A dynamic field of view based vision training method, apparatus and device

Abstract

The application relates to a dynamic visual field-based visual training method, device and equipment, which comprises the following steps: acquiring detection data of a user to be trained and a training duration threshold, wherein the detection data comprises long-distance data, short-distance data and measurement accuracy; detecting the user to be trained according to the detection data to obtain visual data before training; determining training data according to the visual data; and performing long-distance visual field and short-distance visual field alternating cycle training on the user to be trained according to the training data until the duration reaches the training duration threshold, thereby completing the visual training of the user to be trained. The dynamic visual field-based visual training method determines training data by first acquiring visual data of a user to be trained, and then performing long-distance visual field and short-distance visual field alternating cycle training on the user to be trained according to the training data and the training duration threshold, so that the eyes of the user to be trained are truly moved during the training process, thereby significantly improving the training effect of eye fusion and accommodation convergence functions.

Description

Technical Field

[0001] This application relates to the field of vision training technology, and in particular to a vision training method, apparatus and device based on dynamic field of view. Background Technology

[0002] Eye fusion and accommodation / convergence training are important training methods specifically targeting the visual system, offering significant benefits and implications for eye health. Eye fusion training, through a series of visual activities, enhances binocular coordination, improving the eyes' ability to fuse different images into a single stereoscopic image. This helps eliminate visual inhibition, expands the fusion range, enhances stereoscopic vision, and provides adjunctive treatment for visual problems such as strabismus and amblyopia. Eye accommodation / convergence training, by exercising the eyes' accommodation and convergence functions, improves the speed and accuracy of the eyes adapting to objects at different distances, enhances convergence response, and improves visual perception efficiency, helping to prevent the occurrence and development of vision problems such as myopia. Both eye fusion and accommodation / convergence training not only help improve existing visual problems but also enhance the eyes' adaptability and overall visual quality, playing a crucial role in protecting eye health. However, current eye fusion and accommodation / convergence training methods are based on a single two-dimensional plane, failing to fully consider the interaction between spatial changes and the eye fusion and accommodation / convergence systems, resulting in poor visual training effects.

[0003] Currently, there are various training devices on the market based on technologies such as naked-eye 3D, red-green (blue) beam splitting, polarized beam splitting, and virtual reality (VR) for eye fusion and accommodation / convergence. These devices generally achieve the training effect by processing stereoscopic image pairs on a two-dimensional plane to create binocular disparity and generate a sense of depth. The main mechanisms of these training devices include: binocular disparity principle, fusion mechanism, and accommodation / convergence mechanism. The binocular disparity principle refers to these devices presenting slightly different images to the left and right eyes, simulating the situation in the real world where both eyes view the same object from different angles; this difference is interpreted by the brain as depth information, thus forming a sense of depth. The fusion mechanism refers to the merging of the images received by both eyes into a clear stereoscopic image in the brain; this process depends on the eye's fusion ability; training devices promote this ability by providing accurate binocular disparity images. The accommodation / convergence mechanism refers to the need for the eyes to constantly adjust focus (accommodation) and line-of-sight angle (convergence) to maintain binocular single vision.

[0004] The training devices described above simulate the convergence and divergence requirements of the eyes under different fixation states by presenting stereoscopic images with different parallax gradients on the same plane, and the corresponding accommodative responses triggered when convergence and divergence occur, thereby training binocular fusion and accommodative convergence and divergence abilities. The actual training process using these devices typically includes a preparation phase, a training phase, and a conclusion phase.

[0005] The preparation stage begins with selecting the equipment: based on individual circumstances and training needs, choose suitable training equipment, such as a naked-eye 3D vision trainer, a red-green (blue) beam splitter, a polarization beam splitter, or VR training equipment. Next, adjust the equipment: ensure the training equipment is in the correct position and angle so that both eyes can comfortably receive the image; for VR equipment, the tightness and focus of the headset also need to be adjusted to ensure image clarity.

[0006] During the training phase, naked-eye 3D devices using AI pupil tracking technology can be employed, allowing each eye to see two different images. The brain then constructs a virtual three-dimensional image based on the visual information. The eyes then need to track the images in all directions to achieve visual training. Alternatively, red-green (blue) beam splitters can be used with red-green (blue) glasses. By adjusting the position and angle of the red-green (blue) images, visual training can be performed. Polarized beam splitters use polarized lenses to allow each eye to see polarized light images at different angles. These images are then fused to achieve the training objective. VR devices can also be used to enter virtual scenes, training the eyes' fusion and accommodation / convergence abilities by focusing on and tracking virtual objects. As training progresses, the difficulty is gradually increased, such as by increasing image complexity and improving the speed of fixation and tracking, to comprehensively train the eyes' fusion and accommodation / convergence abilities.

[0007] The final stage refers to the end of training. The training effect is evaluated by comparing individual results with historical training data, with a focus on assessing improvements in the eye's fusion and accommodative convergence abilities. Based on this evaluation, subsequent training plans are adjusted according to individual circumstances. Summary of the Invention

[0008] This application provides a visual training method, apparatus, and device based on dynamic field of view, which addresses the technical problem that existing eye fusion and accommodation convergence function training methods are based on a single two-dimensional plane and do not fully consider the linkage between spatial changes and the eye fusion and accommodation convergence system, resulting in poor visual training effects.

[0009] To achieve the above objectives, this application provides the following technical solution:

[0010] On the one hand, a visual training method based on dynamic field of view is provided, including the following steps:

[0011] Acquire the detection data and training duration threshold of the user to be trained, wherein the detection data includes long-distance data, short-distance data, and measurement accuracy;

[0012] The user to be trained is tested based on the test data to obtain vision data before training;

[0013] Training data is determined based on the aforementioned vision data;

[0014] Based on the training data, the user to be trained is subjected to alternating training in the far field and near field until the training duration reaches the training duration threshold, thus completing the visual training of the user to be trained.

[0015] The visual acuity data includes the user's distance visual acuity data, near visual acuity data, distance fusional convergence separation distance, distance fusional divergence separation distance, near fusional convergence separation distance, near fusional divergence separation distance, distance fusion time, and near fusion time. The training data determined based on this visual acuity data includes:

[0016] The number of farsightedness targets in the training data is calculated based on the farsightedness data; the number of nearsightedness targets in the training data is calculated based on the nearsightedness data.

[0017] The far-set separation distance of the training data is calculated based on the far-range fusional set separation distance; the far-spread separation distance of the training data is calculated based on the far-range fusional spread separation distance; and the far-fusional duration of the training data is calculated based on the far-fusional duration and the near-fusional duration.

[0018] The near-set separation distance of the training data is calculated based on the near-set fusional set separation distance; the near-spread separation distance of the training data is calculated based on the near-set fusional spread separation distance; and the near-set fusional duration of the training data is calculated based on the near-set fusional duration required.

[0019] Wherein, the far-field convergence separation distance is not greater than the far-field fusional convergence separation distance, the far-field divergence separation distance is not greater than the far-field fusional divergence separation distance, the far-field fusion duration is not less than 5 times the near-field fusion duration, the near-field convergence separation distance is not greater than the near-field fusional convergence separation distance, the near-field divergence separation distance is not greater than the near-field fusional divergence separation distance, and the near-field fusion duration is not less than 2 times the near-field fusion duration required.

[0020] Preferably, the training data includes the number of far-sighted target poles, the number of near-sighted target poles, the far convergence separation distance, the far divergence separation distance, the far fusion duration, the near convergence separation distance, the near divergence separation distance, and the near fusion duration. Based on the training data, the user to be trained undergoes alternating cyclic training in the far and near fields until the duration reaches the training duration threshold. This completes the visual training of the user to be trained, including:

[0021] The operation of the far field of view equipment is controlled according to the far vision target pole number and the far fusion image duration to perform far field of view training, and the first total training duration at this time is obtained.

[0022] If the first total training time is less than the training time threshold, the myopia field device is controlled to run myopia field training according to the myopia target pole number and the myopia fusion duration, and the second total training time at this time is obtained.

[0023] If the second total training time is less than the training time threshold, return to the step of controlling the operation of the far field of vision device according to the far vision target number and the far fusion duration to perform far field of vision training, and then cyclically execute far field of vision training and near field of vision training until the total training time reaches the training time threshold, thus completing the visual training of the user to be trained.

[0024] If the first total training time or the second total training time is not less than the training time threshold, the visual training of the user to be trained is completed.

[0025] The training of the far field of view includes: the red-green target separation distance of the far field of view device is gradually increased from 0cm to the far convergence separation distance, then gradually changed from the far convergence separation distance to the far divergence separation distance, and then gradually changed from the far divergence separation distance to the far convergence separation distance. This cycle continues until the duration of operation of the far field of view device reaches the far fusion duration, at which point the far field of view device is turned off.

[0026] The training of the myopia field includes: the red-green target separation distance of the myopia field device is gradually increased from 0cm to the near convergence separation distance, then gradually changed from the near convergence separation distance to the near divergence separation distance, and then gradually changed from the near divergence separation distance back to the near convergence separation distance. This cycle continues until the duration of operation of the myopia field device reaches the near fusion duration, at which point the myopia field device is turned off.

[0027] Preferably, the visual acuity data of the user to be trained is obtained by testing the detection data based on the detection data, including:

[0028] The visual acuity of the user to be trained is tested using a visual acuity chart of a far-field device, with the optotypes of the chart arranged from largest to smallest, to obtain far-field visual acuity data; similarly, the visual acuity of the user to be trained is tested using a visual acuity chart of a near-field device, with the optotypes of the chart arranged from largest to smallest, to obtain near-field visual acuity data.

[0029] The fusional aggregation capability of the user to be trained is detected by using the spectroscopic target of the far field of view device according to the fusional aggregation rule, and the long-distance fusional aggregation separation distance is obtained; the fusional divergence capability of the user to be trained is detected by using the spectroscopic target of the far field of view device according to the fusional divergence rule, and the long-distance fusional divergence separation distance is obtained.

[0030] The near-field fusion ability of the user to be trained is detected by using the spectroscopic target of the myopic field device according to the fusion set rule, and the near-range fusion set separation distance is obtained; the near-field fusion divergence ability of the user to be trained is detected by using the spectroscopic target of the myopic field device according to the fusion divergence rule, and the near-range fusion divergence separation distance is obtained.

[0031] The duration of the training is determined by using the far field of view device according to the far fusion duration rule to obtain the required duration of far fusion; the duration of the training is determined by using the near field of view device according to the near fusion duration rule to obtain the required duration of near fusion.

[0032] Preferably, the fusion set rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the inner eye of the user to be trained by a step size until the user to be trained sees the split or blurred spectroscopic target;

[0033] The fusion divergence rule includes: gradually increasing the horizontal distance of the spectroscopic target outwards from the eye of the user to be trained by a step size until the user to be trained sees the split or blurred spectroscopic target;

[0034] Wherein, if the user to be trained is subjected to long-distance detection, the movement step size is the long-distance movement step size, and the product of the long-distance movement step size and the measurement accuracy is used as the long-distance movement step size; if the user to be trained is subjected to short-distance detection, the movement step size is the short-distance movement step size, and the product of one percent of the short-distance data and the measurement accuracy is used as the short-distance movement step size.

[0035] Preferably, the distance fusion duration rule includes: determining distance duration detection data based on the distance fusion set separation distance and the distance fusion divergence separation distance; randomly generating distance target position data based on the distance duration detection data; sorting the distance target position data from far to near according to the target position seen by the user to be trained; and obtaining the duration required to correctly sort the distance target position data as the distance fusion duration; the distance duration detection data includes Y... j 50%Y j Y s 50%Y s And 0, Y j Y represents the distance at which fusion sets separate. sThe near fusion duration rule includes: determining near duration detection data based on the near fusion set separation distance and the near fusion set separation distance; randomly generating myopia target position data based on the near duration detection data; sorting the myopia target position data from far to near according to the target position seen by the user to be trained; and obtaining the time required to correctly sort the myopia target position data as the near fusion duration; the near duration detection data includes J. j 50%J j J s 50%J s and 0, J j J is the near-range fusional set separation distance. s This refers to the near-field fusion dispersion separation distance.

[0036] On the other hand, a visual training device based on dynamic field of view is provided, including a data acquisition module, a detection module, a training data determination module, and a visual training module;

[0037] The data acquisition module is used to acquire the detection data and training duration threshold of the user to be trained. The detection data includes long-distance data, short-distance data and measurement accuracy.

[0038] The detection module is used to detect the user to be trained based on the detection data to obtain vision data before training.

[0039] The training data determination module is used to determine training data based on the vision data;

[0040] The visual training module is used to perform alternating cyclic training of the far field and near field of vision on the user to be trained based on the training data until the duration reaches the training duration threshold, thereby completing the visual training of the user to be trained.

[0041] The vision data includes the user's distance vision data, near vision data, distance fusional convergence separation distance, distance fusional divergence separation distance, near fusional convergence separation distance, near fusional divergence separation distance, distance fusion time required, and near fusion time required. The training data determination module includes:

[0042] The number of farsightedness targets in the training data is calculated based on the farsightedness data; the number of nearsightedness targets in the training data is calculated based on the nearsightedness data.

[0043] The far-set separation distance of the training data is calculated based on the far-range fusional set separation distance; the far-spread separation distance of the training data is calculated based on the far-range fusional spread separation distance; and the far-fusional duration of the training data is calculated based on the far-fusional duration and the near-fusional duration.

[0044] The near-set separation distance of the training data is calculated based on the near-set fusional set separation distance; the near-spread separation distance of the training data is calculated based on the near-set fusional spread separation distance; and the near-set fusional duration of the training data is calculated based on the near-set fusional duration required.

[0045] Wherein, the far-field convergence separation distance is not greater than the far-field fusional convergence separation distance, the far-field divergence separation distance is not greater than the far-field fusional divergence separation distance, the far-field fusion duration is not less than 5 times the near-field fusion duration, the near-field convergence separation distance is not greater than the near-field fusional convergence separation distance, the near-field divergence separation distance is not greater than the near-field fusional divergence separation distance, and the near-field fusion duration is not less than 2 times the near-field fusion duration required.

[0046] Preferably, the training data includes the number of farsighted target poles, the number of nearsighted target poles, the far convergence separation distance, the far divergence separation distance, the far fusion duration, the near convergence separation distance, the near divergence separation distance, and the near fusion duration. The visual training module includes a farsighted field training submodule, a nearsighted field training submodule, a cyclic training submodule, and a judgment submodule.

[0047] The far field training submodule is used to control the operation of the far field equipment to perform far field training according to the far field target pole number and the far fusion duration, and to obtain the first total training duration at this time.

[0048] The myopia field training submodule is used to control the operation of the myopia field device to perform myopia field training according to the myopia target pole number and the myopia fusion duration based on the first total training time being less than the training time threshold, and to obtain the second total training time at this time.

[0049] The loop training submodule is used to return to the far field of vision training submodule and loop through far field of vision training and near field of vision training until the total training time reaches the training time threshold, thus completing the visual training of the user to be trained.

[0050] The judgment submodule is used to complete the visual training of the user to be trained based on the first total training time or the second total training time not being less than the training time threshold.

[0051] The training of the far field of view includes: the red-green target separation distance of the far field of view device is gradually increased from 0cm to the far convergence separation distance, then gradually changed from the far convergence separation distance to the far divergence separation distance, and then gradually changed from the far divergence separation distance to the far convergence separation distance. This cycle continues until the duration of operation of the far field of view device reaches the far fusion duration, at which point the far field of view device is turned off.

[0052] The training of the myopia field includes: the red-green target separation distance of the myopia field device is gradually increased from 0cm to the near convergence separation distance, then gradually changed from the near convergence separation distance to the near divergence separation distance, and then gradually changed from the near divergence separation distance back to the near convergence separation distance. This cycle continues until the duration of operation of the myopia field device reaches the near fusion duration, at which point the myopia field device is turned off.

[0053] Preferably, the detection module includes a first detection submodule, a second detection submodule, a third detection submodule, and a fourth detection submodule;

[0054] The first detection submodule is used to perform vision testing on the user to be trained using a vision chart of a far vision field device and according to the optotypes of the vision chart from largest to smallest to obtain far vision data; and to perform vision testing on the user to be trained using a vision chart of a near vision field device and according to the optotypes of the vision chart from largest to smallest to obtain near vision data.

[0055] The second detection submodule is used to perform fusion set capability detection on the user to be trained using the spectrophotometer of the far field of view device according to the fusion set rule, to obtain the long-distance fusion set separation distance; and to perform fusion divergence capability detection on the user to be trained using the spectrophotometer of the far field of view device according to the fusion divergence rule, to obtain the long-distance fusion divergence separation distance.

[0056] The third detection submodule is used to perform fusional aggregation capability detection on the user to be trained using the spectrophotometer of the myopia field device according to the fusional aggregation rule, to obtain the near-range fusional aggregation separation distance; and to perform fusional divergence capability detection on the user to be trained using the spectrophotometer of the myopia field device according to the fusional divergence rule, to obtain the near-range fusional divergence separation distance.

[0057] The fourth detection submodule is used to perform duration detection on the user to be trained using the far field of view device according to the far fusion duration rule to obtain the required duration of far fusion; and to perform duration detection on the user to be trained using the near field of view device according to the near fusion duration rule to obtain the required duration of near fusion.

[0058] Preferably, the fusion set rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the inner eye of the user to be trained by a movement step size until the user to be trained sees the split or blurred spectroscopic target; the fusion divergence rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the outer eye of the user to be trained by a movement step size until the user to be trained sees the split or blurred spectroscopic target; wherein, if the user to be trained is subjected to far-distance detection, the movement step size is the far-distance movement step size, and the product of the far-distance movement step size and the measurement accuracy is used as the far-distance movement step size; if the user to be trained is subjected to near-distance detection, the movement step size is the near-distance movement step size, and the product of one percent of the near-distance data and the measurement accuracy is used as the near-distance movement step size.

[0059] On another front, a visual training system based on a dynamic field of view is provided, including a dynamic field of view training unit and a far field of view device and a near field of view device connected to the dynamic field of view training unit. The far field of view device and the near field of view device are respectively connected to a flickering vision enhancer. The dynamic field of view training unit controls the operation of the far field of view device and the near field of view device according to the aforementioned visual training method based on a dynamic field of view, so as to complete the visual training of the user to be trained.

[0060] The method, apparatus, and device for visual training based on dynamic field of view include: acquiring detection data and a training duration threshold for the user to be trained; the detection data includes far-range data, near-range data, and measurement accuracy; performing tests on the user to be trained based on the detection data to obtain pre-training visual acuity data; determining training data based on the visual acuity data; and performing alternating cyclic training of the user to be trained in far-range and near-range fields based on the training data until the training duration reaches the training duration threshold, thereby completing the visual training of the user to be trained.

[0061] As can be seen from the above technical solutions, this application has the following advantages: This visual training method based on dynamic field of view first obtains the visual acuity data detected by the user to be trained to determine the training data, and then performs alternating cyclic training of the far field and near field of view on the user to be trained according to the training data and the training duration threshold, so that the eyes of the user to be trained "truly move" during the training process, thereby significantly improving the training effect of eye fusion and accommodation convergence function, and solving the technical problem that the existing eye fusion and accommodation convergence function training methods are based on a single two-dimensional plane and do not fully consider the linkage relationship between spatial changes and eye fusion and accommodation convergence system, resulting in poor visual acuity training effect.

[0062] This dynamic field-of-view visual training device introduces a dynamic field-of-view training method through a data acquisition module, a detection module, a training data determination module, and a visual training module. It systematically considers the linkage between the near and far visual fields, overcoming the limitations of traditional single two-dimensional planar training methods. This dynamic field-of-view visual training device can more realistically simulate visual changes in daily life, allowing the eyes to "truly move" during training, thereby significantly improving the training effect of eye fusion and accommodation / convergence functions. Attached Figure Description

[0063] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0064] Figure 1 This is a flowchart illustrating the steps of the visual training method based on dynamic field of view described in the embodiments of this application.

[0065] Figure 2 This is a flowchart illustrating the visual training method based on dynamic field of view as described in the embodiments of this application.

[0066] Figure 3 This is a flowchart illustrating the framework of iterative training in the visual training method based on dynamic field of view described in the embodiments of this application.

[0067] Figure 4 This is a flowchart illustrating the framework of fusion detection in the visual training method based on dynamic field of view described in the embodiments of this application.

[0068] Figure 5 This is a flowchart illustrating the framework for duration detection in the visual training method based on dynamic field of view described in the embodiments of this application.

[0069] Figure 6 This is a schematic diagram of the framework of the visual training device based on dynamic field of view described in the embodiments of this application;

[0070] Figure 7 This is a schematic diagram of the visual training system based on dynamic field of view described in an embodiment of this application. Detailed Implementation

[0071] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0072] In the description of the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0073] In the embodiments of this application, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0074] Patent terminology used in this application:

[0075] A flashing vision enhancer is a medical device that improves visual function by stimulating the visual system with alternating flashes of multi-colored light. It is mainly suitable for children with amblyopia, strabismus, and accommodative myopia. The device utilizes alternating stimulation with pulsed multi-frequency red, green, and blue light to promote the excitation of cone cells and improve ocular accommodation and blood circulation.

[0076] The myopia field device consists of a main unit and a screen. Its main function is to display myopia field detection and training content, and it also acts as a remote control device when conducting far vision field detection and training.

[0077] The far field of view device consists of a main unit and a screen, and its main function is to display far field of view detection and training content.

[0078] Red-green (blue) split glasses work by splitting red and green (blue) light, allowing the left and right eyes to see red and green images at different positions and angles, thus creating stereoscopic vision.

[0079] This application provides a visual training method, apparatus, and device based on dynamic field of view, which solves the technical problem that existing eye fusion and accommodation convergence function training methods are based on a single two-dimensional plane and do not fully consider the linkage between spatial changes and the eye fusion and accommodation convergence system, resulting in poor visual training effect.

[0080] Example 1:

[0081] Figure 1 This is a flowchart illustrating the steps of the visual training method based on dynamic field of view described in an embodiment of this application. Figure 2 This is a flowchart illustrating the visual training method based on dynamic field of view as described in the embodiments of this application.

[0082] like Figure 1 As shown in the figure, this application provides a visual training method based on dynamic field of view, including the following steps:

[0083] S1. Obtain the detection data and training duration threshold of the user to be trained. The detection data includes long-distance data, short-distance data, and measurement accuracy.

[0084] It should be noted that step S1 involves acquiring the detection data and training duration threshold for the user to be trained. The detection data includes long-distance data Y, short-distance data J, and measurement accuracy K. In this embodiment, the training duration threshold can be set according to requirements, such as a training duration threshold of 5 minutes. The long-distance data Y can be 150cm ≤ Y ≤ 600cm. The short-distance data J can be 20cm ≤ J ≤ 40cm. The measurement accuracy K = 0.25 * n, where n is an integer ranging from 1 to 8.

[0085] S2. Based on the detection data, conduct tests on the users to be trained to obtain pre-training vision data.

[0086] It should be noted that the vision data includes the distance vision data (E) of the user to be trained. y Near vision data E j Long-distance fusion set separation distance Y j Long-distance fusion dispersion separation distance Y s Near-range fusional set separation distance J j Near-range fusional divergence separation distance J s Duration Y required for far-field fusion imaging t The time required for near-perfect fusion imaging J tIn step S2, based on the detection data from step S1, the user to be trained undergoes distance vision testing, near vision testing, distance fusion convergence ability testing, distance fusion divergence ability testing, near fusion convergence ability testing, near fusion divergence ability testing, and distance / near fusion duration testing to obtain vision data, which provides a basis for determining training data in subsequent steps.

[0087] In this embodiment of the application, the visual training method based on dynamic field of view further includes generating a test report based on visual acuity data.

[0088] It should be noted that the test report includes:

[0089] Record distance vision data E y =Best distance vision;

[0090] Record near vision data E j =Best near vision;

[0091] Calculate the long-distance fusion divergence capability (△) = Y s / Y;

[0092] Calculate the long-distance fusion ensemble capability (Δ) = Y j / Y;

[0093] Calculate near-range fusion convergence capability (Δ) = J j / J;

[0094] Calculate near-range fusion divergence capability (Δ) = J s / J;

[0095] The normal reference range for far / near fusion convergence function is as follows:

[0096] Long distance: Gathering ability (△) is 15~20, and spreading ability (△) is 6~10;

[0097] Close range: Convergence ability (△) is 30~40, dispersion ability (△) is 12~16;

[0098] Recording time Y for distant fusion imaging t ;

[0099] Time required to record near-fusion images J t .

[0100] S3. Determine training data based on vision data.

[0101] It should be noted that the training data includes the number of farsighted target poles, the number of nearsighted target poles, the far convergence separation distance, the far divergence separation distance, the far fusion duration, the near convergence separation distance, the near divergence separation distance, and the near fusion duration. In step S3, the training data required for training is obtained based on the visual acuity data from step S2.

[0102] S4. Based on the training data, perform alternating training cycles of far vision and near vision for the user to be trained until the training duration reaches the threshold, thus completing the visual training for the user to be trained.

[0103] It should be noted that in step S4, the visual training of the user to be trained is performed based on the training data obtained in step S3, thus obtaining the training data of the user to be trained.

[0104] In this embodiment, the dynamic field-of-view-based visual training method systematically considers the linkage between the near and far visual fields by introducing a dynamic field-of-view training approach, thus overcoming the limitations of traditional single two-dimensional planar training methods. This dynamic field-of-view-based visual training method can more realistically simulate visual changes in daily life, allowing the eyes to "truly move" during training, thereby significantly improving the training effect of eye fusion and accommodation / convergence functions.

[0105] It should be noted that this dynamic field-of-view-based visual training method combines near-field devices, far-field devices, and flickering vision enhancers (such as red-green / red-blue spectacle glasses) to simulate visual scenes in real-world environments, making the training process more closely resemble actual eye needs. This natural training method not only increases user engagement but also reduces discomfort during training and enhances the sustainability of training.

[0106] This application provides a visual training method based on dynamic field of view, comprising: acquiring detection data and a training duration threshold for the user to be trained; the detection data including far-distance data, near-distance data, and measurement accuracy; conducting tests on the user to be trained based on the detection data to obtain pre-training visual acuity data; determining training data based on the visual acuity data; and performing alternating cyclic training of the user to be trained in far-field and near-field according to the training data until the training duration threshold is reached, thus completing the visual training for the user to be trained. This visual training method based on dynamic field of view first acquires the visual acuity data detected by the user to be trained to determine the training data, and then performs alternating cyclic training of the user to be trained in far-field and near-field according to the training data and the training duration threshold. This allows the user's eyes to "truly move" during the training process, thereby significantly improving the training effect of eye fusion and accommodation convergence functions. It solves the technical problem that existing eye fusion and accommodation convergence function training methods are based on a single two-dimensional plane and do not fully consider the linkage between spatial changes and the eye fusion and accommodation convergence systems, resulting in poor visual acuity training effects.

[0107] In one embodiment of this application, determining training data based on vision data includes:

[0108] The number of farsightedness targets for the training data is calculated based on the distance vision data; the number of nearsightedness targets for the training data is calculated based on the near vision data.

[0109] The far-set separation distance of the training data is calculated based on the far-set fusion separation distance; the far-spread separation distance of the training data is calculated based on the far-set fusion separation distance; and the far-set fusion duration of the training data is calculated based on the far-set fusion duration and the near-set fusion duration.

[0110] The near-convergence separation distance of the training data is calculated based on the near-convergence divergence separation distance; the near-convergence duration of the training data is calculated based on the near-convergence fusion required duration;

[0111] Among them, the far convergence separation distance is not greater than the far-distance fusional convergence separation distance, the far divergence separation distance is not greater than the far-distance fusional divergence separation distance, the far fusional duration is not less than 5 times the near fusional duration, the near convergence separation distance is not greater than the near fusional convergence separation distance, the near divergence separation distance is not greater than the near fusional divergence separation distance, and the near fusional duration is not less than 2 times the near fusional duration required.

[0112] It should be noted that this dynamic field-of-view-based visual training method determines training data based on visual acuity data before conducting visual training on the users to be trained. In determining the training data, one step is to set the optotype size: the farsighted optotype pole number E is set. y0 =E y / 2, setting the myopia target level size E j0 =E j / 2. The higher the optotype level, the greater the training difficulty. The maximum optotype level for training should be ≤ the optimal visual acuity level, such as: E. y0 ≦E y E j0 ≦E j Second, set the far / near fusion separation distance: far fusion separation distance Y j0 =50%Y j Dispersion distance Y s0 =50%Y s Near set separation distance J j0 =50%J j Nearly dispersed separation distance J s0 =50%J s The larger the separation distance, the greater the training difficulty; the maximum limiting factor is the far set separation distance Y. j0≦Y j The maximum divergence separation distance Y s0 ≦Y s The largest near set separation distance J j0 ≦J j The maximum near-dispersion separation distance is J s0 ≦J s Third, set the training duration and the far / near fusion switching time; the default training duration is 5 minutes, and the training time threshold X is limited. t ≥5 minutes; Duration of far-field fusion imaging Y t0 =max(1000%Y t 1000%J t ), duration of near-fusion imaging J t0 =200%J t Limit the shortest possible duration of near-fusion imaging J t0 ≥200%J t And Y t0 ≧5*J t0 .

[0113] Figure 3 This is a flowchart illustrating the framework of iterative training in the visual training method based on dynamic field of view described in the embodiments of this application.

[0114] like Figure 3 As shown, in one embodiment of this application, the visual training of the user to be trained is performed by alternating between far field of vision and near field of vision based on training data until the training duration reaches a training duration threshold, thereby completing the visual training of the user to be trained.

[0115] The operation of the far field of view equipment is controlled according to the far field of view pole number and the duration of far fusion to conduct far field of view training, and the first total training duration at this time is obtained.

[0116] If the first total training time is less than the training time threshold, the operation of the myopia field device is controlled according to the myopia target pole number and the duration of myopia fusion to carry out myopia field training, and the second total training time at this time is obtained.

[0117] If the total training time is less than the training time threshold, return to the step of controlling the operation of the far field of vision device according to the far field of vision pole number and the duration of far fusion to carry out far field of vision training, and then perform far field of vision training and near field of vision training in a loop until the total training time reaches the training time threshold, and complete the visual training of the user to be trained.

[0118] If the first total training time or the second total training time is not less than the training time threshold, the visual training of the user to be trained is completed.

[0119] The training for the far field of view includes: gradually increasing the red-green target separation distance of the far field of view device from 0cm to the far convergence separation distance, then gradually changing from the far convergence separation distance to the far divergence separation distance, and then gradually changing from the far divergence separation distance to the far convergence separation distance, and so on until the duration of operation of the far field of view device reaches the duration of far fusion, and then controlling the far field of view device to be turned off;

[0120] The training content for the myopia field includes: gradually increasing the red-green target separation distance of the myopia field device from 0cm to the near convergence separation distance, then gradually changing the near convergence separation distance to the near divergence separation distance, and then gradually changing the near divergence separation distance back to the near convergence distance. This cycle continues until the duration of operation of the myopia field device reaches the near fusion duration, at which point the myopia field device is turned off.

[0121] It should be noted that during the alternating training of the far-field and near-field devices for the user to be trained based on training data and training duration thresholds, the user is first trained in the far-field mode. The screen of the near-field device is turned off, the screen of the far-field device is turned on, and the user is prompted to carefully observe the screen of the far-field device. The separation distance between the red and green visual targets on the far-field device gradually changes from 0cm to Y. j0 Then Y j0 Stepwise transformation to Y s0 Then Y s0 Then gradually transform to Y j0 This cycle continues until the duration reaches the far-field fusion imaging duration Y. t0 When the total training time is less than the training time threshold, the screen of the far-field device is turned off, the screen of the near-field device is turned on, and the user is prompted to carefully observe the screen of the near-field device. The separation distance between the red and green targets of the near-field device gradually changes from 0 cm to J. j0 Then J j0 Stepwise transformation to J s0 Then J s0 Then gradually transform to J j0 This cycle continues until the duration reaches J. t0 If the total training time is less than the training time threshold, switch back to far field of view training and repeat the far and near field of view training until the total training time reaches the set training time threshold X. t This concludes the visual training for the user.

[0122] Figure 4 This is a flowchart illustrating the framework of fusion detection in the dynamic field-of-view-based visual training method described in this application. Figure 5 This is a flowchart illustrating the duration detection framework in the dynamic field-of-view-based visual training method described in this application embodiment.

[0123] like Figure 4 and Figure 5 As shown, in one embodiment of this application, the vision data of the user to be trained is obtained by testing based on the detection data, including:

[0124] Visual acuity data for the trainee was obtained by using a visual acuity chart with the optotypes on the chart from largest to smallest, and by using a visual acuity chart with the optotypes on the chart from largest to smallest.

[0125] Using a far-field-of-view device, the beam-splitter is used to test the fusion convergence capability of the user to be trained according to the fusion convergence rule, and the far-distance fusion convergence separation distance is obtained; using a far-field-of-view device, the beam-splitter is used to test the fusion divergence capability of the user to be trained according to the fusion divergence rule, and the far-distance fusion divergence separation distance is obtained.

[0126] Using a myopic field device, the spectroscopic target is used to test the fusion convergence ability of the user to be trained according to the fusion convergence rule, and the near-range fusion convergence separation distance is obtained; using a myopic field device, the spectroscopic target is used to test the fusion divergence ability of the user to be trained according to the fusion divergence rule, and the near-range fusion divergence separation distance is obtained.

[0127] The duration of the training user was measured using a far-field-of-view device according to the far-field fusion duration rule to obtain the required duration of far-field fusion; the duration of the training user was measured using a near-field-of-view device according to the near-field fusion duration rule to obtain the required duration of near-field fusion.

[0128] It should be noted that the visual acuity data for visual training is obtained by testing the users to be trained based on the test data. In this embodiment, during the visual acuity test of the users to be trained, the distance visual acuity test is performed by activating the screen of the distance vision device to display a standard distance visual acuity chart (such as a logarithmic visual acuity chart or a children's special graphic visual acuity chart, which can be switched as needed). The optotypes (such as letters, numbers, or graphics) on the visual acuity chart are displayed in descending order of size. The users to be trained identify and report the content of the optotypes they see line by line according to the optotypes on the screen. The distance visual acuity value (such as 5.0, 4.9, etc.) corresponding to the smallest number of optotype lines that the users to be trained can clearly identify is recorded as distance visual acuity data, and the distance visual acuity data is also saved to the user's profile. Near vision testing involves activating the near vision field device and displaying a standard near vision chart (such as the Jaeger or LogMAR near vision chart, which can be switched as needed). The optotypes (such as letters, numbers, or graphics) on the chart are displayed sequentially from largest to smallest. The user to be trained identifies and reports the content of the optotypes seen on the screen line by line. The near vision value corresponding to the smallest number of optotype lines that the user can clearly identify is recorded as near vision data and saved to the user's profile.

[0129] like Figure 4 As shown, in one embodiment of this application, the fusion set rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the inner eye of the user to be trained by a movement step size until the user to be trained sees a split or blurred spectroscopic target; the fusion divergence rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the outer eye of the user to be trained by a movement step size until the user to be trained sees a split or blurred spectroscopic target; wherein, if the user to be trained is subjected to far-distance detection, the movement step size is the far-distance movement step size, and the product of the far-distance movement step size and the measurement accuracy is used as the far-distance movement step size; if the user to be trained is subjected to near-distance detection, the movement step size is the near-distance movement step size, and the product of one percent of the near-distance data and the measurement accuracy is used as the near-distance movement step size.

[0130] It should be noted that during the testing of far-field fusional convergence ability, red-green (blue) spectroscopic targets (target level = best distance visual acuity level / 2) are displayed on the screen of the far-field device. The user to be trained is asked to look at the screen of the far-field device and whether they see a single, clear image. The horizontal separation distance of the red and green targets is gradually increased towards the inner (nasal) side of the eye, according to the step size of each far-field movement (using binocular convergence ability). When the user sees the targets as split or blurred, the separation distance is recorded. This process is repeated three times, and the average of the three separation distances is taken as the far-field fusional convergence separation distance Y. j Calculate the long-distance fusion ensemble capability (Δ) = Y j / Y. During the testing of far-field fusional divergence ability, red-green (blue) spectroscopic targets (target level = best distance visual acuity level / 2) are displayed on the screen of the far-field device; the user to be trained is asked to look at the screen of the far-field device and whether they see a single, clear image; the horizontal separation distance of the red and green targets is gradually increased outwards with each step of far-field movement (using binocular divergence ability); when the target seen by the user is split or blurred, the separation distance is recorded, and this is repeated three times. The average of the three separation distances is taken as the far-field fusional divergence separation distance Y. s Calculate the long-distance fusion divergence capability (Δ) = Y s / Y. During the near-field fusional convergence test, red-green (blue) spectroscopic targets (target level = best near visual acuity level / 2) are displayed on the screen of the near-field device. The user to be trained is asked to look at the screen of the near-field device and whether they see a single, clear image. The horizontal separation distance of the red and green targets is gradually increased towards the inner eye (nasal side) according to the near-field movement step (using binocular convergence ability). When the user sees the targets as split or blurred, the separation distance is recorded. This is repeated three times, and the average of the three separation distances is taken as the near-field fusional convergence separation distance J. jCalculate the near-range fusion convergence capability (Δ) = J j / J. During the testing of near-field fusional divergence ability, red-green (blue) spectroscopic targets (target level = best near-far visual acuity level / 2) are displayed on the screen of the near-field device; the user to be trained is asked to look at the screen of the near-field device and whether they see a single, clear image; the horizontal separation distance of the red and green targets is gradually increased outwards with each near-field movement step (using binocular divergence ability); when the user sees the targets as split or blurred, the separation distance is recorded, and this is repeated three times. The average of the three separation distances is taken as the near-field fusional divergence separation distance J. s Calculate the near-range fusion divergence capability (Δ) = J s / J.

[0131] like Figure 5 As shown, in one embodiment of this application, the distance fusion duration rule includes: determining distance duration detection data based on the distance fusion set separation distance and the distance fusion divergence separation distance; randomly generating distance target position data based on the distance duration detection data; sorting the distance target position data from far to near according to the target position seen by the user to be trained; and obtaining the duration required to correctly sort the distance target position data as the distance fusion duration; the distance duration detection data includes Y... j 50%Y j Y s 50%Y s And 0, Y j Y represents the distance at which fusion sets separate. s For long-distance fusion dispersion separation distance;

[0132] The near fusion duration rule includes: determining near duration detection data based on near fusional convergence separation distance and near fusional divergence separation distance; randomly generating myopia target position data based on the near duration detection data; sorting the myopia target position data from far to near according to the target position seen by the user to be trained; and obtaining the time required to correctly sort the myopia target position data as the near fusion duration; the near duration detection data includes J j 50%J j J s 50%J s and 0, J j J is the near-range fusional set separation distance. s This refers to the near-field fusion dispersion separation distance.

[0133] It should be noted that during the detection of the distance fusion duration, the screen of the near-field device is turned off, and the screen of the far-field device is turned on. The user is prompted to carefully observe the screen of the far-field device, and the distance separation distance is taken as Y. j 50%Yj Y s 50%Y s Five sets of red-green (blue) visual targets (0 and 0) are used as distance fusion time detection data. After binocular fusion is performed on the user, the five sets of red-green (blue) visual targets are presented from far to near. The positions of the five sets of red-green (blue) visual targets are randomly shuffled to generate distance target position data. The user is then asked to arrange the visual targets in order from far to near according to their perceived positions. The time required for the user to correctly complete the arrangement of all visual targets is recorded. This process is repeated three times, and the average of the three times is taken as the distance fusion time Y. t During the near-field fusion duration detection, the screen of the far-field device is turned off, and the screen of the near-field device is turned on. The user is prompted to carefully observe the screen of the near-field device, and the near separation distance is taken as J. j 50%J j J s 50%J s Five sets of red-green (blue) visual targets (0 and 0) were used as near-vision time detection data. After binocular fusion was performed on the user, the five sets of red-green (blue) visual targets were arranged from far to near. The positions of the five sets of red-green (blue) visual targets were randomly shuffled to generate near-vision target position data. The user was then asked to arrange the visual targets in order from far to near according to their perceived positions. The time required for the user to correctly complete the arrangement of all visual targets was recorded. This was repeated three times, and the average of the three times was taken as the near-vision fusion time J. t .

[0134] Example 2:

[0135] Figure 6 This is a schematic diagram of the framework of the visual training device based on dynamic field of view described in the embodiments of this application.

[0136] like Figure 6 As shown, this application embodiment provides a visual training device based on dynamic field of view, including a data acquisition module 10, a detection module 20, a training data determination module 30, and a visual training module 40;

[0137] The data acquisition module 10 is used to acquire the detection data and training duration threshold of the user to be trained. The detection data includes long-distance data, short-distance data and measurement accuracy.

[0138] The detection module 20 is used to detect the user to be trained based on the detection data to obtain vision data before training.

[0139] Training data determination module 30 is used to determine training data based on vision data;

[0140] The vision training module 40 is used to determine training data based on vision data.

[0141] It should be noted that the content of the modules in the device of Embodiment 2 has already been described in the steps of the method of Embodiment 1, and the content of the dynamic field-of-view visual training device module will not be repeated in this embodiment. In this embodiment, the dynamic field-of-view visual training device introduces a dynamic field-of-view training method through a data acquisition module, a detection module, a training data determination module, and a visual training module. It systematically considers the linkage relationship between the far and near fields of vision, overcoming the limitations of traditional single two-dimensional planar training methods. This dynamic field-of-view visual training device can more realistically simulate visual changes in daily life, making the eyes "truly move" during the training process, thereby significantly improving the training effect of eye fusion and accommodation convergence functions.

[0142] In this embodiment of the application, the training data includes the number of farsighted target poles, the number of nearsighted target poles, the far convergence separation distance, the far divergence separation distance, the far fusion duration, the near convergence separation distance, the near divergence separation distance, and the near fusion duration. The visual training module 40 includes a farsighted field training submodule, a nearsighted field training submodule, a loop training submodule, and a judgment submodule.

[0143] The far field training submodule is used to control the operation of the far field equipment to perform far field training according to the far field target pole number and the duration of far fusion, and to obtain the first total training duration at this time.

[0144] The myopia field training submodule is used to control the operation of the myopia field device to perform myopia field training based on the myopia target pole number and the duration of myopia fusion when the first total training time is less than the training time threshold, and to obtain the second total training time at this time.

[0145] The loop training submodule is used to return to the far field of vision training submodule and loop through far field of vision training and near field of vision training until the total training time reaches the training time threshold, thus completing the visual training of the user to be trained.

[0146] The judgment submodule is used to complete the visual training of the user to be trained based on the first total training time or the second total training time not being less than the training time threshold.

[0147] The training for the far field of view includes: gradually increasing the red-green target separation distance of the far field of view device from 0cm to the far convergence separation distance, then gradually changing from the far convergence separation distance to the far divergence separation distance, and then gradually changing from the far divergence separation distance to the far convergence separation distance, and so on until the duration of operation of the far field of view device reaches the duration of far fusion, and then controlling the far field of view device to be turned off;

[0148] The training content for the myopia field includes: gradually increasing the red-green target separation distance of the myopia field device from 0cm to the near convergence separation distance, then gradually changing the near convergence separation distance to the near divergence separation distance, and then gradually changing the near divergence separation distance back to the near convergence distance. This cycle continues until the duration of operation of the myopia field device reaches the near fusion duration, at which point the myopia field device is turned off.

[0149] In this embodiment of the application, the vision data includes the user's distance vision data, near vision data, distance fusional convergence separation distance, distance fusional divergence separation distance, near fusional convergence separation distance, near fusional divergence separation distance, distance fusion time required, and near fusion time required. The training data determination module 30 includes:

[0150] The number of farsightedness targets for the training data is calculated based on the distance vision data; the number of nearsightedness targets for the training data is calculated based on the near vision data.

[0151] The far-set separation distance of the training data is calculated based on the far-set fusion separation distance; the far-spread separation distance of the training data is calculated based on the far-set fusion separation distance; and the far-set fusion duration of the training data is calculated based on the far-set fusion duration and the near-set fusion duration.

[0152] The near-convergence separation distance of the training data is calculated based on the near-convergence divergence separation distance; the near-convergence duration of the training data is calculated based on the near-convergence fusion required duration;

[0153] Among them, the far convergence separation distance is not greater than the far-distance fusional convergence separation distance, the far divergence separation distance is not greater than the far-distance fusional divergence separation distance, the far fusional duration is not less than 5 times the near fusional duration, the near convergence separation distance is not greater than the near fusional convergence separation distance, the near divergence separation distance is not greater than the near fusional divergence separation distance, and the near fusional duration is not less than 2 times the near fusional duration required.

[0154] In this embodiment of the application, the detection module includes a first detection submodule, a second detection submodule, a third detection submodule, and a fourth detection submodule;

[0155] The first detection submodule is used to perform vision tests on the user to be trained using a vision chart of a far vision field device and according to the optotypes of the vision chart from largest to smallest to obtain far vision data; and to perform vision tests on the user to be trained using a vision chart of a near vision field device and according to the optotypes of the vision chart from largest to smallest to obtain near vision data.

[0156] The second detection submodule is used to perform fusion set capability detection on the user to be trained using the spectroscopic target of the far field of view device according to the fusion set rule, and to obtain the long-distance fusion set separation distance; and to perform fusion divergence capability detection on the user to be trained using the spectroscopic target of the far field of view device according to the fusion divergence rule, and to obtain the long-distance fusion divergence separation distance.

[0157] The third detection submodule is used to perform fusion convergence capability detection on the user to be trained using the spectroscopic target of the myopic field device according to the fusion convergence rule, and to obtain the near-range fusion convergence separation distance; and to perform fusion divergence capability detection on the user to be trained using the spectroscopic target of the myopic field device according to the fusion divergence rule, and to obtain the near-range fusion divergence separation distance.

[0158] The fourth detection submodule is used to perform duration detection on the user to be trained using a far field of view device according to the far fusion duration rule to obtain the required duration of far fusion; and to perform duration detection on the user to be trained using a near field of view device according to the near fusion duration rule to obtain the required duration of near fusion.

[0159] It should be noted that the fusion set rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the inner eye of the user to be trained by a step size until the user sees a split or blurred spectroscopic target; the fusion divergence rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the outer eye of the user to be trained by a step size until the user sees a split or blurred spectroscopic target; wherein, if the user to be trained is being tested at a distance, the step size is the distance movement step size, and the product of the distance movement step size and the measurement accuracy is used as the distance movement step size; if the user to be trained is being tested at a near distance, the step size is the near distance movement step size, and the product of one percent of the near distance data and the measurement accuracy is used as the near distance movement step size.

[0160] Example 3:

[0161] Figure 7 This is a schematic diagram of the visual training system based on dynamic field of view described in an embodiment of this application.

[0162] like Figure 7 As shown, this application provides a vision training system based on dynamic field of view, including a dynamic field of view training unit and a far field of view device and a near field of view device connected to the dynamic field of view training unit. The far field of view device and the near field of view device are respectively connected to a flicker vision enhancer. The dynamic field of view training unit controls the operation of the far field of view device and the near field of view device according to the above-described vision training method based on dynamic field of view, so as to complete the vision training for the user to be trained.

[0163] It should be noted that the visual training method based on dynamic field of view has already been described in Embodiment 1, and will not be repeated in this embodiment. The flickering vision enhancer can be red-green (blue) spectacle glasses.

[0164] In the embodiments of this application, before testing the user to be trained based on the detection data and obtaining the vision data before training, the vision training method based on dynamic field of view includes adjusting the far field of view device, the near field of view device and the red-green (blue) spectrophotometer.

[0165] It should be noted that the debugging process includes: 1. Checking whether the near-field device, far-field device, and red-green (blue) spectrophotometer are working properly; 2. Correcting the brightness, contrast, and other parameters of the near-field and far-field devices to ensure clear display; 3. Confirming that the dynamic field-of-view training unit has been started and checking whether its program functions are normal; 4. Initializing the dynamic field-of-view training unit program to ensure that the position of the red-green (blue) optotypes displayed on the screen is consistent with the position of the red-green (blue) spectrophotometer (i.e., assuming the right eye has a red spectrophotometer, the red optotype is located on the right side of the screen, and the green (blue) optotype is located on the left side of the screen); 5. The dynamic field-of-view training unit program automatically obtains the dpi and resolution of the near / far-field devices and converts the pixel unit to centimeters. (Assuming screen resolution = W * H pixels and screen DPI = dpi, then the horizontal width of the screen Wcm = (W / dpi) * 2.54 cm; the vertical width of the screen Hcm = (H / dpi) * 2.54 cm).

[0166] In this embodiment of the application, the dynamic field of view training unit also records the basic information of the user to be trained and optional eye and vision health information.

[0167] It should be noted that basic information includes name, age, and gender. Eye and vision health information includes refractive error type (myopia, hyperopia, astigmatism) and refractive power.

[0168] In this embodiment, the dynamic field-of-view training unit also has core training functions, user role management, system management, data analysis and reporting, security and privacy, and extended functions. The contents of the core training functions, user role management, system management, data analysis and reporting, security and privacy, and extended functions are shown in Table 1.

[0169] Table 1 shows the functional modules of the dynamic field-of-view training unit.

[0170]

[0171] In this embodiment, the dynamic field-of-view-based visual training system can automatically generate personalized training plans based on the user's visual acuity and training progress, and provide real-time feedback and adjustment suggestions. This personalized training method can better meet the needs of different users and help them achieve their training goals more quickly. This dynamic field-of-view-based visual training system significantly improves the effectiveness of visual training and the user experience, while also considering safety and universality, and has significant practical value and social significance.

[0172] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0173] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0174] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0175] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0176] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0177] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A visual training device based on a dynamic field of view, characterized in that, include: Data acquisition module, detection module, training data determination module, and visual training module; The data acquisition module is used to acquire the detection data and training duration threshold of the user to be trained. The detection data includes long-distance data, short-distance data and measurement accuracy. The detection module is used to detect the user to be trained based on the detection data to obtain vision data before training; The training data determination module is used to determine training data based on the vision data; The visual training module is used to perform alternating cyclic training of the far field and near field of vision on the user to be trained based on the training data until the duration reaches the training duration threshold, thereby completing the visual training of the user to be trained. The vision data includes the user's distance vision data, near vision data, distance fusional convergence separation distance, distance fusional divergence separation distance, near fusional convergence separation distance, near fusional divergence separation distance, distance fusion time required, and near fusion time required. The training data determination module includes: The number of farsightedness targets in the training data is calculated based on the farsightedness data; the number of nearsightedness targets in the training data is calculated based on the nearsightedness data. The far-set separation distance of the training data is calculated based on the far-range fusional set separation distance; the far-spread separation distance of the training data is calculated based on the far-range fusional spread separation distance; and the far-fusional duration of the training data is calculated based on the far-fusional duration and the near-fusional duration. The near-set separation distance of the training data is calculated based on the near-set fusional set separation distance; the near-spread separation distance of the training data is calculated based on the near-set fusional spread separation distance; and the near-set fusional duration of the training data is calculated based on the near-set fusional duration required. Wherein, the far convergence separation distance is not greater than the far-distance fusional convergence separation distance, the far divergence separation distance is not greater than the far-distance fusional divergence separation distance, the far fusional duration is not less than 5 times the near fusional duration, the near convergence separation distance is not greater than the near fusional convergence separation distance, the near divergence separation distance is not greater than the near fusional divergence separation distance, and the near fusional duration is not less than 2 times the near fusional duration required. The training data includes farsighted target pole number, nearsighted target pole number, far convergence separation distance, far divergence separation distance, far fusion duration, near convergence separation distance, near divergence separation distance, and near fusion duration. The visual training module includes a farsighted field training submodule, a nearsighted field training submodule, a cyclic training submodule, and a judgment submodule. The far field training submodule is used to control the operation of the far field equipment to perform far field training according to the far field target pole number and the far fusion duration, and to obtain the first total training duration at this time. The myopia field training submodule is used to control the operation of the myopia field device to perform myopia field training according to the myopia target pole number and the myopia fusion duration based on the first total training time being less than the training time threshold, and to obtain the second total training time at this time. The loop training submodule is used to return to the far field of vision training submodule and loop through far field of vision training and near field of vision training until the total training time reaches the training time threshold, thus completing the visual training of the user to be trained. The judgment submodule is used to complete the visual training of the user to be trained based on the first total training time or the second total training time not being less than the training time threshold. The training of the far field of view includes: the red-green target separation distance of the far field of view device is gradually increased from 0cm to the far convergence separation distance, then gradually changed from the far convergence separation distance to the far divergence separation distance, and then gradually changed from the far divergence separation distance to the far convergence separation distance. This cycle continues until the duration of operation of the far field of view device reaches the far fusion duration, at which point the far field of view device is turned off. The training of the myopia field includes: the red-green target separation distance of the myopia field device is gradually increased from 0cm to the near convergence separation distance, then gradually changed from the near convergence separation distance to the near divergence separation distance, and then gradually changed from the near divergence separation distance back to the near convergence separation distance. This cycle continues until the duration of operation of the myopia field device reaches the near fusion duration, at which point the myopia field device is turned off.

2. The visual training device based on dynamic field of view according to claim 1, characterized in that, The detection module includes a first detection submodule, a second detection submodule, a third detection submodule, and a fourth detection submodule; The first detection submodule is used to perform vision testing on the user to be trained using the vision chart of the far field of vision device and according to the optotypes of the vision chart from large to small, to obtain far vision data; The visual acuity of the user to be trained is tested using a visual acuity chart of a near vision field device and the visual acuity chart is used to test the visual acuity of the user from largest to smallest according to the optotypes of the visual acuity chart, so as to obtain near vision data; The second detection submodule is used to perform fusion set capability detection on the user to be trained using the spectrophotometer of the far field of view device according to the fusion set rule, to obtain the long-distance fusion set separation distance; and to perform fusion divergence capability detection on the user to be trained using the spectrophotometer of the far field of view device according to the fusion divergence rule, to obtain the long-distance fusion divergence separation distance. The third detection submodule is used to perform fusional aggregation capability detection on the user to be trained using the spectrophotometer of the myopia field device according to the fusional aggregation rule, to obtain the near-range fusional aggregation separation distance; and to perform fusional divergence capability detection on the user to be trained using the spectrophotometer of the myopia field device according to the fusional divergence rule, to obtain the near-range fusional divergence separation distance. The fourth detection submodule is used to perform duration detection on the user to be trained using the far field of view device according to the far fusion duration rule, so as to obtain the required duration of far fusion. The near vision field device is used to perform duration detection on the user to be trained according to the near fusion duration rule to obtain the required near fusion duration.

3. The visual training device based on dynamic field of view according to claim 2, characterized in that, The fusion set rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the inner eye of the user under training by a step size until the user under training sees the split or blurred spectroscopic target; the fusion divergence rule includes: gradually increasing the horizontal distance of the spectroscopic target towards the outer eye of the user under training by a step size until the user under training sees the split or blurred spectroscopic target; wherein, if the user under training is subjected to far-distance detection, the step size is the far-distance step size, and the product of the far-distance step size and the measurement accuracy is used as the far-distance step size; if the user under training is subjected to near-distance detection, the step size is the near-distance step size, and the product of one percent of the near-distance data and the measurement accuracy is used as the near-distance step size.

4. A visual training system based on dynamic field of view, characterized in that, The system includes a dynamic field of view training unit and a far field of view device and a near field of view device connected to the dynamic field of view training unit. The far field of view device and the near field of view device are respectively connected to a flicker vision enhancer. The dynamic field of view training unit controls the operation of the far field of view device and the near field of view device according to a visual training method based on dynamic field of view, so as to complete the visual training of the user to be trained. The visual training method based on dynamic field of view includes: Acquire the detection data and training duration threshold of the user to be trained, wherein the detection data includes long-distance data, short-distance data, and measurement accuracy; The user to be trained is tested based on the test data to obtain vision data before training; Training data is determined based on the aforementioned vision data; Based on the training data, the user to be trained is subjected to alternating training in the far field and near field until the training duration reaches the training duration threshold, thus completing the visual training of the user to be trained. The visual acuity data includes the user's distance visual acuity data, near visual acuity data, distance fusional convergence separation distance, distance fusional divergence separation distance, near fusional convergence separation distance, near fusional divergence separation distance, distance fusion time, and near fusion time. The training data determined based on this visual acuity data includes: The number of farsightedness targets in the training data is calculated based on the farsightedness data; the number of nearsightedness targets in the training data is calculated based on the nearsightedness data. The far-set separation distance of the training data is calculated based on the far-range fusional set separation distance; the far-spread separation distance of the training data is calculated based on the far-range fusional spread separation distance; and the far-fusional duration of the training data is calculated based on the far-fusional duration and the near-fusional duration. The near-set separation distance of the training data is calculated based on the near-set fusional set separation distance; the near-spread separation distance of the training data is calculated based on the near-set fusional spread separation distance; and the near-set fusional duration of the training data is calculated based on the near-set fusional duration required. Wherein, the far convergence separation distance is not greater than the far-distance fusional convergence separation distance, the far divergence separation distance is not greater than the far-distance fusional divergence separation distance, the far fusional duration is not less than 5 times the near fusional duration, the near convergence separation distance is not greater than the near fusional convergence separation distance, the near divergence separation distance is not greater than the near fusional divergence separation distance, and the near fusional duration is not less than 2 times the near fusional duration required. The training data includes farsighted target pole number, nearsighted target pole number, far convergence separation distance, far divergence separation distance, far fusion duration, near convergence separation distance, near divergence separation distance, and near fusion duration. Based on the training data, the user to be trained undergoes alternating cyclic training in the far and near fields until the duration reaches the training duration threshold. This completes the visual training of the user to be trained, including: The operation of the far field of view equipment is controlled according to the far vision target pole number and the far fusion image duration to perform far field of view training, and the first total training duration at this time is obtained. If the first total training time is less than the training time threshold, the myopia field device is controlled to run myopia field training according to the myopia target pole number and the myopia fusion duration, and the second total training time at this time is obtained. If the second total training time is less than the training time threshold, return to the step of controlling the operation of the far field of vision device according to the far vision target number and the far fusion duration to perform far field of vision training, and then cyclically execute far field of vision training and near field of vision training until the total training time reaches the training time threshold, thus completing the visual training of the user to be trained. If the first total training time or the second total training time is not less than the training time threshold, the visual training of the user to be trained is completed. The training of the far field of view includes: the red-green target separation distance of the far field of view device is gradually increased from 0cm to the far convergence separation distance, then gradually changed from the far convergence separation distance to the far divergence separation distance, and then gradually changed from the far divergence separation distance to the far convergence separation distance. This cycle continues until the duration of operation of the far field of view device reaches the far fusion duration, at which point the far field of view device is turned off. The training of the myopia field includes: the red-green target separation distance of the myopia field device is gradually increased from 0cm to the near convergence separation distance, then gradually changed from the near convergence separation distance to the near divergence separation distance, and then gradually changed from the near divergence separation distance back to the near convergence separation distance. This cycle continues until the duration of operation of the myopia field device reaches the near fusion duration, at which point the myopia field device is turned off.

5. The visual training system based on dynamic field of view according to claim 4, characterized in that, Based on the detection data, the user to be trained is tested to obtain pre-training vision data, including: The visual acuity of the user to be trained is tested using a visual acuity chart of a far-field device, with the optotypes of the chart arranged from largest to smallest, to obtain far-field visual acuity data; similarly, the visual acuity of the user to be trained is tested using a visual acuity chart of a near-field device, with the optotypes of the chart arranged from largest to smallest, to obtain near-field visual acuity data. The fusional aggregation capability of the user to be trained is detected by using the spectroscopic target of the far field of view device according to the fusional aggregation rule, and the long-distance fusional aggregation separation distance is obtained; the fusional divergence capability of the user to be trained is detected by using the spectroscopic target of the far field of view device according to the fusional divergence rule, and the long-distance fusional divergence separation distance is obtained. The near-field fusion ability of the user to be trained is detected by using the spectroscopic target of the myopic field device according to the fusion set rule, and the near-range fusion set separation distance is obtained; the near-field fusion divergence ability of the user to be trained is detected by using the spectroscopic target of the myopic field device according to the fusion divergence rule, and the near-range fusion divergence separation distance is obtained. The duration of the training is determined by using the far field of view device according to the far fusion duration rule to obtain the required duration of far fusion; the duration of the training is determined by using the near field of view device according to the near fusion duration rule to obtain the required duration of near fusion.

6. The visual training system based on dynamic field of view according to claim 5, characterized in that, The fusion set rule includes: gradually increasing the horizontal split distance of the spectroscopic target towards the inner eye of the user to be trained by a moving step size until the user to be trained sees the split or blurred spectroscopic target; The fusion divergence rule includes: gradually increasing the horizontal distance of the spectroscopic target outwards from the eye of the user to be trained by a step size until the user to be trained sees the split or blurred spectroscopic target; Wherein, if the user to be trained is subjected to long-distance detection, the movement step size is the long-distance movement step size, and the product of the long-distance movement step size and the measurement accuracy is used as the long-distance movement step size; if the user to be trained is subjected to short-distance detection, the movement step size is the short-distance movement step size, and the product of one percent of the short-distance data and the measurement accuracy is used as the short-distance movement step size.

7. The visual training system based on dynamic field of view according to claim 5, characterized in that, The distance fusion duration rule includes: determining distance duration detection data based on the distance fusion set separation distance and the distance fusion divergence separation distance; randomly generating distance target position data based on the distance duration detection data; sorting the distance target position data from far to near according to the target position seen by the user to be trained; and obtaining the duration required to correctly sort the distance target position data as the distance fusion duration; the distance duration detection data includes Y... j 50%Y j Y s 50%Y s And 0, Y j Y represents the distance at which fusion sets separate. s For long-distance fusion dispersion separation distance; The near fusion duration rule includes: determining near duration detection data based on the near fusional set separation distance and the near fusional divergence separation distance; randomly generating myopia target position data based on the near duration detection data; sorting the myopia target position data from far to near according to the target position seen by the user to be trained; and obtaining the time required to correctly sort the myopia target position data as the near fusion duration; the near duration detection data includes J j 50%J j J s 50%J s and 0, J j J is the near-range fusional set separation distance. s This refers to the near-field fusion dispersion separation distance.

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