Vision health training method and system based on brain vision function remodeling

Abstract

The invention provides a vision health training method and system based on brain vision function remodeling. The method comprises the following steps: acquiring multi-modal data of a trainer; based on the initial parameter, driving the sighting mark to move according to a preset track so as to perform double-eye dynamic tracking training on the trainee; visual mark visual or tracking training is carried out on the trainee through a prism, magnetic therapy massage is carried out on the eyes of the trainee through a magnetic therapy rod based on magnetic therapy intensity and massage intensity, the training effect of the trainee is evaluated through a training effect evaluation comprehensive index, and training parameters of the next period are optimized. According to the invention, the training state can be monitored in real time; binocular dynamic tracking training is performed on a trainee through sighting marks so as to improve the processing speed and efficiency of the trainee on visual information, visual mark visual or tracking training is performed on the trainee through prisms so as to enlarge a visual comfortable area, tension of eye fascia and ciliary muscles is relieved through massage, the intraocular microenvironment is improved, and the visual comfort of the trainee is improved. And performing training parameter adjustment in the next period according to the training effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of visual function rehabilitation, and in particular to a vision health training method and system based on brain visual function remodeling. BACKGROUND

[0002] Current vision intervention technology has obvious fragmentation, static and symptomatic defects, and it is difficult to fundamentally solve the vision health problem. Optical correction means can only compensate for refractive errors and cannot train visual system function; single function training products lack precise excitation of the brain visual center, and the training parameters are fixed and cannot adapt to the real-time state of the user; physical intervention methods are disconnected from functional training, and the effect is difficult to add and solidify. The existing technology has the following technical defects:

[0003] First, there is a lack of real-time sensing feedback mechanism and quantitative algorithm support, and the training parameters are based on experience setting, which cannot be dynamically adjusted according to the user's eye movement state, accommodation function and physiological load, resulting in insufficient intervention specificity.

[0004] Second, binocular function training only focuses on horizontal direction convergence function, lacks precise algorithm for vertical phoria correction, and the intervention dimension is single.

[0005] Third, there is no clear calculation model for parameter adjustment of structure regulation, which cannot match the organization sensitivity and metabolic state of different users.

[0006] These defects make it difficult for existing technology to build a truly collaborative intervention system for brain, eyes and eye tissue structure, and the intervention effect is limited and prone to bottlenecks, which cannot meet the long-term improvement needs of vision health. SUMMARY

[0007] In view of the deficiencies of the prior art, the present application provides a vision health training method and system based on brain visual function remodeling, which can realize precise remodeling of brain visual function, effective control of myopia progression, and long-term effect of synchronous improvement of visual efficiency and cognitive function.

[0008] To achieve the above purpose, the present application adopts the following technical solutions:

[0009] In a first aspect, the present application provides a vision health training method based on brain visual function remodeling, comprising the following steps:

[0010] S1), obtaining multi-modal data of the trainer;

[0011] S2), driving the target based on the initial parameters to move along the preset trajectory, and the trainer performs binocular dynamic pursuit training; and in the training process, the training speed, trajectory mode and stimulation type are dynamically adjusted through the target parameter optimization algorithm to analyze the data;

[0012] If the eye movement tracking deviation is greater than a preset threshold or the accommodation reaction time is greater than a preset time, the training speed is reduced or switched to intermittent trajectory;

[0013] If the retinal imaging clarity is improved by more than a preset value and the accommodation stability is qualified, the training speed is gradually increased or switched to a complex trajectory mode;

[0014] S3), wearing a prism for target fixation or tracking training for the user, and based on the eye position deviation data and accommodation ratio of step S2), determining the prism power of the training prism through a prism power adaptive algorithm, and adjusting the training prism in real time based on the prism power; after adjustment, a certain distance target fixation or tracking training is performed for a certain period of time; during the training process, the prism power of the training prism is dynamically corrected through a prism parameter iterative correction algorithm;

[0015] S4), adjusting the magnetic therapy intensity and massage intensity based on the physiological signal data, using the magnetic therapy rod to perform magnetic therapy massage on the eyes of the trainer based on the magnetic therapy intensity and massage intensity, and the magnetic therapy massage time is 15-20 minutes;

[0016] S5), obtaining the multi-modal data of the trained trainer and comparing it with the initial multi-modal data, evaluating the training effect of the trainer through a training effect evaluation comprehensive index, and optimizing the training parameters of the next cycle through a parameter iterative optimization algorithm.

[0017] Preferably, in step S1), the multi-modal data includes eye movement data, accommodation function data, physiological signal data, and visual quality data.

[0018] The eye movement data includes binocular fixation point position, eye movement speed, horizontal phoria, and vertical phoria.

[0019] The accommodation function data includes ciliary muscle contraction amplitude, accommodation reaction time, and accommodation stability.

[0020] The physiological signal data includes heart rate variability, skin electrical response, and eye microcirculation blood flow rate.

[0021] The visual quality data includes retinal imaging clarity, contrast sensitivity, and binocular single vision stability.

[0022] Preferably, in step S2), the initial parameters of the training include initial speed of the moving target and initial stimulation type; the initial speed of the moving target is determined according to the age of the trainer; and the initial stimulation type is determined by visual sensitivity score.

[0023] If the visual sensitivity score is less than a first sensitivity threshold, black and white high-contrast visual stimulation is selected.

[0024] if the visual sensitivity score is greater than or equal to the first sensitivity threshold and less than the second sensitivity threshold, a color contrast visual stimulus is selected;

[0025] if the visual sensitivity score is greater than or equal to the second sensitivity threshold, a dynamic flicker visual stimulus is selected.

[0026] Preferably, in step S2), the training target speed is adjusted in real time according to the target speed adjustment amount during the training process; and the trajectory mode is switched in real time through the trajectory adaptation coefficient.

[0027] The trajectory mode includes uniform speed equivalent trajectory, segmented intermittent equivalent trajectory, and acceleration-deceleration alternating equivalent trajectory.

[0028] Preferably, in step S3), the prism degree includes horizontal prism degree and vertical prism degree, the horizontal prism degree is calculated according to the horizontal correction coefficient and the horizontal phoria adjustment ratio, and the vertical prism degree is calculated according to the vertical correction coefficient and the vertical phoria.

[0029] Preferably, in step S3), during the training process, the prism degree is corrected every 2 minutes through a correction amount, and the training prism is adjusted based on the new prism degree; the correction amount is calculated based on the current horizontal phoria and the current binocular single vision stability.

[0030] Preferably, in step S4), the magnetic therapy intensity and the massage intensity are adjusted based on the physiological signal data, specifically as follows:

[0031] The magnetic therapy intensity is calculated based on the heart rate variability before and after the training and the ocular microcirculation blood flow velocity;

[0032] The massage intensity is calculated based on the magnetic therapy intensity and the galvanic skin response data before and after the training;

[0033] The trainer's eyes are subjected to magnetic therapy and massage based on the magnetic therapy intensity and the massage intensity; and essential oils are used during the magnetic therapy and massage.

[0034] Preferably, in step S5), the multi-modal data of the trained trainer is obtained and compared with the initial multi-modal data, the training effect of the trainer is evaluated through the training effect evaluation comprehensive index, and the training parameters of the next cycle are optimized through the parameter iterative optimization algorithm; specifically including the following steps:

[0035] S51), based on the age of the trainer, the initial parameter matrix is constructed based on the basic visual function data;

[0036] S52), the evaluation comprehensive index is constructed based on the multi-modal data before and after the training;

[0037] S53)calculating an initial parameter adjustment amount of the next cycle based on the evaluation comprehensive index, and generating a parameter matrix of the next cycle based on the adjustment amount;

[0038] S54)determining whether to enter the next stage based on the stage determination coefficient.

[0039] In a second aspect, the present application provides a visual health training system based on brain visual function remodeling, comprising:

[0040] An adaptive perceptual feedback module is configured to collect multi-modal data of the user, including eye movement data, accommodation function data, and physiological signal data.

[0041] A first training module is configured to drive the target to move along a preset trajectory based on the initial parameters, so as to perform binocular dynamic pursuit training on the trainee; and in the training process, the training speed, trajectory mode and stimulation type are dynamically adjusted through a target parameter optimization algorithm.

[0042] If the eye movement tracking deviation is greater than a preset threshold or the accommodation reaction time is greater than a preset time, the training speed is reduced or switched to an intermittent trajectory.

[0043] If the retinal imaging clarity is improved by more than a preset value and the accommodation stability meets the standard, the training speed is gradually increased or the complex trajectory mode is switched.

[0044] A second training module is configured to determine the prism power of the training prism through a prism power adaptive algorithm based on the eye position deviation data and the accommodation ratio, and to adjust the training prism in real time based on the prism power. After the adjustment is completed, a specific distance target fixation or tracking training is performed for a certain period of time. In the training process, the prism power of the training prism is dynamically corrected through a prism parameter iterative correction algorithm.

[0045] A third training module is configured to adjust the magnetic therapy intensity and the massage intensity based on the physiological signal data, and to perform magnetic therapy and massage on the trainee's eyes using a magnetic therapy rod based on the magnetic therapy intensity and the massage intensity. The magnetic therapy and massage lasts for 15-20 minutes.

[0046] An evaluation and adjustment module is configured to obtain the multi-modal data of the trainee after the training and compare it with the initial multi-modal data, evaluate the training effect of the trainee through a training effect evaluation comprehensive index, and optimize the training parameters of the next cycle through a parameter iterative optimization algorithm.

[0047] Preferably, the adaptive perceptual feedback module comprises:

[0048] An eye movement tracking unit is configured to collect eye movement data of the fixation point, eye movement speed and eye position deviation in real time.

[0049] The accommodation function monitoring unit is used to dynamically detect accommodation function data of ciliary muscle contraction amplitude and accommodation response time;

[0050] The physiological signal acquisition unit is used to collect physiological signal data of the user's heart rate variability, skin conductance response, and ocular microcirculation blood flow velocity.

[0051] The visual quality assessment unit is used to acquire visual quality data, including retinal image sharpness, contrast sensitivity, and binocular single vision stability.

[0052] The beneficial effects of this invention are as follows:

[0053] 1. This invention achieves real-time monitoring of the training status through an adaptive sensing feedback module; and improves the training effect by adjusting the training process in real time through multimodal data.

[0054] 2. This invention uses visual targets to train trainees in binocular dynamic tracking training to improve their speed and efficiency in processing visual information. Moreover, during the training process, the training speed, trajectory mode and stimulus type are adjusted in real time through feedback data.

[0055] 3. This invention uses a prism to train trainees in visual labeling or tracking training, thereby expanding the visual comfort zone, establishing a stable accommodation-convergence linkage, and adjusting the prism in real time based on feedback information;

[0056] 4. This invention also relieves tension in the fascia and ciliary muscle of the eye through massage, improves the intraocular microenvironment, and adjusts the training parameters for the next cycle based on the training effect. Attached Figure Description

[0057] Figure 1 This is a flowchart illustrating Embodiment 1 of the present invention;

[0058] Figure 2 This is a schematic diagram of the process for evaluating the training effect of trainees in Embodiment 1 of the present invention;

[0059] Figure 3 This is a framework diagram of the system in Embodiment 2 of the present invention. Detailed Implementation

[0060] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0061] Example 1

[0062] like Figure 1 As shown, this embodiment provides a vision health training method based on brain visual function remodeling, including the following steps:

[0063] S1) Acquire multimodal data of trainees through multimodal perception technology;

[0064] The multi-modal data includes: eye movement data, accommodation function data, physiological signal data, visual quality data;

[0065] The eye movement data includes binocular fixation point position, eye movement speed, horizontal phoria, and vertical phoria.

[0066] The accommodation function data includes ciliary muscle contraction amplitude S, accommodation reaction time, and accommodation stability.

[0067] The physiological signal data includes heart rate variability HRV, galvanic skin response GSR, and ocular microcirculation blood flow velocity.

[0068] The visual quality data includes retinal imaging clarity, contrast sensitivity, and binocular single vision stability.

[0069] S2), based on the initial parameters, the target is moved along the preset trajectory, the trainer performs dynamic pursuit training with both eyes, and the trainer is required to continuously and accurately follow the moving target; the training duration is 10-15 minutes, and during the training process, the eye movement data, accommodation function data, and visual quality data are collected every 0.5 seconds, the data is analyzed through a target parameter optimization algorithm, and the training speed, trajectory mode, and stimulation type are dynamically adjusted.

[0070] If the eye movement tracking deviation is greater than 5° or the accommodation reaction time is greater than 300 ms, the training speed is reduced or switched to intermittent trajectory.

[0071] If the retinal imaging clarity is improved by more than 15% and the accommodation stability meets the standard, the training speed is gradually increased or the complex trajectory mode is switched.

[0072] In this embodiment, the training is performed using an intelligent straight line training instrument, which is provided with a high-contrast fine target moving along a straight line track, and the target moving speed and trajectory mode (uniform speed, variable speed, intermittent) can be dynamically adjusted based on the collected multi-modal real-time data through a target parameter optimization algorithm, with an adjustment range of target speed of 0.1-1.5 m / s and target trajectory period of 1-5 seconds, for driving the user to perform precise and extreme pursuit training with both eyes.

[0073] The initial parameters of the training are as follows:

[0074] The initial speed of the moving target based on the age of the trainer is:

[0075] wherein, is the age of the trainer,

[0076] and the constraint condition is established as:​​ ;

[0077] Initial selection of stimulation type: through visual sensitivity score Determination, ;

[0078] Wherein, is the retinal imaging clarity; is the contrast sensitivity;

[0079] If , select black and white high-contrast visual stimulation;

[0080] If , select color contrast visual stimulation;

[0081] If , select dynamic flicker visual stimulation.

[0082] In this embodiment, the data is analyzed by the target parameter optimization algorithm, and the training speed, trajectory mode and stimulation type are dynamically adjusted; Specifically:

[0083] Adjust the training target speed and trajectory in real time during the training process, and the adjustment amount of the target speed is The calculation formula is:

[0084]

[0085] In the formula, is the current retinal imaging clarity; is the initial retinal imaging clarity; is the adjustment stability; is the adjustment reaction time;

[0086] If , the adjusted speed is ;

[0087] If , the adjusted speed is ;

[0088] If , the adjusted speed is ;

[0089] Trajectory mode switching logic: through trajectory adaptation coefficient Determination, that is:

[0090]

[0091] Wherein, is the maximum target speed; is the binocular single vision stability;

[0092] If , maintain constant speed equivalent trajectory;

[0093] If 0.8, switch to segmented intermittent equivalent trajectory;

[0094] If , switch to acceleration-deceleration alternating equivalent trajectory.

[0095] In the embodiment, the formula for calculating the eye tracking deviation D is:

[0096]

[0097] In the formula, is the average fixation point coordinate of both eyes; is the real-time coordinate of the dynamic visual target.

[0098] S3), wearing a prism for target fixation or tracking training, based on the eye position deviation data and the AC and A ratios of step S2), determining the prism power of the training prism through a prism power adaptive algorithm, and adjusting the training prism based on the prism power; After adjustment, specific distance target fixation or tracking training is performed for 10-15 minutes; During the training process, data is collected every 2 minutes; The prism power of the training prism is dynamically corrected through a prism parameter iterative correction algorithm;

[0099] In the embodiment, the prism power of the training prism is determined through a prism power adaptive algorithm, which is as follows:

[0100] Horizontal prism power :

[0101]

[0102] In the formula, is the horizontal correction coefficient; represents the horizontal phoria; is the ratio of accommodative convergence and accommodation;

[0103] Vertical prism power is:

[0104]

[0105] In the formula, is the vertical correction coefficient; represents the vertical phoria;

[0106] Final prism power is:

[0107] ​

[0108] In the embodiment, horizontal outward phoria corresponds to BO, and inward phoria corresponds to BI; in the vertical direction, upward phoria corresponds to BD, and downward phoria corresponds to BU; if outward phoria > 3Δ, BO prism is increased; if inward phoria > 3Δ, BI prism is increased; if there is vertical phoria, BU and BD prisms are superimposed.

[0109] In the embodiment, the correction is made every 2 minutes in the training, and the correction amount is The calculation formula is:

[0110]

[0111] In the formula, is the current horizontal phoria; is the initial horizontal phoria; is the current binocular single vision stability;

[0112] The correction rule is: and the training prism is adjusted based on the new prism In the embodiment, the magnetic therapy intensity The calculation formula is:

[0113]

[0114] In the embodiment, the magnetic therapy intensity The calculation formula is:

[0115]

[0116] In the formula, is the resting heart rate variability; is the post-training heart rate variability; is the pre-training ocular microcirculation blood flow velocity; is the post-training ocular microcirculation blood flow velocity.

[0117] In the embodiment, the massage intensity of the magnetic therapy rod is calculated by the galvanic skin response GSR that is,

[0118]

[0119] In the formula, is the current galvanic skin response; is the resting galvanic skin response; is the magnetic therapy intensity.

[0120] In the embodiment, essential oil can be used in combination when the magnetic therapy rod is used for magnetic therapy massage of the trainer's eyes.

[0121] S5), obtaining the multi-modal data of the trained trainer after training and comparing with the initial multi-modal data, evaluating the training effect of the trainer through the training effect evaluation comprehensive index, and optimizing the training parameters of the next cycle through the parameter iterative optimization algorithm; such as Figure 2 As shown in the figure, it specifically includes the following steps:

[0122] S51), constructing an initial parameter matrix based on the age of the trainer and the basic visual function data That is:

[0123] Among them, is the initial eye movement speed; is the initial visual stimulation frequency; are the initial horizontal and vertical prism degrees, respectively; is the initial magnetic therapy intensity; is the initial massage intensity; is the initial amount of essential oil;

[0124] S52), constructing an evaluation comprehensive index That is:

[0125]

[0126] In the formula, are the retinal imaging clarity before and after training, respectively; , are the horizontal phoria before and after training, respectively; , are the vertical phoria before and after training, respectively; , are the heart rate variability before and after training, respectively; , are the contrast sensitivity before and after training, respectively.

[0127] S53), calculating the initial parameter adjustment amount of the next cycle And based on the adjustment amount generate the parameter matrix of the next cycle ; that is:

[0128] ;

[0129] If , ;

[0130] If , ;

[0131] If , ;

[0132] S54)、based on the stage determination coefficient determine whether to enter the next stage;

[0133] ;

[0134] wherein, , , are the effect evaluation comprehensive indexes of the four sub-training of the current period respectively;

[0135] If , maintain the current stage;

[0136] If , enter the next period;

[0137] If , if the current is the promotion period, enter the consolidation period.

[0138] Embodiment 2

[0139] As shown in Figure 3 , the embodiment provides a vision health training system based on brain visual function remodeling, comprising:

[0140] An adaptive perception feedback module is configured to collect multi-modal data of a user, including eye movement data, accommodation function data, and physiological signal data;

[0141] A first training module is configured to drive an optotype to move along a preset trajectory based on initial parameters, so as to perform binocular dynamic pursuit training on a trainee; and in the training process, the training speed, trajectory mode and stimulation type are dynamically adjusted through an optotype parameter optimization algorithm based on data analysis;

[0142] If the eye movement tracking deviation is greater than a preset threshold or the accommodation reaction time is greater than a preset time, the training speed is reduced or switched to an intermittent trajectory;

[0143] If the retinal imaging clarity is improved by more than a preset value and the accommodation stability meets the standard, the training speed is gradually increased or the trajectory mode is switched to a complex trajectory;

[0144] A second training module is configured to determine the prism power of a training prism through a prism power adaptive algorithm based on eye position deviation data and accommodation ratio, and to adjust the training prism in real time based on the prism power; after the adjustment is completed, a specific distance optotype fixation or tracking training is performed for a certain period of time; and in the training process, the prism power of the training prism is dynamically corrected through a prism parameter iterative correction algorithm;

[0145] A third training module is configured to adjust the magnetic therapy intensity and the massage intensity based on the physiological signal data, and to perform magnetic therapy and massage on the eyes of the trainee using a magnetic therapy rod based on the magnetic therapy intensity and the massage intensity, with a magnetic therapy and massage duration of 15-20 minutes.

[0146] The evaluation and adjustment module obtains the multi-modal data of the trained trainer after training and compares it with the initial multi-modal data, generates a training effect evaluation report of the trainer through a training effect evaluation comprehensive index, and optimizes the training parameters of the next cycle through a parameter iterative optimization algorithm.

[0147] In the embodiment, the adaptive perception feedback module comprises:

[0148] An eye movement tracking unit is configured to collect eye movement data of the fixation points of both eyes, eye movement speed and eye position deviation in real time.

[0149] An accommodation function monitoring unit is configured to dynamically detect accommodation function data of the contraction amplitude of ciliary muscles and accommodation reaction time.

[0150] A physiological signal acquisition unit is configured to collect physiological signal data of heart rate variability, galvanic skin response and ocular microcirculation blood flow velocity of the user.

[0151] A visual quality evaluation unit is configured to obtain visual quality data, including retinal imaging clarity, contrast sensitivity and binocular single vision stability.

[0152] The above embodiments and descriptions are only to illustrate the principles and the best embodiments of the present application, and the present application can be variously changed and improved without departing from the spirit and scope of the present application, and these changes and improvements all fall within the scope of the present application.

Claims

1. A vision health training method based on brain visual function remodeling, characterized in that, Includes the following steps: S1) Obtain multimodal data from trainers; S2) Based on the initial parameters, the visual target is driven to move along a preset trajectory, and the trainee performs binocular dynamic tracking training; and during the training process, the data is analyzed through the visual target parameter optimization algorithm to dynamically adjust the training speed, trajectory mode and stimulus type. If the eye-tracking deviation is detected to be greater than the preset threshold or the accommodation reaction time is greater than the preset time, the training speed is reduced or the training is switched to intermittent trajectory. If the improvement in retinal imaging clarity is detected to be greater than the preset value and the adjustment stability meets the standard, gradually increase the training speed or switch to complex trajectory mode. S3) The user wears a prism, and based on the eye position deviation data and accommodation ratio in step S2), the prism power of the training prism is determined by the prism power adaptive algorithm. The training prism is adjusted in real time based on the prism power. After the adjustment is completed, a specific distance visual annotation or tracking training is performed for a certain period of time. During training, the prism power of the training prism is dynamically corrected through an iterative correction algorithm for prism parameters; S4) Adjust the intensity of magnetic therapy and massage based on physiological signal data, and use a magnetic therapy stick to perform magnetic therapy massage on the trainee's eyes for 15-20 minutes based on the intensity of magnetic therapy and massage. S5) Obtain the multimodal data of the trainees after training and compare it with the initial multimodal data. Evaluate the training effect of the trainees through the comprehensive index of training effect evaluation, and optimize the training parameters for the next cycle through parameter iterative optimization algorithm.

2. The vision health training method based on brain visual function remodeling according to claim 1, characterized in that: In step S1), the multimodal data includes: eye-tracking data, accommodation function data, physiological signal data, and visual quality data; The eye movement data includes binocular fixation point position, eye movement speed, horizontal latancy, and vertical latancy. The aforementioned regulatory function data includes ciliary muscle contraction amplitude and regulatory response time, and regulatory stability; The physiological signal data mentioned include heart rate variability, skin conductance response, and ocular microcirculation blood flow velocity; The visual quality data includes retinal imaging sharpness, contrast sensitivity, and binocular single vision stability.

3. The vision health training method based on brain visual function remodeling according to claim 2, characterized in that: In step S2), the initial parameters for training include the initial velocity of the moving target and the initial stimulus type; wherein, the initial velocity of the moving target is determined according to the age of the trainee; and the initial stimulus type is determined through a visual sensitivity score. If the visual sensitivity score is less than the first sensitivity threshold, then choose high-contrast black and white visual stimulation; If the visual sensitivity score is greater than or equal to the first sensitivity threshold and less than the second sensitivity threshold, select color contrast visual stimulation; If the visual sensitivity score is greater than or equal to the second sensitivity threshold, then dynamic flashing visual stimulation is selected.

4. The vision health training method based on brain visual function remodeling according to claim 3, characterized in that: In step S2), the training target speed is adjusted in real time according to the target speed adjustment amount during the training process; and the trajectory mode is switched in real time through the trajectory adaptation coefficient. The trajectory modes include uniform velocity equivalent trajectory, segmented intermittent equivalent trajectory, and acceleration-deceleration alternating equivalent trajectory.

5. A vision health training method based on brain visual function remodeling according to claim 2, characterized in that: In step S3), the prism power includes horizontal prism power and vertical prism power. The horizontal prism power is calculated based on the horizontal correction coefficient and the horizontal skew adjustment ratio. The vertical prism power is calculated based on the vertical correction coefficient and the vertical skew.

6. A vision health training method based on brain visual function remodeling according to claim 5, characterized in that: In step S3), during the training process, the prism power is corrected every 2 minutes by a correction amount, and the training prism is adjusted based on the new prism power; the correction amount is calculated by the current horizontal phoria and the current binocular single vision stability.

7. A vision health training method based on brain visual function remodeling according to claim 2, characterized in that: In step S4), the intensity of magnetic therapy and massage is adjusted based on physiological signal data, as follows: The intensity of magnetic therapy was calculated based on heart rate variability before and after training, as well as ocular microcirculation blood flow velocity. Massage intensity is calculated based on magnetic therapy intensity and skin electrical response data before and after training. The trainee's eyes are treated with magnetic therapy massage based on the intensity of the magnetic therapy and the intensity of the massage; and essential oils are used in conjunction with the magnetic therapy massage.

8. A vision health training method based on brain visual function remodeling according to claim 2, characterized in that: In step S5), the multimodal data of the trainee after training is obtained and compared with the initial multimodal data. The training effect of the trainee is evaluated by the comprehensive index of training effect evaluation, and the training parameters for the next cycle are optimized by the parameter iterative optimization algorithm. Specifically, it includes the following steps: S51) Construct an initial parameter matrix based on the trainee's age and basic visual function data; S52) Construct a comprehensive evaluation index based on multimodal data before and after training; S53) Calculate the initial parameter adjustment amount for the next period based on the comprehensive evaluation index, and generate the parameter matrix for the next period based on the adjustment amount; S54) Determine whether to proceed to the next stage based on the stage determination coefficient.

9. A vision health training system based on brain visual function remodeling, characterized in that, The system includes: The adaptive perception feedback module is used to collect multimodal data from users, including eye-tracking data, accommodation function data, and physiological signal data. The first training module drives the visual targets to move along a preset trajectory based on initial parameters in order to conduct binocular dynamic tracking training for the trainees; and during the training process, the training speed, trajectory mode and stimulus type are dynamically adjusted by analyzing data through a visual target parameter optimization algorithm. If the eye-tracking deviation is detected to be greater than the preset threshold or the accommodation reaction time is greater than the preset time, the training speed is reduced or the training is switched to intermittent trajectory. If the improvement in retinal imaging clarity is detected to be greater than the preset value and the adjustment stability meets the standard, gradually increase the training speed or switch to complex trajectory mode. The second training module determines the prism power of the training prism based on eye position deviation data and accommodation ratio through a prism power adaptive algorithm, and adjusts the training prism in real time based on the prism power. After adjustment, a specific distance visual annotation or tracking training is conducted for a certain period of time. During the training process, the prism power of the training prism is dynamically corrected through a prism parameter iterative correction algorithm. The third training module adjusts the intensity of magnetic therapy and massage based on physiological signal data, and uses a magnetic therapy stick to perform magnetic therapy massage on the trainee's eyes for 15-20 minutes based on the intensity of magnetic therapy and massage. The evaluation and adjustment module acquires the multimodal data of the trainees after training and compares it with the initial multimodal data. It evaluates the training effect of the trainees through the comprehensive index of training effect evaluation and optimizes the training parameters for the next cycle through the parameter iterative optimization algorithm.

10. A vision health training system based on brain visual function remodeling according to claim 9, characterized in that, The adaptive sensing feedback module includes: An eye-tracking unit is used to collect eye movement data in real time, including binocular fixation points, eye movement speed, and eye position deviation. The accommodation function monitoring unit is used to dynamically detect accommodation function data of ciliary muscle contraction amplitude and accommodation response time; The physiological signal acquisition unit is used to collect physiological signal data of the user's heart rate variability, skin conductance response, and ocular microcirculation blood flow velocity. The visual quality assessment unit is used to acquire visual quality data, including retinal image sharpness, contrast sensitivity, and binocular single vision stability.

Images