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, relates to the technical field of head-mounted devices, and the optical adjustment method is applied to a head-mounted device, the head-mounted device comprising a biological feature acquisition module and a zoom module; the method comprises the following steps: acquiring target biological feature data of a wearer through the biological feature acquisition module; inputting the target biological feature data into a preset optical parameter prediction neural network model to obtain target optical parameters, wherein the optical parameter prediction neural network model is trained by taking biological feature data as samples, and the optical parameters are labels; and adjusting the working parameters of the zoom module based on the target optical parameters. The application enables the head-mounted device to adapt to the biological feature data of different users, automatically adjusts the working parameters of the head-mounted device, and reduces 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 device (hereinafter referred to as "head-mounted display device") technology, the optimization of the performance and user experience of the head-mounted display device 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 vergence-accommodation conflict (VAC) effect. The VAC 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 biological feature acquisition module and an electrochromic layer; the method comprising:

[0008] acquiring target biological feature data of a wearer through the biological feature acquisition module;

[0009] inputting the target biological feature data into a preset optical parameter prediction neural network model to predict target optical parameters, wherein the optical parameter prediction neural network model is trained with biological feature data as samples and optical parameters as labels, and the target optical parameters comprise a dispersion compensation coefficient;

[0010] adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficient.

[0011] In an embodiment, the target optical parameter comprises a focal length compensation value and a lens curvature; and the head-mounted device further comprises a liquid lens array;

[0012] After the step of inputting the target biometric data into the preset optical parameter prediction neural network model to predict the target optical parameter, the method further comprises:

[0013] calculating a target curvature of the liquid lens array based on the focal length compensation value, the lens curvature, and the dispersion compensation coefficient;

[0014] adjusting a control voltage of the liquid lens array based on the target curvature and an initial curvature of the liquid lens array to adjust the curvature of the liquid lens array.

[0015] In an embodiment, the head-mounted device further comprises a score obtaining module;

[0016] After the step of adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficient, the method further comprises:

[0017] obtaining a comfort score through the score obtaining module;

[0018] updating the optical parameter prediction neural network model based on the comfort score, and returning to the step of inputting the target biometric data into the preset optical parameter prediction neural network model.

[0019] In an embodiment, the score obtaining module comprises a physiological signal acquisition unit;

[0020] The step of obtaining a comfort score through the score obtaining module comprises:

[0021] acquiring a target physiological signal of the wearer through the physiological signal acquisition unit;

[0022] determining a comfort score corresponding to the target physiological signal according to a preset mapping relationship.

[0023] In an embodiment, the optical parameter prediction neural network model is a GAN model, and the step of updating the optical parameter prediction neural network model based on the comfort score comprises:

[0024] determining a loss function of the GAN model according to the comfort score;

[0025] updating a weight of a generator in the GAN model based on a preset gradient descent algorithm and the loss function.

[0026] In an embodiment, the step of adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficient comprises:

[0027] calculating a target dispersion of the electrochromic layer based on the dispersion compensation coefficient;

[0028] adjusting the light transmittance based on the target dispersion and an initial dispersion of the electrochromic layer.

[0029] In an embodiment, the biometric feature acquisition module comprises: a camera unit and an eye movement detection unit; and the target biometric feature data comprises: interpupillary distance, corneal curvature, gaze point coordinates and eye movement speed.

[0030] The step of acquiring target biometric feature data of a wearer by the biometric feature acquisition module comprises:

[0031] acquiring an eyeball image of the wearer by the camera unit;

[0032] acquiring interpupillary distance and corneal curvature of the wearer based on the eyeball image;

[0033] acquiring gaze point coordinates and eye movement speed of the wearer by the eye movement detection unit.

[0034] In addition, to achieve the above-mentioned purpose, the present application also proposes an optical adjustment device, which is arranged in a head-mounted device, and the head-mounted device comprises: a biometric feature acquisition module and an electrochromic layer; and the optical adjustment device comprises:

[0035] an acquisition module, configured to acquire target biometric feature data of a wearer by the biometric feature acquisition module;

[0036] an inference module, configured to input the target biometric feature data into a preset optical parameter prediction neural network model to predict target optical parameters, wherein the optical parameter prediction neural network model is trained based on biometric feature data as samples and optical parameters as labels, and the target optical parameters comprise a dispersion compensation coefficient;

[0037] an adjustment module, configured to adjust the light transmittance of the electrochromic layer based on the dispersion compensation coefficient.

[0038] In addition, to achieve the above-mentioned purpose, the present application also proposes a head-mounted device, which comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the optical adjustment method as described above.

[0039] In addition, to achieve the above object, the present application also provides a storage medium, which is a computer readable storage medium, and a computer program is stored on the storage medium, and the computer program realizes the steps of the optical adjustment method when executed by a processor.

[0040] In addition, to achieve the above object, the present application also provides a computer program product, which comprises a computer program, and the computer program realizes the steps of the optical adjustment method when executed by a processor.

[0041] The present application provides an optical adjustment method, which collects target biological feature data of a wearer through a biological feature acquisition module carried by a head-mounted device, inputs the target biological feature data into an optical parameter prediction neural network model trained in advance with biological feature data as samples and optical parameters as labels, obtains target optical parameters output by the optical parameter prediction neural network model, and the target optical parameters include a dispersion compensation coefficient, and adjusts the light transmittance of an electrochromic layer of the head-mounted device based on the dispersion compensation coefficient, so that the optical parameters of the head-mounted device adapt to the biological feature data of the user and reduce the VAC effect of the head-mounted device.

[0042] In summary, the present application uses a pre-trained optical parameter prediction neural network model to predict optical parameters that adapt to the biological feature data of the user based on the collected target biological feature data of the wearer, and then adjusts the light transmittance of the electrochromic layer of the head-mounted device based on the dispersion compensation coefficient in the optical parameters, which is more adaptive to the biological feature data of different users and automatically adjusts the light transmittance of the head-mounted device, thereby reducing the VAC effect of the head-mounted device. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings, which are incorporated into and form part of the specification, illustrate embodiments consistent with the present application and, together with the specification, serve to explain the principles of the application.

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows, and obviously, other drawings can also be obtained by those skilled in the art without creative labor.

[0045] Figure 1 The flowchart provided for the optical adjustment method embodiment one of the present application;

[0046] Figure 2 The structure diagram of the head-mounted device related to the optical adjustment method embodiment of the present application;

[0047] Figure 3 The flowchart provided for the second embodiment of the optical adjustment method of the present application;

[0048] Figure 4 The module structure diagram of the optical adjustment device of the embodiment of the present application;

[0049] Figure 5 The device structure diagram of the hardware operating environment involved in the optical adjustment method in the embodiment of the present application.

[0050] The purpose implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] 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.

[0052] In order to better understand the technical solutions of the present application, the specific embodiments will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] The main solution of the embodiment of the present application is: collecting target biological feature data of the wearer through the biological feature acquisition module; inputting the target biological feature data into a preset optical parameter prediction neural network model to predict target optical parameters, wherein the optical parameter prediction neural network model is trained with biological feature data as samples and optical parameters as labels, and the target optical parameters include dispersion compensation coefficients; adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficients.

[0054] With the rapid development of head-mounted display device (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.

[0055] 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 vergence adjustment conflict (VAC effect). VAC effect refers to the phenomenon 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.

[0056] 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.

[0057] To solve the above problems, the optical adjustment method is provided. The target biological feature data of the wearer is collected by the biological feature collection module carried by the head-mounted device, and the target biological feature data is input into the optical parameter prediction neural network model trained in advance with biological feature data as a sample and optical parameters as labels, to obtain the target optical parameters output by the optical parameter prediction neural network model. The target optical parameters include a dispersion compensation coefficient, and the transmittance of the electrochromic layer of the head-mounted device is adjusted based on the dispersion compensation coefficient, so that the optical parameters of the head-mounted device adapt to the biological feature data of the user, and the VAC effect of the head-mounted device is reduced.

[0058] In summary, the optical parameter prediction neural network model is pre-trained, the target biological feature data of the wearer is collected, the optical parameters suitable for the biological feature data of the user are predicted, and then the transmittance of the electrochromic layer of the head-mounted device is adjusted based on the dispersion compensation coefficient in the optical parameters. Compared with the fixed transmittance or the manual adjustment of the transmittance by the user, the head-mounted device can adapt to the biological feature data of different users, and the transmittance of the head-mounted device is automatically adjusted, thereby reducing the VAC effect of the head-mounted device.

[0059] The head-mounted device in the embodiment 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 embodiment, for ease of description, the head-mounted device is taken as the execution subject for description.

[0060] Based on this, the optical adjustment method is provided in the embodiment of the present application, which is described in detail with reference to Figure 1 , Figure 1 The flowchart of the first embodiment of the optical adjustment method of the present application is shown in the figure.

[0061] In the embodiment, the optical adjustment method is applied to the head-mounted device, and the head-mounted device includes a biological feature collection module and an electrochromic layer. The optical adjustment method includes steps S10-S30:

[0062] Step S10, collecting target biological feature data of the wearer by the biological feature collection module;

[0063] It should be noted that in the present embodiment, the head-mounted device (i.e. head-mounted display device) mainly applies AR (Augmented Reality) technology, VR (Virtual Reality) technology and MR (Mixed Reality) technology, based on which the user can display virtual content in a real scene through various sensors, cameras or optical components in the device when wearing the head-mounted device.

[0064] It should be noted that in the present embodiment, the biological collection module can include a plurality of hardware modules for collecting different types of biological feature data, and has a biological feature recognition capability for collecting a plurality of key biological feature data of the wearer.

[0065] In the present embodiment, the biological feature collection module built-in the head-mounted device automatically starts the biological feature data collection function after detecting that the user correctly wears the head-mounted device, and collects the biological feature data of the wearer related to the VAC effect, including but not limited to inter-pupillary distance (IPD), corneal curvature, iris texture, etc.

[0066] Further, in a feasible implementation, the biological feature collection module includes a camera unit and an eye movement detection unit; the target biological feature data includes inter-pupillary distance, corneal curvature, gaze point coordinates and eye movement speed; and the step S10 can include steps S11-S15.

[0067] Step S11, acquiring the eye image of the wearer through the camera unit;

[0068] It should be noted that in the present embodiment, the biological feature collection module can include a camera unit and an eye movement detection unit, the camera unit can be used to collect the eye image of the wearer, and the eye movement detection unit can be used to track the eye movement data of the wearer.

[0069] 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 eye images can be obtained under different lighting conditions. During the collection process, the camera unit will capture the eye region of the wearer in real time to obtain a series of continuous eye image frames, which contain the appearance information of the eye, such as pupil size, position, corneal shape and other features.

[0070] Step S12, acquiring the inter-pupillary distance and corneal curvature of the wearer based on the eye image;

[0071] In this embodiment, after the image of the eyeball is collected, the image processing algorithm built in the head-mounted device analyzes and processes the image of the eyeball collected by the camera unit to extract biological feature data such as the interpupillary distance and the corneal curvature.

[0072] As an example, specifically, the algorithm first pre-processes the image of the eyeball, including image enhancement, edge detection and the like, to highlight the outline of the pupil, and then determines the center positions of the two pupils by image recognition technology and calculates the distance between them, that is, the interpupillary distance.

[0073] Then, the corneal curvature radius is calculated by using the corneal shape information in the image of the eyeball, 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.

[0074] Step S13, obtaining the gaze point coordinates and eye movement speed of the wearer through the eye movement detection unit;

[0075] 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, that is, 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 the like.

[0076] In addition, the historical comfort scores of the user can also be included in the input model data, specifically including:

[0077] Step S14, obtaining a plurality of historical reference scores according to a preset window;

[0078] In this embodiment, in order to more comprehensively evaluate the comfort feeling of the wearer, the head-mounted device also considers the historical use data of the wearer, obtains 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.). These historical reference scores can be the comfort scores obtained by the scoring acquisition module when the wearer uses the head-mounted device in the past, or the scores automatically calculated by the device according to other indicators (such as the use time, the number of abnormal exits, etc.).

[0079] Step S15, calculating the average value of each historical reference score to obtain a historical score.

[0080] In this embodiment, after obtaining a plurality of historical reference scores, the head-mounted device averages the scores to obtain a comprehensive historical score, which reflects the overall comfort feeling of the wearer to the head-mounted device in the past period of time. This score can be used as an important reference for current comfort assessment, helping the device to better understand the needs and preferences of the wearer.

[0081] In a further example, referring to Figure 2 , the head-mounted device is provided with left and right infrared cameras, left and right iris cameras, and in the zoom module, there are liquid lens arrays for adjusting focal length and electrochromic layers for adjusting light transmittance. Through the left and right lenses and the display screen, the user can see the virtual image through the head-mounted device. Adjusting the optical parameters is to let the user focus from virtual image A to virtual image B through the head-mounted device, and vice versa. Based on this hardware structure, when collecting biometric data, the head-mounted device can measure the interpupillary distance of the wearer through the infrared camera at a sampling rate of 60Hz, then store the IPD data in CSV format in the storage space of the head-mounted device, and collect the eye image of the wearer through the iris camera, identify the deformation features on the surface of the wearer's eye, measure the radius of curvature of the wearer's cornea based on the deformation features, store the collected corneal curvature image in JPEG format with a fixed size of 640*480 pixels in the storage space, and facilitate image processing. Then measure the fixation point coordinates and eye movement speed of the wearer through the eye tracker at a sampling rate of 120Hz, and store the detected data in JSON format.

[0082] After collecting the biometric data of the wearer, in order to facilitate subsequent model analysis and calculation, the biometric data also needs to be preprocessed. First, the abnormal values in the biometric data are removed, for example, when the IPD does not meet the range of 54mm to 74mm, 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 stamps of the collected data are used to synchronize the multi-modal data, and finally the 10-dimensional feature vector is output , where X IPD : IPD data; X cornea : corneal curvature data; X gaze : fixation point coordinates, the 10-dimensional feature vector includes:

[0083] 1) interpupillary distance (IPD, 1 dimension);

[0084] 2) corneal curvature (3 dimensions, extracted from iris image features);

[0085] 3) Gaze point coordinates (2D, x / y axis normalized values);

[0086] 4) Eye movement velocity (2D, horizontal / vertical components);

[0087] 5) User historical comfort score (2D, sliding average of the last 5 scores).

[0088] Step S20, input the target biological feature data into the preset optical parameter prediction neural network model, and predict the target optical parameter, wherein the optical parameter prediction neural network model is trained by taking the biological feature data as a sample and the optical parameter as a label, and the target optical parameter includes a dispersion compensation coefficient;

[0089] It should be noted that in the present embodiment, the head-mounted device is pre-integrated with 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 as a basic neural network model, and a large number of trained optical parameter prediction neural network models. The optical parameter prediction neural network model uses a large number of biological feature data of different age, gender and other populations, and its corresponding sample data set of the best wearing experience and the best visual comfort degree of the current biological signal corresponding to the optical parameter, through a deep learning algorithm, the optical parameter prediction neural network model can learn the complex mapping relationship between the biological feature data and the optical parameter.

[0090] In the present embodiment, the head-mounted device inputs the collected target biological feature data as input data into the preset optical parameter prediction neural network model, the optical parameter prediction neural network model quickly processes and analyzes the input target biological feature data, and uses the learned mapping relationship to predict and output the target optical parameter matched with the wearer's biological feature data. These target optical parameters directly determine the VAC effect of the head-mounted device.

[0091] The optical parameter prediction neural network 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.

[0092] As an example, the optical parameter prediction neural network can be a GAN (Generative Adversarial Network) model, which includes a generator and a discriminator. The generator (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 d).

[0093] The discriminator (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.

[0094] In step S30, the transmittance of the electrochromic layer is adjusted based on the dispersion compensation coefficient.

[0095] It should be noted that in the present embodiment, electrochromism is a phenomenon in which the optical properties (reflectivity, transmittance, absorptivity, etc.) of a material change stably and reversibly under the action of an applied electric field, and in appearance, it shows reversible changes in color and transparency. By setting an electrochromic layer, the transmittance of the head-mounted device eye lens can be adjusted, thereby reducing the VAC effect of the head-mounted device.

[0096] In the present embodiment, after the head-mounted device receives the target optical parameters output by the optical parameter prediction neural network model, the transmittance of the electrochromic layer is immediately adjusted.

[0097] In this embodiment, the head-mounted device adjusts the light transmittance of the electrochromic layer in real time through electronic control means according to the received target optical parameters. In addition, other hardware parameters can also be adjusted synchronously to adjust the optical parameters of the head-mounted device, for example, the focal length is changed by fine-tuning the thickness, position or angle of the lens, the aberration is corrected by adjusting the distance between the lens groups, etc. After the adjustment is completed, the optical parameters of the head-mounted device will be automatically optimized to the best state matched with the biological feature data of the wearer.

[0098] Further, in a possible implementation, the target optical parameters include a focal length compensation value and a lens curvature; the head-mounted device further includes a liquid lens array; and the step S30 can include steps S31-S32.

[0099] In step S31, the target curvature of the liquid lens array is calculated based on the focal length compensation value, the lens curvature and a dispersion compensation coefficient.

[0100] It should be noted that in this embodiment, the liquid lens can change the interface curvature of the liquid through the electro-wetting effect, hydraulic pressure or air pressure, so as to adjust the focal length or the light path. A common design includes two immiscible liquids (such as conductive aqueous solution and insulating oil), which can change the contact angle through voltage to achieve fast zooming.

[0101] 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 difference in biological features of the wearer (such as the difference in interpupillary distance and corneal curvature), so as to ensure clear and accurate imaging.

[0102] The lens curvature is used to describe the bending degree of the liquid lens array, and different curvatures will affect the refraction and focusing effect of light.

[0103] The dispersion compensation coefficient is used to correct the dispersion phenomenon caused by the difference in refractive index of different color light, reduce the chromatic aberration, and improve the color restoration degree and clarity of the image.

[0104] In this embodiment, the target curvature of the lens in the zoom module can be calculated according to the focal length compensation value, the lens curvature and the dispersion compensation coefficient output by the model.

[0105] In step S32, the control voltage of the liquid lens array is adjusted based on the target curvature and the initial curvature of the liquid lens array, so as to adjust the curvature of the liquid lens array.

[0106] In this embodiment, before the working parameter adjustment is performed, the initial curvature of the zoom module needs to be obtained, the target curvature is compared with the initial curvature, the curvature deviation is calculated, and then the value of the control voltage can be calculated according to the curvature-voltage coefficient. The curvature of the lens can be adjusted to the target curvature by adjusting the control voltage to the value.

[0107] Specifically, the radius of curvature R of the liquid lens is strictly related to the focal length f by the thin lens formula:

[0108] ;

[0109] wherein:

[0110] n: refractive index of the liquid lens material (e.g. PDMS, n = 1.43);

[0111] R fix : radius of the fixed curvature surface (determined by the mechanical structure of the lens, e.g. R fix = 500 pm);

[0112] R: dynamically adjusted radius of curvature;

[0113] The relationship between the radius of curvature R of the liquid lens and the driving voltage V is:

[0114] ;

[0115] wherein:

[0116] R: target radius of curvature (pm), calculated from the curvature parameter in the generator output Y.

[0117] V: driving voltage (V), target value to be solved.

[0118] k: lens material characteristic constant (e.g. dielectric elastomer k = 0.12 pm / V2).

[0119] , : vacuum permittivity (8.85e-12 F / m) and material relative permittivity (e.g. PDMS = 2.8).

[0120] d: electrode spacing (pm, default 50 pm).

[0121] R0: zero-voltage radius of curvature (determined by the initial shape of the lens, usually 200 pm).

[0122] In one possible implementation, the setting position of the liquid lens array can also be adjusted based on the collected IPD.

[0123] In this embodiment, the liquid lens array can be arranged in a set of mechanical structures that can adjust the position respectively. After the IPD of the wearer is collected, the setting position of the liquid lens array is adjusted according to the IPD, so that the visual center of the liquid lens array matches the IPD, further adapting to the physiological data of the user.

[0124] Further, in a possible implementation, the step S30 can further include steps S33-S34.

[0125] In step S33, the target dispersion of the electrochromic layer is calculated based on the dispersion compensation coefficient.

[0126] 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.

[0127] In this embodiment, according to the dispersion compensation coefficient, the target dispersion to which the zoom module needs to be adjusted at present can be calculated.

[0128] In step S34, the transmittance is adjusted based on the target dispersion and the initial dispersion of the electrochromic layer.

[0129] In this embodiment, according to the target dispersion to which the zoom module 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.

[0130] Specifically, the formula for dynamically adjusting the transmittance based on the dispersion compensation coefficient is as follows:

[0131]

[0132] α: material characteristic constant;

[0133] T: transmittance;

[0134] δ: dispersion compensation coefficient

[0135] T0: initial transmittance.

[0136] By collecting the target biological feature data of the wearer and using the optical parameter prediction neural network model to predict the optical parameters suitable for the user's biological feature data, the head-mounted device can achieve personalized optical adaptation, which ensures that each user can obtain the best wearing experience regardless of their biological feature data. Traditional head-mounted devices often have difficulty achieving precise matching due to the use of fixed focal length or manual adjustment methods, resulting in a significant increase in visual vergence accommodation conflict (VAC effect), while the optical adjustment method of the present application automatically adjusts the working parameters of the zoom module, so that the optical parameters of the head-mounted device can adapt to the user's biological feature data, 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.

[0137] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as the above-mentioned embodiment one can refer to the above introduction, and the subsequent will not be described in detail. On this basis, please refer to Figure 3 Figure 3 The flowchart of the second embodiment of the optical adjustment method of the present application is shown.

[0138] In this embodiment, the head-mounted device further comprises a score acquisition module; after the step S30, the method further comprises steps S40-S50:

[0139] Step S40, obtaining the comfort score through the score acquisition module;

[0140] In this embodiment, after the head-mounted device adjusts the working parameters of the zoom module based on the target optical parameters, the score acquisition module built-in the device is used to monitor and evaluate the comfort feeling of the wearer in real time. Specifically, the score acquisition module can obtain the comfort score in various ways, for example:

[0141] Physiological index monitoring: using the built-in sensor to monitor the heart rate, skin electrical response and other physiological indexes of the wearer, which can reflect the tension and comfort state of the wearer.

[0142] Visual feedback collection: through the camera or eye tracking technology built-in the head-mounted device, the blinking frequency, visual line movement and other visual behaviors of the wearer are observed to infer the visual comfort.

[0143] User active feedback: providing comfort score options on the user interface of the head-mounted device, allowing the wearer to score according to his subjective feeling.

[0144] The score acquisition module integrates the above information and calculates the current comfort score through a preset algorithm, which reflects the wearer's satisfaction and comfort feeling of the current optical parameters of the head-mounted device.

[0145] Further, in a feasible implementation, the score acquisition module comprises a physiological signal acquisition unit;

[0146] The above-mentioned step S40 can comprise steps S41-S42:

[0147] Step S41, acquiring the target physiological signal of the wearer through the physiological signal acquisition unit;

[0148] In this embodiment, when feedback detection is performed, the physiological signal acquisition unit acquires the target physiological signal of the wearer.

[0149] Step S42, determining the comfort score corresponding to the target physiological signal according to a preset mapping relationship.​

[0150] In the present embodiment, in the head-mounted device, a mapping relationship between physiological signals and comfort scores is pre-stored, and according to the mapping relationship, the comfort score corresponding to the target physiological signal can be determined.

[0151] Specifically, when evaluating the comfort of the user of the head-mounted device, the entire evaluation process integrates interactive technology, biological signal detection technology, and data analysis algorithms. Specifically, in the process of obtaining the comfort score, the head-mounted device first displays an interactive interface in the form of a graphical user interface (GUI) through its built-in display module, guiding the user to complete a series of preset operation tasks or evaluation processes. At the same time, in order to enhance the diversity and convenience of the interaction, the device is also equipped with a speaker system, which can output voice prompts to guide the user on how to submit their subjective score of the current use experience through voice commands, touch clicks, or other preset interaction methods (such as gesture recognition, eye tracking triggering, etc.).

[0152] At the same time of the interaction between the user and the device, the head-mounted device collects the physiological data of the user in a non-invasive manner. On the one hand, the device measures the change in skin conductivity around the user's eyes through the bioelectricity detection sensors integrated around the eyes or the frame. Skin conductivity is a sensitive indicator of sympathetic nervous system activity, and its change is closely related to the emotional state, cognitive load, and physiological stress response of the individual. When the user experiences visual fatigue, motion sickness, or accommodation conflict during the use of the head-mounted device, the excitability of the sympathetic nervous system will significantly increase, prompting the sweat glands to increase secretion, thereby causing the skin conductivity to rise. On the contrary, in a comfortable use state, the activity of the parasympathetic nervous system dominates, and the skin conductivity tends to be stable or decrease. Therefore, by continuously monitoring the change in skin conductivity, the comfort level of the user can be objectively reflected.

[0153] On the other hand, the head-mounted device also uses the built-in camera or special eye tracking sensors to monitor the user's blinking behavior in real time. Blinking frequency and eye closure time are another important indicator reflecting the user's physiological state and cognitive load, and their changes can also reveal the user's comfort level. Specifically, when the user feels uncomfortable or tired, the blinking frequency often increases, and the eye closure time may also be extended, which are natural reactions of the body to alleviate discomfort by reducing visual stimulation.

[0154] Based on the above-mentioned user biological signals obtained from skin conductivity and blinking behavior, the head-mounted device can use an algorithm model for comprehensive analysis, thereby objectively and accurately evaluating the comfort of the user. In order to further improve the accuracy and reliability of the evaluation, the device will also collect scoring data from multiple data sources, at multiple different time points or in different use scenarios, and use a weighted average method to integrate and process these data, and finally obtain a comprehensive and objective comfort score.

[0155] Further, in a specific embodiment, the target physiological signal includes: blink frequency and skin conductivity; and the step S42 can include:

[0156] determining a first comfort score corresponding to the blink frequency and a second comfort score corresponding to the skin conductivity according to a preset mapping relationship, wherein the blink frequency is negatively correlated with the first comfort score, and the skin conductivity is negatively correlated with the second comfort score;

[0157] performing weighted calculation on the first comfort score and the second comfort score according to a preset weight to obtain the comfort score.

[0158] It should be noted that, under normal circumstances, when a person is in a relaxed and comfortable state, the blink frequency is relatively stable and natural. When the head-mounted device is worn and discomfort such as visual fatigue and dry eyes occurs, the wearer will unconsciously increase the blink frequency in an attempt to alleviate the discomfort. Therefore, the higher the blink frequency, the lower the comfort of the wearer, and the lower the first comfort score. Similarly, the skin conductivity reflects the electrical properties of the skin. When the human body is in a state of tension, anxiety or discomfort, the sweat glands of the skin will increase secretion, causing the skin conductivity to rise. When the head-mounted device is worn and discomfort occurs, the wearer's mood may be affected, thereby causing changes in skin conductivity. Therefore, the higher the skin conductivity, the lower the comfort of the wearer, and the lower the second comfort score.

[0159] In this embodiment, through a large amount of experimental data collection and analysis, a mapping relationship between the blink frequency and the first comfort score can be established. For example, different blink frequency intervals are set to correspond to different first comfort scores. When the blink frequency is in a lower interval, the first comfort score is higher. As the blink frequency increases, the first comfort score gradually decreases. Similarly, based on experimental data, a mapping relationship between the skin conductivity and the second comfort score is established. Different skin conductivity thresholds or ranges are set to correspond to different second comfort scores. The lower the skin conductivity value, the higher the second comfort score. The higher the skin conductivity value, the lower the second comfort score.

[0160] In this embodiment, the first comfort score and the second comfort score are weighted and calculated according to preset weights, so as to obtain a final comfort score, and the preset weights are determined according to the importance of the blink frequency and the skin conductance in reflecting the comfort of the wearer.

[0161] By comprehensively considering the two physiological signals of blink frequency and skin conductance, determining the corresponding comfort scores respectively, and then performing weighted calculation, the comfort state of the wearer can be more comprehensively and accurately evaluated. Compared with the single physiological signal evaluation method, this method can reduce the errors caused by individual differences and signal fluctuations, and improve the reliability of the comfort score.

[0162] In step S50, the optical parameter prediction neural network model is updated based on the comfort score, and the step of inputting the target biological feature data into the preset optical parameter prediction neural network model is returned.

[0163] In this embodiment, after the head-mounted device obtains the comfort score, the score is analyzed and processed. If the analysis result shows that the comfort score is at a high level, it indicates that the target optical parameters generated by the current optical parameter prediction neural network model are highly matched with the actual needs of the wearer, not only achieving the user's expectations in visual effect, but also not causing obvious discomfort in a long wearing process. This positive feedback shows that the model has high accuracy in capturing subtle differences in user biological features and predicting optimal optical parameters. Therefore, the device will further take action to store the biological feature data (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 user given comfort score collected this time as a complete training sample in the database of the device. These samples not only record the user's current physiological state and preferences, but also reflect the output performance of the optical parameter prediction neural network model under specific conditions, providing data resources for the continuous optimization of the subsequent model.

[0164] Subsequently, these newly added training samples will be included in the training process of the optical parameter prediction neural network model, and the parameters and structure of the optical parameter prediction neural network model will be continuously iteratively optimized through machine learning algorithms, so as to more accurately predict and generate target optical parameters that meet the individual needs of users in future use. This process is a cycle of continuous improvement, aiming to continuously improve the comfort and performance of the head-mounted device.

[0165] Conversely, if the analysis result shows a lower comfort score, it indicates that the current optical parameter prediction neural network model has deviations in outputting the target optical parameters, and fails to fully consider the differences in biological characteristics of the wearer or the needs in specific use scenarios. The device will use this time's comfort score as direct feedback signal, and propagate it back to each layer of the optical parameter prediction neural network model, so as to guide it to learn a more accurate and more user-demand-oriented optical parameter generation strategy by adjusting the weight parameters of each layer of the neural network.

[0166] After adjusting the weight parameters of the optical parameter prediction neural network model, the device will re-collect the target biological characteristic data of the wearer and input it into the updated optical parameter prediction neural network model, so as to generate new target optical parameters. This process may be repeated for several times until the newly generated optical parameters can obtain a higher comfort score in subsequent user tests. Through this continuous iteration and optimization, the head-mounted device can gradually approach the limit of user demand and provide more comfortable and more personalized visual experience for the user.

[0167] Further, in a feasible implementation, the optical parameter prediction neural network model is a GAN model, and the step of updating the optical parameter prediction neural network model based on the comfort score in step S50 can include steps S51-S52:

[0168] Step S51, determining the loss function of the GAN model according to the comfort score;

[0169] In this embodiment, the loss function of the GAN model can be obtained by weighted calculation according to the comfort score.

[0170] Specifically, the loss function L combines the adversarial loss and the user score weighting:

[0171] ;

[0172] L adv : Adversarial Loss, which measures the probability that the generated parameter output by the generator is recognized by the discriminator.

[0173] L score : Comfort Loss, which quantifies the matching degree between the generated parameter and the user comfort.

[0174] λ1, λ2: weight coefficients, used to balance the adversarial training and user feedback. The default values are λ1=0.7, λ2=0.3, which need to be adjusted according to the verification set.

[0175] Step S52, updating the weight of the generator in the GAN model based on the preset gradient descent algorithm and the loss function.

[0176] In this embodiment, further, a preset gradient current price algorithm is adopted to update the weight of the generator in the GAN model in combination with the loss function, so as to gradually adjust the model to an optimal state.

[0177] Specifically, the Mini-batch SGD (Mini-batch Stochastic Gradient Descent) can be adopted to update the weight of the generator:

[0178] ;

[0179] θ G : trainable parameters of the generator;

[0180] η: learning rate (Learning Rate), which controls the step size of parameter update. The initial value of η is set to 0.0002, and is exponentially decayed with the training round: initial learning rate η0=0.0002;

[0181] ▽ θG : gradient of the loss function with respect to the parameters of the generator.

[0182] S, Y: user score and the currently generated optical parameters.

[0183] In a possible implementation, before the step S50, the optical adjustment method further includes steps B10-B20:

[0184] Step B10: determining whether the comfort score is less than a first preset score.

[0185] 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 visual comfort of the wearer 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 judgment, helping the system to determine whether the optical parameter prediction neural network model needs to be updated. If the target comfort score of the wearer is less than the first preset score, it indicates that the current optical settings fail to fully meet the visual needs of the user, and the optical parameter prediction neural network model has room for improvement; on the contrary, if the target comfort score is equal to or higher than the first preset score, it means that the current settings can already provide a good user experience, and there is no need to further update the optical parameter prediction neural network model.

[0186] By introducing the concept of the first preset score, the embodiment can intelligently determine when to start the update process of the optical parameter prediction neural network model under the premise of ensuring user experience, thereby improving the prediction accuracy of the optical parameter prediction neural network model and ensuring the best visual experience for the user. This mechanism not only helps to reduce unnecessary resource consumption, but also significantly improves user satisfaction and overall device performance.

[0187] Step B20, in the case where it is determined that the target comfort score is less than the first preset score, performing a step of updating the optical parameter prediction neural network model based on the comfort score.

[0188] In the present embodiment, once it is determined that the 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 a more obvious VAC problem. At this time, the system will automatically start the update process and enter step S50 to update the optical parameter prediction neural network model based on the comfort score.

[0189] 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 model is updated only when the wearer's experience needs to be improved, which helps to maintain the stability of the device performance and the long-term satisfaction of the user.

[0190] The present embodiment realizes the personalized prediction of the target optical parameter by introducing a scoring module to evaluate the comfort of the wearer in real time and dynamically updating the optical parameter prediction neural network model based on the evaluation result (i.e., the comfort score), so that the target optical parameter predicted by the optical parameter prediction neural network model is more matched with the target biological feature data of the wearer, thereby effectively reducing the visual vergence accommodation conflict (VAC effect) in the head-mounted device and improving the user wearing experience.

[0191] Further, in a feasible embodiment, after step B10, the optical adjustment method further comprises steps B30-B40:

[0192] Step B30, calibrating the target optical parameter based on the comfort score;

[0193] 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 parameter according to the deviation between the 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 parameter.

[0194] Step B40, 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.

[0195] After the calibration of the target optical parameter is completed, the embodiment can convert these optimized parameters (i.e., the calibrated target optical parameter) 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 can be dynamically regulated by applying different voltages. Therefore, the embodiment calculates the corresponding control voltage value according to the calibrated target optical parameter and loads it onto the corresponding liquid lens unit, thereby changing the curvature distribution of the lens surface.

[0196] The embodiment realizes a closed-loop feedback mechanism from "perceived comfort" to "physical optical adjustment": the comfort score of the wearer is converted into actual optical parameter changes, which are ultimately reflected in the physical form adjustment of the liquid lens array, making the virtual image more consistent with the user's visual focus, effectively relieving the VAC effect and improving the display quality and user experience.

[0197] In summary, by calibrating the target optical parameter based on the comfort score in step B30 and applying the calibration result to the actual adjustment of the liquid lens array in step B40, the embodiment constructs a dynamic adaptive optical adjustment mechanism that combines score feedback and physical adjustment, significantly improving the visual adaptation ability of the head-mounted device in complex application scenarios and user satisfaction.

[0198] 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. Further simple transformations based on this technical concept are within the scope of protection of the present application.

[0199] 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, the head-mounted device comprises: a biological feature acquisition module and a zoom module; the optical adjustment device comprises:

[0200] The acquisition module 10 is configured to acquire target biological feature data of a wearer through the biological feature acquisition module.

[0201] The inference module 20 is configured to input the target biological feature data into a preset optical parameter prediction neural network model to predict a target optical parameter, wherein the optical parameter prediction neural network model is trained with biological feature data as samples and optical parameters as labels, and the target optical parameter comprises a dispersion compensation coefficient.

[0202] The adjustment module 30 is configured to adjust the light transmittance of the electrochromic layer based on the dispersion compensation coefficient.

[0203] Optionally, the head-mounted device further comprises a score obtaining module; and the optical adjustment device further comprises:

[0204] a feedback module configured to: obtain a comfort score through the score obtaining module; update the optical parameter prediction neural network model based on the comfort score; and return to the step of inputting the target biological feature data into the preset optical parameter prediction neural network model.

[0205] Optionally, the score obtaining module comprises a physiological signal acquisition unit; and the feedback module is further configured to:

[0206] acquire a target physiological signal of the wearer through the physiological signal acquisition unit;

[0207] determine a comfort score corresponding to the target physiological signal according to a preset mapping relationship.

[0208] Optionally, the optical parameter prediction neural network model is a GAN model, and the feedback module is further configured to:

[0209] determine a loss function of the GAN model according to the comfort score;

[0210] update a weight of a generator in the GAN model based on a preset gradient descent algorithm and the loss function.

[0211] Optionally, the target optical parameter comprises a focal length compensation value and a lens curvature; and the head-mounted device further comprises a liquid lens array; and the adjustment module is further configured to:

[0212] calculate a target curvature of the zoom module based on the focal length compensation value, the lens curvature and the dispersion compensation coefficient;

[0213] adjust a control voltage of the liquid lens array based on the target curvature and an initial curvature of the liquid lens array, so as to adjust the curvature of the liquid lens array.

[0214] Optionally, the adjustment module is further configured to:

[0215] calculate a target dispersion of the electrochromic layer based on the dispersion compensation coefficient;

[0216] adjust the light transmittance based on the target dispersion and an initial dispersion of the electrochromic layer.

[0217] Optionally, the biological feature acquisition module comprises a camera unit and an eye movement detection unit; the target biological feature data comprises an interpupillary distance, a corneal curvature, a gaze point coordinate and an eye movement speed; and the acquisition module is further configured to:

[0218] acquire an eyeball image of the wearer through the camera unit;

[0219] acquire an interpupillary distance and a corneal curvature of the wearer based on the eyeball image;

[0220] acquire a gaze point coordinate and an eye movement speed of the wearer through the eye movement detection unit.

[0221] The optical adjustment device provided in the 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 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.

[0222] The application provides a head-mounted device, which comprises at least one processor and a memory connected with the at least one processor; 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 execute the optical adjustment method in the above embodiment one.

[0223] The head-mounted device in the embodiments of the 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.

[0224] As Figure 5As shown, the head-mounted device can include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. Various programs and data required for operation of the head-mounted device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; the storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the head-mounted device to communicate wirelessly or wired with other devices to exchange data. Although the head-mounted device with various systems is shown in the figure, it should be understood that all the shown systems are not required to be implemented or possessed. More or less systems can be alternatively implemented or possessed.

[0225] In particular, the processes described above with reference to the flowcharts can be implemented as computer software programs according to embodiments of the present disclosure. For example, embodiments of the present disclosure 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 through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the methods of embodiments of the present disclosure are performed.

[0226] The head-mounted device provided by the present disclosure adopts the optical adjustment method in the above-mentioned 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 head-mounted device provided by the present disclosure has the same beneficial effects as the optical adjustment method provided by the above-mentioned embodiments, 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 repeated here.

[0227] It should be understood that various aspects of the disclosure can be implemented in 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 appropriate manner in any one or more embodiments or examples.

[0228] The above description is merely illustrative of the application and is not intended to limit the scope of the application. Any modifications or equivalents of the application should be construed as falling within the scope of the application. The scope of the application should be determined by the appended claims.

[0229] The application provides a computer readable storage medium having stored thereon computer readable program instructions (i.e., a computer program) for performing the optical adjustment method in the above embodiments.

[0230] The computer readable storage medium provided by the application may, for example, be a U disk, but is not limited to an electric, 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 electric 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 appropriate 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 by or in conjunction 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 appropriate medium, including but not limited to an electric wire, an optical cable, an RF (Radio Frequency), etc., or any appropriate combination thereof.

[0231] The above computer readable storage medium can be contained in a head-mounted device, or can exist separately and not be assembled into a head-mounted device.

[0232] The computer readable storage medium described above 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: acquire target biological feature data of a wearer through the biological feature acquisition module; input the target biological feature data into a preset optical parameter prediction neural network model to predict target optical parameters, wherein the optical parameter prediction neural network model is trained with biological feature data as samples and optical parameters as labels, and the target optical parameters include a dispersion compensation coefficient; and adjust the light transmittance of the electrochromic layer based on the dispersion compensation coefficient.

[0233] 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).

[0234] The computer program instructions can also be loaded onto a computer or other programmable information processing apparatus to cause a series of operations to be performed on the computer or other programmable information processing apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable information processing apparatus implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0235] The modules described in the embodiments of the present application can be implemented in the form of software or in the form of hardware. In some cases, the names of the modules do not constitute a limitation on the modules themselves.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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, direct / indirect application in other related technical fields based on the technical concept of the present application, and the content of the specification and drawings are included in the patent protection scope of the present application.

Claims

1. A method of optical adjustment, characterized by, The optical adjustment method is applied to a head-mounted device, and the head-mounted device comprises a biological feature acquisition module and an electrochromic layer; the method comprises: acquiring target biological feature data of a wearer through the biological feature acquisition module; inputting the target biological feature data into a preset optical parameter prediction neural network model to predict target optical parameters, wherein the optical parameter prediction neural network model is trained by taking biological feature data as samples and optical parameters as labels, and the target optical parameters comprise a dispersion compensation coefficient; adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficient; wherein the biological feature acquisition module comprises a camera unit and an eye movement detection unit; and the target biological feature data comprises interpupillary distance, corneal curvature, gaze point coordinates, eye movement speed and historical scores; the step of acquiring target biological feature data of a wearer through the biological feature acquisition module comprises: obtaining an eyeball image of the wearer through the camera unit; obtaining the interpupillary distance and corneal curvature of the wearer based on the eyeball image; obtaining the gaze point coordinates and eye movement speed of the wearer through the eye movement detection unit; obtaining a plurality of historical reference scores according to a preset window, wherein the historical reference scores are historical comfort scores; calculating the average of each historical reference score to obtain a historical score.

2. The method of optical adjustment of claim 1, wherein, The target optical parameters comprise focal length compensation values and lens curvatures; and the head-mounted device further comprises a liquid lens array; after the step of inputting the target biological feature data into a preset optical parameter prediction neural network model to predict target optical parameters, the method further comprises: calculating the target curvature of the liquid lens array based on the focal length compensation values, the lens curvatures and the dispersion compensation coefficient; adjusting the control voltage of the liquid lens array based on the target curvature and the initial curvature of the liquid lens array to adjust the curvature of the liquid lens array.

3. The method of optical adjustment of claim 1, wherein, The head-mounted device further comprises a score acquisition module; after the step of adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficient, the method further comprises: obtaining a comfort score through the score acquisition module; updating the optical parameter prediction neural network model based on the comfort score, and returning to the step of inputting the target biological feature data into a preset optical parameter prediction neural network model.

4. The method of optical adjustment of claim 3, wherein, The score acquisition module comprises a physiological signal acquisition unit; the step of obtaining a comfort score through the score acquisition module comprises: acquiring target physiological signals of the wearer through the physiological signal acquisition unit; determining the comfort score corresponding to the target physiological signals according to a preset mapping relationship.

5. The method of optical adjustment of claim 3, wherein, The optical parameter prediction neural network model is a GAN model, and the step of updating the optical parameter prediction neural network model based on the comfort score comprises: determining the loss function of the GAN model according to the comfort score; updating the weight of the generator in the GAN model based on a preset gradient descent algorithm and the loss function.

6. The method of optical adjustment of claim 1, wherein, The step of adjusting the light transmittance of the electrochromic layer based on the dispersion compensation coefficient comprises: calculating a target dispersion of the electrochromic layer based on the dispersion compensation coefficient; adjusting the light transmittance based on the target dispersion and an initial dispersion of the electrochromic layer.

7. An optical adjustment device, characterized by The device is arranged in a head-mounted device, and the head-mounted device comprises a biometric feature acquisition module and an electrochromic layer; the optical adjustment device comprises: an acquisition module configured to acquire target biometric feature data of a wearer through the biometric feature acquisition module; an inference module configured to input the target biometric feature data into a preset optical parameter prediction neural network model to predict a target optical parameter, wherein the optical parameter prediction neural network model is trained with biometric feature data as samples and optical parameters as labels, and the target optical parameter comprises a dispersion compensation coefficient; an adjustment module configured to adjust the light transmittance of the electrochromic layer based on the dispersion compensation coefficient. The biometric feature acquisition module comprises a camera unit and an eye movement detection unit, and the target biometric feature data comprises interpupillary distance, corneal curvature, gaze point coordinates, eye movement speed, and a historical score. The acquisition module is further configured to: obtain an eyeball image of the wearer through the camera unit; obtain the interpupillary distance and the corneal curvature of the wearer based on the eyeball image; obtain the gaze point coordinates and the eye movement speed of the wearer through the eye movement detection unit; obtain a plurality of historical reference scores according to a preset window, wherein the historical reference scores are historical comfort scores; calculate the average of the historical reference scores to obtain the historical score.

8. A head-mounted device, comprising: The device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the optical adjustment method according to any one of claims 1 to 6.

9. A storage medium, characterized by The storage medium is a computer-readable storage medium, and the storage medium stores a computer program, and the computer program is executed by the processor to implement the steps of the optical adjustment method according to any one of claims 1 to 6.

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