In-vehicle button backlight adjustment method and system based on user posture

Abstract

The present invention provides a method and system for adjusting the backlight of a button in a car based on user posture, which relates to the field of automobile intelligent control technology, including collecting body posture information of a user terminal through an in-car posture sensor, processing the body posture information based on an image recognition algorithm, and obtaining key posture information corresponding to the user terminal; real-time monitoring of the light intensity inside and outside the car through an in-car light sensor to judge the current lighting conditions; determining the optimal key backlight brightness according to the key posture information and the current lighting conditions; and automatically controlling the key backlight group based on the optimal key backlight brightness to adjust the in-car backlight brightness in real time, thereby automatically sensing the driver's body posture and the external lighting environment, and adjusting the key backlight brightness in real time to ensure that the driver can clearly identify the buttons under any conditions, improve driving safety, and reduce safety hazards. At the same time, it can enhance the driving experience, improve the convenience and comfort of driving, and avoid energy waste.

Description

Technical Field

[0001] The present invention relates to the field of automobile intelligent control technology, and in particular to a method and system for adjusting the backlight of a button in a vehicle based on user posture. Background Art

[0002] With the continuous progress of automobile engineering technology and the acceleration of the intelligent trend, the internal control interface of modern automobiles is undergoing unprecedented changes. Among them, the complexity and diversity of the in-car button system have increased significantly. As a core element to improve the convenience and safety of driving operations at night and in low-light environments, the performance optimization of button backlight technology is particularly important. Specifically, the brightness and uniformity control technology of the button backlight has become the focus of research and development. Through refined optical design and intelligent control technology, it ensures that the button logos are clearly visible under different lighting conditions, thereby effectively reducing the risk of misoperation and improving the driver's operating efficiency and comfort.

[0003] In the prior art, traditional key backlight adjustment methods are mostly limited to manual adjustment or preset brightness modes, which lack flexibility and intelligence. This static adjustment method is difficult to respond to the driver's personalized needs and changes in external lighting conditions in real time, especially in extreme light environments, such as low light in a tunnel late at night or strong light at noon in summer, which may cause the key backlight to be too dark and difficult to identify, or too bright to produce glare, seriously interfering with the driver's vision and posing a safety hazard.

[0004] Therefore, it is necessary to provide a method and system for adjusting the backlight of in-vehicle buttons based on user posture to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides an in-vehicle button backlight adjustment method and system based on user posture to solve the problem that traditional button backlight adjustment methods are mostly limited to manual adjustment or preset brightness modes, lack flexibility and intelligence, and are difficult to respond to the driver's personalized needs and changes in external lighting conditions in real time, especially in extreme lighting environments, such as low light in a tunnel late at night or strong light at noon in summer, which may cause the button backlight to be too dark and difficult to identify, or too bright to produce glare, seriously interfering with the driver's vision and posing a safety hazard.

[0006] The present invention provides a method and system for adjusting the backlight of a button in a vehicle based on user gestures, comprising:

[0007] Collecting body posture information of the user terminal through an in-vehicle posture sensor, and processing the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal;

[0008] The light sensor in the car monitors the light intensity inside and outside the car in real time to determine the current lighting conditions;

[0009] Determining an optimal key backlight brightness according to the key posture information and current lighting conditions;

[0010] Based on the optimal button backlight brightness, the button backlight group is automatically controlled to perform real-time adjustment of the backlight brightness in the vehicle.

[0011] Preferably, the collecting of the user's body posture information by the in-vehicle posture sensor and processing of the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user specifically include:

[0012] Starting the in-vehicle posture sensor to collect body posture information of the user terminal in real time through the in-vehicle posture sensor;

[0013] Performing a preprocessing operation on the body posture information to generate input posture information;

[0014] Key posture information in the input posture information is extracted based on the image recognition algorithm.

[0015] Preferably, the key posture information in the input posture information is extracted based on the image recognition algorithm and calculated by the following feature extraction algorithm:

[0016] grayscale the input posture information to obtain grayscaled input posture information, i.e., intermediate posture information;

[0017] Feature extraction is performed on the intermediate posture information to obtain an intermediate posture information matrix, and the expression of the intermediate posture information matrix is:

[0018] ;

[0019] In the formula, Represents the intermediate posture information matrix; 、 Indicates the position of the pixel in the intermediate posture information; Neighborhood standard deviation of pixels representing intermediate pose information; Indicates the intermediate posture information at the pixel position The second derivative of Indicates the intermediate posture information at the pixel position 、 The second derivative of Indicates the intermediate posture information at the pixel position The second derivative of

[0020] Calculate the extreme value of the intermediate posture information matrix, the expression of the extreme value of the intermediate posture information matrix is:

[0021] ;

[0022] In the formula, Represents the intermediate posture information matrix; Represents the extreme value of the intermediate posture information matrix; 、 Indicates the position of the pixel in the intermediate posture information; Neighborhood standard deviation of pixels representing intermediate pose information; Indicates the intermediate posture information at the pixel position The second derivative of Indicates the intermediate posture information at the pixel position 、 The second derivative of Indicates the intermediate posture information at the pixel position The second derivative of

[0023] Select the extreme value of the intermediate posture information matrix The corresponding pixel point is used as the intermediate posture information feature point, and the stable intermediate posture information feature point is determined. The intermediate posture information feature direction is determined according to the wavelet feature of the circular neighborhood of the stable intermediate posture information feature point, and the 4-dimensional image around the stable intermediate posture information feature point is obtained. 4 rectangular area, the sum of the horizontal direction values, the sum of the vertical direction values, the sum of the horizontal direction absolute values ​​and the sum of the vertical direction absolute values ​​of the stable intermediate posture information feature points are counted to obtain the intermediate posture information features, that is, the key posture information.

[0024] Preferably, the method of monitoring the light intensity inside and outside the vehicle in real time through an in-vehicle light sensor to determine the current lighting conditions specifically includes:

[0025] Starting the in-vehicle light sensor to collect light information inside and outside the vehicle in real time through the in-vehicle light sensor;

[0026] Converting the vehicle interior and exterior light signals into the vehicle interior and exterior light intensities based on a digital converter;

[0027] Based on a preset illumination judgment algorithm, the current illumination condition is judged according to the light intensity inside and outside the vehicle.

[0028] Preferably, judging the current lighting condition based on the intensity of light inside and outside the vehicle based on a preset lighting judgment algorithm specifically includes:

[0029] ;

[0030] In the formula, represents the logarithmic probability of predicting that the current light condition is night; represents the probability of predicting that the current lighting condition is night; Indicates the initial value of the logarithmic probability when the light intensity is 0; It represents the rate of change of log probability when the light intensity changes; Indicates the input light intensity inside and outside the car;

[0031] ;

[0032] In the formula, represents the probability of predicting that the current lighting condition is night; represents the exponential function; represents the logarithmic probability of predicting that the current light condition is night;

[0033] The probability that the current lighting condition is night is determined based on the light intensity inside and outside the vehicle.

[0034] Preferably, determining the optimal key backlight brightness according to the key posture information and the current lighting conditions specifically includes:

[0035] Based on the intelligent control algorithm, the key posture information is analyzed to obtain the sight direction angle and the arm position distance;

[0036] Taking the sight direction angle, arm position distance and current lighting conditions as input features;

[0037] Based on a preset decision model, the optimal key backlight brightness is determined according to the input feature.

[0038] Preferably, the optimal key backlight brightness is determined based on the preset decision model according to the input feature, and is calculated by the following backlight brightness judgment formula:

[0039] ;

[0040] In the formula, Indicates the best key backlight brightness; Represents a preset decision model; Indicates The weight coefficient of the input features; Indicates Non-linear transformation function of input features; Indicates Input features; represents the bias term, which is the reference value for adjusting the backlight brightness;

[0041] Based on the preset decision model, the optimal key backlight brightness is determined according to the input feature.

[0042] Preferably, the real-time adjustment of the in-vehicle backlight brightness by automatically controlling the key backlight group based on the optimal key backlight brightness also includes supporting the user terminal to manually adjust the key backlight group through the in-vehicle touch screen, collecting the manual adjustment information of the user terminal, and optimizing the automatic adjustment process according to the manual adjustment information.

[0043] The in-vehicle button backlight adjustment system based on user gesture of the present invention comprises:

[0044] A collection module, used to collect body posture information of the user terminal through an in-vehicle posture sensor, and process the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal;

[0045] The monitoring module is used to monitor the light intensity inside and outside the car in real time through the in-car light sensor to determine the current lighting conditions;

[0046] A determination module, configured to determine an optimal key backlight brightness according to the key posture information and current lighting conditions;

[0047] The adjustment module is used to automatically control the key backlight group based on the optimal key backlight brightness to perform real-time adjustment of the backlight brightness in the vehicle.

[0048] Compared with the related art, the method and system for adjusting the backlight of in-vehicle buttons based on user gestures provided by the present invention have the following beneficial effects:

[0049] The present invention collects body posture information of a user terminal through an in-vehicle posture sensor, processes the body posture information based on an image recognition algorithm, and obtains key posture information corresponding to the user terminal; monitors the light intensity inside and outside the vehicle in real time through an in-vehicle light sensor to judge the current lighting conditions; determines the optimal key backlight brightness according to the key posture information and the current lighting conditions; automatically controls the key backlight group based on the optimal key backlight brightness to adjust the in-vehicle backlight brightness in real time, thereby automatically sensing the driver's body posture and the external lighting environment, and adjusting the key backlight brightness in real time to ensure that the driver can clearly identify the keys under any conditions, improve driving safety, and reduce safety hazards caused by misoperation; through an intelligent backlight adjustment method, the driver can enjoy a comfortable driving environment without manual settings, enhance the driving experience, and improve the convenience and comfort of driving; by dynamically adjusting the backlight brightness, unnecessary energy waste is avoided, which is in line with the design concept of energy saving and environmental protection of modern automobiles. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the method for adjusting the backlight of a button in a vehicle based on user gestures of the present invention;

[0051] Figure 2A schematic diagram of automatically adjusting the brightness of the backlight in a vehicle according to the present invention;

[0052] Figure 3 The system block diagram of the in-vehicle button backlight adjustment system based on user gesture of the present invention. DETAILED DESCRIPTION

[0053] The present invention will be further described below in conjunction with the accompanying drawings and implementation modes.

[0054] Embodiment 1

[0055] like Figure 1 As shown, the method for adjusting the backlight of a button in a vehicle based on a user's gesture includes:

[0056] S1, collecting body posture information of a user terminal through an in-vehicle posture sensor, processing the body posture information based on an image recognition algorithm, and obtaining key posture information corresponding to the user terminal;

[0057] In the context of intelligent and humanized vehicle design, firstly, the high-precision in-vehicle posture sensor array can be used to capture and analyze the body posture information of the user (i.e., driver and passenger) in an all-round and blind-angle manner. These sensors include accelerometers, gyroscopes, and pressure distribution sensors. And through advanced image recognition algorithms, the raw data collected in real time can be deeply processed and analyzed, and then the key posture information of the user can be accurately identified and extracted, such as the stability of the driving posture, the extension angle of the arm, and the tilt of the head.

[0058] S2, monitors the light intensity inside and outside the car in real time through the in-car light sensor to determine the current lighting conditions;

[0059] Understandably, a highly sensitive light sensor network can be equipped inside the vehicle, which not only covers key areas inside the vehicle, but also has the ability to perceive changes in ambient light. By continuously monitoring and comparing real-time data of light intensity inside and outside the vehicle, the current lighting conditions can be quickly and accurately determined, whether it is strong sunlight during the day, soft twilight in the evening, or a dark driving environment at night, it can be accurately identified and classified.

[0060] S3, determining an optimal key backlight brightness according to the key posture information and current lighting conditions;

[0061] It should be noted that based on the collected key posture information and current lighting condition data, the optimal key backlight brightness can be dynamically calculated by comprehensively considering the user's posture comfort requirements and the impact of current lighting conditions on visual clarity. This process not only ensures that the keys are clearly visible under different lighting conditions, but also takes into account the driver's visual comfort, effectively avoiding the risk of visual fatigue or misoperation caused by too strong or too weak light.

[0062] S4, automatically controlling the key backlight group based on the optimal key backlight brightness to adjust the backlight brightness in the vehicle in real time.

[0063] In actual applications, commands can be sent to the key backlight group based on the calculated optimal key backlight brightness to achieve real-time and automatic adjustment of the backlight brightness in the car. This process is not only highly automated, but also responsive, and can instantly adapt to changes in the car environment, providing a safe and comfortable driving environment for drivers and passengers.

[0064] In addition, the adjustment system of the present invention can also continuously optimize and adjust the backlight brightness strategy according to the user's usage habits and feedback, thereby further improving the user experience.

[0065] The collecting of the body posture information of the user terminal by the in-vehicle posture sensor and processing of the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal specifically include:

[0066] Starting the in-vehicle posture sensor to collect body posture information of the user terminal in real time through the in-vehicle posture sensor;

[0067] Performing a preprocessing operation on the body posture information to generate input posture information;

[0068] Key posture information in the input posture information is extracted based on the image recognition algorithm.

[0069] Specifically, first, the in-vehicle posture sensor array integrated in the vehicle seat or interior can be activated. These sensors usually adopt a non-contact or micro-contact design, such as infrared sensors, pressure distribution sensors or inertial measurement units (IMUs), etc., so that the passenger's body contours, posture angles and tiny movement changes can be captured in an all-round and high-precision manner, thereby realizing real-time collection of body posture information.

[0070] Subsequently, the collected raw body posture information can be preprocessed to generate input posture information suitable for subsequent algorithm processing. The preprocessing process includes noise filtering (using digital filtering technology to remove environmental interference), data calibration (linearization or nonlinear correction according to sensor characteristics), and data compression (reducing the amount of data without losing key information and improving processing efficiency), which can improve the accuracy and reliability of the data. Among them, noise filtering refers to the technology of using digital filtering technology to remove environmental interference; data calibration refers to the technology of linearization or nonlinear correction according to sensor characteristics; data compression refers to the technology of reducing the amount of data without losing key information and improving processing efficiency.

[0071] After preprocessing is completed, based on the image recognition algorithm, the input posture information can be deeply analyzed to automatically identify and extract key posture information closely related to driving safety, passenger comfort or specific interaction needs.

[0072] For example, by analyzing changes in a passenger's sitting posture, it is possible to determine whether the passenger is in a state of fatigue driving; or according to the passenger's body tilt angle, the seat angle can be automatically adjusted to provide optimal support.

[0073] In addition, the accuracy and efficiency of image recognition can be continuously optimized as the usage time increases, achieving a more personalized service experience. For example, the user's preferred posture can be learned and recognized in real time, so that the user's most comfortable sitting posture can be automatically adjusted every time the car is used.

[0074] Through real-time collection of in-vehicle posture sensors, data preprocessing, and extraction of key posture information based on image recognition algorithms, users can be provided with a safer, more comfortable and personalized riding experience.

[0075] In a specific implementation process, the key posture information in the input posture information is extracted based on the image recognition algorithm and calculated by the following feature extraction algorithm:

[0076] grayscale the input posture information to obtain grayscaled input posture information, i.e., intermediate posture information;

[0077] Feature extraction is performed on the intermediate posture information to obtain an intermediate posture information matrix, and the expression of the intermediate posture information matrix is:

[0078] ;

[0079] In the formula, Represents the intermediate posture information matrix; 、 Indicates the position of the pixel in the intermediate posture information; Neighborhood standard deviation of pixels representing intermediate pose information; Indicates the intermediate posture information at the pixel position The second derivative of Indicates the intermediate posture information at the pixel position 、 The second derivative of Indicates the intermediate posture information at the pixel position The second derivative of

[0080] Calculate the extreme value of the intermediate posture information matrix, the expression of the extreme value of the intermediate posture information matrix is:

[0081] ;

[0082] In the formula, Represents the intermediate posture information matrix; Represents the extreme value of the intermediate posture information matrix; 、 Indicates the position of the pixel in the intermediate posture information; Neighborhood standard deviation of pixels representing intermediate pose information; Indicates the intermediate posture information at the pixel position The second derivative of Indicates the intermediate posture information at the pixel position 、 The second derivative of Indicates the intermediate posture information at the pixel position The second derivative of

[0083] Select the extreme value of the intermediate posture information matrix The corresponding pixel point is used as the intermediate posture information feature point, and the stable intermediate posture information feature point is determined. The intermediate posture information feature direction is determined according to the wavelet feature of the circular neighborhood of the stable intermediate posture information feature point, and the 4-dimensional image around the stable intermediate posture information feature point is obtained. 4 rectangular area, the sum of the horizontal direction values, the sum of the vertical direction values, the sum of the horizontal direction absolute values ​​and the sum of the vertical direction absolute values ​​of the stable intermediate posture information feature points are counted to obtain the intermediate posture information features, that is, the key posture information.

[0084] Specifically, by graying the input posture information, the color information in the input posture information can be removed, the data complexity can be simplified, and a grayscale image, that is, the intermediate posture information, can be produced, thereby reducing the amount of calculation and enhancing the robustness of subsequent feature extraction.

[0085] At the same time, an intermediate posture information matrix can be constructed, which uses Gaussian derivative filters to calculate the second-order derivatives of the image at multiple scales. These derivatives reflect the curvature changes of the local structure of the image. Each element in the matrix contains the spatial position and its neighborhood statistical characteristics, which can effectively reflect the spatial structure information of the image at different scales.

[0086] Furthermore, in order to extract the most representative feature points from the matrix, the extreme values ​​of the intermediate posture information matrix can be calculated, and the extreme values ​​can determine stable feature points that show significant changes in different directions through an optimization algorithm.

[0087] After determining the stable feature points, the wavelet transform can be used to analyze the wavelet features of the circular neighborhood around these points to finely determine the direction of each feature point, thereby capturing the subtle texture and directional information around the feature points, which is convenient for subsequent posture recognition and matching. In order to construct key posture information, a 4x4 rectangular area can be extracted around each stable feature point, and the horizontal and vertical values ​​of the pixels in the area and the sum of their absolute values ​​are counted.

[0088] Through the above method, the brightness distribution of pixels can be comprehensively considered and combined with their directional information to generate corresponding key posture information as input for subsequent posture recognition, classification and tracking tasks, providing accurate and robust posture representation for the adjustment system.

[0089] The method of monitoring the light intensity inside and outside the vehicle in real time by using the vehicle light sensor to determine the current lighting conditions specifically includes:

[0090] Starting the in-vehicle light sensor to collect light information inside and outside the vehicle in real time through the in-vehicle light sensor;

[0091] Converting the vehicle interior and exterior light signals into the vehicle interior and exterior light intensities based on a digital converter;

[0092] Based on a preset illumination judgment algorithm, the current illumination condition is judged according to the light intensity inside and outside the vehicle.

[0093] When the vehicle is started, the light sensor in the vehicle is activated and enters standby mode. The sensor uses photoelectric conversion technology to capture and sense light signals from the vehicle's internal and external environments in real time. These light signals cover a variety of lighting scenarios, from faint morning light to glaring midday sunlight, and even street lighting at night, thus ensuring comprehensive and accurate monitoring.

[0094] Next, in order to convert these light signals into computer-recognizable data, the adjustment system of the present invention has a built-in high-performance digital converter that can convert the light signals into specific numerical parameters to facilitate subsequent data analysis and processing.

[0095] The preset light judgment algorithm can be used to intelligently analyze the converted light intensity inside and outside the car. The preset light judgment algorithm is based on a large amount of actual test data and driving habit data, and can comprehensively consider multiple factors, such as season, time, geographical location, and vehicle driving status, to make intelligent and accurate light condition judgments.

[0096] For example, on a clear day, when the light intensity outside the car is much higher than that inside the car, the position of the sunshade can be automatically adjusted or the automatic dimming rearview mirror can be started to reduce the impact of glare on the driver's vision; at night or on rainy days, the brightness of the interior ambient light can be adjusted to create a warmer and more comfortable riding environment for passengers.

[0097] The in-car light sensor monitors the light intensity inside and outside the car in real time, and based on precise digital conversion and intelligent light judgment algorithms, it can accurately identify and flexibly respond to lighting conditions, improve driving safety and convenience, and enrich the personalized riding experience.

[0098] The step of judging the current lighting condition based on the preset lighting judgment algorithm and according to the light intensity inside and outside the vehicle specifically includes:

[0099] ;

[0100] In the formula, represents the logarithmic probability of predicting that the current light condition is night; represents the probability of predicting that the current lighting condition is night; Indicates the initial value of the logarithmic probability when the light intensity is 0; It represents the rate of change of log probability when the light intensity changes; Indicates the input light intensity inside and outside the car;

[0101] ;

[0102] In the formula, represents the probability of predicting that the current lighting condition is night; represents the exponential function; represents the logarithmic probability of predicting that the current light condition is night;

[0103] The probability that the current lighting condition is night is determined based on the light intensity inside and outside the vehicle.

[0104] Through the above method, based on the preset lighting judgment algorithm, by measuring the light intensity inside and outside the vehicle, the probability that the current lighting condition is night can be accurately calculated, thereby improving the accuracy and efficiency of lighting condition judgment, providing technical support for vehicle intelligent lighting, night safety driving assistance and other functions, and significantly improving user driving comfort and safety.

[0105] The step of determining the optimal key backlight brightness according to the key posture information and the current lighting conditions specifically includes:

[0106] Based on the intelligent control algorithm, the key posture information is analyzed to obtain the sight direction angle and the arm position distance;

[0107] Taking the sight direction angle, arm position distance and current lighting conditions as input features;

[0108] Based on a preset decision model, the optimal key backlight brightness is determined according to the input feature.

[0109] like Figure 2 As shown in the figure, the schematic diagram of the automatic adjustment of the backlight brightness in the car of the present invention can first use advanced intelligent control algorithms to finely analyze and process the collected key posture information. Specifically, by identifying and analyzing the user's key posture information, which includes head posture, eye movement trajectory, etc., based on mathematical models and image processing technology, the user's line of sight direction angle can be accurately calculated. At the same time, sensor technology (such as infrared sensors or ultrasonic sensors) can be used to measure the position distance of the user's arm relative to the button in real time, which is convenient for subsequent evaluation of the user's operating comfort and light interference.

[0110] Furthermore, the sight direction angle, arm position distance, and current lighting conditions obtained by the in-car light sensor can be used as input features to form a multi-dimensional data set. The preset decision model is built based on machine learning or deep learning technology. Through data training and optimization in a large number of actual usage scenarios, it has powerful pattern recognition and prediction capabilities. After receiving the input features, the preset decision model can quickly perform internal calculations and reasoning, comprehensively considering factors such as line of sight clarity, ease of operation, energy efficiency, and avoiding visual fatigue caused by light reflection, and output the optimal key backlight brightness setting value.

[0111] By comprehensively using intelligent control algorithms, sensor technology, machine learning models and adaptive learning mechanisms, it is possible to automatically determine the optimal button backlight brightness based on key posture information and current lighting conditions, thereby improving the intelligence level of in-car button backlight adjustment and providing users with a more convenient, comfortable and personalized user experience.

[0112] Based on the preset decision model, the optimal key backlight brightness is determined according to the input feature and is calculated by the following backlight brightness judgment formula:

[0113] ;

[0114] In the formula, Indicates the best key backlight brightness; Represents a preset decision model; Indicates The weight coefficient of the input features; Indicates Non-linear transformation function of input features; Indicates Input features; represents the bias term, which is the reference value for adjusting the backlight brightness;

[0115] Based on the preset decision model, the optimal key backlight brightness is determined according to the input feature.

[0116] in, As the target variable, it represents the optimal key backlight brightness, which directly determines the visual comfort and efficiency of users when operating the keyboard. Represents a preset decision model that can output the optimal backlight brightness value based on a nonlinear combination of input features, using a machine learning algorithm to capture complex relationships and patterns in the data. It is The weight coefficient of each input feature reflects the importance of the input feature in determining the optimal backlight brightness. These weight coefficients are automatically learned through the training process of the model, thereby ensuring the objectivity and accuracy of the decision-making process. is a nonlinear transformation function used to Input features These functions can be polynomial, exponential, logarithmic, or a combination thereof, so that the original input features can be mapped into a space that is more suitable for model processing. Indicates The input features are gaze direction angle, arm position distance, and current lighting conditions. Represents a bias item, which serves as a reference value for adjusting the backlight brightness. It can be fine-tuned according to different usage scenarios or user preferences to achieve a more personalized backlight brightness setting.

[0117] Through the above method, multiple input features and their weights, nonlinear transformations, and the influence of bias terms can be comprehensively considered, and the optimal key backlight brightness can be calculated using a preset decision model.

[0118] The real-time adjustment of the in-vehicle backlight brightness by automatically controlling the key backlight group based on the optimal key backlight brightness also includes supporting the user terminal to manually adjust the key backlight group through the in-vehicle touch screen, collecting the manual adjustment information of the user terminal, and optimizing the automatic adjustment process according to the manual adjustment information.

[0119] During the manual adjustment process, the adjustment system of the present invention can capture and record the user's manual adjustment information in real time, including the starting point of the adjustment, the end brightness value, and the adjustment rate. Through the machine learning algorithm, the user's preference mode and adjustment habits can be learned from this information, and then the automatic adjustment process can be fine-tuned to make it closer to the user's actual needs. This two-way adjustment mechanism can enhance the user's experience and promote the continuous optimization of the adjustment system.

[0120] Through the above method, real-time automatic control of the button backlight group in the car can be achieved, and the flexibility of user interaction can be integrated, supporting users to directly manually adjust the brightness of the button backlight group through the touch screen interface integrated in the car, fully considering the user's personalized needs and preferences in different situations, allowing users to make instant adjustments according to the current driving environment or visual comfort.

[0121] Embodiment 2

[0122] like Figure 3 As shown, the in-vehicle button backlight adjustment system based on user gestures includes:

[0123] A collection module, used to collect body posture information of the user terminal through an in-vehicle posture sensor, and process the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal;

[0124] The monitoring module is used to monitor the light intensity inside and outside the car in real time through the in-car light sensor to determine the current lighting conditions;

[0125] A determination module, configured to determine an optimal key backlight brightness according to the key posture information and current lighting conditions;

[0126] The adjustment module is used to automatically control the key backlight group based on the optimal key backlight brightness to perform real-time adjustment of the backlight brightness in the vehicle.

[0127] Through the introduction of the above embodiments, the present invention adopts a method and system for adjusting the in-vehicle button backlight based on user posture, collects the body posture information of the user end through the in-vehicle posture sensor, processes the body posture information based on the image recognition algorithm, and obtains the key posture information corresponding to the user end; monitors the light intensity inside and outside the car in real time through the in-vehicle light sensor to judge the current lighting conditions; determines the optimal button backlight brightness according to the key posture information and the current lighting conditions; automatically controls the button backlight group based on the optimal button backlight brightness to adjust the in-vehicle backlight brightness in real time, so that the driver's body posture and the external lighting environment can be automatically sensed, and the button backlight brightness can be adjusted in real time to ensure that the driver can clearly identify the buttons under any conditions, improve driving safety, and reduce safety hazards caused by misoperation; through the intelligent backlight adjustment method, the driver can enjoy a comfortable driving environment without manual settings, enhance the driving experience, and improve the convenience and comfort of driving; by dynamically adjusting the backlight brightness, unnecessary energy waste is avoided, which is in line with the design concept of energy saving and environmental protection of modern automobiles.

[0128] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0129] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0130] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

Claims

1. A method for adjusting the backlight of a button in a vehicle based on user gestures, characterized in that: include: Collecting body posture information of the user terminal through an in-vehicle posture sensor, and processing the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal; The light sensor in the car monitors the light intensity inside and outside the car in real time to determine the current lighting conditions; Determining an optimal key backlight brightness according to the key posture information and current lighting conditions; Automatically controlling the key backlight group based on the optimal key backlight brightness to adjust the backlight brightness in the vehicle in real time; The step of determining the optimal key backlight brightness according to the key posture information and the current lighting conditions specifically includes: Based on the intelligent control algorithm, the key posture information is analyzed to obtain the sight direction angle and the arm position distance; Taking the sight direction angle, arm position distance and current lighting conditions as input features; Based on a preset decision model, determining the optimal key backlight brightness according to the input feature; Based on the preset decision model, the optimal key backlight brightness is determined according to the input feature and is calculated by the following backlight brightness judgment formula: ； In the formula, Indicates the best key backlight brightness; Represents a preset decision model; Indicates The weight coefficient of the input features; Indicates Non-linear transformation function of input features; Indicates Input features; represents the bias term, which is the reference value for adjusting the backlight brightness; Based on the preset decision model, the optimal key backlight brightness is determined according to the input feature.

2. The method for adjusting the backlight of a button in a vehicle based on user gesture according to claim 1, characterized in that: The collecting of the body posture information of the user terminal by the in-vehicle posture sensor and processing of the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal specifically include: Starting the in-vehicle posture sensor to collect body posture information of the user terminal in real time through the in-vehicle posture sensor; Performing a preprocessing operation on the body posture information to generate input posture information; Key posture information in the input posture information is extracted based on the image recognition algorithm.

3. The method for adjusting the backlight of a button in a vehicle based on user gesture according to claim 2, characterized in that: The key posture information in the input posture information is extracted based on the image recognition algorithm and calculated by the following feature extraction algorithm: grayscale the input posture information to obtain grayscaled input posture information, i.e., intermediate posture information; Feature extraction is performed on the intermediate posture information to obtain an intermediate posture information matrix, and the expression of the intermediate posture information matrix is: ； In the formula, Represents the intermediate posture information matrix; 、 Indicates the position of the pixel in the intermediate posture information; Neighborhood standard deviation of pixels representing intermediate pose information; Indicates the intermediate posture information at the pixel position The second derivative of Indicates the intermediate posture information at the pixel position 、 The second derivative of Indicates the intermediate posture information at the pixel position The second derivative of Calculate the extreme value of the intermediate posture information matrix, the expression of the extreme value of the intermediate posture information matrix is: ； In the formula, Represents the intermediate posture information matrix; Represents the extreme value of the intermediate posture information matrix; 、 Indicates the position of the pixel in the intermediate posture information; Neighborhood standard deviation of pixels representing intermediate pose information; Indicates the intermediate posture information at the pixel position The second derivative of Indicates the intermediate posture information at the pixel position 、 The second derivative of Indicates the intermediate posture information at the pixel position The second derivative of Select the extreme value of the intermediate posture information matrix The corresponding pixel point is used as the intermediate posture information feature point, and the stable intermediate posture information feature point is determined. The intermediate posture information feature direction is determined according to the wavelet feature of the circular neighborhood of the stable intermediate posture information feature point, and the 4-dimensional image around the stable intermediate posture information feature point is obtained. 4 rectangular area, the sum of the horizontal direction values, the sum of the vertical direction values, the sum of the horizontal direction absolute values ​​and the sum of the vertical direction absolute values ​​of the stable intermediate posture information feature points are counted to obtain the intermediate posture information features, that is, the key posture information.

4. The method for adjusting the backlight of a button in a vehicle based on user gesture according to claim 1, characterized in that: The method of monitoring the light intensity inside and outside the vehicle in real time by using the vehicle light sensor to determine the current lighting conditions specifically includes: Starting the in-vehicle light sensor to collect light information inside and outside the vehicle in real time through the in-vehicle light sensor; Converting the light information inside and outside the vehicle into the light intensity inside and outside the vehicle based on a digital converter; Based on a preset illumination judgment algorithm, the current illumination condition is judged according to the light intensity inside and outside the vehicle.

5. The method for adjusting the backlight of a button in a vehicle based on user gesture according to claim 4, characterized in that: The step of judging the current lighting condition based on the preset lighting judgment algorithm and according to the light intensity inside and outside the vehicle specifically includes: ； In the formula, represents the logarithmic probability of predicting that the current light condition is night; represents the probability of predicting that the current lighting condition is night; Indicates the initial value of the logarithmic probability when the light intensity is 0; It represents the rate of change of log probability when the light intensity changes; Indicates the input light intensity inside and outside the car; ； In the formula, represents the probability of predicting that the current lighting condition is night; represents the exponential function; represents the logarithmic probability of predicting that the current light condition is night; The probability that the current lighting condition is night is determined based on the light intensity inside and outside the vehicle.

6. The method for adjusting the backlight of a button in a vehicle based on user gesture according to claim 1, characterized in that: The real-time adjustment of the in-vehicle backlight brightness by automatically controlling the key backlight group based on the optimal key backlight brightness also includes supporting the user terminal to manually adjust the key backlight group through the in-vehicle touch screen, collecting the manual adjustment information of the user terminal, and optimizing the automatic adjustment process according to the manual adjustment information.

7. A system for adjusting the backlight of a button in a vehicle based on user gestures, applied to the method for adjusting the backlight of a button in a vehicle based on user gestures as claimed in any one of claims 1 to 6, wherein the adjustment system comprises: A collection module, used to collect body posture information of the user terminal through an in-vehicle posture sensor, and process the body posture information based on an image recognition algorithm to obtain key posture information corresponding to the user terminal; The monitoring module is used to monitor the light intensity inside and outside the vehicle in real time through the in-vehicle light sensor to determine the current lighting conditions; A determination module, configured to determine an optimal key backlight brightness according to the key posture information and current lighting conditions; The adjustment module is used to automatically control the key backlight group based on the optimal key backlight brightness to perform real-time adjustment of the backlight brightness in the vehicle.

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