An eye health status monitoring and evaluation method and device based on big data

Abstract

The present invention provides a method and device for monitoring and evaluating the eye health status based on big data, relating to the technical field of health monitoring, including obtaining first information, second information, and third information; performing three-dimensional reconstruction processing on the first information to construct three-dimensional eye feature data; performing pattern recognition processing on the second information to obtain historical health feature data; performing time series analysis on the historical health feature data and the third information to obtain behavior-environment cumulative effect data; performing data fusion processing on the historical health feature data and the behavior-environment cumulative effect data, and obtaining potential risk data by predicting potential risk factors for children's eye health; and performing evaluation processing on the three-dimensional eye feature data and the risk data to obtain an evaluation result. By using big data mining and pattern recognition technologies, the present invention can identify patterns and trends related to specific eye diseases and can identify potential health problems in advance.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring. Specifically, it relates to a method and device for monitoring and evaluating the ocular health status based on big data Background Art

[0002] With the continuous development of computer technology, especially the progress of big data processing technology and artificial intelligence algorithms, the field of health monitoring is gradually developing towards the direction of intelligence and refinement. In the aspect of ocular health status monitoring, traditional methods such as visual acuity chart testing and regular ophthalmic examinations rely on manual observation and subjective judgment, and cannot obtain and process a large amount of health data in real time. This method not only has a long detection cycle, is difficult to achieve continuous monitoring of individual ocular health, but also cannot capture the subtle changes in structures such as the retina and cornea, especially being more ineffective in the early prediction and prevention of ocular diseases such as myopia development

[0003] The introduction of big data technology provides new possibilities for solving these problems. By integrating various data sources such as electronic health records, daily behavior monitoring, and environmental sensors, a computer system can process a large amount of multi-dimensional data and analyze the state changes of children's ocular health in real time. Using machine learning and pattern recognition technologies, potential health risks can be mined from a large amount of health data, and early abnormal patterns and trends can be identified. In addition, big data processing frameworks such as MapReduce and Hadoop can effectively support the storage and calculation of large-scale data, making the monitoring of ocular health no longer limited to regular physical examinations, but turning towards a more dynamic and real-time global health status analysis

[0004] Therefore, the application of the existing technology in ocular health monitoring is far from reaching the technical potential of the big data era. There is an urgent need for a method and device for monitoring and evaluating the ocular health status based on big data, which can make full use of computer intelligence, big data processing, and machine learning technologies to improve the accuracy, real-time performance, and coverage of ocular health monitoring Summary of the Invention

[0005] The purpose of the present invention is to provide a method and device for monitoring and evaluating the ocular health status based on big data to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows

[0006] On the one hand, the present application provides a method for monitoring and evaluating the ocular health status based on big data, including

[0007] Obtaining first information, second information, and third information, where the first information includes three-dimensional ocular structure data of the child to be monitored, the second information includes electronic health records, ophthalmic examination records, treatment records, and family medical history, and the third information includes daily behavior data and environmental data

[0008] Perform three-dimensional reconstruction processing based on the first information, and construct three-dimensional eye feature data by identifying retinal thickness, corneal curvature, and lens morphology. The three-dimensional eye feature data includes retinal thickness data, corneal curvature data, and lens morphology data;

[0009] Perform pattern recognition processing based on the second information. By performing correlation analysis on historical ophthalmic examination records, treatment records, and family medical history in the electronic health record, identify patterns and trends related to specific eye diseases to obtain historical health feature data;

[0010] Perform time series analysis based on the historical health feature data and the third information. By quantitatively calculating the cumulative effect of eye damage under various behavior-environment combination scenarios and mapping it to the transformation of the three-dimensional eye structure, obtain behavior-environment cumulative effect data;

[0011] Perform data fusion processing based on the historical health feature data and the behavior-environment cumulative effect data, and use big data analysis and supervised learning algorithms to train a prediction model. Obtain potential risk data by predicting potential risk factors for children's eye health;

[0012] Perform evaluation processing based on the three-dimensional eye feature data and the risk data to obtain an evaluation result. The evaluation result includes the current monitoring result and future eye health management suggestions.

[0013] On the other hand, the present application also provides an apparatus for monitoring and evaluating eye health status based on big data, including:

[0014] An acquisition module for acquiring the first information, the second information, and the third information. The first information includes three-dimensional eye structure data of the child to be monitored. The second information includes electronic health records, ophthalmic examination records, treatment records, and family medical history. The third information includes daily behavior data and environmental data;

[0015] A reconstruction module for performing three-dimensional reconstruction processing based on the first information, and constructing three-dimensional eye feature data by identifying retinal thickness, corneal curvature, and lens morphology. The three-dimensional eye feature data includes retinal thickness data, corneal curvature data, and lens morphology data;

[0016] An identification module for performing pattern recognition processing based on the second information. By performing correlation analysis on historical ophthalmic examination records, treatment records, and family medical history in the electronic health record, identify patterns and trends related to specific eye diseases to obtain historical health feature data;

[0017] An analysis module for performing time series analysis based on the historical health characteristic data and the third information, quantifying and calculating the cumulative effect on eye damage in various combinations of behaviors and environments, and mapping it to the transformation of the three-dimensional eye structure to obtain behavior-environment cumulative effect data;

[0018] A fusion module for performing data fusion processing based on the historical health characteristic data and the behavior-environment cumulative effect data, and training a prediction model using big data analysis and supervised learning algorithms to obtain potential risk data by predicting potential risk factors for children's eye health;

[0019] An evaluation module for performing evaluation processing based on the three-dimensional eye characteristic data and the risk data to obtain an evaluation result, where the evaluation result includes the current monitoring result and future eye health management suggestions.

[0020] The beneficial effects of the present invention are as follows:

[0021] By identifying the retinal thickness, corneal curvature, and lens morphology, the present invention constructs three-dimensional eye characteristic data to achieve high-precision three-dimensional reconstruction of children's eye structures; by performing correlation analysis on historical ophthalmic examination records, treatment records, and family medical histories in electronic health records, and using big data mining and pattern recognition technologies, patterns and trends related to specific eye diseases are identified, enabling potential health problems to be identified in advance.

[0022] Other features and advantages of the present invention will be described in the subsequent specification, and, in part, will become apparent from the specification, or can be understood by implementing the embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a schematic flowchart of the method for monitoring and evaluating eye health status based on big data described in the embodiments of the present invention;

[0025] Figure 2 It is a flowchart of the U-Net algorithm described in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Components of the embodiments of the present invention usually described and illustrated in the accompanying drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed present invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0028] Embodiment 1:

[0029] This embodiment provides a method for monitoring and evaluating the eye health status based on big data.

[0030] See Figure 1 , which shows that this method includes step S100, step S200, step S300, step S400, step S500, and step S600.

[0031] Step S100: Obtain the first information, the second information, and the third information. The first information includes the three-dimensional eye structure data of the child to be monitored. The second information includes the electronic health record, which includes the ophthalmological examination record, the treatment record, and the family medical history. The third information includes the daily behavior data and the environmental data;

[0032] It is understandable that the three-dimensional eye structure data of children to be monitored is obtained through eye imaging devices, such as optical coherence tomography (OCT) or high-resolution magnetic resonance imaging (MRI). Electronic health records are a large data set containing multi-dimensional health data, covering children's past medical history, diagnosis results, treatment processes, and medication situations. Ophthalmic examination records contain detailed data for each ophthalmic examination, such as vision tests, intraocular pressure measurements, etc. All this information is aggregated and processed through a big data system. Treatment records and family medical history are integrated by combining multiple data sources. The family medical history can be generated from the genetic information module in the electronic health record or through automated data mining and analysis algorithms of the big data system, thereby helping to identify potential genetic risk factors. Daily behavior data is obtained through wearable devices (such as smartwatches), smartphone applications, etc., covering children's activity levels, screen usage time, reading habits, and sleep quality. Environmental data is automatically collected through environmental sensors and smart home systems, involving external factors such as the lighting conditions, air quality, and noise levels where children are located. This data can be collected and transmitted in real time through Internet of Things (IoT) devices, providing important references for the impact of the environment on eye health. The big data platform integrates multi-dimensional environmental data from different devices and sensors through real-time data stream processing technology and a distributed computing framework, providing comprehensive support and references for analyzing the long-term impact of the environment on eye health.

[0033] Step S200: Perform three-dimensional reconstruction processing based on the first information. Construct three-dimensional eye feature data by identifying the retinal thickness, corneal curvature, and lens shape. The three-dimensional eye feature data includes retinal thickness data, corneal curvature data, and lens shape data.

[0034] Preferably, the three main features of retinal thickness, corneal curvature, and lens shape are identified through image processing and computer vision techniques. The measurement of retinal thickness is achieved by analyzing the density changes in different layers of the three-dimensional eye image; the measurement of corneal curvature is completed by detecting the shape and curvature changes of the corneal surface; the lens shape data is obtained by extracting the morphological features of the lens through an image processing algorithm. The lens shape data includes the anterior and posterior surface curvatures, thickness, and position, etc.

[0035] Step S300: Perform pattern recognition processing based on the second information. Identify patterns and trends related to specific eye diseases by conducting correlation analysis on historical ophthalmic examination records, treatment records, and family medical history in the electronic health record, and obtain historical health feature data.

[0036] It is understandable that in this step, multiple data sources in the electronic health record are aggregated and standardized, and by using a correlation analysis algorithm, frequent patterns and association rules in the data can be effectively identified, helping to find potential patterns related to eye diseases.

[0037] Step S400: Perform time series analysis based on historical health characteristic data and the third information. By quantitatively calculating the cumulative effects on eye injuries under various combinations of behavior-environment scenarios and mapping them to the transformation of the three-dimensional eye structure, obtain behavior-environment cumulative effect data;

[0038] It can be understood that in this step, by simulating and visualizing the specific manifestations of these cumulative effects on the three-dimensional eye structure, and precisely calculating the changes in retinal thickness, corneal curvature, and lens morphology, the behavior-environment cumulative effects are concretized into visible three-dimensional structure changes.

[0039] Step S500: Perform data fusion processing based on historical health characteristic data and behavior-environment cumulative effect data, and use big data analysis and supervised learning algorithms to train a prediction model to obtain potential risk data by predicting potential risk factors for children's eye health;

[0040] It can be understood that in this step, first, fuse historical health characteristic data from multiple sources (including children's physical examination records, treatment records, family medical history, etc.) with behavior-environment cumulative effect data (such as the duration of using electronic devices, lighting conditions, etc.). With the support of big data technology, through parallel computing and distributed processing, these heterogeneous data can be effectively integrated and standardized to ensure the relevance and consistency of multi-dimensional data. Next, use supervised learning algorithms, such as decision tree or random forest models, to train and predict these fused data. The advantage of big data technology lies in its ability to process a large amount of health and behavior data, improve the accuracy of the prediction model, and identify potential health risk factors. Through this method, the system can efficiently extract important patterns related to children's eye health from a large dataset, reveal potential health risks, and provide a basis for individualized health monitoring.

[0041] Step S600: Perform evaluation processing based on three-dimensional eye feature data and risk data to obtain an evaluation result, where the evaluation result includes the current monitoring result and future eye health management suggestions.

[0042] These evaluation results can help doctors and parents better understand the eye health status of children, take effective preventive and intervention measures, and thus improve the eye health level of children.

[0043] It should be noted that step S200 includes step S210, step S220, step S230, and step S240.

[0044] Step S210: Perform voxelization processing based on the first information. By converting eye structure data into three-dimensional voxel data, obtain voxelized eye data;

[0045] Specifically, a voxel (volume pixel) is a small cube in three-dimensional space, which represents a data point in this spatial region. The value of each voxel usually represents a certain characteristic value at that position, such as density, color, or intensity. In the eye structure data, voxelization will associate each voxel with specific eye tissue characteristics, such as retinal thickness, corneal curvature, etc. To achieve this process, first, the input two-dimensional image needs to be preprocessed, including denoising, enhancing contrast, etc. Next, interpolation algorithms (such as bilinear interpolation, cubic spline interpolation) are used to fill the gaps in three-dimensional space to ensure that the reconstructed three-dimensional model has high resolution and high precision. At this time, the generated three-dimensional voxel data can be used for further analysis and modeling. Through this voxelization process, a detailed three-dimensional eye structure model can be obtained. The voxelized eye data not only provides rich spatial information but also enables more refined three-dimensional analysis and visualization.

[0046] Step S220: Perform retinal thickness extraction processing on the voxelized eye data based on the U-Net segmentation algorithm, and automatically segment the retinal region and calculate the thickness to obtain retinal thickness data by combining the thickness variation characteristics of the retina in different growth stages of children;

[0047] It can be understood that the U-Net algorithm is a deep learning network suitable for medical image segmentation tasks. Its structure is a symmetric U shape, including a contracting path to capture background information and a symmetric expanding path to ensure accurate positioning. This structure makes it suitable for the precise segmentation of small targets in medical images. When processing the extraction of retinal thickness, U-Net can effectively separate the retinal region from complex eye images and maintain a high-precision segmentation effect even when the structure of the retina changes at various growth stages. In this step, the voxelized three-dimensional eye data is first input into the U-Net model. The model has been trained to recognize different characteristics and boundaries of the retina by learning a large number of similar medical image data. Specifically, the algorithm analyzes the characteristics of each voxel point and automatically marks the retinal region. Then, by calculating the thickness of these marked regions, an accurate measurement of the retinal thickness is obtained. As Figure 2 shown, Figure 2The flowchart of the U-Net algorithm is shown. The U-Net architecture includes an input layer, a downsampling convolutional layer, a bottom layer (bottleneck layer), an upsampling convolutional layer, and an output layer. Among them, the input layer is the starting point of the network, and the input is the voxelized three-dimensional data of the eye. The downsampling convolutional layer includes downsampling convolutional layer 1 and downsampling convolutional layer 2, which extract low-level features of the image, such as edges and textures, through convolutional operations. After passing through each convolutional layer, the spatial dimension of the image is halved by max pooling, and at the same time, the number of feature channels increases. The bottom layer (bottleneck layer) is located at the deepest part of the network and is designed to be a layer with the maximum feature extraction ability. Here, no pooling is performed, but more convolutional operations are used to deepen the feature learning to provide rich feature information for the upsampling path. The upsampling convolutional layer includes upsampling convolutional layer 1 and upsampling convolutional layer 2: These layers restore the details and size of the image through upsampling (gradually increasing the spatial dimension of the image) and convolutional operations. During upsampling, the feature maps in the corresponding downsampling path are merged (through skip connections) into the upsampling path. This is done to retain important spatial information that may be lost in the downsampling path, thereby improving the accuracy of segmentation. The output layer finally converts the feature map into the final segmentation map through a convolutional layer. This map marks the exact area of the retina and can be used for further analysis such as thickness calculation. Such a design aims to capture more abstract high-level features while reducing computational complexity. The technical effects of this method include the ability to provide fast and automated retinal thickness measurement, which is particularly important for the monitoring of children's vision development. The retinal thickness calculation formula is:

[0048]

[0049] Among them, x and y represent the abscissa and ordinate of the central pixel point of the retinal image, d(x, y) represents the retinal thickness at the position (x, y), R represents the local neighborhood, i and j represent the abscissa and ordinate of the points in the R region, G is a function used to simulate the change characteristics of the children's retina at different growth stages, w represents the weight assigned by the U-Net model to each pixel in the R region, F ij represents the eigenvalue at (i, j), F xy represents the eigenvalue at (x, y), and σ represents the standard deviation.

[0050] Step S230: Perform corneal curvature calculation processing based on the voxelized eye data, capture the curvature characteristics at different development stages through an adaptive filtering algorithm, and perform surface fitting processing to obtain corneal curvature data;

[0051] It can be understood that the adaptive filtering algorithm can dynamically adjust its parameters according to the local characteristics of the data, enabling it to better adapt to different regions in the image. For the measurement of corneal curvature, this means that the algorithm can adjust the processing method according to the unique morphology and curvature changes in different parts of the cornea (such as the central and peripheral regions). After adaptive filtering processing, surface fitting technology is used to create a continuous and smooth surface model. The calculation formula is as follows:

[0052]

[0053] Where x and y represent the abscissa and ordinate of the central pixel point of the retinal image, K(x, y) represents the corneal curvature at the position (x, y), N represents the local neighborhood, i and j represent the abscissa and ordinate of the points in the N region, W represents the adaptive weight, and Z represents the height function of the eye structure. This calculation method uses differential geometry and image processing techniques to quantitatively describe the curvature of the cornea. By evaluating the local curvature of the cornea through a second-order partial differential equation, a detailed view of the corneal morphology can be provided, especially in regions where the surface changes are complex or irregular.

[0054] Step S240: Perform lens morphology analysis processing on the voxelized eye data, and extract the main morphological features of the lens to obtain lens morphology data.

[0055] Specifically, first use image segmentation technology to distinguish the lens from other eye structures. Thereafter, morphological analysis is performed on the segmented lens region, including its size, shape, and any morphological abnormalities. Through quantitative analysis, geometric parameters of the lens are extracted, such as length, width, thickness, and the complexity of its three-dimensional structure, and three-dimensional reconstruction is performed using voxelized data to observe the morphology of the lens from different perspectives.

[0056] It should be noted that step S300 includes step S310, step S320, step S330, and step S340.

[0057] Step S310: Perform data integration processing according to the second information to obtain comprehensive health data;

[0058] It can be understood that the comprehensive health data includes merging fields, creating new data dimensions (such as calculating the long-term trends of certain health indicators), and transforming the data to adapt to subsequent analysis.

[0059] Step S320: Perform feature extraction processing according to the comprehensive health data, and obtain health feature data by identifying key health features;

[0060] Preferably, in this step, statistical analysis and machine learning algorithms are used to identify those features that have a significant impact on the health status. These features include the patient's biomarkers, historical symptoms, treatment responses, etc.

[0061] Step S330: Perform pattern recognition processing based on the health feature data, and obtain disease pattern data by applying a Hidden Markov Model to identify patterns related to specific eye diseases.

[0062] It can be understood that the Hidden Markov Model is a statistical model that assumes the system can be described by a series of hidden states, and these states have the Markov property (i.e., the probability of the next state depends only on the current state). In the identification of pediatric eye diseases, each disease can be regarded as a series of state changes, and these changes are observed and identified through the patient's health feature data. The process of using the Hidden Markov Model for disease pattern recognition first involves training the model to identify the state transition patterns in the data, and these patterns correspond to the development stages or characteristics of the disease. For example, in the analysis of the development of glaucoma, the state transitions can be marked by the progression of the lesion (such as the gradual narrowing of the visual field). In addition, the Hidden Markov Model can handle time series data, making it particularly suitable for tracking health indicators that change over time, such as visual acuity test results or intraocular pressure data.

[0063] Step S340: Perform trend analysis processing based on the disease pattern data, and identify the potential trends in the development of the disease by predicting the trends of the disease patterns to obtain historical health feature data.

[0064] Preferably, in this step, by applying time series analysis methods, such as the Autoregressive Integrated Moving Average (ARIMA) model, to predict the development trends of pediatric eye diseases, and finally generate historical health feature data. This analysis is particularly important for the management of pediatric eye health because it can help doctors detect the signs of diseases early, such as the initial signs of myopia progression, astigmatism, or other vision problems. For example, when analyzing the visual acuity test results of children, by capturing the patterns and trends of visual acuity changes, it is possible to predict the possible development paths of these diseases and take appropriate preventive measures in advance. This trend analysis enhances the predictive ability of changes in pediatric eye health, enabling ophthalmologists to develop personalized monitoring and intervention strategies based on data-driven insights.

[0065] It should be noted that step S400 includes step S410, step S420, step S430, and step S440.

[0066] Step S410: Perform time series processing based on the historical health feature data and the third information, and obtain time series data by eliminating the noise and trends in the data.

[0067] This step improves the quality of children's eye health monitoring data and enhances the ability to monitor and understand the long-term changes in children's eye health.

[0068] Step S420: Perform scenario recognition processing based on time series data, and obtain behavior-environment data by using the dynamic time warping algorithm to identify and match typical behaviors and environmental scenarios in the time series data.

[0069] It can be understood that the dynamic time warping algorithm is particularly suitable for analyzing sequence data that may be distorted on the time axis, such as sequences with inconsistent time lengths or different rates. In children's eye health monitoring, the dynamic time warping algorithm can effectively compare and analyze the relationship between health indicators (such as intraocular pressure or vision changes) recorded at different time points and environmental factors (such as lighting conditions, frequency of using electronic products). Through the processing of the dynamic time warping algorithm, specific behaviors (such as study time, outdoor activities) and environmental factors (such as light conditions, frequency of using electronic devices) in the time series data can be associated with the eye health status (such as vision changes). This helps to determine which specific combinations of behaviors and environments affect children's visual health.

[0070] Step S430: Perform quantization processing based on the behavior-environment data, and obtain cumulative effect data by applying a long short-term memory network to quantify the cumulative damage effects on the eyes in various behavior-environment combination scenarios.

[0071] It can be understood that in this step, the data of various behaviors (such as reading, outdoor activities) and environmental factors (such as lighting conditions, frequency of using electronic products) are standardized to ensure that they are suitable for input into the long short-term memory network model. Then, the network structure of the long short-term memory network is adjusted according to these data characteristics, including increasing the depth of the network and the number of memory units, so that it can better capture the behavior patterns of children in different environments and their potential impacts on vision. The core of the long short-term memory network lies in its ability to effectively process and remember long-term data dependencies, which is crucial for analyzing how behaviors and environments accumulate over time to affect children's vision. Through training, the long short-term memory network model can identify the degree of influence of specific behaviors and environmental factors on eye health and predict possible future health trends. Specifically, the model can identify the vision problems that may be caused by reading for a long time under low light conditions, or evaluate how regular outdoor activities can effectively prevent the development of myopia.

[0072] Step S440: Perform data mapping processing based on the cumulative effect data, and obtain behavior-environment cumulative effect data by reverse modeling the eye structure and mapping the cumulative effect data into specific eye structure changes.

[0073] It is understandable that the core of cumulative effect data is to understand how various behavioral and environmental factors interact over time to jointly affect the eye structure and health of children. It can capture the additive effects between single factors, as well as the interactions between different factors, and even the offset effects they may produce. Specifically, cumulative effect data can show that long-term use of electronic devices indoors (low-light environment and close focusing) may lead to a gradual decline in vision, but this effect may be offset or slowed down by regular outdoor activities (providing better lighting and opportunities for distant focusing). These data not only record the effects of each independent factor, but also reveal their combined effects under different combinations through comprehensive analysis. Further, this inverse modeling is carried out using finite element analysis (FEA) and other modeling techniques. These techniques can detail the response of a physical structure when subjected to external forces (in this scenario, the physiological changes caused by behavioral and environmental factors). By inputting the cumulative effect data into these models, it is possible to detail the simulation and prediction of how behavioral and environmental factors affect the specific structure of the eye over time, such as changes in retinal thickness, corneal curvature, and lens shape, and predict how specific behavior-environment combinations specifically affect the health status of the eye structure. By considering the additive effects of multiple variables, a more comprehensive and accurate health management tool is provided.

[0074] It should be noted that step S440 includes step S441, step S442, step S443, and step S444.

[0075] Step S441: Conduct statistical analysis and processing based on the cumulative effect data. By using multivariate regression analysis, quantify the specific impact of each behavioral and environmental factor on the change of the eye structure to obtain regression coefficients.

[0076] Through this analysis, the impact of each factor is not only measured individually, but also how they interact with other factors is considered. For example, multivariate regression can identify which specific behavioral patterns or environmental settings have the greatest impact on vision loss, or discover that certain combinations of seemingly unrelated behavioral and environmental factors may lead to unexpected eye health problems. Regression analysis provides specific regression coefficients, which quantify the specific contributions of each factor to the change of the eye structure, enabling an accurate understanding and prediction of how behavior and environment affect children's eye health.

[0077] Step S442: Conduct inverse modeling processing of the eye structure based on the regression coefficients. Through finite element analysis, simulate the stress and deformation of the eye structure under different external influences and display the specific changes in the eye structure caused by different cumulative effects to obtain an inverse model.

[0078] It can be understood that reverse modeling infers the factors (behavioral and environmental factors) that cause these changes based on known results (changes in the eye structure). Using regression coefficients, the contribution of each factor to the changes in the eye structure can be quantified, and these data are used to set the input parameters of the finite element model to simulate in detail the effects of various forces that the eye structure may be subjected to in real life. For example, the specific stress patterns generated by prolonged head-down reading or using electronic products, and the effects of light changes during outdoor activities on eye structures such as the cornea and retina. The model converts these factors into stress and deformation data of the eye structure, providing a visualized picture of the changes.

[0079] Step S443: Perform data mapping processing according to the reverse model. The mapping result is obtained by using the support vector regression algorithm to associate the cumulative effect data with the changes in the eye structure.

[0080] It can be understood that the support vector regression algorithm is suitable for processing data sets with complex non-linear relationships and can find the best fitting line in a high-dimensional space. In this embodiment, the support vector regression algorithm defines a loss function to minimize the error between the actual observed values and the predicted values, while trying to maintain the smoothness of the model and avoid overfitting. Specifically, first, the data of the changes in the eye structure output by the reverse model and the cumulative effect data are integrated and converted into a format suitable for support vector regression analysis. The support vector regression algorithm is used to train the data, and parameters such as the kernel function type, penalty parameter, and kernel coefficient are adjusted to adapt to the characteristics of the eye health data and optimize the prediction accuracy. Through the trained model, the cumulative effect data is mapped to specific changes in the eye structure, such as specific indicators such as corneal thinning or retinal deformation, to provide a quantitative association result. The support vector regression algorithm can provide highly accurate prediction results, which helps to accurately determine how different factors individually and jointly affect the eye structure.

[0081] Step S444: Perform data conversion processing according to the mapping result. The behavioral-environmental cumulative effect data is obtained by converting continuous variables into categorical data.

[0082] It can be understood that converting continuous variables into categorical data simplifies the decision-making process and makes eye health management measures easier to implement, especially in situations that require quick responses.

[0083] It should be noted that step S500 includes step S510, step S520, step S530, and step S540.

[0084] Step S510: Perform data standardization processing according to the historical health characteristic data and the behavioral-environmental cumulative effect data. The dimensionality of the two types of data is unified through min-max scaling, and parallel processing of heterogeneous data is performed based on the MapReduce framework to obtain a normalized data set.

[0085] In this process, each data point is rescaled to a specified range so that all data items have the same scale when being analyzed and compared. Such processing not only improves the consistency of data processing but also helps with statistical analysis and the application of machine learning models. Further, parallel processing of multi-source heterogeneous data is performed based on the MapReduce framework, including:

[0086] Step S511, Map phase: Preprocess data from different sources (such as health monitoring data, behavior data, environmental data, etc.), split it into multiple data segments (splits), and assign a mapping task to each data segment. Each mapping task maps the input data into the key-value pair form required for standardized processing. For example, health features are used as keys and numerical data as values. For multi-source heterogeneous data, the Map phase can also unify the data format, remove invalid data, or fill in missing data to ensure consistent data formats.

[0087] Step S512, Shuffle phase: Classify and organize the key-value pair data generated in the Map phase by key, so that data with the same health feature converges to the same Reducer node. This phase classifies similar data from different sources to ensure that health data, behavior data, and environmental data from different sources can be efficiently integrated and analyzed in the Reduce phase.

[0088] Step S513, Reduce phase: Perform standardized processing on each Reducer node, and use the min-max scaling algorithm to perform standardized calculations on health data from different sources. Specifically, the Reducer normalizes the values corresponding to each key (such as health features) to ensure that all health data, behavior data, and environmental data have a unified dimension, and finally outputs a standardized normalized data set.

[0089] Step S520, Perform modeling processing based on the normalized data set to construct a decision tree model;

[0090] It is understandable that this step constructs a decision tree model using a normalized dataset, specifically for the children's glasses health status monitoring project. The establishment of this model is based on decision tree algorithms such as CART (Classification and Regression Tree) or C4.5, which can automatically learn and identify key decision paths from the dataset. Each decision node represents an assessment of a specific variable (such as living environment, reading habits, etc.), and the path from the root node to the leaf node reflects a series of decision points until the final health status classification. By calculating information gain or Gini impurity to select the best splitting attribute, the decision tree precisely analyzes how different behavioral and environmental factors collectively affect children's eye health. The main advantage of this model lies in its high interpretability, enabling doctors and parents to easily understand the key factors influencing children's eye health and accordingly formulate preventive or intervention measures. In addition, the classification results of the decision tree directly support clinical decision-making, helping to implement personalized health management plans and improve the pertinence and timeliness of preventive measures.

[0091] Step S530: Perform cross-validation processing based on the decision tree model. Optimize the model by splitting the dataset into at least two groups for training and validation and using the K-fold cross-validation method to evaluate the generalization ability and stability of the model to obtain an optimized model.

[0092] It is understandable that cross-validation is a statistical analysis method used to evaluate and improve the performance of a model on unseen data. In this process, the entire dataset is split into at least two subsets, usually using the K-fold cross-validation method, where the dataset is divided into K subgroups. Each time, one group is set aside as the validation dataset, and the remaining K - 1 groups are used to train the model. By repeating training and testing on different data subsets, the model can demonstrate its stability and reliability under various data conditions, thereby improving its generalization ability.

[0093] Step S540: Perform risk assessment processing on the latest patient data in the historical health feature data according to the optimized model, and obtain potential risk data by predicting the development risk of myopia.

[0094] Specifically, the model compares the new patient data with the historical health feature data and uses the trained parameters to evaluate possible future health problems, such as the progression of myopia. Using the classification results of the decision tree model, it can identify which children are at high risk of developing myopia. The model analyzes the influence of various factors such as genetic factors, living habits, and learning environment. The optimized model can more accurately predict the specific impact of various factors on children's eye health, especially the risk assessment of myopia development.

[0095] It should be noted that step S600 includes step S610, step S620, step S630, and step S640.

[0096] Step S610: Perform modeling processing based on the three-dimensional eye feature data and risk data. Use machine learning algorithms to integrate the genetic factors and lifestyle data in the risk data with the three-dimensional eye structure data, and construct a digital twin model capable of dynamically simulating eye pathological changes.

[0097] It can be understood that in this step, a digital twin model is constructed using machine learning technology. This model integrates the three-dimensional eye structure data and risk data including genetic factors and lifestyle, and is used to dynamically simulate and predict the pathological changes of children's eyes. By analyzing in detail an individual's genetic background, environmental exposure, and behavioral habits, combined with fine three-dimensional eye imaging, the model can accurately predict pathological states such as myopia progression and corneal thickness changes. The model uses algorithms such as deep learning to identify and learn the complex patterns hidden in the health data, and can not only see the current state of eye diseases but also predict future change trends.

[0098] Step S620: Perform simulation processing based on the digital twin model. By simulating the potential changes of different risk factors on the eye structure, and at the same time simulating the long-term effects of different lifestyles and treatment plans on eye health, obtain a pathological development prediction model.

[0099] Specifically, in this step, simulation processing is performed through the digital twin model. These simulations examine the possible changes of various risk factors on children's eye structures, as well as the long-term effects of different lifestyles and treatment plans on eye health. This model integrates data such as genetic factors, behavioral habits, and environmental impacts, and uses machine learning technology to predict future pathological developments, such as the progression of myopia and changes in corneal diseases, providing a comprehensive method for evaluating and predicting children's eye health. The advantage of the digital twin model lies in its ability to simulate real-world medical scenarios and foresee the effects of different medical interventions. For example, by simulating different vision correction strategies, the specific impacts of these measures on children's vision can be predicted, and then the treatment plan can be optimized. This simulation is not only based on existing medical data but can also be updated in real time, continuously optimizing the prediction results as new data is added.

[0100] Step S630: Generate a health risk assessment based on the pathological development prediction model, and use the SHAP value to explain the decision-making process of the prediction model to obtain a health assessment report.

[0101] It is understandable that in this step, a risk assessment of children's eye health is carried out through a pathological development prediction model, and the SHAP value is used to explain the prediction decision-making process of the model in detail. Specifically, first, the pathological development prediction model combines multi-source data, including personal genetic information, living habits, and environmental factors. These data are analyzed through machine learning methods to identify and predict possible eye health problems. By learning the relationships between these data, the model predicts the likelihood of pathological states such as increased myopia. Subsequently, the SHAP value is used to explain the model decision. The SHAP value provides an intuitive way to understand the operation mechanism of the model by quantifying the contribution of each feature to the prediction result. This interpretability not only improves the transparency of the model but also enables doctors and parents to better understand the reasons for the prediction output. Through this method, the potential effects of various health intervention measures can be demonstrated and evaluated in detail, such as the long-term health impacts of changing living habits or medical interventions.

[0102] Step S640: Perform language conversion processing according to the health assessment report, and generate coherent natural language text from the structured data explained by the SHAP value through the use of natural language generation technology to obtain the assessment result.

[0103] This process involves extracting key information from the data and presenting it in an easy-to-understand language, enabling non-professionals to easily understand the model's decision-making process and results. Specifically, first, through natural language generation technology, the structured SHAP value data is parsed. These data detail how various variables affect the model's prediction results. Natural language generation technology can automatically generate text descriptions from these explanations, such as "Based on the patient's genetic background and current living habits, the model predicts an increased risk of myopia." This technology not only saves a large amount of time in writing reports but also ensures the accuracy and consistency of the information. This step significantly improves the acceptability and readability of the report, enabling doctors and patients to better understand and trust the model's output. In this way, complex data analysis results are transformed into concise and clear health advice, greatly facilitating the practical application and implementation of medical advice.

[0104] Embodiment 2:

[0105] This embodiment provides an apparatus for monitoring and evaluating the eye health status based on big data. The apparatus includes:

[0106] An acquisition module, configured to acquire first information, second information, and third information. The first information includes three-dimensional eye structure data of the child to be monitored. The second information includes electronic health records, which include ophthalmic examination records, treatment records, and family medical history. The third information includes daily behavior data and environmental data;

[0107] A reconstruction module, configured to perform three-dimensional reconstruction processing based on first information, and construct three-dimensional eye feature data by identifying retinal thickness, corneal curvature, and lens morphology. The three-dimensional eye feature data includes retinal thickness data, corneal curvature data, and lens morphology data;

[0108] An identification module, configured to perform pattern recognition processing based on second information, and identify patterns and trends related to specific eye diseases by performing correlation analysis on historical ophthalmic examination records, treatment records, and family medical history in electronic health records, to obtain historical health feature data;

[0109] An analysis module, configured to perform time series analysis based on the historical health feature data and third information, and obtain behavior-environment cumulative effect data by quantifying and calculating the cumulative effect of various behavior-environment combination scenarios on eye damage and mapping it to the transformation of the three-dimensional eye structure;

[0110] A fusion module, configured to perform data fusion processing based on the historical health feature data and the behavior-environment cumulative effect data, and train a prediction model using big data analysis and supervised learning algorithms, to obtain potential risk data by predicting potential risk factors for children's eye health;

[0111] An evaluation module, configured to perform evaluation processing based on the three-dimensional eye feature data and the risk data to obtain an evaluation result. The evaluation result includes the current monitoring result and future eye health management suggestions.

[0112] In a specific embodiment of the present disclosure, the reconstruction module includes:

[0113] A first processing unit, configured to perform voxelization processing based on first information, and obtain voxelized eye data by converting eye structure data into three-dimensional voxel data;

[0114] A first extraction unit, configured to perform retinal thickness extraction processing on the voxelized eye data based on the U-Net segmentation algorithm, and automatically segment the retinal region and calculate the thickness by combining the thickness variation characteristics of the child's retina at different growth stages to obtain retinal thickness data;

[0115] A first calculation unit, configured to perform corneal curvature calculation processing based on the voxelized eye data, and obtain corneal curvature data by capturing the curvature characteristics at different development stages through an adaptive filtering algorithm and performing surface fitting processing;

[0116] A second extraction unit, configured to perform lens morphology analysis processing on the voxelized eye data, and extract the main morphological characteristics of the lens to obtain lens morphology data.

[0117] In a specific embodiment of the present disclosure, the identification module includes:

[0118] The first integration unit is configured to perform data integration processing based on the second information to obtain comprehensive health data;

[0119] The third extraction unit is configured to perform feature extraction processing based on the comprehensive health data, and obtain health feature data by identifying key health features;

[0120] The first recognition unit is configured to perform pattern recognition processing based on the health feature data, and obtain disease pattern data by applying a Hidden Markov Model to identify patterns related to specific eye diseases;

[0121] The second recognition unit is configured to perform trend analysis processing based on the disease pattern data, and obtain historical health feature data by predicting the trend of the disease pattern to identify potential trends in the development of the disease.

[0122] It should be noted that for the devices in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0123] The above are only the preferred embodiments of the present invention, and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0124] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present invention, and all should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for monitoring and evaluating eye health status based on big data, characterized in that: include: Acquire first information, second information and third information, wherein the first information includes three-dimensional eye structure data of the child to be monitored, the second information includes electronic health records, the electronic health records include eye examination records, treatment records and family medical history, and the third information includes daily behavior data and environmental data; Performing three-dimensional reconstruction processing according to the first information, and obtaining three-dimensional eye feature data by identifying retinal thickness, corneal curvature and lens morphology, wherein the three-dimensional eye feature data includes retinal thickness data, corneal curvature data and lens morphology data; performing pattern recognition processing based on the second information, identifying patterns and trends related to specific eye diseases by performing correlation analysis on historical eye examination records, treatment records, and family medical history in the electronic health record, and obtaining historical health feature data; Performing a time series analysis based on the historical health characteristic data and the third information, by quantitatively calculating the cumulative effects of various behavior-environment combinations on eye damage and mapping them to the transformation of the three-dimensional structure of the eye, to obtain behavior-environment cumulative effect data; Performing data fusion processing based on the historical health characteristic data and the behavior-environment cumulative effect data, and training a prediction model using big data analysis and supervised learning algorithms to obtain potential risk data by predicting potential risk factors for children's eye health; Performing an evaluation process based on the three-dimensional eye feature data and the risk data to obtain an evaluation result, wherein the evaluation result includes a current monitoring result and future eye health management suggestions; Wherein, the time series analysis is performed based on the historical health characteristic data and the third information, and the cumulative effects of eye damage under various behavior-environment combination scenarios are quantitatively calculated and mapped to the transformation of the three-dimensional structure of the eye to obtain the behavior-environment cumulative effect data, including: Performing time series processing according to the historical health characteristic data and the third information to obtain time series data by eliminating noise and trends in the data; Performing scenario recognition processing according to the time series data, and obtaining behavior-environment data by using a dynamic time warping algorithm to identify and match typical behaviors and environmental scenarios in the time series data; Quantitative processing is performed according to the behavior-environment data, and cumulative effect data is obtained by applying a long short-term memory network to quantify the cumulative damage effects on the eyes under various behavior-environment combination scenarios; Performing data mapping processing according to the cumulative effect data, performing reverse modeling on the eye structure and mapping the cumulative effect data to specific eye structure changes, thereby obtaining behavior-environment cumulative effect data; Wherein, data mapping processing is performed according to the cumulative effect data, by reverse modeling the eye structure and mapping the cumulative effect data to specific eye structure changes, the behavior-environment cumulative effect data is obtained, including: Statistical analysis is performed on the cumulative effect data to quantify the specific effects of each behavior and environmental factor on the changes in eye structure by using multivariate regression analysis to obtain regression coefficients; Performing reverse modeling of the eye structure according to the regression coefficient, simulating the stress and deformation of the eye structure under different external influences through finite element analysis and showing the specific changes of the eye structure caused by different cumulative effects, thereby obtaining a reverse model; Performing data mapping processing according to the inverse model, and obtaining a mapping result by associating the cumulative effect data with changes in eye structure using a support vector regression algorithm; Data conversion processing is performed according to the mapping results, and the behavior-environment cumulative effect data are obtained by converting continuous variables into categorical data.

2. The eye health status monitoring and evaluation method based on big data according to claim 1 is characterized in that , performing three-dimensional reconstruction processing according to the first information, and obtaining three-dimensional eye feature data by identifying retinal thickness, corneal curvature and lens morphology, including: Performing voxel processing according to the first information, and obtaining voxelized eye data by converting the eye structure data into three-dimensional voxel data; Extracting and processing the retinal thickness of the voxelized eye data based on the U-Net segmentation algorithm, automatically segmenting the retinal area and calculating the thickness to obtain retinal thickness data by combining the thickness variation characteristics of the children's retina at different growth stages; Performing corneal curvature calculation processing based on the voxelized eye data, capturing curvature characteristics of different developmental stages through an adaptive filtering algorithm and performing surface fitting processing to obtain corneal curvature data; Lens morphology analysis is performed based on the voxelized eye data, and main morphological features of the lens are extracted to obtain lens morphology data.

3. The eye health status monitoring and evaluation method based on big data according to claim 1 is characterized in that , performing pattern recognition processing based on the second information, identifying patterns and trends related to specific eye diseases by performing correlation analysis on historical eye examination records, treatment records and family medical history in the electronic health record, and obtaining historical health feature data, including: Performing data integration processing according to the second information to obtain comprehensive health data; Performing feature extraction processing according to the comprehensive health data, and obtaining health feature data by identifying key health features; Performing pattern recognition processing on the health feature data, and obtaining disease pattern data by identifying patterns related to specific eye diseases based on a hidden Markov model; Trend analysis is performed on the disease pattern data, and historical health characteristic data is obtained by identifying the potential trend of disease development through trend prediction of the disease pattern.

4. The eye health status monitoring and evaluation method based on big data according to claim 1 is characterized in that , perform data fusion processing based on the historical health characteristic data and the behavior-environment cumulative effect data, and use big data analysis and supervised learning algorithms to train the prediction model, and obtain potential risk data by predicting potential risk factors for children's eye health, including: Performing data standardization processing on the historical health characteristic data and the behavior-environment cumulative effect data, unifying the dimensions of the two types of data through minimum and maximum scaling, and performing parallel processing on heterogeneous data based on a MapReduce framework to obtain a normalized data set; Performing modeling processing according to the normalized data set to construct a decision tree model; Performing cross-validation processing based on the decision tree model, splitting the data set into at least two groups for training and validation, and using a K-fold cross-validation method to evaluate the model to obtain generalization ability and stability to obtain an optimized model; The latest patient data in the historical health characteristic data is subjected to risk assessment processing according to the optimization model, and potential risk data is obtained by predicting the risk of myopia development.

5. The eye health status monitoring and evaluation method based on big data according to claim 1 is characterized in that , performing evaluation processing according to the three-dimensional eye feature data and the risk data to obtain an evaluation result, including: Modeling is performed based on the three-dimensional eye feature data and the risk data, and the genetic factors and lifestyle data in the risk data are integrated with the three-dimensional eye structure data using a machine learning algorithm to construct a digital twin model that can dynamically simulate eye pathological changes; Performing simulation processing according to the digital twin model, by simulating the potential changes of eye structure caused by different risk factors and simulating the long-term effects of different lifestyles and treatment plans on eye health, a pathology development prediction model is obtained; Generate a health risk assessment based on the pathology development prediction model, and use SHAP values ​​to explain the decision-making process of the prediction model to obtain a health assessment report; Language conversion processing is performed according to the health assessment report, and a coherent natural language text is generated from the structured data explained by the SHAP value by using natural language generation technology to obtain an assessment result.

6. A device for monitoring and evaluating eye health based on big data, characterized in that: include: an acquisition module, configured to acquire first information, second information, and third information, wherein the first information includes three-dimensional eye structure data of the child to be monitored, the second information includes electronic health records, the electronic health records include eye examination records, treatment records, and family medical history, and the third information includes daily behavior data and environmental data; a reconstruction module, configured to perform three-dimensional reconstruction processing according to the first information, and obtain three-dimensional eye feature data by identifying retinal thickness, corneal curvature and lens morphology, wherein the three-dimensional eye feature data includes retinal thickness data, corneal curvature data and lens morphology data; an identification module, configured to perform pattern recognition processing according to the second information, identify patterns and trends related to specific eye diseases by performing correlation analysis on historical eye examination records, treatment records and family medical history in the electronic health record, and obtain historical health feature data; An analysis module, configured to perform time series analysis based on the historical health feature data and the third information, and obtain behavior-environment cumulative effect data by quantitatively calculating the cumulative effects of various behavior-environment combination scenarios on eye damage and mapping them to the transformation of the three-dimensional structure of the eye; A fusion module, used to perform data fusion processing based on the historical health characteristic data and the behavior-environment cumulative effect data, and use big data analysis and supervised learning algorithms to train a prediction model to obtain potential risk data by predicting potential risk factors for children's eye health; An evaluation module, configured to perform evaluation processing based on the three-dimensional eye feature data and the risk data to obtain an evaluation result, wherein the evaluation result includes a current monitoring result and future eye health management suggestions; Wherein, the time series analysis is performed based on the historical health characteristic data and the third information, and the cumulative effects of eye damage under various behavior-environment combination scenarios are quantitatively calculated and mapped to the transformation of the three-dimensional structure of the eye to obtain the behavior-environment cumulative effect data, including: Performing time series processing according to the historical health characteristic data and the third information to obtain time series data by eliminating noise and trends in the data; Performing scenario recognition processing according to the time series data, and obtaining behavior-environment data by using a dynamic time warping algorithm to identify and match typical behaviors and environmental scenarios in the time series data; Quantitative processing is performed according to the behavior-environment data, and cumulative effect data is obtained by applying a long short-term memory network to quantify the cumulative damage effects on the eyes under various behavior-environment combination scenarios; Performing data mapping processing according to the cumulative effect data, performing reverse modeling on the eye structure and mapping the cumulative effect data to specific eye structure changes, thereby obtaining behavior-environment cumulative effect data; Wherein, data mapping processing is performed according to the cumulative effect data, by reverse modeling the eye structure and mapping the cumulative effect data to specific eye structure changes, the behavior-environment cumulative effect data is obtained, including: Statistical analysis is performed on the cumulative effect data to quantify the specific effects of each behavior and environmental factor on the changes in eye structure by using multivariate regression analysis to obtain regression coefficients; Performing reverse modeling of the eye structure according to the regression coefficient, simulating the stress and deformation of the eye structure under different external influences through finite element analysis and showing the specific changes of the eye structure caused by different cumulative effects, thereby obtaining a reverse model; Performing data mapping processing according to the inverse model, and obtaining a mapping result by associating the cumulative effect data with changes in eye structure using a support vector regression algorithm; Data conversion processing is performed according to the mapping results, and the behavior-environment cumulative effect data are obtained by converting continuous variables into categorical data.

7. The eye health status monitoring and evaluation device based on big data according to claim 6 is characterized in that , the reconstruction module includes: A first processing unit, configured to perform voxelization processing according to the first information, and obtain voxelized eye data by converting the eye structure data into three-dimensional voxel data; A first extraction unit extracts and processes the retinal thickness of the voxelized eye data based on a U-Net segmentation algorithm, automatically segments the retinal area and calculates the thickness to obtain retinal thickness data by combining the thickness variation characteristics of the children's retina at different growth stages; A first calculation unit is used to calculate and process corneal curvature according to the voxelized eye data, capture curvature characteristics of different development stages through an adaptive filtering algorithm, and perform surface fitting processing to obtain corneal curvature data; The second extraction unit is used to perform lens morphology analysis according to the voxelized eye data, and extract the main morphological features of the lens to obtain lens morphology data.

8. The eye health status monitoring and evaluation device based on big data according to claim 6 is characterized in that , the identification module includes: A first integration unit, configured to perform data integration processing according to the second information to obtain comprehensive health data; A third extraction unit is used to perform feature extraction processing according to the comprehensive health data, and obtain health feature data by identifying key health features; A first recognition unit is used to perform pattern recognition processing according to the health feature data, and obtain disease pattern data by identifying patterns related to specific eye diseases based on a hidden Markov model; The second identification unit is used to perform trend analysis processing based on the disease pattern data, and identify the potential trend of disease development by trend prediction on the disease pattern to obtain historical health characteristic data.

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