Visual function diagnosis and training integrated system based on Internet big data

Abstract

The invention discloses a visual function diagnosis and training integrated system based on Internet big data, and relates to the technical field of medical health informatization. Comprising two entrances of a doctor user side and a patient user side. The doctor user side is used for inputting basic information of a patient, performing visual function examination and diagnosis and treatment arrangement and tracking the training condition of the patient in real time; and the patient user side is used for receiving diagnosis and treatment suggestions of doctors, performing visual function training and feeding back training effects in real time. The system also has a big data analysis function, can predict the development trend of the visual function of the patient according to the training data of the patient, and provides a scientific diagnosis and treatment basis for doctors.

Description

Technical Field

[0001] This invention belongs to the field of medical and health information technology, and in particular relates to an integrated system for visual function diagnosis and training based on Internet big data. Background Technology

[0002] With the increasing emphasis on visual health in modern society and the increased eye strain caused by the widespread use of electronic products, visual function abnormalities are becoming increasingly prominent, especially among children and adolescents. Traditional visual function diagnosis and treatment models suffer from inefficiency and uneven resource allocation, particularly in remote areas where high-quality medical resources are scarce. Therefore, developing an efficient, convenient, and widely applicable visual function diagnosis and training system is of paramount importance.

[0003] Currently, there are some visual function training software or platforms on the market, but most of these products have limited functions, lack personalized training programs, and cannot provide real-time tracking and feedback on patients' training progress. Furthermore, existing visual function diagnostic methods largely rely on traditional medical equipment and manual examination, which is time-consuming, labor-intensive, and easily affected by human factors. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated system for visual function diagnosis and training based on Internet big data. This system can collect data related to patients' visual function and provide personalized visual function training programs based on this data, while assisting doctors in making accurate diagnoses and treatments.

[0005] To solve the above-mentioned technical problems, the present invention is achieved through the following technical solution: This invention is an integrated system for visual function diagnosis and training based on Internet big data, comprising: The Internet visual function platform includes two different versions of the platform: a doctor user version and a patient user version, which are interactive in both directions. The Internet visual function platform can collect data related to the patient's visual function and provide personalized visual function training programs based on this data. The doctor user version platform includes at least a section for entering basic patient information, a section for patient visual function examination and treatment arrangements, a section for follow-up records and treatment effects, a section for doctor treatment suggestions, a section for treatment statistics, and a section for submitting medical science popularization articles. The patient user version of the platform should include at least the following sections: patient basic information, patient visual function examination and treatment suggestions, visual function training, follow-up records and treatment effects, and medical knowledge popular science articles. A data prediction model that uses collected multi-data, through specific algorithmic model building steps and data processing flow, to analyze and predict the development trend of myopia or other visual function abnormalities in users.

[0006] As a preferred embodiment of the present invention, the data prediction model specifically includes the following steps: Data collection: Collect data related to myopia; Data preprocessing: Cleaning, deduplication, and missing value handling of the data to ensure data quality; Model building: Using nomograms, determine the weight and threshold of each feature based on the relationship between the features and the target variable; Model evaluation: The accuracy and reliability of the model are evaluated using cross-validation, and adjustments and optimizations are made accordingly; Model application: Applying the model to new datasets for prediction and analysis.

[0007] As a preferred technical solution of the present invention, the data collected during the data collection includes, but is not limited to, patient eye biometric data, family history of genetic diseases, environmental factors, lifestyle data, as well as traditional age, gender, eye habits, vision training records and vision test results. When constructing the model, an ensemble learning method is used to combine convolutional neural networks (CNN) and long short-term memory networks (LSTM). CNN is used to extract local features from ocular medical images and visual training image data, and LSTM is used to process time series data. The outputs of the two are integrated in the fusion layer and then connected to a fully connected layer for prediction. The specific operation of the model evaluation is as follows: optimize the model hyperparameters using Bayesian optimization algorithm or random search combined with cross-validation technology, and dynamically adjust the hyperparameters according to different types of visual function abnormalities and patient groups.

[0008] As a preferred technical solution of the present invention, it also includes a user information feedback module: collecting and integrating the patient's training data, including but not limited to training content, duration, degree and effect, analyzing and predicting the patient's myopia development trend through statistical methods, and providing it to doctors and patients as a reference. Doctors can make timely adjustments and provide feedback guidance to patients based on the feedback results, and patients can also understand their vision and visual function improvement.

[0009] As a preferred embodiment of the present invention, the doctor user platform also integrates remote consultation functionality, allowing doctors to communicate online, share cases, and collaborate remotely to improve diagnostic efficiency and accuracy. The patient user platform also provides online appointment booking services, enabling patients to choose suitable doctors and times for their medical visits according to their needs, reducing on-site waiting time and enhancing their medical experience.

[0010] As a preferred technical solution of the present invention, the treatment statistics module in the doctor user version platform also includes a statistical analysis function for the types of visual function abnormalities in patients. By analyzing a large amount of patient data, it provides doctors with the distribution of types of visual function abnormalities, assisting doctors in clinical research and decision-making.

[0011] As a preferred embodiment of the present invention, the visual function training module in the patient user platform specifically includes a myopia training module, an amblyopia training module, a strabismus training module, a binocular vision function training module, and an eye fatigue training module.

[0012] As a preferred embodiment of the present invention, the myopia training module specifically includes, but is not limited to: Flipping beat training: Provides online guidance for flipping beat training, including personalized settings for training methods, training time and training difficulty. During the training process, the system records and analyzes the patient's training data and provides real-time feedback on the training effect. Outdoor exercise duration statistics: Through manual recording or automatic tracking by users, the system collects patients' outdoor exercise time and exercise data, and provides reasonable exercise suggestions and myopia prevention tips based on this data; Correcting reading and writing posture and reading and writing time: The front-facing camera is used to identify the user's reading and writing posture. When the posture is incorrect, a reminder is issued to help the user correct the reading and writing posture, prevent myopia, and count the time the user uses the correct posture and the time spent looking down, looking out, and too close or too far from the book. The data is then used to generate a ratio chart to give the user more intuitive feedback in order to correct the reading and writing posture. Instructions for use of atropine eye drops: Provides instructions on how to use atropine eye drops and precautions, and includes a medication reminder function to ensure patients take the medication on time.

[0013] As a preferred embodiment of the present invention, the patient user platform further includes an intelligent reminder and notification module, which can automatically send training reminders, follow-up visit reminders, and health advice notifications to the patient based on the patient's visual function data, training progress, and appointment time information.

[0014] As a preferred embodiment of the present invention, the system further includes supporting hardware facilities: Smart visual monitoring glasses: integrate a miniature camera, distance sensor and photosensor to monitor eye distance, posture and ambient light intensity in real time, and upload data to the platform via wireless connection; Portable ocular biometer: Supports optical coherence tomography (OCT) and corneal curvature measurement, with encrypted data transmission to the doctor's user terminal; Multifunctional visual training device: Includes an adjustable visual target screen and an eye-tracking sensor, which works in conjunction with the platform to adjust training parameters; Wearable physiological monitoring bracelet: collects outdoor exercise time and physiological indicators, and synchronizes them to the patient user's device; Integrated remote consultation terminal: Supports high-definition image sharing and real-time consultation, and seamlessly connects with doctor user terminals; Intelligent reading and writing posture correction desk lamp: It monitors reading and writing posture through a camera and pressure sensor and triggers light reminders.

[0015] The present invention has the following beneficial effects: Improved diagnostic and treatment efficiency: Online consultations and examinations avoid the inconvenience of patients frequently traveling to hospitals, while doctors can process patient information more efficiently, thus improving overall diagnostic and treatment efficiency.

[0016] Personalized training programs: Personalized training programs are developed based on the patient's specific condition, making training more precise and effective, and avoiding the blindness and inefficiency of traditional training methods.

[0017] Real-time tracking and feedback: Track and provide feedback on the patient's training progress in real time, adjust the training plan in a timely manner, and ensure the maximum training effect.

[0018] Big data analysis and prediction: By using big data analysis technology, we can deeply mine and analyze patients' training data to predict the development trend of their visual function, providing doctors with a more scientific basis for diagnosis and treatment.

[0019] Access to high-quality medical resources: This system breaks down geographical barriers, enabling patients in remote areas to enjoy high-quality medical resources and services, effectively alleviating the problem of uneven distribution of medical resources.

[0020] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the overall framework structure of the present invention; Figure 2 This is a schematic diagram of the visual function training module in this invention; Figure 3 This is a schematic diagram of the myopia training module in this invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1 like Figure 1 As shown: This invention provides an integrated system for visual function diagnosis and training based on internet big data, comprising: an internet visual function platform, which includes two different versions of an interactive platform for doctors and a platform for patients. The internet visual function platform can collect data related to patients' visual function and provide personalized visual function training programs based on this data; the doctor's platform includes at least a section for entering basic patient information, a section for patient visual function examination and treatment arrangements, a section for follow-up records and treatment effects, a section for doctor's treatment suggestions, a section for treatment statistics, and a section for submitting medical science articles; the patient's platform includes at least a section for basic patient information, a section for patient visual function examination and treatment suggestions, a section for visual function training, a section for follow-up records and treatment effects, and a section for reading medical science articles; and a data prediction model that can use the collected patient data to analyze and predict the development trend of myopia or other visual function abnormalities in users.

[0025] In this embodiment, the system mainly includes an Internet vision function platform, which has two different versions of entry points: a doctor user version platform and a patient user version platform, to meet the different needs of doctors and patients.

[0026] The functional modules of the doctor user version platform include: Patient Basic Information Entry Section: Doctors can enter basic patient information in this section, such as name, age, gender, and contact information, to provide basic data for subsequent diagnosis and treatment.

[0027] Patient Visual Function Examination and Treatment Arrangement Section: Doctors can view the patient's visual function examination results in this section and formulate a treatment plan based on the results, arranging subsequent examinations and treatments.

[0028] Follow-up Records and Treatment Outcomes Section: Doctors can record patients' follow-up information in this section, including changes in vision and symptom improvement after treatment, in order to assess the treatment effect; Doctor's Treatment Suggestions Section: In this section, doctors can provide personalized treatment suggestions based on the patient's specific situation, including medication, visual function training, etc. Treatment Statistics Section: Doctors can view patients' treatment statistics in this section, including the number of treatments and treatment effects, to facilitate clinical research and decision-making. Submit Medical Science Popularization Articles: Doctors can submit medical science popularization articles to this section to provide patients with scientific visual health knowledge and improve their health literacy.

[0029] The patient user version platform includes the following functional modules: Patient Basic Information Section: Patients can view and edit their basic information in this section to ensure the accuracy and completeness of the information; Patient Visual Function Examination and Treatment Suggestions Section: Patients can view their visual function examination results and obtain treatment suggestions from their doctors in this section; Visual function training section: Patients can conduct personalized visual function training in this section to improve their visual acuity. Follow-up Records and Treatment Results Section: Patients can view their follow-up records and treatment results in this section to understand the progress of treatment and changes in vision. Medical Knowledge Popular Science Articles Reading Section: Patients can read medical science popularization articles submitted by doctors in this section to obtain scientific visual health knowledge and prevention and health care methods.

[0030] The doctor's user platform and the patient's user platform can interact in two directions. Doctors can view patients' test results and treatment progress on the platform and communicate with patients as needed. Patients can also view doctors' treatment suggestions and plans on the platform and provide feedback on treatment effectiveness and any problems encountered. This two-way interactive mechanism helps improve diagnostic efficiency and accuracy, and enhances patients' medical experience and health. One of the core improvements of this invention is the introduction of a data prediction model that can accurately predict the development trend of myopia or other visual function abnormalities in users by using patient data collected from an internet vision function platform.

[0031] In summary, the integrated visual function diagnosis and training system based on Internet big data provided by this invention has the characteristics of comprehensive functions, simple operation, and accurate data, which can meet the different needs of doctors and patients and provide strong support for the diagnosis and treatment of visual function abnormalities.

[0032] Example 2 Based on Embodiment 1, the difference of the present invention is: The data prediction model specifically includes the following steps: Data collection: Collect data related to myopia; Data preprocessing: Clean, deduplicate, and handle missing values ​​of the data to ensure data quality; Model building: Use nomograms to determine the weight and threshold of each feature based on the relationship between the features and the target variable; Model evaluation: Use cross-validation to evaluate the accuracy and reliability of the model, and make adjustments and optimizations; Model application: Apply the model to new datasets for prediction and analysis.

[0033] The data collected during the data collection process includes, but is not limited to, patient ocular biometric data, family history of genetic diseases, environmental factors, lifestyle data, as well as traditional data on age, gender, eye habits, visual training records, and visual acuity test results. When building the model, an ensemble learning method is used to combine convolutional neural networks (CNN) and long short-term memory networks (LSTM) to build the model. CNN is used to extract local features from ocular medical images and visual training image data, LSTM is used to process time series data, and the outputs of the two are integrated in the fusion layer and then connected to a fully connected layer for prediction. The specific steps for model evaluation are as follows: optimize the model hyperparameters using Bayesian optimization algorithms or random search combined with cross-validation techniques, and dynamically adjust the hyperparameters according to different types of visual function abnormalities and patient groups.

[0034] It also includes a user feedback module: collecting and integrating patients' training data, including but not limited to training content, duration, degree and effect, analyzing and predicting the patient's myopia development trend through statistical methods, and providing it to doctors and patients as a reference. Doctors can make timely adjustments and provide feedback guidance to patients based on the feedback results, and patients can also understand their vision and visual function improvement.

[0035] Based on Embodiment 1, this embodiment further expands and optimizes the original system by introducing a data prediction model and a user information feedback module to more comprehensively meet the needs of visual function diagnosis and training.

[0036] The specific steps of a data prediction model include: Data collection: Collect data related to myopia and visual function abnormalities from internet vision function platforms, including but not limited to patients' age, gender, eye habits, vision training records, and vision test results; Data preprocessing: The collected data is cleaned, deduplicated, and missing values ​​are removed to ensure data quality and accuracy. This step is an important preparatory work before building the model and can directly affect the model's predictive performance. Model building: Use nomograms (or other suitable machine learning algorithms, such as: An ensemble learning approach is employed, combining a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network to construct the model. The CNN is used to extract local features from ocular medical images and visual training image data; the LSTM is used to process time-series data and capture temporal dependencies. The outputs of the CNN and LSTM are integrated in a fusion layer and connected to a fully connected layer to build a predictive model. A noctilinear plot is a graphical statistical tool that visually illustrates the relationship between features and the target variable. During model construction, the weights and thresholds of each feature need to be determined so that the model can accurately predict the development trend of myopia or visual dysfunction in users. Model Evaluation: Evaluation methods such as cross-validation are used to verify the accuracy and reliability of the model. By adjusting and optimizing the model's parameters, the predictive accuracy of the model can be further improved. Model Application: The trained and validated model is applied to new datasets for prediction and analysis. The prediction results can provide important reference information for doctors and patients, helping them better understand the development trend of myopia or visual dysfunction.

[0037] In addition to the data prediction model, this invention also introduces a user feedback module for collecting and integrating patient training data, including but not limited to training content, duration, level, and effect.

[0038] The specific functions of this module include: Data Collection: Training data from patients is collected through an internet-based vision function platform, ensuring data integrity and accuracy; diverse data related to myopia is also collected, including but not limited to: Patient's ocular biometric data (such as corneal curvature, axial length, etc.), family history of myopia, understanding the genetic factors of myopia, environmental factors, such as lighting conditions, eye use environment, lifestyle data, such as reading habits, electronic device usage time, etc., traditional information, such as age, gender, eye use habits, vision training records, and vision test results.

[0039] Data analysis: Statistical methods are used to analyze the collected training data to predict the progression of myopia in patients. This step provides users with personalized predictions, helping them better understand their vision.

[0040] Results Feedback: The prediction results will be promptly communicated to both doctors and patients. Doctors can adjust treatment plans accordingly and provide personalized recommendations. Patients can also monitor their vision and visual function improvement, enabling them to better cooperate with treatment.

[0041] Interactive Communication: Provides a platform for interactive communication between doctors and patients, enabling them to engage in in-depth exchanges and discussions regarding predicted outcomes and treatment recommendations. This two-way interactive mechanism helps improve diagnostic and treatment efficiency and accuracy, enhancing the patient's healthcare experience and overall health.

[0042] In summary, this embodiment introduces a data prediction model and a user information feedback module into the original system, further enhancing the system's functionality and performance. Through the synergistic effect of these two modules, users can be provided with more accurate and personalized visual function diagnosis and training services.

[0043] Implementation Three Based on Embodiment 1, the difference in this embodiment is: The doctor's user version platform also integrates remote consultation functionality, allowing doctors to communicate online, share cases, and collaborate remotely to improve diagnostic efficiency and accuracy. The patient's user version platform also provides online appointment booking services, allowing patients to choose suitable doctors and times for their appointments based on their needs, reducing on-site waiting time and improving the overall medical experience. The treatment statistics section of the doctor's user version platform also includes statistical analysis functions for patient visual function abnormality types. Through the analysis of a large amount of patient data, it provides doctors with information on the distribution of visual function abnormality types, assisting them in clinical research and decision-making.

[0044] Building upon Embodiment 1, this embodiment further enriches the system's functionality, particularly by integrating remote consultation capabilities into the doctor's user platform and providing online appointment booking services in the patient's user platform. Furthermore, the treatment statistics section of the doctor's user platform has been expanded, adding statistical analysis functions for patient visual function abnormality types.

[0045] Remote consultation functions: Online communication: Doctors can communicate online in real time through the platform, sharing case information, clinical experience, and treatment suggestions, thereby improving diagnostic efficiency and accuracy. Case sharing: Doctors can upload and download case data, conduct in-depth case analysis and discussion with other doctors, and jointly explore more effective treatment plans. Remote collaboration: Supports remote collaboration between doctors, especially when dealing with complex cases, allowing them to invite other experts for remote consultations to jointly develop treatment plans.

[0046] Treatment Statistics Section: Statistical Analysis of Patient Visual Function Abnormalities: Through the analysis of a large amount of patient data, the system can automatically generate reports on the distribution of different types of visual function abnormalities. These reports can help doctors understand key information such as the incidence rate, age distribution, and gender differences of different visual function abnormalities, providing strong support for clinical research and decision-making. Data Visualization: The system provides rich data visualization tools, such as bar charts, line charts, and pie charts, enabling doctors to intuitively view and analyze statistical data and quickly discover patterns and trends in the data.

[0047] Patient User Platform Online appointment registration service: Choosing a doctor and appointment time: Patients can select a suitable doctor and appointment time on the platform according to their needs and preferences. This greatly reduces on-site waiting time and improves the patient's medical experience.

[0048] Appointment Management: Patients can view and manage their appointment information at any time, including appointment status, appointment time, doctor information, etc. If they need to cancel or change an appointment, they can also easily do so on the platform.

[0049] In summary, this embodiment further enhances the system's practicality and convenience by integrating remote consultation and online appointment booking functions, as well as expanding the functionality of the treatment statistics section. These improvements not only increase doctors' diagnostic efficiency and accuracy but also significantly improve patients' medical experience.

[0050] Example 4 Based on Embodiment 1, the difference in this embodiment is: like Figure 2 and Figure 3 As shown: The visual function training section of the patient user platform specifically includes modules for myopia training, amblyopia training, strabismus training, binocular vision training, and eye strain training. The myopia training module specifically includes, but is not limited to: Flipping beat training: Provides online guidance for flipping beat training, including personalized settings for training methods, training time and training difficulty. During the training process, the system records and analyzes the patient's training data and provides real-time feedback on the training effect. Outdoor exercise duration statistics: Through manual recording or automatic tracking by users, the system collects patients' outdoor exercise time and exercise data, and provides reasonable exercise suggestions and myopia prevention tips based on this data; Correcting reading and writing posture and reading and writing time: The front-facing camera is used to identify the user's reading and writing posture. When the posture is incorrect, a reminder is issued to help the user correct the reading and writing posture, prevent myopia, and count the time the user uses the correct posture and the time spent looking down, looking out, and too close or too far from the book. The data is then used to generate a ratio chart to give the user more intuitive feedback in order to correct the reading and writing posture. Instructions for use of atropine eye drops: Provides instructions on how to use atropine eye drops and precautions, and includes a medication reminder function to ensure patients take the medication on time.

[0051] The patient user version of the platform also includes an intelligent reminder and notification module, which can automatically send training reminders, follow-up visit reminders, and health advice notifications to patients based on their visual function data, training progress, and appointment time information.

[0052] Based on Embodiment 1, this embodiment further refines the visual function training module within the patient user platform and adds an intelligent reminder and notification module, aiming to provide patients with more personalized and comprehensive visual function training and health management services. The following is a detailed description of this embodiment: Visual function training section Myopia training module Flip-and-shoot training: ① Training: Each training session can consist of looking at the optotype for 2 minutes, resting for half a minute, and then looking again, or looking at the entire vision card at once (depending on eye condition). Generally, training should last about 15 minutes per day, 5 minutes for the right eye, 5 minutes for the left eye, and 5 minutes for both eyes. The training time should not be too long. Optotype cards and flip-and-shoots can be sold together. The information on each optotype card can be imported by scanning a QR code.

[0053] The information includes: the correct answer to the letter on the card, the number of times the card has been used, and historical scores.

[0054] ② Testing: Generally, testing can be conducted every 5-7 days (about a week) to check the training effect. The simplest timing method is to start timing from when the plate is placed on it and count how many numbers you can see in one minute. Generally, when you can see the 24th letter in one minute, you can consolidate the training for 3-5 days. Change the training difficulty. Set up a reverse-rate performance measurement module in the platform, import the corresponding information of the visual target cards, and record the correctness and error of the user's pronunciation of the letters by detecting whether the pronunciation is correct or incorrect. Finally, summarize the score of the test.

[0055] Daily training and regular tests can be reminded via platform notifications. After each test, the training and test results for that period will be synchronized to the doctor's end for evaluation and feedback. For other training that cannot be conducted online, such as beading, ball-laying, and gap-finding exercises, patients will purchase the corresponding assistive tools and watch the doctor's guidance videos posted on the platform before engaging in the corresponding training. The training process will be recorded and uploaded for the doctor to correct and suggest. Outdoor Exercise Duration Statistics: In the section for tracking outdoor exercise duration, users can manually click to enter outdoor exercise mode and begin recording the time and distance of their outdoor activity. They can also monitor indicators such as blood pressure and heart rate via a smartwatch, receiving reasonable exercise suggestions. Setting specific goals and awarding rewards upon achievement optimizes the user experience. After outdoor exercise ends, users can manually stop the activity and the system will automatically record exercise duration and other information. Daily, weekly, and monthly statistics can be compiled and analyzed to predict the user's myopia trend.

[0056] Correcting reading and writing posture and time: This section uses the front-facing camera to recognize the user's face, starts timing, and can display animated food from various regions above the user's head. When the user is in an incorrect reading or writing posture (including looking down, tilting their head, being too close to the book, or being too far from the book), the animated image above their head will fall due to virtual gravity, reminding the user of the incorrect posture. When the user returns to the correct posture, the animated image above their head will reset. The system can be paused or stopped at any time during use. When finished, the system calculates the time spent in correct posture and the time spent looking down, tilting their head, or being too close or too far from the book, generating a graph to provide more intuitive feedback and help correct reading and writing posture. Furthermore, statistical methods can be used to analyze and predict the user's myopia trend. Instructions for using atropine eye drops: The platform's eye drop recording module allows users to set a specific sleep time and provides a reminder half an hour in advance. After completing the eye drop application, users can confirm on the platform, and the system will record it as "dropped." If no confirmation is made, the platform will send a maximum of two reminders before recording it as "not dropped." Because atropine eye drops can affect vision for a period of time, users can set a pop-up window with precautions after each nightly confirmation.

[0057] Users can record a date for an intraocular pressure (IOP) check in the platform's calendar module. The platform will remind users to have an IOP check every three months. A QR code can be added to the IOP check report, allowing users to directly import the results by scanning the code. The backend can compare IOP changes with stored reports and issue alerts if a certain deviation is detected.

[0058] Amblyopia training module (specific training content is customized according to the type and degree of amblyopia, such as visual stimulation training, fine motor skills training, etc.); Strabismus training module (including eye movement training, visual direction perception training, etc.); Binocular vision function training modules (such as stereo vision training, fusion function training, etc.); Visual fatigue training module (provides eye exercises, distance relaxation training, etc.); Smart Reminder and Notification Module Training reminders: Based on the patient's visual function data and training progress, training reminders are automatically sent to the patient to ensure that the patient completes the training plan on time.

[0059] Follow-up appointment reminder: Based on the patient's appointment time information, send a follow-up appointment reminder to the patient in advance to avoid the patient missing the follow-up appointment.

[0060] Health advice notification: Based on the patient's visual function data and training status, we provide personalized health advice, such as adjusting eye habits and increasing outdoor activities, to help patients better protect their vision.

[0061] In summary, this embodiment provides patients with more personalized and comprehensive visual function training and health management services by meticulously dividing the visual function training module and adding an intelligent reminder and notification module. These improvements not only enhance the training effect for patients but also strengthen their health awareness and self-management capabilities.

[0062] Example 5 In another optional embodiment, the system further includes the following supporting hardware facilities, detailed in the following description: 1. Smart visual monitoring glasses Function Description: Smart glasses equipped with a miniature camera, distance sensor, gyroscope and photosensor to monitor the user's eye habits (such as eye distance, viewing angle tilt, ambient light intensity) and head posture in real time.

[0063] System integration: Connects to patient user terminals via Bluetooth or Wi-Fi to upload data to the system platform in real time; It supports voice reminder function, which will automatically trigger voice prompts when it detects that the viewing distance is too close, the posture is incorrect or the lighting is insufficient; Built-in storage module supports offline data caching to ensure data integrity when the network is unstable.

[0064] 2. Portable eye biometer Function Description: A lightweight handheld device that integrates optical coherence tomography (OCT) and corneal curvature measurement modules to collect patients' ocular biological data (such as axial length, corneal curvature, lens thickness, etc.).

[0065] System integration: Measurement data is directly synchronized to the doctor's user platform via USB or wireless transmission, automatically generating structured reports; It supports patient self-operation, and the device has a built-in guided interface that guides the measurement process through voice and animation. Data is transmitted in encrypted form, in compliance with medical data security standards such as HIPAA.

[0066] 3. Multifunctional visual training device Function Description: Hardware device designed specifically for visual function training, including adjustable optotype screen, dynamic stereo vision module, eye-tracking sensor and haptic feedback device, supporting multiple training modes such as myopia, amblyopia, and strabismus.

[0067] System integration: Real-time interaction with patient user platforms, automatically adjusting the difficulty of visual targets, dynamic scenes, and training duration based on the personalized training plan generated by the system; Eye-tracking sensors record eye movement trajectories during training, and AI algorithms analyze the training effect, providing real-time feedback to doctors. The haptic feedback device provides vibration alerts when the user's attention is distracted, ensuring focus during training.

[0068] 4. Wearable physiological monitoring bracelet Function Description: This medical-grade wristband integrates a heart rate sensor, blood oxygen monitoring module, and activity tracking function to collect data on a patient's outdoor exercise duration, exercise intensity, and physiological indicators.

[0069] System integration: Automatically collects daily outdoor exercise data and generates exercise suggestions based on system algorithms (such as "Add 30 minutes of outdoor activity today"). The data is synchronized to the "Outdoor Exercise Duration Statistics" module on the patient's client and linked with the myopia development trend prediction model; Low-power design, supporting up to 7 days of continuous use.

[0070] 5. Integrated Remote Consultation Terminal Function Description: A terminal device equipped with a high-definition camera, microphone array and medical image display screen, supporting remote doctor consultation, case discussion and real-time image sharing.

[0071] System integration: Seamlessly integrates with the doctor's user platform, supporting one-click consultation initiation, multi-screen split-screen, and medical image annotation functions; Built-in AI noise reduction algorithm ensures clear remote communication; It conforms to the DICOM standard and can directly read and display professional medical images such as eye OCT and fundus imaging.

[0072] 6. Intelligent reading and writing posture correction desk lamp Function Description: A desk lamp with a built-in wide-angle camera and pressure sensor to monitor the user's reading and writing posture, book distance, and eye usage time in real time.

[0073] System integration: Connects to the patient's user terminal via Wi-Fi. When incorrect posture is detected (such as looking down more than 15°) or continuous eye use exceeds 40 minutes, a flashing light reminder is automatically triggered. The pressure sensor records the book's position, generates a posture correction report, and synchronizes it to the system. It supports color temperature adjustment and automatically optimizes lighting conditions based on ambient light.

[0074] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0075] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A visual function diagnosis and training integrated system based on Internet big data, characterized in that, include: An internet vision function platform includes two different versions of the platform: a doctor user version and a patient user version, which are interactive in two directions. The internet vision function platform can collect data related to the patient's vision function and provide personalized vision function training programs based on this data. The doctor user version platform includes at least a section for entering basic patient information, a section for patient visual function examination and treatment arrangements, a section for follow-up records and treatment effects, a section for doctor treatment suggestions, a section for treatment statistics, and a section for submitting medical science popularization articles. The patient user version of the platform should include at least the following sections: patient basic information, patient visual function examination and treatment suggestions, visual function training, follow-up records and treatment effects, and medical knowledge popular science articles. A data prediction model that uses collected multi-data, through specific algorithmic model building steps and data processing flow, to analyze and predict the development trend of myopia or other visual function abnormalities in users.

2. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 1, characterized in that, The data prediction model specifically includes the following steps: Data collection: Collect data related to myopia; Data preprocessing: Cleaning, deduplication, and missing value handling of the data to ensure data quality; Model building: Using nomograms, determine the weight and threshold of each feature based on the relationship between the features and the target variable; Model evaluation: The accuracy and reliability of the model are evaluated using cross-validation, and adjustments and optimizations are made accordingly; Model application: Applying the model to new datasets for prediction and analysis.

3. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 2, characterized in that, The data collected during the data collection process includes, but is not limited to, patient ocular biometric data, family history of genetic diseases, environmental factors, lifestyle data, as well as traditional data on age, gender, eye habits, visual training records, and visual acuity test results. When constructing the model, an ensemble learning method is used to combine convolutional neural networks (CNN) and long short-term memory networks (LSTM). CNN is used to extract local features from ocular medical images and visual training image data, and LSTM is used to process time series data. The outputs of the two are integrated in the fusion layer and then connected to a fully connected layer for prediction. The specific operation of the model evaluation is as follows: optimize the model hyperparameters using Bayesian optimization algorithm or random search combined with cross-validation technology, and dynamically adjust the hyperparameters according to different types of visual function abnormalities and patient groups.

4. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 3, characterized in that, It also includes a user feedback module: collecting and integrating patients' training data, including but not limited to training content, duration, degree and effect, analyzing and predicting the patient's myopia development trend through statistical methods, and providing it to doctors and patients as a reference. Doctors can make timely adjustments and provide feedback guidance to patients based on the feedback results, and patients can also understand their vision and visual function improvement.

5. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 1, characterized in that, The doctor user version platform also integrates remote consultation functions, allowing doctors to communicate online, share cases, and collaborate remotely to improve diagnostic efficiency and accuracy. The patient user version platform also provides online appointment registration services, making it convenient for patients to choose suitable doctors and times for their visits according to their own needs, reducing on-site waiting time and improving the medical experience.

6. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 5, characterized in that, The treatment statistics section of the doctor user version platform also includes a statistical analysis function for the types of visual function abnormalities in patients. By analyzing a large amount of patient data, it provides doctors with information on the distribution of types of visual function abnormalities, assisting them in clinical research and decision-making.

7. The integrated system for visual function diagnosis and training based on Internet big data according to claim 1, characterized in that, The visual function training module in the patient user version platform specifically includes a myopia training module, an amblyopia training module, a strabismus training module, a binocular vision function training module, and an eye fatigue training module.

8. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 7, characterized in that, The myopia training module specifically includes, but is not limited to: Flipping beat training: Provides online guidance for flipping beat training, including personalized settings for training methods, training time and training difficulty. During the training process, the system records and analyzes the patient's training data and provides real-time feedback on the training effect. Outdoor exercise duration statistics: Through manual recording or automatic tracking by users, the system collects patients' outdoor exercise time and exercise data, and provides reasonable exercise suggestions and myopia prevention tips based on this data; Correcting reading and writing posture and reading and writing time: The front-facing camera is used to identify the user's reading and writing posture. When the posture is incorrect, a reminder is issued to help the user correct the reading and writing posture, prevent myopia, and count the time the user uses the correct posture and the time spent looking down, looking out, and too close or too far from the book. The data is then used to generate a ratio chart to give the user more intuitive feedback in order to correct the reading and writing posture. Instructions for use of atropine eye drops: Provides instructions on how to use atropine eye drops and precautions, and includes a medication reminder function to ensure patients take the medication on time.

9. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 8, characterized in that, The patient user version platform also includes an intelligent reminder and notification module, which can automatically send training reminders, follow-up visit reminders, and health advice notifications to patients based on their visual function data, training progress, and appointment time information.

10. The integrated system for visual function diagnosis and training based on Internet big data as described in claim 8, characterized in that, The system also includes supporting hardware facilities: Smart visual monitoring glasses: integrate a miniature camera, distance sensor and photosensor to monitor eye distance, posture and ambient light intensity in real time, and upload data to the platform via wireless connection; Portable ocular biometer: Supports optical coherence tomography (OCT) and corneal curvature measurement, with encrypted data transmission to the doctor's user terminal; Multifunctional visual training device: Includes an adjustable visual target screen and an eye-tracking sensor, which works in conjunction with the platform to adjust training parameters; Wearable physiological monitoring bracelet: collects outdoor exercise time and physiological indicators, and synchronizes them to the patient user's device; Integrated remote consultation terminal: Supports high-definition image sharing and real-time consultation, and seamlessly connects with doctor user terminals; Intelligent reading and writing posture correction desk lamp: It monitors reading and writing posture through a camera and pressure sensor and triggers light reminders.

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