A method and system for early warning of myopia in children and adolescents

Abstract

The present invention discloses a method and system for early warning of myopia in children and adolescents, which relates to the field of health monitoring technology, including real-time collection of bioelectric signal data and environmental sensor data; combining a convolutional neural network with a recurrent neural network to extract features from the collected bioelectric signal data, identify fatigue signals and fatigue levels, and evaluate the current visual fatigue state of children and adolescents, outputting a fatigue score; real-time monitoring of the fatigue scores of children and adolescents, and automatically triggering a warning signal when the fatigue score exceeds a myopia warning threshold, and generating prompts and vision protection suggestions for the current myopia risk of children and adolescents. By combining the present invention with environmental data, it can automatically adjust environmental conditions in combination with smart devices when monitoring fatigue scores in real time, optimize children's learning and living environments, and further reduce the risk of myopia.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring, and in particular to a method and system for early warning of myopia in children and adolescents. Background Art

[0002] With the rapid development of artificial intelligence and sensing technology, health monitoring systems based on bioelectric signals and environmental sensor data have made significant progress in multiple fields. In the field of visual health for children and adolescents, researchers are attempting to improve their vision using various technologies, such as visual fatigue monitoring and myopia warning. In recent years, deep learning algorithms, particularly the combination of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have been widely used in medical signal processing and health management. By collecting bioelectric signals, behavioral data, and environmental data in real time, combined with intelligent algorithms, early identification and intervention of visual fatigue can be achieved, preventing further deterioration of myopia.

[0003] However, existing technologies still have limitations when it comes to accurately assessing visual fatigue and health status in children and adolescents. Most existing systems rely primarily on static thresholds for health monitoring and lack the ability to personalize and dynamically adjust. In particular, there are technical deficiencies in the relationship between fatigue assessment and myopia warning, as well as in environmental adaptation. Furthermore, the warning mechanisms of existing systems are overly simplistic, unable to adjust monitoring strategies in real time based on environmental changes and individual differences, which can lead to misjudgments or miss the optimal time for intervention. Therefore, how to integrate multiple data sources and adopt more flexible and personalized algorithms for real-time warnings has become a key issue that needs to be addressed. Summary of the Invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for early warning of myopia in children and adolescents to solve the problem that the existing technology lacks a personalized, real-time, and dynamically adjusted early warning system in visual health monitoring of children and adolescents.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for early warning of myopia in children and adolescents, which includes real-time collection of bioelectric signal data and environmental sensor data;

[0008] By combining convolutional neural networks with recurrent neural networks, the system extracts features from the collected bioelectrical signal data, identifies fatigue signals and fatigue levels, and assesses the current visual fatigue status of children and adolescents, outputting a fatigue score.

[0009] Based on fatigue scores and the personal health records of children and adolescents, combined with reinforcement learning algorithms, a personalized dynamic warning model is built based on the physiological and behavioral characteristics of children in bioelectric signal data, and a myopia warning threshold is set;

[0010] Analyze current environmental conditions by combining environmental sensor data and myopia warning thresholds, and control environmental conditions in real time through connected smart devices;

[0011] Monitor the fatigue scores of children and adolescents in real time. When the fatigue score exceeds the myopia warning threshold, it will automatically trigger a warning signal and generate prompts and vision protection suggestions for the current myopia risk of children and adolescents.

[0012] As a preferred solution of the method for early warning of myopia in children and adolescents according to the present invention, the bioelectric signal data includes electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG);

[0013] The environmental sensor data includes temperature and humidity sensors, gas sensors, and light sensors.

[0014] As a preferred embodiment of the method for early warning of myopia in children and adolescents described in the present invention, the method combines a convolutional neural network with a recurrent neural network to extract features from the collected bioelectric signal data and identify fatigue signals and fatigue levels. The specific steps are as follows:

[0015] Remove noise and standardize bioelectric signal data to retain valid bioelectric signals;

[0016] Extract the convolutional neural network spatial features from the effective bioelectrical signals and use wavelet transform to enhance the high-frequency and low-frequency features of the bioelectrical signals;

[0017] Input high-frequency features and low-frequency features into the LSTM network to learn the temporal features of high-frequency features and low-frequency features. Then, the temporal features are fused through the fully connected layer and the fused features are output.

[0018] The fused features are input into the fully connected network to identify the fatigue signal. SoftMax receives the fatigue signal and outputs the classification probability of the fatigue degree.

[0019] As a preferred solution of the method for early warning of myopia in children and adolescents according to the present invention, the steps of evaluating the current visual fatigue state of children and adolescents and outputting a fatigue score are as follows:

[0020] Extract the probability value of each fatigue level from the classification probability output by the fully connected network;

[0021] Set corresponding scoring intervals of mild fatigue, moderate fatigue and severe fatigue for each fatigue level;

[0022] The classification probability value of each fatigue level is used to perform a weighted average of the corresponding scoring intervals to obtain the final fatigue score.

[0023] As a preferred embodiment of the method for early warning of myopia in children and adolescents described in the present invention, the method comprises the following steps: based on fatigue scores and personal health records of children and adolescents, combined with a reinforcement learning algorithm, a personalized dynamic early warning model is constructed according to the physiological and behavioral characteristics of children in bioelectric signal data, and a myopia warning threshold is set.

[0024] Real-time collection of children and adolescents' daily eye use time and sitting posture behavior characteristics;

[0025] Combining bioelectrical signal data and behavioral characteristics of children and adolescents to build personal health records;

[0026] The data in the personal health record is input into the multimodal neural network for feature fusion to obtain a comprehensive personalized feature vector;

[0027] Based on the personalized feature vector, bioelectric signal data and behavioral characteristics, a personalized dynamic early warning model is constructed, which is expressed as:

[0028]

[0029] F(ΔX(τ))=ΔX(τ) 2 ;

[0030] Among them, P(t) is the output of the personalized dynamic warning model at time t, n is the bioelectric signal feature, m is the number of behavioral features, X i (t) is the value of the i-th feature in the bioelectric signal data at time t, w i is the weight corresponding to the i-th feature, Y j (t) is the value of the jth feature in the behavior feature at time t, v j is the weight corresponding to the jth feature; ΔX(τ) is the change in the time interval, τ is the integral variable, dτ is the small change, λ is the decay parameter, and F(ΔX(τ)) is the time difference function;

[0031] Use reinforcement learning algorithms to train personalized dynamic warning models;

[0032] Based on the training results of the personalized dynamic warning model, the myopia warning threshold is set through dynamic adjustment strategies.

[0033] As a preferred solution of the method for early warning of myopia in children and adolescents described in the present invention, the method analyzes the current environmental conditions by combining environmental sensor data and myopia warning thresholds, and regulates the environmental conditions in real time through associated smart devices. The specific steps are as follows:

[0034] Collect light, temperature, humidity, and air quality data in real time through environmental sensors;

[0035] Analyze current environmental conditions by combining environmental sensor data with myopia warning thresholds;

[0036] Use Q-learning algorithm to calculate and analyze results and generate optimized environmental adjustment strategies;

[0037] Based on the optimized environmental adjustment strategy, by associating smart devices, device parameters are automatically adjusted and environmental conditions are controlled in real time.

[0038] As a preferred embodiment of the method for early warning of myopia in children and adolescents described in the present invention, wherein: the fatigue score of children and adolescents is monitored in real time, and when the fatigue score exceeds the myopia warning threshold, an early warning signal is automatically triggered and a prompt and vision protection suggestion for the current myopia risk of children and adolescents are generated. The specific steps are as follows:

[0039] Under environmental condition monitoring, a personalized dynamic early warning model is used to collect and analyze children and adolescents' fatigue scores in real time;

[0040] When the fatigue score exceeds the myopia warning threshold, the warning signal is automatically triggered;

[0041] When the warning signal is triggered, the associated smart device will issue a reminder about the current myopia risk to alert children and adolescents;

[0042] Input myopia risk alerts, fatigue scores, smart devices, and bioelectric signal data feedback into a personalized dynamic early warning model to further optimize the early warning strategy;

[0043] Based on the optimized early warning strategy and real-time data, vision protection recommendations are generated.

[0044] In a second aspect, the present invention provides a myopia warning system for children and adolescents, comprising a data acquisition module, a feature extraction module, a model building module, an environment control module, and a warning prompt module;

[0045] The data acquisition module is used to collect bioelectric signal data and environmental sensor data in real time;

[0046] The feature extraction module is used to extract features from the collected bioelectric signal data through a combination of a convolutional neural network and a recurrent neural network, identify fatigue signals and fatigue levels, and assess the current visual fatigue state of children and adolescents, and output a fatigue score;

[0047] The model building module is used to build a personalized dynamic warning model based on fatigue scores and personal health records of children and adolescents, combined with a reinforcement learning algorithm, according to the physiological and behavioral characteristics of children in bioelectric signal data, and set a myopia warning threshold;

[0048] The environmental control module is used to analyze the current environmental conditions by combining environmental sensor data and myopia warning thresholds, and to control the environmental conditions in real time through associated smart devices;

[0049] The early warning prompt module is used to monitor the fatigue scores of children and adolescents in real time. When the fatigue score exceeds the myopia warning threshold, it automatically triggers a warning signal and generates prompts and vision protection suggestions for the current myopia risk of children and adolescents.

[0050] In a third aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for early warning of myopia in children and adolescents as described in the first aspect of the present invention is implemented.

[0051] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for early warning of myopia in children and adolescents as described in the first aspect of the present invention is implemented.

[0052] The beneficial effects of the present invention are as follows: the present invention proposes a personalized dynamic early warning model by combining bioelectric signals, environmental sensor data and deep learning algorithms, which can monitor the visual fatigue status of children and adolescents in real time and make intelligent adjustments. Compared with the traditional static threshold early warning, the present invention effectively improves the recognition accuracy of fatigue signals through the feature extraction method that combines convolutional neural network (CNN) with recurrent neural network (RNN), and can dynamically adapt to the differences in individual physiological characteristics. On this basis, combined with the reinforcement learning algorithm, it can intelligently adjust the early warning model according to the child’s health file and real-time data, and provide personalized health intervention measures. In particular, the combination with environmental data can automatically adjust the environmental conditions in combination with smart devices when monitoring fatigue scores in real time, optimize children’s learning and living environment, and further reduce the risk of myopia. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 This is a flow chart of the myopia warning method for children and adolescents in Example 1.

[0055] Figure 2 This is a module diagram of the myopia early warning system for children and adolescents in Example 1. DETAILED DESCRIPTION

[0056] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0057] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0058] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0059] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for early warning of myopia in children and adolescents, comprising the following steps:

[0060] S1. Real-time collection of bioelectric signal data and environmental sensor data.

[0061] Furthermore, bioelectric signal data includes electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG); environmental sensor data includes temperature and humidity sensors, gas sensors and light sensors.

[0062] Specifically, bioelectric signal data is collected through high-precision electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG) sensors. These devices can monitor the physiological status of children and adolescents in real time, including brain electrical activity, heart rate, and muscle tension, thereby accurately reflecting their fatigue level. Through Bluetooth and Wi-Fi wireless technologies, bioelectric signal data is quickly transmitted to the data processing center for real-time analysis. At the same time, environmental sensor data is collected through temperature and humidity sensors, gas sensors, and light sensors to obtain real-time information such as temperature, humidity, air quality, and light intensity in the environment. All collected data is aggregated through the wireless network and transmitted to the cloud platform or local server for analysis and processing.

[0063] Preferably, fatigue scores and personalized early warning models provide comprehensive and real-time data support to ensure the dynamism and efficiency of the program, conduct accurate health monitoring and timely intervention based on real-time data, greatly improving the accuracy and timeliness of visual health management, thereby effectively reducing the risk of vision problems caused by fatigue or uncomfortable environment in children and adolescents.

[0064] S2. By combining convolutional neural networks with recurrent neural networks, the collected bioelectric signal data is feature extracted to identify fatigue signals and fatigue levels, and the current visual fatigue status of children and adolescents is evaluated to output a fatigue score.

[0065] Furthermore, the bioelectric signal data is subjected to noise removal and standardization to retain the effective bioelectric signal;

[0066] It should be noted that effective bioelectric signals refer to signals that can truly reflect the physiological state of the human body after removing noise and interference. These signals include electroencephalogram (EEG), electrocardiogram (ECG), and electromyogram (EMG), and can accurately represent physiological changes such as fatigue and stress. Effective bioelectric signals retain their true physiological information after noise removal and are standardized to facilitate subsequent analysis and feature extraction.

[0067] Specifically, a bandpass filter is used to select the frequency range of interest in the signal and filter out noise above or below this range. For example, for EEG signals, the frequency range of 0.5-50 Hz is usually of interest, while noise with a frequency higher than 50 Hz (such as power frequency noise) or artifacts with a frequency lower than 0.5 Hz (such as eye movement artifacts) can be removed by bandpass filtering. The bandpass filter effectively retains the useful frequency components in the signal while eliminating unnecessary noise.

[0068] Normalization is the process of converting data into a unified scale so that signals from different sources can be processed under the same standard. Bioelectric signal data often vary significantly over time, under different environmental conditions, and between individuals. Normalization can eliminate these differences, making subsequent feature extraction and pattern recognition more accurate and stable.

[0069] Use ICA to preserve valid physiological signals. ICA is a signal separation technology that can extract independent source signals from multi-channel signals. In EEG or ECG signal processing, ICA can effectively separate EEG signals from noise sources such as eye movement artifacts and ECG artifacts, removing these interferences and preserving valid physiological signals.

[0070] Optimally, noise removal methods can remove various electromagnetic interferences, artifacts, and other irrelevant signals from bioelectric signals, retaining valid data that truly reflects physiological status, effectively improving signal quality and providing an accurate basis for subsequent feature extraction and analysis. Standardization eliminates differences between different signal sources and devices, allowing signals to be processed uniformly under the same standard. This not only improves consistency between different signal channels, but also enhances the algorithm's ability to process various signal features, which is particularly important for subsequent processing combined with CNN, LSTM, and other algorithms.

[0071] Extract the convolutional neural network spatial features from the effective bioelectrical signals and use wavelet transform to enhance the high-frequency and low-frequency features of the bioelectrical signals;

[0072] Specifically, bioelectric signals (such as EEG, ECG, and EMG) have complex structures and multi-level features in spatial and temporal dimensions. The successful application of convolutional neural networks (CNNs) in the field of computer vision has shown that they can effectively extract local features from images and signals and capture spatial structural information through multi-layer convolution operations. Convolutional neural networks (CNNs) are applied to the spatial feature extraction of bioelectric signals.

[0073] The convolution layer uses multiple convolution kernels to locally filter the input bioelectric signals and extract local features. In bioelectric signal processing, each convolution kernel can learn the signal pattern of a specific frequency or spatial position. For example, in EEG signal processing, CNN can extract spatial distribution features from signals at different electrode positions, which is particularly important for understanding the relationship between signal sources (such as brain area activity).

[0074] CNN gradually extracts features from low to high layers through multi-layer convolution operations. The initial convolution kernel extracts low-level features, such as simple electrical signal waveform features, while the deep convolution kernel can extract more abstract features, such as spectral structure and time series patterns. This hierarchical learning enables CNN to capture complex spatial information in the signal, thereby more accurately reflecting the physical and physiological background of the signal.

[0075] Wavelet transform can decompose the signal into components of multiple scales (frequency). Through wavelet decomposition, the high-frequency and low-frequency parts of the signal are effectively distinguished. The high-frequency part usually corresponds to faster changes (such as fluctuations in a short time), while the low-frequency part corresponds to the stable part of the signal (such as changes on a longer time scale). In EEG signals, high-frequency components usually represent rapid brain activity, while low-frequency components represent relatively slow EEG activity.

[0076] Finally, wavelet transform is performed on the signal to enhance its high-frequency and low-frequency features. For example, high-frequency feature enhancement helps capture rapidly changing physiological phenomena (such as sudden brain wave changes or abnormal states), while low-frequency feature enhancement helps reflect long-term trends and regularities (such as brain wave patterns in deep sleep). By processing the wavelet coefficients, these features can be effectively amplified, making them more prominent in subsequent analysis.

[0077] It should be noted that bioelectric signals contain different physiological information in different frequency ranges. In particular, in EEG signals, low-frequency (such as delta waves and theta waves) and high-frequency (such as alpha waves and beta waves) signals often reflect different brain activity states (such as wakefulness, fatigue, relaxation, etc.). Wavelet transform is a multi-resolution analysis method that can perform detailed decomposition of signals in two dimensions: time and frequency. It is a very effective tool in the field of signal processing.

[0078] Wavelet transforms can provide more detailed local time-frequency information than Fourier transforms, making them particularly effective for analyzing complex bioelectrical signals. By enhancing both high- and low-frequency characteristics of bioelectrical signals, they can effectively capture both rapidly changing and long-term signal patterns under fatigue conditions, providing accurate data for subsequent fatigue identification.

[0079] Input high-frequency features and low-frequency features into the LSTM network to learn the temporal features of high-frequency features and low-frequency features. Then, the temporal features are fused through the fully connected layer and the fused features are output.

[0080] Specifically, the LSTM network can learn how signals change over time through its memory units, identify long-term dependencies in the signal, and accurately capture the accumulation of fatigue. LSTM can effectively avoid the vanishing gradient problem that may occur in traditional RNNs when processing long sequences of data, thereby better analyzing the temporal information in bioelectric signals.

[0081] The better approach is to introduce LSTM, which can more accurately identify the changing trend of fatigue status, provide stronger time series modeling capabilities for fatigue signal detection, and improve the accuracy of response to persistent fatigue signals.

[0082] The fused features are input into the fully connected network to identify the fatigue signal. The SoftMax receives the fatigue signal and outputs the classification probability of the fatigue degree.

[0083] Specifically, the input of the fully connected layer is the fused feature vector output by CNN and LSTM, which contains the information of the signal in spatial and temporal dimensions;

[0084] The fully connected layer assigns a weight to each feature input and combines all input features into a unified output vector through weighted summation;

[0085] The result after weighted summation will be transformed nonlinearly through activation functions (such as ReLU, Sigmoid, etc.), so that the network can learn complex signal features;

[0086] The output of the fully connected layer is a vector, where each element represents the score of a specific category. Suppose the network needs to output the classification results of three categories: mild fatigue, moderate fatigue, and severe fatigue. The SoftMax receives a vector of length 3, which contains the scores of these three categories.

[0087] SoftMax calculates the exponential function of each category and then standardizes these values so that the sum of the probabilities of all categories is 1. In this way, the score of each category is converted into the corresponding probability, reflecting the possibility that the signal belongs to that category.

[0088] It should be noted that the fully connected network (FCN) is a common layer structure in neural networks, usually used for comprehensive processing of signals or features. Fusion features refer to features obtained by combining convolutional neural networks (CNN) and long short-term memory networks (LSTM). These features contain spatial and temporal information of bioelectric signals.

[0089] The SoftMax function is a commonly used activation function in multi-classification problems. It converts the output of the neural network into a probability distribution and is usually used in the output layer of the neural network to perform probability estimation on the classification task. In the present invention, the SoftMax function receives the fusion features output by the fully connected layer and converts them into the classification probability of the fatigue degree.

[0090] Extract the probability value of each fatigue level from the classification probability output by the fully connected network;

[0091] Set corresponding scoring intervals of mild fatigue, moderate fatigue and severe fatigue for each fatigue level;

[0092] Using the classification probability value of each fatigue level, the corresponding scoring interval is weighted averaged to obtain the final fatigue score;

[0093] It should be noted that the classification probability value refers to the probability that the input signal belongs to a certain category (here, fatigue level) after the neural network is trained. The classification probability value here measures the credibility of different fatigue levels.

[0094] The fatigue level can be used to determine different fatigue states (such as mild, moderate, and severe). The fatigue level reflects the individual fatigue state assessed based on bioelectric signals and other relevant data (such as physiological characteristics and time series information).

[0095] Each fatigue level is assigned a specific scoring interval (e.g., mild fatigue corresponds to one scoring interval, severe fatigue corresponds to another scoring interval), which is set according to the critical criteria of the level in order to weight the final fatigue score according to the value of the classification probability;

[0096] Weighted average means that when calculating the final fatigue score, different weights are assigned to the probability values of each fatigue level, and then the weighted average is obtained. This can more accurately determine the final score based on the classification probability value and avoid errors caused by a single classification result;

[0097] The best approach is to perform weighted averaging on the classification probability values output by the fully connected network and combine them with the scoring range of each fatigue level to obtain the final fatigue score, which solves several key problems in traditional fatigue assessment methods. First, it uses the classification probability output by the deep learning model rather than a single classification result, making fatigue assessment more flexible and accurate. Second, it integrates the probability information of different fatigue levels through weighted averaging, avoiding the accumulation of classification errors and improving the comprehensive assessment ability of fatigue status.

[0098] S3. Based on fatigue scores and the personal health records of children and adolescents, combined with reinforcement learning algorithms, a personalized dynamic warning model is constructed according to the physiological and behavioral characteristics of children in bioelectric signal data, and a myopia warning threshold is set.

[0099] Furthermore, the behavioral characteristics of children and adolescents’ daily eye use time and sitting posture are collected in real time;

[0100] Specifically, smart glasses, wearable devices (such as smart watches) or mobile phone applications are used to monitor children's visual activities. These devices use built-in light sensors, accelerometers and other sensors to detect and record children's visual activity time in real time. For example, the device can detect changes in screen brightness or the intensity of ambient light when wearing glasses to infer the length of time children use their eyes;

[0101] Use smart wearable devices (such as smart clothing, seat sensors, etc.) to monitor children's sitting posture in real time, and use accelerometers, gyroscopes, etc. to detect children's body posture, especially changes in the back, waist and spine;

[0102] It should be noted that behavioral characteristics refer to the activity data of children and adolescents in their daily lives, such as eye usage time, sitting posture, and exercise volume. These behavioral characteristics can affect their visual health and therefore need to be monitored and quantified in real time.

[0103] Combining bioelectrical signal data and behavioral characteristics of children and adolescents to build personal health records;

[0104] Specifically, first, real-time collection of children and adolescents’ bioelectrical signal data (such as electroencephalogram, electrocardiogram, electromyogram) and behavioral characteristic data (such as eye use time, sitting posture);

[0105] Secondly, clean and standardize the data to ensure its accuracy and consistency;

[0106] Then, the bioelectric signals and behavioral characteristics are fused through a multimodal neural network to extract personalized health feature vectors;

[0107] Finally, based on these integrated characteristics and combined with the individual differences of children, a personal health profile is constructed, and updated and optimized based on real-time data to ensure the accuracy of health monitoring and early warning.

[0108] The data in the personal health record is input into the multimodal neural network for feature fusion to obtain a comprehensive personalized feature vector;

[0109] It should be noted that fusing feature data from different sources (such as bioelectric signals and behavioral characteristics) can effectively extract individual health information in all aspects. This method can not only improve the accuracy of data processing, but also tap into more potential correlations. Compared with traditional single data source processing methods, multimodal neural networks can achieve more accurate and comprehensive feature representation, thereby enhancing the accuracy of personalized health assessments.

[0110] Based on the personalized feature vector, bioelectric signal data and behavioral characteristics, a personalized dynamic early warning model is constructed, which is expressed as:

[0111]

[0112] F(ΔX(τ))=ΔX(τ) 2 ;

[0113] Among them, P(t) is the output of the personalized dynamic warning model at time t, n is the bioelectric signal feature, m is the number of behavioral features, X i (t) is the value of the i-th feature in the bioelectric signal data at time t, w i is the weight corresponding to the i-th feature, Y j (t) is the value of the jth feature in the behavior feature at time t, v j is the weight corresponding to the jth feature; ΔX(τ) is the change in the time interval, τ is the integral variable, dτ is the small change, λ is an attenuation parameter that controls the range of influence of signal changes, and F(ΔX(τ)) is the time difference function that measures the change of the signal in the time interval [0, t].

[0114] Specifically, P(t) represents the output result at time t, for example, dynamically changing indicators such as fatigue score and health risk;

[0115]

[0116] This part calculates each factor X i (t), and the influence of historical changes is added. The exponential decay term reflects that the influence of historical changes on the current output gradually decays over time;

[0117] Among them, w i is the weight of factors such as children's physiological characteristics and behavioral habits, i (t) is derived from environmental sensors (such as light, temperature and humidity) and children's behavioral characteristics (such as eye use time, rest time, etc.). λ is the rate constant that controls the reaction of the personalized dynamic early warning model. It is usually a positive number and determines the response rate of the change. F(ΔX(τ)) is a function that reflects changes in fatigue, visual burden or other factors. A memory decay process that reflects feature changes over a period of time in the past;

[0118] in, Combining different features Y j The weighted sum of (t) represents the influence of the feature on the final output;

[0119] It should be noted that the output P(t) of the personalized dynamic early warning model is determined by the weighted sum of the current factors (the numerator) and the weighted sum of the feature data (the denominator). Historical data (through the exponential decay term) has a gradually decreasing effect on the output.

[0120] Use reinforcement learning algorithms to train personalized dynamic warning models;

[0121] It should be noted that by collecting bioelectric signal data (such as electroencephalogram, electrocardiogram, electromyogram) and behavioral characteristics (such as eye usage time and sitting posture) of children and adolescents in real time, combined with their personal health records, a personalized feature vector is constructed, and the bioelectric signal data and behavioral characteristics are input into the reinforcement learning algorithm for training;

[0122] Specifically, reinforcement learning uses a reward function to evaluate the performance of a personalized dynamic warning model by defining a state space (such as fatigue score and environmental conditions) and an action space (such as adjusting environmental parameters and issuing alarms). When the personalized dynamic warning model outputs a valid myopia warning signal, the system will give a positive reward; otherwise, a negative reward will be given. Through repeated training and feedback, the reinforcement learning algorithm continuously adjusts the parameters of the personalized dynamic warning model.

[0123] Based on the training results of the personalized dynamic warning model, the myopia warning threshold is set through dynamic adjustment strategies.

[0124] Specifically, when the personalized dynamic warning model identifies fatigue signals or other relevant health indicators reaching a certain critical value, it will dynamically adjust the myopia warning threshold to ensure that the warning signal is issued under the most appropriate circumstances for the individual;

[0125] The optimal dynamic adjustment mechanism enables the personalized dynamic early warning model to adapt to the changes and differences of each child and adolescent, avoiding the blindness and inaccuracy of traditional static threshold setting, and improving the accuracy and response speed of the early warning.

[0126] S4. Analyze current environmental conditions based on environmental sensor data and myopia warning thresholds, and adjust environmental conditions in real time through associated smart devices.

[0127] Furthermore, environmental sensors are used to collect data on light, temperature, humidity, and air quality in real time;

[0128] Specifically, a light sensor (such as a photoelectric sensor or a light intensity sensor) is used to monitor the ambient light intensity, which is usually expressed in lux.

[0129] Temperature and humidity sensors (such as DHT11 and DHT22) can collect temperature and humidity data in the environment in real time;

[0130] Air quality sensors (such as the MQ series sensors and CCS811 sensors) can detect harmful gases (such as carbon dioxide and volatile organic compounds) and particulate matter (such as PM2.5 and PM10) in the air. These sensors output an air quality index (AQI) or pollutant concentration value to indicate the quality of air.

[0131] Analyze current environmental conditions by combining environmental sensor data with myopia warning thresholds;

[0132] It should be noted that the myopia warning threshold refers to a standard value set by a personalized dynamic warning model based on the individual health status of children and adolescents. When environmental conditions or behavioral characteristics reach this standard, a myopia warning signal will be triggered.

[0133] Use Q-learning algorithm to calculate and analyze results and generate optimized environmental adjustment strategies;

[0134] It should be noted that the Q-learning algorithm, as a reinforcement learning algorithm, optimizes decision-making strategies through a reward and punishment mechanism. It can continuously adjust its action plan in a dynamic and complex environment to maximize the effect of the goal. Q-learning analyzes environmental data to generate environmental adjustment strategies related to individual myopia warning thresholds.

[0135] The best approach is to use the Q-learning algorithm to achieve personalized and dynamic adjustments. Taking into account the physiological differences and individual needs of children and adolescents in different environmental conditions, it not only optimizes the warning threshold setting, but also intelligently adjusts environmental conditions to mitigate the risk of myopia. For example, when the light is strong, it automatically adjusts the indoor lighting to the appropriate brightness, or when the humidity is low, it uses smart humidification equipment to adjust the humidity to ensure children's eye health.

[0136] Based on the optimized environmental adjustment strategy, by associating smart devices, device parameters are automatically adjusted and environmental conditions are controlled in real time.

[0137] Specifically, the temperature and humidity sensor monitors the indoor temperature and humidity in real time. For example, if the humidity is lower than 30% or higher than 70%, it may cause dry eyes and fatigue, affecting eye comfort. The appropriate temperature and humidity range is set (such as: temperature 22℃ to 26℃, humidity 40% to 60%). When the temperature and humidity exceed the set range, the intelligent air conditioner, humidifier or dehumidifier is used to adjust the humidity.

[0138] The air quality sensor monitors the indoor air quality index (AQI) in real time, especially the concentration of harmful substances such as PM2.5 and carbon dioxide. If the indoor air quality is poor (for example, the PM2.5 concentration exceeds 50μg / m 3 ), which may cause dry eyes, irritation and fatigue in children and adolescents, and even increase the risk of myopia. According to air quality monitoring results, if the air quality index exceeds the preset health threshold (for example, AQI>100), the air purifier will be activated and the wind speed will be adjusted to reduce the concentration of harmful gases and particulate matter indoors;

[0139] Preferably, the present invention combines environmental sensor data with a personalized dynamic early warning model and intelligently regulates the environment based on an adjustment strategy generated by a Q-learning algorithm, thereby achieving personalized and adaptive myopia prevention and control. The Q-learning optimization strategy can perform real-time adaptive adjustments in a constantly changing environment, ensuring that every child and adolescent uses their eyes under optimal environmental conditions, thereby effectively reducing the risk of myopia.

[0140] S5. Real-time monitoring of the fatigue scores of children and adolescents. When the fatigue score exceeds the myopia warning threshold, the warning signal is automatically triggered and prompts and vision protection suggestions are generated for the current myopia risk of children and adolescents.

[0141] Furthermore, under environmental condition monitoring, a personalized dynamic early warning model is used to collect and analyze children and adolescents’ fatigue scores in real time;

[0142] Specifically, by wearing wearable devices (such as smart bracelets and smart glasses), children and adolescents can collect electroencephalograms (EEGs), electrocardiograms (ECGs), and electromyograms (EMGs) in real time. Bioelectric signals reflect the physical fatigue state of children and adolescents. For example, within a day, if a child's EEG data shows signs of fatigue in brain wave activity, their EEG activity fluctuates more between 10:00 and 12:00, with a lower frequency, indicating a high level of fatigue.

[0143] Environmental sensor data shows that the indoor light intensity reaches over 1000 lux at noon, which is considered strong light and will aggravate eye fatigue;

[0144] A personalized dynamic early warning model calculates a fatigue score in real time. For example, in the scenario above, assume the personalized dynamic early warning model calculates the child's fatigue score as 85 (out of 100) based on their EEG and ECG data. This score exceeds the preset threshold of 70, indicating that the child is currently fatigued and at risk of vision loss.

[0145] In a data set of 100 children and adolescents, suppose 60% of the children scored above 70 points in a specific time period (most of these children concentrated on studying for more than 2 hours), and 30% of them scored above 80 points, showing obvious signs of fatigue;

[0146] According to environmental data analysis, when the light intensity exceeds 800 lux (i.e., when the indoor light source is strong), the average fatigue score increases by 15%. Excessive light aggravates visual fatigue;

[0147] When the fatigue score exceeds the myopia warning threshold, the warning signal is automatically triggered;

[0148] It should be noted that when the fatigue score derived from the personalized dynamic warning model analysis exceeds the set myopia warning threshold, a warning signal is automatically triggered to remind the user that the current fatigue state may pose a risk to vision health;

[0149] Ideally, by setting reasonable thresholds and automatically triggering warning signals, it can effectively prevent children and adolescents from continuing activities that could cause significant vision damage when they are overly fatigued. Intelligent warnings based on real-time data can more accurately reflect an individual's actual fatigue level, provide timely feedback, and avoid potential risks to vision health.

[0150] When the warning signal is triggered, the associated smart device will issue a reminder about the current myopia risk to alert children and adolescents;

[0151] Specifically, smart devices push signals and instructions to devices through Bluetooth signals (such as smart watches, smart glasses, and smart bracelets), Wi-Fi signals (such as smart lights, smart air conditioners, and smart speakers), MQTT protocol (message queue telemetry transmission protocol), and HTTP / HTTPS protocol;

[0152] Input myopia risk alerts, fatigue scores, smart devices, and bioelectric signal data feedback into a personalized dynamic early warning model to further optimize the early warning strategy;

[0153] It should be noted that all relevant data (such as fatigue scores, smart device data, environmental data, and bioelectric signals) will be fed back into the personalized dynamic early warning model. The model will dynamically adjust and optimize the early warning strategy based on this data to achieve more efficient monitoring and early warning.

[0154] Ideally, real-time data will be fed back into a personalized dynamic warning model to further optimize the personalized warning strategy, making it adaptable to the individual needs of different users. With continuous model training and data accumulation, the accuracy and adaptability of warnings will gradually improve, helping to reduce false alarms and missed reports, and improving the vision protection effect of children and adolescents;

[0155] Based on the optimized early warning strategy and real-time data, vision protection recommendations are generated.

[0156] It should be noted that based on the combination of optimized dynamic warning strategies and real-time data, personalized vision protection recommendations will be automatically generated, such as reasonable eye use time, rest frequency, appropriate sitting posture, light intensity, etc., to help children and adolescents protect their eyesight scientifically and rationally.

[0157] Preferably, the steps for generating vision protection recommendations combine all environmental conditions, fatigue scores, bioelectric signal data and other health data to provide comprehensive vision protection measures for children and adolescents; unlike traditional preventive measures, by comprehensively considering various data to provide scientific recommendations, it can effectively reduce the risks of excessive eye use, myopia development and other problems, and constantly adjust itself according to actual feedback, making protection measures more precise and personalized, further improving the accuracy and scientific nature of health management.

[0158] This embodiment also provides a myopia warning system for children and adolescents, including: a data acquisition module, a feature extraction module, a model building module, an environmental control module and an early warning prompt module; the data acquisition module is used to collect bioelectric signal data and environmental sensor data in real time; the feature extraction module is used to combine a convolutional neural network with a recurrent neural network to extract features from the collected bioelectric signal data, identify fatigue signals and fatigue levels, and evaluate the current visual fatigue state of children and adolescents, and output a fatigue score; the model building module is used to build a personalized dynamic early warning model based on the fatigue score and the personal health records of children and adolescents, combined with a reinforcement learning algorithm, and according to the physiological and behavioral characteristics of the children in the bioelectric signal data, and set a myopia warning threshold; the environmental control module is used to analyze the current environmental conditions in combination with the environmental sensor data and the myopia warning threshold, and to control the environmental conditions in real time through associated smart devices; the early warning prompt module is used to monitor the fatigue score of children and adolescents in real time, and when the fatigue score exceeds the myopia warning threshold, automatically trigger a warning signal and generate prompts and vision protection suggestions for the current myopia risk of children and adolescents.

[0159] This embodiment also provides a computer device, which is suitable for the situation based on the myopia early warning method for children and adolescents, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the myopia early warning method for children and adolescents proposed in the above embodiment.

[0160] The computer device may be a terminal, comprising a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a button, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse.

[0161] This embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for early warning of myopia in children and adolescents proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk.

[0162] In summary, the present invention proposes a personalized dynamic early warning model by combining bioelectric signals, environmental sensor data and deep learning algorithms, which can monitor the visual fatigue status of children and adolescents in real time and make intelligent adjustments. Compared with the traditional static threshold early warning, the present invention effectively improves the recognition accuracy of fatigue signals through the feature extraction method of combining convolutional neural network (CNN) and recurrent neural network (RNN), and can dynamically adapt to the differences in individual physiological characteristics. On this basis, combined with the reinforcement learning algorithm, it is possible to intelligently adjust the early warning model according to the child’s health file and real-time data, and provide personalized health intervention measures. In particular, the combination with environmental data can automatically adjust the environmental conditions in combination with smart devices when monitoring fatigue scores in real time, optimize children’s learning and living environment, and further reduce the risk of myopia.

[0163] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for early warning of myopia in children and adolescents, characterized by: include, Real-time collection of bioelectric signal data and environmental sensor data; By combining convolutional neural networks with recurrent neural networks, the system extracts features from the collected bioelectrical signal data, identifies fatigue signals and fatigue levels, and assesses the current visual fatigue status of children and adolescents, outputting a fatigue score. Based on fatigue scores and the personal health records of children and adolescents, combined with reinforcement learning algorithms, a personalized dynamic warning model is built based on the physiological and behavioral characteristics of children in bioelectric signal data, and a myopia warning threshold is set; Analyze current environmental conditions by combining environmental sensor data and myopia warning thresholds, and control environmental conditions in real time through connected smart devices; Real-time monitoring of children and adolescents' fatigue scores. When the fatigue score exceeds the myopia warning threshold, it will automatically trigger a warning signal and generate prompts and vision protection suggestions for children and adolescents' current myopia risks. Based on fatigue scores and personal health records of children and adolescents, combined with reinforcement learning algorithms, a personalized dynamic warning model is constructed according to the physiological and behavioral characteristics of children in bioelectric signal data, and a myopia warning threshold is set. The specific steps are as follows: Real-time collection of children and adolescents' daily eye use time and sitting posture behavior characteristics; Combining bioelectrical signal data and behavioral characteristics of children and adolescents to build personal health records; The data in the personal health record is input into the multimodal neural network for feature fusion to obtain a comprehensive personalized feature vector; Based on the personalized feature vector, bioelectric signal data and behavioral characteristics, a personalized dynamic early warning model is constructed, which is expressed as: F(ΔX(τ))=ΔX(τ) 2 ; Among them, P(t) is the output of the personalized dynamic warning model at time t, n is the bioelectric signal feature, m is the number of behavioral features, X i (t) is the value of the i-th feature in the bioelectric signal data at time t, w i is the weight corresponding to the i-th feature, Y j (t) is the value of the jth feature in the behavior feature at time t, v j is the weight corresponding to the jth feature, ΔX(τ) is the change in the time interval, τ is the integral variable, dτ is the small change, λ is the decay parameter, and F(ΔX(τ)) is the time difference function; Use reinforcement learning algorithms to train personalized dynamic warning models; Based on the training results of the personalized dynamic warning model, the myopia warning threshold is set through dynamic adjustment strategies.

2. The method for early warning of myopia in children and adolescents according to claim 1, characterized in that: The bioelectric signal data includes electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG); The environmental sensor data includes temperature and humidity sensors, gas sensors, and light sensors.

3. The method for early warning of myopia in children and adolescents according to claim 2, characterized in that: The convolutional neural network is combined with the recurrent neural network to extract features from the collected bioelectric signal data and identify fatigue signals and fatigue levels. The specific steps are as follows: Remove noise and standardize bioelectric signal data to retain valid bioelectric signals; Extract the convolutional neural network spatial features from the effective bioelectrical signals and use wavelet transform to enhance the high-frequency and low-frequency features of the bioelectrical signals; Input high-frequency features and low-frequency features into the LSTM network to learn the temporal features of high-frequency features and low-frequency features. Then, the temporal features are fused through the fully connected layer and the fused features are output. The fused features are input into the fully connected network to identify the fatigue signal. SoftMax receives the fatigue signal and outputs the classification probability of the fatigue degree.

4. The method for early warning of myopia in children and adolescents according to claim 3, characterized in that: The specific steps of evaluating the current visual fatigue status of children and adolescents and outputting fatigue scores are as follows: Extract the probability value of each fatigue level from the classification probability output by the fully connected network; Set corresponding scoring intervals of mild fatigue, moderate fatigue and severe fatigue for each fatigue level; The classification probability value of each fatigue level is used to perform a weighted average of the corresponding scoring intervals to obtain the final fatigue score.

5. The method for early warning of myopia in children and adolescents according to claim 1, characterized in that: The method combines environmental sensor data and myopia warning thresholds to analyze current environmental conditions, and controls environmental conditions in real time through associated smart devices. The specific steps are as follows: Collect light, temperature, humidity, and air quality data in real time through environmental sensors; Analyze current environmental conditions by combining environmental sensor data with myopia warning thresholds; Use Q-learning algorithm to calculate and analyze results and generate optimized environmental adjustment strategies; Based on the optimized environmental adjustment strategy, by associating smart devices, device parameters are automatically adjusted and environmental conditions are controlled in real time.

6. The method for early warning of myopia in children and adolescents according to claim 5, characterized in that: The real-time monitoring of the fatigue scores of children and adolescents, when the fatigue scores exceed the myopia warning threshold, automatically triggers a warning signal and generates prompts and vision protection suggestions for the current myopia risk of children and adolescents. The specific steps are as follows: Under environmental condition monitoring, a personalized dynamic early warning model is used to collect and analyze children and adolescents' fatigue scores in real time; When the fatigue score exceeds the myopia warning threshold, the warning signal is automatically triggered; When the warning signal is triggered, the associated smart device will issue a reminder about the current myopia risk to alert children and adolescents; Input myopia risk alerts, fatigue scores, smart devices, and bioelectric signal data feedback into a personalized dynamic early warning model to further optimize the early warning strategy; Based on the optimized early warning strategy and real-time data, vision protection recommendations are generated.

7. A myopia warning system for children and adolescents, based on the myopia warning method for children and adolescents according to any one of claims 1 to 6, characterized in that: Including data acquisition module, feature extraction module, model building module, environment control module and early warning module; The data acquisition module is used to collect bioelectric signal data and environmental sensor data in real time; The feature extraction module is used to extract features from the collected bioelectric signal data through a combination of a convolutional neural network and a recurrent neural network, identify fatigue signals and fatigue levels, and assess the current visual fatigue state of children and adolescents, and output a fatigue score; The model building module is used to build a personalized dynamic warning model based on fatigue scores and personal health records of children and adolescents, combined with a reinforcement learning algorithm, according to the physiological and behavioral characteristics of children in bioelectric signal data, and set a myopia warning threshold; The environmental control module is used to analyze the current environmental conditions by combining environmental sensor data and myopia warning thresholds, and to control the environmental conditions in real time through associated smart devices; The early warning prompt module is used to monitor the fatigue scores of children and adolescents in real time. When the fatigue score exceeds the myopia warning threshold, it automatically triggers a warning signal and generates prompts and vision protection suggestions for the current myopia risk of children and adolescents.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for early warning of myopia in children and adolescents according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for early warning of myopia in children and adolescents according to any one of claims 1 to 6 are implemented.

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