Teenager myopia prevention and control method and device, computer equipment and storage medium

Abstract

The invention discloses a teenager myopia prevention and control method and device, computer equipment and a storage medium, and relates to the technical field of VR myopia training. Receiving a starting instruction sent by a target user, constructing a three-dimensional environment according to the starting instruction, and generating a positioning instruction; a calibration model is generated according to the positioning instruction, the calibration model moves according to a random track, so that the target user moves eyeballs according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and a confrontation instruction is generated; a confrontation instruction is received, and a confrontation environment is generated according to the confrontation instruction, so that the target user executes confrontation training; recording the eyeball activity of the target user, calculating the following degree of the eyeball activity, and determining a training plan according to the following degree. Eyeball movement is guided through random movement of the calibration model, eyestrain is intelligently evaluated, personalized confrontation training is generated accordingly, the system intelligently adjusts a training plan, fatigue is effectively prevented, the training effect is optimized, efficient and scientific vision training is achieved, and the training cost is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of VR myopia training technology, and specifically relates to a method, device, computer equipment and storage medium for preventing and controlling myopia in adolescents. Background Art

[0002] In the field of myopia prevention and control for adolescents, in-depth training using virtual reality equipment has become an innovative and effective means. This method uses VR technology to simulate various visual environments to provide adolescents with personalized visual training, aiming to strengthen eye muscles, improve accommodation ability, and help adolescents develop good eye habits. Through this in-depth VR training, the occurrence and development of myopia can be effectively prevented and controlled, safeguarding the visual health of adolescents.

[0003] Patent No. CN115547497A, the present invention discloses a myopia prevention and control system and method based on multi-source data; based on image data, it is judged whether myopia prediction is needed, and no professional myopia detection equipment is required, which can reduce the judgment cost and perform non-sensory myopia detection on users; the myopia assessment model is combined with the myopia risk factor, basic data and environmental data to realize myopia prediction, and the prevention and control plan is matched when the myopia prediction result is abnormal; the myopia prediction model is obtained through reasonable training, and periodic myopia prediction is performed, and the prevention and control plan is provided to the user in time to avoid the deterioration of myopia. Although the above technology solves some problems, it does not take into account the actual situation of the user. The user may have eye fatigue, which leads to a decrease in the training effect of the myopia prevention and control plan and an increase in the training cost. Summary of the invention

[0004] The purpose of the present invention is to solve the problem and to propose a method, device, computer equipment and storage medium for preventing and controlling myopia in adolescents.

[0005] In the first aspect of the present invention, a method for preventing and controlling myopia in adolescents is first proposed, the method comprising:

[0006] Receiving a start instruction from a target user, constructing a three-dimensional environment according to the start instruction, and generating a positioning instruction;

[0007] Generate a calibration model according to the positioning instruction, the calibration model moves according to a random trajectory, so that the target user moves the eyeball according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates a countermeasure instruction; the calibration model is a three-dimensional model;

[0008] receiving the confrontation instruction, and generating a confrontation environment according to the confrontation instruction, so that the target user performs confrontation training;

[0009] The eye movement parameters of the target user are recorded, the tracking degree of the eye movement is calculated according to the eye movement parameters and the abnormal fatigue coefficient, and a training plan is determined according to the tracking degree.

[0010] Optionally, the target user moves the eyeballs according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, including:

[0011] Determining the plane displacement of the calibration model according to the random trajectory, and simulating the depth movement of the object by scaling the size of the calibration model;

[0012] Acquire a video stream of the target user, and extract frames from the video stream according to a preset period to obtain key frames; the video stream is a video of the target user performing focus calibration according to the positioning instruction;

[0013] identifying blink features and pupil features in the key frame, and calculating an abnormal fatigue coefficient according to the blink features and pupil features;

[0014]

[0015] Where P is the abnormal fatigue coefficient, t 0 is the preset time period, Z is the number of blinks, PERCLOS is the proportion of eye closure time in the preset time period, T is the pupil change score, α and β are weight coefficients, both greater than 0.

[0016] Optionally, calculating the tracking degree of the eye movement according to the eye movement parameter and the abnormal fatigue coefficient includes:

[0017] The degree of following is calculated according to the eye movement parameters; the eye movement parameters include: the number of attention loss, the object movement speed and the eye movement speed;

[0018]

[0019] Among them, F is the following degree, v 0 is the object moving speed, v is the eyeball moving speed, P is the abnormal fatigue coefficient, D is the number of times of attention loss, σ, and ω are weight coefficients, both greater than 0.

[0020] Optionally, determining a training plan according to the following degree includes:

[0021] If the following degree is equal to the standard threshold, the original myopia prevention and control training plan is executed;

[0022] If the following degree is less than the standard threshold, the training time of the original myopia prevention and control training plan will be reduced.

[0023] In a second aspect of the present invention, a device for preventing and controlling myopia in adolescents is provided, including: the device includes: a training start module, a fatigue detection module, a simulation training module and a training correction module:

[0024] Optionally, the device includes: a training start module, a fatigue detection module, a simulation training module and a training correction module:

[0025] The training start module is used to receive a start instruction issued by a target user, construct a three-dimensional environment according to the start instruction, and generate a positioning instruction;

[0026] The fatigue detection module is used to generate a calibration model according to the positioning instruction, and the calibration model moves according to a random trajectory, so that the target user moves the eyeball according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates a countermeasure instruction; the calibration model is a three-dimensional model;

[0027] The simulation training module is used to receive the confrontation instruction and generate a confrontation environment according to the confrontation instruction so that the target user performs confrontation training;

[0028] The training correction module is used to record the eye movement parameters of the target user, calculate the tracking degree of the eye movement according to the eye movement parameters and the abnormal fatigue coefficient, and determine the training plan according to the tracking degree.

[0029] Optionally, the fatigue detection module includes: a model motion module, a key frame extraction module and a first calculation module:

[0030] The model motion module is used to determine the plane displacement of the calibration model according to the random trajectory, and simulate the depth motion of the object by scaling the size of the calibration model;

[0031] The key frame extraction module is used to obtain the video stream of the target user, and extract frames from the video stream according to a preset period to obtain key frames; the video stream is a video of the target user performing focus calibration according to the positioning instruction;

[0032] The first calculation module is used to identify the blinking features and pupil features in the key frame, and calculate the abnormal fatigue coefficient according to the blinking features and the pupil features;

[0033]

[0034] Where P is the abnormal fatigue coefficient, t 0 is the preset time period, Z is the number of blinks, PERCLOS is the proportion of eye closure time in the preset time period, T is the pupil change score, α and β are weight coefficients, both greater than 0.

[0035] Optionally, the training correction module includes: a second calculation module:

[0036] The second calculation module is used to calculate the tracking degree according to the eye movement parameters; the eye movement parameters include: the number of attention loss, the object movement speed and the eye movement speed;

[0037]

[0038] Among them, F is the following degree, v 0 is the object moving speed, v is the eyeball moving speed, P is the abnormal fatigue coefficient, D is the number of times of attention loss, σ, and ω are weight coefficients, both greater than 0.

[0039] Optionally, the training correction module further includes: a normal training module and an abnormal training module:

[0040] The normal training module is used to execute the original myopia prevention and control training plan if the following degree is equal to the standard threshold;

[0041] The abnormal training module is used to reduce the training time of the original myopia prevention and control training plan if the following degree is less than the standard threshold.

[0042] In a third aspect of the present invention, a computer device is provided, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0043] Memory, used to store computer programs;

[0044] The processor is used to implement any of the above-mentioned steps of a method for dynamic planning of a transport vehicle when executing a program stored in the memory.

[0045] In a fourth aspect of the implementation of the present invention, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any of the steps of a method for dynamic planning of a transport vehicle described above is implemented.

[0046] Beneficial effects of the present invention:

[0047] The present invention proposes a method for preventing and controlling myopia in adolescents, which guides users to perform eye movements through the random movement of a calibration model to detect and evaluate the degree of eye fatigue. This innovative fatigue detection method allows the system to automatically generate a confrontation environment based on the abnormal fatigue coefficient, providing users with personalized confrontation training; the system also records the user's eye movement and calculates the tracking degree, and then intelligently adjusts the training plan to ensure that while avoiding eye fatigue, the training effect is optimized, the training cost is reduced, and a more scientific and effective vision training is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be further described below in conjunction with the accompanying drawings.

[0049] Figure 1 A flowchart of a method for preventing and controlling myopia in adolescents is provided for an embodiment of the present invention;

[0050] Figure 2 A structural schematic diagram of a device for preventing and controlling myopia in adolescents is provided for an embodiment of the present invention;

[0051] Figure 3 A structural diagram of a computer device is provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. The term "and / or" herein is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the present invention, the description of "first", "second", etc. is only used for descriptive purposes, and cannot be understood as indicating or implying its relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" can explicitly or implicitly include at least one of the features. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0053] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative work shall fall within the scope of protection of the present invention.

[0054] The embodiment of the present invention provides a method for preventing and controlling myopia in adolescents. Figure 1 , Figure 1A flowchart of a method for preventing and controlling myopia in adolescents provided by an embodiment of the present invention. The method comprises the following steps:

[0055] S101, receiving a start instruction from a target user, constructing a three-dimensional environment according to the start instruction, and generating a positioning instruction.

[0056] S102, generating a calibration model according to the positioning instruction, the calibration model moves along a random trajectory, so that the target user moves the eyeballs according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates a countermeasure instruction.

[0057] S103, receiving a confrontation instruction, and generating a confrontation environment according to the confrontation instruction, so that the target user performs confrontation training.

[0058] S104, recording the eye movement parameters of the target user, calculating the tracking degree of the eye movement according to the eye movement parameters and the abnormal fatigue coefficient, and determining the training plan according to the tracking degree.

[0059] The calibration model is a three-dimensional solid model.

[0060] Based on the method for preventing and controlling myopia in adolescents provided by the embodiment of the present invention, the user's eye movement is guided by the random movement of the calibration model to detect and evaluate the degree of eye fatigue. This innovative fatigue detection method allows the system to automatically generate a confrontation environment based on the abnormal fatigue coefficient, providing users with personalized confrontation training; the system also records the user's eye movement and calculates the tracking degree, and then intelligently adjusts the training plan to ensure that while avoiding eye fatigue, the training effect is optimized, the training cost is reduced, and a more scientific and effective vision training is achieved.

[0061] In one implementation, the user presses the start button of the device, that is, sends a start instruction, builds an initial three-dimensional environment according to the start instruction, and sends a positioning instruction. The positioning instruction is used to locate the pupil position, and a calibration model is generated according to the positioning instruction. The calibration model moves according to a random trajectory. The user's eyeballs move according to the calibration model and shoot the video stream at this time. Fatigue detection is performed based on the video stream to obtain an abnormal fatigue coefficient, and a confrontation environment is determined based on the abnormal fatigue coefficient. For example, the confrontation environment corresponding to the abnormal fatigue coefficient under normal circumstances is table tennis, if the abnormal fatigue coefficient is slightly tired, it is badminton, and if the abnormal fatigue coefficient is very tired, it is tennis. After determining that the confrontation environment is completed, a confrontation instruction is sent, and a three-dimensional confrontation environment is generated according to the confrontation instruction. The target user performs confrontation training, records the target user's eye movement parameters, and calculates the eye movement tracking degree based on the eye movement parameters and the abnormal fatigue coefficient. The training plan is determined based on the tracking degree, that is, the training plan for confrontation training.

[0062] In one implementation, the VR device first constructs a three-dimensional environment in the screen and generates a calibration model. The calibration model is a three-dimensional stereo model. For example, the three-dimensional stereo model can be a sphere or a cube, etc. The calibration model can be enlarged or reduced to simulate the movement of an object from far to near or from near to far. The random trajectory is generated by a computer.

[0063] In one implementation, confrontation training is a training method for training eye activities, for example, table tennis confrontation training, in which users can play in the three-dimensional environment to train the activity of eye muscles and the contraction of pupils to achieve the effect of active eyes.

[0064] In one implementation, the system automatically selects and generates a confrontation environment based on the abnormal fatigue coefficient to provide users with personalized confrontation training; the system also records the user's eye movement and calculates the tracking degree, and then intelligently adjusts the training plan to ensure that the training effect is optimized while avoiding eye fatigue. Users can reasonably adjust the eye training time according to their own conditions to achieve more scientific and effective vision training.

[0065] In one embodiment, step S102 includes:

[0066] The plane displacement of the calibration model is determined according to the random trajectory, and the depth motion of the object is simulated by scaling the size of the calibration model.

[0067] The target user's video stream is obtained, and key frames are obtained by extracting frames from the video stream according to a preset period; the video stream is a video of the target user performing focus calibration according to the positioning instruction.

[0068] The blink features and pupil features in the key frames are identified, and the abnormal fatigue coefficient is calculated based on the blink features and pupil features.

[0069]

[0070] Where P is the abnormal fatigue coefficient, t 0 is the preset time period, Z is the number of blinks, PERCLOS is the proportion of eye closure time in the preset time period, T is the pupil change score, α and β are weight coefficients, both greater than 0.

[0071] In one implementation, a camera is used to capture a video stream of a target user, and necessary preprocessing is performed on the video stream, such as grayscale, denoising, contrast enhancement, etc., to improve the accuracy of subsequent feature recognition; key frames are extracted from the video stream according to a preset period, and a face recognition algorithm is used to detect faces, such as a deep learning model; the eye area is located, blinking actions are detected, and changes in the state of the eyes between consecutive frames are analyzed in the eye area; blinking is usually manifested as a process in which the eye area quickly closes and then reopens within a few frames, and blinking events can be identified by comparing pixel changes or shape changes in the eye area in consecutive frames.

[0072] In one implementation, the pupil is further located in the eye area. The pupil is the darker part of the eye and can be identified by threshold segmentation, morphological operations, or deep learning models. The characteristics of the pupil can be obtained by analyzing the size, shape, and position changes of the pupil. The pupil contraction process is judged by comparing the pupil scaling ratio, that is, the area of ​​the black area in the pupil. The average pupil change is obtained by calculating the pupil change amount and the number of changes in the preset time period. The average pupil change amount is compared with the preset pupil change amount to obtain the change difference, and the pupil change amount score is determined in the score table according to the change difference.

[0073] In one implementation, PERCLOS calculates the ratio of closed-eye time to total observation time. t3 is the end time of the eyes-closed state, t2 is the start time of the eyes-closed state, t4 is the end time of the observation, and t1 is the start time of the observation.

[0074] In one embodiment, step S104 includes:

[0075] The degree of following is calculated based on eye movement parameters; the eye movement parameters include: the number of attention losses, the object movement speed and the eye movement speed.

[0076]

[0077] Among them, F is the following degree, v 0 is the object moving speed, v is the eyeball moving speed, P is the abnormal fatigue coefficient, D is the number of times of attention loss, σ, and ω are weight coefficients, both greater than 0.

[0078] In one implementation, if the number of times of attention loss is during the training process, when the eyes are not focused on the three-dimensional object and the time focused on other places exceeds the attention threshold, it is determined as attention loss, and the user's mental state may be very tired. The attention threshold is determined manually. For example, during the training process, the attention threshold is 10 times, but the number of times of attention loss is 15 times, it is determined that the attention threshold is exceeded.

[0079] In one implementation, adversarial training is performed based on the target user to obtain the object movement speed, v 0 is the object movement speed, the movement speed of a three-dimensional object in a three-dimensional environment, for example, the movement speed of a table tennis ball in table tennis confrontation training; v is the eye movement speed, the speed at which both eyes focus on a three-dimensional object and the focus point moves with the three-dimensional object.

[0080] In one embodiment, step S104 further includes:

[0081] If the following degree is equal to the standard threshold, the original myopia prevention and control training plan will be implemented.

[0082] If the following degree is less than the standard threshold, the training time of the original myopia prevention and control training plan will be reduced.

[0083] In one implementation, when the following degree is equal to the standard threshold, the standard threshold is determined manually, for example: the following degree is 5, and the standard threshold is 10, then the following degree is determined to be less than the standard threshold; it indicates that the target user's eye tracking ability is at a normal or expected level, and continuing to implement the original myopia prevention and control training plan can continue to consolidate the training effect. When the following degree reaches the standard threshold, it indicates that the target user's eye tracking ability is at a normal or expected level. At this time, continuing to implement the original myopia prevention and control training plan can maintain and consolidate the training effect. Continuous visual training helps to enhance the application ability of the binocular visual system, improve the accommodation function, convergence function and eye movement function, thereby helping to prevent further vision loss. By maintaining appropriate training intensity and time, the visual health of the target user can be promoted, and visual fatigue and other eye problems caused by long-term eye use can be reduced.

[0084] In one implementation, if the tracking degree is less than the standard threshold, it means that the target user's eye tracking ability may be insufficient. Reducing the training time of the original myopia prevention and control training plan can avoid visual fatigue or discomfort caused by overtraining, thereby protecting the user's eye health. Adjusting the training plan according to the actual level of tracking degree can achieve personalized training arrangements and ensure that the training content and methods are more in line with the actual situation and needs of the target user. By reducing unnecessary training time, you can focus on more effective training and improve training efficiency and effectiveness.

[0085] Based on the same inventive concept, the present invention also provides a device for preventing and controlling myopia in adolescents. Figure 2 , Figure 2 The schematic diagram of the structure of the device for preventing and controlling myopia in adolescents provided by the embodiment of the present invention includes: modules: training start module, fatigue detection module, simulation training module and training correction module:

[0086] The training start module is used to receive the start instruction issued by the target user, build a three-dimensional environment according to the start instruction, and generate a positioning instruction.

[0087] The fatigue detection module is used to generate a calibration model according to the positioning instructions. The calibration model moves along a random trajectory so that the target user moves his eyes according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates countermeasure instructions; the calibration model is a three-dimensional model.

[0088] The simulation training module is used to receive adversarial instructions and generate an adversarial environment according to the adversarial instructions so that the target user can perform adversarial training.

[0089] The training correction module is used to record the eye movement parameters of the target user, calculate the tracking degree of the eye movement according to the eye movement parameters and the abnormal fatigue coefficient, and determine the training plan according to the tracking degree.

[0090] Based on the method for preventing and controlling myopia in adolescents provided by the embodiment of the present invention, the user's eye movement is guided by the random movement of the calibration model to detect and evaluate the degree of eye fatigue. This innovative fatigue detection method allows the system to automatically generate a confrontation environment based on the abnormal fatigue coefficient, providing users with personalized confrontation training; the system also records the user's eye movement and calculates the tracking degree, and then intelligently adjusts the training plan to ensure that while avoiding eye fatigue, the training effect is optimized, the training cost is reduced, and a more scientific and effective vision training is achieved.

[0091] In one embodiment, the fatigue detection module includes: a model motion module, a key frame extraction module and a first calculation module:

[0092] The model motion module is used to determine the plane displacement of the calibration model according to the random trajectory and simulate the depth motion of the object by scaling the size of the calibration model.

[0093] The key frame extraction module is used to obtain the video stream of the target user and extract frames from the video stream according to a preset period to obtain key frames; the video stream is a video of the target user performing focus calibration according to the positioning instruction.

[0094] The first calculation module is used to identify the blinking features and pupil features in the key frame, and calculate the abnormal fatigue coefficient according to the blinking features and pupil features.

[0095]

[0096] Where P is the abnormal fatigue coefficient, t 0 is the preset time period, Z is the number of blinks, PERCLOS is the proportion of eye closure time in the preset time period, T is the pupil change score, α and β are weight coefficients, both greater than 0.

[0097] In one embodiment, the training correction module includes: a second calculation module:

[0098] The second calculation module is used to calculate the tracking degree according to the eye movement parameters; the eye movement parameters include: the number of attention loss, the object movement speed and the eye movement speed;

[0099]

[0100] Among them, F is the following degree, v 0 is the object moving speed, v is the eyeball moving speed, P is the abnormal fatigue coefficient, D is the number of times of attention loss, σ, and ω are weight coefficients, both greater than 0.

[0101] In one embodiment, the training correction module further includes: a normal training module and an abnormal training module:

[0102] The normal training module is used to execute the original myopia prevention and control training plan if the following degree is equal to the standard threshold.

[0103] The abnormal training module is used to reduce the training time of the original myopia prevention and control training plan if the following degree is less than the standard threshold.

[0104] The embodiment of the present invention also provides a computer device, such as Figure 3 As shown, it includes a processor 301, a communication interface 302, a memory 303 and a communication bus 304, wherein the processor 301, the communication interface 302, and the memory 303 communicate with each other through the communication bus 304, and the memory 303 is used to store computer programs; when the processor 301 is used to execute the program stored in the memory 303, the following steps are implemented:

[0105] Receiving a start instruction from a target user, constructing a three-dimensional environment according to the start instruction, and generating a positioning instruction;

[0106] Generate a calibration model according to the positioning instruction, the calibration model moves according to a random trajectory, so that the target user moves the eyeball according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates a countermeasure instruction; the calibration model is a three-dimensional model;

[0107] receiving the confrontation instruction, and generating a confrontation environment according to the confrontation instruction, so that the target user performs confrontation training;

[0108] The eye movement parameters of the target user are recorded, the tracking degree of the eye movement is calculated according to the eye movement parameters and the abnormal fatigue coefficient, and a training plan is determined according to the tracking degree.

[0109] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0110] The communication interface is used for communication between the above terminal and other devices.

[0111] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0112] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0113] In another embodiment provided by the present invention, a computer-readable storage medium is also provided, in which a computer program is stored. When the computer program is executed by a processor, any of the methods for preventing and controlling myopia in adolescents in the above-mentioned embodiments is implemented.

[0114] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for preventing and controlling myopia in adolescents, characterized in that: The method comprises: Receiving a start instruction from a target user, constructing a three-dimensional environment according to the start instruction, and generating a positioning instruction; Generate a calibration model according to the positioning instruction, the calibration model moves according to a random trajectory, so that the target user moves the eyeball according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates a countermeasure instruction; the calibration model is a three-dimensional model; receiving the confrontation instruction, and generating a confrontation environment according to the confrontation instruction, so that the target user performs confrontation training; The eye movement parameters of the target user are recorded, the tracking degree of the eye movement is calculated according to the eye movement parameters and the abnormal fatigue coefficient, and a training plan is determined according to the tracking degree.

2. The method for preventing and controlling myopia in adolescents according to claim 1, characterized in that: The target user moves the eyeballs according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, which includes: Determining the plane displacement of the calibration model according to the random trajectory, and simulating the depth movement of the object by scaling the size of the calibration model; Acquire a video stream of the target user, and extract frames from the video stream according to a preset period to obtain key frames; the video stream is a video of the target user performing focus calibration according to the positioning instruction; identifying blink features and pupil features in the key frame, and calculating an abnormal fatigue coefficient according to the blink features and pupil features; Among them, P is the abnormal fatigue coefficient, t0 is the preset time period, Z is the number of blinks, PERCLOS is the proportion of eye closure time in the preset time period, T is the pupil change score, α and β are weight coefficients, both greater than 0.

3. The method for preventing and controlling myopia in adolescents according to claim 1, characterized in that: The following degree of the eye movement is calculated according to the eye movement parameter and the abnormal fatigue coefficient, including: The degree of following is calculated according to the eye movement parameters; the eye movement parameters include: the number of attention loss, the object movement speed and the eye movement speed; Among them, F is the degree of following, v0 is the object moving speed, v is the eye moving speed, P is the abnormal fatigue coefficient, D is the number of times of attention loss, σ, and ω are weight coefficients, both greater than 0.

4. The method for preventing and controlling myopia in adolescents according to claim 1, characterized in that: Determining a training plan based on the following degree includes: If the following degree is equal to the standard threshold, the original myopia prevention and control training plan is executed; If the following degree is less than the standard threshold, the training time of the original myopia prevention and control training plan will be reduced.

5. A device for preventing and controlling myopia in young people, characterized in that: The device comprises: a training start module, a fatigue detection module, a simulation training module and a training correction module: The training start module is used to receive a start instruction issued by a target user, construct a three-dimensional environment according to the start instruction, and generate a positioning instruction; The fatigue detection module is used to generate a calibration model according to the positioning instruction, and the calibration model moves according to a random trajectory, so that the target user moves the eyeball according to the calibration model and performs fatigue detection to obtain an abnormal fatigue coefficient, and generates a countermeasure instruction; the calibration model is a three-dimensional model; The simulation training module is used to receive the confrontation instruction and generate a confrontation environment according to the confrontation instruction so that the target user can perform confrontation training; The training correction module is used to record the eye movement parameters of the target user, calculate the tracking degree of the eye movement according to the eye movement parameters and the abnormal fatigue coefficient, and determine the training plan according to the tracking degree.

6. The device for preventing and controlling myopia in adolescents according to claim 5, characterized in that: The fatigue detection module includes: a model motion module, a key frame extraction module and a first calculation module: The model motion module is used to determine the plane displacement of the calibration model according to the random trajectory, and simulate the depth motion of the object by scaling the size of the calibration model; The key frame extraction module is used to obtain the video stream of the target user, and extract frames from the video stream according to a preset period to obtain key frames; the video stream is a video of the target user performing focus calibration according to the positioning instruction; The first calculation module is used to identify the blinking features and pupil features in the key frame, and calculate the abnormal fatigue coefficient according to the blinking features and the pupil features; Among them, P is the abnormal fatigue coefficient, t0 is the preset time period, Z is the number of blinks, PERCLOS is the proportion of eye closure time in the preset time period, T is the pupil change score, α and β are weight coefficients, both greater than 0.

7. The device for preventing and controlling myopia in adolescents according to claim 5, characterized in that: The training correction module includes: a second calculation module: The second calculation module is used to calculate the tracking degree according to the eye movement parameters; the eye movement parameters include: the number of attention loss, the object movement speed and the eye movement speed; Among them, F is the degree of following, v0 is the object moving speed, v is the eye moving speed, P is the abnormal fatigue coefficient, D is the number of times of attention loss, σ, and ω are weight coefficients, both greater than 0.

8. The device for preventing and controlling myopia in adolescents according to claim 5, characterized in that: The training correction module also includes: a normal training module and an abnormal training module: The normal training module is used to execute the original myopia prevention and control training plan if the following degree is equal to the standard threshold; The abnormal training module is used to reduce the training time of the original myopia prevention and control training plan if the following degree is less than the standard threshold.

9. A computer device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 4 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 4 are implemented.

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