Method, device and equipment for automatically adjusting vision focal length and storage medium

Abstract

The invention relates to the technical field of optics, and discloses an automatic vision focal length adjusting method, device and equipment and a storage medium, and the method comprises the steps: obtaining personalized vision data of a user; performing vision detection on the user to obtain vision detection data; generating initial training data according to the vision detection data and the personalized vision data; performing visual training according to the initial training data to obtain an initial training result; evaluating the initial training result to obtain an evaluation result, and generating a training strategy according to the evaluation result; according to the invention, a closed loop from data acquisition, training, evaluation and strategy adjustment is formed, individual requirements of different users can be met, and a more targeted vision adjustment scheme is provided.

Description

Technical Field

[0001] The present invention relates to the field of optical technologies, and particularly to a method, device, equipment, and storage medium for automatically adjusting visual acuity focus. Background Art

[0002] In modern society, vision problems are extremely common. Most of the existing vision adjustment methods rely on general training models, which are often based on unified standards and processes and do not fully consider the differences in individual vision conditions. In fact, the causes of vision problems are complex and diverse, such as genetic factors, eye - using habits, living environments, etc. Since the general training model cannot accurately target the personalized vision conditions of each user for effective training, the existing vision adjustment methods are greatly reduced in actual application, and the training effect is not good. Therefore, there is an urgent need for a more targeted vision adjustment method. Summary of the Invention

[0003] In order to overcome the deficiencies of the prior art, the purpose of the present invention is to provide a method, device, equipment, and storage medium for automatically adjusting visual acuity focus. It obtains the personalized vision data of the user, combines the vision detection data to generate initial training data, realizes the initial vision training for different individuals, then uses the initial training data to conduct preliminary vision training on the user to obtain the initial training result, and then conducts an effective evaluation based on the initial training result, thereby setting a targeted training strategy. Compared with the traditional general training model, the present invention forms a closed - loop from data collection, training, evaluation to strategy adjustment, which can meet the personalized needs of different users and provide a more targeted vision adjustment solution.

[0004] The first aspect of the present invention provides a method for automatically adjusting visual acuity focus, including:

[0005] Obtain the personalized vision data of the user;

[0006] Conduct a vision test on the user to obtain vision test data;

[0007] Generate initial training data according to the vision test data and the personalized vision data;

[0008] Conduct vision training according to the initial training data to obtain an initial training result;

[0009] Evaluate the initial training result to obtain an evaluation result, and generate a training strategy according to the evaluation result.

[0010] Optionally, in the first implementation manner of the first aspect of the present invention, the obtaining of the user's personalized vision data includes: obtaining the user's historical vision data, eye usage time data, fatigue state data, and eye usage environment data; generating personalized vision data based on the historical vision data, eye usage time data, fatigue state data, and eye usage environment data.

[0011] Optionally, in the second implementation manner of the first aspect of the present invention, the performing of a vision test on the user to obtain vision test data includes: performing a refractive test on the user to obtain an optical imaging test result; performing an eye movement tracking test on the user to obtain an eye movement tracking test result; performing an iris response test on the user to obtain an iris response test result; generating vision test data based on the optical imaging test result, the eye movement tracking test result, and the iris response test result.

[0012] Optionally, in the third implementation manner of the first aspect of the present invention, the generating of initial training data based on the vision test data and the personalized vision data includes: performing a feature comparison on the vision test data, the personalized vision data, and preset reference vision data to obtain a comparison result; generating initial training data based on the comparison result and a preset vision training strategy.

[0013] Optionally, in the fourth implementation manner of the first aspect of the present invention, the generating of initial training data based on the comparison result and a preset vision training strategy includes: the comparison result includes: recent vision decline, pseudo-myopia tendency, and stable myopia for many years; the vision training strategy includes a fatigue recovery strategy, a pseudo-myopia reversal strategy, and a correction reinforcement strategy; when the comparison result is recent vision decline, initial training data is generated according to the fatigue recovery strategy; when the comparison result is pseudo-myopia tendency, initial training data is generated according to the pseudo-myopia reversal strategy; when the comparison result is stable myopia for many years, initial training data is generated according to the correction reinforcement strategy.

[0014] Optionally, in the fifth implementation manner of the first aspect of the present invention, the performing of vision training based on the initial training data to obtain an initial training result includes: performing refractive correction training on the user according to the initial training data to obtain corrected retinal imaging data and an optical correction training result; generating stereoscopic parallax projection data based on the corrected retinal imaging data, and performing binocular coordination training on the user using the stereoscopic parallax projection data to obtain optic nerve coordination data and a binocular coordination training result; generating RGB dynamic light data based on the optic nerve coordination data, and performing light and dark scene adaptation training on the user using the RGB dynamic light data to obtain a scene adaptation training result; generating an initial training result based on the optical correction training result, the coordination training result, and the scene adaptation training result.

[0015] Optionally, in the sixth implementation manner of the first aspect of the present invention, the evaluating the initial training result to obtain an evaluation result and generating a training strategy based on the evaluation result includes: evaluating the training effect of the initial training result to obtain an effect evaluation result, and generating optimized training data based on the effect evaluation result; evaluating the user fatigue of the initial training result to obtain a fatigue evaluation result, and generating training frequency data based on the fatigue evaluation result; and generating a training strategy based on the optimized training data and the training frequency data.

[0016] The second aspect of the present invention provides an automatic vision focus adjustment device, including: an acquisition module, configured to acquire personalized vision data of a user; a detection module, configured to perform vision detection on the user to obtain vision detection data; a configuration module, configured to generate initial training data based on the vision detection data and the personalized vision data; a training module, configured to perform vision training based on the initial training data to obtain an initial training result; and an evaluation module, configured to evaluate the initial training result to obtain an evaluation result and generate a training strategy based on the evaluation result.

[0017] The third aspect of the present invention provides an automatic vision focus adjustment device, where the automatic vision focus adjustment device includes: a memory and at least one processor, and instructions are stored in the memory; at least one of the processors invokes the instructions in the memory so that the automatic vision focus adjustment device executes each step of the automatic vision focus adjustment method described in any one of the above.

[0018] The fourth aspect of the present invention provides a computer-readable storage medium, where instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, each step of the automatic vision focus adjustment method described in any one of the above is implemented.

[0019] In the technical solution of the present invention, by acquiring the personalized vision data of the user and combining the vision detection data to generate initial training data, personalized initial vision training for different individuals is realized. Then, the initial training data is used to perform preliminary vision training on the user to obtain an initial training result, and then an effective evaluation is performed based on the initial training result, so as to set a targeted training strategy. Compared with the traditional general training mode, the present invention forms a closed loop from data collection, training, evaluation to strategy adjustment, which can meet the personalized needs of different users and provide a more targeted vision adjustment solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and easy to understand from the description of the embodiments in conjunction with the following drawings, where:

[0021] Figure 1 is the first flowchart of the automatic vision focus adjustment method provided by the embodiment of the present invention;

[0022] Figure 2 It is the second flowchart of the method for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0023] Figure 3 It is the third flowchart of the method for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0024] Figure 4 It is the fourth flowchart of the method for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0025] Figure 5 It is the fifth flowchart of the method for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0026] Figure 6 It is the sixth flowchart of the method for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0027] Figure 7 It is the seventh flowchart of the method for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0028] Figure 8 It is a schematic structural diagram of the device for automatically adjusting visual acuity focus provided by the embodiments of the present invention;

[0029] Figure 9 It is a schematic structural diagram of the device for automatically adjusting visual acuity focus provided by the embodiments of the present invention. Specific embodiments

[0030] The present invention provides a method, device, equipment and storage medium for automatically adjusting visual acuity focus. By obtaining the personalized visual acuity data of users and generating initial training data in combination with visual acuity detection data, it realizes initial visual acuity training tailored to individuals. Then, the initial training data is used to conduct preliminary visual acuity training on users to obtain initial training results, and based on the initial training results, effective evaluation is carried out, so as to set targeted training strategies. Compared with the traditional general training mode, the present invention forms a closed loop from data collection, training to evaluation and then to strategy adjustment, which can meet the personalized needs of different users and provide a more targeted visual acuity adjustment plan.

[0031] In the description, claims and above-mentioned drawings of the present invention, terms such as "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, device, product or equipment comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or equipment.

[0032] For ease of understanding, the specific process of the embodiment of the present invention is described below. Please refer to Figure 1 , an embodiment of the method for automatically adjusting the vision focal length in the embodiment of the present invention includes:

[0033] 101. Obtain the personalized vision data of the user;

[0034] In this embodiment, through the device supporting software, the user is guided to actively fill in personal basic information, such as historical eye use data (including myopia, hyperopia, astigmatism degrees, etc.), daily eye use habits (such as the daily duration of using electronic devices, reading duration), recent fatigue status, and daily eye use environment, so as to obtain the user's personalized vision data.

[0035] 102. Conduct a vision test on the user to obtain vision test data;

[0036] In this embodiment, using the optical detection module of the device, a refractive detection is performed on the user to measure the myopia, hyperopia, astigmatism degrees, eye movement coordination, fixation stability of the user's eyes, and the response changes of the iris under different light stimuli. Combining these detection results, comprehensive vision test data is generated.

[0037] 103. Generate initial training data according to the vision test data and the personalized vision data;

[0038] In this embodiment, the vision test data and the personalized vision data are integrated and analyzed; the user's current vision condition is compared with the normal vision range of the same age group, combined with the user's eye use habits and historical vision change trends, to judge the type of the user's vision problem; according to different judgment results, corresponding initial training data is generated.

[0039] 104. Conduct vision training according to the initial training data to obtain an initial training result;

[0040] In this embodiment, according to the initial training data, the eye use requirements under different vision states are simulated; during the training process, the user's training performance is continuously recorded, such as the accuracy of completing tasks, the speed and accuracy of adjusting the focal length, etc. After the training is completed, these data are sorted out to obtain the initial training results.

[0041] 105. Evaluate the initial training results to obtain an evaluation result, and generate a training strategy according to the evaluation result;

[0042] In this embodiment, the initial training results are evaluated from two aspects: training effect and user fatigue level; the training effect evaluation includes comparing the changes in vision detection data before and after training and analyzing the improvement of the user's performance in the training tasks; the user fatigue level evaluation is judged by monitoring the user's blink frequency, changes in eye movement speed, and the user's subjective feedback; a new training strategy is generated according to the evaluation result to provide clear guidance for the user's next-stage vision training.

[0043] In the embodiment of the present invention, by obtaining the user's personalized vision data and combining the vision detection data to generate the initial training data, the initial vision training for different individuals is realized. Then, the initial training data is used to conduct preliminary vision training on the user to obtain the initial training results, and then an effective evaluation is carried out based on the initial training results, so as to set a targeted training strategy. Compared with the traditional general training mode, the present invention forms a closed loop from data collection, training, evaluation to strategy adjustment, which can meet the personalized needs of different users and provide a more targeted vision adjustment plan.

[0044] Please refer to Figure 2 , two embodiments of the method for automatically adjusting the vision focal length in the embodiment of the present invention include:

[0045] 201. Obtain the user's historical vision data, eye use time data, fatigue state data, and eye use environment data;

[0046] In this embodiment, when the user first uses the automatic vision focal length adjustment device, the automatic vision focal length adjustment device guides the user to input relevant data. Among them, the historical vision data is the vision examination results in the past at least 5 years, including information such as the myopia degree, hyperopia degree, astigmatism degree, and axis position of the left and right eyes; the eye use time data is the duration data of the user using electronic devices every day; the fatigue state data is the eye fatigue state statistically obtained by the user according to his own feelings, including dry eyes, soreness, blurred vision, etc.; the eye use environment data is the main scene of the user's daily eye use environment, including indoor office, outdoor direct sunlight, indoor dim light, etc.

[0047] 202. Generate personalized vision data according to the historical vision data, eye use time data, fatigue state data, and eye use environment data;

[0048] In this embodiment, the acquired data are standardized to unify the data format and unit. For example, the degrees in the historical visual acuity data are uniformly converted into the international standard diopter unit; the eye - using time data are uniformly converted into hours; the light intensity data in the eye - using environment are classified and quantified according to different environment types; then the historical visual acuity data are analyzed to extract the visual acuity change trend data, which include the growth rate of myopia degree, the change of astigmatism, etc.; the eye - using time data, fatigue state data and eye - using environment data are comprehensively analyzed to extract the eye - using pattern data; the eye - using pattern data include the proportion of different eye - using scenarios and the time distribution rule, the correlation between the frequency and degree of fatigue appearance and eye - using behavior; finally, the personalized visual acuity data are generated by integrating the visual acuity change trend data and the eye - using pattern data; the personalized visual acuity data can comprehensively reflect the user's visual acuity condition, including multiple dimensions such as historical genetic factors, eye - using habits, and living environment, so as to provide comprehensive and accurate data support for formulating personalized training strategies in the follow - up.

[0049] Please refer to Figure 3 , three embodiments of the method for automatically adjusting the vision focus in the embodiments of the present invention include:

[0050] 301. Conduct refractive detection and aberration detection on the user to obtain an optical imaging detection result;

[0051] In this embodiment, the automatic vision focus adjustment device includes a liquid - crystal focusing lens (such as Optotune EL - 16 - 40 - TC) and a photodetector (such as Hamamatsu S1336 - 18BK). The liquid - crystal focusing lens is used to generate a dynamic defocus spot, and then the photodetector is used to capture the light reflected from the eye in real time, record the refraction and scattering of the light, realize refractive detection, and obtain the optical imaging detection result (including myopia, hyperopia, and astigmatism degrees); the optical imaging detection result is used to measure the user's basic fundus imaging condition.

[0052] 302. Conduct eye movement tracking detection on the user to obtain an eye movement tracking detection result;

[0053] In this embodiment, the automatic vision focus adjustment device includes a display screen, an infrared emitting diode (such as Osram SFH 4550) and a CMOS sensor (such as Tobii Pro Fusion); at the beginning of the detection, a series of patterns moving near and far are projected on the display screen, and the infrared emitting diode emits invisible red light to illuminate the eyeball, so as to cooperate with the CMOS sensor to sample the dynamic features in front of the eyes; the eye movement parameters such as the rotation angle, fixation direction and saccade speed of the eyeball are calculated to obtain the eye movement tracking detection result; the eye movement tracking result is used to measure the user's dynamic visual acuity and binocular coordination ability.

[0054] 303. Perform iris response detection on the user to obtain the iris response detection result;

[0055] In this embodiment, the automatic vision focusing device includes a multispectral fundus camera (such as Topcon TRC-NW400). By alternately transmitting 450 nm (blue light) and 620 nm (red light) to the user and recording the pupil contraction delay time, iris response characteristic parameters are obtained, which are the iris response detection results. The iris response detection results are used to evaluate the user's adaptability to bright and dark scenes.

[0056] 304. Generate vision detection data based on the optical imaging detection result, eye movement tracking detection result, and iris response detection result;

[0057] In this embodiment, the refractive power in the optical imaging detection result, the eye movement parameters in the eye movement tracking detection result, and the iris response characteristic parameters in the iris response detection result are integrated in a unified data format to form a data set containing multi-dimensional vision information, that is, the vision detection data is obtained. The vision detection data is a comprehensive evaluation of the user's vision condition and provides a basis for formulating initial training data in the future.

[0058] Please refer to Figure 4 , the four embodiments of the automatic vision focusing method in the embodiments of the present invention include:

[0059] 401. Perform feature comparison on the vision detection data, personalized vision data, and preset reference vision data to obtain a comparison result;

[0060] In this embodiment, the preset reference vision data includes vision data of normal people of different ages and typical data of different types of vision patients (such as myopia, hyperopia, and astigmatism patients), such as the normal growth slope of vision data of the same age group, the standard range of eye movement coordination, etc. Then, from multiple dimensions such as vision value, refractive power, eye axis length, eye use habit characteristics (such as eye use duration, eye use frequency, etc.), eye fatigue-related characteristics (fatigue degree, fatigue occurrence frequency, etc.), and eye use environment characteristics (light intensity, color temperature, etc.), the vision detection data and personalized vision data are respectively compared with the reference vision data to clarify the differences and similarities between the user's vision data and the reference data, that is, the comparison result. The comparison result is used to analyze the vision problems corresponding to the user's current vision state.

[0061] 402. Generate initial training data according to the comparison result and the preset vision training strategy;

[0062] In this embodiment, after the comparison result is determined, corresponding preset vision training strategies are matched according to different results; if the comparison result shows that the user's vision state is a rapid decline in vision recently, initial training data can be formulated from the preset fatigue recovery strategies; if the comparison result shows that the user's vision state is a tendency of pseudomyopia, initial training data can be formulated from the preset pseudomyopia reversal strategies; different vision training strategies are selected according to different comparison results, and then different initial training data are formulated to conduct the initial vision training for the user.

[0063] Please refer to Figure 5 , five embodiments of the method for automatically adjusting the vision focal length in the embodiments of the present invention include:

[0064] The comparison results include: a rapid decline in vision recently, a tendency of pseudomyopia, and stable myopia for many years;

[0065] In this embodiment, if it is found in the comparison that the user's recent vision detection data shows that the myopia degree has increased by more than a certain amplitude (such as 50 degrees or more) within a short period of time (such as the recent 3 months), and the personalized vision data indicates that the eye use duration has increased significantly and the fatigue state frequently appears, and at the same time the eye movement tracking detection result shows that the eye movement coordination becomes worse, it can be determined that the vision has declined recently;

[0066] If it is found in the comparison that the user's vision detection data shows that the refractive degree is unstable and fluctuates greatly at different detection time points, and the eye movement tracking detection finds that the ciliary muscle tension degree is abnormal when the eyes view near objects, and at the same time the personalized vision data reflects that the eye use has been excessive recently but the historical vision is relatively stable, it is comprehensively judged that there is a tendency of pseudomyopia;

[0067] If it is found in the comparison that the user's vision detection data shows that the myopia degree has changed little within many years (such as more than 3 years) (such as an annual increase of no more than 25 degrees), and the eye movement tracking and iris response detection results are basically stable, and the personalized vision data shows that the eye use habits and environment are relatively fixed, it can be determined that the user has stable myopia for many years.

[0068] The vision training strategies include fatigue recovery strategies, pseudomyopia reversal strategies, and correction and strengthening strategies;

[0069] 501. When the comparison result is a rapid decline in vision recently, initial training data is generated according to the fatigue recovery strategy;

[0070] In this embodiment, when the comparison result is a rapid decline in vision recently, the fatigue recovery strategy is selected from the preset vision training strategies, and initial training data is generated according to the fatigue recovery strategy; at this time, the focus of the initial training data is to relieve eye fatigue, and it is necessary to increase the proportion of light and dark scene adaptation training, adjust the light and dark to simulate different outdoor light intensities, and promote eye relaxation.

[0071] 502. When the comparison result shows a tendency of pseudomyopia, initial training data is generated according to the pseudomyopia reversal strategy.

[0072] In this embodiment, when the comparison result shows a tendency of pseudomyopia, the pseudomyopia reversal strategy is selected from the preset vision training strategies, and initial training data is generated according to the pseudomyopia reversal strategy. At this time, the focus of the initial training data is to restore the elasticity and accommodation ability of the ciliary muscle. It is necessary to increase the proportion of binocular coordination training, train the user's ability to alternate focus between near and far with both eyes, and effectively stimulate the ciliary muscle.

[0073] 503. When the comparison result shows stable myopia over the years, initial training data is generated according to the correction and strengthening strategy.

[0074] In this embodiment, when the comparison result shows stable myopia over the years, the correction and strengthening strategy is selected from the preset vision training strategies, and initial training data is generated according to the correction and strengthening strategy. At this time, the focus of the initial training data is to stabilize the user's vision and improve visual quality. At this time, it is necessary to increase the proportion of refractive correction training and train the user's adaptability to different focal lengths.

[0075] Please refer to Figure 6 , six embodiments of the method for automatically adjusting the vision focal length in the embodiments of the present invention include:

[0076] 601. Perform refractive correction training on the user according to the initial training data to obtain corrected retinal imaging data and optical correction training results.

[0077] In this embodiment, the vision training steps are based on the physiological cascade of the human visual function. Because only by repairing the optical distortion at the physical level can the effective output of neural processing be guaranteed, and then all-round vision training can be achieved. Therefore, the initial vision training needs to sequentially complete refractive correction training (the purpose is to optimize the optical imaging quality through optical correction), binocular coordination training (strengthening neural signals is carried out on the basis of completed optical correction), and light and dark scene adaptation training (finally improving the scene adaptation ability after the first two stages of training);

[0078] The purpose of refractive correction training is to improve the user's imaging quality and reduce the quality difference between the binocular images. Specifically, binocular refractive correction is performed by dynamically adjusting the refractive compensation value of the liquid crystal focusing lens, and then defocus is applied to the dominant eye to forcibly activate the accommodation function of the weak eye. During the above training process, the photodetector is used to dynamically analyze the intensity distribution of the retinal reflected light to generate the retinal imaging MTF (corrected retinal imaging data); during the training process, at the same time, record the parameters of each lens adjustment, the user's fixation time, fixation stability and other data, and synthesize these data to obtain the optical correction training results.

[0079] 602. Generate stereoscopic parallax projection data based on the calibrated retinal imaging data, and use the stereoscopic parallax projection data to conduct binocular coordination training for the user to obtain optic nerve coordination data and binocular coordination training results.

[0080] In this embodiment, after the refractive correction training is completed, the calibrated retinal imaging data is analyzed and processed to calculate the position differences of image feature points at different viewing angles, and left and right eye images with parallax (i.e., stereoscopic parallax projection data) are generated to simulate a stereoscopic scene. The stereoscopic parallax projection data is presented to the user in the form of left and right eye split screens for binocular coordination training. During this period, eye movement data is collected using a CMOS sensor, including the fixation time of both eyes, saccade speed, binocular coordination index, etc. These data reflect the control ability of the optic nerve over binocular coordinated movement, that is, optic nerve coordination data. By analyzing the optic nerve coordination data, the effect of binocular coordination training is evaluated to obtain the binocular coordination training results.

[0081] 603. Generate RGB dynamic light data based on the optic nerve coordination data, and use the RGB dynamic light data to conduct light and dark scene adaptation training for the user to obtain scene adaptation training results.

[0082] In this embodiment, after the binocular coordination training is completed, the optic nerve coordination data is analyzed to extract the visual response characteristics of the user under different visual tasks. According to these characteristics, combined with the ambient light data real-time monitored by the ambient light sensor, RGB dynamic light data is generated. Specifically, if the user's visual response is slow when facing strong light, the system will generate RGB dynamic light data with gradually increasing light intensity to train the user's strong light adaptation ability; if the user's visual sensitivity is low in dim light, the system will generate RGB dynamic light data with gradually decreasing light intensity to improve the user's dim light adaptation ability. The display screen simulates different light and dark scenes according to the generated RGB dynamic light data, thereby conducting light and dark scene adaptation training for the user. During the training process, a multispectral camera is used to real-time detect the change rate of pupil diameter, and then analyze the adjustment process and stability of the user's eyes under different light conditions to evaluate the user's light and dark scene adaptation ability to obtain the scene adaptation training results.

[0083] 604. Generate initial training results based on the optical correction training results, coordination training results, and scene adaptation training results.

[0084] In this embodiment, the optical correction training results, binocular coordination training results, and scene adaptation training results are integrated to form a data set containing multi-dimensional training effect information. The initial training results include specific data of each training result, analysis of the user's performance during the training process, the gap from the training objectives, etc., providing a comprehensive and accurate basis for subsequent training strategy adjustment.

[0085] Please refer toFigure 7 , seven embodiments of the method for automatically adjusting the vision focal length in the embodiments of the present invention include:

[0086] 701. Evaluate the training effect of the initial training result to obtain an effect evaluation result, and generate optimized training data according to the effect evaluation result;

[0087] In this embodiment, first, for the initial training result, set multi-dimensional evaluation indicators; correspond the optical correction training result, the collaborative training result, and the scene adaptation training result in the initial training result to each evaluation indicator respectively; specifically, extract the imaging clarity improvement data from the optical correction training result to evaluate the vision improvement; obtain the coordination data of binocular collaborative movement from the collaborative training result to evaluate the improvement of eye movement function; collect the accuracy of the user's visual task completion under different lights and scenes from the scene adaptation training result to evaluate the enhancement of visual sensitivity;

[0088] Then, according to the importance of different training results, assign corresponding weights to each indicator; for example, for a user whose comparison result is a recent decline in vision and the selected training strategy is a fatigue recovery strategy, the score weight on the scene adaptation training result is higher; similarly, for a user whose comparison result is a tendency of pseudomyopia and the selected training strategy is a pseudomyopia reversal strategy, the score weight on the binocular collaborative training result is higher; for a user whose comparison result is stable myopia for many years and the selected training strategy is a correction enhancement strategy, the score weight on the optical correction training result is higher; through weighted calculation, obtain a value that comprehensively reflects the user's training effect, which is the effect evaluation result;

[0089] Finally, generate optimized training data according to the effect evaluation result; if the vision improvement does not meet the expectation, increase the training intensity for refractive errors, such as extending the training time and increasing the training difficulty; if the improvement of eye movement function is slow, design more targeted eye movement training tasks, such as eye tracking training in specific directions and speeds; if the enhancement of visual sensitivity is not obvious, adjust the parameters of visual stimulation training, such as increasing the complexity of the pattern and changing the color combination, etc.; organize the optimized training content, parameters, duration and other information into optimized training data.

[0090] 702. Evaluate the user fatigue of the initial training result to obtain a fatigue evaluation result, and generate training frequency data according to the fatigue evaluation result;

[0091] In this embodiment, first extract the data related to eye movement tracking from the initial training result. When the blink frequency is lower than a certain proportion of the normal range, it is regarded as a fatigue sign; when the fixation point shows frequent jitter or drift, it is also regarded as a fatigue sign;

[0092] Then, integrate the fatigue-related metrics in the eye-tracking data and the user's subjective feedback data, and assign weights to different fatigue assessment factors. For example, the weight of the change in blink frequency is set to 0.4, the weight of the fixation point stability is set to 0.3, and the weight of the user's subjective feedback is set to 0.3. Calculate the comprehensive fatigue assessment value through weighted calculation; then, according to the preset fatigue level criteria, determine whether the user is in mild fatigue, moderate fatigue, or severe fatigue to obtain the fatigue assessment result;

[0093] If the user is in mild fatigue, appropriately shorten the duration of a single training session while keeping the number of training sessions per day unchanged; if in moderate fatigue, reduce the number of training sessions per day and increase the training interval; if in severe fatigue, suspend the training. After the user's fatigue is relieved, readjust the training frequency and start to resume from low-intensity and low-frequency training; organize these training frequency adjustment plans for different fatigue levels into training frequency data.

[0094] 703. Generate a training strategy based on the optimized training data and the training frequency data;

[0095] In this embodiment, combine the training content and parameter adjustment in the optimized training data with the number of training sessions, duration, and interval in the training frequency data to generate a training strategy; the training strategy fully considers the most suitable training content and training plan for the user, and can be dynamically adjusted adaptively after each training session, providing clear guidance for the user's next-stage vision training, forming a closed loop from data collection, training, evaluation to strategy adjustment, which can meet the personalized needs of different users and provide a more targeted vision adjustment plan.

[0096] The method for automatically adjusting the vision focal length in the embodiment of the present invention has been described above. Next, the device for automatically adjusting the vision focal length in the embodiment of the present invention will be described. Please refer to Figure 8 One embodiment of the device for automatically adjusting the vision focal length in the embodiment of the present invention includes:

[0097] An acquisition module 801, configured to acquire the personalized vision data of the user;

[0098] A detection module 802, configured to perform vision detection on the user to obtain vision detection data;

[0099] A configuration module 803, configured to generate initial training data according to the vision detection data and the personalized vision data;

[0100] A training module 804, configured to perform vision training according to the initial training data to obtain an initial training result;

[0101] An evaluation module 805, configured to evaluate the initial training result to obtain an evaluation result, and generate a training strategy according to the evaluation result.

[0102] In this embodiment, first, the acquisition module 801 acquires the personalized vision data of the user, and then the detection module 802 conducts a preliminary vision detection on the user to obtain vision detection data; then the configuration module 803 generates initial training data by combining the vision detection data and the personalized vision data; the training module 804 conducts preliminary vision training on the user using the initial training data to obtain an initial training result; finally, the evaluation module 805 conducts an effective evaluation based on the initial training result, thereby setting a targeted training strategy. Compared with the traditional general training mode, the present invention forms a closed loop from data acquisition, training, evaluation to strategy adjustment, which can meet the personalized needs of different users and provide a more targeted vision adjustment solution.

[0103] Above Figure 8 The automatic vision focus adjustment device in the embodiment of the present invention is described in detail from the perspective of modular functional entities. Next, the automatic vision focus adjustment device in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0104] Figure 9 FIG. is a schematic structural diagram of an automatic vision focus adjustment device provided by an embodiment of the present invention. The automatic vision focus adjustment device 900 may vary greatly due to configuration or performance differences, and may include one or more processors (central processing units, CPUs) 910 (for example, one or more processors) and a memory 920, and one or more storage media 930 for storing application programs 933 or data 932 (for example, one or more mass storage devices). Among them, the memory 920 and the storage media 930 may be transient storage or persistent storage. The program stored in the storage media 930 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the automatic vision focus adjustment device 900. Further, the processor 910 may be configured to communicate with the storage media 930 and execute a series of instruction operations in the storage media 930 on the automatic vision focus adjustment device 900 to implement the steps of the automatic vision focus adjustment method provided in the above method embodiments.

[0105] The automatic vision focus adjustment device 900 may further include one or more power supplies 940, one or more wired or wireless network interfaces 950, one or more input / output interfaces 960, and / or one or more operating devices 931, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand, Figure 9The shown structure of the automatic vision focusing device does not constitute a limitation on the automatic vision focusing device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0106] The present invention also provides a computer-readable storage medium. The computer-readable storage medium can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer is caused to execute the steps of the automatic vision focusing method.

[0107] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described device or devices, units can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0108] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0109] Finally, it should be noted that the above are only preferred examples of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for automatically adjusting visual focus, characterized in that: include: Obtain the user's personalized vision data; Performing vision testing on the user to obtain vision testing data; Generate initial training data based on vision test data and personalized vision data; Performing vision training according to the initial training data to obtain an initial training result; The initial training results are evaluated to obtain evaluation results, and a training strategy is generated based on the evaluation results.

2. The method for automatically adjusting the visual focus according to claim 1, characterized in that: The obtaining of the user's personalized vision data comprises: Obtain the user's historical vision data, eye usage time data, fatigue status data, and eye usage environment data; Generate personalized vision data based on historical vision data, eye usage time data, fatigue status data and eye usage environment data.

3. The method for automatically adjusting the visual focus according to claim 1, characterized in that: The performing vision testing on the user to obtain vision testing data includes: Perform refractive testing on the user to obtain optical imaging test results; Performing eye tracking detection on the user to obtain eye tracking detection results; Performing iris response detection on the user to obtain an iris response detection result; Vision detection data is generated based on the optical imaging detection results, the eye tracking detection results, and the iris response detection results.

4. The method for automatically adjusting the visual focus according to claim 1, characterized in that: The generating of initial training data according to the vision detection data and the personalized vision data comprises: Perform feature comparison on vision test data, personalized vision data and preset reference vision data to obtain comparison results; Initial training data is generated according to the comparison results and the preset vision training strategy.

5. The method for automatically adjusting the visual focus according to claim 4, characterized in that: The generating of initial training data according to the comparison result and the preset vision training strategy includes: The comparison results include: recent visual acuity decline, pseudomyopia tendency and stable myopia for many years; the vision training strategies include fatigue recovery strategy, pseudomyopia reversal strategy and correction and reinforcement strategy; When the comparison result is recent visual impairment, initial training data is generated according to the fatigue recovery strategy; When the comparison result is a pseudomyopia tendency, initial training data is generated according to the pseudomyopia reversal strategy; When the comparison result is stable myopia for many years, initial training data are generated according to the correction and enhancement strategy.

6. The method for automatically adjusting the visual focus according to claim 1, characterized in that: The performing vision training according to the initial training data to obtain an initial training result includes: Perform refractive correction training on the user according to the initial training data to obtain corrected retinal imaging data and optical correction training results; Generate stereo disparity projection data based on the corrected retinal imaging data, and use the stereo disparity projection data to perform binocular coordination training on the user to obtain optic nerve coordination data and binocular coordination training results; Generate RGB dynamic light data according to the optic nerve coordination data, and use the RGB dynamic light data to train the user to adapt to light and dark scenes, and obtain scene adaptation training results; An initial training result is generated according to the optical correction training result, the collaborative training result and the scene adaptation training result.

7. The method for automatically adjusting the visual focus according to claim 1, characterized in that: The initial training result is evaluated to obtain an evaluation result, and a training strategy is generated according to the evaluation result, including: Performing training effect evaluation on the initial training results to obtain effect evaluation results, and generating optimized training data according to the effect evaluation results; Performing user fatigue assessment on the initial training results to obtain fatigue assessment results, and generating training frequency data according to the fatigue assessment results; Generate a training strategy based on the optimized training data and training frequency data.

8. A device for automatically adjusting the visual focus, characterized in that: include: An acquisition module, used to acquire the user's personalized vision data; A detection module, used to perform vision detection on a user to obtain vision detection data; A configuration module, used for generating initial training data according to vision test data and personalized vision data; A training module, used for performing vision training according to initial training data to obtain initial training results; The evaluation module is used to evaluate the initial training results to obtain evaluation results and generate training strategies based on the evaluation results.

9. A device for automatically adjusting the visual focus, characterized in that: The device for automatically adjusting vision focus includes: a memory and at least one processor, wherein instructions are stored in the memory; at least one of the processors calls the instructions in the memory so that the device for automatically adjusting vision focus performs each step of the method for automatically adjusting vision focus as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the method for automatically adjusting vision focus as described in any one of claims 1-7 are implemented.

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