition engines can use lightweight decision tree models. Lightweight decision trees are decision trees obtained by simplifying the model structure through limiting the depth of the decision tree, selecting core features, histogram optimization, and pruning quantization. Because lightweight decision tree models have small memory footprint, low inference latency, and low power consumption, they can be deployed in vehicle edge computing units to achieve high-precision and low-latency classification of driving scenes.

[0041] The control algorithm module is used to determine the user's tolerance for noise in the corresponding driving scenario, and to determine the scenario noise weight coefficient based on the tolerance. The scenario noise weight coefficient is a normalized coefficient between 0 and 1. The scenario noise weight coefficient is negatively correlated with the tolerance. The noise weight coefficient is substituted into the pre-established nonlinear charging parameter adjustment formula to calculate the adjusted charging parameters.

[0042] For example, in a fatigued driving scenario, a user's noise tolerance is lower than in a quiet nighttime scenario; in a quiet nighttime scenario, noise tolerance is lower than in a congested urban area; in a congested urban area, noise tolerance is lower than in a high-speed cruising scenario; and in a high-speed cruising scenario, noise tolerance is lower than in a high-temperature emergency scenario. Correspondingly, the noise weighting coefficient S=0.85 for a fatigued driving scenario, S=0.75 for a quiet nighttime scenario, S=0.5 for a congested urban area, S=0.2 for a high-speed cruising scenario, and S=0 for a high-temperature emergency scenario. This ensures that charging parameters are reduced during user fatigue and nighttime modes, minimizing the impact on driver and passenger focus, thereby ensuring cabin quietness and driving safety. It also ensures that sufficient charging power is provided during high-temperature emergencies, achieving a dynamic balance between cabin quietness and charging efficiency. Furthermore, it ensures that power automatically decreases during fatigue and nighttime scenarios, while still providing the required charging power during high-temperature emergency scenarios, achieving a dynamic balance between safety and efficiency.

[0043] The wireless charging device is used to adjust the charging parameters of the vehicle-mounted wireless charging device according to the charging parameters calculated by the control algorithm module, and to charge the terminal device placed in the wireless charging area according to the adjusted charging parameters.

[0044] The following is combined Figures 2 to 5 The in-vehicle wireless charging method provided in the embodiments of this application will be described in detail.

[0045] Figure 2 This is a schematic flowchart of an in-vehicle wireless charging method provided in an embodiment of this application.

[0046] For example, Figure 2 The method 200 shown can be performed by a vehicle, or can be performed by... Figure 1 The onboard wireless charging parameters shown are executed by the control system, or by the controller in the vehicle, or by the vehicle's processor or chip.

[0047] like Figure 2 As shown, the vehicle-mounted wireless charging method 200 includes steps S210 to S240. The vehicle-mounted wireless charging method shown in steps S210 to S240 will be described in detail below.

[0048] S210 detects the user status of the user inside the vehicle and the driving environment perception data.

[0049] The users include the driver and passengers in the vehicle, and the user status includes fatigued and non-fatigued states. The driving environment perception data includes at least one of the following: light intensity, cabin voice volume, road type, and vehicle speed.

[0050] In one implementation, in response to a wireless charging command from a terminal device, the user's status and driving environment perception data are detected. The wireless charging command refers to a control command issued by the in-vehicle wireless charging system to the charging execution unit or the terminal device being charged, used to control the wireless charging operation status of the terminal device. The terminal device refers to an electronic device that supports in-vehicle wireless charging, including but not limited to smartphones, tablets, and in-vehicle wearable devices.

[0051] For example, when the vehicle's wireless charging function is activated and a terminal device placed in the wireless charging area and completing a charging handshake is detected, a wireless charging command is triggered. The wireless charging area is typically located in the storage area of ​​the front center console or the center armrest area of ​​the vehicle, and is used to place the terminal device to be charged.

[0052] For example, ambient light intensity can be detected by an ambient light sensor integrated in the center console; vehicle speed, road type and other user status data can be obtained in real time through the vehicle controller local area network bus; and cabin voice volume can be collected by a microphone array installed on the A-pillar or roof of the vehicle.

[0053] The following is a further explanation of the user status detection method: In one implementation, the user status of the user inside the vehicle is detected by: determining whether the driver is fatigued based on the driver's blinking frequency, gaze deviation angle, and head tilt angle, where the gaze deviation angle represents the angle by which the driver's gaze deviates from the direction of the lane center; and determining whether the passenger is fatigued based on the passenger's blinking frequency and head tilt angle.

[0054] Among them, blink frequency refers to the number of blinks a user makes per unit of time; gaze deviation angle refers to the angle at which the driver's gaze deviates from the center of the road on which the vehicle is traveling, that is, the angle at which the user's eyes focus on the center of the road directly in front of the vehicle deviates from the reference direction; and head tilt angle refers to the angle at which the user's head tilts forward relative to the head posture in a standard sitting position.

[0055] Understandably, drivers are responsible for vehicle control and road condition observation, requiring them to continuously focus their gaze on the road ahead. The angle of gaze deviation can effectively reflect their attention state. Therefore, blinking frequency, gaze deviation angle, and head tilt angle are used as criteria for judging driver fatigue. Passengers, on the other hand, do not participate in driving operations, and their gaze direction is not constrained by driving. Gaze deviation is a normal behavior, and fatigue cannot be determined based on the angle of gaze deviation. Therefore, passenger fatigue is judged solely based on blinking frequency and head tilt angle.

[0056] For example, in-vehicle vision cameras can be used to collect real-time image data of the user's face and head. Image recognition algorithms can then be used to determine the user's blink count, gaze deviation angle, and head tilt angle. For instance, key eye points in multiple consecutive frames of image data can be detected to identify the user's eye opening and closing state. The number of complete blinks per unit time can be counted and converted into blink frequency. Key points of the driver's pupils and eye area can be located, and the deviation angle of the gaze point relative to the reference direction can be calculated, using the vehicle's forward driving direction as the reference direction. Facial contours and head feature points can be extracted, and the angle of head tilt forward can be calculated, using the user's standard normal sitting posture as the reference, to obtain the head tilt angle.

[0057] Optionally, user eye movement data can be collected by a high-precision infrared eye-tracking camera integrated inside the steering wheel; and the user's head tilt angle can be detected by a high-precision six-axis head posture sensor deployed on the vehicle's dashboard or rearview mirror area, reducing algorithm complexity and enabling quick and direct acquisition of the user's blink count, gaze deviation angle, and head tilt angle.

[0058] After acquiring the user's blink frequency, gaze deviation angle, and head tilt angle, the user's state is determined by comparing these parameters with preset thresholds. Specifically, if the driver's blink frequency is greater than a first frequency, the gaze deviation angle is greater than a first preset angle, and the driver's head tilt angle is greater than a second preset angle, the driver is determined to be fatigued; otherwise, the driver is determined not to be fatigued. For example, if a blink frequency greater than 8 times / minute, a gaze deviation angle greater than 15°, and the user's head tilt angle greater than 16° are detected, the driver is determined to be fatigued. Similarly, if a passenger's blink frequency is greater than a first frequency and the passenger's head tilt angle is greater than a second preset angle, the driver is determined to be fatigued; otherwise, the driver is determined not to be fatigued.

[0059] Understandably, when users are fatigued, their eyes are prone to soreness and strain, leading to a significant increase in blinking frequency. Simultaneously, decreased attention and weakened motor control make it difficult to maintain a stable focus on the vehicle ahead, resulting in a larger angle of visual deviation. Furthermore, insufficient neck muscle support causes the head to tilt forward noticeably, with a correspondingly larger angle of forward head tilt. Therefore, when blinking frequency, visual deviation angle, and head tilt angle are all detected to be in an excessively high range, it is determined that the user is fatigued.

[0060] In the embodiments of this application, the fatigue state of the driver and passengers is detected based on different detection indicators to ensure that the user status of different users in the vehicle can be identified. Compared with single indicator detection, this effectively improves the accuracy of user status identification and reduces the probability of false positives and false negatives.

[0061] Optionally, after determining that the user is in a state of fatigue, the user's fatigue level can be further determined. For example, the fatigue level can be determined based on the difference between the blink frequency, the angle of gaze deviation, and the angle of head tilt and preset thresholds. The larger the difference, the higher the corresponding fatigue level.

[0062] S220 obtains the first parameter based on user status and driving environment perception data.

[0063] The first parameter represents the user's tolerance for noise. The value of this first parameter is determined by both the user's state and the driving environment perception data. When user fatigue levels, light intensity, cabin voice volume, vehicle speed, and road type change, the user's tolerance for in-vehicle noise will change accordingly. Therefore, different user states and different driving environment perception data correspond to different values ​​for the first parameter.

[0064] The method for determining the first parameter will be further explained below: In one implementation, a first parameter is obtained based on user state and driving environment perception data, including: obtaining a second parameter based on user state, the second parameter being negatively correlated with the fatigue level corresponding to the user state; obtaining a third parameter based on driving environment perception data, the driving environment perception data including at least one of light intensity, cabin voice volume, road type, and vehicle speed, the third parameter being positively correlated with light intensity, positively correlated with cabin voice volume, negatively correlated with vehicle speed, and negatively correlated with the noise level corresponding to the road type; and obtaining the first parameter based on the second parameter and the third parameter.

[0065] The second parameter represents the user's tolerance to noise, determined based on the user's state. For example, if the user is a driver, the second parameter is derived based on the driver's state and is negatively correlated with the fatigue level corresponding to that state. The third parameter represents the user's tolerance to noise, determined based on driving environment perception data.

[0066] Understandably, when users are fatigued, they experience mental agitation, heightened sensory sensitivity, and decreased tolerance to noise; conversely, when users are alert and in good condition, and not fatigued, they have greater noise tolerance. Therefore, the user's fatigue level is negatively correlated with their noise tolerance.

[0067] When there is sufficient light, users have a good visual experience, are more relaxed, and have a more stable mental state. They are less resistant to additional noise inside the car and have a higher noise tolerance. When the light is dim and the visibility is poor, users are more likely to feel depressed and irritable, and are more sensitive to noise, resulting in a lower noise tolerance. Therefore, the third parameter is positively correlated with light intensity.

[0068] When normal voice conversations occur in the vehicle cabin, they create a basic background sound, weakening the human ear's perception of noise. The higher the cabin voice volume is within a reasonable range, the fuller the cabin's inherent sound field, and the stronger the user's tolerance for new noise. Conversely, the lower the cabin voice volume, the more even weak noises will be amplified, reducing tolerance. Therefore, the third parameter is positively correlated with the cabin voice volume.

[0069] When the vehicle speed is high, the wind noise will increase accordingly, and the overall cabin noise will rise. Users who are in a noisy environment for a long time are prone to auditory fatigue, and their emotions are more easily disturbed by additional noise. At the same time, when driving at high speed, users are under mental tension, their aversion to abnormal noise becomes stronger, and their noise tolerance gradually decreases. That is, the higher the speed, the lower the tolerance. Therefore, the third parameter is negatively correlated with vehicle speed.

[0070] Different roads generate varying levels of environmental noise. For example, tunnels, continuous curves, and highways have high noise levels, while ordinary urban roads and straight suburban roads have low noise levels. Higher road noise levels result in a harsher cabin acoustic environment, a heavier auditory burden on users, and a lower tolerance for additional noise, particularly from charging. In other words, lower road noise levels correlate with higher tolerance. Therefore, the third parameter is negatively correlated with the noise level corresponding to the road type.

[0071] For example, after calculating the second and third parameters, the weight coefficients of the user state and the driving environment perception data are determined according to the proportion of the influence of user state and driving environment perception data on noise tolerance; the weight coefficient of the user state is used as the weight of the second parameter, and the weight coefficient of the driving environment perception data is determined as the weight of the third parameter; the first parameter is obtained by weighted summation based on the second and third parameters.

[0072] It should be noted that since user status and driving environment have varying degrees of impact on noise tolerance, priority can be flexibly configured through weighting. For example, during long-distance night driving, the weight of user status can be increased to prioritize changes in noise tolerance caused by driver fatigue; during low-speed urban commuting, the weight of driving environment perception data can be increased to focus on the impact of road conditions and cabin acoustics. Compared to simply superimposing the two types of parameters, quantifying their respective influence through weighting can improve the calculation accuracy of the first parameter and more closely reflect the user's actual auditory experience.

[0073] In the embodiments of this application, the first parameter is calculated step by step based on the user status and driving environment perception data. By incorporating multi-dimensional information such as user fatigue level, light intensity, cabin voice volume, vehicle speed, and road noise level into the parameter calculation process, the combined impact of the user's real-time status and the vehicle's driving environment on noise tolerance can be comprehensively reflected. This ensures that the user's tolerance to noise under the current user status and driving environment perception data can be accurately measured, thereby obtaining a more accurate first parameter.

[0074] In another implementation, the first parameter is obtained based on user state and driving environment perception data, including: obtaining the current driving scenario of the vehicle based on user state and driving environment perception data; and determining the first parameter under the current driving scenario based on the current driving scenario and the first mapping relationship, wherein the first mapping relationship is used to represent the mapping relationship between the vehicle's driving scenario and the first parameter.

[0075] For example, the current driving scenario of the vehicle is determined based on the user's status and driving environment perception data and multiple preset driving scenarios. The multiple preset driving scenarios are scenarios in which charging noise needs to be adjusted by adjusting charging parameters.

[0076] The following is a further explanation of how the current scenario was determined: For example, based on user status and driving environment perception data, the current driving scenario of the vehicle is obtained, including: if the user is fatigued, the light intensity is less than a first light intensity, and the vehicle speed is greater than a first preset speed, the current driving scenario is determined to be a first driving scenario; if the user is not fatigued, the light intensity is less than a second light intensity, the cabin voice volume is less than a first preset volume, and the vehicle speed is less than a second preset speed, the current driving scenario is determined to be a second driving scenario, where the second preset speed is greater than the first preset speed, and the second light intensity is less than the first light intensity; if the user is not fatigued, the vehicle speed is less than a third preset speed, and the cabin voice volume is greater than the second preset volume, the current driving scenario is determined to be a third driving scenario, where the third preset speed is less than the first preset speed; if the user is not fatigued, the vehicle speed is greater than the first preset speed, and the light intensity is greater than the third light intensity, the current driving scenario is determined to be a fourth driving scenario, where the third light intensity is greater than the first light intensity.

[0077] Among them, the first parameter of the first driving scenario is greater than the first parameter of the second driving scenario, the first parameter of the second driving scenario is greater than the first parameter of the third driving scenario, and the first parameter of the third driving scenario is greater than the first parameter of the fourth driving scenario.

[0078] For example, when the user is fatigued, the light intensity is <50 lux, and the vehicle speed is >80 km / h, it is determined to be the first driving scenario; when the user is not fatigued, the light intensity is <30 lux, the cabin voice volume is <35 dB, and the vehicle speed is <60 km / h, it is determined to be the second driving scenario; when the user is not fatigued, the vehicle speed is <30 km / h, and the cabin voice volume is >40 dB, it is determined to be the third driving scenario; when the user is not fatigued, the vehicle speed is >80 km / h, and the light intensity is >100 lux, it is determined to be the fourth driving scenario.

[0079] The first driving scenario can be a fatigue driving scenario, the second driving scenario can be a quiet night driving scenario, the third driving scenario can be a city traffic jam scenario, and the fourth driving scenario can be a highway cruising scenario.

[0080] This embodiment solves the problem in existing technologies where, during long-distance nighttime driving or when the user is fatigued, the in-vehicle wireless charging device continues to operate at maximum power, resulting in high-frequency electromagnetic noise and mechanical resonance that interferes with auditory perception and exacerbates drowsiness and distraction. It avoids the continuous high-power operation of the wireless charging device, preventing psychological discomfort and decreased concentration for the user, thereby improving the driving and riding experience for users inside the vehicle.

[0081] In the embodiments of this application, the driving scenario is divided according to the user status and various driving environment perception data points to determine the current driving scenario; the recognition accuracy of the driving scenario is improved to ensure that different first parameters are obtained under different driving scenarios, and the target charging parameters adapted to the current driving scenario are calculated based on the first parameters.

[0082] Optionally, the method further includes: detecting user adjustments to the target charging parameters under different driving scenarios; and updating the first mapping relationship based on the adjustments.

[0083] Optionally, the user's historical behavior of manually adjusting the charging power can be recorded locally through automotive-grade memory. The personalized first parameter can be corrected based on a weighted moving average algorithm to achieve adaptive optimization of the first parameter. Since the perception data and learning model are processed in the local controller (Electronic Control Unit, ECU) and are not uploaded to the cloud, the user's privacy data is protected.

[0084] For example, if it is detected that a user reduces wireless charging parameters in the first driving scenario to reduce noise generated during wireless charging, it is determined that the user's noise tolerance is reduced in the first driving scenario, and the first parameter corresponding to the first driving scenario in the first mapping relationship is reduced. As another example, if it is detected that a user increases wireless charging parameters in the second driving scenario to improve wireless charging efficiency, it is determined that the user's noise tolerance is increased in the second driving scenario, and that charging efficiency is considered to have a higher priority than the quiet charging effect. Therefore, the first parameter corresponding to the second driving scenario in the first mapping relationship is increased.

[0085] In the embodiments of this application, the first mapping relationship is updated based on the user's adjustment operations of the target charging parameters under different driving scenarios. The first mapping relationship is a pre-set mapping relationship between different driving scenarios and different first parameters. The first mapping relationship is updated according to the user's adjustment parameters, so that the updated first mapping relationship is more adapted to the user's personalized needs. This ensures that when determining the first parameters based on the driving scenario and the first mapping relationship, the first parameters that better meet the user's personalized needs can be obtained, thereby improving the intelligence level of charging parameter adjustment.

[0086] After determining the current driving scenario, the first parameter under the current driving scenario is determined based on the first mapping relationship between the current driving scenario and the first mapping relationship. The first mapping relationship can be a mapping relationship in the form of a function or a mapping relationship in the form of a table. This application does not limit the specific form of the mapping relationship.

[0087] For example, the first parameter is a quantifiable value. The larger the value of the first parameter, the higher the user's tolerance for noise. The smaller the value of the first parameter, the lower the user's tolerance for noise. For example, the first parameter for the first driving scenario is 0.15, the first parameter for the second driving scenario is 0.25, the first parameter for the third driving scenario is 0.5, and the first parameter for the fourth driving scenario is 0.8.

[0088] It should be noted that the above are examples illustrating the values ​​of the first parameters corresponding to each driving scenario in the first mapping relationship, and this application does not limit the specific values ​​of each first parameter.

[0089] In the embodiments of this application, the current driving scenario is determined based on user status and driving environment perception data; and a first parameter is determined based on the current driving scenario and a first mapping relationship. Using user status and driving environment perception data ensures accurate identification of the vehicle's current driving scenario; determining the first parameter using a pre-established first mapping relationship between different driving scenarios and the first parameter allows for rapid and accurate matching of actual operating conditions under different driving scenarios, improving parameter acquisition efficiency and judgment accuracy. Adjusting charging parameters based on this effectively adapts to noise tolerance requirements in various scenarios, balancing charging efficiency and device operational stability.

[0090] Optionally, the driving scenario also includes a high-temperature emergency scenario. When the temperature of the terminal device is greater than the preset temperature (e.g., 40°C) and there are no signs of fatigue (e.g., blinking frequency ≤ 8 times / minute, head tilt angle ≤ 15°), it is determined to be a high-temperature emergency mode. In the high-temperature emergency scenario, the user's tolerance for noise is 1, and the charging efficiency of the terminal device is prioritized.

[0091] S230, adjust the first charging parameter of wireless charging based on the first parameter to obtain the target charging parameter of wireless charging.

[0092] The first charging parameters include charging power, driving frequency of the charging coil, current fluctuation amplitude, and magnetic core vibration amplitude.

[0093] For example, charging power represents the output power of a wireless charging system, determines the charging rate of the terminal device, and is a core parameter for controlling the operating noise and heat generation of the charging module. The higher the power, the faster the charging speed, but the power consumption, heat generation, and fan speed of the charging module will increase accordingly, easily generating fan noise and electromagnetic howling; while reducing the power will reduce noise and heat generation simultaneously.

[0094] The driving frequency of the charging coil represents the alternating operating frequency applied to the transmitting coil. It is a key resonant parameter for wireless power transmission, affecting wireless charging efficiency and electromagnetic noise performance. The charging coil achieves the highest transmission efficiency when operating at its resonant frequency; as the frequency deviates from the resonant point, transmission efficiency decreases, and electromagnetic noise and coil rattles are more likely to occur. The magnitude and timbre of electromagnetic radiation and howling noise vary at different driving frequencies.

[0095] The amplitude of current fluctuation represents the range of change in the operating current of the charging coil. The magnitude of the fluctuation affects the stability of system operation and the intensity of additional noise. Excessive current fluctuation will cause power supply instability, while also increasing device losses and amplifying electromagnetic and vibration noise; the more stable the fluctuation, the quieter the system operation and the longer the device life.

[0096] The vibration amplitude of the magnetic core represents the mechanical vibration displacement of the magnetic core that is matched with the charging coil under the action of an electromagnetic field. The vibration amplitude affects the magnitude of mechanical vibration noise. The larger the vibration amplitude, the more obvious the structural abnormal noise and resonance noise will be.

[0097] It is understood that this application defines charging power, coil drive frequency, current fluctuation amplitude, and magnetic core vibration amplitude as adjustable target charging parameters. It can coordinately manage the electromagnetic noise, fan noise, and structural noise of the charging system from multiple dimensions such as power output, electromagnetic resonance, current stability, and mechanical vibration, so as to ensure more comprehensive noise suppression, while taking into account wireless charging transmission efficiency, operational stability, and device lifespan, and adapting to the control requirements under different noise tolerance levels.

[0098] The process of adjusting the charging parameters will be explained further below.

[0099] Specifically, adjusting the first charging parameter of wireless charging based on the first parameter to obtain the target charging parameter of wireless charging includes: determining the adjustment amount of the first charging parameter based on the first parameter, wherein the adjustment amount of the first charging parameter is negatively correlated with the first parameter; and adjusting the first charging parameter based on the adjustment amount of the first charging parameter to obtain the target charging parameter.

[0100] For example, the adjustment amount of the first charging parameter is determined based on the first parameter. The larger the first parameter, the higher the user's tolerance for noise, that is, the higher their tolerance for noise generated during charging. Therefore, the smaller the adjustment amount of the first charging parameter, the more priority is given to ensuring charging efficiency. The smaller the first parameter, the lower the user's tolerance for noise, that is, the lower their tolerance for noise generated during charging. Therefore, the larger the adjustment amount of the first charging parameter, the greater the noise generated during charging is generated by significantly reducing the charging parameter.

[0101] In the embodiments of this application, since there is a negative correlation between the adjustment amount of the first parameter and the first charging parameter, the target charging parameter is obtained by correcting the charging parameter according to the adjustment amount. This enables precise adjustment of the charging parameter, ensuring that the charging output state matches the current noise tolerance level. When the noise tolerance level is low, the downward adjustment of the charging parameter is increased to reduce the noise of the charging module and improve the cabin acoustic environment. When the noise tolerance level is high, the downward adjustment of the charging parameter is reduced to ensure charging efficiency, thereby balancing driving comfort and charging efficiency.

[0102] In one implementation, determining the adjustment amount of the first charging parameter based on the first parameter includes: determining the adjustment amount of the charging power based on the first parameter and the charging power in the first charging parameter; if the first parameter is less than a preset threshold, determining the adjustment amount of the driving frequency, the adjustment amount of the current fluctuation amplitude, and the adjustment amount of the magnetic core vibration amplitude based on the difference between the first parameter and the preset threshold.

[0103] The first charging parameter is the initial charging parameter corresponding to the terminal device. For example, the first charging parameter can be the rated charging parameter of the wireless charging device, or the charging parameter that matches the device type and model of the terminal device.

[0104] For example, the first parameter reflects the user's noise tolerance level. Based on this first parameter and combined with the first charging power, the corresponding power adjustment amount is calculated to achieve adaptive adjustment of the charging power. The preset threshold is the critical value of noise tolerance. When the first parameter is greater than or equal to the preset threshold, the user's noise tolerance is high, and adjusting the charging power alone can meet the requirements without changing other parameters, thus ensuring the original transmission status and efficiency. When the first parameter is less than the preset threshold, the user's noise tolerance is low. At this time, adjusting the charging power alone has limited noise reduction effect. Therefore, the system calculates the difference between the first parameter and the preset threshold. The larger the difference, the worse the tolerance and the stronger the noise reduction requirement. Based on this difference, the adjustment amounts of the driving frequency, current fluctuation amplitude, and magnetic core vibration amplitude are determined sequentially. All three types of parameters that are prone to generating electromagnetic noise, current noise, and mechanical vibration noise are adjusted to suppress noise from multiple dimensions.

[0105] For example, the preset threshold of the first parameter is 0.6. When the first parameter is less than 0.5, it is determined that the current situation is a low noise scenario. Then the driving frequency of the wireless charging coil is reduced from 120kHz (high noise band) to 80kHz (low noise band) to reduce current fluctuation and magnetic core vibration amplitude, and suppress noise generation from both electromagnetic and mechanical sources.

[0106] Optionally, when the first parameter is less than a preset threshold, the DC-DC converter (DC-DC converter) is synchronously switched to a low-ripple mode to suppress noise and vibration caused by current and voltage fluctuations. The DC-DC converter, a voltage / current conversion module in the vehicle-mounted wireless charging system, is responsible for providing stable power to the charging coil and main control circuit, and is the core unit for power transmission. The low-ripple mode is a special operating mode of the DC-DC converter that significantly reduces the fluctuation amplitude of output voltage and current by optimizing switching logic, filtering strategies, and control algorithms.

[0107] In the embodiments of this application, the adjustment amount of the charging power is determined based on the first parameter and the charging power in the first charging parameter; when the first parameter is less than a preset threshold, the adjustment amounts of the driving frequency, current fluctuation amplitude, and magnetic core vibration amplitude are determined based on the difference between the first parameter and the preset threshold. This ensures that multiple charging parameters in the charging parameters can be adjusted, so that the noise generated by the charging parameters matches the current noise tolerance level in real time.

[0108] Optionally, determining the adjustment amount of the charging power based on the first parameter and the charging power in the first charging parameter includes: determining a first adjustment amount of the charging power based on the first parameter and the charging power in the first charging parameter; determining the temperature difference between the temperature of the terminal device and the preset safe temperature, and determining a second adjustment amount of the charging power based on the temperature difference; and determining the sum of the first adjustment amount and the second adjustment amount as the adjustment amount of the charging power.

[0109] For example, the charging power in the current first parameter and the first charging parameter is retrieved; a preset mapping relationship, calculation formula or lookup table model is called, and the first parameter and the current charging power are substituted to perform calculation to obtain the first adjustment amount of the charging power; wherein, the smaller the first parameter (the lower the noise tolerance), the larger the negative value of the first adjustment amount, and the corresponding charging power is reduced; the larger the first parameter, the closer the first adjustment amount is to 0 or a small positive adjustment.

[0110] For example, the surface temperature of the charging terminal device is collected in real time using a temperature sensor; the collected temperature is then compared with the system's preset safe temperature to obtain a temperature difference value; if the temperature difference is greater than 0, it indicates that the terminal device's temperature is higher than the safe temperature, posing an overheating risk; if the temperature difference is less than or equal to 0, the terminal temperature is within the safe range. A second adjustment amount is calculated based on the preset temperature control rules and the magnitude of the difference: the larger the temperature difference, the larger the value of the second adjustment amount, forcibly and significantly reducing the power; when the temperature is normal, the second adjustment amount is 0, and no temperature control intervention is performed.

[0111] In the embodiments of this application, an adjustment amount of charging power is determined based on the first parameter and the charging power in the first charging parameter; a second adjustment amount is determined based on the temperature difference between the temperature of the terminal device and the preset safe temperature; the adjustment amount of charging power is obtained by combining the first adjustment amount and the second adjustment amount, which can take into account both the user's tolerance for noise and the safety requirements for temperature control of the terminal device. On the one hand, the charging noise performance is dynamically optimized according to the driving scenario to ensure that the noise generated during wireless charging is adapted to the current user's tolerance for noise, taking into account both driving comfort and charging efficiency; on the other hand, the second adjustment amount of power is determined based on the temperature difference to manage the risk of overheating in real time and avoid high temperature triggering protection or device damage; ensuring that the adjustment of charging power is more comprehensive, achieving a balance between driving comfort and charging efficiency while ensuring charging safety and the lifespan of the terminal device.

[0112] Optionally, a noise reduction weight coefficient can be determined based on a first parameter, which is negatively correlated with the noise reduction weight coefficient; and the adjustment amount of the charging power can be determined based on the scene noise reduction coefficient, the noise reduction weight coefficient, and the charging power in the first charging parameter.

[0113] For example, the adjustment amount of charging power can be calculated using the method shown in Formula 1: ;(Formula 1) in, This indicates the amount of adjustment to the charging power. This represents the silence weighting coefficient. This represents the scene noise reduction factor (the value is usually between 0.6 and 1.0). This represents the charging power in the first charging parameter.

[0114] For example, after calculating the adjustment amount of the charging power, the difference between the charging power in the first charging parameter and the adjustment amount of the charging power is determined as the charging power of the target charging parameter; the difference between the driving frequency in the first charging parameter and the adjustment amount of the driving frequency is determined as the driving frequency in the adjusted target charging parameter; the difference between the current fluctuation amplitude in the first charging parameter and the adjustment amount of the current fluctuation amplitude is determined as the current fluctuation amplitude in the target charging parameter; and the difference between the magnetic core vibration amplitude in the first charging parameter and the adjustment amount of the magnetic core vibration amplitude is determined as the magnetic core vibration amplitude in the target charging parameter.

[0115] For example, the charging power in the target charging parameters can be calculated using the method shown in Formula 2: ;(Formula 2) For the meaning of each parameter, please refer to the explanation of the parameter meaning in Formula 1.

[0116] The S240 wirelessly charges terminal devices based on target charging parameters.

[0117] For example, after calculating the target charging parameters, a corresponding wireless charging control command is generated and sent to the wireless charging device (module). This causes the wireless charging device to operate according to the parameters set in the target charging parameters, such as charging power, coil drive frequency, current fluctuation amplitude, and magnetic core vibration amplitude, to perform wireless charging on the terminal device placed in the vehicle's wireless charging area, thus completing the charging operation. If the user's status, driving environment perception data, or device temperature changes during the wireless charging process based on the target charging parameters, the parameters are recalculated and the charging operation status is dynamically updated.

[0118] In the above embodiments, a first parameter is determined in real time based on user status and driving environment perception data. This first parameter measures the user's tolerance for noise. Combining the first parameter with the first charging parameter, the target charging parameter for wireless charging is dynamically adjusted to ensure that the obtained target charging parameter matches the current user's noise tolerance. Furthermore, by adjusting the charging parameters of the wireless charging device, the noise of the wireless charging device is adjusted to ensure that the noise of the wireless charging device matches the current user's noise tolerance. This avoids situations where the charging parameter is too small, resulting in low charging efficiency of the terminal device and affecting the user's wireless charging experience. It also avoids situations where the noise generated by the charging parameter is too large, affecting the user's concentration and preventing noise from distracting the driver. This improves the driving and riding experience of users inside the vehicle while ensuring vehicle driving safety.

[0119] Figure 3 This is a schematic flowchart of another vehicle-mounted wireless charging method provided in the embodiments of this application.

[0120] For example, Figure 3 The method 300 shown can be performed by a vehicle, or can be performed by... Figure 1 The onboard wireless charging parameters shown are executed by the control system, or by the controller in the vehicle, or by the vehicle's processor or chip.

[0121] like Figure 3 As shown, the vehicle-mounted wireless charging method 300 includes S301 to S306. The vehicle-mounted wireless charging method shown in S301 to S306 will be described in detail below.

[0122] S301, a wireless charging command for the terminal device has been detected.

[0123] For example, when the vehicle's wireless charging function is turned on and a terminal device placed in the wireless charging area and completing a charging handshake is detected, a wireless charging command is triggered.

[0124] S302, acquire multimodal perception data of the vehicle.

[0125] For example, multimodal perception data includes the user's blink frequency, gaze deviation angle and head tilt angle, as well as driving environment perception data, which includes at least one of light intensity, cabin voice volume, road type and vehicle speed.

[0126] Alternatively, the implementation methods of S301 to S302 can be found in [reference needed]. Figure 2 The relevant description of S210 will not be repeated here.

[0127] S303 determines the vehicle's current driving scenario based on multimodal perception data.

[0128] For example, it can be determined whether a user is fatigued based on the user's blinking frequency, gaze deviation angle, head tilt angle, and driving environment perception data.

[0129] If the user is fatigued, the light intensity is less than the first light intensity, and the vehicle speed is greater than the first preset speed, the current driving scenario is determined to be the first driving scenario. If the user is not fatigued, the light intensity is less than the second light intensity, the cabin voice volume is less than the first preset volume, and the vehicle speed is less than the second preset speed, the current driving scenario is determined to be the second driving scenario, where the second preset speed is greater than the first preset speed, and the second light intensity is less than the first light intensity. If the user is not fatigued, the vehicle speed is less than the third preset speed, and the cabin voice volume is greater than the second preset volume, the current driving scenario is determined to be the third driving scenario, where the third preset speed is less than the first preset speed. If the user is not fatigued, the vehicle speed is greater than the first preset speed, and the light intensity is greater than the third light intensity, the current driving scenario is determined to be the fourth driving scenario.

[0130] S304, determine the first parameter for the current driving scenario based on the first mapping relationship.

[0131] For example, based on the current driving scenario and the first mapping relationship, a first parameter under the current driving scenario is determined. The first mapping relationship is used to represent the mapping relationship between the vehicle's driving scenario and the first parameter.

[0132] Alternatively, the implementation methods of S303 and S304 can be found in [reference needed]. Figure 2 The relevant descriptions of the S220 are not repeated here.

[0133] S305, adjust the first charging parameter of wireless charging according to the first parameter to obtain the target charging parameter.

[0134] For example, based on the first parameter, an adjustment amount for the first charging parameter is determined, and the adjustment amount of the first charging parameter is negatively correlated with the first parameter; the first charging parameter is adjusted based on the adjustment amount of the first charging parameter to obtain the target charging parameter.

[0135] Alternatively, the implementation of S305 can be found in [reference needed]. Figure 2 The relevant descriptions of the S230 are not repeated here.

[0136] S306, charges the terminal device according to the target charging parameters.

[0137] For example, the wireless charging device is controlled to operate according to the parameters set by the target charging parameters, such as charging power, coil driving frequency, current fluctuation amplitude, and magnetic core vibration amplitude, to perform wireless charging on the terminal device placed in the vehicle wireless charging area.

[0138] Alternatively, the implementation of S306 can be found in [reference needed]. Figure 2 The relevant descriptions of S240 will not be repeated here.

[0139] In the embodiments of this application, the current driving scenario of the vehicle is identified based on multi-dimensional perception data; the first parameter is determined by using a first mapping relationship between different driving scenarios and the first parameter, which can quickly and accurately match the actual working conditions under different driving scenarios, improve the efficiency of parameter acquisition and the accuracy of judgment, and adjust the charging parameters accordingly, which can effectively adapt to the noise tolerance requirements under various scenarios, and take into account both charging efficiency and equipment operation stability.

[0140] Figure 4 This is a schematic flowchart illustrating another vehicle-mounted wireless charging method provided in the embodiments of this application.

[0141] Figure 4 The method 400 shown can be performed by a vehicle, or can be performed by... Figure 1 The onboard wireless charging parameters shown are executed by the control system, or by the controller in the vehicle, or by the vehicle's processor or chip.

[0142] like Figure 4 As shown, the vehicle-mounted wireless charging method 400 includes S401 to S408. The vehicle-mounted wireless charging method shown in S401 to S408 will be described in detail below.

[0143] S401, a wireless charging command for the terminal device has been detected.

[0144] Alternatively, the implementation of S401 can be found in [reference needed]. Figure 3 The relevant descriptions of S301 will not be repeated here.

[0145] S402, acquire multimodal perception data of the vehicle.

[0146] Alternatively, the implementation of S401 can be found in [reference needed]. Figure 3 The relevant descriptions of S302 will not be repeated here.

[0147] S403 determines the user's status based on multimodal perception data.

[0148] For example, the user's state is determined by comparing blink frequency, gaze deviation angle, and head tilt angle with preset thresholds. If the blink frequency is greater than a first frequency, the gaze deviation angle is greater than a first preset angle, and the user's head tilt angle is greater than a second preset angle, the user is determined to be fatigued; otherwise, the user is determined not to be fatigued.

[0149] S404, determine the second parameter based on the user status.

[0150] For example, a second parameter is obtained based on the user's state; the second parameter represents the user's tolerance to noise as determined by the user's state, and the second parameter is negatively correlated with the fatigue level corresponding to the user's state.

[0151] S405 determines the third parameter based on the driving environment perception data in the multimodal perception data.

[0152] The third parameter represents the user's tolerance for noise, determined based on driving environment perception data.

[0153] For example, a third parameter is obtained based on driving environment perception data, which includes at least one of light intensity, cabin voice volume, road type and vehicle speed. The third parameter is positively correlated with light intensity, positively correlated with cabin voice volume, negatively correlated with vehicle speed, and negatively correlated with the noise level corresponding to the road type.

[0154] S406, determine the first parameter based on the second and third parameters.

[0155] For example, the sum of the second parameter and the third parameter can be used to determine the first parameter; or, based on the proportion of the influence of user state and driving environment perception data on noise tolerance, the weight coefficients of user state and driving environment perception data can be determined; the weight coefficient of user state can be used as the weight of the second parameter, the weight coefficient of driving environment perception data can be used as the weight of the third parameter, and the first parameter can be obtained by weighted summation based on the second parameter and the third parameter.

[0156] Alternatively, the implementation methods of S403 to S406 can be found in [reference needed]. Figure 2 The relevant descriptions of the S220 are not repeated here.

[0157] S407, adjust the first charging parameter of wireless charging according to the first parameter to obtain the target charging parameter.

[0158] Alternatively, the implementation of S407 can be found in [reference needed]. Figure 3 The relevant descriptions of S305 will not be repeated here.

[0159] S408 charges the terminal device according to the target charging parameters.

[0160] Alternatively, the implementation of S408 can be found in [reference needed]. Figure 3 The relevant descriptions of S306 will not be repeated here.

[0161] In the embodiments of this application, the first parameter is calculated step by step based on the user status and driving environment perception data. By incorporating multi-dimensional information such as user fatigue level, light intensity, cabin voice volume, vehicle speed, and road noise level into the parameter calculation process, the combined impact of the user's real-time status and the vehicle's driving environment on noise tolerance can be comprehensively reflected. This ensures that the user's tolerance to noise under the current user status and driving environment perception data can be accurately measured, thereby obtaining a more accurate first parameter.

[0162] Figure 5 This is a schematic flowchart illustrating a method for adjusting wireless charging parameters provided in an embodiment of this application.

[0163] For example, Figure 5 The method 500 shown can be performed by a vehicle, or can be performed by... Figure 1 The onboard wireless charging parameters shown are executed by the control system, or by the controller in the vehicle, or by the vehicle's processor or chip.

[0164] like Figure 5 As shown, the vehicle-mounted wireless charging method 500 includes S501 to S508. The vehicle-mounted wireless charging method shown in S501 to S508 will be described in detail below.

[0165] S501, Obtain the first parameter and the first charging parameter of the wireless charging device.

[0166] For example, the first parameter represents the user's tolerance for noise, and the first charging parameter represents the initial charging parameter corresponding to the terminal device. For instance, the first charging parameter can be the rated charging parameter of the wireless charging device, or the charging parameter that matches the device type and model of the terminal device.

[0167] S502, based on the first parameter and the charging power of the first charging parameter, determine the first adjustment amount of the first charging power.

[0168] For example, the adjustment amount of the first charging parameter is determined based on the first parameter. The larger the first parameter, the higher the user's tolerance for noise, that is, the higher their tolerance for noise generated during charging. Therefore, the smaller the adjustment amount of the first charging parameter, the more priority is given to ensuring charging efficiency. The smaller the first parameter, the lower the user's tolerance for noise, that is, the lower their tolerance for noise generated during charging. Therefore, the larger the adjustment amount of the first charging parameter, the greater the noise generated during charging is generated by significantly reducing the charging parameter.

[0169] S503 determines a second adjustment amount of the first charging power based on the temperature difference between the terminal device's temperature and the preset safe temperature.

[0170] For example, the surface temperature of the charging terminal device is collected in real time using a temperature sensor; the collected temperature is then compared with the system's preset safe temperature to obtain a temperature difference value; if the temperature difference is greater than 0, it indicates that the terminal device's temperature is higher than the safe temperature, posing an overheating risk; if the temperature difference is less than or equal to 0, the terminal temperature is within the safe range. A second adjustment amount is calculated based on the preset temperature control rules and the magnitude of the difference: the larger the temperature difference, the larger the value of the second adjustment amount.

[0171] S504, the sum of the first adjustment amount and the second adjustment amount is determined as the adjustment amount of the charging power.

[0172] S505 adjusts the charging power according to the amount of adjustment.

[0173] For example, the difference between the charging power in the first charging parameter and the adjustment amount of the charging power is determined as the charging power of the target charging parameter.

[0174] S506, Is the first parameter less than the preset threshold? If so, execute S507.

[0175] For example, it is determined whether the first parameter is less than a preset threshold. If the first parameter is less than the preset threshold, the adjustment amount of the driving frequency, the adjustment amount of the current fluctuation amplitude, and the adjustment amount of the magnetic core vibration amplitude are determined based on the difference between the first parameter and the preset threshold.

[0176] S507, based on the difference between the first parameter and the preset threshold, determine the adjustment amount of the driving frequency, the adjustment amount of the current fluctuation amplitude, and the adjustment amount of the magnetic core vibration amplitude.

[0177] For example, when the first parameter is greater than or equal to the preset threshold, the user's noise tolerance is high, and adjusting the charging power alone is sufficient to meet the requirements without changing other parameters, thus ensuring the original transmission status and efficiency. When the first parameter is less than the preset threshold, the user's noise tolerance is low, and adjusting the charging power alone has limited noise reduction effect. Therefore, the system calculates the difference between the first parameter and the preset threshold. The larger the difference, the worse the tolerance and the stronger the noise reduction requirement. Based on this difference, the adjustment amounts of the driving frequency, current fluctuation amplitude, and magnetic core vibration amplitude are determined sequentially.

[0178] S508 adjusts the drive frequency according to the adjustment amount of the drive frequency; adjusts the current fluctuation amplitude according to the adjustment amount of the current fluctuation amplitude; and adjusts the magnetic core vibration amplitude according to the adjustment amount of the magnetic core vibration amplitude.

[0179] For example, the difference between the charging power in the first charging parameter and the adjustment amount of the charging power is determined as the charging power of the target charging parameter; the difference between the driving frequency in the first charging parameter and the adjustment amount of the driving frequency is determined as the driving frequency in the adjusted target charging parameter; the difference between the current fluctuation amplitude in the first charging parameter and the adjustment amount of the current fluctuation amplitude is determined as the current fluctuation amplitude in the target charging parameter; and the difference between the magnetic core vibration amplitude in the first charging parameter and the adjustment amount of the magnetic core vibration amplitude is determined as the magnetic core vibration amplitude in the target charging parameter.

[0180] In the embodiments of this application, the adjustment amount of the charging power is determined based on the first parameter and the charging power in the first charging parameter; when the first parameter is less than a preset threshold, the adjustment amounts of the driving frequency, current fluctuation amplitude, and magnetic core vibration amplitude are determined based on the difference between the first parameter and the preset threshold. This ensures that multiple charging parameters in the charging parameters can be adjusted, so that the noise generated by the charging parameters matches the current noise tolerance level in real time.

[0181] Figure 6 This is a schematic diagram of the structure of an in-vehicle wireless charging device provided in an embodiment of this application.

[0182] For example, such as Figure 6 As shown, the in-vehicle wireless charging device 600 includes: The detection module 610 is used to detect the user status of the user inside the vehicle and the driving environment perception data; The processing module 620 is used to obtain a first parameter based on user status and driving environment perception data, the first parameter being used to represent the user's tolerance for noise; adjust the first charging parameter of wireless charging based on the first parameter to obtain the target charging parameter of wireless charging; and perform wireless charging on the terminal device based on the target charging parameter.

[0183] Optionally, as an embodiment, the processing module 620 is specifically used to: obtain a second parameter based on the user's state, wherein the second parameter is negatively correlated with the fatigue level corresponding to the user's state; obtain a third parameter based on driving environment perception data, wherein the driving environment perception data includes at least one of light intensity, cabin voice volume, road type, and vehicle speed, wherein the second parameter is positively correlated with light intensity, the second parameter is positively correlated with cabin voice volume, the third parameter is negatively correlated with vehicle speed, and the third parameter is negatively correlated with the noise level corresponding to the road type; and obtain a first parameter based on the second parameter and the third parameter.

[0184] Optionally, as an embodiment, the processing module 620 is specifically used to: obtain the current driving scenario of the vehicle based on the user state and driving environment perception data; and determine the first parameter under the current driving scenario based on the current driving scenario and the first mapping relationship, wherein the first mapping relationship is used to represent the mapping relationship between the vehicle's driving scenario and the first parameter.

[0185] Optionally, as an embodiment, the processing module 620 is specifically used to: determine the adjustment amount of the first charging parameter based on the first parameter, wherein the adjustment amount of the first charging parameter is negatively correlated with the first parameter; and adjust the first charging parameter based on the adjustment amount of the first charging parameter to obtain the target charging parameter.

[0186] Optionally, as an embodiment, the processing module 620 is specifically used to: determine the adjustment amount of the charging power based on the first parameter and the charging power in the first charging parameter; if the first parameter is less than a preset threshold, determine the adjustment amount of the driving frequency, the adjustment amount of the current fluctuation amplitude, and the adjustment amount of the magnetic core vibration amplitude based on the difference between the first parameter and the preset threshold.

[0187] Optionally, as an embodiment, the processing module 620 is specifically used to: determine a first adjustment amount of the charging power based on the first parameter and the charging power in the first charging parameter; determine the temperature difference between the temperature of the terminal device and the preset safe temperature, and determine a second adjustment amount of the charging power based on the temperature difference; and determine the sum of the first adjustment amount and the second adjustment amount as the adjustment amount of the charging power.

[0188] Optionally, as an embodiment, the processing module 620 is specifically used to: if the user is in a fatigued state, the light intensity is less than a first light intensity, and the vehicle speed is greater than a first preset speed, determine the current driving scenario as a first driving scenario; if the user is not in a fatigued state, the light intensity is less than a second light intensity, the cabin voice volume is less than a first preset volume, and the vehicle speed is less than a second preset speed, determine the current driving scenario as a second driving scenario, where the second preset speed is greater than the first preset speed, and the second light intensity is less than the first light intensity; if the user is not in a fatigued state, the vehicle speed is less than a third preset speed, and the cabin voice volume is greater than the second preset volume, determine the current driving scenario as a third driving scenario, where the third preset speed is less than the first preset speed; if the user is not in a fatigued state, the vehicle speed is greater than the first preset speed, and the light intensity is greater than the third light intensity, determine the current driving scenario as a fourth driving scenario, where the third light intensity is greater than the first light intensity; wherein, the first parameter of the first driving scenario is greater than the first parameter of the second driving scenario, the first parameter of the second driving scenario is greater than the first parameter of the third driving scenario, and the first parameter of the third driving scenario is greater than the first parameter of the fourth driving scenario.

[0189] Optionally, as an embodiment, the detection module 610 is also used to: detect the user's adjustment operation of the target charging parameters in different driving scenarios; The processing module 620 is also used to update the first mapping relationship based on the adjustment operation.

[0190] Optionally, as an embodiment, the detection module 610 is specifically used to: determine whether the driver is fatigued based on the driver's blinking frequency, gaze deviation angle, and head tilt angle inside the vehicle, where the gaze deviation angle represents the angle at which the driver's gaze deviates from the direction of the lane center; and determine whether the passengers are fatigued based on the passengers' blinking frequency and head tilt angle inside the vehicle.

[0191] It should be noted that the aforementioned in-vehicle wireless charging device is embodied in the form of a functional unit. The term "module" here can be implemented in software and / or hardware, without specific limitations.

[0192] For example, a "module" can be a software program, hardware circuit, or a combination of both that implements the above functions. Hardware circuits may include application-specific integrated circuits (ASICs), electronic circuits, processors (e.g., shared processors, proprietary processors, or group processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functions.

[0193] Therefore, the units of the various examples described in the embodiments of this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0194] Figure 7 This is a schematic diagram of the structure of a vehicle provided in an embodiment of this application.

[0195] For example, vehicle 700 includes processor 710, memory 720 and executable program code 730.

[0196] For example, vehicle 700 includes one or more processors 710 that can support the implementation of the on-board wireless charging method in the method embodiments of vehicle 700. Processor 710 can be a general-purpose processor or a special-purpose processor. For example, processor 710 can be a central processing unit (CPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0197] For example, the processor 710 can be used to control the vehicle 700, execute software programs, and process data from the software programs. The vehicle 700 may also include a communication unit for receiving and transmitting signals.

[0198] For example, the vehicle 700 may include one or more memories 720 storing executable program code 730, which can be run by the processor 710 to generate instructions, causing the processor 710 to execute the in-vehicle wireless charging method described in the above method embodiments according to the instructions.

[0199] Optionally, the memory 720 may also store data. Optionally, the processor 710 may also read data stored in the memory 720, which may be stored at the same memory address as the executable program code 730, or the data may be stored at a different memory address than the executable program code 730.

[0200] For example, the processor 710 and memory 720 can be configured separately or integrated together, for example, integrated on a system-on-a-chip of the terminal device.

[0201] For example, the memory 720 can be used to store related programs of the vehicle-mounted wireless charging method provided in the embodiments of this application, and the processor 710 can be used to call the executable program code 730 stored in the memory 720 when controlling the vehicle to execute the vehicle-mounted wireless charging method of the embodiments of this application; for example, in response to a wireless charging command for a terminal device, the user status and driving environment perception data of the user in the vehicle are detected; based on the user status and the driving environment perception data, a first parameter is obtained, the first parameter being used to represent the user's tolerance to noise; based on the first parameter, the first charging parameter of wireless charging is adjusted to obtain the target charging parameter of wireless charging; based on the target charging parameter, the terminal device is wirelessly charged.

[0202] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the vehicle-mounted wireless charging method of any of the foregoing embodiments.

[0203] The computer-readable storage medium may include, but is not limited to, any type of disk, including floppy disks, optical disks, Digital Video Discs (DVDs), Compact Disc Read-Only Memory (CD-ROMs), microdrives, and magneto-optical disks, read-only memory (ROMs), random access memory (RAMs), erasable programmable read-only memory (EPROMs), electrically erasable programmable read-only memory (EEPROMs), dynamic random access memory (DRAMs), video random access memory (VRAMs), flash memory devices, magnetic cards or optical cards, nanosystems (including molecular memory ICs), or any type of medium or device suitable for storing instructions and / or data.

[0204] This application also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned related steps to implement a vehicle-mounted wireless charging method as described in the above embodiments.

[0205] In addition, the vehicle provided in the embodiments of this application may specifically be a chip, component or module, and the vehicle may include a connected processor and a memory; wherein, the memory is used to store instructions, and the processor can call and execute the instructions to make the chip execute an in-vehicle wireless charging method in the above embodiments.

[0206] The vehicle, computer-readable storage medium, computer program product or chip provided in this application are all used to execute the corresponding vehicle-mounted wireless charging method provided above. Therefore, the beneficial effects that can be achieved can be referred to the beneficial effects in the corresponding vehicle-mounted wireless charging method provided above, and will not be repeated here.

[0207] Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0208] In the embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0209] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A vehicle-mounted wireless charging method, characterized in that, The method includes: Detects user status and driving environment perception data within the vehicle; Based on the user's state and the driving environment perception data, a first parameter is obtained, which represents the user's tolerance for noise. Based on the first parameter, the first charging parameter of wireless charging is adjusted to obtain the target charging parameter of wireless charging. Based on the target charging parameters, the terminal device is wirelessly charged.

2. The method according to claim 1, characterized in that, The first parameter is obtained based on the user state and the driving environment perception data, including: Based on the user's state, a second parameter is obtained, and the second parameter is negatively correlated with the fatigue level corresponding to the user's state; Based on the driving environment perception data, a third parameter is obtained. The driving environment perception data includes at least one of light intensity, cabin voice volume, road type, and vehicle speed. The third parameter is positively correlated with the light intensity, positively correlated with the cabin voice volume, negatively correlated with the vehicle speed, and negatively correlated with the noise level corresponding to the road type. The first parameter is obtained based on the second parameter and the third parameter.

3. The method according to claim 1, characterized in that, The first parameter is obtained based on the user state and the driving environment perception data, including: Based on the user's state and the driving environment perception data, the current driving scenario of the vehicle is obtained; Based on the current driving scenario and the first mapping relationship, the first parameter under the current driving scenario is determined, and the first mapping relationship is used to represent the mapping relationship between the driving scenario of the vehicle and the first parameter.

4. The method according to claim 1, characterized in that, The step of adjusting the first charging parameter of wireless charging based on the first parameter to obtain the target charging parameter of wireless charging includes: Based on the first parameter, the adjustment amount of the first charging parameter is determined, and the adjustment amount of the first charging parameter is negatively correlated with the first parameter. The first charging parameter is adjusted based on the adjustment amount of the first charging parameter to obtain the target charging parameter.

5. The method according to claim 4, characterized in that, The first charging parameters include charging power, driving frequency of the charging coil, current fluctuation amplitude, and magnetic core vibration amplitude; Determining the adjustment amount of the first charging parameter based on the first parameter includes: Based on the first parameter and the charging power in the first charging parameter, determine the adjustment amount of the charging power; If the first parameter is less than a preset threshold, the adjustment amount of the driving frequency, the adjustment amount of the current fluctuation amplitude, and the adjustment amount of the magnetic core vibration amplitude are determined based on the difference between the first parameter and the preset threshold.

6. The method according to claim 5, characterized in that, The step of determining the adjustment amount of the charging power based on the first parameter and the charging power in the first charging parameter includes: Based on the first parameter and the charging power in the first charging parameter, a first adjustment amount of the charging power is determined; The temperature difference between the terminal device's temperature and the preset safe temperature is determined, and based on the temperature difference, a second adjustment amount of the charging power is determined; The sum of the first adjustment amount and the second adjustment amount is determined as the adjustment amount of the charging power.

7. The method according to claim 3, characterized in that, The driving environment perception data includes at least one of light intensity, cabin voice volume, road type and vehicle speed; The process of obtaining the current driving scenario of the vehicle based on the user's state and the driving environment perception data includes: If the user is in a fatigued state, the light intensity is less than the first light intensity, and the vehicle speed is greater than the first preset speed, then the current driving scenario is determined to be the first driving scenario. If the user is in a non-fatigue state, the light intensity is less than the second light intensity, the cockpit voice volume is less than the first preset volume, and the vehicle speed is less than the second preset vehicle speed, then the current driving scenario is determined to be the second driving scenario, where the second preset vehicle speed is greater than the first preset vehicle speed, and the second light intensity is less than the first light intensity. If the user is in a non-fatigue state, the vehicle speed is less than the third preset speed and the cabin voice volume is greater than the second preset volume, the current driving scenario is determined to be the third driving scenario, and the third preset speed is less than the first preset speed. If the user is in a non-fatigue state, the vehicle speed is greater than the first preset vehicle speed, and the light intensity is greater than the third light intensity, then the current driving scenario is determined to be the fourth driving scenario where the third light intensity is greater than the first light intensity. Wherein, the first parameter of the first driving scenario is greater than the first parameter of the second driving scenario, the first parameter of the second driving scenario is greater than the first parameter of the third driving scenario, and the first parameter of the third driving scenario is greater than the first parameter of the fourth driving scenario.

8. The method according to claim 3, characterized in that, The method further includes: Detect user adjustments to the target charging parameters under different driving scenarios; Based on the adjustment operation, the first mapping relationship is updated.

9. The method according to any one of claims 1 to 8, characterized in that, The user status includes driver status and passenger status. Detecting the user status of users inside the vehicle includes: Based on the driver's blinking frequency, gaze deviation angle, and head tilt angle inside the vehicle, it is determined whether the driver is fatigued. The gaze deviation angle represents the angle by which the driver's gaze deviates from the direction of the lane center. Based on the blinking frequency and head tilt angle of the passengers inside the vehicle, it is determined whether the passengers are in a state of fatigue.

10. A vehicle, characterized in that, The vehicles include: Memory, used to store executable program code; A processor for calling and running the executable program code from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 9.

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