Adaptive display method and system for interface information and storage medium

Abstract

The invention relates to the technical field of automotive electronics, and provides an interface information self-adaptive display method and system and a storage medium, current user data is obtained in real time, the state of a user is judged in real time, then an interface adjustment strategy can be executed according to the current attention level and fatigue state of the user, and the user experience is improved. A personalized display interface is generated based on the newly-added wallpaper layer, and the interface information dynamic display function is achieved through dynamic interaction of wallpaper; on the basis of the original wallpaper scheme of the vehicle machine, the wallpaper overlapping display function is set, the wallpaper overlapping display function only appears when a user needs, the original wallpaper cannot be completely replaced, and therefore the driving safety and the user experience are improved.

Description

Technical Field

[0001] The present invention relates to the field of interface interaction technology, and in particular to an adaptive display method, system and storage medium for interface information. Background Art

[0002] At present, all vehicles on the market, whether traditional fuel vehicles or smart cars, have cockpit applications. When designing the display effect of the cockpit application, that is, designing the cockpit screen, R&D personnel generally refer to user survey results, personal preferences, existing cockpit application products, etc. to improve the efficiency of interface interaction.

[0003] In the current interface interaction technology, the wallpaper setting of the central control screen is relatively simple and cannot be dynamically adjusted according to the user's real-time status. Therefore, there are the following defects:

[0004] (1) Static wallpaper: The wallpaper of the central control screen is usually a preset static image or a simple dynamic effect, which cannot change in real time according to the user's status.

[0005] (2) Lack of interactivity: Existing wallpapers cannot interact with the user’s status. For example, they cannot adjust according to the user’s fatigue or emotional state, thus failing to provide a personalized user experience.

[0006] (3) Insufficient safety: The user’s attention and emotional state are crucial to driving safety. Existing wallpapers cannot dynamically adjust to remind users to stay focused or relieve fatigue, resulting in insufficient safety. Summary of the invention

[0007] The present invention provides an adaptive display method, system and storage medium for interface information, which solve the technical problems that the existing automobile central control display lacks interactivity and has low security, resulting in poor user experience.

[0008] In order to solve the above technical problems, the present invention provides an adaptive display method of interface information, comprising the steps of:

[0009] Acquire current user data in real time, and evaluate the user's attention level and the user's fatigue state based on the user data;

[0010] Executing an interface adjustment strategy according to the attention level and the fatigue state, and generating a display interface based on the newly added wallpaper layer;

[0011] identifying the attention level, and modifying a first font attribute in the display interface;

[0012] Identifying the fatigue state, and correcting wallpaper attributes in the display interface;

[0013] The corresponding second font attribute is optimized based on the wallpaper attribute, and then the display interface is laid out for visual display.

[0014] This basic solution obtains current user data in real time, judges the user's status in real time, and then can execute interface adjustment strategies according to the user's current attention level and fatigue status, and generate a personalized display interface based on the newly added wallpaper layer, and realize the dynamic display function of interface information through the dynamic interaction of wallpaper; based on the original wallpaper solution of the car computer, set up the wallpaper overlay display function, which only appears when the user needs it, and will not completely replace the original wallpaper, thereby improving driving safety and user experience.

[0015] In a further embodiment, before real-time acquisition, the method further comprises:

[0016] Conduct data collection to obtain experimental driving data from different users;

[0017] Training the evaluation model based on the experimental driving data: setting initial parameters; performing state prediction according to the initial parameters to obtain prediction results, wherein the prediction results include predicted fatigue state and predicted attention level; obtaining user subjective evaluation, comparing it with the prediction result to obtain prediction error, optimizing the initial parameters based on the prediction error, obtaining target parameters and configuring the evaluation model;

[0018] The calculation formula of the prediction error is as follows:

[0019]

[0020] In the formula, is the fatigue index predicted by the system, S actual,i is the user’s actual fatigue score, and n is the number of samples.

[0021] By collecting and analyzing users’ subjective evaluations, this solution can better understand users’ needs and expectations, and thus optimize the model in a targeted manner. Adjustments based on user feedback can significantly improve the accuracy and reliability of the model, making it more in line with users’ actual needs.

[0022] In a further embodiment, the target parameters include weight parameters and target thresholds;

[0023] The optimization of the initial parameters based on the prediction error is specifically as follows: for each weight parameter ω j , calculate the partial derivative of the prediction error MSE with respect to it, and update the weight until the error converges or reaches the set number of iterations; calculate the corresponding target threshold based on all the updated weight parameters; the target threshold includes a fatigue threshold and an attention threshold;

[0024] The calculation formula for parameter optimization is as follows:

[0025]

[0026] Where η is the learning rate, usually a small positive number, used to control the step size of each update.

[0027] On the one hand, this solution can improve the convergence speed while ensuring stability by dynamically adjusting the learning rate, avoiding gradient explosion and ensuring the stability and effect of training; on the other hand, the corresponding target threshold is calculated based on all the updated weight parameters, which can more accurately identify the user's current state and accurately determine whether to perform interface display adjustments.

[0028] In a further embodiment, evaluating the user's attention level and the user's fatigue state based on the user data comprises:

[0029] The eye closing time, blinking frequency, head posture, facial features, and driving operation are obtained from the user data, and the user fatigue index S is calculated by weighted average method. fatigue :

[0030] Obtain the eye track, head orientation, blink duration, and driving operation from the user data, and calculate the user attention level S by weighted average method attention .

[0031] This solution adopts a non-contact testing method to collect real-time user data such as eye closure time, blinking frequency, head posture, facial features, driving operations, line of sight trajectory, head direction, blinking duration, driving operations, etc., and realizes the recognition of user status through the fusion of facial expression features and vehicle control features. It will not interfere with the user and can effectively reduce traffic accidents caused by fatigue driving or inattention, thereby improving road traffic safety.

[0032] In a further embodiment, an interface adjustment strategy is executed according to the attention level and the fatigue state, and a display interface is generated based on the newly added wallpaper layer, including:

[0033] Determine whether to execute the interface adjustment strategy according to the attention level, the fatigue state and the corresponding fatigue threshold and attention threshold, and if so, proceed to the next step of triggering dynamic wallpaper superposition;

[0034] Overlay a new wallpaper image on the current interface and add a new wallpaper layer;

[0035] Determine a target display text according to the attention level and the fatigue state;

[0036] A new plug-in is added to the new wallpaper image to insert the target display text to generate a display interface.

[0037] When determining the execution interface adjustment strategy, this solution directly overlays a new wallpaper image on the current interface based on the interface visualization requirements, adds a new wallpaper layer, and inserts a target display text plug-in to generate a display interface, helping users to grasp the vehicle situation and their own driving conditions at any time, so as to adjust driving operations in time and improve driving safety; the wallpaper overlay also enhances the interactivity and fun of the system, adapts to the driving habits of different users, and will not interfere with the use of the original application wallpaper.

[0038] In a further embodiment, the identifying the attention level and modifying the first font attribute in the display interface includes:

[0039] Acquire the attention level, adjust the font size in combination with the basic font size, determine the target font size of the target display text in the display interface, and obtain a corrected first font attribute;

[0040] The target font size is determined by the following formula:

[0041]

[0042] In the formula, F s is the target font size, F base is the base font size, S attention is the user's attention level, and k is the gain coefficient.

[0043] This solution automatically adjusts the interface font size according to the attention level, which not only improves the user experience, but also enhances readability and reduces reading fatigue, thereby optimizing the overall interface effect.

[0044] In a further embodiment, the identifying the fatigue state and correcting the wallpaper attributes in the display interface include:

[0045] Identify the newly added wallpaper layer and determine its base color;

[0046] Acquire the fatigue state, and adjust the contrast and brightness of the newly added wallpaper layer in combination with the basic color to obtain corrected wallpaper attributes;

[0047] Among them, the correction formula of wallpaper attributes is as follows:

[0048] C=C base ×(1+α·ln(1+S fatigue )+b·sin(S fatigue ))

[0049] C is the adjusted wallpaper attribute, Cbase As the base color, S fatigue is the user's fatigue state, a and b are the adjustment coefficients for controlling the contrast and brightness change intensity respectively.

[0050] This solution adjusts the wallpaper brightness and contrast according to the fatigue status. Adjusting the wallpaper brightness and contrast to an appropriate level can make images and texts clearer, reduce the burden on the eyes, thereby alleviating eye fatigue, and reduce visual interference, making the attention more focused, and assisting users in obtaining interface information.

[0051] In a further embodiment, the optimizing the corresponding second font attribute based on the wallpaper attribute includes:

[0052] Obtaining a brightness parameter from the wallpaper attribute, and comparing it with a preset threshold, selecting a target font color of the newly added wallpaper layer corresponding to the brightness parameter according to the comparison result, and obtaining a corrected second font attribute;

[0053] The font color selection algorithm is as follows:

[0054] L bg =log(1+0.299×R+0.587×G+0.114×B)

[0055]

[0056] Where, L bg is the brightness parameter of the new wallpaper layer; T is the target font color; K is the preset threshold; y is the probability of selecting a dark font; X is the input feature, including f(L bg ), R, G, B; P is the intermediate quantity; β0, β1, β2, β3, β4 are model parameters.

[0057] This solution adjusts the font color to match the wallpaper brightness and contrast, so that the display interface adapts to different scene environments, making the text clearer and easier to read, which is conducive to the realization of interface visualization effects, thereby further improving driving safety.

[0058] The present invention also provides an adaptive display system for interface information, which is applied to implement the adaptive display method for interface information as described above, comprising:

[0059] A data collection system, used to obtain current user data in real time, and evaluate the user's attention level and the user's fatigue state based on the user data;

[0060] A font content generator, configured to execute an interface adjustment strategy according to the attention level and the fatigue state, and generate a display interface based on a newly added wallpaper layer;

[0061] a font size adjuster, identifying the attention level, and modifying a first font attribute in the display interface, the first font attribute including a target font size;

[0062] A wallpaper color adjuster, which identifies the fatigue state and corrects the wallpaper attributes in the display interface;

[0063] a font color adjuster, which optimizes a corresponding second font attribute based on the wallpaper attribute, wherein the second font attribute includes a target font color;

[0064] A display screen is used to layout the display interface for information interaction.

[0065] This system uses various modules to implement various steps in the adaptive display method of interface information, providing a hardware basis for the method and facilitating the implementation of the method.

[0066] The present invention also provides a storage medium on which a computer program is stored, and the computer program is used to implement the above-mentioned method for adaptively displaying interface information. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM). BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 It is a workflow diagram of an adaptive display method of interface information provided by an embodiment of the present invention;

[0068] Figure 2 It is a system framework diagram of an adaptive display system for interface information provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The following specifically illustrates the implementation mode of the present invention in conjunction with the accompanying drawings. The embodiments are provided for illustrative purposes only and cannot be understood as limiting the present invention. The accompanying drawings are provided for reference and illustration only and do not constitute a limitation on the scope of patent protection of the present invention, because many changes can be made to the present invention without departing from the spirit and scope of the present invention.

[0070] An embodiment of the present invention provides an adaptive display method for interface information, such as Figure 1 As shown, in this embodiment, steps S01, S02, S1-S6 are included:

[0071] S01. Collect data to obtain experimental driving data of different users;

[0072] Specifically, driving data were collected from multiple users, including information such as eye closure time, blinking frequency, head posture, facial features, and driving operations, and users were asked to subjectively evaluate their fatigue status with a quantification range of [0-10].

[0073] S02. Training an evaluation model based on the experimental driving data, the training is as follows:

[0074] A. Set initial parameters;

[0075] B. performing state prediction according to the initial parameters to obtain prediction results, wherein the prediction results include predicted fatigue state and predicted attention level;

[0076] Set the initial weight values ​​ω1, ω2, ω3, ω4, ω5 (ω1+ω2+ω3+ω4+ω5=1) and calculate the initial fatigue index S fatigue ;

[0077] Set the initial weight values ​​ω6, ω7, ω8, ω9 (ω6+ω7+ω8+ω9=1) and calculate the initial attention level S attention .

[0078] C. Obtaining the user's subjective evaluation, comparing it with the prediction result to obtain a prediction error, optimizing the initial parameters based on the prediction error, obtaining the target parameters and configuring the evaluation model;

[0079] Taking fatigue index as an example, the calculation formula of the prediction error is as follows:

[0080]

[0081] In the formula, is the fatigue index predicted by the system, S actual,i is the user’s actual fatigue score, and n is the number of samples.

[0082] By collecting and analyzing the subjective evaluation of users, this embodiment can better understand the needs and expectations of users, so as to optimize the model in a targeted manner. Adjustments based on user feedback can significantly improve the accuracy and reliability of the model, making it more in line with the actual needs of users.

[0083] In this embodiment, the target parameters include a weight parameter and a target threshold;

[0084] The optimization of the initial parameters based on the prediction error is specifically as follows: for each weight parameter ω j, calculate the partial derivative of the prediction error MSE with respect to it, and update the weight until the error converges or reaches the set number of iterations; calculate the corresponding target threshold based on all the updated weight parameters; the target threshold includes a fatigue threshold and an attention threshold;

[0085] The calculation formula for parameter optimization is as follows:

[0086]

[0087] Where η is the learning rate, usually a small positive number, used to control the step size of each update. j Contains ω1~ω9.

[0088] Among them, the feature weight values ​​of ω1, ω2, ω3, ω4, and ω5 are determined through experimental tuning until the error converges or the set number of iterations is reached. After multiple iterations, the weight parameters are obtained and the corresponding fatigue threshold V1 is calculated.

[0089] The feature weight values ​​of ω1, ω2, ω3, ω4, and ω5 are determined through experimental tuning until the error converges or the set number of iterations is reached. After multiple iterations, the weight parameters are obtained and the corresponding attention threshold V2 is calculated.

[0090] On the one hand, this embodiment can improve the convergence speed while ensuring stability by dynamically adjusting the learning rate, avoiding gradient explosion and ensuring the stability and effect of training; on the other hand, the corresponding target threshold is calculated based on all the updated weight parameters, which can more accurately identify the user's current state and accurately determine whether to perform interface display adjustment.

[0091] S1. Acquire current user data in real time, and evaluate the user's attention level and the user's fatigue state based on the user data, including:

[0092] Acquire current user data in real time, including eye closure time, blink frequency, head posture, facial features, driving operation, sight track, head orientation, blink duration, etc.

[0093] The eye closing time, blinking frequency, head posture, facial features, and driving operation are obtained from the user data, and the user fatigue index S is calculated by weighted average method. fatigue :

[0094] S fatigue =ω1×eye closure time+ω2×blink frequency+ω3×head posture+ω4

[0095] ×Facial features +ω5×Driving operation

[0096] Among them, the feature weight values ​​of ω1, ω2, ω3, ω4, and ω5 are determined by experimental tuning to satisfy Σω i =1; eye closure time, blinking frequency, head posture, facial features, and driving operation are all standardized values, ranging from [0,1].

[0097] Obtain the eye track, head orientation, blink duration, and driving operation from the user data, and calculate the user attention level S by weighted average method attention .

[0098] S attention =ω6×eye gaze trajectory+ω7×head orientation+ω8×blink duration+ω9

[0099] × Driving Operation

[0100] Among them, the feature weight values ​​of ω6, ω7, ω8, and ω9 are determined through experimental tuning to satisfy Σω i =1; gaze trajectory, head orientation, blink duration, and driving operation are all standardized values, ranging from [0,1].

[0101] This embodiment adopts a non-contact testing method to collect and obtain user data such as eye closure time, blinking frequency, head posture, facial features, driving operations, line of sight trajectory, head direction, blinking duration, driving operations, etc. in real time. By integrating facial expression features and vehicle control features, the user status can be recognized without interfering with the user. It can effectively reduce traffic accidents caused by fatigue driving or inattention, thereby improving road traffic safety.

[0102] S2. Executing an interface adjustment strategy according to the attention level and the fatigue state, and generating a display interface based on the newly added wallpaper layer, including:

[0103] S21, judging whether to execute the interface adjustment strategy according to the attention level, the fatigue state and the corresponding fatigue threshold and attention threshold, and if so, proceeding to the next step of triggering dynamic wallpaper superposition;

[0104] S22, adding a new wallpaper image on the current interface, adding a new wallpaper layer;

[0105] S23, determining the target display text according to the attention level and the fatigue state, as follows:

[0106]

[0107] For example:

[0108] The user is in a high fatigue state and the attention level is S attention =0.5, fatigue state Sfatigue =0.8. At this time, the overlay wallpaper is triggered, such as black 18-point font, with the text "Please stay awake".

[0109] The above target display text is not limited in this embodiment and can be customized in stages according to needs.

[0110] S24, adding a new plug-in to the newly added wallpaper image and inserting the target display text to generate a display interface.

[0111] When determining the execution interface adjustment strategy, this embodiment directly overlays a new wallpaper image on the current interface based on the interface visualization requirements, adds a new wallpaper layer, and inserts a target display text plug-in to generate a display interface, helping users to grasp the vehicle situation and their own driving conditions at any time, so as to adjust driving operations in time and improve driving safety; the wallpaper overlay also enhances the interactivity and fun of the system, adapts to the driving habits of different users, and will not interfere with the use of the original application wallpaper.

[0112] S3, identifying the attention level, and modifying the first font attribute in the display interface, including:

[0113] Acquire the attention level, adjust the font size in combination with the basic font size, determine the target font size of the target display text in the display interface, and obtain a corrected first font attribute;

[0114] The target font size is determined by the following formula:

[0115]

[0116] In the formula, F s is the target font size, F base is the base font size (regular font size, which is the system default font size), S attention is the user's attention level (ranging from 0 to 1), and k is the gain coefficient, which can be set according to the actual prediction, for example, set to 5.

[0117] This embodiment automatically adjusts the interface font size according to the attention level, which not only improves the user experience, but also enhances readability and reduces reading fatigue, thereby optimizing the overall interface effect.

[0118] S4, identifying the fatigue state, and correcting the wallpaper attributes in the display interface, including:

[0119] Identify the newly added wallpaper layer and determine its base color, for example, taking the base color of the system wallpaper as the main color;

[0120] Acquire the fatigue state, and adjust the contrast and brightness of the newly added wallpaper layer in combination with the basic color to obtain corrected wallpaper attributes;

[0121] Among them, the correction formula of wallpaper attributes is as follows:

[0122] C=C base ×(1+α·ln(1+S fatigue )+b·sin(S fatigue ))

[0123] C is the adjusted wallpaper attribute, expressed in RGB value; C base As the base color, S fatigue is the user's fatigue state (the value range is 0 to 1), a and b are the adjustment coefficients for controlling the contrast and brightness change intensity respectively.

[0124] This embodiment adjusts the wallpaper brightness and contrast according to the fatigue status. Adjusting the wallpaper brightness and contrast to an appropriate level can make images and texts clearer, reduce the burden on the eyes, thereby alleviating eye fatigue, and reduce visual interference, so that the attention is more focused, and assist the user in obtaining interface information.

[0125] S5. Optimizing a corresponding second font attribute based on the wallpaper attribute includes:

[0126] A brightness parameter is obtained from the wallpaper attribute, and compared with a preset threshold value, and a target font color of the newly added wallpaper layer corresponding to the brightness parameter is selected according to the comparison result to obtain a corrected second font attribute; for example, when the newly added wallpaper layer is in high brightness, black is selected as the font color; when the newly added wallpaper layer is in low brightness, white is selected as the font color.

[0127] The font color selection algorithm is as follows:

[0128] L bg =log(1+0.299×R+0.587×G+0.114×B)

[0129]

[0130] Where, L bg is the brightness parameter of the new wallpaper layer, ranging from 1 to 255; T is the target font color; K is the preset threshold, that is, the demarcation threshold for font color selection; y is the probability of selecting a dark font; X is the input feature, including f(L bg ), R, G, B; P is the intermediate quantity; β0, β1, β2, β3, β4 are model parameters, which are learned through training data.

[0131] This embodiment adjusts the font color to match the wallpaper brightness and contrast so that the display interface adapts to different scene environments, which can make the text clearer and easier to read, facilitate the realization of interface visualization effects, and further improve driving safety.

[0132] S6. Laying out the display interface for visual display according to the first font attribute, the wallpaper attribute and the second font attribute.

[0133] For example, the DMS system comprehensively evaluates the user status (attention level and fatigue status), and calls the font size adjuster, wallpaper color adjuster, font color adjuster and font content generator respectively, and displays a personalized wallpaper suitable for the current user status on the central control screen (i.e., the display screen in Example 2).

[0134] This basic solution obtains current user data (such as driver monitoring data) in real time, judges the user's status in real time, and then can execute interface adjustment strategies according to the user's current attention level and fatigue status, and generate a personalized display interface based on the newly added wallpaper layer, and realizes the dynamic display function of interface information through dynamic interaction of wallpaper to remind users at the same time; based on the original wallpaper solution of the car computer, set up wallpaper overlay display function, which only appears when the user needs it and will not completely replace the original wallpaper, thereby improving driving safety and user experience.

[0135] Example 2

[0136] The reference numerals in the embodiment of the present invention include: data acquisition system 1, font size adjuster 2, wallpaper color adjuster 3, font color adjuster 4, font content generator 5, display screen 6;

[0137] The embodiment of the present invention further provides an adaptive display system for interface information, which is used to implement an adaptive display method for interface information as described in the above embodiment 1. Figure 2 As shown, including:

[0138] A data acquisition system 1 (i.e., a DMS system), which is used to obtain current user data in real time and evaluate the user's attention level and the user's fatigue state based on the user data;

[0139] A font content generator 5, configured to execute an interface adjustment strategy according to the attention level and the fatigue state, and generate a display interface based on a newly added wallpaper layer;

[0140] A font size adjuster 2, which identifies the attention level and modifies a first font attribute in the display interface, wherein the first font attribute includes a target font size;

[0141] Wallpaper color adjuster 3, identifying the fatigue state and correcting the wallpaper attributes in the display interface;

[0142] A font color adjuster 4, which optimizes a corresponding second font attribute based on the wallpaper attribute, wherein the second font attribute includes a target font color;

[0143] The display screen 6 is used to layout the display interface for information interaction.

[0144] This system uses various modules to implement various steps in the adaptive display method of interface information, providing a hardware basis for the method and facilitating the implementation of the method.

[0145] Example 3

[0146] The embodiment of the present invention further provides a storage medium on which a computer program is stored, and the computer program is used to implement the adaptive display method of interface information as described in the above embodiment 1. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0147] The above embodiments are preferred implementation modes of the present invention, but the implementation modes of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, and simplifications made without departing from the spirit and principles of the present invention shall be equivalent replacement methods and shall be included in the protection scope of the present invention.

Claims

1. A method for adaptively displaying interface information, characterized in that: Includes steps: Acquire current user data in real time, and evaluate the user's attention level and the user's fatigue state based on the user data; Executing an interface adjustment strategy according to the attention level and the fatigue state, and generating a display interface based on the newly added wallpaper layer; identifying the attention level, and modifying a first font attribute in the display interface; Identifying the fatigue state, and correcting wallpaper attributes in the display interface; The corresponding second font attribute is optimized based on the wallpaper attribute, and then the display interface is laid out for visual display.

2. The method for adaptively displaying interface information according to claim 1, characterized in that: Before real-time acquisition, it also includes: Conduct data collection to obtain experimental driving data from different users; Training the evaluation model based on the experimental driving data: setting initial parameters; performing state prediction according to the initial parameters to obtain prediction results, wherein the prediction results include predicted fatigue state and predicted attention level; obtaining user subjective evaluation, comparing it with the prediction result to obtain prediction error, optimizing the initial parameters based on the prediction error, obtaining target parameters and configuring the evaluation model; The calculation formula of the prediction error is as follows: In the formula, is the fatigue index predicted by the system, S actual,i is the user’s actual fatigue score, and n is the number of samples.

3. The method for adaptively displaying interface information according to claim 2, characterized in that: The target parameters include weight parameters and target thresholds; The optimization of the initial parameters based on the prediction error is specifically as follows: for each weight parameter ω j , calculate the partial derivative of the prediction error MSE with respect to it, and update the weight until the error converges or reaches the set number of iterations; calculate the corresponding target threshold based on all the updated weight parameters; the target threshold includes a fatigue threshold and an attention threshold; The calculation formula for parameter optimization is as follows: Where η is the learning rate, which is a positive number used to control the step size of each update.

4. The method for adaptively displaying interface information according to claim 2, characterized in that: The user's attention level and the user's fatigue state are evaluated based on the user data, including: The eye closing time, blinking frequency, head posture, facial features, and driving operation are obtained from the user data, and the user fatigue index S is calculated by weighted average method. fatigie : Obtain the eye track, head orientation, blink duration, and driving operation from the user data, and calculate the user attention level S by weighted average method attention .

5. The method for adaptively displaying interface information according to claim 4, characterized in that: Executing an interface adjustment strategy according to the attention level and the fatigue state, and generating a display interface based on the newly added wallpaper layer, including: Determine whether to execute the interface adjustment strategy according to the attention level, the fatigue state and the corresponding fatigue threshold and attention threshold, and if so, proceed to the next step of triggering dynamic wallpaper superposition; Overlay a new wallpaper image on the current interface and add a new wallpaper layer; Determine a target display text according to the attention level and the fatigue state; A new plug-in is added to the new wallpaper image to insert the target display text to generate a display interface.

6. The method for adaptively displaying interface information according to claim 1, characterized in that: The identifying the attention level and modifying the first font attribute in the display interface includes: Acquire the attention level, adjust the font size in combination with the basic font size, determine the target font size of the target display text in the display interface, and obtain a corrected first font attribute; The formula for determining the target font size is as follows: In the formula, F s is the target font size, F base is the base font size, S attention is the user's attention level, and k is the gain coefficient.

7. The method for adaptively displaying interface information according to claim 1, characterized in that: The identifying the fatigue state and correcting the wallpaper attributes in the display interface includes: Identify the newly added wallpaper layer and determine its base color; Acquire the fatigue state, and adjust the contrast and brightness of the newly added wallpaper layer in combination with the basic color to obtain corrected wallpaper attributes; Among them, the correction formula of wallpaper attributes is as follows: C=C base ×(1+α·ln(1+S fatigue )+b·sin(S fatigue )) C is the adjusted wallpaper attribute, C base As the base color, S fatigue is the user's fatigue state, a and b are the adjustment coefficients for controlling the contrast and brightness change intensity respectively.

8. The method for adaptively displaying interface information according to claim 7, characterized in that: The optimizing the corresponding second font attribute based on the wallpaper attribute includes: Obtaining a brightness parameter from the wallpaper attribute, and comparing it with a preset threshold, selecting a target font color of the newly added wallpaper layer corresponding to the brightness parameter according to the comparison result, and obtaining a corrected second font attribute; The font color selection algorithm is as follows: L bg =log(1+0.299×R+0.587×G+0.114×B) Where, L bg is the brightness parameter of the new wallpaper layer; T is the target font color; K is the preset threshold; y is the probability of selecting a dark font; X is the input feature, including f(L bg ), R, G, B; P is the intermediate quantity; β0, β1, β2, β3, β4 are model parameters.

9. An adaptive display system for interface information, used to implement an adaptive display method for interface information as claimed in any one of claims 1 to 8, characterized in that: include: A data collection system, used to obtain current user data in real time, and evaluate the user's attention level and the user's fatigue state based on the user data; A font content generator, configured to execute an interface adjustment strategy according to the attention level and the fatigue state, and generate a display interface based on a newly added wallpaper layer; a font size adjuster, identifying the attention level, and modifying a first font attribute in the display interface, the first font attribute including a target font size; A wallpaper color adjuster, which identifies the fatigue state and corrects the wallpaper attributes in the display interface; a font color adjuster, which optimizes a corresponding second font attribute based on the wallpaper attribute, wherein the second font attribute includes a target font color; A display screen is used to layout the display interface for information interaction.

10. A storage medium having a computer program stored thereon, characterized in that: The computer program is used to implement an adaptive display method of interface information as described in any one of claims 1-8.

Images