An intelligent monitoring system for postoperative rehabilitation of ophthalmic patients

Abstract

The present invention relates to the field of intelligent monitoring technology, specifically to an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients, the system comprising a structural association analysis module, an intraocular pressure trend module, a postoperative recovery assessment module, a drug prediction response module, a vision future recovery prediction module, an image reconstruction module, an image quality assessment module, and a comprehensive health assessment module. In the present invention, by applying a graph neural network algorithm to analyze the interaction of eye structures, time series analysis improves the accuracy of identifying the law of intraocular pressure changes and predicting future trends in the intraocular pressure trend module, deep belief networks and support vector machines realize personalized analysis in vision recovery assessment and drug response prediction, dynamic Bayesian networks and sparse coding algorithms improve the accuracy of prediction and image reconstruction quality in vision recovery prediction and eye image reconstruction, variational autoencoder technology enhances the accuracy of image quality assessment, and integrated learning strategies comprehensively evaluate the overall health status of patients.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring technology, and in particular to an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients. Background Art

[0002] The field of intelligent monitoring technology mainly involves the use of advanced computer technology, data analysis, artificial intelligence (AI) and the Internet of Things (IoT) to monitor, analyze and manage patients' health conditions. In the context of ophthalmic patient rehabilitation, this technology pays special attention to providing accurate and real-time health monitoring, as well as personalized rehabilitation guidance. By integrating a variety of sensors, such as wearable devices, to collect patients' physiological data (such as heart rate, blood sugar), as well as ophthalmology-specific real-time analysis of data, the monitoring intelligent system can promptly detect any abnormalities in the rehabilitation process, thereby initiating corresponding intervention measures. The key to this technology is that it can establish a continuous and dynamic health management bridge between doctors and patients, improving the efficiency and safety of rehabilitation.

[0003] Among them, the ophthalmic patient rehabilitation intelligent monitoring system is a health management tool designed specifically for ophthalmic patient rehabilitation. The core purpose of the system is to improve the efficiency and safety of patient rehabilitation and reduce the risk of spasms and complications. By continuously monitoring the patient's physiological parameters and eye health status, the rehabilitation process can be evaluated in real time, and timely medical advice or intervention can be provided when necessary. The system is designed to help patients conduct home rehabilitation more safely and effectively under the guidance of professional medical personnel, and also provides doctors with a platform for continuous monitoring of patients' health status to ensure the best implementation of treatment plans. The system achieves its functions by integrating multiple technical means, using various biosensors and wearable devices to collect patients' physiological data and eye health indicators in real time. The data is transmitted to a central processing unit, which is a computing platform with artificial intelligence analysis capabilities. The data is analyzed by advanced algorithms to identify any health problems or deviations in the rehabilitation process.

[0004] Traditional ophthalmic patient rehabilitation intelligent monitoring systems rely on relatively simple analysis tools and models in eye health assessment, which limits the ability to capture complex eye structure relationships and subtle changes. The lack of support for advanced algorithms leads to inaccurate eye structure analysis, inaccurate prediction of intraocular pressure trends, and inadequate assessment of vision recovery and drug response. These deficiencies lead to misjudgment of the patient's condition, affect the formulation and implementation of treatment plans, and thus delay recovery or lead to unnecessary medical interventions. The limitations of traditional systems in processing complex medical data and providing personalized medical advice have become their main shortcomings. Summary of the invention

[0005] This application provides an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients, which solves the problem that traditional intelligent monitoring systems for ophthalmic patient rehabilitation rely on relatively simple analysis tools and models in terms of eye health assessment, which limits the ability to capture complex eye structure relationships and subtle changes, lacks the support of advanced algorithms, and leads to inaccurate eye structure analysis, inaccurate prediction of intraocular pressure trends, and inadequate assessment of vision recovery and drug response. The inadequacy leads to misjudgment of the patient's condition, affects the formulation and implementation of treatment plans, and thus delays recovery or causes unnecessary medical interventions. The limitations of traditional systems in processing complex medical data and providing personalized medical advice have become its main shortcomings.

[0006] In view of the above problems, the present application provides an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients.

[0007] The present application provides an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients, wherein the system includes a structural correlation analysis module, an intraocular pressure trend module, a postoperative recovery assessment module, a drug prediction response module, a vision future recovery prediction module, an image reconstruction module, an image quality assessment module, and a comprehensive health assessment module;

[0008] The structural association analysis module is based on the eye structure data and adopts the graph neural network algorithm to analyze the mutual influence and connection characteristics between the eye structures, and to construct the association network between the structures by verifying the relationship strength and pattern, and output the association map to show the connection between the structures, thereby generating the eye structure association map;

[0009] The intraocular pressure trend module uses a time series analysis method based on the eye structure association map to perform trend analysis on the intraocular pressure data, identifies the regularity and variation points of intraocular pressure changes through the model, predicts the future trend of intraocular pressure, and generates intraocular pressure trend analysis results;

[0010] The postoperative recovery assessment module uses a deep belief network model to analyze and pattern recognize the patient's vision recovery data based on the intraocular pressure trend analysis results, evaluate the trend and degree of vision recovery, and perform a comprehensive score for vision recovery based on the analysis output results to generate a vision recovery probability score table;

[0011] The drug prediction response module uses a support vector machine method to analyze the patient's medical history and genetic information based on the visual rehabilitation probability score table, performs predictive analysis on drug response, identifies the drug result type that matches the patient, and generates drug response prediction results;

[0012] The future vision recovery prediction module uses a dynamic Bayesian network algorithm to perform probability modeling on the vision recovery process based on the drug response prediction results, predicts the future vision recovery trend with reference to the impact of multiple factors on the recovery progress, and predicts the output model to generate a vision recovery prediction optimization model;

[0013] The image reconstruction module reconstructs the eye damage image based on the visual restoration prediction optimization model and adopts a sparse coding algorithm, extracts key features of the eye image through the algorithm, reconstructs a complete eye image using the key features, and generates an eye image enhancement display image;

[0014] The image quality assessment module uses variational autoencoder technology based on the eye image enhancement display to perform in-depth analysis on the reconstructed image, evaluate the accuracy and gain of image restoration, and evaluate the degree of image restoration by comparing the original and reconstructed images. The image quality is quantitatively scored based on the analysis results to generate a comprehensive image quality score;

[0015] The comprehensive health assessment module uses an integrated learning strategy based on the comprehensive image quality score to comprehensively assess the patient's overall health status, analyze the patient's rehabilitation profile and health risks, and conduct a health assessment based on this to generate a health status visualization dashboard.

[0016] Preferably, the eye structure association map is specifically a network diagram of eye structure data, the intraocular pressure trend analysis result includes a trend line showing the change pattern of intraocular pressure over time and marked intraocular pressure variation points, the vision rehabilitation probability score table includes evaluation indicators for vision recovery speed, stability and multi-dimensional scoring, the drug response prediction result includes the patient's score for differentiated drug response and the type of drug, the visual recovery prediction optimization model includes probability estimates of objective factors affecting vision recovery and future trend predictions, the eye image enhancement display diagram includes extraction of key feature combinations and reconstructed complete images, the comprehensive image quality score includes comparative analysis of original and reconstructed images and quality grade classification, and the health status visualization dashboard includes a rehabilitation overview, risk assessment, and overall rating of health status.

[0017] Preferably, the structural association analysis module includes a structural data processing submodule, a graph network construction submodule, and a correlation graph output submodule;

[0018] The structural data processing submodule uses data processing technology based on the eye structure data to standardize the data, unify the original data format, perform data integrity checks, identify and process missing and abnormal values, and perform data conversion to convert the processed data into a format that matches subsequent analysis to generate standardized eye structure data;

[0019] The graph network construction submodule uses a graph convolutional network to extract node features based on the normalized eye structure data, extracts key features and constructs a graph structure by analyzing the attribute relationship between nodes, and generates an eye structure association network by defining nodes to express the relationship between eye structures.

[0020] The association map output submodule is based on the eye structure association network and adopts network visualization technology to perform graph layout design. It optimizes the map layout by determining the location of nodes and the path of edges, and performs color and size coding to enhance the readability and visual effect of the map, thereby generating an eye structure association map.

[0021] Preferably, the intraocular pressure trend module includes a trend modeling submodule, a pattern recognition submodule, and a trend report output submodule;

[0022] The trend modeling submodule uses a time series analysis method based on the eye structure association map to perform data serialization to convert the intraocular pressure data into a time series analysis format, and performs pattern analysis to identify time-dependent patterns and periodic changes in the intraocular pressure data to generate an intraocular pressure trend model;

[0023] The pattern recognition submodule uses an abnormality detection method based on the intraocular pressure trend model to identify variation points, detect abnormal changes in intraocular pressure by analyzing mutation points in the time series, and perform trend prediction, analyze future trends and change patterns in the intraocular pressure data, and generate intraocular pressure change prediction results;

[0024] The trend report output submodule is based on the intraocular pressure change prediction results, adopts a report generation algorithm, performs data integration, merges the intraocular pressure change prediction results with the analysis conclusions, and uses an automated layout algorithm to structure and format the report content to generate intraocular pressure trend analysis results.

[0025] Preferably, the postoperative recovery assessment module includes a data reconstruction submodule, a model training submodule, and a first scoring output submodule;

[0026] The data reconstruction submodule uses a layer neural network algorithm based on the intraocular pressure trend analysis results to gradually extract key features in the vision recovery data through multiple layers and activation functions, uses a pooling layer to reduce the data dimension and enhance the model's abstract ability, and performs data reconstruction operations to integrate and reconstruct key features to generate a visual data feature set;

[0027] The model training submodule is based on the visual data feature set, adopts the deep belief network algorithm, and uses the layered training strategy and layer-by-layer adjustment to enable the model to learn and recognize the pattern of vision restoration, perform model training, and generate a vision restoration recognition model;

[0028] The first scoring output submodule is based on a visual recovery recognition model and adopts a logistic regression algorithm to predict and analyze the degree of vision recovery through statistical methods, combines the probability generation distribution of patient data, evaluates the degree of vision recovery, and generates a vision recovery probability score table.

[0029] Preferably, the drug prediction response module includes a drug information integration submodule, a response analysis submodule, and a prediction report output submodule;

[0030] The drug information integration submodule is based on the visual rehabilitation probability score table and adopts data fusion technology to standardize the format and structure of multi-source medical history data through information unification processing, and performs data integration, and analyzes and integrates drug information to generate a drug prediction data set;

[0031] The response analysis submodule is based on the drug prediction data set and adopts the support vector machine algorithm to characterize the characteristics of the drug response data by constructing a feature space, divides the differentiated drug response categories by the principle of interval boundaries, performs drug response analysis, distinguishes and identifies drug effects, and generates a drug effect distinction map;

[0032] The prediction report output submodule is based on the drug effect differentiation map, adopts a classification decision tree algorithm, classifies differentiated drug types through a tree structure model, builds a decision path based on patient characteristics, predicts drug types, and generates drug response prediction results.

[0033] Preferably, the future vision recovery prediction module includes a Bayesian model configuration submodule, a trend prediction submodule, and a model output submodule;

[0034] The Bayesian model configuration submodule adopts a dynamic Bayesian network algorithm based on the drug response prediction results, designs a network structure to represent the time series relationship of the state of the vision recovery process, and establishes an overall probability model structure by configuring the conditional probability distribution between nodes to generate a Bayesian network configuration;

[0035] The trend prediction submodule is based on the Bayesian network configuration and uses the Bayesian reasoning method to perform probability analysis on the vision recovery process. By combining historical data, the probability of the differentiated recovery stage is calculated to generate the vision recovery probability analysis result;

[0036] The model output submodule is based on the results of the vision recovery probability analysis, uses data fusion and model optimization algorithms, integrates the prediction data and analysis results, improves the accuracy and reliability of the prediction by merging differentiated data sources through algorithms, and uses model optimization technology to adjust and improve model parameters to generate a vision recovery prediction optimization model.

[0037] Preferably, the image reconstruction module includes an image embedding submodule, a feature reconstruction submodule, and an image output submodule;

[0038] The image embedding submodule is based on a visual restoration prediction optimization model and adopts a sparse coding algorithm to represent the key features of the eye damage image by constructing a feature dictionary, and by using the dictionary to calculate the sparse representation of the image, encodes and extracts the key features of the eye image to generate an eye image feature embedding;

[0039] The feature reconstruction submodule is based on eye image feature embedding, adopts image reconstruction technology in sparse coding, combines image features by matching elements in the feature dictionary, uses the combined features to restore the complete structure of the damaged eye image, and generates an eye image structure restoration map;

[0040] The image output submodule is based on the eye image structure recovery map and adopts image integration and formatting technology to integrate and format the processed image data, and generates an eye image enhancement display map by obtaining subsequent analysis and diagnosis requirements of the image data.

[0041] Preferably, the image quality assessment module includes an image analysis submodule, a quality calculation submodule, and a second score output submodule;

[0042] The image analysis submodule is based on the eye image enhancement display diagram, adopts the variational autoencoder algorithm, compresses the image data through the encoder part, obtains the image feature representation, uses the decoder part to reconstruct the image from the features, captures the original image features and reconstructs the image, and generates the image feature analysis result;

[0043] The quality calculation submodule uses a pixel-level comparison analysis method based on the image feature analysis result to compare the original image and the reconstructed image at the pixel level, analyze the difference between the pixel values, evaluate the effect of image reconstruction and restoration, and generate an image quality calculation result;

[0044] The second scoring output submodule adopts a comprehensive scoring model based on the image quality calculation results, performs weighted processing on the data in the comparative analysis, comprehensively evaluates the quality level of the image according to the pixel contrast difference and the overall image quality standard, and generates a comprehensive image quality score.

[0045] Preferably, the comprehensive health assessment module includes a health data fusion submodule, an assessment and analysis submodule, and a report display submodule;

[0046] The health data fusion submodule uses a multidimensional data fusion algorithm based on comprehensive image quality scores, combines medical records, physiological data and individual characteristic information, integrates multi-source data in multiple dimensions through algorithms for analysis, and generates a comprehensive health data set;

[0047] The evaluation and analysis submodule uses statistical analysis methods based on the comprehensive health data set to conduct in-depth analysis of the integrated health data, identify health trends and potential risk factors, evaluate the health status of the patient, and generate health risk analysis results;

[0048] The report display submodule is based on the health risk analysis results and uses information visualization technology to convert the evaluation data into intuitive charts and graphs. By displaying the individual's health status and potential risks, it analyzes the user's understanding and application of information and generates a health status visualization dashboard.

[0049] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0050] The application of graph neural network algorithm in the structural association analysis module enables more accurate analysis of the mutual influence and connection characteristics between eye structures, thereby constructing a more refined structural association map. The use of time series analysis method in the intraocular pressure trend module improves the ability to identify the regularity of intraocular pressure changes and mutation points, and enhances the accuracy of predicting future trends. The application of deep belief network model and support vector machine method in vision recovery assessment and drug response prediction respectively provides patients with more personalized and detailed assessment and prediction. The application of dynamic Bayesian network algorithm and sparse coding algorithm in vision recovery prediction and image reconstruction enhances the accurate prediction of vision recovery trend and the reconstruction quality of damaged eye images. The use of variational autoencoder technology improves the accuracy assessment of image restoration in image quality assessment, and the integrated learning strategy realizes a comprehensive assessment of the overall health status of patients in comprehensive health assessment.

[0051] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A module diagram of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients is proposed for the present invention;

[0053] Figure 2 The present invention proposes a system framework diagram of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0054] Figure 3 The present invention proposes a specific flow chart of a structural correlation analysis module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0055] Figure 4The present invention proposes a specific flow chart of an intraocular pressure trend module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0056] Figure 5 The present invention proposes a specific flow chart of a postoperative recovery assessment module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0057] Figure 6 The present invention proposes a specific flow chart of a drug prediction response module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0058] Figure 7 The present invention proposes a specific flow chart of a future vision recovery prediction module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0059] Figure 8 The present invention proposes a specific flow chart of an image reconstruction module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0060] Fig. 9 The present invention proposes a specific flow chart of an image quality assessment module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients;

[0061] Fig.10 The present invention proposes a specific flow chart of a comprehensive health assessment module of an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients. DETAILED DESCRIPTION

[0062] The present application provides an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients.

[0063] Application Overview

[0064] The existing technology of traditional ophthalmology patient rehabilitation intelligent monitoring system relies on relatively simple analysis tools and models in eye health assessment, which limits the ability to capture complex eye structure relationships and subtle changes. The lack of support for advanced algorithms leads to inaccurate eye structure analysis, inaccurate prediction of intraocular pressure trends, and inadequate assessment of vision recovery and drug response. The inadequacy leads to misjudgment of the patient's condition, affects the formulation and implementation of treatment plans, and thus delays recovery or leads to unnecessary medical intervention. The limitations of traditional systems in processing complex medical data and providing personalized medical advice have become their main shortcomings.

[0065] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows:

[0066] like Figure 1 , 2As shown, the present application provides an intelligent monitoring system for postoperative rehabilitation of ophthalmic patients, wherein the system includes a structural correlation analysis module, an intraocular pressure trend module, a postoperative recovery assessment module, a drug prediction response module, a future vision recovery prediction module, an image reconstruction module, an image quality assessment module, and a comprehensive health assessment module;

[0067] The structural association analysis module is based on eye structure data and adopts a graph neural network algorithm to analyze the mutual influence and connection characteristics between eye structures, and to build an association network between structures by verifying the relationship strength and pattern. It outputs an association map to show the connection between structures and generates an eye structure association map.

[0068] The intraocular pressure trend module uses time series analysis methods based on the eye structure association map to perform trend analysis on intraocular pressure data. It uses the model to identify the regularity and variation points of intraocular pressure changes, predict the future trend of intraocular pressure, and generate intraocular pressure trend analysis results.

[0069] The postoperative recovery assessment module uses a deep belief network model to analyze and pattern-recognize the patient's vision recovery data based on the intraocular pressure trend analysis results, evaluate the trend and degree of vision recovery, and make a comprehensive score of vision recovery based on the analysis output results to generate a vision recovery probability score table;

[0070] The drug prediction response module uses the support vector machine method to analyze the patient's medical history and genetic information based on the visual rehabilitation probability score table, conducts predictive analysis on drug response, identifies the drug result type that matches the patient, and generates drug response prediction results;

[0071] The future vision recovery prediction module uses a dynamic Bayesian network algorithm to model the vision recovery process based on the drug response prediction results, and predicts the future vision recovery trend by referring to the impact of multiple factors on the recovery progress. It also predicts the output model and generates a visual recovery prediction optimization model.

[0072] The image reconstruction module is based on the visual restoration prediction optimization model and adopts a sparse coding algorithm to reconstruct the eye damage image. The algorithm extracts the key features of the eye image, reconstructs the complete eye image using the key features, and generates an enhanced display of the eye image.

[0073] The image quality assessment module uses variational autoencoder technology to conduct in-depth analysis of the reconstructed image based on the eye image enhancement display diagram, evaluate the accuracy and gain of image restoration, and evaluate the degree of image restoration by comparing the original and reconstructed images. The image quality is quantitatively scored based on the analysis results to generate a comprehensive image quality score.

[0074] The comprehensive health assessment module uses an integrated learning strategy based on the comprehensive image quality score to comprehensively evaluate the patient's overall health status, analyze the patient's recovery profile and health risks, and conduct a health assessment based on this to generate a health status visualization dashboard.

[0075] The eye structure association map is specifically a network diagram of eye structure data. The intraocular pressure trend analysis results include trend lines showing the changes in intraocular pressure over time and marked intraocular pressure variation points. The vision recovery probability score table includes indicators for evaluating the speed and stability of vision recovery and multi-dimensional scoring. The drug response prediction results include the scores of patients for differentiated drug responses and the types of drugs. The visual recovery prediction optimization model includes probability estimates of objective factors affecting vision recovery and future trend predictions. The eye image enhancement display diagram includes the extraction of key feature combinations and the reconstruction of the complete image. The comprehensive image quality score includes comparative analysis of the original and reconstructed images and quality grade classification. The health status visualization dashboard includes a rehabilitation overview, risk assessment, and an overall rating of health status.

[0076] Specifically, if Figure 2 , 3 As shown, the structural association analysis module includes a structural data processing submodule, a graph network construction submodule, and a correlation graph output submodule;

[0077] The structural data processing submodule uses data processing technology based on the eye structure data to standardize the data, unify the original data format, and perform data integrity checks, identify and process missing and outliers, and perform data conversion to convert the processed data into a format that matches subsequent analysis. The execution process for generating standardized eye structure data is as follows;

[0078] The structural data processing submodule is based on the eye structure data. First, the raw data is formatted, including unifying all timestamps to UTC standard time and converting various measurement units to international standard units. The formatting formula is used. ,in Represents the original data, Represents the conversion function, performs integrity check on the data, and uses the data integrity index ,in is the number of non-null data points, is the total number of data points to ensure data quality. For missing values, linear interpolation based on surrounding points is used to fill them, and outliers are identified and processed by comparing them with three times the standard deviation of neighboring values. The processed data is converted according to the requirements of the analysis module, such as discretizing continuous data to ensure that the data format is compatible with subsequent analysis modules.

[0079] The graph network construction submodule uses a graph convolutional network to extract node features based on the normalized eye structure data, extracts key features by analyzing the attribute relationship between nodes, constructs a graph structure, and defines nodes to express the relationship between eye structures. The execution process of generating an eye structure association network is as follows;

[0080] The graph network construction submodule is based on the normalized eye structure data. First, each node is defined to represent a different part of the eye structure, such as the cornea, retina, etc. By extracting the features of each node such as shape, size and relative position, the vector processing formula Vi=g(Si, Pi) is used, where Si represents the structural attribute, Pi represents the position attribute, and g is the feature extraction function. The relationship between nodes is further analyzed, such as adjacent relationships and functional connections, by calculating the relationship strength Rij=h(Vi,Vj) between nodes, where h is the relationship metric function. Relationships are used to construct a graph structure, in which nodes represent eye structures and edges represent interactions between structures. This structured graph model can clearly depict the relationship network between the components of the eye, providing a basis for subsequent analysis.

[0081] The association map output submodule is based on the eye structure association network and uses network visualization technology to design the graph layout. It optimizes the map layout by determining the location of nodes and the path of edges, and performs color and size coding to enhance the readability and visual effect of the map. The execution process of generating the eye structure association map is as follows;

[0082] The association graph output submodule is based on the eye structure association network. First, the force-directed algorithm is used for graph layout design. The interaction force between nodes is balanced by simulating the physical spring force. The layout optimization formula is used ,in is the spring constant, Is a node and The actual distance between The color and size coding of nodes are set according to their structural importance and functionality. The color depth indicates the activity of the structure, and the size indicates its importance in the eye structure. The layout of the atlas not only optimizes the visual effect, but also improves the readability and educational significance of the atlas information, making it easier for professionals and patients to better understand the complex relationship of the eye structure.

[0083] Specifically, if Figure 2 , 4 As shown, the intraocular pressure trend module includes a trend modeling submodule, a pattern recognition submodule, and a trend report output submodule;

[0084] The trend modeling submodule uses the time series analysis method based on the eye structure association map to perform data serialization to convert the intraocular pressure data into the format of time series analysis, and performs pattern analysis to identify the time-dependent patterns and periodic changes in the intraocular pressure data. The execution process of generating the intraocular pressure trend model is as follows;

[0085] The trend modeling submodule is based on the eye structure association map. First, the intraocular pressure data is time-stamped to record the specific time point of each measurement to ensure the time accuracy of the data. The intraocular pressure data is standardized to eliminate the deviation under different measurement conditions, such as adjusting the influence of temperature and air pressure, using the formula ,in is the measured value, and are the mean and standard deviation of the data set, respectively. After this step is completed, the sliding window method is used to extract the feature values ​​in each period, the average intraocular pressure and standard deviation of each window are calculated, and the periodic changes and trends are identified through statistical data. Finally, combined with the information of the eye structure association map, a linear regression model is constructed to predict the future intraocular pressure trend. The specific form of the model is ,in is the time variable, and is the regression coefficient.

[0086] The pattern recognition submodule uses anomaly detection method based on the intraocular pressure trend model to identify mutation points, detect abnormal changes in intraocular pressure by analyzing mutation points in the time series, and perform trend prediction, analyze future trends and change patterns in intraocular pressure data, and generate intraocular pressure change prediction results. The execution process is as follows;

[0087] The pattern recognition submodule is based on the intraocular pressure trend model. The initial step is to calculate the sliding standard deviation of each data point using the formula ,in is the window size, is the intraocular pressure value within the window, It is the average intraocular pressure within the window. Data points that are higher or lower than two standard deviations can be quickly located and are considered potential abnormal points. Cluster analysis is performed on the abnormal points to determine the type of abnormality (for example, a sudden increase or decrease in pressure). The center and distribution range of each cluster are used to update the intraocular pressure trend model to enhance its ability to predict future abnormal events. The data is used to predict the future trend of intraocular pressure by comparing intraocular pressure data for consecutive days to determine whether there is a long-term upward or downward trend.

[0088] The trend report output submodule uses a report generation algorithm to integrate data based on the intraocular pressure change prediction results, merges the intraocular pressure change prediction results with the analysis conclusions, and uses an automated layout algorithm to structure and format the report content. The execution process for generating intraocular pressure trend analysis results is as follows;

[0089] Based on the prediction results of intraocular pressure changes, the trend report output submodule first compares all the predicted data with the historical data, marks all the significant trend change points, and records the specific date and value of the points. Through marking, a detailed report is generated, including a detailed explanation and cause analysis of each marked point. In order to make the report easy to understand, each change point is accompanied by a chart showing the trend of intraocular pressure changes from the past to the future. The last part of the report includes a comprehensive trend forecast, using a graphical timeline to show the expected intraocular pressure changes in the next week. All information is automatically sent to doctors and patients in the form of emails to ensure that the latest intraocular pressure conditions can be obtained immediately.

[0090] Specifically, if Figure 2 , 5 As shown, the postoperative recovery assessment module includes a data reconstruction submodule, a model training submodule, and a first scoring output submodule;

[0091] The data reconstruction submodule uses a layer neural network algorithm based on the intraocular pressure trend analysis results to gradually extract key features from the vision recovery data through multiple layers and activation functions, uses a pooling layer to reduce the data dimension and enhance the model's abstract ability, and performs data reconstruction operations to integrate and reconstruct key features. The execution process of generating a visual data feature set is as follows;

[0092] The data reconstruction submodule performs feature extraction of vision restoration data based on the results of intraocular pressure trend analysis, and defines the vision restoration dataset D, where D = {d1, d2, ..., dn}, where di represents the vision data of the i-th data point. By configuring multiple processing layers, key features F are gradually analyzed and extracted, where F = {f1, f2, ..., fm}, where fi represents the i-th key feature. Dimensionality reduction techniques such as pooling operations are applied to reduce the dimension of the data and enhance the abstraction ability of the model. The key features are integrated and reconstructed to generate a visual data feature set V, which shows the integrated key features of vision restoration.

[0093] The model training submodule is based on the visual data feature set and adopts the deep belief network algorithm. Through the layered training strategy and layer-by-layer adjustment, the model is trained and the execution process of generating the visual restoration recognition model is as follows;

[0094] The model training submodule learns and recognizes vision recovery patterns based on the visual data feature set V. Define the training strategy, and gradually improve the model's ability to recognize vision recovery patterns through layer-by-layer adjustment and optimization. Set the parameters and conditions for model training to ensure that each layer of training can effectively extract and learn the features of vision data. Based on the training results, generate a visual recovery recognition model M that can recognize and predict different stages and conditions of vision recovery.

[0095] The first scoring output submodule is based on the visual recovery recognition model, adopts the logistic regression algorithm, uses statistical methods to predict and analyze the degree of visual recovery, combines the probability generation distribution of patient data, evaluates the degree of visual recovery, and generates the visual recovery probability score table. The execution process is as follows;

[0096] The first scoring output submodule predicts and analyzes the degree of vision recovery based on the visual recovery recognition model M, defines the scoring logic, and analyzes the degree of vision recovery through statistical methods based on the patient's vision recovery data and the output of the model M. The probability distribution P of vision recovery is calculated, P = {p1, p2, ..., pk}, where pi represents the probability of recovery to the i-th level of vision. Combining the probabilities, a vision recovery probability score table S is generated, which shows in detail the predicted score and probability distribution of the patient's vision recovery.

[0097] Specifically, if Figure 2 , 6 As shown, the drug prediction response module includes a drug information integration submodule, a response analysis submodule, and a prediction report output submodule;

[0098] The drug information integration submodule is based on the visual rehabilitation probability score table and uses data fusion technology to standardize the format and structure of multi-source medical history data through information unification processing, perform data integration, and analyze and integrate drug information to generate the drug prediction data set. The execution process is as follows;

[0099] The drug information integration submodule is based on the visual rehabilitation probability score table. It first unifies the time stamps of the data collected from different medical databases to ensure that all data points are recorded in the same time format (such as YYYY-MM-DD). Then, it uses predefined conversion rules to unify the drug names and dosage units, such as converting all dosage units to milligrams or micrograms. Use the conversion formula ,in, Represents the original data, is a unit mapping function. Next, the data integrity is verified and missing values ​​are filled. The missing values ​​are estimated based on adjacent data points through interpolation methods to ensure the integrity and consistency of the data set. All processed data items are integrated to form a drug prediction data set in a unified format, which is convenient for subsequent analysis and model training.

[0100] The response analysis submodule is based on the drug prediction data set and adopts the support vector machine algorithm to characterize the characteristics of drug response data by constructing a feature space. It uses the principle of interval boundaries to divide differentiated drug response categories, conduct drug response analysis, and distinguish and identify drug effects. The execution process of generating a drug effect distinction map is as follows;

[0101] The response analysis submodule is based on the drug prediction data set. It first extracts features from the drug response data to identify key variables that affect drug effects, such as dosage, medication frequency, and patient age. The variables are converted into feature vectors and standardized using the formula ,in is the eigenvector, and They are the mean and standard deviation respectively. The feature vector is analyzed, and the classification boundary of drug response is determined using the interval analysis method. The parameters of the classification model are optimized based on the maximum interval principle to ensure the best distinction between different categories. Detailed drug effect distinction maps can be generated to clearly show the differences in the effects of different drugs and doses on different patient groups.

[0102] The prediction report output submodule uses a classification decision tree algorithm based on the drug effect differentiation map to classify differentiated drug types through a tree structure model, builds a decision path based on patient characteristics, predicts drug types, and generates drug response prediction results. The execution process is as follows;

[0103] The prediction report output submodule is based on the drug effect classification map. First, a decision tree model is built based on the results of drug effect classification. A judgment condition is defined for each decision node, such as the patient's age boundary, the drug dosage threshold, etc. ,,in represents a decision node, is the splitting condition. The tree structure is optimized through recursive segmentation technology to ensure that each terminal node (leaf node) has the highest prediction accuracy. Finally, the decision tree model is used to predict the drug response type of new patients and generate a detailed drug response prediction report, which includes the prediction results and their credibility scores, to provide doctors with accurate treatment recommendations.

[0104] Specifically, if Figure 2 , 7 As shown, the future vision recovery prediction module includes a Bayesian model configuration submodule, a trend prediction submodule, and a model output submodule;

[0105] The Bayesian model configuration submodule uses a dynamic Bayesian network algorithm based on the drug response prediction results, designs a network structure to represent the time series relationship of the state of the vision recovery process, and configures the conditional probability distribution between nodes to establish an overall probability model structure. The execution process of generating the Bayesian network configuration is as follows;

[0106] The Bayesian model configuration submodule models the vision recovery process based on the drug response prediction results. First, the time series data set T of the vision recovery process state is defined, T = {t1, t2, ..., tn}, where ti represents the vision state at the i-th time point. The network structure is designed to represent the time series relationship between states, and the conditional probability distribution of each node is configured. By setting the network nodes and edges, the overall probability model structure P is established, which can describe the probabilistic dependency between vision recovery states. The structure and probability data are integrated to generate a Bayesian network configuration diagram, which shows the dynamic probabilistic connection between the states in the vision recovery process.

[0107] The trend prediction submodule is based on the Bayesian network configuration and uses the Bayesian reasoning method to perform probability analysis on the vision recovery process. By combining historical data, the probability of the differentiated recovery stage is calculated and the execution process of generating the vision recovery probability analysis result is as follows;

[0108] The trend prediction submodule performs probability analysis of the vision recovery process based on the Bayesian network configuration, using the Bayesian reasoning method and combining the historical data set H, H = {h1, h2, ..., hm}, where hi represents the vision recovery data at the i-th time point in history. The Bayesian network is used to analyze the probability of each stage of vision recovery Q, Q = {q1, q2, ..., qn}, where qi represents the probability of recovery to the i-th stage, and generates a vision recovery probability analysis result graph to show the probability distribution and trend prediction of different recovery stages.

[0109] The model output submodule is based on the results of the visual recovery probability analysis, uses data fusion and model optimization algorithms to integrate the prediction data and analysis results, and improves the accuracy and reliability of the prediction by merging differentiated data sources through algorithms. It also uses model optimization technology to adjust and improve model parameters to generate the visual recovery prediction optimization model. The execution process is as follows;

[0110] The model output submodule performs model optimization based on the results of the visual restoration probability analysis. First, the prediction data set F and the analysis result set A are defined, F = {f1, f2, ..., fk}, A = {a1, a2, ..., al}, where fi and ai represent the data points in the prediction and analysis, respectively. Data fusion technology is used to integrate data to improve the accuracy and reliability of prediction. Model optimization technology is used to adjust and improve model parameters to enhance the performance of the model. After integration and optimization, a visual restoration prediction optimization model diagram is generated, which describes the optimized model structure and prediction performance in detail.

[0111] Specifically, if Figure 2 , 8 As shown, the image reconstruction module includes an image embedding submodule, a feature reconstruction submodule, and an image output submodule;

[0112] The image embedding submodule is based on the visual restoration prediction optimization model and adopts the sparse coding algorithm. It constructs a feature dictionary to represent the key features of the eye damage image, and uses the dictionary to calculate the sparse representation of the image to encode and extract the key features of the eye image. The execution process of generating the eye image feature embedding is as follows;

[0113] The image embedding submodule is based on the visual restoration prediction optimization model. It starts by collecting multiple eye injury images and preprocessing the images, including grayscale conversion and noise filtering. Then, a feature dictionary is designed, which consists of multiple basic image elements, each of which represents a typical eye injury feature, such as a ruptured blood vessel or an inflammatory area. The dictionary learning method is used to automatically extract elements from a large number of preprocessed images to ensure the broad representativeness and restoration ability of the dictionary. Then, sparse coding technology is applied to each target image to solve the optimization problem , where y is the image data, D is the feature dictionary, x is the sparse coefficient matrix, and λ is the regularization parameter, which realizes the sparse representation of the image. Finally, the obtained sparse coefficients are used to reconstruct the key features in the image and effectively extract the damage features.

[0114] The feature reconstruction submodule is based on the embedding of eye image features and uses the image reconstruction technology in sparse coding. It combines the image features by matching the elements in the feature dictionary and uses the combined features to restore the complete structure of the damaged eye image. The execution process of generating the eye image structure restoration map is as follows;

[0115] The feature reconstruction submodule is based on the embedding of eye image features. In the initial stage, the feature coefficients obtained by sparse coding are combined with the feature dictionary, and each feature element in the dictionary is weighted. The contribution of each feature is determined according to the size corresponding to the sparse coefficient x. By linearly combining the dictionary elements, according to r=Dx, where r is the reconstructed image, D is the feature dictionary, and x is the feature coefficient, the complete structural reconstruction of the image is achieved, ensuring the accurate reproduction of each image feature in the reconstructed image, so that the structure of the damaged area is accurately depicted. By comparing the original image and the reconstructed image, the dictionary elements are further adjusted to optimize the reconstruction quality, ensuring the retention of details and accurate mapping of damage features during the image reconstruction process.

[0116] The image output submodule is based on the eye image structure recovery map and uses image integration and formatting technology to integrate and format the processed image data. By obtaining the subsequent analysis and diagnosis requirements of the image data, the execution process of generating the eye image enhancement display map is as follows;

[0117] The image output submodule is based on the eye image structure recovery map. It first integrates multiple reconstructed image data and uses advanced image processing technology to perform color correction and contrast enhancement on the integrated image data to ensure the best visual effect of the image. ,in, is a single reconstructed image, is a weighting factor, which is adjusted according to the quality and information content of the image. The integrated image is formatted, such as adjusting the resolution and compression ratio to meet the display requirements of different devices. The generated eye image enhancement display will provide detailed visual information in a format suitable for doctors and patients to view, facilitating further analysis and diagnosis.

[0118] Specifically, if Figure 2 , 9 As shown, the image quality assessment module includes an image analysis submodule, a quality calculation submodule, and a second score output submodule;

[0119] The image parsing submodule is based on the eye image enhancement display diagram and adopts the variational autoencoder algorithm. The encoder part compresses the image data to obtain the image feature representation, and the decoder part is used to reconstruct the image from the features. The original image features are captured and reconstructed. The execution process of generating the image feature parsing result is as follows;

[0120] The image parsing submodule performs feature extraction and image reconstruction based on the eye image enhancement display map. First, the eye image dataset I is defined, I = {i1, i2, ..., in}, where each i represents an image instance. The encoder part extracts features from each image and compresses the image data to obtain its feature representation F, F = {f1, f2, ..., fn}, where f represents the compressed image features. The decoder part is used to reconstruct the image from the features and generate a reconstructed image set R, R = {r1, r2, ..., rn}. This process aims to capture and reconstruct the key features of the original image and generate an image feature parsing result map to show the comparison between the original and reconstructed images.

[0121] The quality calculation submodule uses a pixel-level comparison analysis method based on the image feature analysis results to compare the original image and the reconstructed image at the pixel level, analyze the difference between the pixel values, evaluate the effect of image reconstruction and restoration, and generate the image quality calculation results. The execution process is as follows;

[0122] The quality calculation submodule performs quality assessment based on the image feature analysis results. Each original image i is compared with the corresponding reconstructed image r through pixel-level comparison and analysis. The pixel value difference D between the two is calculated, where D = {d1, d2, ..., dn}, where d represents the difference value between the two corresponding images. By analyzing the difference, the restoration effect of the image reconstruction is evaluated. An image quality calculation result graph is generated, which describes in detail the pixel-level comparison and difference analysis of each pair of images.

[0123] The second scoring output submodule uses a comprehensive scoring model based on the image quality calculation results, performs weighted processing on the data in the comparative analysis, and comprehensively evaluates the image quality level according to the pixel contrast difference and the overall image quality standard. The execution process of generating a comprehensive image quality score is as follows;

[0124] The second scoring output submodule performs a comprehensive scoring based on the image quality calculation results, defines a scoring model, combines the pixel contrast difference D with the overall image quality standard Q, Q = {q1, q2, ..., qn}, performs weighted processing on each image, and calculates the comprehensive quality level L of each image, L = {l1, l2, ..., ln}, where each l represents the quality level of an image. Through the comprehensive evaluation model, a comprehensive image quality scoring graph is generated to show the quality level of each image and its calculation process.

[0125] Specifically, if Figure 2 , 10 As shown, the comprehensive health assessment module includes a health data fusion submodule, an assessment and analysis submodule, and a report display submodule;

[0126] The health data fusion submodule is based on the comprehensive image quality score, adopts a multidimensional data fusion algorithm, combines medical records, physiological data and individual characteristic information, integrates multi-source data in multiple dimensions through the algorithm for analysis, and generates the execution process of the health comprehensive data set as follows;

[0127] The health data fusion submodule performs multidimensional data fusion based on the comprehensive image quality score and defines health data sets from different sources: medical records M, physiological data P and individual feature information F, M = {m1, m2, ..., mn}, P = {p1, p2, ..., pn}, F = {f1, f2, ..., fn}. Through the multidimensional data fusion algorithm, the data are integrated in multiple dimensions to generate a comprehensive health data set H, H = {h1, h2, ..., hn}, where hi represents each data point after fusion. The process aims to provide a comprehensive view of health data and generate a health comprehensive data set result diagram to show the results and methods of data fusion.

[0128] The evaluation and analysis submodule uses statistical analysis methods based on the comprehensive health data set to conduct in-depth analysis of the integrated health data, identify health trends and potential risk factors, evaluate the health status of patients, and generate health risk analysis results. The execution process is as follows;

[0129] The assessment and analysis submodule identifies health trends and potential risk factors based on the comprehensive health data set H, applies statistical analysis methods, deeply analyzes the comprehensive data, identifies key health trends and risk factors, assesses the patient's overall health status, calculates the health risk assessment value R, R = {r1, r2, ..., rn}, where ri represents the health risk assessment result of patient i, generates a health risk analysis result graph, and provides the health status and risk level of each patient.

[0130] The report display submodule is based on the health risk analysis results and uses information visualization technology to convert the evaluation data into intuitive charts and graphs. By displaying the individual's health status and potential risks, it analyzes the user's understanding and application of information, and generates the health status visualization dashboard. The execution process is as follows;

[0131] The report display submodule uses information visualization technology to display data based on the health risk analysis results, defines the visualization parameter set V, V = {v1, v2, ..., vn}, selects appropriate charts and graph types to represent health data and risk analysis results, and enables patients and medical professionals to intuitively understand the individual's health status and potential risks through graphical representation. It generates a health status visualization dashboard that displays detailed health assessments and risk levels, making it easier for users to understand and apply information.

[0132] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. An intelligent monitoring system for postoperative rehabilitation of ophthalmic patients, characterized in that: The system includes a structural correlation analysis module, an intraocular pressure trend module, a postoperative recovery assessment module, a drug prediction response module, a vision future recovery prediction module, an image reconstruction module, an image quality assessment module, and a comprehensive health assessment module; The structural association analysis module is based on the eye structure data and adopts the graph neural network algorithm to analyze the mutual influence and connection characteristics between the eye structures, and to construct the association network between the structures by verifying the relationship strength and pattern, and output the association map to show the connection between the structures, thereby generating the eye structure association map; The intraocular pressure trend module uses a time series analysis method based on the eye structure association map to perform trend analysis on the intraocular pressure data, identifies the regularity and variation points of intraocular pressure changes through the model, predicts the future trend of intraocular pressure, and generates intraocular pressure trend analysis results; The postoperative recovery assessment module uses a deep belief network model to analyze and pattern recognize the patient's vision recovery data based on the intraocular pressure trend analysis results, evaluate the trend and degree of vision recovery, and perform a comprehensive score for vision recovery based on the analysis output results to generate a vision recovery probability score table; The drug prediction response module uses a support vector machine method to analyze the patient's medical history and genetic information based on the visual rehabilitation probability score table, performs predictive analysis on drug response, identifies the drug result type that matches the patient, and generates drug response prediction results; The future vision recovery prediction module uses a dynamic Bayesian network algorithm to perform probability modeling on the vision recovery process based on the drug response prediction results, predicts the future vision recovery trend with reference to the impact of multiple factors on the recovery progress, and predicts the output model to generate a vision recovery prediction optimization model; The image reconstruction module reconstructs the eye damage image based on the visual restoration prediction optimization model and adopts a sparse coding algorithm, extracts key features of the eye image through the algorithm, reconstructs a complete eye image using the key features, and generates an eye image enhancement display image; The image quality assessment module uses variational autoencoder technology based on the eye image enhancement display to perform in-depth analysis on the reconstructed image, evaluate the accuracy and gain of image restoration, and evaluate the degree of image restoration by comparing the original and reconstructed images. The image quality is quantitatively scored based on the analysis results to generate a comprehensive image quality score; The comprehensive health assessment module uses an integrated learning strategy based on the comprehensive image quality score to comprehensively assess the patient's overall health status, analyze the patient's rehabilitation profile and health risks, and conduct a health assessment based on this to generate a health status visualization dashboard.

2. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The eye structure association map is specifically a network diagram of eye structure data, the intraocular pressure trend analysis result includes a trend line showing the change pattern of intraocular pressure over time and the marked intraocular pressure variation points, the vision rehabilitation probability score table includes evaluation indicators for vision recovery speed, stability and multi-dimensional scoring, the drug response prediction result includes the patient's score for differentiated drug response and the type of drug, the visual recovery prediction optimization model includes probability estimation and future trend prediction of objective factors affecting vision recovery, the eye image enhancement display diagram includes extraction of key feature combinations and reconstruction of the complete image, the comprehensive image quality score includes comparative analysis of original and reconstructed images and quality grade classification, and the health status visualization dashboard includes rehabilitation overview, risk assessment and overall rating of health status.

3. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The structural association analysis module includes a structural data processing submodule, a graph network construction submodule, and a correlation graph output submodule; The structural data processing submodule uses data processing technology based on the eye structure data to standardize the data, unify the original data format, perform data integrity checks, identify and process missing and abnormal values, and perform data conversion to convert the processed data into a format that matches subsequent analysis to generate standardized eye structure data; The graph network construction submodule uses a graph convolutional network to extract node features based on the normalized eye structure data, extracts key features and constructs a graph structure by analyzing the attribute relationship between nodes, and generates an eye structure association network by defining nodes to express the relationship between eye structures. The association map output submodule is based on the eye structure association network and adopts network visualization technology to perform graph layout design. It optimizes the map layout by determining the location of nodes and the path of edges, and performs color and size coding to enhance the readability and visual effect of the map, thereby generating an eye structure association map.

4. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The intraocular pressure trend module includes a trend modeling submodule, a pattern recognition submodule, and a trend report output submodule; The trend modeling submodule uses a time series analysis method based on the eye structure association map to perform data serialization to convert the intraocular pressure data into a time series analysis format, and performs pattern analysis to identify time-dependent patterns and periodic changes in the intraocular pressure data to generate an intraocular pressure trend model; The pattern recognition submodule uses an abnormality detection method based on the intraocular pressure trend model to identify variation points, detect abnormal changes in intraocular pressure by analyzing mutation points in the time series, and perform trend prediction, analyze future trends and change patterns in the intraocular pressure data, and generate intraocular pressure change prediction results; The trend report output submodule is based on the intraocular pressure change prediction results, adopts a report generation algorithm, performs data integration, merges the intraocular pressure change prediction results with the analysis conclusions, and uses an automated layout algorithm to structure and format the report content to generate intraocular pressure trend analysis results.

5. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The postoperative recovery assessment module includes a data reconstruction submodule, a model training submodule, and a first scoring output submodule; The data reconstruction submodule uses a layer neural network algorithm based on the intraocular pressure trend analysis results to gradually extract key features in the vision recovery data through multiple layers and activation functions, uses a pooling layer to reduce the data dimension and enhance the model's abstract ability, and performs data reconstruction operations to integrate and reconstruct key features to generate a visual data feature set; The model training submodule is based on the visual data feature set, adopts the deep belief network algorithm, and uses the layered training strategy and layer-by-layer adjustment to enable the model to learn and recognize the pattern of vision restoration, perform model training, and generate a vision restoration recognition model; The first scoring output submodule is based on a visual recovery recognition model and adopts a logistic regression algorithm to predict and analyze the degree of vision recovery through statistical methods, combines the probability generation distribution of patient data, evaluates the degree of vision recovery, and generates a vision recovery probability score table.

6. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The drug prediction response module includes a drug information integration submodule, a response analysis submodule, and a prediction report output submodule; The drug information integration submodule is based on the visual rehabilitation probability score table and adopts data fusion technology to standardize the format and structure of multi-source medical history data through information unification processing, and performs data integration, and analyzes and integrates drug information to generate a drug prediction data set; The response analysis submodule is based on the drug prediction data set and adopts the support vector machine algorithm to characterize the characteristics of the drug response data by constructing a feature space, divides the differentiated drug response categories by the principle of interval boundaries, performs drug response analysis, distinguishes and identifies drug effects, and generates a drug effect distinction map; The prediction report output submodule is based on the drug effect differentiation map, adopts a classification decision tree algorithm, classifies differentiated drug types through a tree structure model, builds a decision path based on patient characteristics, predicts drug types, and generates drug response prediction results.

7. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The vision future recovery prediction module includes a Bayesian model configuration submodule, a trend prediction submodule, and a model output submodule; The Bayesian model configuration submodule adopts a dynamic Bayesian network algorithm based on the drug response prediction results, designs a network structure to represent the time series relationship of the state of the vision recovery process, and establishes an overall probability model structure by configuring the conditional probability distribution between nodes to generate a Bayesian network configuration; The trend prediction submodule is based on the Bayesian network configuration and uses the Bayesian reasoning method to perform probability analysis on the vision recovery process. By combining historical data, the probability of the differentiated recovery stage is calculated to generate the vision recovery probability analysis result; The model output submodule is based on the results of the vision recovery probability analysis, uses data fusion and model optimization algorithms, integrates the prediction data and analysis results, improves the accuracy and reliability of the prediction by merging differentiated data sources through algorithms, and uses model optimization technology to adjust and improve model parameters to generate a vision recovery prediction optimization model.

8. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The image reconstruction module includes an image embedding submodule, a feature reconstruction submodule, and an image output submodule; The image embedding submodule is based on a visual restoration prediction optimization model and adopts a sparse coding algorithm to represent the key features of the eye damage image by constructing a feature dictionary, and by using the dictionary to calculate the sparse representation of the image, encodes and extracts the key features of the eye image to generate an eye image feature embedding; The feature reconstruction submodule is based on eye image feature embedding, adopts image reconstruction technology in sparse coding, combines image features by matching elements in the feature dictionary, uses the combined features to restore the complete structure of the damaged eye image, and generates an eye image structure restoration map; The image output submodule is based on the eye image structure recovery map and adopts image integration and formatting technology to integrate and format the processed image data, and generates an eye image enhancement display map by obtaining subsequent analysis and diagnosis requirements of the image data.

9. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The image quality assessment module includes an image analysis submodule, a quality calculation submodule, and a second score output submodule; The image analysis submodule is based on the eye image enhancement display diagram, adopts the variational autoencoder algorithm, compresses the image data through the encoder part, obtains the image feature representation, uses the decoder part to reconstruct the image from the features, captures the original image features and reconstructs the image, and generates the image feature analysis result; The quality calculation submodule uses a pixel-level comparison analysis method based on the image feature analysis result to compare the original image and the reconstructed image at the pixel level, analyze the difference between the pixel values, evaluate the effect of image reconstruction and restoration, and generate an image quality calculation result; The second scoring output submodule adopts a comprehensive scoring model based on the image quality calculation results, performs weighted processing on the data in the comparative analysis, comprehensively evaluates the quality level of the image according to the pixel contrast difference and the overall image quality standard, and generates a comprehensive image quality score.

10. The intelligent monitoring system for postoperative rehabilitation of ophthalmic patients according to claim 1, characterized in that: The comprehensive health assessment module includes a health data fusion submodule, an assessment and analysis submodule, and a report display submodule; The health data fusion submodule uses a multidimensional data fusion algorithm based on comprehensive image quality scores, combines medical records, physiological data and individual characteristic information, integrates multi-source data in multiple dimensions through algorithms for analysis, and generates a comprehensive health data set; The evaluation and analysis submodule uses statistical analysis methods based on the comprehensive health data set to conduct in-depth analysis of the integrated health data, identify health trends and potential risk factors, evaluate the health status of the patient, and generate health risk analysis results; The report display submodule is based on the health risk analysis results and uses information visualization technology to convert the evaluation data into intuitive charts and graphs. By displaying the individual's health status and potential risks, it analyzes the user's understanding and application of information and generates a health status visualization dashboard.

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