Eye monitoring and early warning method, system and equipment for myopia prevention and control

Abstract

The invention relates to an eye use monitoring and early warning method, system and device for myopia prevention and control, and relates to the field of eye use monitoring and early warning, and the method comprises the steps: carrying out the recognition of a user eye position, obtaining a user eye position sequence, and carrying out the calculation to obtain an eye use distance sequence; a light intensity sequence is obtained according to the user eye position sequence index, light intensity eye use influence analysis is carried out, and light intensity influence parameters and light change influence parameters are obtained; distance eye use influence analysis is carried out according to the eye use distance sequence, distance influence parameters are obtained, light intensity influence parameters and light change influence parameters are combined, total eye use influence parameters are obtained through calculation, and judgment and early warning are carried out. The technical problem that excessive eye use early warning cannot be accurately performed in real time due to the fact that the dynamic eye use behavior of the user is difficult to comprehensively evaluate in real time in a traditional monitoring method can be solved; the influence of the eye using environment and behavior of the user on the vision can be comprehensively evaluated, and the real-time performance, comprehensiveness and accuracy of eye using monitoring and early warning are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of eye monitoring and early warning, and in particular to an eye monitoring and early warning method, system and equipment for myopia prevention and control. Background Art

[0002] With the widespread use of modern electronic devices, especially the popularity of smartphones, tablets and computers, more and more people, especially teenagers and office workers, are facing the problem of long-term close-up eye use. Studies have shown that long-term bad eye habits, such as excessive close-up eye use, reading in low-light environments, and long-term continuous eye use, may lead to eye health problems such as visual fatigue and worsening myopia.

[0003] Most existing eye monitoring technologies rely on static vision tests or regular inspections and are unable to track and analyze users' eye behaviors in real time, which makes it impossible to detect users' dynamic eye behaviors (such as changes in eye distance, changes in head position, etc.) in the first place. In addition, traditional monitoring methods usually only rely on light intensity sensors to assess the eye environment, ignoring other factors that potentially affect vision, such as eye distance, head posture, and changes in lighting, resulting in insufficient accuracy of monitoring results and an inability to comprehensively assess users' eye risks. Summary of the invention

[0004] The present invention aims to solve the technical problem that traditional eye monitoring methods are difficult to evaluate the user's dynamic eye behavior in real time and comprehensively, resulting in insufficient real-time, comprehensiveness and accuracy of excessive eye monitoring and warning. An eye monitoring and early warning method, system and equipment for myopia prevention and control are provided to solve the problem.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: In the first aspect, the present invention provides an eye monitoring and early warning method for myopia prevention and control, comprising: testing and constructing a light intensity field in the user's eye environment; collecting a user image sequence during the user's eye use process, identifying the user's eye position, obtaining a user eye position sequence, and calculating an eye distance sequence; according to the user's eye position sequence, indexing a light intensity sequence in the light intensity field, performing light intensity eye impact analysis, and obtaining light intensity impact parameters and light change impact parameters; according to the eye distance sequence, performing distance eye impact analysis, obtaining distance impact parameters, combining the light intensity impact parameters and light change impact parameters, calculating total eye impact parameters, and performing discrimination and early warning.

[0006] Preferably, the eye monitoring and early warning method for myopia prevention and control also includes: constructing a spatial coordinate system in the user's eye environment; selecting multiple test position coordinates in the spatial coordinate system, and using a light sensor to test and obtain multiple light intensity parameters of the multiple test position coordinates; and interpolating the light intensity parameters of other position coordinates in the spatial coordinate system based on the multiple light intensity parameters to obtain a light intensity field.

[0007] Preferably, the eye monitoring and early warning method for myopia prevention and control also includes: collecting user images during the user's eye use process, and arranging them in time sequence to obtain a user image sequence; training a user image recognition channel; inputting multiple user images in the user image sequence into the user image recognition channel, and identifying and outputting a user eye position sequence; obtaining desktop position coordinates; and according to the user eye position sequence, calculating the distance between each user's eye position and the desktop position coordinates to obtain an eye distance sequence.

[0008] Preferably, the eye monitoring and early warning method for myopia prevention and control also includes: collecting a set of sample user images based on the user's eye monitoring sample data in the historical time, and marking the user's eye position coordinates in each sample user image to obtain a set of sample user eye positions; constructing a user image recognition channel based on a convolutional neural network; using the sample user image set and the sample user eye position set as input images and output features, and performing supervised training on the user image recognition channel until convergence requirements are met.

[0009] Preferably, the eye monitoring and early warning method for myopia prevention and control also includes: performing light intensity indexing in the light intensity field according to each user eye position in the user eye position sequence to obtain a light intensity sequence; calculating the number of light intensities greater than an excessively bright light intensity threshold or less than an excessively dark light intensity threshold in the light intensity sequence to obtain the number of light intensity anomalies as a light intensity influencing parameter; and performing light intensity change impact analysis according to the light intensity sequence to obtain a light change impact parameter.

[0010] Preferably, the eye monitoring and early warning method for myopia prevention and control also includes: calculating the number of light intensity changes within a preset time window that are greater than a preset light intensity change threshold based on the light intensity sequence, to obtain the number of light intensity mutations; and using the number of light intensity mutations as a light change influencing parameter.

[0011] Preferably, the eye monitoring and early warning method for myopia prevention and control also includes: according to the eye distance sequence, screening the number of eye distances that are less than a preset eye distance threshold as a distance influencing parameter; performing weighted calculation on the light intensity influencing parameter, the light change influencing parameter and the distance influencing parameter to obtain a total eye influencing parameter; judging whether the total eye influencing parameter is greater than or equal to the eye influencing parameter threshold, and if so, issuing an early warning, and if not, not issuing an early warning.

[0012] In the second aspect, the present invention provides an eye monitoring and early warning system for myopia prevention and control, including: a light intensity field construction module, used to test and construct a light intensity field in the user's eye environment; an eye position recognition module, used to collect a user image sequence during the user's eye use process, perform user eye position recognition, obtain a user eye position sequence, and calculate an eye distance sequence; an eye impact analysis module, used to index and obtain a light intensity sequence in the light intensity field according to the user eye position sequence, perform light intensity eye impact analysis, and obtain light intensity impact parameters and light change impact parameters; an excessive eye use warning module, used to perform distance eye impact analysis according to the eye distance sequence, obtain distance impact parameters, combine the light intensity impact parameters and light change impact parameters, calculate the total eye impact parameters, and perform discrimination and early warning.

[0013] In a third aspect, the present invention further provides an eye monitoring and early warning device for myopia prevention and control, which can be controlled by an eye monitoring and early warning method for myopia prevention and control as described in any one of the first aspects above.

[0014] The beneficial effects of the present invention are as follows: by testing and constructing a light intensity field in the user's eye environment; then collecting a user image sequence during the user's eye use process, performing user eye position recognition, obtaining a user eye position sequence, and calculating an eye distance sequence; further, according to the user eye position sequence, indexing a light intensity sequence in the light intensity field, performing light intensity eye impact analysis, and obtaining light intensity impact parameters and light change impact parameters; on the other hand, according to the eye distance sequence, performing distance eye impact analysis, and obtaining distance impact parameters; then calculating the total eye impact parameters based on the distance impact parameters, light intensity impact parameters, and light change impact parameters, and performing a judgment and early warning. In other words, by combining multidimensional data such as light intensity, light change, and eye distance for comprehensive eye analysis, it is possible to comprehensively evaluate the impact of the user's eye environment and behavior on vision, thereby significantly improving the real-time, comprehensiveness, and accuracy of eye monitoring and early warning, and effectively preventing vision problems caused by bad eye habits. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A schematic diagram of a flow chart of an eye monitoring and early warning method for myopia prevention and control provided by the present invention; Figure 2 A schematic structural diagram of an eye monitoring and early warning system for myopia prevention and control provided by the present invention.

[0016] In the accompanying drawings, the components represented by the reference numerals are described as follows: A light intensity field construction module 11, an eye position recognition module 12, an eye use impact analysis module 13, and an excessive eye use warning module 14. DETAILED DESCRIPTION

[0017] 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. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0018] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0019] In the description of the present invention, the term "for example" is used to mean "used as an example, illustration or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present invention.

[0020] Embodiment 1, as Figure 1 As shown, the embodiment of the present invention provides an eye monitoring and early warning method for myopia prevention and control, which specifically includes the following steps: S10: Test and construct a light intensity field in the user's eye environment.

[0021] Furthermore, step S10 of the present invention further includes: S11: Construct a spatial coordinate system in the user's eye environment; S12: Select multiple test position coordinates in the spatial coordinate system, use a light sensor to test and obtain multiple light intensity parameters of the multiple test position coordinates; S13: Based on the multiple light intensity parameters, interpolate the light intensity parameters of other position coordinates in the spatial coordinate system to obtain a light intensity field.

[0022] Specifically, a three-dimensional coordinate system is established in the user's eye environment (such as near a desk) to locate and measure the distribution of light intensity. For example, with the desk as the center, the origin is the center point of the desk, the horizontal direction (such as the length of the desk) is the X-axis, the vertical direction (such as the height of the desk) is the Y-axis, and the depth direction (such as the width of the desk) is the Z-axis. A three-dimensional coordinate system is defined and set as a spatial coordinate system. The coordinate system divides the space of the user's eye environment into quantifiable areas to facilitate subsequent light intensity measurement and analysis.

[0023] Next, select multiple representative positions in the spatial coordinate system as test position coordinates for measuring light intensity, and obtain multiple test position coordinates. For example, select the main areas where the user uses the eyes as test points, including but not limited to the desk surface (the area where the user reads or writes), the front of the screen (the area where the user watches electronic devices), and the eye position of the user when sitting (the focal area of ​​the user's line of sight); these test points should be evenly distributed in the spatial coordinate system to ensure the accuracy of subsequent interpolation results. Further use a light sensor (such as an ambient light sensor) to measure the light intensity at each test point, record the light intensity value of each test point, and form a set of light intensity parameters, such as the light intensity of test point A (X1, Y1, Z1) is L1, and the light intensity of test point B (X2, Y2, Z2) is L2; ​​obtain multiple light intensity parameters of the multiple test position coordinates.

[0024] Then, based on the multiple light intensity parameters, the light intensity parameters of other position coordinates in the spatial coordinate system are interpolated, that is, the light intensity of unmeasured points is calculated using an interpolation algorithm (such as linear interpolation, bilinear interpolation or cubic spline interpolation). For example, if the light intensity of test points A, B, and C is known, the light intensity L4 of test point D (X4, Y4, Z4) can be estimated by the interpolation algorithm; through interpolation processing, the light intensity field of the entire spatial coordinate system is generated, covering every position of the user's eye environment. By constructing a light intensity field, the light distribution of the user's eye environment can be comprehensively and accurately evaluated, providing a scientific basis for myopia prevention and control.

[0025] S20: Collecting a user image sequence during the user's eye use process, identifying the user's eye position, obtaining the user's eye position sequence, and calculating the eye use distance sequence.

[0026] Further, step S20 of the present invention further includes: S21: Collect user images during the user's eye use process, and arrange them in time sequence to obtain a user image sequence.

[0027] Specifically, a camera or other image acquisition device is used to capture images of the user's eyes in real time. The image acquisition frequency can be set according to needs, such as capturing 1 frame per second or 1 frame per minute. The captured user images are then arranged in chronological order to form a user image sequence, in which each frame of the image should contain timestamp information to facilitate subsequent analysis and comparison.

[0028] S22: Training user image recognition channel.

[0029] Further, step S22 of the present invention further includes: S221: Based on the eye monitoring sample data of the user in the historical time, a set of sample user images is collected, and the coordinates of the user's eye position in each sample user image are marked to obtain a set of sample user eye positions; S222: Based on the convolutional neural network, a user image recognition channel is constructed; S223: Using the sample user image set and the sample user eye position set as input images and output features, the user image recognition channel is supervised and trained until the convergence requirements are met.

[0030] Specifically, first, obtain eye monitoring sample data within the user's historical time (such as within the last month), and collect sample user images based on the eye monitoring sample data to obtain a set of sample user images; then, annotate the user's eye position in each sample user image, that is, mark the center coordinates of the eye area in the image (such as the center point coordinates of the left eye and the right eye), and use image annotation tools (such as LabelImg, VIA, etc.) for manual or semi-automatic annotation to obtain a set of sample user eye positions.

[0031] Convolutional neural network (CNN) is a deep learning model specially designed for processing data with grid structure (such as images and videos). Its core idea is to extract local features through convolution operation and gradually abstract high-level feature representation through multi-layer network structure. CNN has achieved remarkable success in the field of computer vision (such as image classification, target detection, and semantic segmentation). Next, a user image recognition channel is constructed based on the convolutional neural network. The user image recognition channel includes an input layer (receiving user images), a convolution layer (extracting local features of the image), a pooling layer (reducing the spatial dimension of the feature map), a fully connected layer (mapping the features to the output space) and an output layer (outputting the coordinates of the eye position). Next, the sample user image is used as input, the sample user eye position is used as supervision, and the sample user image set and the sample user eye position set are used as training data to perform supervised training on the user image recognition channel. During the training process, the mean square error (MSE) or smoothed L1 loss function is used to calculate the error between the predicted coordinates and the true coordinates; then the stochastic gradient descent (SGD) or Adam optimizer is used to update the network parameters; then the network parameters are continuously adjusted through the back propagation algorithm until the loss function converges; during the verification process, the verification set is used to evaluate the performance of the model, and the accuracy and error of the eye position prediction are calculated. According to the evaluation results, the network structure or hyperparameters (such as learning rate, batch size, etc.) are adjusted to further optimize the model performance until the model output accuracy meets the expected convergence index, and a trained user image recognition channel is obtained.

[0032] By building a user image recognition channel based on a convolutional neural network and using labeled data for supervised training, high-precision and high-robustness eye position recognition can be achieved, which significantly improves the intelligence, accuracy and efficiency of eye position recognition and provides reliable technical support for eye behavior monitoring.

[0033] S23: inputting multiple user images in the user image sequence into the user image recognition channel, and obtaining a user eye position sequence through recognition output; S24: obtaining desktop position coordinates; S25: calculating the distance between each user eye position and the desktop position coordinates according to the user eye position sequence, and obtaining an eye distance sequence.

[0034] Specifically, multiple user images in the user image sequence are sequentially input into the user image recognition channel, the user eye position in each frame is automatically identified, the user eye position coordinates in each frame are output, and then the eye position coordinates of all frames are arranged in chronological order to obtain the user eye position sequence. On the other hand, the desktop position coordinates, such as the center point coordinates of the desktop, are measured in real time; then, based on the user eye position sequence and the desktop position coordinates, the distance between the user's eyes and the desktop in each frame is calculated, and the eye distances of all frames are arranged in chronological order to obtain the eye distance sequence. The eye position is identified through the user image sequence, and the eye distance sequence is dynamically calculated in combination with the desktop position coordinates, providing data support for the subsequent comprehensive and accurate evaluation of the user's eye behavior.

[0035] S30: According to the user's eye position sequence, indexing in the light intensity field obtains a light intensity sequence, performs eye impact analysis on the light intensity, and obtains light intensity impact parameters and light change impact parameters.

[0036] Further, step S30 of the present invention further includes: S31: According to each user eye position in the user eye position sequence, light intensity indexing is performed in the light intensity field to obtain a light intensity sequence; S32: The number of light intensities in the light intensity sequence that are greater than an excessively bright light intensity threshold or less than an excessively dark light intensity threshold is calculated to obtain the number of light intensity anomalies as a light intensity influencing parameter.

[0037] Specifically, first, according to each user eye position in the user eye position sequence, light intensity indexing is performed in the light intensity field to obtain the light intensity value of the corresponding position, and the light intensity values ​​of all frames are arranged in chronological order to obtain a light intensity sequence.

[0038] Next, configure and define the over-bright light intensity threshold and the over-dark light intensity threshold. The over-bright light intensity threshold and the over-dark light intensity threshold can be set according to the actual scenario and user needs. The definition of the over-bright light intensity threshold and the over-dark light intensity threshold is to quantify the healthy range of the lighting environment. When the light intensity exceeds the bright threshold or is lower than the over-dark threshold, it may have a negative impact on the user's vision. By setting reasonable thresholds, the lighting environment can be scientifically evaluated and optimization suggestions can be provided to the user. For example, the over-dark light intensity threshold is set to 300 lux. A value lower than this may cause eye fatigue; the over-bright light intensity threshold is set to 1000 lux. A value higher than this may cause glare or discomfort.

[0039] The light intensity sequence is further traversed to count the number of light intensities greater than the too-bright light intensity threshold or less than the too-dark light intensity threshold as the number of light intensity anomalies. The number of light intensity anomalies serves as a light intensity influencing parameter, reflecting the potential impact of the lighting environment on the user's vision. Too many light intensity anomalies indicate that the lighting environment is unstable or uncomfortable, which may have a negative impact on the user's vision.

[0040] S33: performing light intensity change impact analysis according to the light intensity sequence to obtain light change impact parameters.

[0041] Further, step S33 of the present invention also includes: S331: According to the light intensity sequence, the number of light intensity changes within a preset time window that are greater than a preset light intensity change threshold is calculated to obtain the number of light intensity mutations; S332: The number of light intensity mutations is used as a light change influencing parameter.

[0042] Specifically, first, a preset time window (such as 20 seconds) and a light intensity change threshold (such as 100 lux) are defined, that is, a 20-second time window means that the system will count the number of light intensity mutations every 20 seconds, and a threshold of 100 lux means that when the light intensity change between adjacent time points exceeds 100 lux, a light intensity mutation is considered to have occurred, wherein the time window and the change threshold can be adjusted according to actual scenarios and user needs; then, within the preset time window, the light intensity sequence is traversed, the light intensity change amplitude between adjacent time points is calculated, and the number of times the light intensity change amplitude is greater than the preset light intensity change threshold is counted to obtain the number of light intensity mutations; then the number of light intensity mutations is used as a light change influencing parameter, which can be used to evaluate the stability of the lighting environment and reflect the potential impact of changes in the lighting environment on the user's vision.

[0043] S40: Perform distance eye impact analysis according to the eye distance sequence to obtain distance impact parameters, and calculate total eye impact parameters in combination with the light intensity impact parameters and light change impact parameters to perform judgment and warning.

[0044] Further, step S40 of the present invention further includes: S41: According to the eye distance sequence, the number of eye distances that are less than a preset eye distance threshold is screened as a distance influencing parameter; S42: Weighted calculation is performed on the light intensity influencing parameter, the light change influencing parameter and the distance influencing parameter to obtain a total eye influencing parameter; S43: Determine whether the total eye influencing parameter is greater than or equal to the eye influencing parameter threshold, if so, issue a warning, if not, do not issue a warning.

[0045] Specifically, a preset eye distance threshold is obtained, and the preset eye distance threshold can be based on a standard eye distance or set according to customer needs, for example, 30 cm; then, according to the eye distance sequence, the number of eye distances that are less than the preset eye distance threshold is filtered as a distance influencing parameter. Next, configure the light intensity influence weight, light change influence weight and distance influence weight. When comprehensively evaluating eye health, the contribution of light intensity influence parameter, light change influence parameter and distance influence parameter to the total eye influence parameter may be different. By configuring the weights, the importance of each parameter can be reflected more scientifically, making the calculation of the total eye influence parameter more reasonable and accurate. For example, the weights are dynamically adjusted according to the user's eye habits, vision conditions and environmental requirements, where the sum of the light intensity influence weight, light change influence weight and distance influence weight is 1. The greater the impact of an indicator on eye health, the higher the weight. For example, the light intensity influence weight, light change influence weight and distance influence weight are 0.4, 0.3 and 0.3 respectively. Then, the light intensity influence parameter, light change influence parameter and distance influence parameter are weightedly calculated according to the light intensity influence weight, light change influence weight and distance influence weight, and the weighted calculation result is used as the total eye influence parameter.

[0046] It is further determined whether the total eye influence parameter is greater than or equal to the eye influence parameter threshold, and the eye influence parameter threshold can be set according to customer needs; if the total eye influence parameter is greater than or equal to the eye influence parameter threshold, an early warning is triggered, wherein the early warning prompt unit is located on the side of the eye monitoring and early warning device, and an early warning prompt can be issued by sound, vibration or flashing light to remind the user to adjust the eye environment or take a rest; if the total eye influence parameter is less than the eye influence parameter threshold, no early warning is issued.

[0047] In addition, the eye monitoring and early warning device is also equipped with a time monitoring unit, which is built into the core control board of the device and works with the sensor module to record the user's continuous eye use time. When the continuous eye use time exceeds the set threshold, the early warning mechanism is triggered, and an early warning prompt is issued through sound, vibration or flashing lights to remind the user to adjust the eye environment or take a rest.

[0048] The eye monitoring and early warning method for myopia prevention and control provided by the embodiment of the present invention has at least the following technical effects: By testing and constructing a light intensity field in the user's eye environment; then collecting a user image sequence during the user's eye use process, identifying the user's eye position, obtaining the user's eye position sequence, and calculating the eye distance sequence; further based on the user's eye position sequence, indexing the light intensity sequence in the light intensity field, performing light intensity eye impact analysis, and obtaining light intensity impact parameters and light change impact parameters; on the other hand, based on the eye distance sequence, performing distance eye impact analysis, and obtaining distance impact parameters; then calculating the total eye impact parameters based on the distance impact parameters, light intensity impact parameters, and light change impact parameters, and performing a judgment and warning. In other words, by combining multi-dimensional data such as light intensity, light change, and eye distance for comprehensive eye analysis, it is possible to comprehensively evaluate the impact of the user's eye environment and behavior on vision, thereby significantly improving the real-time, comprehensiveness, and accuracy of eye monitoring and early warning, and effectively preventing vision problems caused by bad eye habits.

[0049] Embodiment 2, as Figure 2 As shown, based on the same inventive concept as the eye monitoring and early warning method for myopia prevention and control provided in Example 1, an embodiment of the present invention further provides an eye monitoring and early warning system for myopia prevention and control, including: a light intensity field construction module 11, used to test and construct a light intensity field in the user's eye environment; an eye position recognition module 12, used to collect a user image sequence in the user's eye use process, perform user eye position recognition, obtain a user eye position sequence, and calculate an eye distance sequence; an eye influence analysis module 13, used to obtain a light intensity sequence by indexing in the light intensity field according to the user's eye position sequence, perform light intensity eye influence analysis, and obtain light intensity influence parameters and light change influence parameters; an excessive eye use warning module 14, used to perform distance eye influence analysis according to the eye distance sequence, obtain distance influence parameters, and calculate the total eye influence parameters in combination with the light intensity influence parameters and light change influence parameters to perform discrimination and early warning.

[0050] Furthermore, the eye monitoring and early warning system for myopia prevention and control is also used to: construct a spatial coordinate system in the user's eye environment; select multiple test position coordinates in the spatial coordinate system, and use light sensors to test and obtain multiple light intensity parameters of the multiple test position coordinates; based on the multiple light intensity parameters, interpolate the light intensity parameters of other position coordinates in the spatial coordinate system to obtain a light intensity field.

[0051] Furthermore, the eye monitoring and early warning system for myopia prevention and control is also used to: collect user images during the user's eye use process, and arrange them in time sequence to obtain a user image sequence; train a user image recognition channel; input multiple user images in the user image sequence into the user image recognition channel, and obtain a user eye position sequence through recognition output; obtain desktop position coordinates; and based on the user eye position sequence, calculate the distance between each user's eye position and the desktop position coordinates to obtain an eye distance sequence.

[0052] Furthermore, the eye monitoring and early warning system for myopia prevention and control is also used to: collect a set of sample user images based on the user's eye monitoring sample data in a historical period, and mark the user's eye position coordinates in each sample user image to obtain a set of sample user eye positions; construct a user image recognition channel based on a convolutional neural network; use the sample user image set and the sample user eye position set as input images and output features, and perform supervised training on the user image recognition channel until convergence requirements are met.

[0053] Furthermore, the eye monitoring and early warning system for myopia prevention and control is also used to: perform light intensity indexing in the light intensity field according to each user eye position in the user eye position sequence to obtain a light intensity sequence; calculate the number of light intensities in the light intensity sequence that are greater than an excessively bright light intensity threshold or less than an excessively dark light intensity threshold to obtain the number of light intensity anomalies as a light intensity influencing parameter; perform light intensity change impact analysis according to the light intensity sequence to obtain a light change impact parameter.

[0054] Furthermore, the eye monitoring and early warning system for myopia prevention and control is also used to: calculate the number of light intensity changes within a preset time window that are greater than a preset light intensity change threshold based on the light intensity sequence, and obtain the number of light intensity mutations; and use the number of light intensity mutations as a light change influencing parameter.

[0055] Furthermore, the eye monitoring and early warning system for myopia prevention and control is also used to: screen the number of eye distances that are less than a preset eye distance threshold according to the eye distance sequence as a distance influencing parameter; perform weighted calculation on the light intensity influencing parameter, the light change influencing parameter and the distance influencing parameter to obtain a total eye influencing parameter; determine whether the total eye influencing parameter is greater than or equal to the eye influencing parameter threshold, and if so, issue an early warning, otherwise, do not issue an early warning.

[0056] Embodiment 3, the embodiment of the present invention provides an eye monitoring and early warning device for myopia prevention and control, which is controlled by an eye monitoring and early warning method for myopia prevention and control as described in any one of Embodiment 1.

[0057] Although preferred embodiments of the present invention have been described, additional changes and modifications may occur to these embodiments once those skilled in the art understand the basic inventive concepts.

[0058] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for eye monitoring and early warning for myopia prevention and control, characterized in that: Methods include: The test builds a light intensity field within the user's eye environment; Collecting a user image sequence during the user's eye use process, identifying the user's eye position, obtaining the user's eye position sequence, and calculating the eye use distance sequence; According to the user's eye position sequence, indexing in the light intensity field to obtain a light intensity sequence, performing an eye impact analysis of the light intensity, and obtaining a light intensity impact parameter and a light change impact parameter; According to the eye distance sequence, the distance eye impact analysis is performed to obtain the distance impact parameter, and the total eye impact parameter is calculated by combining the light intensity impact parameter and the light change impact parameter to perform a judgment and early warning.

2. The eye monitoring and early warning method for myopia prevention and control according to claim 1, characterized in that: The test builds a light intensity field within the user’s eye environment, including: Construct a spatial coordinate system in the user's eye environment; In the spatial coordinate system, multiple test position coordinates are selected, and a light sensor is used to test and obtain multiple light intensity parameters of the multiple test position coordinates; According to the multiple light intensity parameters, interpolation processing is performed on the light intensity parameters of other position coordinates in the space coordinate system to obtain a light intensity field.

3. The eye monitoring and early warning method for myopia prevention and control according to claim 1, characterized in that: Collecting a user image sequence during the user's eye use process, identifying the user's eye position, obtaining the user's eye position sequence, and calculating the eye use distance sequence, including: Collect user images during the user's eye use process and arrange them in time sequence to obtain a user image sequence; Train the user image recognition channel; Inputting a plurality of user images in the user image sequence into the user image recognition channel, and obtaining a user eye position sequence through recognition output; Get the desktop position coordinates; According to the user eye position sequence, the distance between each user's eye position and the desktop position coordinates is calculated to obtain an eye distance sequence.

4. The eye monitoring and early warning method for myopia prevention and control according to claim 3, characterized in that: Train the user image recognition pipeline, including: According to the eye monitoring sample data of the user in the historical time, a sample user image set is collected, and the eye position coordinates of the user in each sample user image are marked to obtain a sample user eye position set; Based on convolutional neural network, build user image recognition channel; The sample user image set and the sample user eye position set are used as input images and output features, and supervised training is performed on the user image recognition channel until convergence requirements are met.

5. The eye monitoring and early warning method for myopia prevention and control according to claim 1, characterized in that: According to the user's eye position sequence, a light intensity sequence is obtained by indexing in the light intensity field, and light intensity eye impact analysis is performed to obtain light intensity impact parameters and light change impact parameters, including: According to each user eye position in the user eye position sequence, performing light intensity indexing in the light intensity field to obtain a light intensity sequence; Calculate the number of light intensities in the light intensity sequence that are greater than an excessively bright light intensity threshold or less than an excessively dark light intensity threshold, and obtain the number of light intensity anomalies as a light intensity influencing parameter; According to the light intensity sequence, light intensity change impact analysis is performed to obtain light change impact parameters.

6. The eye monitoring and early warning method for myopia prevention and control according to claim 5, characterized in that: According to the light intensity sequence, light intensity change impact analysis is performed to obtain light change impact parameters, including: According to the light intensity sequence, the number of light intensity changes within a preset time window that are greater than a preset light intensity change threshold is calculated to obtain the number of light intensity mutations; The number of light intensity mutations is used as a light change influencing parameter.

7. The eye monitoring and early warning method for myopia prevention and control according to claim 1, characterized in that: According to the eye distance sequence, the distance eye influence analysis is performed to obtain the distance influence parameter, and the total eye influence parameter is calculated by combining the light intensity influence parameter and the light change influence parameter to perform a judgment and early warning, including: According to the eye distance sequence, the number of eye distances that are less than a preset eye distance threshold is selected as a distance influencing parameter; Performing weighted calculation on the light intensity influence parameter, the light change influence parameter and the distance influence parameter to obtain a total eye influence parameter; Determine whether the total eye-use influence parameter is greater than or equal to the eye-use influence parameter threshold. If so, issue a warning; if not, do not issue a warning.

8. An eye monitoring and early warning system for myopia prevention and control, characterized in that: The steps for implementing the eye monitoring and early warning method for myopia prevention and control as described in any one of claims 1 to 7 include: A light intensity field construction module is used to test and construct a light intensity field in the user's eye environment; The eye position recognition module is used to collect a user image sequence during the user's eye use process, perform user eye position recognition, obtain a user eye position sequence, and calculate an eye use distance sequence; An eye influence analysis module is used to obtain a light intensity sequence by indexing in the light intensity field according to the user's eye position sequence, perform eye influence analysis on the light intensity, and obtain light intensity influence parameters and light change influence parameters; The excessive eye use warning module is used to perform distance eye use impact analysis based on the eye use distance sequence, obtain distance impact parameters, combine the light intensity impact parameters and light change impact parameters, calculate the total eye use impact parameters, and perform judgment and warning.

9. An eye monitoring and early warning device for myopia prevention and control, characterized in that: The eye monitoring and early warning device is controlled by an eye monitoring and early warning method for myopia prevention and control as described in any one of claims 1 to 7.

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