Visual function training method and device, intelligent glasses and storage medium

Abstract

This application relates to the field of visual function training technology, and discloses a visual function training method, device, smart glasses, and storage medium. The method is applied to smart glasses equipped with a zoom lens unit. The method includes: controlling the zoom lens unit to switch to a preset refractive power while the user is wearing the smart glasses for visual function training; collecting the user's current eye movement data and determining the user's accommodative reaction time based on the current eye movement data, the accommodative reaction time being the time elapsed from the start of the zoom lens unit switching to the preset refractive power to the user's fixation on the target visual object; adjusting the preset refractive power based on the accommodative reaction time, and controlling the zoom lens unit to switch to the adjusted preset refractive power for visual function training. This application, by introducing an eye movement feedback closed loop, can sense the user's accommodative ability and adaptively optimize the refractive power. During visual function training, the user can directly obtain personalized training intensity, improving the effectiveness of the training.

Description

Technical Field

[0001] This application relates to the field of visual function training technology, and in particular to a visual function training method, device, smart glasses, and storage medium. Background Technology

[0002] With the widespread adoption of digital life, children and adolescents are spending significantly more time on close-range visual tasks, and abnormal accommodative function has become a key risk factor for the onset and progression of myopia. Reversal lens training, a classic method for improving accommodative ability, works by alternating between positive and negative lenses, forcing the ciliary muscle to alternate between relaxation and tension, thereby enhancing accommodative sensitivity and slowing axial elongation. In recent years, electronic reversal lenses have automated lens switching, making training more convenient.

[0003] Electronic reversal lenses typically use pre-set training parameters (such as training duration and number of repetitions). The device automatically switches lenses with the preset diopter at fixed time intervals, and the user tries to see the target clearly. The number of repetitions completed is recorded as the training result. However, in current methods, the diopter value remains constant throughout the training process. For beginners with weak accommodative abilities, this constant difficulty may be too high, leading to frustration and an inability to complete the training. For users who have already adapted, the constant difficulty fails to provide sustained effective stimulation, causing the training to quickly plateau and the effect to rapidly diminish. Therefore, the existing method of using a fixed diopter is prone to technical problems that result in poor training effectiveness. Summary of the Invention

[0004] The main purpose of this application is to provide a visual function training method that aims to solve the technical problem that the existing method of using a fixed refractive power is prone to poor training results.

[0005] To achieve the above objectives, this application proposes a visual function training method, which is applied to smart glasses equipped with a zoom lens unit;

[0006] The method includes: When a user wears the smart glasses for visual function training, the zoom lens unit is controlled to switch to a preset diopter. The user's current eye movement data is collected, and the user's accommodation reaction time is determined based on the current eye movement data. The accommodation reaction time is the time elapsed from the start of switching from the zoom lens unit to the preset refractive power until the user fixates on the target visual object. The preset diopter is adjusted according to the adjustment response time, and the zoom lens unit is controlled to switch to the adjusted preset diopter for visual function training.

[0007] In one embodiment, the step of acquiring the user's current eye movement data and determining the user's accommodation reaction time based on the current eye movement data includes: Record the first moment when the zoom lens unit begins to switch to the preset diopter; Collect the user's current eye movement data, and record the second moment when determining that the user is fixating on the target visual object based on the current eye movement data; The difference between the first time point and the second time point is taken as the user's adjustment response time.

[0008] In one embodiment, the smart glasses are further provided with an eye-tracking unit and a distance acquisition unit; The step of collecting the user's current eye movement data and recording the second moment when determining that the user is fixating on a target based on the current eye movement data includes: The eye-tracking unit collects the user's current eye movement data and determines the user's current gaze coordinates based on the current eye movement data. The required gaze depth between the target and the distance acquisition unit is acquired, and the target coordinates are determined based on the required gaze depth. If the deviation between the current gaze point coordinates and the target coordinates is within a preset deviation range, it is determined that the user is gazing at the target and a second moment is recorded.

[0009] In one embodiment, the step of adjusting the preset diopter based on the adjustment response time includes: If it is determined that the user is gazing at the target object, the current number of training sessions completed is increased; If the current number of training sessions completed has not reached the preset target number, the preset diopter is adjusted according to the adjustment reaction time.

[0010] In one embodiment, the step of adjusting the preset diopter based on the adjustment response time includes: Obtain at least two historical conditioning reaction times of the user during the historical visual function training; Based on the current adjustment response time and the at least two historical adjustment response times, determine the user's response time trend and average response time; If the reaction time shows a continuous decreasing trend and the average reaction time is lower than a first preset threshold, the preset diopter is increased. If the reaction time shows a continuous increasing trend and the average reaction time is higher than the second preset threshold, the preset diopter is reduced. The preset diopter is maintained if the reaction time trend is neither a continuous decrease nor a continuous increase, and the average reaction time is not lower than the first preset threshold and not higher than the second preset threshold.

[0011] In one embodiment, the smart glasses are further provided with an eye-tracking unit; After the steps of adjusting the preset diopter according to the adjustment reaction time and controlling the zoom lens unit to switch to the adjusted preset diopter for visual function training, the method further includes: The eye-tracking unit acquires images of the user's pupils and determines the user's pupil diameter data based on the pupil images. A time series of pupil diameter changes over time is generated based on the pupil diameter data, and the fluctuation characteristic parameters of the time series are determined. When the fluctuation characteristic parameters meet the preset fatigue conditions, a fatigue warning is generated.

[0012] In one embodiment, the smart glasses are further provided with an eye-tracking unit; The step of controlling the zoom lens unit to switch to a preset diopter when the user is wearing the smart glasses for visual function training includes: When the user is wearing the smart glasses for visual function training, the eye-tracking unit collects the user's current eye movement data and determines the user's current gaze coordinates based on the current eye movement data; The rate of change of the current gaze point coordinates is determined, and if the rate of change is lower than a preset rate threshold, the zoom lens unit is controlled to switch to a preset diopter.

[0013] Furthermore, to achieve the above objectives, this application also proposes a visual function training device, the device comprising: The switching module is used to control the zoom lens unit to switch to a preset diopter when the user is wearing smart glasses for visual function training. The acquisition module is used to acquire the user's current eye movement data and determine the user's accommodation reaction time based on the current eye movement data. The accommodation reaction time is the time elapsed from the start of switching from the zoom lens unit to the preset refractive power to the time when the user fixates on the target visual object. The adjustment module is used to adjust the preset diopter according to the adjustment response time, and control the zoom lens unit to switch to the adjusted preset diopter for visual function training.

[0014] In addition, to achieve the above objectives, this application also proposes a smart glasses, which is equipped with a zoom lens unit; The smart glasses further include: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the visual function training method described above when executed by the processor.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium that is a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the visual function training method described above.

[0016] This application discloses a visual function training method, device, smart glasses, and storage medium. The method is applied to smart glasses equipped with a zoom lens unit. The method includes: when a user is wearing the smart glasses for visual function training, controlling the zoom lens unit to switch to a preset diopter; collecting the user's current eye movement data and determining the user's accommodative reaction time based on the current eye movement data, the accommodative reaction time being the time elapsed from the start of the zoom lens unit switching to the preset diopter to the user fixing their gaze on a target; adjusting the preset diopter based on the accommodative reaction time, and controlling the zoom lens unit to switch to the adjusted preset diopter for visual function training.

[0017] This application's visual function training method and smart glasses can be equipped with a refractive power adaptive adjustment mechanism based on eye-tracking feedback. During user training, the system can control the zoom lens unit to switch to a preset refractive power and measure the user's accommodative reaction time in real time by collecting eye-tracking data. Furthermore, during actual training, the system dynamically adjusts the preset refractive power based on the accommodative reaction time, matching the training difficulty to the user's real-time visual function state. Compared to existing visual function training methods that use a fixed refractive power and cannot dynamically adjust according to individual user differences and real-time states, resulting in limited training effectiveness or insufficient adaptability, this application, by introducing an eye-tracking feedback closed loop, can perceive the user's accommodative ability in real time and adaptively optimize the refractive power. Therefore, during visual function training, users can directly obtain personalized and precisely matched training intensity, improving training effectiveness and user experience. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] 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, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the smart glasses structure of the hardware operating environment involved in the embodiments of this application; Figure 2 This is a flowchart illustrating the visual function training method proposed in an embodiment of this application; Figure 3 This is a flowchart of the first embodiment of the visual function training method proposed in this application; Figure 4 This is a flowchart of a second embodiment of the visual function training method proposed in this application. Figure 5 This is a flowchart of a third embodiment of the visual function training method proposed in this application. Figure 6 This is an example diagram illustrating the workflow of the eye-tracking unit corresponding to the method proposed in the embodiments of this application; Figure 7 A diagram of a visual function training device provided in an embodiment of this application.

[0021] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not intended to limit this application.

[0023] Reference Figure 1 , Figure 1 This is a schematic diagram of the smart glasses structure of the hardware operating environment involved in the embodiments of this application.

[0024] like Figure 1As shown, the smart glasses may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may be connected to a display screen. Optionally, the user interface 1003 may include a standard wired interface or a wireless interface; in this application, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wireless-Fidelity (Wi-Fi) interface). The memory 1005 may be high-speed random access memory (RAM) or non-volatile memory (NVM), such as a disk storage device. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0025] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on smart glasses and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0026] like Figure 1 As shown, the memory 1005, which is identified as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a visual function training program.

[0027] exist Figure 1 In the smart glasses shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to the user device; the smart glasses call the defogging program stored in the memory 1005 through the processor 1001 and execute the steps of the defogging method provided in the embodiments of this application.

[0028] It should also be understood that the smart glasses described in this embodiment are further provided with a zoom lens unit, the specific implementation of which can be referred to the description of the following embodiments.

[0029] Understandably, with the widespread adoption of digital life, children and adolescents are spending significantly more time on close-range visual tasks, and abnormal accommodative function has become a key risk factor for the onset and progression of myopia. Reversal lens training, a classic method for improving accommodative ability, works by alternating between positive and negative lenses, forcing the ciliary muscle to alternate between relaxation and tension, thereby enhancing accommodative sensitivity and slowing axial elongation. In recent years, electronic reversal lenses have automated lens switching, making training more convenient.

[0030] Electronic reversal lenses typically use pre-set training parameters (such as training duration and number of repetitions). The device automatically switches lenses with the preset diopter at fixed time intervals, and the user tries to see the target clearly. The number of repetitions completed is recorded as the training result. However, in current methods, the diopter value remains constant throughout the training process. For beginners with weak accommodative abilities, this constant difficulty may be too high, leading to frustration and an inability to complete the training. For users who have already adapted, the constant difficulty fails to provide sustained effective stimulation, causing the training to quickly plateau and the effect to rapidly diminish. Therefore, the existing method of using a fixed diopter is prone to technical problems that result in poor training effectiveness.

[0031] Therefore, to solve the above-mentioned technical problems, this embodiment proposes a visual function training method, which is applied to smart glasses equipped with a zoom lens unit. The method includes: when a user is wearing the smart glasses for visual function training, controlling the zoom lens unit to switch to a preset diopter; collecting the user's current eye movement data, and determining the user's accommodation reaction time based on the current eye movement data, wherein the accommodation reaction time is the time elapsed from the start of the zoom lens unit switching to the preset diopter to the user fixing their gaze on the target visual object; adjusting the preset diopter based on the accommodation reaction time, and controlling the zoom lens unit to switch to the adjusted preset diopter for visual function training.

[0032] This embodiment of the visual function training method and smart glasses can be equipped with a refractive power adaptive adjustment mechanism based on eye-tracking feedback. During user training, the system can control the zoom lens unit to switch to a preset refractive power and measure the user's accommodative reaction time in real time by collecting eye-tracking data. Furthermore, during actual training, the system dynamically adjusts the preset refractive power based on the accommodative reaction time, matching the training difficulty to the user's real-time visual function state. Compared to existing visual function training methods that use fixed refractive powers or preset training sequences, which cannot dynamically adjust according to individual user differences and real-time states, resulting in limited training effectiveness or insufficient adaptability, this embodiment, by introducing an eye-tracking feedback closed loop, can perceive the user's accommodative ability in real time and adaptively optimize the refractive power. Therefore, during visual function training, the user can directly obtain personalized and precisely matched training intensity, improving training effectiveness and user experience.

[0033] For ease of understanding, the following is combined with Figures 1 to 7 The visual function training method provided in the embodiments of this application, as well as the visual function training method, device, smart glasses, and storage medium provided in the following embodiments, will be described in detail.

[0034] This application provides a visual function training method, referring to... Figure 2 as well as Figure 3 , Figure 2 This is a flowchart illustrating the visual function training method proposed in an embodiment of this application. Figure 3 This is a flowchart of the first embodiment of the visual function training method proposed in this application.

[0035] like Figure 3 As shown, the method includes: Step S10: When the user is wearing the smart glasses for visual function training, control the zoom lens unit to switch to the preset diopter.

[0036] It should be noted that the execution subject in this embodiment can be a multifunctional machine device with visual function training capabilities, such as smart glasses, or a device capable of performing the aforementioned functions. This embodiment uses the processor in smart glasses as the execution subject for explanation.

[0037] Furthermore, it should be noted that the aforementioned visual function training can be a training process that forces the ciliary muscle of the eye to alternate between relaxation and tension by switching lenses of different diopters. For example, training to improve the eye's accommodative sensitivity by alternating between positive and negative lenses. The aforementioned zoom lens unit can be an optical element capable of adjusting its own diopter according to control commands, such as an electronic zoom lens unit or a driveable physical lens group. In this embodiment, the aforementioned zoom lens unit can be used as a lens for smart glasses. The aforementioned preset diopter can be a pre-set or dynamically adjusted lens focal length value used to apply a specific degree of accommodative load to the user's eyes, such as +2.0D (positive lens, relaxed accommodation) or -2.0D (negative lens, tense accommodation).

[0038] like Figure 2 As shown, in a specific implementation, the aforementioned smart glasses are used in scenarios where users wear the device for visual function training (i.e., Figure 2 (Entering training mode) The processor first determines whether the training start conditions are met; when the start conditions are met, the processor generates a diopter switching instruction and sends the instruction to the zoom lens unit; after receiving the instruction, the zoom lens unit adjusts its diopter to a preset value.

[0039] It should be emphasized that the above-mentioned entry into the training mode can be activated by the user or automatically when the smart glasses enter a preset environment or detect a preset target. This embodiment uses the user-activated training mode for explanation, but does not impose any specific limitations on this embodiment.

[0040] Step S20: Collect the user's current eye movement data and determine the user's accommodation reaction time based on the current eye movement data. The accommodation reaction time is the time elapsed from the start of switching from the zoom lens unit to the preset refractive power until the user fixates on the target visual object.

[0041] Understandably, the aforementioned eye-tracking data can reflect information about the user's eye movement state, including parameters such as fixation point coordinates, pupil diameter, eye movement trajectory, and gaze stability, such as the spatial coordinates of the user's current fixation point quaternion acquired by an eye-tracking camera. The aforementioned accommodative reaction time can be the time elapsed from the moment the zoom lens unit begins switching diopter to the moment the user successfully fixates on the target visual target; for example, the 0.8 seconds it takes for the user to fully see the target after switching lenses to -2.0D. The aforementioned target visual target can be a visual marker pre-set at a specific distance directly in front of the user to guide fixation and accommodation training, such as characters on a vision chart fixed at 2.5 meters away or a pattern on a near-field reading card.

[0042] like Figure 2 As shown, in a specific implementation, the processor described above collects the user's current eye movement data in real time through an eye-tracking unit (i.e., Figure 2 The processor acquires eye movements in real time and then analyzes the data to extract key parameters such as fixation point coordinates and fixation stability. Next, based on these parameters, it determines whether the user has successfully fixed their gaze on the target. When the user is determined to have completed fixation, the processor, combined with the moment the zoom lens unit begins switching, calculates the time elapsed from the start of the switching to the completion of fixation; this time elapsed is the user's accommodative reaction time.

[0043] Furthermore, in order to obtain an accurate accommodative reaction time, the step of collecting the user's current eye movement data and determining the user's accommodative reaction time based on the current eye movement data includes: Step S21: Record the first moment when the zoom lens unit starts to switch to the preset diopter.

[0044] It should be noted that the aforementioned "first moment" can be the precise time at which the zoom lens unit begins to execute the diopter switching action, such as the timestamp 15:30:00.000 recorded synchronously when the processor sends the switching command to the zoom lens unit. In the specific implementation, the processor sends the diopter switching command to the zoom lens unit. After receiving the command, the zoom lens unit prepares to execute the switching action. At the instant it actually begins to change its diopter, the zoom lens unit sends a feedback signal to the processor indicating that the switching has begun. Upon receiving this feedback signal, the processor immediately calls the system clock to obtain the current time and records this time as the "first moment."

[0045] Step S22: Collect the user's current eye movement data, and record the second moment when determining that the user is gazing at the target based on the current eye movement data.

[0046] It should be noted that the aforementioned second moment can be the precise time point at which the processor determines, based on eye-tracking data, that the user has successfully gazed at the target visual object. For example, it could be the timestamp 15:30:00.850, corresponding to the point where the user's gaze coordinates coincide with and remain stable with the target visual object's coordinates. In the specific implementation, the processor continuously acquires the user's current eye-tracking data and performs real-time analysis to extract the user's current gaze state information. When the processor determines, based on the gaze state information, that the user has successfully gazed at the target visual object, it immediately calls the system clock to obtain the current time and records this time point as the second moment.

[0047] Furthermore, to determine whether the user is looking at the target, the smart glasses are also equipped with an eye-tracking unit and a distance acquisition unit. The distance acquisition unit can be positioned anywhere in front of the smart glasses; this embodiment describes it as being positioned in the center of the nose bridge support, but this is not a specific limitation of this embodiment.

[0048] It is understood that the aforementioned eye-tracking unit can be a sensor component used to collect the user's eye movements and gaze direction. Specifically, it can include an infrared light source and a high-speed camera, calculating the gaze point coordinates by capturing corneal reflection and pupil position. For example, two miniature infrared cameras can be placed inside the frame of the smart glasses, facing the user's eyes. The aforementioned distance acquisition unit can be a sensor that uses the time-of-flight method or structured light principle to measure the distance between a target object in front and the smart glasses, such as a ToF sensor.

[0049] Accordingly, the step of collecting the user's current eye movement data and recording the second moment when determining that the user is fixating on a target based on the current eye movement data includes: Step S221: Collect the user's current eye movement data through the eye tracking unit, and determine the user's current gaze coordinates based on the current eye movement data.

[0050] It should be noted that the above-mentioned current gaze point coordinates can be the positional parameters corresponding to the intersection of the user's eyes in three-dimensional space. For example, the spatial coordinates (0, 0, 250) corresponding to the user's gaze at the target 2.5 meters directly in front of them, where the Z-axis value of 250 indicates a distance of 250 centimeters.

[0051] In its implementation, the processor sends a data acquisition command to the eye-tracking unit. Upon receiving the command, the eye-tracking unit activates the infrared light source and high-speed camera to begin acquiring images of the user's eyes, transmitting the acquired raw eye image data to the processor in real time. The processor preprocesses the received eye images and extracts pupil and corneal reflection features from the preprocessed images. Then, based on the relative positional relationship between the pupil center and the corneal reflection center, combined with pre-calibrated parameters, it calculates the user's current gaze direction. Next, it projects this gaze direction onto a preset viewing surface in three-dimensional space and uses spatial geometric calculations to determine the coordinates of the intersection point between the gaze and the viewing surface. These intersection point coordinates are the user's current fixation point coordinates.

[0052] Step S222: Acquire the required gaze depth between the target and the target object using the distance acquisition unit, and determine the target coordinates based on the required gaze depth.

[0053] It is understandable that the required depth of gaze can be the spatial depth position to which the user's eyes need to adjust, such as the 250 cm distance value between the user's eyes and a target 2.5 meters away, as measured by a ToF sensor. The target target can be a visual marker pre-set at a specific distance directly in front of the user to guide the user in gaze and accommodation training, such as characters on a vision chart fixed at 2.5 meters away or a pattern on a near-field reading card. The target coordinates can be the precise positional parameters of the target target in three-dimensional space, such as the spatial coordinates (-21.8, 8.7, 250) corresponding to the target target being located 5 degrees to the left and 2 degrees above, 250 cm away.

[0054] like Figure 2 As shown, in a specific implementation, the processor sends a ranging command to the distance acquisition unit. Upon receiving the command, the distance acquisition unit emits a detection signal towards the target and receives the reflected signal. Based on the time difference between the signal emission and reception, it calculates the required gaze depth between the target and the smart glasses and transmits this required gaze depth to the processor. After obtaining the required gaze depth, the processor performs trigonometric function calculations using the preset horizontal and vertical azimuth angles of the target to determine the target's coordinates in three-dimensional space (i.e., the target's coordinates). Figure 2 The ToF sensor reads the gaze depth and combines the gaze depth with the eye movement gaze direction.

[0055] Step S223: If the deviation between the current gaze point coordinates and the target coordinates is within a preset deviation range, determine that the user is gazing at the target and record the second moment.

[0056] like Figure 2 As shown, it should be noted that the aforementioned preset deviation range can be an allowable error interval for determining whether the gaze point is aligned with the target, such as a cubic space with a side length of 10 centimeters centered on the target coordinates. In the specific implementation, the processor obtains the current gaze point coordinates and the target coordinates, calculates the deviation values ​​between the current gaze point coordinates and the target coordinates in each coordinate axis direction, and then compares the calculated deviation values ​​with the preset deviation range. When it is determined that all deviation values ​​between the current gaze point coordinates and the target coordinates are within the preset deviation range, it is determined that the user has successfully gazed at the target (i.e., ...). Figure 2 (The gaze position is accurate), and at the moment the judgment is completed, the system clock is called to obtain the current time and the time point is recorded as the second moment.

[0057] Step S23: The difference between the first time point and the second time point is taken as the user's adjustment response time.

[0058] It should be noted that the aforementioned accommodation reaction time can be the length of time elapsed from the moment the zoom lens unit actually begins to switch diopter to the moment the user successfully focuses on the target visual target. For example, it could be 0.845 seconds from the moment the lens begins to change its power until the user can clearly see the target. In specific implementation, the processor reads the previously recorded first and second moments from memory. The processor then performs a subtraction operation, subtracting the first moment from the second moment to calculate the time difference between the two moments, and determines this time difference as the user's current accommodation reaction time.

[0059] Step S30: Adjust the preset diopter according to the adjustment reaction time, and control the zoom lens unit to switch to the adjusted preset diopter for visual function training.

[0060] It should be noted that the aforementioned preset diopter can be a pre-set or dynamically adjusted lens focal length value, used to apply a specific degree of accommodative load to the user's eyes, such as +2.0D (positive lens, relaxed accommodation) or -2.0D (negative lens, tense accommodation). The aforementioned adjusted preset diopter can be a new diopter value calculated by an algorithm based on the user's accommodative reaction time. This value may be the same as the original diopter, or it may be increased or decreased compared to the original diopter. For example, the diopter may be adjusted from -2.0D to -2.5D based on the user's reaction time of 0.85 seconds.

[0061] like Figure 2As shown, in the specific implementation, the processor obtains the currently calculated adjustment response time, then matches the current adjustment response time with the preset difficulty adjustment rules, and determines whether the diopter needs to be adjusted and the specific adjustment value based on the matching result. When it is determined that adjustment is needed, the processor generates a new diopter value as the adjusted preset diopter, and then sends a switching command containing the adjusted preset diopter value to the zoom lens unit. After receiving the command, the zoom lens unit adjusts its own diopter to the adjusted preset diopter (i.e., Figure 2 (Adjusting the focus in the middle), thus starting a new round of visual function training.

[0062] For ease of understanding, the following example is provided, but it does not impose any specific limitations on this embodiment. For instance, the processor calculates that the current adjustment reaction time is 0.85 seconds. The preset difficulty adjustment rule is: when the reaction time is less than 1.0 second, the difficulty is too low, and the diopter needs to be increased by 0.5D. According to the rule, the processor determines to adjust the diopter from -2.0D to -2.5D. Subsequently, the processor sends a command to the zoom lens unit to switch to -2.5D. After receiving the command, the zoom lens unit begins to change its curvature to provide the user with a more difficult adjustment stimulus and begin a new round of training.

[0063] This embodiment introduces an eye-tracking feedback loop, which can sense the user's accommodative ability in real time and adaptively optimize training parameters. As a result, when the user is training their visual function, they can directly obtain personalized and precisely matched training intensity, thereby improving the effectiveness of training and the user experience.

[0064] Based on the first embodiment, in the second embodiment, the content that is the same as or similar to that in Embodiment 1 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 4 , Figure 4 The flowchart below shows the first embodiment of the method proposed in this application. Further, to ensure the validity of the training records, the step of adjusting the preset diopter based on the adjustment reaction time includes: Step S31: If it is determined that the user is gazing at the target object, increase the current number of training sessions completed.

[0065] like Figure 2 As shown, it should be noted that the aforementioned current number of training completions can be the cumulative number of times the user has successfully completed effective fixations during this training process. For example, the count value corresponding to the user successfully seeing and fixing on the target 3 times is 3. In the specific implementation, after determining that the user has successfully fixed on the target, the processor reads the value of the current number of training completions from memory and increments this value by 1 to obtain the new number of training completions (i.e., ...). Figure 2The number of successful training sessions is incremented by 1, and the updated number of training sessions is written back to memory for storage.

[0066] Step S32: If the current number of training sessions completed has not reached the preset target number, adjust the preset diopter according to the adjustment reaction time.

[0067] It should be noted that the aforementioned preset target number of times can be the total number of successful fixations required in a single training session, for example, a training target of 20 effective fixations. In the specific implementation, the processor reads the current number of completed training sessions and the preset target number of times from memory, comparing the current number of completed training sessions with the preset target number. When it is determined that the current number of completed training sessions is less than the preset target number, the processor retrieves the accommodation reaction time from memory for this training session, then uses the accommodation reaction time as an input parameter to call the difficulty adjustment algorithm, and determines a new preset diopter value (i.e., ...) based on the algorithm's output. Figure 2 (Adjusting the focus). Since this embodiment only determines that the training is effective when the user is looking at the target, compared to the existing method that only determines that the training is effective after confirming the flip, the accuracy of the training record can be further improved.

[0068] Furthermore, to ensure the accuracy and long-term effectiveness of the training, the step of adjusting the preset refractive power based on the adjustment reaction time includes: Step S33: Obtain at least two historical conditioning reaction times of the user during the historical visual function training; Step S34: Based on the current adjustment response time and the at least two historical adjustment response times, determine the user's response time change trend and average response time.

[0069] It is understandable that the aforementioned historical adjustment reaction time can be the adjustment reaction time value recorded and saved by the user in the historical training cycles before this training, such as 0.85 seconds recorded in the last training and 0.92 seconds recorded in the training two years ago. The aforementioned current adjustment reaction time can be the adjustment reaction time value just calculated by the user in the current training cycle, such as 0.78 seconds measured in this training. The aforementioned reaction time change trend can be a statistical characteristic determined by the changing direction of multiple consecutive adjustment reaction time values, such as a continuous downward trend showing reaction times of 0.92 seconds, 0.85 seconds, and 0.78 seconds in three consecutive training sessions. The aforementioned average reaction time can be the average value obtained by arithmetically averaging multiple adjustment reaction time values, such as 0.85 seconds obtained by averaging the three values ​​of 0.92 seconds, 0.85 seconds, and 0.78 seconds.

[0070] In its implementation, the processor reads at least two historical adjustment reaction time values ​​saved by the user during historical training from the training record storage area in memory, and simultaneously obtains the current adjustment reaction time calculated during the current training. The current adjustment reaction time is combined with the at least two historical adjustment reaction times to form a time series dataset. Linear regression analysis or differencing is then performed on this time series dataset to determine the trend of reaction time changes based on the direction of data point increases or decreases over time. Finally, the average reaction time is calculated by summing all the values ​​in the time series dataset and dividing by the number of values.

[0071] Step S35: When the reaction time trend is continuously decreasing and the average reaction time is lower than the first preset threshold, increase the preset diopter.

[0072] It should be noted that the aforementioned reaction time trend can be a statistical characteristic determined by the changing direction of multiple consecutive adjustment reaction time values. For example, a continuous downward trend showing reaction times of 0.92 seconds, 0.85 seconds, and 0.78 seconds in three consecutive training sessions. The aforementioned average reaction time can be the average value obtained by arithmetically averaging multiple adjustment reaction time values, such as averaging 0.85 seconds from 0.92 seconds, 0.85 seconds, and 0.78 seconds. The aforementioned first preset threshold can be a pre-set upper limit for reaction time used to determine whether the user's adjustment ability has entered the efficient range, such as a 1.0-second threshold. When the average reaction time is below 1.0 second, it indicates that the user's current adjustment ability is relatively strong.

[0073] In a specific implementation, the processor obtains the calculated reaction time change trend and average reaction time. The processor determines whether the reaction time change trend is continuously decreasing and whether the average reaction time is lower than a first preset threshold. When the processor determines that the reaction time change trend is continuously decreasing and the average reaction time is lower than the first preset threshold, the processor determines that the training difficulty needs to be increased. Subsequently, the processor reads the current preset diopter value from memory and performs an increment operation on the value to obtain a new preset diopter.

[0074] Step S36: If the reaction time shows a continuous increasing trend and the average reaction time is higher than the second preset threshold, reduce the preset diopter.

[0075] It should be noted that the aforementioned reaction time trend can be a statistical characteristic determined by the changing direction of multiple consecutive adjustment reaction time values. For example, the reaction times of three consecutive training sessions, which are 0.78 seconds, 0.85 seconds, and 0.92 seconds respectively, show a continuous increasing trend. The aforementioned second preset threshold can be a pre-set upper limit for reaction time used to determine whether the user is experiencing adjustment fatigue or that the training difficulty is too high. For example, a threshold of 1.2 seconds is set. When the average reaction time is higher than 1.2 seconds, it indicates that the user's current adjustment ability has declined or the training difficulty is too high.

[0076] In its implementation, the processor acquires the calculated reaction time trend and average reaction time, determines whether the reaction time trend is continuously increasing, and simultaneously determines whether the average reaction time is higher than a second preset threshold. When the processor determines that the reaction time trend is continuously increasing and the average reaction time is higher than the second preset threshold, the processor determines that the training difficulty needs to be reduced. Subsequently, the processor reads the current preset diopter value from memory and performs a reduction operation on that value to obtain a new preset diopter.

[0077] Step S37: If the reaction time change trend is not continuously decreasing or continuously increasing, and the average reaction time is not lower than the first preset threshold and not higher than the second preset threshold, maintain the preset diopter.

[0078] In its specific implementation, the processor acquires the calculated reaction time trend and average reaction time. The processor first determines whether the reaction time trend is continuously decreasing or continuously increasing. When the processor determines that the reaction time trend is neither continuously decreasing nor continuously increasing, the processor further determines the relationship between the average reaction time and the first preset threshold and the second preset threshold. When the processor determines that the average reaction time is not lower than the first preset threshold and not higher than the second preset threshold, the processor determines that the current training difficulty is suitable for the user's current ability level. Subsequently, the processor reads the current preset diopter value from memory. The processor does not modify the value in any way and keeps the original preset diopter value unchanged for subsequent training.

[0079] For ease of understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment. For instance, the reaction time change trend obtained by the processor is stable, neither continuously decreasing nor continuously increasing, with an average reaction time of 1.10 seconds. The first preset threshold is 1.0 seconds, and the second preset threshold is 1.2 seconds. The processor determines that 1.10 seconds is neither lower than 1.0 seconds nor higher than 1.2 seconds, therefore determining that the current difficulty level is suitable for the user. The processor reads the current preset diopter from memory as -2.0D. The processor does not modify this value and continues to maintain -2.0D for subsequent training.

[0080] Furthermore, to enhance user interest, the step of adjusting the preset diopter based on the adjustment response time includes: Step S38: Determine whether the adjustment response time meets the preset pass requirements corresponding to the preset refractive power. Different preset refractive powers correspond to different preset pass requirements.

[0081] It should be noted that the aforementioned preset diopter can be the focal length value of the zoom lens unit currently in use during the training cycle, such as -2.0D or +2.0D. The aforementioned preset passing requirements can be pre-set adjustment reaction time standards for different diopter levels; for example, the passing requirement for -2.0D diopter is an adjustment reaction time of less than 1.0 second, and the passing requirement for -2.5D diopter is an adjustment reaction time of less than 1.2 seconds.

[0082] In its implementation, the processor obtains the accommodation response time calculated during this training and reads the currently used diopter value from memory. Based on the preset diopter value, the processor looks up the corresponding preset pass requirement in a pre-stored pass requirement mapping table. Different diopter levels correspond to different preset pass requirements in the mapping table. The processor compares the accommodation response time with the found preset pass requirement to determine whether the accommodation response time meets the standard required for that diopter level.

[0083] Step S39: If the adjustment reaction time meets the preset clearance requirements corresponding to the preset diopter, determine the current clearance progress of the user based on the preset diopter.

[0084] It should be noted that the current progress mentioned above can be the user's position level in the entire training level system, which is determined based on the refractive error level that the user has already passed. For example, if the user has already met the requirements of the -2.0D level, the user is currently in the state of having passed the -2.0D level and is waiting to enter the -2.5D level.

[0085] In its implementation, after determining that the adjustment response time meets the preset pass requirements corresponding to the preset refractive power, the processor reads the preset refractive power value from memory and matches it with a pre-stored pass progress mapping table. This pass progress mapping table records the correspondence between each refractive power level and the progress status. Based on the matching result, the processor determines the user's current pass progress, which indicates that the user has completed the preset refractive power level's qualification requirements.

[0086] Step S310: Determine the next target refractive power based on the current clearance progress.

[0087] Step S311: Adjust the preset refractive power according to the next target refractive power.

[0088] It should be noted that the aforementioned next target refractive error can be the refractive error value of the next difficulty level that the user needs to challenge after completing the preset refractive error level. For example, after completing the -2.0D level, the next target refractive error is -2.5D. In the specific implementation, the processor obtains the current progress and matches it with a pre-stored level difficulty mapping table. The level difficulty mapping table records the next refractive error value corresponding to each progress level. The next target refractive error is determined based on the matching result. Subsequently, the processor reads the current preset refractive error value from memory, updates the current preset refractive error value to the next target refractive error value, and stores the updated preset refractive error value in memory for subsequent training.

[0089] Based on the first and second embodiments, in the third embodiment, the content that is the same as or similar to that in Embodiments 1 and 2 above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 5 , Figure 5 The second partitioning result block diagram corresponding to the method proposed in this application embodiment is further shown below. To prevent eye fatigue warnings during user training, after the steps of adjusting the preset refractive power based on the adjustment reaction time and controlling the zoom lens unit to switch to the adjusted preset refractive power for visual function training, the method further includes: Step S40: Acquire the user's pupil image through the eye-tracking unit, and determine the user's pupil diameter data based on the pupil image.

[0090] It should be noted that the aforementioned pupil image can be an eye image frame containing the user's pupil area captured by the eye-tracking unit, such as a 640×480 pixel grayscale image captured by the left eye camera, containing the pupil, iris, and corneal reflective spot. The aforementioned pupil diameter data can be the pupil size value extracted from the pupil image, specifically referring to the diameter of the pupil on the image plane. For example, the diameter of the left eye pupil calculated through image analysis is 4.5 mm.

[0091] In its implementation, the processor sends an image acquisition command to the eye-tracking unit. Upon receiving the command, the eye-tracking unit activates an infrared light source and a high-speed camera to continuously capture images of the user's eye region at a preset frame rate, generating pupil images. These pupil images are then transmitted to the processor in real time. The processor performs image preprocessing on the received pupil images, segmenting the pupil region from the preprocessed image. It then performs ellipse fitting or circle detection on the segmented pupil region, calculating the pupil's diameter in pixels on the image. Combining this with a pre-calibrated pixel-to-actual-size conversion factor, the pupil diameter in pixels is converted into actual pupil diameter data. This conversion factor can be a scaling factor that converts the pixel length on the image to the actual physical length, for example, a conversion factor of 0.0375 mm per pixel.

[0092] Step S50: Generate a time series of pupil diameter changes over time based on the pupil diameter data, and determine the fluctuation characteristic parameters of the time series.

[0093] It is understandable that the aforementioned time series can be a dataset formed by arranging multiple pupil diameter data points in chronological order of collection time. For example, a sequence of pupil diameter values ​​[4.5, 4.5, 4.6, 4.7, 4.6, 4.5] recorded at a collection time of 60 times per second. The aforementioned fluctuation characteristic parameters can be quantitative indicators used to describe the variation and fluctuation degree of pupil diameter over time, specifically including statistical characteristics such as variance, standard deviation, range, and fluctuation frequency. For example, the calculated standard deviation of the time series is 0.08 mm.

[0094] In its implementation, the processor continuously receives and stores pupil diameter data collected by the eye-tracking unit during training, appending a corresponding acquisition timestamp to each pupil diameter data point. The processor then arranges the timestamped pupil diameter data in chronological order, generating a pupil diameter time series that varies over time. Statistical analysis is performed on this time series, calculating the variance, standard deviation, difference between the maximum and minimum values, and rate of change between adjacent data points. Finally, the statistical results are output as fluctuation characteristic parameters.

[0095] Step S60: If the fluctuation characteristic parameters meet the preset fatigue conditions, generate a fatigue warning.

[0096] It should be noted that the aforementioned preset fatigue conditions can be pre-set pupil fluctuation thresholds or fluctuation patterns used to determine whether a user is experiencing accommodative fatigue. For example, a fatigue state is defined as a standard deviation exceeding 0.10 mm and a range exceeding 0.5 mm. The aforementioned fatigue alert can be a reminder signal issued to the user to inform them that accommodative fatigue has occurred and to suggest pausing training or resting, for example, through motor vibration or display screen flashing.

[0097] In its implementation, the processor acquires the calculated fluctuation characteristic parameters and reads pre-stored preset fatigue conditions from memory. It then compares the fluctuation characteristic parameters with the preset fatigue conditions to determine whether the fluctuation characteristic parameters meet or exceed the threshold set in the preset fatigue conditions. When the processor determines that the fluctuation characteristic parameters meet the preset fatigue conditions, it sends a fatigue alert command to the interaction module, which then issues a fatigue alert to the user in a preset manner.

[0098] For ease of understanding, the following example illustrates the concept, but does not impose specific limitations on this embodiment. For instance, the processor calculates that the standard deviation of the pupil diameter time series is 0.12 mm and the range is 0.6 mm. The preset fatigue condition is that the standard deviation is greater than 0.10 mm and the range is greater than 0.5 mm. The processor determines that 0.12 mm is greater than 0.10 mm and 0.6 mm is greater than 0.5 mm, thus meeting the preset fatigue condition. The processor generates a fatigue warning command and sends it to the motor drive circuit. After receiving the command, the motor vibrates three times in a specific vibration mode, prompting the user that fatigue has occurred and suggesting a rest.

[0099] refer to Figure 6 , Figure 6 This is an example diagram illustrating the workflow of the eye-tracking unit corresponding to the method proposed in this application. Further, to improve the reliability of the training data, the step of controlling the zoom lens unit to switch to a preset diopter while the user is wearing the smart glasses for visual function training includes: Step S11: When the user is wearing the smart glasses for visual function training, the eye-tracking unit collects the user's current eye movement data and determines the user's current gaze coordinates based on the current eye movement data.

[0100] It should be noted that the above-mentioned current gaze point coordinates can be the position parameters corresponding to the current intersection point of the user's eyes in three-dimensional space, specifically represented by values ​​in a three-dimensional coordinate system, such as the spatial coordinate point (0, 0, 250) corresponding to 2.5 meters directly in front of the user who is looking.

[0101] like Figure 6 As shown, in a specific implementation, when the user is wearing the aforementioned smart glasses for visual function training, the processor sends a data acquisition command to the aforementioned eye-tracking unit. Upon receiving the command, the eye-tracking unit activates the infrared light source and high-speed camera to transmit the acquired raw eye image data to the processor in real time. The processor performs image preprocessing on the received eye images and extracts pupil and corneal reflection features from the preprocessed images. Then, based on the relative positional relationship between the pupil center and the corneal reflection center, combined with pre-calibrated parameters, it calculates the user's current gaze direction. Next, it projects this gaze direction onto a preset visual plane in three-dimensional space and determines the coordinates of the intersection point between the gaze and the visual plane through spatial geometric calculations. These intersection point coordinates are the user's current fixation point coordinates (i.e.,...). Figure 6 The raw eye-tracking data stream (coordinates, velocity) in the data.

[0102] Step S12: Determine the rate of change of the current gaze point coordinates, and if the rate of change is lower than a preset rate threshold, control the zoom lens unit to switch to a preset diopter.

[0103] It should be noted that the aforementioned rate of change can be the distance the gaze point coordinates move per unit time, specifically the displacement between two adjacent moments divided by the time interval, such as a speed of 50 millimeters per second. The aforementioned preset speed threshold can be a pre-set upper limit value for determining whether the user's gaze is in a stable gaze state, such as a threshold of 20 millimeters per second. When the rate of change is less than 20 millimeters per second, it indicates that the user is gazing steadily.

[0104] like Figure 2 as well as Figure 6 As shown, in a specific implementation, the processor continuously acquires the current gaze point coordinates at multiple moments and calculates the spatial distance between gaze point coordinates at adjacent moments. This spatial distance is divided by the time interval between adjacent moments to obtain the rate of change of the gaze point coordinates. Subsequently, the processor reads a preset speed threshold from memory and compares the calculated rate of change with the preset speed threshold (i.e.,...). Figure 6 (Whether the speed exceeds the judgment threshold), when the processor determines that the change speed is lower than the preset speed threshold (i.e., Figure 6 (No in the "low speed / stationary" field) confirms that the user's gaze is in a stable, fixed state (i.e., ... Figure 6 If the processor determines that the image is fixed (gazing), it then generates a diopter switching command and sends it to the zoom lens unit. Upon receiving the command, the zoom lens unit adjusts its diopter to a preset diopter value. When the processor determines that the rate of change is higher than a preset rate threshold (i.e., ...), ... Figure 6The key is: high-speed movement), confirming that the user's gaze is in a scanning state (i.e., Figure 6 If the scan is determined to be a scan, then the subsequent steps are stopped (i.e., ...). Figure 2 (Eye movement pattern recognition in [the context of this text]).

[0105] Furthermore, to ensure the accuracy of the training data, the smart glasses are also equipped with an inertial measurement unit; The step of controlling the zoom lens unit to switch to a preset diopter when the user is wearing the smart glasses for visual function training includes: Step S13: While the user is wearing the smart glasses for visual function training, the user's head posture data is collected through the inertial measurement unit.

[0106] It should be noted that the aforementioned visual function training can be a training process that forces the ciliary muscle of the eye to alternate between relaxation and tension by switching lenses of different refractive powers. For example, training to improve the eye's accommodative sensitivity by alternating positive and negative lenses. The aforementioned inertial measurement unit can be a sensor module used to detect the three-axis acceleration and three-axis angular velocity of an object, specifically composed of an accelerometer and a gyroscope, such as a six-axis IMU sensor that integrates a three-axis accelerometer and a three-axis gyroscope.

[0107] refer to Figure 2 In a specific implementation, when the user is wearing the aforementioned smart glasses for visual function training, the processor sends a data acquisition command to the inertial measurement unit (IMU). Upon receiving the command, the IMU activates the accelerometer and gyroscope to detect the motion state. The accelerometer collects linear acceleration data along three axes, and the gyroscope collects angular velocity data along three axes. The acquired raw sensor data is transmitted in real time to the processor (i.e., ...). Figure 2 (Reading IMU status). The processor filters and performs sensor fusion calculations on the received raw data, and obtains the user's current head pose data through integration and attitude calculation algorithms.

[0108] Step S14: Determine whether the user's head is currently stable based on the head posture data; Step S15: When the user's head is currently stable, control the zoom lens unit to switch to the preset diopter.

[0109] like Figure 2As shown, in the specific implementation, the processor acquires head posture data collected and calculated by the inertial measurement unit, extracts angular velocity data and angle change data from the head posture data, compares the angular velocity data with a preset angular velocity stability threshold, and compares the angle change data with a preset angle fluctuation range. When it is determined that the angular velocity data is below the angular velocity stability threshold and the angle change data remains within the angle fluctuation range, it is determined that the user's head is currently in a stable state (i.e., Figure 2 (Whether the head in the middle is stable), then a diopter switching command is generated and sent to the zoom lens unit. After receiving the command, the zoom lens unit adjusts its own diopter to the preset diopter value.

[0110] This embodiment also provides a first embodiment of a vision function training device; please refer to [reference needed]. Figure 7 , Figure 7 This is a diagram of a visual function training device provided in an embodiment of this application. The visual function training device includes: The switching module 601 is used to control the zoom lens unit to switch to a preset diopter when the user is wearing smart glasses for visual function training. The acquisition module 602 is used to acquire the user's current eye movement data and determine the user's accommodation reaction time based on the current eye movement data. The accommodation reaction time is the time elapsed from the start of switching from the zoom lens unit to the preset refractive power to the time when the user fixates on the target visual object. The adjustment module 603 is used to adjust the preset diopter according to the adjustment response time, and control the zoom lens unit to switch to the adjusted preset diopter for visual function training.

[0111] The acquisition module 602 is further configured to record a first moment when the zoom lens unit begins to switch to a preset diopter; acquire the user's current eye movement data, and record a second moment when the user is determining the target visual object based on the current eye movement data; and use the difference between the first moment and the second moment as the user's accommodation reaction time.

[0112] The acquisition module is further configured to acquire the user's current eye movement data through the eye-tracking unit, and determine the user's current gaze point coordinates based on the current eye movement data; acquire the required gaze depth between the user and the target visual target through the distance acquisition unit, and determine the visual target coordinates based on the required gaze depth; and determine that the user is gazing at the target visual target and record a second moment when the deviation between the current gaze point coordinates and the visual target coordinates is within a preset deviation range.

[0113] Referring to the first embodiment of the visual function training device, this embodiment also proposes a second embodiment of the visual function training device. The contents that are the same as or similar to those in the first embodiment of the visual function training device can be referred to the above description, and will not be repeated hereafter.

[0114] The adjustment module 603 is further configured to increase the current number of training sessions completed when it is determined that the user is gazing at the target visual object; and to adjust the preset diopter according to the adjustment reaction time when the current number of training sessions completed has not reached the preset target number.

[0115] The adjustment module 603 is further configured to acquire at least two historical accommodation reaction times of the user during historical visual function training; determine the user's reaction time change trend and average reaction time based on the current accommodation reaction time and the at least two historical accommodation reaction times; increase the preset diopter when the reaction time change trend is continuously decreasing and the average reaction time is lower than a first preset threshold; decrease the preset diopter when the reaction time change trend is continuously increasing and the average reaction time is higher than a second preset threshold; and maintain the preset diopter when the reaction time change trend is neither continuously decreasing nor continuously increasing, and the average reaction time is not lower than the first preset threshold and not higher than the second preset threshold.

[0116] Referring to the first embodiment and the second embodiment of the visual function training device, this embodiment also proposes a third embodiment of the visual function training device. The contents that are the same as or similar to the first embodiment and the second embodiment of the visual function training device can be referred to the above description, and will not be repeated hereafter.

[0117] The adjustment module 603 is further configured to acquire the user's pupil image through the eye-tracking unit, and determine the user's pupil diameter data based on the pupil image; generate a time series that changes over time based on the pupil diameter data, and determine the fluctuation characteristic parameters of the time series; and generate a fatigue prompt when the fluctuation characteristic parameters meet a preset fatigue condition.

[0118] The switching module 601 is further configured to, when the user is wearing the smart glasses for visual function training, collect the user's current eye movement data through the eye tracking unit, and determine the user's current fixation point coordinates based on the current eye movement data; determine the rate of change of the current fixation point coordinates, and control the zoom lens unit to switch to a preset diopter when the rate of change is lower than a preset rate threshold.

[0119] The visual function training device provided in this embodiment, employing the visual function training method described in the above embodiments, can solve the technical problem of how to improve the training effect of visual function training. Compared with the prior art, the beneficial effects of the visual function training device provided in this embodiment are the same as those of the visual function training method described in the above embodiments, and other technical features in the visual function training device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0120] This embodiment provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the visual function training method in the above embodiment.

[0121] The computer-readable storage medium provided in this embodiment may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0122] The aforementioned computer-readable storage medium may be included in the visual function training device; or it may exist independently and not be assembled into the visual function training device.

[0123] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the visual function training device, cause the visual function training device to perform visual function training.

[0124] Computer program code for performing the operations of this embodiment can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0125] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of the system, method, and display according to various embodiments of this embodiment. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0126] The modules described in this embodiment can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0127] The readable storage medium provided in this embodiment is a computer-readable storage medium, which stores computer-readable program instructions (i.e., a computer program) for executing the above-described visual function training method, and can solve the technical problem of how to improve the viewing experience of the display screen. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this embodiment are the same as the beneficial effects of the visual function training method provided in the above embodiments, and will not be repeated here.

[0128] The above descriptions are only some embodiments and do not limit the patent scope of this embodiment. All equivalent structural transformations made based on the technical concept of this application and the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the patent protection scope of this application.

Claims

1. A visual function training method, characterized in that, The method is applied to smart glasses equipped with a zoom lens unit; The method includes: When a user wears the smart glasses for visual function training, the zoom lens unit is controlled to switch to a preset diopter. The user's current eye movement data is collected, and the user's accommodation reaction time is determined based on the current eye movement data. The accommodation reaction time is the time elapsed from the start of switching from the zoom lens unit to the preset refractive power until the user fixates on the target visual object. The preset diopter is adjusted according to the adjustment response time, and the zoom lens unit is controlled to switch to the adjusted preset diopter for visual function training.

2. The method as described in claim 1, characterized in that, The step of collecting the user's current eye movement data and determining the user's accommodation reaction time based on the current eye movement data includes: Record the first moment when the zoom lens unit begins to switch to the preset diopter; Collect the user's current eye movement data, and record the second moment when determining that the user is fixating on the target visual object based on the current eye movement data; The difference between the first time point and the second time point is taken as the user's adjustment response time.

3. The method as described in claim 2, characterized in that, The smart glasses are also equipped with an eye-tracking unit and a distance acquisition unit; The step of collecting the user's current eye movement data and recording the second moment when determining that the user is fixating on a target based on the current eye movement data includes: The eye-tracking unit collects the user's current eye movement data and determines the user's current gaze coordinates based on the current eye movement data. The required gaze depth between the target and the distance acquisition unit is acquired, and the target coordinates are determined based on the required gaze depth. If the deviation between the current gaze point coordinates and the target coordinates is within a preset deviation range, it is determined that the user is gazing at the target and a second moment is recorded.

4. The method as described in claim 1, characterized in that, The step of adjusting the preset diopter based on the adjustment response time includes: If it is determined that the user is gazing at the target object, the current number of training sessions completed is increased; If the current number of training sessions completed has not reached the preset target number, the preset diopter is adjusted according to the adjustment reaction time.

5. The method as described in claim 1, characterized in that, The step of adjusting the preset diopter based on the adjustment response time includes: Obtain at least two historical conditioning reaction times of the user during the historical visual function training; Based on the current adjustment response time and the at least two historical adjustment response times, determine the user's response time trend and average response time; If the reaction time shows a continuous decreasing trend and the average reaction time is lower than a first preset threshold, the preset diopter is increased. If the reaction time shows a continuous increasing trend and the average reaction time is higher than the second preset threshold, the preset diopter is reduced. The preset diopter is maintained if the reaction time trend is neither a continuous decrease nor a continuous increase, and the average reaction time is not lower than the first preset threshold and not higher than the second preset threshold.

6. The method as described in claim 1, characterized in that, The smart glasses are also equipped with an eye-tracking unit; After the steps of adjusting the preset diopter according to the adjustment reaction time and controlling the zoom lens unit to switch to the adjusted preset diopter for visual function training, the method further includes: The eye-tracking unit acquires images of the user's pupils and determines the user's pupil diameter data based on the pupil images. A time series of pupil diameter changes over time is generated based on the pupil diameter data, and the fluctuation characteristic parameters of the time series are determined. When the fluctuation characteristic parameters meet the preset fatigue conditions, a fatigue warning is generated.

7. The method as described in claim 1, characterized in that, The smart glasses are also equipped with an eye-tracking unit; The step of controlling the zoom lens unit to switch to a preset diopter when the user is wearing the smart glasses for visual function training includes: When the user is wearing the smart glasses for visual function training, the eye-tracking unit collects the user's current eye movement data and determines the user's current gaze coordinates based on the current eye movement data; The rate of change of the current gaze point coordinates is determined, and if the rate of change is lower than a preset rate threshold, the zoom lens unit is controlled to switch to a preset diopter.

8. A visual function training device, characterized in that, The device includes: The switching module is used to control the zoom lens unit to switch to a preset diopter when the user is wearing smart glasses for visual function training. The acquisition module is used to acquire the user's current eye movement data and determine the user's accommodation reaction time based on the current eye movement data. The accommodation reaction time is the time elapsed from the start of switching from the zoom lens unit to the preset refractive power to the time when the user fixates on the target visual object. The adjustment module is used to adjust the preset diopter according to the adjustment response time, and control the zoom lens unit to switch to the adjusted preset diopter for visual function training.

9. A type of smart glasses, characterized in that, The smart glasses are equipped with a zoom lens unit; The smart glasses further include: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the visual function training method as described in any one of claims 1 to 7.

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

Images