Optical adjustment method and apparatus, head-mounted device, and storage medium

Abstract

The application discloses an optical adjustment method and device, a head-mounted device and a storage medium, and relates to the technical field of head-mounted devices. The optical adjustment method is applied to a head-mounted device, the head-mounted device comprises a liquid lens array and a scoring module, and the method comprises the following steps: after the curvature of the liquid lens array is adjusted based on target optical parameters of the head-mounted device, determining a target comfort score of a wearer by using the scoring module, wherein the target optical parameters are obtained by predicting a light parameter prediction model based on target biological feature data of the wearer, and the light parameter prediction model is obtained by training biological feature data as a sample and optical parameters as a label; and based on the target comfort score, the curvature of the liquid lens array is calibrated. The application can reduce the VAC effect of the head-mounted device.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of head-mounted display devices, and particularly relates to an optical adjustment method and device, a head-mounted display device and a storage medium. BACKGROUND

[0002] With the rapid development of head-mounted display devices (hereinafter referred to as head-mounted display devices), the optimization of the performance and user experience of head-mounted display devices has become the focus of industry research.

[0003] The traditional head-mounted display device mainly relies on fixed focal length design or user manual adjustment in the visual adjustment mechanism. This static adjustment mode cannot flexibly adapt to the individualized biological feature differences of different users, such as interpupillary distance, corneal curvature and other key parameters. Due to the significant differences in biological features among individuals, fixed focal length or manual adjustment often cannot achieve accurate matching, thereby significantly increasing the visual vergence accommodation conflict (VAC effect). The VAC (Vergence-Accommodation Conflict) effect is that when the user tries to focus on the object in the virtual scene, the visual lines of the two eyes cannot naturally converge at the same focal point, causing visual fatigue, dizziness and other discomfort.

[0004] Therefore, how to reduce the VAC effect of the head-mounted display device has become a problem to be solved by those skilled in the art.

[0005] The above content is only used to assist in understanding the technical solutions of the present application and does not represent the acknowledgement of the above content as prior art. SUMMARY

[0006] The main purpose of the present application is to provide an optical adjustment method, device, head-mounted display device and storage medium, which aims to solve the technical problem of how to reduce the VAC effect of the head-mounted display device.

[0007] To achieve the above purpose, the present application provides an optical adjustment method, which is applied to a head-mounted display device, the head-mounted display device comprising a liquid lens array and a scoring module, the scoring module comprising at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is used to determine a first comfort score according to a target physiological signal of a wearer, and the active scoring unit is used to receive a second comfort score input by the wearer, and the method comprises the following steps:

[0008] determining a target comfort score of the wearer by the scoring module after adjusting the curvature of the liquid lens array based on a target optical parameter of the head-mounted device, wherein the target optical parameter is predicted by an optical parameter prediction model based on target biometric data of the wearer, the optical parameter prediction model is trained based on biometric data as samples and optical parameters as labels, and the target comfort score is determined based on the first comfort score and / or the second comfort score;

[0009] calibrating the curvature of the liquid lens array based on the target comfort score.

[0010] In an embodiment, before the step of calibrating the curvature of the liquid lens array based on the target comfort score, the method further comprises:

[0011] determining whether the comfort score is less than a first preset score;

[0012] in a case where it is determined that the comfort score is less than the first preset score, performing the step of calibrating the curvature of the liquid lens array based on the target comfort score.

[0013] In an embodiment, the step of calibrating the curvature of the liquid lens array based on the target comfort score comprises:

[0014] calibrating the target optical parameter based on the target comfort score;

[0015] adjusting the control voltage of the liquid lens array according to the calibrated target optical parameter to adjust the curvature of the liquid lens array.

[0016] In an embodiment, the target optical parameter comprises a focal length compensation value, a lens curvature, and a dispersion compensation coefficient, and the step of adjusting the control voltage of the liquid lens array according to the calibrated target optical parameter comprises:

[0017] calculating a curvature correction term of the liquid lens array according to the calibrated focal length compensation value and the calibrated dispersion compensation coefficient;

[0018] adding the calibrated lens curvature and the curvature correction term to obtain a target curvature of the liquid lens array;

[0019] obtaining an initial curvature of the liquid lens array and calculating a curvature difference value between the initial curvature and the target curvature;

[0020] According to the curvature difference and a preset curvature-voltage coefficient, a target voltage of the liquid lens array is calculated, and a control voltage of the liquid lens array is adjusted according to the target voltage.

[0021] In an embodiment, the head-mounted device further comprises an electrochromic layer, and after the step of calibrating the target optical parameter based on the target comfort score, the method further comprises:

[0022] According to the calibrated dispersion compensation coefficient, a target dispersion of the electrochromic layer is calculated;

[0023] Based on the target dispersion and an initial dispersion of the electrochromic layer, a transmittance of the electrochromic layer is adjusted.

[0024] In an embodiment, the head-mounted device further comprises a biometric acquisition module, and before the step of adjusting the curvature of the liquid lens array based on the target optical parameter of the head-mounted device, the method further comprises:

[0025] An eyeball image of the wearer is acquired by the camera unit;

[0026] Based on the eyeball image, an interpupillary distance and a corneal curvature of the wearer are acquired;

[0027] A gaze point coordinate and an eye movement speed of the wearer are acquired by the eye movement detection unit;

[0028] The interpupillary distance, the corneal curvature, the gaze point coordinate and the eye movement speed are taken as target biometric data of the wearer;

[0029] The target biometric data is input into the optical parameter prediction model to obtain a target optical parameter of the head-mounted device.

[0030] In an embodiment, the method further comprises:

[0031] In a case where the target comfort score satisfies a condition of being less than a first preset score, a loss function of the optical parameter prediction model is determined according to the target comfort score;

[0032] Based on a preset gradient descent algorithm and the loss function, a trainable parameter of the optical parameter prediction model is updated.

[0033] In an embodiment, after the step of calibrating the curvature of the liquid lens array based on the target comfort score, the method further comprises:

[0034] A curvature calibration frequency of the liquid lens array is counted;

[0035] in a case where the curvature calibration number is less than or equal to a preset number, returning to performing the step of determining the target comfort score of the wearer by the scoring module;

[0036] in a case where the curvature calibration number is greater than the preset number, returning to performing the step of acquiring the eye image of the wearer by the camera unit, and setting the curvature calibration number to zero.

[0037] In addition, to achieve the above object, the present application further provides an optical adjustment device, which is arranged in a head-mounted device, the head-mounted device comprising a liquid lens array and a scoring module, the scoring module comprising at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is configured to determine a first comfort score according to a target physiological signal of a wearer, and the active scoring unit is configured to receive a second comfort score input by the wearer, and the device comprises:

[0038] a scoring module configured to determine a target comfort score of the wearer by the scoring module after adjusting the curvature of the liquid lens array based on a target optical parameter of the head-mounted device, wherein the target optical parameter is predicted by an optical parameter prediction model based on target biological feature data of the wearer, the optical parameter prediction model is trained based on biological feature data as samples and optical parameters as labels, and the target comfort score is determined based on the first comfort score and / or the second comfort score;

[0039] a calibration module configured to calibrate the curvature of the liquid lens array based on the target comfort score.

[0040] In addition, to achieve the above object, the present application further provides a head-mounted device, which comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the optical adjustment method as described above.

[0041] In addition, to achieve the above object, the present application further provides a storage medium, which is a computer-readable storage medium, the storage medium storing a computer program, the computer program being executable by a processor to implement the steps of the optical adjustment method as described above.

[0042] In addition, to achieve the above object, the present application further provides a computer program product, which comprises a computer program, the computer program being executable by a processor to implement the steps of the optical adjustment method as described above.

[0043] The embodiment of the present application provides an optical adjustment method and device, a head-mounted device and a storage medium, and relates to the technical field of head-mounted devices. The optical adjustment method is applied to a head-mounted device, the head-mounted device comprises a liquid lens array and a scoring module, the scoring module comprises at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is used for determining a first comfort score according to a target physiological signal of a wearer, and the active scoring unit is used for receiving a second comfort score input by the wearer, the method comprises the following steps: after adjusting the curvature of the liquid lens array based on a target optical parameter of the head-mounted device, determining a target comfort score of the wearer by using the scoring module, wherein the target optical parameter is obtained by predicting a light parameter prediction model based on target biological feature data of the wearer, the light parameter prediction model is obtained by training biological feature data as a sample and optical parameters as labels, and the target comfort score is determined based on the first comfort score and / or the second comfort score; and based on the target comfort score, calibrating the curvature of the liquid lens array.

[0044] The embodiment of the present application introduces a scoring module to evaluate the comfort of the wearer in real time, and dynamically calibrates the curvature of the liquid lens array based on the evaluation result (i.e. the target comfort score), thereby effectively reducing the visual accommodation adjustment conflict (VAC effect) in the head-mounted device and improving the user experience. Specifically, the embodiment of the present application predicts the target optical parameter based on the target biological feature data of the wearer, adjusts the curvature of the liquid lens array, and further obtains the first comfort score (automatic scoring unit determines according to physiological signals such as eye movement and blink frequency) and / or the second comfort score (user actively inputs feedback) of the wearer by using the scoring module, and comprehensively forms the target comfort score; the system analyzes the feedback of the current optical state according to the score, and if it is identified that the VAC effect is still obvious, the curvature of the liquid lens array is dynamically calibrated and optimized, so that the optical system is more suitable for the actual visual needs of the user. This closed-loop adaptive adjustment mechanism not only makes up for the defects that the static adjustment cannot adapt to individual differences, but also introduces a subjective and objective combined comfort evaluation system, improves the accuracy and individualization of optical adjustment, thereby significantly alleviating the visual fatigue and dizziness caused by the VAC effect, and enhancing the immersion and use comfort of the head-mounted device. BRIEF DESCRIPTION OF DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application.

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0047] Figure 1 A flowchart of an optical adjustment method in the embodiments of the present application;

[0048] Figure 2 A scene diagram of an optical adjustment method in the embodiments of the present application;

[0049] Figure 3 A structural diagram of an optical adjustment method in the embodiments of the present application;

[0050] Figure 4 A module structure diagram of an optical adjustment device in the embodiments of the present application;

[0051] Figure 5 A device structure diagram of a hardware running environment related to an optical adjustment method in the embodiments of the present application.

[0052] The purposes, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application, and are not used to limit the present application.

[0054] In order to better understand the technical solutions of the present application, the following will be described in detail with reference to the drawings and specific embodiments in the specification.

[0055] With the rapid development of head-mounted display device (hereinafter referred to as "head-mounted device") technology, the optimization of performance and user experience of head-mounted device has become the focus of industry research.

[0056] The traditional head-mounted device mainly relies on fixed focal length design or user manual adjustment in visual adjustment mechanism. This static adjustment mode cannot flexibly adapt to the individualized biological feature differences of different users, such as pupil distance, corneal curvature and other key parameters. Due to the significant differences in biological features among individuals, fixed focal length or manual adjustment often cannot achieve accurate matching, which in turn leads to a significant increase in visual accommodation adjustment conflict (VAC effect). The VAC effect is that when the user's eyes try to focus on the objects in the virtual scene, the visual lines of the two eyes cannot naturally converge at the same focal point, causing visual fatigue, dizziness and other discomfort.

[0057] Therefore, how to reduce the VAC effect of the head-mounted device has become a problem to be solved by those skilled in the art.

[0058] To solve the above problems, the embodiment of the present application provides an optical adjustment method, which is applied to a head-mounted device, the head-mounted device comprising a liquid lens array and a scoring module, the scoring module comprising at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is configured to determine a first comfort score according to a target physiological signal of a wearer, and the active scoring unit is configured to receive a second comfort score input by the wearer, the method comprising: after adjusting the curvature of the liquid lens array based on a target optical parameter of the head-mounted device, determining a target comfort score of the wearer by the scoring module, wherein the target optical parameter is predicted by an optical parameter prediction model based on target biological feature data of the wearer, the optical parameter prediction model being trained based on biological feature data as samples and optical parameters as labels, and the target comfort score is determined based on the first comfort score and / or the second comfort score; and calibrating the curvature of the liquid lens array based on the target comfort score.

[0059] The embodiment of the present application can effectively reduce the visual accommodation adjustment conflict (VAC effect) in the head-mounted device and improve the user experience by introducing a scoring module to evaluate the comfort of the wearer in real time and dynamically calibrating the curvature of the liquid lens array based on the evaluation result (i.e., the target comfort score). Specifically, the embodiment of the present application predicts the target optical parameter based on the target biological feature data of the wearer, adjusts the curvature of the liquid lens array, and further obtains the first comfort score of the wearer (judged by the automatic scoring unit according to physiological signals such as eye movement and blink frequency) and / or the second comfort score (actively input by the user) by using the scoring module, and comprehensively forms the target comfort score; the system analyzes the feedback of the current optical state according to the score, and if it is identified that the VAC effect is still obvious, the curvature of the liquid lens array is dynamically calibrated and optimized, so that the optical system is more suitable for the actual visual needs of the user. This closed-loop adaptive adjustment mechanism not only makes up for the defects of static adjustment that cannot adapt to individual differences, but also improves the accuracy and individuality of optical adjustment by introducing a comfort evaluation system combining subjective and objective evaluation, thereby significantly alleviating the visual fatigue and dizziness caused by the VAC effect, and enhancing the immersion and use comfort of the head-mounted device.

[0060] The head-mounted device in the embodiments of the present application can include, but is not limited to, a head-mounted display device such as a Mixed Reality (MR) device (for example, MR glasses or an MR helmet), an Augmented Reality (AR) device (for example, AR glasses or an AR helmet), a Virtual Reality (VR) device (for example, VR glasses or a VR helmet), an Extended Reality (XR) device or some combination thereof, and the like. In the embodiments, the head-mounted device is taken as an execution subject for the convenience of description.

[0061] Based on this, the embodiments of the present application provide an optical adjustment method, referring to Figure 1 , Figure 1 The flowchart of the optical adjustment method in the embodiments of the present application is shown in FIG. 1.

[0062] In the embodiments, the optical adjustment method is applied to a head-mounted device, and the head-mounted device includes a liquid lens array and a scoring module, the scoring module includes at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is configured to determine a first comfort score according to a target physiological signal of a wearer, and the active scoring unit is configured to receive a second comfort score input by the wearer, and the optical adjustment method includes steps S100-S200:

[0063] Step S100, after adjusting the curvature of the liquid lens array based on a target optical parameter of the head-mounted device, determining a target comfort score of the wearer through the scoring module;

[0064] Wherein, the target optical parameter is predicted by a light parameter prediction model based on target biological feature data of the wearer, the light parameter prediction model is trained based on biological feature data as samples and optical parameters as labels, and the target comfort score is determined based on the first comfort score and / or the second comfort score.

[0065] In the embodiments, the head-mounted device refers to a head-mounted display device for AR (Augmented Reality), VR (Virtual Reality) or MR (Mixed Reality) experience, which provides an immersive visual experience for users through various built-in sensors and display technologies.

[0066] The liquid lens array is a special lens system whose lens shape (curvature) can be dynamically adjusted by changing the voltage applied to it. This feature allows it to adapt to different focal length requirements, thereby improving the user's visual experience and reducing visual fatigue. In addition, the curvature can also be adjusted by adjusting the pose of each micro liquid lens in the liquid lens array.

[0067] The scoring module is a component in the head-mounted device responsible for assessing the comfort level of the wearer. The scoring module includes an automatic scoring unit and / or an active scoring unit. The automatic scoring unit collects the target physiological signals of the wearer (such as blink frequency, skin conductance, etc.) using the sensors built-in the head-mounted device, and calculates the first comfort score through a pre-set algorithm. The whole process does not require direct user participation, realizing the automation of monitoring the comfort of the wearer. The active scoring unit allows the wearer to input the second comfort score through the device interface according to their subjective feelings, so that the user can express their satisfaction with the current visual experience.

[0068] It is not difficult to understand that the first comfort score is a comfort score automatically calculated by the automatic scoring unit according to the target physiological signals of the wearer (such as blink frequency, skin conductance, etc.). This scoring method does not rely on the active input of the user, but indirectly reflects the visual comfort of the user by monitoring their physical reactions. For example, if the wearer's blink frequency significantly increases or the skin conductance value rises during use, it may indicate that they are experiencing some degree of discomfort, resulting in a lower first comfort score.

[0069] The second comfort score is obtained by the active scoring unit receiving the subjective evaluation directly input by the wearer. The wearer can input a specific score or choose the corresponding option through the interface provided by the device according to their actual feelings, such as visual clarity, whether there is a sense of dizziness, etc. to express their satisfaction with the current visual experience. This method allows the user to directly participate in the comfort assessment process, providing valuable subjective feedback information.

[0070] The target comfort score is determined based on the first comfort score and / or the second comfort score. When only the automatic scoring unit is enabled, the target comfort score can be the first comfort score, or the product of the first comfort score and a first pre-set weight coefficient. When only the active scoring unit is enabled, the target comfort score can be the second comfort score, or the product of the second comfort score and a second pre-set weight coefficient. When both the automatic scoring unit and the active scoring unit are enabled, the target comfort score can be calculated by taking an arithmetic mean or a weighted mean of the first comfort score and the second comfort score. The first pre-set weight coefficient and the second pre-set weight coefficient can be obtained flexibly according to the actual needs of the user or pre-calibrated according to the actual situation of the system, which is not limited in the present embodiment.

[0071] It is worth mentioning that when the automatic scoring unit and the active scoring unit are enabled at the same time, the target comfort score not only considers the objective data obtained through physiological signal analysis (the first comfort score), but also incorporates the subjective feedback of the wearer (the second comfort score). By combining these two different types of scores, the target comfort score can more accurately judge the actual experience quality of the wearer, comprehensively reflect the overall visual comfort of the wearer in the current display state, so that the embodiment can thereby more accurately adjust the curvature of the liquid lens array to reduce the VAC effect and improve the comfort of the wearer, ensuring that the best visual experience is provided for the wearer while minimizing visual fatigue and other discomfort symptoms caused by individual differences.

[0072] It should be noted that the biological feature data refers to specific physiological indicators related to the visual vergence accommodation conflict (VAC effect). These data are crucial for reducing the VAC effect of the head-mounted device to reduce visual fatigue and discomfort. Specifically, the biological feature data includes but is not limited to the following types of information:

[0073] Inter-pupillary distance (IPD): The distance between the centers of the pupils of both eyes. This parameter is very important to ensure that virtual images can be correctly focused on the user's retina.

[0074] Corneal curvature: The degree of curvature of the corneal surface, which affects the refraction path of light entering the eye. Differences in corneal curvature among individuals can cause misfocus or visual distortion.

[0075] Gaze point coordinates: The specific position coordinates of the user's gaze on the screen. This helps to understand the direction of the user's gaze and its changes, so as to adjust the position and clarity of the display content.

[0076] Eye movement speed: The speed of eye movement, which can reflect the changes in the user's attention during browsing or interaction. Rapid eye movement may indicate that the user is experiencing some form of discomfort or trying to find a more comfortable viewing angle.

[0077] Optical parameters refer to parameters used to adjust the curvature of the liquid lens array to reduce the VAC effect and reduce visual discomfort. These parameters directly affect the clarity, color restoration, and visual comfort of the user, and are crucial for mitigating the visual vergence accommodation conflict (VAC effect). Specifically, the optical parameters include but are not limited to the following aspects:

[0078] Focal length compensation value: This is an adjustment value set to compensate for the deviation between the actual focal length and the ideal focal length caused by the differences in the biological characteristics of the wearer, such as the pupil distance and the curvature of the cornea. By accurately calculating and applying the focal length compensation value, it can ensure clear and accurate imaging, and adapt to the needs of different users.

[0079] Lens curvature: A numerical value that describes the degree of bending of the lens surface, which determines how light is refracted and focused through the lens. Different lens curvatures will affect the magnification and focusing effect of the image, so personalized adjustments need to be made according to the specific biological characteristic data of the user to achieve the best visual experience.

[0080] Dispersion compensation coefficient: Used to correct the chromatic aberration phenomenon caused by different refractive indices of different color light in the lens. Proper dispersion compensation can reduce the appearance of colored edges or blurred areas, and improve the color restoration and clarity of the image.

[0081] Other related parameters: such as luminous flux adjustment, contrast enhancement coefficient, etc., these parameters work together to optimize display quality, so that the virtual scene is presented to the user as naturally as possible, reducing visual fatigue and other discomfort.

[0082] In this embodiment, the light parameter prediction model is a neural network model trained through the corresponding relationship between massive biological characteristic data (such as pupil distance, corneal curvature, eye movement speed, gaze point coordinates, etc.) and optimal optical parameters (such as focal length compensation value, lens curvature, dispersion compensation coefficient, etc.). The light parameter prediction model extracts the hidden space correlation in the biological characteristic data and outputs the optical parameters that adapt to the current user's physiological state.

[0083] In this embodiment, based on the biological characteristic data (i.e. target biological characteristic data) collected from the wearer, the light parameter prediction model predicts the best optical parameters (i.e. target optical parameters) that match the target biological characteristic data, so that this embodiment can accurately adjust the curvature of the liquid lens array based on the target optical parameters, ensuring that the head-mounted device can provide a more personalized wearing experience for the wearer, maximizing the user's visual comfort and immersion, while effectively reducing the negative effects of VAC effect.

[0084] It should be noted that in the present embodiment, the head-mounted device is built-in with a biometric feature acquisition module, which can include a plurality of hardware modules for acquiring different types of biometric feature data, has biometric feature recognition capability, and is used to acquire a plurality of key biometric feature data of the wearer. After detecting that the user correctly wears the head-mounted device, the biometric feature acquisition module can automatically start the biometric feature data acquisition function, acquire the biometric feature data related to the VAC effect of the wearer, and output the biometric feature data to the light parameter prediction model to predict the target optical parameter required for adjusting the curvature of the liquid lens array.

[0085] Exemplarily, in a feasible implementation, the head-mounted device further includes a biometric feature acquisition module, which includes a camera unit and an eye movement detection unit. Before the step of adjusting the curvature of the liquid lens array based on the target optical parameter of the head-mounted device, the optical adjustment method can further include steps S10-S50:

[0086] Step S10: acquiring an eyeball image of the wearer by the camera unit;

[0087] It should be noted that in the present embodiment, the biometric feature acquisition module can include a camera unit and an eye movement detection unit. The camera unit can be used to acquire an eye image of the wearer, and the eye movement detection unit can be used to track eye movement data of the wearer.

[0088] In the present embodiment, when the wearer correctly wears the head-mounted device, the camera unit starts to work. The camera unit usually adopts a high-resolution camera with automatic focusing and light compensation functions to ensure that clear and accurate eyeball images can be acquired under different lighting conditions. During the acquisition process, the camera unit will capture the eyeball region of the wearer in real time to acquire a series of continuous eyeball image frames. These image frames contain appearance information of the eyeball, such as pupil size, position, corneal shape, and the like.

[0089] Step S20: acquiring an interpupillary distance and a corneal curvature of the wearer based on the eyeball image;

[0090] In the present embodiment, after the image of the eyeball is acquired, the image processing algorithm built-in the head-mounted device will analyze and process the eyeball image acquired by the camera unit to extract biometric feature data such as interpupillary distance and corneal curvature.

[0091] As an example, specifically, the algorithm first pre-processes the eyeball image, including image enhancement, edge detection, and the like, to highlight the outline of the pupil. Then, the center positions of the two pupils are determined by image recognition technology, and the distance between them is calculated, which is the interpupillary distance.

[0092] Then, the corneal curvature radius is calculated by using the corneal shape information in the eye image, combining a preset corneal model and a mathematical algorithm. The corneal curvature is an important indicator for measuring the bending degree of the cornea and is crucial for the optical design of the head-mounted device.

[0093] In step S30, the gaze point coordinates and the eye movement speed of the wearer are obtained by the eye movement detection unit.

[0094] In this embodiment, the eye movement detection unit calculates the coordinates of the current gaze point of the wearer according to the movement trajectory of the eyeball and the position information of the pupil. The gaze point coordinates reflect the visual focus position of the wearer. In addition to the gaze point coordinates, the eye movement detection unit can also measure the movement speed of the eyeball, i.e., the eye movement speed. Changes in the eye movement speed can reflect the degree of concentration of the wearer's attention and the efficiency of visual search and other information.

[0095] In step S40, the interpupillary distance, the corneal curvature, the gaze point coordinates and the eye movement speed are taken as the target biological feature data of the wearer.

[0096] In step S50, the target biological feature data is input into the optical parameter prediction model to obtain the target optical parameters of the head-mounted device.

[0097] In this embodiment, the optical parameter prediction model is based on a GAN (Generative Adversarial Network) model, an LSTM (Long Short Term Memory) model, a CNN (Convolutional Neural Network) model or an RNN (Recurrent Neural Network) model. A large amount of biological feature data of different age, gender and other groups of people and sample data sets of their corresponding optical parameters with the best wearing experience and the best visual comfort degree of the current biological signals are used in the model training stage. Through deep learning algorithm, the optical parameter prediction model can learn the complex mapping relationship between the biological feature data and the optical parameters.

[0098] In this embodiment, the head-mounted device inputs the collected target biological feature data into the optical parameter prediction model as input data. The optical parameter prediction model quickly processes and analyzes the input target biological feature data, uses the learned mapping relationship inside to predict and output the target optical parameters matched with the biological feature data of the wearer. These target optical parameters directly determine the VAC effect of the head-mounted device.

[0099] The light parameter prediction model can be one of a GAN (Generative Adversarial Network) model, an LSTM (Long Short Term Memory) model, a CNN (Convolutional Neural Network) model, and an RNN (Recurrent Neural Network) model, and the present embodiment does not make specific limitations on this.

[0100] As an example, the light parameter prediction model can be a GAN (Generative Adversarial Network) model, which includes a generator and a discriminator. The generator includes an input layer (a 10-dimensional biological feature vector), a fully connected layer (512 nodes, Leaky ReLU), a fully connected layer (256 nodes), and an output layer (3-dimensional optical parameters: focal length compensation value f, lens curvature r, and dispersion coefficient δ).

[0101] The discriminator includes an input layer (3-dimensional optical parameters), a convolutional layer (64 filters, step 2), a fully connected layer (128 nodes), and a binary output (real / generated probability). Among the multiple inference results output by the generator, the group of data with the highest real / generated probability is selected as the final output result.

[0102] Further, in a feasible implementation, before step S20, the method can further include steps A10-A20:

[0103] Step A10, if the target biological feature data does not conform to the preset normal range, reacquire the target biological feature data;

[0104] In the present embodiment, after the target biological feature data of the wearer is collected, the head-mounted device performs preliminary legality verification on the data. The preset normal range is determined according to a large amount of sample data and actual application requirements, and covers the biological feature data range of different user groups in the normal state. For example, a reasonable interval range is set for the interpupillary distance, and there is also a corresponding normal value range for the corneal curvature.

[0105] When the head-mounted device determines that the collected target biometric feature data is not within the preset normal range, there can be various reasons, such as sensor failure, collection environment interference, non-standard wearing by the wearer, etc. At this time, the head-mounted device will trigger a re-collection mechanism, and through interface prompts or sound prompts, etc., it will inform the wearer to re-collect biometric feature data. During the re-collection process, the biometric feature collection module will be started again, and new target biometric feature data will be obtained according to the same collection process and standard. The head-mounted device will again perform legality verification on the re-collected data, and the process will continue until the collected data meets the preset normal range, so as to ensure that the data used in the subsequent steps is accurate and reliable.

[0106] Taking interpupillary distance (IPD) as an example, the normal range preset by the head-mounted device can not only include the statistical mean ± 2σ interval, but also introduce quantile correction based on user age group (such as allowing ± 3σ fluctuation for child users), dynamic compensation of device wearing angle (± 1mm tilt tolerance), and calibration coefficient of real-time environmental light influence (such as IPD value may shrink by 0.5-1mm in strong light environment).

[0107] When the head-mounted device detects that the collected data exceeds the preset range through the verification algorithm, a multi-dimensional abnormality diagnosis mechanism will be triggered. First, the device analyzes the state parameters of the sensor itself (such as infrared light source intensity, camera exposure time, temperature drift, etc.), compares the historical baseline values to determine whether there is a hardware failure; second, it combines environmental sensor data (such as light intensity, humidity, electromagnetic interference intensity) to evaluate external interference factors; finally, it analyzes the wearer's head posture (accelerometer / gyroscope data), device wearing pressure distribution (pressure sensor array data), and user interaction behavior (such as blink frequency, eye tremor pattern) to determine whether there is non-standard wearing (such as device slipping, light leakage, orbital compression) or user physiological abnormalities (such as fatigue, drug effects), etc.

[0108] If it is determined that the abnormality is retryable (such as temporary environmental interference or slight wearing deviation), the head-mounted device will immediately start the re-collection mechanism. The device will guide the wearer to adjust the device position, clean the collection area, or re-enter the collection state through multi-modal interaction methods such as visual prompts (such as pupil position calibration animation, device wearing angle diagram), auditory prompts (multilingual voice instructions, environmental sound compensation), and tactile feedback (slight vibration prompt). During the re-collection process, the biometric feature collection module (including infrared camera array, iris scanner, eye tracker, etc.) will operate in a higher precision mode, enabling self-calibration algorithms (such as auto-focus, white balance adjustment, distortion correction), and increasing data redundancy (such as collecting 3 sets of data and taking the median).

[0109] Step A20, input the target biometric feature data into the preset convolutional network to obtain vectorized target biometric feature data.

[0110] In this embodiment, after the target biometric feature data passes the legality check, it needs to be vectorized for subsequent processing and analysis in the GAN model. The preset convolutional network is a deep learning model specially designed for this purpose.

[0111] Specifically, after collecting the biometric feature data of the wearer, the biometric feature data needs to be preprocessed for subsequent model analysis and calculation. First, the abnormal values in the biometric feature data are removed, for example, when the IPD does not meet the range of 54 to 74 mm, it is considered as abnormal data and needs to be re-measured. After removing the abnormal data, the IPD, corneal curvature and other parameters are normalized to the interval [0, 1], then the time stamp of the collected data is used to synchronize the multi-modal data, and finally the data including the user's historical comfort score is input into the convolutional network for feature extraction, and a 10-dimensional feature vector is output. wherein X IPD : IPD data; X cornea : corneal curvature data; X gaze : gaze point coordinates, the 10-dimensional feature vector includes:

[0112] 1) interpupillary distance (IPD, 1 dimension);

[0113] 2) corneal curvature (3 dimensions, extracted from iris image features);

[0114] 3) gaze point coordinates (2 dimensions, x / y axis normalized values);

[0115] 4) eye movement speed (2 dimensions, horizontal / vertical components);

[0116] 5) user's historical comfort score (2 dimensions, sliding average of the last 5 scores).

[0117] It is worth mentioning that the user's historical comfort score can also be included in the data input to the light parameter prediction model. Specifically, step S50 can further include step S51:

[0118] Step S51, obtain a plurality of historical reference scores according to a preset window;

[0119] In this embodiment, in order to more comprehensively evaluate the comfort perception of the wearer, the head-mounted device will also consider the historical use data of the wearer, obtain a plurality of historical reference scores from the historical data according to a preset time window (such as the last 5 scores, the last month, etc.), which can be the comfort scores obtained by the wearer through the score acquisition module when using the head-mounted device in the past, or scores automatically calculated by the device according to other indicators (such as use time, number of abnormal exits, etc.).

[0120] Step S52, the average of each historical reference score is calculated to obtain a historical score.

[0121] In this embodiment, after obtaining a plurality of historical reference scores, the head-mounted device will average the scores to obtain a comprehensive historical score, which reflects the overall comfort perception of the wearer to the head-mounted device in the past period of time. This score can be an important reference for current comfort assessment, helping the device better understand the needs and preferences of the wearer.

[0122] Step S53, input the historical score and the target biological feature data into the optical parameter prediction model to obtain the target optical parameter of the head-mounted device.

[0123] This embodiment inputs the historical score and the target biological feature data collected at the current time into the optical parameter prediction model, so that the optical parameter prediction model can more accurately predict the optical parameter suitable for the current state on the basis of comprehensively considering the physiological characteristics of the wearer and the historical use experience. Specifically, the optical parameter prediction model not only calculates the optical parameter based on the individual biological characteristics of the wearer (such as interpupillary distance, corneal curvature, etc.), but also introduces the comfort trend information formed by the wearer in the past use process, thereby realizing the leap from “static adaptation” to “dynamic personalized adaptation”.

[0124] This fusion of historical scores makes the optical parameter prediction process have certain memory and adaptability: on the one hand, it can identify the long-term preferences of the wearer for certain optical settings and avoid repeated uncomfortable adjustment results; on the other hand, when facing similar biological feature inputs, the model can correct the current prediction result through the historical score, making it closer to the actual perception needs of the user, and further reducing the discomfort caused by the VAC effect.

[0125] Therefore, by including the historical score as one of the input features in the optical parameter prediction process, it is helpful to improve the response ability of the head-mounted device to the visual comfort of the user in different scenarios, enhance the intelligent level and personalized degree of optical adjustment, and ultimately realize a more natural, stable and comfortable immersive wearing experience.

[0126] In a further example, please refer toFigure 2 The head-mounted device integrates an optical perception system inside, the core components of which include left and right symmetrical infrared camera modules and left and right iris recognition camera arrays. These cameras use high-sensitivity sensors with low noise and high dynamic range characteristics, enabling accurate capture of subtle features in the eye area. In the zoom module, a liquid lens array technology is used, which consists of multiple micro liquid lens units. Each unit can be independently adjusted in focal length through electric field driving, achieving millisecond-level response continuous zoom function. It can also adjust the focal length by adjusting the position through motor driving. At the same time, it is matched with an electrochromic layer (a layer or multiple layers made of electrochromic material, which can undergo reversible optical property changes such as transmittance, reflectance or absorbance when a certain voltage is applied across its two ends), which can precisely control the transmittance by applying different voltages, thereby dynamically optimizing the luminous flux and contrast of the optical system. Through the synergistic effect of the zoom module, left and right light splitting prisms, and high-precision optical lens groups, the virtual image is finally accurately projected onto the display screen, and the distortion is corrected through the free-form surface optical system, so that the user can observe clear and distortion-free virtual imaging through the eyepiece.

[0127] For dynamic adjustment of optical parameters, the head-mounted device can realize real-time switching of virtual image focal length through built-in calibration algorithms. Specifically, when the user needs to switch from focusing on virtual image A (such as a close-up virtual object) to virtual image B (such as a long-distance virtual scene), the head-mounted device can complete focal length adjustment through the fast response characteristics of the liquid lens array, while the electrochromic layer synchronously optimizes the transmittance to match the light intensity requirement under the new focal length. Conversely, when returning from virtual image B to virtual image A, the head-mounted device can also complete the reverse adjustment with the same accuracy and speed, ensuring the coherence and comfort of the user's visual experience.

[0128] Based on the above hardware architecture, when the head-mounted device collects biological feature data, the left and right infrared cameras work synchronously at a sampling rate of 60 Hz, and the three-dimensional coordinates of the pupil center of the wearer are calculated in real time through a binocular stereo vision algorithm, and then the accurate IPD value is obtained. The data is stored in the CSV (Comma-Separated Values) format (including timestamp, left / right pupil X / Y / Z coordinates, IPD value, etc.) frame by frame in the flash memory built-in the head-mounted device, the iris camera collects dynamic images of the wearer's eyeball at a high frame rate (≥ 30 fps), the head-mounted device uses deep learning algorithm to identify the micro-deformation features (such as blood vessel distribution, wrinkle shape, etc.) on the iris surface, and combines with the geometric optics model to inverse the corneal curvature radius, the collected corneal curvature image is stored in JPEG (Joint Photographic Experts Group, a lossy compression digital image format) format (640x480 pixels, 8-bit grayscale), the eye tracker module captures the rotation angle and micro-movement of the wearer's eyeball at a sampling rate of 120 Hz, calculates the gaze point coordinates, and analyzes the eye movement speed and acceleration using the optical flow method, and the detection data is stored in JSON (JavaScript Object Notation) format (including timestamp, gaze point X / Y coordinates, eye movement speed vector, etc.) format), which supports real-time gaze point heat map generation and eye movement trajectory playback function.

[0129] As shown in Figure 3 In an example, the head-mounted device is divided into left eye region and right eye region, and the zoom module includes left and right VAC adjustment modules and left and right lenses. The VAC adjustment module includes a liquid lens array and an electrochromic layer. In this example, in addition to directly controlling the voltage of the left and right VAC adjustment modules to achieve curvature adjustment, the microcontroller unit can also control the pose of each liquid lens in the liquid lens array in the left and right VAC adjustment modules through the left and right motors, thereby changing the curvature of the liquid lens array.

[0130] Step S200, based on the target comfort score, calibrate the curvature of the liquid lens array.

[0131] After quantitatively evaluating the actual visual experience of the wearer and obtaining the target comfort score, the embodiment can determine whether the current optical settings have reached the optimal state. If the target comfort score indicates that the wearer has discomfort or dissatisfaction, the system will use this score information to dynamically adjust the control voltage of the liquid lens array or adjust the pose of each liquid lens in the liquid lens array through a preset algorithm, thereby optimizing the lens curvature and ensuring that it better matches the individual needs of the wearer. This adaptive adjustment mechanism not only effectively reduces the visual accommodation convergence conflict (VAC effect), but also significantly improves the user's visual comfort and overall use experience.

[0132] For example, in a feasible implementation, before step S200, the optical adjustment method further includes steps B10-B20:

[0133] Step B10, determine whether the comfort score is less than a first preset score.

[0134] It should be noted that the first preset score refers to a reference value or threshold value set in the optical adjustment system of the head-mounted device, which is used to evaluate whether the wearer's visual comfort has reached an acceptable standard. This score is based on a series of pre-defined rules, standards or ideal comfort levels obtained through extensive user testing and data analysis. Specifically, the first preset score is a numerical standard representing the minimum level of visual comfort that the wearer should achieve when using the head-mounted device, which is usually determined by the device manufacturer based on a large amount of user experience data, physiological research and the needs of specific application scenarios. This score serves as a basis for the system to determine whether the current optical settings need to be adjusted. If the target comfort score of the wearer is less than the first preset score, it indicates that the current optical settings do not fully meet the user's visual needs and there is room for improvement; otherwise, if the target comfort score is equal to or higher than the first preset score, it means that the current settings can already provide good user experience and do not need further adjustment.

[0135] By introducing the concept of the first preset score, the embodiment can intelligently determine when to start the calibration process while ensuring user experience, thereby improving the efficiency and response speed of the system and ensuring the best visual experience for users. This mechanism not only helps to reduce unnecessary resource consumption, but also significantly improves user satisfaction and overall performance of the device.

[0136] Step B20, in the case where the comfort score is determined to be less than the first preset score, perform the step of calibrating the curvature of the liquid lens array based on the target comfort score.

[0137] In the present embodiment, once it is determined that the target comfort score is less than the first preset score, it means that the current visual experience of the wearer does not reach the ideal state, and there may be more obvious VAC problems. At this time, the system will automatically start the calibration program and enter step S200 to calibrate the curvature of the liquid lens array based on the target comfort score.

[0138] The present embodiment adopts a mechanism of judging first and then acting, which not only improves the response efficiency of the system and avoids unnecessary resource consumption, but also ensures that the corresponding adjustment is only made when the wearer's experience really needs to be improved, thereby helping to maintain the stability of the device performance and the long-term satisfaction of the user.

[0139] The present embodiment effectively reduces the visual vergence accommodation conflict (VAC effect) in the head-mounted device and improves the user wearing experience by introducing a scoring module to evaluate the comfort of the wearer in real time and dynamically calibrating the curvature of the liquid lens array based on the evaluation result (i.e., the target comfort score). Specifically, based on predicting the target optical parameters based on the target biological feature data of the wearer and adjusting the curvature of the liquid lens array, the present embodiment further utilizes the scoring module to obtain the first comfort score of the wearer (judged by the automatic scoring unit based on physiological signals such as eye movement and blink frequency) and / or the second comfort score (actively input by the user), and comprehensively forms the target comfort score. The system analyzes the feedback of the current optical state according to the score, and if it is identified that the VAC effect is still obvious, the curvature of the liquid lens array is dynamically calibrated and optimized, so that the optical system is more suitable for the actual visual needs of the user. This closed-loop adaptive adjustment mechanism not only makes up for the defect that the static adjustment cannot adapt to individual differences, but also improves the precision and individualization of optical adjustment by introducing a comfort evaluation system combining subjective and objective factors, thereby significantly alleviating the visual fatigue and dizziness caused by the VAC effect and enhancing the immersion and comfort of the head-mounted device.

[0140] In a feasible implementation, step S200 of calibrating the curvature of the liquid lens array based on the target comfort score can include steps S210-S220:

[0141] Step S210: Calibrating the target optical parameters based on the target comfort score.

[0142] In the present embodiment, when it is determined that the current optical setting fails to fully meet the visual comfort needs of the wearer and there is a certain degree of VAC effect or other discomfort factors, the system can use a feedback control algorithm (such as PID control, gradient descent method, etc.) to fine-tune the original target optical parameters based on the deviation between the target comfort score and the ideal score, to generate a set of optimized optical parameters that are more in line with the subjective feelings and physiological state of the wearer, i.e., the calibrated target optical parameters.

[0143] At step S220, the control voltage of the liquid lens array is adjusted according to the calibrated target optical parameters, so as to adjust the curvature of the liquid lens array.

[0144] After the calibration of the target optical parameters is completed, the embodiment can convert the optimized parameters (i.e., the calibrated target optical parameters) into specific control instructions and input them to the driving module of the liquid lens array. Each lens unit in the liquid lens array has variable curvature characteristics, and the curvature thereof can be dynamically regulated by applying different voltages. Therefore, the embodiment calculates the corresponding control voltage value according to the calibrated target optical parameters and loads it to the corresponding liquid lens unit, so as to change the curvature distribution of the lens surface.

[0145] The embodiment realizes a closed-loop feedback mechanism from "perceived comfort" to "physical optical adjustment": the target comfort score of the wearer is converted into actual optical parameter changes, which are finally embodied in the physical form adjustment of the liquid lens array, so that the virtual image is more consistent with the user's visual focus, effectively alleviates the VAC effect, and improves the display quality and user experience.

[0146] In summary, by calibrating the target optical parameters based on the comfort score at step S210 and applying the calibration results to the actual adjustment of the liquid lens array at step S220, the embodiment constructs a dynamic adaptive optical adjustment mechanism that integrates subjective feedback and objective adjustment, significantly improving the visual adaptation capability and user satisfaction of the head-mounted device in complex application scenarios.

[0147] Further, in a feasible embodiment, the target optical parameters include a focal length compensation value, a lens curvature, and a dispersion compensation coefficient, and step S220 can further include steps S221-S224:

[0148] At step S221, a curvature correction term of the liquid lens array is calculated according to the calibrated focal length compensation value and the calibrated dispersion compensation coefficient.

[0149] It should be noted that, in the present embodiment, the focal length compensation value is used to compensate for the deviation between the actual focal length and the ideal focal length caused by the differences in biological characteristics of the wearer (such as different interpupillary distances and corneal curvatures), so as to ensure clear and accurate imaging.

[0150] The lens curvature is used to describe the bending degree of the lens surface, and different curvatures will affect the refraction and focusing effect of light.

[0151] In addition, the dispersion compensation coefficient is used to correct the dispersion phenomenon caused by the different refractive indices of different color light, reduce chromatic aberration, and improve the color restoration and clarity of the image.

[0152] In the embodiment, the curvature correction term of the liquid lens array can be calculated according to the focal length compensation value and the dispersion compensation coefficient, that is, the curvature that needs to be compensated.

[0153] In step S32, the calibrated lens curvature is added to the curvature correction term to obtain the target curvature of the liquid lens array.

[0154] In the embodiment, after the curvature correction term is calculated, the lens curvature is added to the curvature correction term to obtain the target curvature to which the liquid lens array needs to be adjusted.

[0155] In step S33, the initial curvature of the liquid lens array is obtained, and the curvature difference between the initial curvature and the target curvature is calculated.

[0156] In the embodiment, in order to calculate the control voltage that needs to be adjusted, the curvature difference between the initial curvature and the target curvature of the lens in the liquid lens array also needs to be calculated.

[0157] In step S34, the target voltage of the liquid lens array is calculated according to the curvature difference and the preset curvature-voltage coefficient, and the control voltage of the liquid lens array is adjusted according to the target voltage.

[0158] In the embodiment, after the curvature difference is calculated, the target voltage to which the liquid lens array needs to be adjusted can be calculated according to the preset curvature-voltage coefficient, and then the control voltage of the liquid lens array is adjusted to the target voltage, so that the curvature of the liquid lens array can be adjusted to the target curvature.

[0159] Specifically, in one example, the curvature radius R of the liquid lens is strictly related to the focal length f through the thin lens formula:

[0160] ;

[0161] Wherein:

[0162] n: refractive index of liquid lens material (such as PDMS, n = 1.43);

[0163] R fix : radius of fixed curvature surface (determined by lens mechanical structure, such as R fix = 500 μm);

[0164] R: dynamically adjusted curvature radius;

[0165] The relationship between the curvature radius R of the liquid lens and the driving voltage V is:

[0166] ;

[0167] Wherein:

[0168] R: target radius of curvature (pm), calculated from the curvature parameter in the generator output Y.

[0169] V: driving voltage (V), target value to be solved.

[0170] k: lens material characteristic constant (e.g. dielectric elastomer k=0.12 pm / V2).

[0171] , : vacuum permittivity (8.85e-12 F / m) and material relative permittivity (e.g. PDMS =2.8).

[0172] d: electrode spacing (pm, default 50 pm).

[0173] R0: zero-voltage radius of curvature (determined by the initial shape of the lens, usually 200 pm).

[0174] In one possible implementation, the head-mounted device further comprises an electrochromic layer, and after the target optical parameter is calibrated based on the target comfort score in step S210, the optical adjustment method can further comprise steps S230-S240:

[0175] In step S230, the target dispersion of the electrochromic layer is calculated according to the calibrated dispersion compensation coefficient.

[0176] It should be noted that in this embodiment, the transmittance refers to the ratio of the actual transmitted light intensity to the incident light intensity when the light passes through the electrochromic layer, and the transmittance directly affects the brightness, color saturation and clarity of the display picture of the head-mounted device.

[0177] In this embodiment, according to the dispersion compensation coefficient, the target dispersion to which the electrochromic layer needs to be adjusted at present can be calculated.

[0178] In step S240, the transmittance of the electrochromic layer is adjusted based on the target dispersion and the initial dispersion of the electrochromic layer.

[0179] In this embodiment, according to the target dispersion to which the electrochromic layer needs to be adjusted at present and the initial dispersion of the zoom module, the transmittance of the electrochromic layer is adjusted, so that the electrochromic layer is adjusted to the target dispersion.

[0180] Specifically, the formula for dynamically adjusting the transmittance based on the dispersion compensation coefficient is as follows:

[0181]

[0182] a: material characteristic constant;

[0183] T: transmittance;

[0184] δ: dispersion compensation coefficient

[0185] T0: initial transmittance.

[0186] By collecting the target biological feature data of the wearer and predicting the target optical parameters matched with the target biological feature data by using the light parameter prediction model, the head-mounted device can realize personalized optical setting (including curvature setting of the liquid lens array and / or transmittance setting of the electrochromic layer), ensuring that different wearers can obtain the best wearing experience. Due to the fixed focal length or manual adjustment mode of the traditional head-mounted device, it is often difficult to achieve precise matching, resulting in a significant increase in visual accommodation convergence adjustment conflict (VAC effect), while the optical adjustment method of the present embodiment can automatically adjust the working parameters of the liquid lens array and / or the electrochromic layer, so that the optical parameters of the head-mounted device can adapt to the biological feature data of the user, thereby effectively reducing the VAC effect and reducing visual fatigue and dizziness and other discomfort. In addition, since the head-mounted device can automatically adapt to the biological feature data of different users and adjust the working parameters in real time to reduce the VAC effect, the user does not need to perform complex manual adjustment operations during use, which not only improves the convenience of use, but also significantly improves the overall experience of the user.

[0187] In a feasible implementation, the optical adjustment method further comprises steps C10-C20:

[0188] Step C10, in the case that the target comfort score meets a condition of being less than a first preset score, determining a loss function of the light parameter prediction model according to the target comfort score;

[0189] Step C20, updating the trainable parameters of the light parameter prediction model based on a preset gradient descent algorithm and the loss function.

[0190] In this embodiment, after the head-mounted device determines the target comfort score of the wearer, it analyzes and processes the score. If the analysis result shows that the target comfort score is at a high level (i.e., greater than or equal to the first preset score), it indicates that the target optical parameters generated by the current light parameter prediction model are highly matched with the actual needs of the wearer, not only achieving the user's expectations in visual effects, but also not causing significant discomfort during long-term wearing. This positive feedback indicates that the model has high accuracy in capturing subtle differences in user biological characteristics and predicting optimal optical parameters. Therefore, the device further takes action to store the biological characteristic data collected this time (such as interpupillary distance, corneal curvature, eye movement trajectory, etc.), target optical parameters (such as focal length, light field distribution, color temperature adjustment, etc.), and the second comfort score given by the user as a complete training sample in the device's database. These samples not only record the user's current physiological state and preferences, but also reflect the output performance of the light parameter prediction model under specific conditions, providing data resources for the continuous optimization of subsequent models.

[0191] Subsequently, these newly added training samples will be included in the training process of the light parameter prediction model, and the parameters and structure of the light parameter prediction model will be continuously iteratively optimized through machine learning algorithms, so as to more accurately predict and generate target optical parameters that meet the user's individual needs in future use. This process is a continuous improvement process that aims to continuously improve the comfort and performance of the head-mounted device.

[0192] On the contrary, if the analysis result shows that the target comfort score is low, it indicates that the current light parameter prediction model has deviations in outputting target optical parameters, and has not fully considered the biological characteristic differences of the wearer or the needs in specific use scenarios. The device will use the target comfort score this time as direct feedback signal, and propagate it back to the generator part of the light parameter prediction model, to guide it to learn more accurate and user demand-oriented optical parameter generation strategies by adjusting the weight parameters of the generator.

[0193] After adjusting the weight parameters of the light parameter prediction model, the device will restart the optical parameter prediction process of the light parameter prediction model, and use the updated model to analyze the biological characteristic data of the wearer again and generate new target optical parameters. This process may be repeated multiple times until the newly generated optical parameters can obtain a high target comfort score in subsequent user tests. Through this continuous iteration and continuous optimization, the head-mounted device can gradually approach the limit of user needs and provide users with more comfortable and personalized visual experiences.

[0194] Exemplarily, the light parametric prediction model can be a GAN (Generative Adversarial Network) model, and steps C10-C20 can be: determining a loss function of the GAN model according to the comfort score; and updating the weight of the generator in the GAN model based on a preset gradient descent algorithm and the loss function.

[0195] In this example, the loss function of the GAN model can be obtained according to the comfort score weighted calculation.

[0196] Specifically, the loss function L combines the adversarial loss and the user score weighting:

[0197] ;

[0198] L adv : adversarial loss, which measures the probability that the generator output parameter is recognized by the discriminator.

[0199] L score : user score loss (Comfort Loss), which quantifies the matching degree of the generated parameter and the user comfort.

[0200] λ1, λ2: weight coefficients, used to balance the adversarial training and user feedback. The default values are λ1=0.7 and λ2=0.3, which need to be adjusted according to the validation set.

[0201] Further, the weight of the generator in the GAN model is updated using a preset gradient descent algorithm combined with the loss function, and the model is gradually adjusted to the optimal state.

[0202] Specifically, the Mini-batch SGD can be used to update the weight of the generator:

[0203] ;

[0204] θ G : trainable parameters of the generator;

[0205] η: learning rate (Learning Rate), which controls the step size of parameter update. The initial value of η is set to 0.0002, and it is exponentially decayed with the training round: the initial learning rate η0=0.0002;

[0206] ▽ θG : gradient of the loss function with respect to the generator parameters.

[0207] S, Y: user score and currently generated optical parameters.

[0208] It is worth mentioning that in the present embodiment, after the target optical parameter is generated by the light parameter prediction model each time and the curvature of the liquid lens array is adjusted based on the generated target optical parameter, the target comfort score of the wearer under the current optical setting is determined by the scoring module, and the corresponding target biological feature data, target light parameter and target comfort score are associated and stored. In the associated storage, according to the actual design, it can be stored by account, stored by wearer, or not distinguished, that is, when the head-mounted device associates and stores the target biological feature data, the target light parameter and the target comfort score, it can associate and store the above data under the same account to the same light parameter storage space (i.e. the storage space for associating and storing the above data), or the identity of the wearer can be determined by iris authentication and the like, and then the above data of the same wearer is associated and stored in the same light parameter storage space, or not distinguished, and all the above data of the head-mounted device is associated and stored in the same light parameter storage space.

[0209] It can be understood that the trainable parameters of the light parameter prediction model corresponding to different light parameter storage spaces are different, that is, the light parameter prediction model of the head-mounted display device can have multiple different sets of trainable parameters, each set of trainable parameters corresponding to a light parameter storage space (i.e. corresponding to an account or corresponding to a wearer), and each time the head-mounted device is used, the logged account or the identity of the wearer is detected first, and the corresponding set of trainable parameters is loaded into the light parameter prediction model, so that the light parameter prediction model is matched with the current logged account or the current wearer. Accordingly, when it is detected that the logged account or the wearer changes, the set of trainable parameters loaded by the light parameter prediction model is switched.

[0210] For the same light parameter storage space, each target biological feature data is only stored once, that is, only the latest associated target optical parameter and target comfort score of the target biological feature data are retained.

[0211] It is not difficult to understand that for the same optical parameter storage space corresponding to the trainable parameter set, as the optical parameter prediction model is continuously updated, the target optical parameters predicted will be more and more matched with the target biological feature data of the wearer. Correspondingly, after adjusting the curvature of the liquid lens array based on this, the target comfort score of the wearer will also be higher and higher. When the target comfort score associated with each target biological feature data in the entire optical parameter storage space is greater than the first preset score, or the target comfort score of the latest data associated and stored in the optical parameter storage space in the last certain number of times (such as the last 100 times) or the last certain time (such as the last month) is greater than the first preset score, it can be considered that the trainable parameter set corresponding to the optical parameter storage space has been trained and is in an optimal state, and the trainable parameter set is marked as a target state. At this time, in subsequent use, the trainable parameter set does not need to be optimized, that is, the optical parameter prediction model loaded with the trainable parameter set does not need to be updated, and at the same time, after the target biological feature data of the wearer is collected, the optical parameter prediction model loaded with the trainable parameter set does not need to be predicted, and the target optical parameters associated with the target biological feature data can be directly called from the optical parameter storage space to adjust the curvature of the liquid lens array, thereby improving the optical adjustment speed of the head-mounted device and saving the computing resources required for model prediction.

[0212] Exemplarily, after step S40 takes the interpupillary distance, corneal curvature, fixation point coordinates and eye movement speed as the target biological feature data of the wearer, the optical adjustment method can further include:

[0213] determining the optical parameter storage space corresponding to the wearer, and determining whether the trainable parameter set corresponding to the optical parameter storage space is marked as a target state;

[0214] In the case where the trainable parameter set is marked as a target state, querying the optical parameters associated with the target biological feature data from the optical parameter storage space, and adjusting the curvature of the liquid lens array based on the queried optical parameters.

[0215] Wherein, the trainable parameter set being marked as a target state means that the trainable parameter set has been trained and is in an optimal state, and does not need to be updated.

[0216] It is not difficult to understand that in the present example, when the above-mentioned data is associated and stored by dividing the optical parameter storage space according to the account, determining the optical parameter storage space corresponding to the wearer is essentially determining the optical parameter storage space corresponding to the currently logged-in account.

[0217] In a feasible implementation, after step S200 adjusts the curvature of the liquid lens array based on the target comfort score, the optical adjustment method can further include steps D10-D30:

[0218] Step D10, count the curvature calibration times of the liquid lens array;

[0219] It should be noted that the curvature calibration times refer to the cumulative number of times of calibrating the curvature of the liquid lens array.

[0220] In this embodiment, the curvature of each pair of liquid lens array is calibrated once, and the curvature calibration times are counted once, so that the curvature calibration times are incremented by one.

[0221] Step D20, if the curvature calibration times are less than or equal to a preset number of times, return to the step of determining the target comfort score of the wearer by the scoring module;

[0222] It should be noted that the preset number of times refers to a threshold or upper limit set for the curvature calibration of the liquid lens array in the optical adjustment process of the head-mounted device. It specifies the maximum number of attempts for the system to adjust the curvature based on the same set of biometric data. Once this preset number of times is reached and the ideal visual comfort level is still not achieved, the system will trigger additional operation processes, such as reacquiring the target biometric data of the wearer.

[0223] In this embodiment, if the current curvature calibration times do not exceed the preset number of times (for example, set to 3 times), the system considers that there is still room for further fine-tuning of the current target optical parameters. At this time, the system will return to step S100 and again obtain the latest target comfort score of the wearer through the scoring module. This cyclic mechanism allows the system to gradually approach the optimal optical settings based on the immediate feedback of the wearer until the target comfort score of the wearer is greater than the first preset score.

[0224] Step D30, if the curvature calibration times are greater than the preset number of times, return to the step of obtaining the eye image of the wearer by the camera unit and set the curvature calibration times to zero.

[0225] In this embodiment, when the curvature calibration times exceed the preset number of times (such as 3 times in the above example), it indicates that despite multiple attempts, the ideal comfort level has not been achieved. In this case, the system speculates that the initial biometric data (such as interpupillary distance, corneal curvature, etc.) may not be accurate enough or has changed, and therefore decides to start the entire optical adjustment process again, i.e., returns to the step of obtaining the eye image of the wearer by the camera unit. At the same time, this embodiment also sets the curvature calibration times to zero to prevent the curvature calibration times of the current optical adjustment from being accumulated to the next optical adjustment.

[0226] It is understandable that the ultimate purpose of the embodiment is to ensure that the target comfort degree is greater than or equal to the first preset score, thereby reducing the VAC effect of the user when using the head-mounted device. Therefore, in actual application, the biological feature acquisition module can detect the target biological feature data of the wearer in real time or periodically. When the target biological feature data is detected to change, according to actual needs, the latest acquired target biological feature data can be directly input to the optical parameter prediction model to start a new round of optical adjustment, or the scoring module can be triggered to determine the target comfort degree score of the wearer. When the latest determined target comfort degree score is less than the first preset score, the latest acquired target biological feature data is input to the optical parameter prediction model, and a new round of optical adjustment is started.

[0227] In addition, in actual application, the scoring module can also determine the target comfort degree score of the wearer in real time or periodically, and trigger the execution of step S200 to calibrate the curvature of the liquid lens array based on the target comfort degree score when the target comfort degree score is less than the first preset score.

[0228] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the optical adjustment method of the present application. More forms of simple transformation based on this technical concept are within the protection scope of the present application.

[0229] The present application also provides an optical adjustment device, please refer to Figure 4 The optical adjustment device is arranged in a head-mounted device, and the head-mounted device includes a liquid lens array and a scoring module. The scoring module includes at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is used to determine a first comfort degree score based on a target physiological signal of a wearer, and the active scoring unit is used to receive a second comfort degree score input by the wearer. The device includes:

[0230] A scoring module 10 is configured to determine a target comfort degree score of the wearer by the scoring module after adjusting the curvature of the liquid lens array based on a target optical parameter of the head-mounted device. The target optical parameter is predicted by an optical parameter prediction model based on target biological feature data of the wearer. The optical parameter prediction model is trained based on biological feature data as samples and optical parameters as labels. The target comfort degree score is determined based on the first comfort degree score and / or the second comfort degree score.

[0231] A calibration module 20 is configured to calibrate the curvature of the liquid lens array based on the target comfort degree score.

[0232] In an embodiment, the calibration module 20 is further configured to:

[0233] determining whether the comfort score is less than a first preset score;

[0234] in a case where it is determined that the comfort score is less than the first preset score, performing the step of calibrating the curvature of the liquid lens array based on the target comfort score.

[0235] In an embodiment, the calibration module 20 is further configured to:

[0236] calibrating the target optical parameter based on the target comfort score;

[0237] adjusting the control voltage of the liquid lens array according to the calibrated target optical parameter, so as to adjust the curvature of the liquid lens array.

[0238] In an embodiment, the target optical parameter comprises a focal length compensation value, a lens curvature and a dispersion compensation coefficient, and the calibration module 20 is further configured to:

[0239] calculating a curvature correction term of the liquid lens array according to the calibrated focal length compensation value and the calibrated dispersion compensation coefficient;

[0240] adding the calibrated lens curvature and the curvature correction term to obtain a target curvature of the liquid lens array;

[0241] obtaining an initial curvature of the liquid lens array and calculating a curvature difference value between the initial curvature and the target curvature;

[0242] calculating a target voltage of the liquid lens array according to the curvature difference value and a preset curvature-voltage coefficient, and adjusting the control voltage of the liquid lens array according to the target voltage.

[0243] In an embodiment, the head-mounted device further comprises an electrochromic layer, and the calibration module 20 is further configured to:

[0244] calculating a target dispersion of the electrochromic layer according to the calibrated dispersion compensation coefficient;

[0245] adjusting the light transmittance of the electrochromic layer based on the target dispersion and an initial dispersion of the electrochromic layer.

[0246] In an embodiment, the head-mounted device further comprises a biometric acquisition module, the biometric acquisition module comprising a camera unit and an eye movement detection unit, and the device further comprises a prediction module (not shown), the prediction module being configured to:

[0247] obtaining an eye image of the wearer through the camera unit;

[0248] acquire an interpupillary distance and a corneal curvature of the wearer based on the eye image;

[0249] acquire a gaze point coordinate and an eye movement speed of the wearer by the eye movement detection unit;

[0250] input the interpupillary distance, the corneal curvature, the gaze point coordinate and the eye movement speed as target biometric data of the wearer;

[0251] input the target biometric data into the optical parameter prediction model to obtain a target optical parameter of the head-mounted device.

[0252] In an embodiment, the device further comprises an updating module (not shown) configured to:

[0253] determine a loss function of the optical parameter prediction model according to the target comfort score in a case that the target comfort score is less than a first preset score;

[0254] update a trainable parameter of the optical parameter prediction model based on a preset gradient descent algorithm and the loss function.

[0255] In an embodiment, the calibration module 20 is further configured to:

[0256] count a curvature calibration frequency of the liquid lens array;

[0257] return to execute the step of determining the target comfort score of the wearer by the scoring module in a case that the curvature calibration frequency is less than or equal to a preset frequency;

[0258] return to execute the step of acquiring the eye image of the wearer by the camera unit and reset the curvature calibration frequency to zero in a case that the curvature calibration frequency is greater than the preset frequency.

[0259] The optical adjustment device provided in the present application adopts the optical adjustment method in the above embodiments, and can solve the technical problem of how to reduce the VAC effect of the head-mounted device. Compared with the prior art, the optical adjustment device provided in the present application has the same beneficial effects as the optical adjustment method provided in the above embodiments, and other technical features in the optical adjustment device are the same as the features disclosed in the above embodiments, which will not be repeated here.

[0260] The present application provides a head-mounted device, which comprises at least one processor and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the optical adjustment method in the above embodiment one.

[0261] The head-mounted display device in this application embodiment may include, but is not limited to, head-mounted display devices such as Mixed Reality (MR) devices (e.g., MR glasses or MR helmets), Augmented Reality (AR) devices (e.g., AR glasses or AR helmets), Virtual Reality (VR) devices (e.g., VR glasses or VR helmets), Extended Reality (XR) devices, or some combination thereof.

[0262] like Figure 5 As shown, the head-mounted display device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory 1002 or a program loaded from a storage device 1003 into a random access memory 1004. The random access memory 1004 also stores various programs and data required for the operation of the head-mounted display device. The processing unit 1001, the read-only memory 1002, and the random access memory 1004 are interconnected via a bus 1005. An input / output interface 1006 is also connected to the bus. Typically, the following systems can be connected to the input / output interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. The communication device 1009 allows the head-mounted display to communicate wirelessly or wiredly with other devices to exchange data. While head-mounted display devices with various systems are shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0263] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0264] The head-mounted device provided in the present application adopts the optical adjustment method in the above embodiment, and can solve the technical problem of how to reduce the VAC effect of the head-mounted device. Compared with the prior art, the head-mounted device provided in the present application has the same beneficial effects as the optical adjustment method provided in the above embodiment, and other technical features in the head-mounted device are the same as the features disclosed in the previous embodiment method, which will not be described here.

[0265] It should be understood that parts of the present application can be realized by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0266] The above describes only the specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0267] The present application provides a computer-readable storage medium having stored thereon computer-readable program instructions (i.e., computer programs) for executing the optical adjustment method in the above embodiment.

[0268] The computer-readable storage medium provided in the present application may, for example, be a U disk, but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system or device, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more conductive wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, a system or a device. The program code contained on the computer-readable storage medium can be transmitted by any suitable medium, including but not limited to: an electrical wire, an optical cable, an RF (Radio Frequency: radio frequency), etc., or any suitable combination thereof.

[0269] The computer readable storage medium can be included in the head-mounted device, or can exist separately and not be assembled into the head-mounted device.

[0270] The computer readable storage medium carries one or more programs, when the one or more programs are executed by the head-mounted device, the head-mounted device is caused to: determine a target comfort score of the wearer by the scoring module after adjusting the curvature of the liquid lens array based on the target optical parameters of the head-mounted device, wherein the target optical parameters are predicted by an optical parameter prediction model based on target biological feature data of the wearer, the optical parameter prediction model is trained with biological feature data as samples and optical parameters as labels, and the target comfort score is determined based on the first comfort score and / or the second comfort score; and calibrate the curvature of the liquid lens array based on the target comfort score.

[0271] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0272] The flow and block diagrams in the drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flow and block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or the blocks may be executed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0273] The modules involved in the embodiments of the present application can be implemented in software or in hardware. In some cases, the names of the modules do not limit the modules themselves.

[0274] The readable storage medium provided by the present application is a computer readable storage medium, which stores computer readable program instructions (i.e., a computer program) for executing the optical adjustment method described above, and can solve the technical problem of how to reduce the VAC effect of the head-mounted device. Compared with the prior art, the computer readable storage medium provided by the present application has the same beneficial effects as the optical adjustment method provided by the above embodiments, which will not be repeated here.

[0275] The present application also provides a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the optical adjustment method as described above.

[0276] The computer program product provided by the present application can solve the technical problem of how to reduce the VAC effect of the head-mounted device. Compared with the prior art, the computer program product provided by the present application has the same beneficial effects as the optical adjustment method provided by the above embodiments, which will not be repeated here.

[0277] The above only describes some embodiments of the present application, and does not limit the patent scope of the present application. Any equivalent structural transformation made by using the content of the present application specification and drawings, or direct / indirect application in other related technical fields is included in the patent protection scope of the present application.

Claims

1. An optical adjustment method, characterized in that, The optical adjustment method is applied to a head-mounted display device, which includes a liquid lens array and a scoring module. The scoring module includes at least one of an automatic scoring unit and an active scoring unit. The automatic scoring unit is used to determine a first comfort score based on the wearer's target physiological signals, and the active scoring unit is used to receive a second comfort score input by the wearer. The method includes: After adjusting the curvature of the liquid lens array based on the target optical parameters of the head-mounted display device, the target comfort score of the wearer is determined by the scoring module. The target optical parameters are predicted by the optical parameter prediction model based on the wearer's target biometric data. The optical parameter prediction model is trained using biometric data as samples and optical parameters as labels. The target comfort score is determined based on the first comfort score and / or the second comfort score. Based on the target comfort score, the curvature of the liquid lens array is calibrated; The head-mounted display device further includes a biometric acquisition module, which includes a camera unit and an eye-tracking detection unit. Prior to the step of adjusting the curvature of the liquid lens array based on the target optical parameters of the head-mounted display device, the method further includes: The camera unit acquires an image of the wearer's eyeball; Based on the eye image, the wearer's interpupillary distance and corneal curvature are obtained; The eye movement detection unit obtains the wearer's gaze coordinates and eye movement velocity. The interpupillary distance, corneal curvature, fixation point coordinates, and eye movement velocity are used as the target biometric data of the wearer; Obtain multiple historical reference scores according to the preset window; The historical score is obtained by averaging the historical reference scores. Historical scores and target biometric data are input into the optical parameter prediction model to obtain the target optical parameters of the head-mounted display device.

2. The optical adjustment method as described in claim 1, characterized in that, Before the step of calibrating the curvature of the liquid lens array based on the target comfort score, the method further includes: Determine whether the comfort score is less than the first preset score; If the comfort score is determined to be less than the first preset score, the step of calibrating the curvature of the liquid lens array based on the target comfort score is performed.

3. The optical adjustment method as described in claim 2, characterized in that, The step of calibrating the curvature of the liquid lens array based on the target comfort score includes: Based on the target comfort score, calibrate the target optical parameters; Based on the calibrated target optical parameters, the control voltage of the liquid lens array is adjusted to adjust the curvature of the liquid lens array.

4. The optical adjustment method as described in claim 3, characterized in that, The target optical parameters include focal length compensation value, lens curvature, and dispersion compensation coefficient. The step of adjusting the control voltage of the liquid lens array according to the calibrated target optical parameters includes: The curvature correction term of the liquid lens array is calculated based on the calibrated focal length compensation value and the calibrated dispersion compensation coefficient. The calibrated lens curvature is added to the curvature correction term to obtain the target curvature of the liquid lens array; The initial curvature of the liquid lens array is obtained, and the curvature difference between the initial curvature and the target curvature is calculated. The target voltage of the liquid lens array is calculated based on the curvature difference and the preset curvature-voltage coefficient, and the control voltage of the liquid lens array is adjusted according to the target voltage.

5. The optical adjustment method as described in claim 4, characterized in that, The head-mounted display device further includes an electrochromic layer, and after the step of calibrating the target optical parameters based on the target comfort score, the method further includes: The target dispersion of the electrochromic layer is calculated based on the calibrated dispersion compensation coefficient. The transmittance of the electrochromic layer is adjusted based on the target dispersion and the initial dispersion of the electrochromic layer.

6. The optical adjustment method according to any one of claims 1 to 5, characterized in that, The method further includes: If the target comfort score is less than a first preset score, the loss function of the optical parameter prediction model is determined based on the target comfort score. The trainable parameters of the optical parameter prediction model are updated based on the preset gradient descent algorithm and the loss function.

7. The optical adjustment method as described in claim 6, characterized in that, After the step of calibrating the curvature of the liquid lens array based on the target comfort score, the method further includes: The number of curvature calibrations performed on the liquid lens array is counted. If the number of curvature calibrations is less than or equal to the preset number, return to the step of determining the wearer's target comfort score through the scoring module; If the number of curvature calibrations exceeds the preset number, return to the step of acquiring the wearer's eye image through the camera unit, and set the number of curvature calibrations to zero.

8. An optical adjustment device, characterized in that, The device is disposed on a head-mounted display device, the head-mounted display device including a liquid lens array and a scoring module, the scoring module including at least one of an automatic scoring unit and an active scoring unit, wherein the automatic scoring unit is used to determine a first comfort score based on the wearer's target physiological signals, and the active scoring unit is used to receive a second comfort score input by the wearer, the device comprising: The scoring module is used to determine the wearer's target comfort score after adjusting the curvature of the liquid lens array based on the target optical parameters of the head-mounted display device. The target optical parameters are predicted by an optical parameter prediction model based on the wearer's target biometric data. The optical parameter prediction model is trained using biometric data as samples and optical parameters as labels. The target comfort score is determined based on the first comfort score and / or the second comfort score. A calibration module is used to calibrate the curvature of the liquid lens array based on the target comfort score; The head-mounted display device further includes a biometric acquisition module, which comprises a camera unit and an eye-tracking detection unit. The device is also used for: The camera unit acquires an image of the wearer's eyeball; Based on the eye image, the wearer's interpupillary distance and corneal curvature are obtained; The eye movement detection unit obtains the wearer's gaze coordinates and eye movement velocity. The interpupillary distance, corneal curvature, fixation point coordinates, and eye movement velocity are used as the target biometric data of the wearer; Obtain multiple historical reference scores according to the preset window; The historical score is obtained by averaging the historical reference scores. Historical scores and target biometric data are input into the optical parameter prediction model to obtain the target optical parameters of the head-mounted display device.

9. A head-mounted display device, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the optical adjustment method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the optical adjustment method as described in any one of claims 1 to 7.

Images