Myopia adjusting system and method for AI intelligent visual training

Abstract

The invention relates to the technical field of vision health care and myopia prevention and control, and discloses a myopia adjusting system for AI intelligent vision training, which comprises a vision training device for dynamically adjusting the vision of a user; the AI algorithm module is used for generating a personalized training scheme according to the vision data of the user and adjusting training parameters in real time; the cloud data processing module is used for storing and analyzing vision data and training records of a user, and the invention further discloses a myopia adjusting method for AL intelligent vision training, and the method comprises the following steps: S1, collecting the vision data of the user, and inputting the vision data into the AI algorithm module; a personalized training scheme is generated by using an AI algorithm, multiple training modes are supported, training parameters are adjusted in real time through a dynamic focusing assembly, the optimization effect is ensured, the system automatically generates a training report for a user to check and adjust, meanwhile, real-time monitoring is achieved through a range finder and a wearing sensor, and training safety and intelligence are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vision care and myopia prevention and control, and specifically to a myopia adjustment system and method for AI intelligent vision training. Background Art

[0002] In recent years, with the popularization of digital devices, the incidence of myopia has shown a significant upward trend worldwide, especially among adolescents and children. Myopia has become a global public health challenge. According to statistics from the World Health Organization (WHO), about 30% of the world's population suffers from myopia, and it is estimated that by 2050, myopia patients will account for more than half of the world's total population. The high incidence and rapid progression of myopia are closely related to modern lifestyles, especially excessive use of electronic devices, long-term close-up use of the eyes, and reduced outdoor activities, which are all important factors leading to myopia.

[0003] Traditional myopia correction methods mainly rely on glasses, contact lenses or laser surgery. Although these methods can effectively correct vision problems, they are only passive corrections for myopia and cannot fundamentally improve the eye's ability to adjust, nor can they prevent the further development of myopia. Especially in children and adolescents, myopia progresses faster, and traditional correction methods are limited in controlling and slowing the development of myopia.

[0004] In response to this problem, in recent years, vision training has gradually become an important direction for vision correction and prevention. Vision training is a training method that improves visual ability by exercising eye muscles and improving eye adjustment function. It helps restore the eye's adjustment ability, relieves visual fatigue, and prevents the progression of myopia to a certain extent through targeted visual stimulation and focusing training. Vision training usually combines physical training, optical stimulation, and targeted focusing exercises.

[0005] However, most of the visual training devices currently on the market have the following shortcomings:

[0006] 1. Single training mode: Many devices can only provide fixed training modes, such as single far-point or near-point training, which cannot cover the training needs of various vision problems and the training effect is relatively limited.

[0007] 2. Lack of personalization: Most training devices are unable to tailor training programs according to the user's specific vision conditions. The lack of personalized adjustments causes the training effects to vary from person to person, making it difficult to achieve the best results.

[0008] 3. Lack of real-time monitoring and dynamic feedback: Existing equipment generally does not have the function of real-time monitoring of the user's training status, nor can it make dynamic adjustments based on the user's feedback during the training process, resulting in a lack of flexibility and timeliness in the training process.

[0009] In view of the above problems, the present invention proposes a myopia adjustment system and method for AI intelligent vision training. Summary of the invention

[0010] In view of the shortcomings of the prior art, the present invention provides a myopia adjustment system and method for AI intelligent vision training, which solves the problems of the existing vision training equipment having a single training mode, lacking personalized adjustment, and being unable to perform real-time monitoring and dynamic feedback.

[0011] To achieve the above objectives, the present invention is implemented through the following technical solutions: a myopia adjustment system for AI intelligent vision training, comprising:

[0012] A visual training device for dynamically adjusting the user's vision;

[0013] AI algorithm module, used to generate personalized training plans based on the user's vision data and adjust training parameters in real time;

[0014] Cloud data processing module, used to store and analyze users’ vision data and training records;

[0015] The mobile device application module is used to communicate with the vision training device via a wireless connection to provide user data input, training mode selection and device control.

[0016] Preferably, the vision training device comprises:

[0017] The training frame is used to provide a physical carrier for users' vision training and cooperate with the system's focus adjustment and training mode operation;

[0018] A dynamic focus adjustment component, which is composed of a lens adjustment structure driven by a stepper motor and is arranged at the front side of the bottom of the training frame, and is used to adjust the focal length according to the AI ​​algorithm instructions during the training process;

[0019] A front rangefinder, which is arranged on the front side of the training frame and is used to detect the distance between the user and the device and provide feedback information;

[0020] The wearing sensor is set on the back of the training frame to detect whether the user is wearing the device correctly. If the user is not wearing the device, the system will automatically pause the training;

[0021] The switch button is arranged on the outer wall of the training frame and is used to control the opening and closing of the device.

[0022] Preferably, the AI ​​algorithm module includes:

[0023] A vision data processing unit, used for receiving and processing vision data input by a user, including diopter, astigmatism, axis position and pupil distance;

[0024] A training program generating unit generates a personalized training program suitable for the user based on the processed data;

[0025] The dynamic adjustment unit is used to adjust the focal length of the lens, training duration and frequency according to real-time training feedback.

[0026] Preferably, the cloud data processing module includes:

[0027] A user profile storage unit for storing the user's historical vision data and training progress;

[0028] Data analysis unit, used to analyze user training data and provide optimization suggestions for AI algorithms.

[0029] Preferably, the mobile device application module includes:

[0030] a data input unit through which the user inputs vision parameters;

[0031] a mode selection unit that allows the user to choose between far point training, near point training, and reading modes;

[0032] The training feedback unit is used to provide status feedback and data during training to the user.

[0033] A myopia adjustment method for AI intelligent vision training comprises the following steps:

[0034] S1. Collect user vision data and input it into the AI ​​algorithm module;

[0035] S2. Generate a training plan, where the plan is automatically generated by the AI ​​algorithm module based on the user's vision data;

[0036] S3, performing training by using a visual training device, adjusting the focal length by using a dynamic focusing component, and performing far point training or near point training;

[0037] S4, monitor the training status in real time through the sensor module, and adjust the training parameters according to the feedback through the AI ​​algorithm;

[0038] S5. After training is completed, upload the data to the cloud and generate a training report for users to view

[0039] Preferably, the operation steps of the far point training in step S3 are to use a positive lens to gradually increase the focal length to simulate long-distance vision training, and the operation steps of the near point training in step S3 are to use a negative lens to gradually reduce the focal length to simulate short-distance vision training.

[0040] Preferably, the step S2 specifically includes the following steps:

[0041] S2.1. Data preprocessing: The AI ​​algorithm module analyzes the received vision data to determine whether there are inconsistencies or outliers;

[0042] S2.2, Training needs analysis: Based on the refractive power, astigmatism and axial position data input by the user, the AI ​​algorithm evaluates the main vision problems that need training;

[0043] S2.3, Select training mode: The AI ​​algorithm decides to use far point training, near point training or mixed training mode based on the vision data;

[0044] S2.4. Generate training parameters: The AI ​​algorithm generates training parameters based on the user's vision data, including training intensity, lens focal length variation range, training duration and adjustment frequency;

[0045] S2.5, Personalized adjustment: AI algorithm combines the user's historical data or training feedback to perform personalized optimization and adjust the training plan;

[0046] S2.6, training plan confirmation: After the generated training plan is presented to the user for confirmation via the mobile device application, the system is ready to execute.

[0047] Preferably, the step S4 specifically includes the following steps:

[0048] S4.1. Front rangefinder monitoring: The front rangefinder monitors the distance between the user and the visual training device in real time, allowing the user to use the device within a reasonable range;

[0049] S4.2, Wearing status detection: The rear wearing sensor detects whether the user is wearing the device correctly. If it detects that the device is not worn correctly, the training will be automatically paused;

[0050] S4.3, Real-time data feedback: The sensor module feeds back the user's training posture, distance and wearing condition to the AI ​​algorithm module;

[0051] S4.4. Dynamically adjust training parameters: The AI ​​algorithm adjusts training parameters based on real-time sensor data, including focusing speed and focal length range;

[0052] S4.5. User intervention support: Users can view real-time feedback data through mobile device applications. If visual fatigue or discomfort occurs, they can manually adjust the training intensity or pause the training.

[0053] Preferably, the step S5 specifically includes the following steps:

[0054] S5.1, training end signal: the visual training device automatically ends the training and sends a completion signal to the mobile device application;

[0055] S5.2. Generate training data: The AI ​​algorithm module automatically generates detailed data of this training, including training duration, changes in lens adjustment parameters, and sensor data;

[0056] S5.3, Upload data to the cloud: After the training is completed, the data is uploaded to the cloud data storage module through the mobile device application to update the user profile;

[0057] S5.4. Generate training report: The AI ​​algorithm generates a report based on the training data, including training results, adjustment suggestions and vision change trends;

[0058] S5.5. User viewing report: The user views the generated training report through the mobile device application and can select the time and mode of the next training;

[0059] S5.6. Historical data analysis: The cloud data storage module analyzes the user's historical training data for the AI ​​algorithm to optimize subsequent training plans.

[0060] The present invention provides a myopia adjustment system and method for AI intelligent vision training.

[0061] Beneficial effects:

[0062] 1. The present invention, through the AI ​​algorithm module, can generate an exclusive training plan according to the user's specific vision data, including refraction, astigmatism, axis position and pupil distance, support far point training and near point training, and also introduce a reading mode. Combined training can be performed according to user needs, avoiding the drawbacks of the unified training mode in existing equipment, realizing personalized and precise training, and improving the pertinence and effect of training.

[0063] 2. The present invention uses a dynamic focusing component and an AI algorithm to automatically adjust the focal length, training duration and frequency according to the user's real-time feedback during the training process, ensuring timely optimization of the training and avoiding the problem of poor training effects caused by fixed parameters in traditional equipment.

[0064] 3. The present invention automatically generates a detailed training report after each training is completed. Users can view the training duration, focal length adjustment and other relevant data through mobile devices. The generation of the training report enables users to clearly understand their training progress and make personalized adjustments based on feedback, thereby improving user experience and training effects.

[0065] 4. The present invention uses a front rangefinder and a wearing sensor to monitor the distance between the user and the device and the wearing status in real time, automatically correct unreasonable operations, and automatically suspend training when the user does not wear the device correctly or is at an inappropriate distance from the device, ensuring safety and avoiding improper operations that affect the training effect. This design greatly improves the intelligence level of the device and the safety of training. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is the overall framework diagram of the system of the present invention;

[0067] Figure 2 It is a framework diagram of the A1 algorithm module of the present invention;

[0068] Figure 3 This is a framework diagram of the cloud data processing module of the present invention;

[0069] Figure 4 A framework diagram of an application module of a mobile device of the present invention;

[0070] Figure 5 is a flow chart of the method of the present invention;

[0071] Figure 6 It is a structural schematic diagram of the vision training device of the present invention;

[0072] Figure 7 It is a rear view schematic diagram of the vision training device of the present invention.

[0073] Among them, 1. Training frame; 2. Dynamic focusing component; 3. Front rangefinder; 4. Switch button; 5. Wearing sensor. DETAILED DESCRIPTION

[0074] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0075] Please see attached Figure 1 The embodiment of the present invention provides a myopia adjustment system for AI intelligent vision training, including:

[0076] A visual training device for dynamically adjusting the user's vision;

[0077] AI algorithm module, used to generate personalized training plans based on the user's vision data and adjust training parameters in real time;

[0078] Cloud data processing module, used to store and analyze users’ vision data and training records;

[0079] The mobile device application module is used to communicate with the vision training device via a wireless connection to provide user data input, training mode selection and device control.

[0080] Please see attached Figure 6 and attached Figure 7 , the visual training device comprises:

[0081] The training frame 1 is used to provide a physical carrier for the user's vision training and cooperate with the system's focus adjustment and training mode operation;

[0082] The dynamic focus adjustment component 2 is composed of a lens adjustment structure driven by a stepping motor, which is arranged at the front side of the bottom of the training frame 1 and is used to adjust the focal length according to the AI ​​algorithm instructions during the training process;

[0083] A front rangefinder 3, which is arranged at the front side of the training frame 1, and is used to detect the distance between the user and the device and provide feedback information;

[0084] A wearing sensor 5, which is arranged at the rear side of the training frame 1, is used to detect whether the user wears the device correctly. If the user does not wear the device, the system automatically pauses the training;

[0085] The switch button 4 is arranged on the outer wall of the training frame 1 and is used to control the opening and closing of the device.

[0086] Specifically, the AI ​​intelligent vision training device is started by the switch button 4. The wearing sensor 5 first detects whether the user wears the device correctly. Only when it is worn correctly will the system enter the training mode. During the training process, the front rangefinder 3 continuously monitors the distance between the user and the device to ensure that the user maintains an appropriate use distance. If an abnormal distance is found, the system will provide timely feedback to remind the user to adjust. The device automatically adjusts the focal length of the lens according to the training plan generated by the AI ​​algorithm through the dynamic focusing component 2, thereby realizing different vision training modes, such as far point and near point training. During the training process, the system dynamically adjusts the focal length and training duration according to the user's real-time feedback and sensor data to ensure the accuracy and effectiveness of the training. After the training is completed, the system generates a report to prompt the user to complete the training. The device then automatically turns off or is manually turned off by a button.

[0087] Please see attached Figure 2 , AI algorithm modules include:

[0088] A vision data processing unit, used for receiving and processing vision data input by a user, including diopter, astigmatism, axis position and pupil distance;

[0089] A training program generating unit generates a personalized training program suitable for the user based on the processed data;

[0090] The dynamic adjustment unit is used to adjust the focal length of the lens, training duration and frequency according to real-time training feedback.

[0091] Please see attached Figure 3 , the cloud data processing module includes:

[0092] A user profile storage unit for storing the user's historical vision data and training progress;

[0093] Data analysis unit, used to analyze user training data and provide optimization suggestions for AI algorithms.

[0094] Please see attached Figure 4 , the mobile device application modules include:

[0095] a data input unit through which the user inputs vision parameters;

[0096] a mode selection unit that allows the user to choose between far point training, near point training, and reading modes;

[0097] The training feedback unit is used to provide status feedback and data during training to the user.

[0098] Please see attached Figure 5 , a myopia adjustment method of AI intelligent vision training, comprising the following steps:

[0099] S1. Collect user vision data and input it into the AI ​​algorithm module;

[0100] S2. Generate a training plan, where the plan is automatically generated by the AI ​​algorithm module based on the user's vision data;

[0101] S3, performing training through the visual training device, adjusting the focal length through the dynamic focusing component 2, and performing far point training or near point training;

[0102] S4, monitor the training status in real time through the sensor module, and adjust the training parameters according to the feedback through the AI ​​algorithm;

[0103] S5. After the training is completed, the data is uploaded to the cloud and a training report is generated for the user to view.

[0104] The operation steps of the far point training in step S3 are to use a positive lens to gradually increase the focal length to simulate long-distance vision training, and the operation steps of the near point training in step S3 are to use a negative lens to gradually reduce the focal length to simulate short-distance vision training.

[0105] The S2 step specifically includes the following steps:

[0106] S2.1. Data preprocessing: The AI ​​algorithm module analyzes the received vision data to determine whether there are inconsistencies or outliers;

[0107] S2.2, Training needs analysis: Based on the refractive power, astigmatism and axial position data input by the user, the AI ​​algorithm evaluates the main vision problems that need training;

[0108] S2.3, Select training mode: The AI ​​algorithm decides to use far point training, near point training or mixed training mode based on the vision data;

[0109] S2.4. Generate training parameters: The AI ​​algorithm generates training parameters based on the user's vision data, including training intensity, lens focal length variation range, training duration and adjustment frequency;

[0110] S2.5, Personalized adjustment: AI algorithm combines the user's historical data or training feedback to perform personalized optimization and adjust the training plan;

[0111] S2.6, training plan confirmation: After the generated training plan is presented to the user for confirmation via the mobile device application, the system is ready to execute.

[0112] The S4 step specifically includes the following steps:

[0113] S4.1, front rangefinder 3 monitoring: the front rangefinder 3 monitors the distance between the user and the visual training device in real time, so that the user uses the device within a reasonable range;

[0114] S4.2, wearing status detection: the rear wearing sensor 5 detects whether the user wears the device correctly. If it is detected that the device is not worn correctly, the training is automatically paused;

[0115] S4.3, Real-time data feedback: The sensor module feeds back the user's training posture, distance and wearing condition to the AI ​​algorithm module;

[0116] S4.4. Dynamically adjust training parameters: The AI ​​algorithm adjusts training parameters based on real-time sensor data, including focusing speed and focal length range;

[0117] S4.5. User intervention support: Users can view real-time feedback data through mobile device applications. If visual fatigue or discomfort occurs, they can manually adjust the training intensity or pause the training.

[0118] Step S5 specifically includes the following steps:

[0119] S5.1, training end signal: the visual training device automatically ends the training and sends a completion signal to the mobile device application;

[0120] S5.2. Generate training data: The AI ​​algorithm module automatically generates detailed data of this training, including training duration, changes in lens adjustment parameters, and sensor data;

[0121] S5.3, Upload data to the cloud: After the training is completed, the data is uploaded to the cloud data storage module through the mobile device application to update the user profile;

[0122] S5.4. Generate training report: The AI ​​algorithm generates a report based on the training data, including training results, adjustment suggestions and vision change trends;

[0123] S5.5. User viewing report: The user views the generated training report through the mobile device application and can select the time and mode of the next training;

[0124] S5.6. Historical data analysis: The cloud data storage module analyzes the user's historical training data for the AI ​​algorithm to optimize subsequent training plans.

[0125] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A myopia adjustment system for AI intelligent vision training, characterized in that: include: A visual training device for dynamically adjusting the user's vision; AI algorithm module, used to generate personalized training plans based on the user's vision data and adjust training parameters in real time; Cloud data processing module, used to store and analyze users’ vision data and training records; The mobile device application module is used to communicate with the vision training device via a wireless connection to provide user data input, training mode selection and device control.

2. The myopia adjustment system of AI intelligent vision training according to claim 1, characterized in that: The vision training device comprises: The training frame (1) is used to provide a physical carrier for the user's vision training and cooperate with the system's focus adjustment and training mode operation; A dynamic focus adjustment component (2), which is composed of a lens adjustment structure driven by a stepping motor and is arranged on the front side of the bottom of the training frame (1) and is used to adjust the focal length according to AI algorithm instructions during the training process; A front rangefinder (3) is arranged on the front side of the training frame (1) and is used to detect the distance between the user and the device and provide feedback information; A wearing sensor (5), which is arranged on the rear side of the training frame (1) and is used to detect whether the user is wearing the device correctly. If the user is not wearing the device correctly, the system automatically pauses the training; A switch button (4) is arranged on the outer wall of the training frame (1) and is used to control the opening and closing of the device.

3. The myopia adjustment system of AI intelligent vision training according to claim 1, characterized in that: The AI ​​algorithm module includes: A vision data processing unit, used for receiving and processing vision data input by a user, including diopter, astigmatism, axis position and pupil distance; A training program generating unit generates a personalized training program suitable for the user based on the processed data; The dynamic adjustment unit is used to adjust the focal length of the lens, training duration and frequency according to real-time training feedback.

4. The myopia adjustment system of AI intelligent vision training according to claim 1, characterized in that: The cloud data processing module includes: A user profile storage unit for storing the user's historical vision data and training progress; Data analysis unit, used to analyze user training data and provide optimization suggestions for AI algorithms.

5. The myopia adjustment system of AI intelligent vision training according to claim 1, characterized in that: The mobile device application module comprises: a data input unit through which the user inputs vision parameters; a mode selection unit that allows the user to choose between far point training, near point training, and reading modes; The training feedback unit is used to provide status feedback and data during training to the user.

6. A myopia adjustment method for AI intelligent vision training, characterized in that: The following steps are involved: S1. Collect user vision data and input it into the AI ​​algorithm module; S2. Generate a training plan, where the plan is automatically generated by the AI ​​algorithm module based on the user's vision data; S3, performing training by using the visual training device, adjusting the focal length by using the dynamic focusing component (2), and performing far point training or near point training; S4, monitor the training status in real time through the sensor module, and adjust the training parameters according to the feedback through the AI ​​algorithm; S5. After the training is completed, the data is uploaded to the cloud and a training report is generated for the user to view.

7. The myopia adjustment method of AI intelligent vision training according to claim 6, characterized in that: The operation steps of the far point training in step S3 are to use a positive lens to gradually increase the focal length to simulate long-distance vision training, and the operation steps of the near point training in step S3 are to use a negative lens to gradually reduce the focal length to simulate short-distance vision training.

8. The myopia adjustment method of AI intelligent vision training according to claim 6, characterized in that: The S2 step specifically includes the following steps: S2.

1. Data preprocessing: The AI ​​algorithm module analyzes the received vision data to determine whether there are inconsistencies or outliers; S2.2, Training needs analysis: Based on the refractive power, astigmatism and axial position data input by the user, the AI ​​algorithm evaluates the main vision problems that need training; S2.3, Select training mode: The AI ​​algorithm decides to use far point training, near point training or mixed training mode based on the vision data; S2.

4. Generate training parameters: The AI ​​algorithm generates training parameters based on the user's vision data, including training intensity, lens focal length variation range, training duration and adjustment frequency; S2.5, Personalized adjustment: AI algorithm combines the user's historical data or training feedback to perform personalized optimization and adjust the training plan; S2.6, training plan confirmation: After the generated training plan is presented to the user for confirmation via the mobile device application, the system is ready to execute.

9. The myopia adjustment method of AI intelligent vision training according to claim 6, characterized in that: The S4 step specifically includes the following steps: S4.

1. Monitoring by front rangefinder (3): The front rangefinder (3) monitors the distance between the user and the visual training device in real time, so that the user can use the device within a reasonable range; S4.2, wearing status detection: the rear wearing sensor (5) detects whether the user wears the device correctly. If it is detected that the device is not worn correctly, the training is automatically paused; S4.3, Real-time data feedback: The sensor module feeds back the user's training posture, distance and wearing condition to the AI ​​algorithm module; S4.

4. Dynamically adjust training parameters: The AI ​​algorithm adjusts training parameters based on real-time sensor data, including focusing speed and focal length range; S4.

5. User intervention support: Users can view real-time feedback data through mobile device applications. If visual fatigue or discomfort occurs, they can manually adjust the training intensity or pause the training.

10. The myopia adjustment method of AI intelligent vision training according to claim 6, characterized in that: The step S5 specifically includes the following steps: S5.1, training end signal: the visual training device automatically ends the training and sends a completion signal to the mobile device application; S5.

2. Generate training data: The AI ​​algorithm module automatically generates detailed data of this training, including training duration, changes in lens adjustment parameters, and sensor data; S5.3, Upload data to the cloud: After the training is completed, the data is uploaded to the cloud data storage module through the mobile device application to update the user profile; S5.

4. Generate training report: The AI ​​algorithm generates a report based on the training data, including training results, adjustment suggestions and vision change trends; S5.

5. User viewing report: The user views the generated training report through the mobile device application and can select the time and mode of the next training; S5.

6. Historical data analysis: The cloud data storage module analyzes the user's historical training data for the AI ​​algorithm to optimize subsequent training plans.

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