Human body aging information analysis method and system based on big data

Abstract

The invention provides a human body aging information analysis method and system based on big data. The method belongs to the technical field of big data analysis, and comprises the following steps: carrying out real-time acquisition on multi-source data through a real-time data stream processing system based on Kafka, and carrying out primary processing on the acquired multi-source data; and through a deep learning model, extracting multi-dimensional senescence-related features from the preliminarily processed multi-source data, and based on the extracted features, constructing a senescence feature map in combination with a map neural network. Through the Kafka-based real-time data stream processing system, multi-source data can be collected and processed in real time, the data processing efficiency and speed are improved, and human body aging information can be captured and analyzed in time.

Description

Technical Field

[0001] The present invention proposes a human aging information analysis method and system based on big data, belonging to the technical field of big data analysis. Background Art

[0002] As the population ages, the understanding and management of the human aging process has become an important research topic in the fields of medicine, biology, and information technology. Traditional aging assessment methods often rely on single-dimensional physiological indicators or questionnaires. These methods are limited in comprehensiveness and accuracy and are difficult to capture the complex physiological and environmental interactions in the aging process. At the same time, with the rapid development of big data technology, massive, multi-dimensional health and environmental data have been accumulated, providing unprecedented opportunities for in-depth research on the mechanisms of human aging and the development of personalized intervention strategies.

[0003] In this context, a method for analyzing human aging information based on big data has emerged. This method uses advanced data collection, processing and analysis technologies to collect human physiological data (such as heart rate, blood pressure, blood sugar, skin elasticity, etc.) and environmental data (such as air quality, temperature and humidity, light intensity, etc.) in real time and comprehensively. These data are crucial for understanding the interaction between internal and external factors in the aging process.

[0004] However, faced with such a huge amount of data, how to efficiently and accurately extract features closely related to aging and build a model that can reflect the aging state has become an urgent problem to be solved. Deep learning technology, especially convolutional neural networks (CNN) and long short-term memory networks (LSTM), has achieved remarkable results in image recognition, natural language processing and other fields with its powerful feature extraction and time series data processing capabilities, and has also provided new ideas for aging feature extraction. In addition, the introduction of graph neural networks (GNN) enables the construction of more complex and comprehensive aging feature maps, further improving the model's interpretability and predictive capabilities.

[0005] In terms of model construction and optimization, although traditional machine learning algorithms can build aging assessment models based on extracted features, their static nature and limitations gradually become apparent in the face of ever-changing data and user needs. Therefore, the introduction of online learning algorithms (such as incremental learning, meta-learning) and user feedback mechanisms can achieve dynamic adjustment and optimization of model parameters, making the model more adaptable to actual application scenarios and improving the accuracy and personalization of assessments. Summary of the invention

[0006] The present invention provides a method and system for analyzing human aging information based on big data to solve the problems mentioned in the above background technology:

[0007] The present invention proposes a method for analyzing human aging information based on big data, the method comprising:

[0008] S1. Through the real-time data stream processing system based on Kafka, multi-source data is collected in real time and the collected multi-source data is preliminarily processed;

[0009] S2. Through deep learning models, we extract multi-dimensional aging-related features from the preliminarily processed multi-source data, and build an aging feature map based on the extracted features combined with graph neural networks.

[0010] S3. Based on the extracted features and the constructed aging feature map, the initial aging assessment model is constructed using a machine learning algorithm. The model parameters are continuously adjusted and optimized through an online learning algorithm and user feedback mechanism.

[0011] S4. Based on the specific situation and needs of the user, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, and generate personalized health reports and recommendations.

[0012] The present invention proposes a human aging information analysis system based on big data, the system comprising:

[0013] Data collection module: collects multi-source data in real time through the real-time data stream processing system based on Kafka, and performs preliminary processing on the collected multi-source data;

[0014] Graph construction module: Through deep learning models, multi-dimensional aging-related features are extracted from the preliminarily processed multi-source data. Based on the extracted features, an aging feature graph is constructed in combination with a graph neural network.

[0015] Adjustment and optimization module: Based on the extracted features and the constructed aging feature map, the initial aging assessment model is constructed using machine learning algorithms. The model parameters are continuously adjusted and optimized through online learning algorithms and user feedback mechanisms.

[0016] Report generation module: Based on the user's specific situation and needs, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, and generate personalized health reports and recommendations.

[0017] The invention has the following beneficial effects: through the real-time data stream processing system based on Kafka, multi-source data can be collected and processed in real time, the efficiency and speed of data processing are improved, and it is helpful to timely capture and analyze human aging information; by using deep learning models and graph neural networks, multi-dimensional aging-related features can be extracted from multi-source data, and aging feature maps can be constructed to more comprehensively reflect the aging state of the human body; through online learning algorithms and user feedback mechanisms, the aging assessment model can continuously adjust and optimize parameters, improve the accuracy and adaptability of the model, and better meet personalized needs; in combination with the specific situation and needs of users, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, which is helpful to improve the effectiveness and pertinence of intervention measures; by monitoring the Kafka cluster and stream processing tasks, as well as the model monitoring and evaluation system, the stable operation and continuous optimization of the system are ensured, and the scalability and robustness of the system are enhanced; by collecting user feedback, analyzing user needs, and continuously optimizing models and intervention strategies, the user's participation and satisfaction are improved, which is helpful to improve the quality of health management services; the generated personalized health reports and recommendations can provide users with customized health management plans, which is helpful to improve the health status and quality of life of users. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a step diagram of the method of the present invention;

[0019] Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0020] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. The embodiments described are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0023] One embodiment of the present invention, as Figure 1As shown, a method for analyzing human aging information based on big data, the method comprising:

[0024] S1. Real-time data collection and preliminary processing of multi-source data are performed through a real-time data stream processing system based on Kafka; the multi-source data includes human physiological data and environmental data;

[0025] S2. Extract multi-dimensional aging-related features from the preliminarily processed multi-source data through deep learning models (such as convolutional neural networks (CNN) and long short-term memory networks (LSTM)), and construct an aging feature map based on the extracted features in combination with graph neural networks (GNN);

[0026] S3. Based on the extracted features and the constructed aging feature map, the initial aging assessment model is constructed using a machine learning algorithm, and the model parameters are continuously adjusted and optimized through online learning algorithms (such as incremental learning, meta-learning) and user feedback mechanisms;

[0027] S4. Based on the specific situation and needs of the user, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, and generate personalized health reports and recommendations.

[0028] The working principle of the above technical solution is: using Kafka, a distributed stream processing platform, to collect human physiological data (such as heart rate, blood pressure, number of steps, etc.) and environmental data (such as temperature, humidity, air quality, etc.) in real time from multiple data sources (such as wearable devices, medical testing equipment, environmental sensors, etc.). Kafka's high throughput and fault tolerance ensure that data can be transmitted stably and efficiently; the collected multi-source data is preliminarily processed, including data cleaning (removing noise and outliers), data compression (reducing storage and transmission costs), data format conversion (unifying data formats for subsequent processing), etc. This step aims to improve data quality and provide a reliable data foundation for subsequent analysis; deep learning models (such as CNN, LSTM) are used to conduct in-depth analysis of the preliminarily processed multi-source data to extract multi-dimensional features related to aging. CNN is good at processing the spatial features of images or sequence data, while LSTM is good at capturing long-term dependencies in time series data. These features may include the changing trends of physiological indicators, the periodic changes of environmental factors, etc.; based on the extracted features, a graph neural network (GNN) is used to construct an aging feature map. GNN can process graph-structured data and reveal the complex relationships and potential rules between features through nodes (features) and edges (relationships between features). The aging feature map not only shows the status of each feature, but also reveals the interactions and influences between them, providing comprehensive information support for subsequent aging assessment; the initial aging assessment model is constructed by combining the extracted features and the constructed aging feature map using machine learning algorithms (such as random forests, gradient boosting trees, neural networks, etc.). The model can evaluate the aging status of individuals based on the input feature data; the model is continuously optimized through online learning algorithms (such as incremental learning, meta-learning) and user feedback mechanisms. Incremental learning allows the model to automatically update its parameters and structure when receiving new data to adapt to changes in data. Meta-learning improves the generalization ability and adaptability of the model by learning how to learn. The user feedback mechanism collects user feedback on the evaluation results to guide further optimization of the model; through the above optimization process, adaptive learning of the personalized evaluation model is achieved. The model can conduct customized assessments based on the specific circumstances and needs of users, providing more accurate and personalized aging assessment results; combined with the specific circumstances and needs of users (such as age, gender, health status, economic conditions, etc.), a multi-objective optimization algorithm (such as NSGA-II, MOEA / D, etc.) is used to generate personalized aging intervention strategies. These strategies aim to balance multiple goals such as anti-aging effects, costs, and risks, and provide users with the best intervention plan; natural language generation technology is used to convert complex intervention strategies into easy-to-understand language, and generate personalized health reports and recommendations.These reports and recommendations not only contain specific intervention measures (such as dietary advice, exercise plans, mental health guidance, etc.), but also provide detailed explanations and instructions to help users better understand and implement these measures.

[0029] The effects of the above technical solutions are: using the Kafka real-time data stream processing system, data from multiple sources can be collected and processed in real time to ensure the timeliness and accuracy of the data. This is crucial for capturing the immediate changes in the physiological state of the human body and the immediate impact of environmental factors; extracting multi-dimensional aging-related features through deep learning models (CNN, LSTM, etc.), these features are more refined and comprehensive, and can more accurately reflect the aging state and change trend of the human body; based on the extracted features and aging feature maps, combined with the initial aging assessment model constructed by the machine learning algorithm, it is continuously optimized and adjusted through online learning algorithms (incremental learning, meta-learning) and user feedback mechanisms, realizing the personalized and adaptive learning of the model. This means that the model can be customized according to the specific circumstances and needs of different users, providing more accurate and personalized evaluation results; the personalized aging intervention strategy generated by the multi-objective optimization algorithm not only takes into account the anti-aging effect, but also balances multiple goals such as cost and risk, ensuring the feasibility and practicality of the strategy. At the same time, natural language generation technology is used to convert complex strategies into easy-to-understand language, which improves user acceptance and execution efficiency; the aging feature map constructed by combining graph neural network (GNN) reveals the complex relationship and potential laws between features, providing strong support for the comprehensive evaluation of human aging status. This comprehensive evaluation method helps to understand the aging process of the human body more comprehensively and provide a scientific basis for formulating effective intervention strategies; through continuous learning and optimization, the technical solution also has a certain predictive ability. It can predict future aging trends and potential risks based on historical data and current status, providing possibilities for early prevention and intervention; the generated personalized health reports and recommendations use natural language generation technology, and the content is concise and easy to understand, which improves the user's reading experience and acceptance. At the same time, the user feedback mechanism enhances the interaction between users and the system, enabling users to participate in the evaluation and optimization process, and improving the transparency and credibility of the entire analysis process.

[0030] In one embodiment of the present invention, the S1 includes:

[0031] S11. Determine the types of data sources required, including wearable devices (such as heart rate monitors, pedometers), medical testing equipment (such as blood pressure monitors, blood glucose meters), and environmental sensors (such as temperature and humidity sensors, air quality monitors), negotiate data interface protocols with various data source suppliers, access various data sources, deploy data integration middleware, and conduct stress testing and stability verification;

[0032] S12. Build the Kafka cluster architecture, including the configuration and deployment of Broker nodes, Zookeeper nodes, etc., configure Kafka topics, and divide different topics according to data types and uses;

[0033] S13. Write Kafka consumer and producer codes to collect and send data in real time, and perform preliminary data processing based on stream processing logic, such as data cleaning and format conversion;

[0034] S14. Monitor the running status of the Kafka cluster and stream processing tasks, and adjust resource allocation and processing logic in real time based on the monitoring results;

[0035] S15. Conduct a preliminary check on the collected raw data, identify and handle obvious errors and outliers; handle missing values ​​by filling them with interpolation, mean filling, mode filling or model-based prediction, and standardize and normalize the data;

[0036] S16. Reduce the data dimension through data dimensionality reduction algorithms (such as PCA and LDA), and divide the processed data into training set, validation set and test set.

[0037] The working principle of the above technical solution is: identify and determine the required multi-source data types, including wearable devices (such as heart rate monitors, pedometers), medical testing equipment (such as blood pressure monitors, blood glucose meters) and environmental sensors (such as temperature and humidity sensors, air quality monitors), etc.; negotiate data interface protocols with various data source suppliers to ensure the uniformity of data formats and the stability of transmission. This step is the basis of data integration and helps with the centralized management and scheduling of subsequent data; access various data sources to achieve centralized management and scheduling of data. Deploy data integration middleware to support data exchange and sharing between different data sources; stress test and stability verification of the data integration system to ensure that the system can handle high-concurrency data streams and meet real-time requirements; build a Kafka cluster architecture, including the configuration and deployment of Broker nodes and Zookeeper nodes. Kafka is a distributed stream processing platform that can handle high-throughput data streams; configure Kafka topics and divide different topics according to data types and uses. This helps with the classification management and efficient processing of data; write Kafka consumer and producer code for real-time data collection and transmission. Consumers receive data from data sources, and producers send data to Kafka clusters. Preliminary data processing is performed based on stream processing logic, such as data cleaning and format conversion. This step is a key step to ensure data quality. Preliminary checks are performed on the collected raw data to identify and handle obvious errors and outliers. Missing values ​​are filled by interpolation, mean filling, mode filling, or model-based prediction. Standardization and normalization of data are performed to eliminate the impact of different dimensions and ranges on data analysis. This step helps improve the efficiency of subsequent processing and the accuracy of results. Real-time monitoring of the operating status of Kafka clusters and stream processing tasks is performed, and resource allocation and processing logic are adjusted in real time based on the monitoring results. This helps ensure processing efficiency and stability, and timely discover and solve potential problems. Reduce data dimensions through data dimensionality reduction algorithms (such as PCA and LDA) to improve subsequent processing efficiency. The reduced data is more compact and easy to process. The processed data is divided into training sets, validation sets, and test sets, providing a basis for the construction and evaluation of subsequent machine learning models.

[0038] The effects of the above technical solutions are: by negotiating the data interface protocol with each data source supplier, the uniformity of the data format and the stability of the transmission are ensured. This avoids the processing difficulties and errors caused by inconsistent data formats, and improves the efficiency and accuracy of data processing; it realizes the centralized management and scheduling of multi-source data, simplifies the complexity of data processing, and improves the flexibility and scalability of data processing; it builds a Kafka cluster architecture that can handle high-concurrency data streams and ensure the real-time and high efficiency of data processing. The distributed nature of Kafka enables the system to cope with the challenges of large-scale data processing; through stress testing and stability verification, it ensures the stability and reliability of the system under high load conditions, reducing the risk of system crashes and data loss; writes Kafka consumer and producer codes to realize real-time data collection and transmission. This helps to capture changes in human physiological status and environmental factors in a timely manner, and provide the latest and most accurate data for subsequent analysis; it performs preliminary processing of data based on stream processing logic, such as data cleaning, format conversion, etc. This helps reduce the complexity and error rate of subsequent processing and improve the accuracy and efficiency of data analysis; real-time monitoring of the operating status of the Kafka cluster and stream processing tasks can promptly discover and handle potential problems and bottlenecks; real-time adjustment of resource allocation and processing logic based on monitoring results to ensure processing efficiency and stability. This adaptive adjustment mechanism enables the system to cope with different load conditions and processing requirements, improving the flexibility and response speed of the system; preliminary inspection of the collected raw data to identify and handle obvious errors and outliers. This helps reduce analysis errors and misleading caused by data quality issues; a variety of methods are used to fill missing values, such as interpolation, mean filling, mode filling or model-based prediction. This helps maintain the integrity and consistency of data and improve the reliability of data analysis; standardization and normalization of data eliminates the impact of different dimensions and ranges on data analysis. This helps improve the efficiency of subsequent processing and the comparability of results; reducing data dimensions through data dimensionality reduction algorithms improves the efficiency of subsequent processing. The data after dimensionality reduction is more compact and easier to process, which helps reduce computational complexity and storage costs; the processed data is divided into training sets, validation sets, and test sets, which provides a basis for the construction and evaluation of subsequent machine learning models. This division method helps ensure the generalization ability and accuracy of the model.

[0039] In one embodiment of the present invention, the S2 includes:

[0040] S21. Analyze the characteristics of the data and determine the type of features to be extracted based on the analysis results, including time series features, statistical features, image features, etc. Extract the features through feature extraction algorithms, such as time series feature extraction based on sliding windows, statistical feature extraction based on histograms, image feature extraction based on CNN, etc., and perform quality assessment on the extracted features to check whether there are redundant or irrelevant features, and perform corresponding screening and optimization; wherein, the feature quality assessment is obtained through the following formula:

[0041]

[0042] Among them, I(f i ) is the feature f i The importance score of , which can be calculated by a model-dependent measure (e.g., feature importance based on random forests);

[0043] R(f i , F -i ) is the feature f i and other features F in the feature set -i The redundancy score can be calculated by the mutual information or other correlation coefficients between features;

[0044] C(f i , T) is the characteristic f i The correlation score with the target variable T can be calculated by correlation coefficient or information gain;

[0045] V(f i ) is the feature f i The variability score of can be obtained by calculating the variance or entropy of the feature;

[0046] ω I ,ω R ,ω C ,ω V Represent the weights of importance, redundancy, relevance, and variability, and the sum is 1;

[0047] P(F) is the penalty term of the feature set FF, which is used to penalize overfitting features. For example, L1 or L2 regularization can be used.

[0048] N(F) is the regularization term of the feature set F, which is used to ensure the diversity of the feature set and can be based on the entropy of the feature distribution or other diversity measures;

[0049] λ P and λ N are the coefficients of the penalty term and the regularization term, which are determined by the optimization method.

[0050] S22, defining a graph structure, taking the extracted aging-related features as nodes, and the correlation or influence relationship between the features as edges;

[0051] S23. Select a GNN model (such as GCN, GAT), configure model parameters, write GNN model training code, and train the model using training set data;

[0052] S24. After training, use the validation set data to validate the model and evaluate the performance and generalization ability of the model; adjust the model parameters and structure according to the validation results to optimize the model performance;

[0053] S25. Use the optimized model to predict the test set data and generate an aging feature map.

[0054] The working principle of the above technical solution is as follows: first, conduct an in-depth analysis of the data set to understand the type of information it contains, such as time series data, statistical data or image data; based on the characteristics of the data, determine the type of features that need to be extracted, such as time series features, statistical features, image features, etc.; use appropriate feature extraction algorithms, such as time series feature extraction based on sliding windows, statistical feature extraction based on histograms, image feature extraction based on convolutional neural networks (CNNs), etc., to extract features; perform quality assessment on the extracted features to check whether there are redundant or irrelevant features, and perform corresponding screening and optimization to ensure the effectiveness of subsequent analysis; use the extracted aging-related features as nodes in a graph structure, with each node representing a specific feature; and define the edges between nodes based on the correlation or influence relationship between the features. These edges can represent direct connections or potential relationships between features; select appropriate graph neural network (GNN) models, such as graph convolutional networks (GCNs), graph attention networks (GATs), etc., according to task requirements and data characteristics; configure corresponding parameters, such as learning rate, number of iterations, number of layers, etc., according to the requirements of the selected model; write the training code for the GNN model, including data loading, model construction, loss function definition, optimizer selection, etc.; use the training set data to train the model, and adjust the model parameters through the back propagation algorithm so that the model can learn the complex relationship between features; after training, use the validation set data to verify the model and evaluate the performance and generalization ability of the model; according to the verification results, adjust the model parameters and structure to optimize the model performance. This may include changing the model architecture, adjusting the learning rate, adding regularization terms, etc.; use the optimized model to predict the test set data and generate an aging feature map; analyze the prediction results to understand the performance of the model under different conditions, and conduct further research or application based on this.

[0055] The effects of the above technical solutions are as follows: by conducting an in-depth analysis of the data characteristics, determining the appropriate feature type and using a special feature extraction algorithm, more representative and discriminative features can be extracted. This not only improves the effectiveness of subsequent analysis, but also reduces the interference of redundant and irrelevant features on model performance; the quality of the extracted features is evaluated, redundant or irrelevant features are discovered and removed in a timely manner, and the feature set is optimized, so that the model can focus more on factors that have a substantial impact on the aging process; the aging-related features are used as nodes in the graph structure, and the edges are used to represent the correlation or influence relationship between the features. This representation method can intuitively reflect the complex relationship between the features and provide strong support for the subsequent graph neural network model processing; the graph structure not only helps the model learn the interaction between features, but also makes the model results more interpretable. By analyzing the key nodes and edges in the graph structure, we can deeply understand which features have an important impact on the aging process; select appropriate graph neural network models (such as GCN, GAT, etc.) according to task requirements and data characteristics. These models can make full use of the information in the graph structure, learn the complex relationship between features, and generate effective prediction results; through the training, verification and optimization process, the model parameters and structure are continuously adjusted to improve the performance and generalization ability of the model. This enables the model to predict aging characteristics more accurately and maintain good performance on new data sets; the optimized model is used to predict the test set data and generate aging characteristic maps. These maps can intuitively show the changes in individual characteristics at different aging stages, providing strong support for fields such as medical care, health management, and aging research; based on the generated aging characteristic maps, medical professionals can formulate more accurate diagnosis and treatment plans, health management companies can provide more personalized health management services, and researchers can explore the occurrence and development of aging mechanisms and related diseases in more depth. The above feature quality evaluation formula; comprehensively considers multiple dimensions such as feature importance, redundancy, correlation with target variables, and feature variability, so that the quality of features can be more comprehensively evaluated. This comprehensive evaluation helps to screen out features that contribute greatly to the model's predictive performance and have low mutual redundancy; by setting different weights, the importance of each evaluation dimension can be flexibly adjusted according to the needs of specific tasks and the characteristics of the data. This flexibility makes feature quality evaluation more adaptable to different application scenarios and data sets; the introduction of penalty terms and regularization terms helps prevent model overfitting and enhance the diversity of feature sets. Overfitting is a common problem in machine learning, especially when there are a large number of features. The generalization ability of the model can be improved by penalizing overfitting features and ensuring the diversity of the feature set; based on the evaluation results of this formula, the extracted features can be screened and optimized, redundant or irrelevant features can be removed, and features that contribute greatly to the model's prediction performance can be retained.This optimization process can not only reduce the computational complexity of the model, but also improve the prediction accuracy and stability of the model; by comprehensively evaluating and optimizing the feature quality, higher quality data input can be provided for subsequent model training. High-quality input data helps improve the training efficiency and prediction performance of the model, allowing the model to better capture useful information in the data and make accurate predictions; each evaluation indicator in the formula has clear mathematical and statistical meanings, making the results of feature quality evaluation more interpretable and credible. This helps researchers and engineers better understand the impact of data features on model performance, so as to make more scientific and reasonable decisions.

[0056] In one embodiment of the present invention, S3 includes:

[0057] S31. In-depth analysis of the distribution characteristics of the data, including outlier detection, refinement of missing value filling strategies (such as time series-based interpolation, filling based on similar user groups, etc.), application of feature selection techniques (such as chi-square test, mutual information, recursive feature elimination, etc.), screening out the most influential feature subsets for aging assessment from a large number of candidate features; and encoding and transforming the features; taking a variety of machine learning algorithms (such as logistic regression, random forest, gradient boosting tree, deep learning model, etc.) as candidates, and using cross-validation techniques (such as K-fold cross-validation) to evaluate the performance of each candidate model, including accuracy, recall, F1 score, area under the ROC curve (AUC) and other indicators;

[0058] S32. Select the optimal model framework based on the evaluation results, preliminarily train the model, and adjust the model parameters using the training set data; test the preliminarily trained model on the validation set to evaluate its generalization ability;

[0059] S33. Analyze the model's incorrect prediction cases on the validation set to identify possible biases and limitations; adjust the model architecture or parameters based on the validation results, such as adding hidden layers, changing activation functions, adjusting regularization strength, etc., to improve model performance;

[0060] S34. Combine multiple single models into an integrated model through integrated learning techniques, such as bagging, boosting or stacking, evaluate the impact of different integration strategies on model performance, select the optimal integration method, train the integrated model, and adjust the weights and parameters of each base model;

[0061] S35. Analyze the interactions between features, identify the feature combinations that have a significant impact on aging assessment, and capture complex feature interactions and temporal dependencies through deep learning models (such as convolutional neural networks (CNN), recurrent neural networks (RNN), or graph neural networks (GNN); design the architecture of deep models, such as a combination of multi-layer perceptrons, convolutional layers, recurrent layers, or graph convolutional layers, to better learn high-level feature representations in the data, and design incremental learning mechanisms that allow the model to automatically update its parameters and structure when receiving new data;

[0062] S36. The performance of the model on new data is regularly evaluated through the model monitoring and evaluation system, and the incremental learning process is triggered. In the incremental learning process, transfer learning technology is applied to transfer the knowledge on the old data to the new data;

[0063] S37. Analyze the uncertainty of model predictions, estimate the confidence interval of prediction results through Bayesian methods, and evaluate the model performance based on the robustness test scheme; including the performance under conditions such as noisy data, abnormal data or changes in data distribution; further optimize and adjust the model based on the uncertainty and robustness evaluation results;

[0064] S38. Based on the user feedback mechanism, collect users’ satisfaction, suggestions and improvement opinions on the evaluation results, organize and classify user feedback, identify common problems and personalized needs, analyze the impact of user feedback on model performance, and evaluate the applicability of the model in real scenarios;

[0065] S39. Based on user feedback and model performance evaluation results, formulate an iterative optimization plan to optimize the model in a targeted manner, such as adjusting feature selection strategies, improving model architecture, optimizing training algorithms, etc., and regularly update and retrain the model.

[0066] The working principle of the above technical solution is as follows: first, analyze the distribution characteristics of the data, including outlier detection and missing value processing. By refining the missing value filling strategy (such as time series-based interpolation, filling based on similar user groups, etc.) and feature selection techniques (such as chi-square test, mutual information, recursive feature elimination, etc.), screen out the feature subsets that have the greatest impact on aging assessment; encode and transform the features to meet the needs of different machine learning algorithms. For example, use one-hot encoding to process categorical features, polynomial or logarithmic transformation to process numerical features, etc.; comprehensively consider factors such as the model's predictive performance, interpretability, and training efficiency, select a variety of machine learning algorithms as candidates, and use cross-validation technology to evaluate the performance of each candidate model; select the optimal model framework based on the evaluation results, and use the training set data to preliminarily train the model and adjust the model parameters; test the preliminarily trained model on the validation set to evaluate its generalization ability; analyze the model's incorrect prediction cases on the validation set to identify possible biases and limitations; adjust the model architecture or parameters based on the verification results, such as adding hidden layers, changing activation functions, adjusting regularization strength, etc., to improve model performance; through Combine multiple single models into an integrated model through integrated learning techniques (such as Bagging, Boosting or Stacking); evaluate the impact of different integration strategies on model performance, select the optimal integration method, and adjust the weights and parameters of each base model to maximize the overall performance; capture complex feature interactions and temporal dependencies through deep learning models (such as CNN, RNN or GNN); design the architecture of the deep model to better learn high-level feature representations in the data, and design an incremental learning mechanism to allow the model to automatically update its parameters and structure when receiving new data; regularly evaluate the performance of the model on new data through the model monitoring and evaluation system, and trigger the incremental learning process. In the incremental learning process, transfer learning technology is applied to accelerate the training process of new models; the confidence interval of the prediction results is estimated through the Bayesian method, and the uncertainty of the model prediction is analyzed; based on the robustness test scheme, the performance of the model in the case of noisy data, abnormal data or data distribution changes is evaluated, and the model is further optimized and adjusted according to the evaluation results; through the user feedback mechanism, user satisfaction, suggestions and improvement opinions on the evaluation results are collected; user feedback is sorted and classified, common problems and personalized needs are identified, and their impact on model performance is analyzed; according to user feedback and model performance evaluation results, an iterative optimization plan is formulated to optimize the model in a targeted manner, and the model is regularly updated and retrained.

[0067] The effects of the above technical solutions are as follows: by deeply analyzing the distribution characteristics of the data, implementing outlier detection and missing value filling strategies, the data quality is effectively improved, laying a solid foundation for subsequent analysis; using feature selection technology to screen out the most influential feature subsets for aging assessment, reducing data redundancy and noise, and improving the prediction efficiency and accuracy of the model; encoding and converting features according to the needs of the machine learning algorithm, making the features more suitable for algorithm processing, and further improving the model performance; using cross-validation technology to evaluate the performance of multiple machine learning algorithms to ensure the scientificity and rationality of model selection; selecting the optimal model framework based on the evaluation results, and verifying the generalization ability of the model through preliminary training and verification; continuously optimizing model performance through analysis of erroneous prediction cases and adjustment of model architecture or parameters to ensure the effectiveness of the model in practical applications; combining multiple single models into an integrated model through integrated learning technology, effectively improving the stability and prediction accuracy of the model; using deep learning models to capture features The complex interactions and temporal dependencies between them improve the model's ability to handle complex data; an incremental learning mechanism is designed to enable the model to automatically adapt to new data and maintain the timeliness and accuracy of the model; the performance of the model on new data is regularly evaluated through the model monitoring and evaluation system to ensure real-time monitoring of the model performance; the transfer learning technology is applied in the incremental learning process to accelerate the training process of the new model and improve the efficiency of model updating; the confidence interval of the prediction result is estimated through the Bayesian method, which provides a reliable confidence assessment for the prediction result of the model; the performance of the model in the case of noisy data, abnormal data or data distribution changes is evaluated based on the robustness test scheme, which improves the stability and reliability of the model; the user satisfaction, suggestions and improvement opinions on the evaluation results are collected through the user feedback mechanism, which provides a valuable reference for the optimization of the model; an iterative optimization plan is formulated according to user feedback and model performance evaluation results, and the model is optimized in a targeted manner to ensure the applicability and continuous optimization of the model in real scenarios.

[0068] In one embodiment of the present invention, the S4 includes:

[0069] S41. Define the optimization target set, including anti-aging effect, cost, risk and other multiple targets, select a multi-objective optimization algorithm (such as NSGA-II, MOEA / D, etc.), and configure the algorithm parameters;

[0070] S42. Through the multi-objective optimization algorithm code, the optimization objectives are converted into a form that can be processed by the algorithm, and the candidate intervention strategies are evaluated and optimized based on the algorithm to generate the Pareto frontier solution set;

[0071] S43. Select the most appropriate intervention strategy from the Pareto frontier solution set based on the user's specific situation and needs;

[0072] S44. According to the selected intervention strategy, set up intervention measures, including dietary advice, exercise plan, mental health guidance, etc., and transform the intervention measures through natural language generation technology;

[0073] S45. Integrate intervention measures and assessment results into health report and recommendation templates, generate personalized health reports and recommendations, and send them to users through multiple methods (email, APP push);

[0074] S46. Collect user feedback on the intervention strategy, and analyze and process the feedback.

[0075] The working principle of the above technical solution is as follows: First, clarify the set of optimization objectives of the anti-aging intervention strategy, which usually includes anti-aging effects, costs, risks and other aspects. These objectives may conflict with each other, and it is necessary to find the best balance point through a multi-objective optimization algorithm; according to the complexity and needs of the problem, select a suitable multi-objective optimization algorithm, such as NSGA-II (non-dominated sorting genetic algorithm II), MOEA / D (multi-objective evolutionary algorithm based on decomposition), etc. At the same time, the algorithm parameters are reasonably configured to ensure the effectiveness and efficiency of the algorithm; by writing or calling the code of the multi-objective optimization algorithm, the optimization objectives are converted into a form that the algorithm can handle. This usually involves converting the objective function, constraints, etc. into mathematical expressions that the algorithm can understand; based on the selected algorithm, the candidate anti-aging intervention strategies are evaluated and optimized. The algorithm generates a series of candidate solutions, and finally generates a Pareto frontier solution set through comparison and screening. The Pareto frontier solution set contains solutions that cannot be completely dominated by other solutions under given conditions, that is, in these solutions, the improvement of any one goal will lead to the deterioration of at least one other goal; select the most appropriate anti-aging intervention strategy from the Pareto frontier solution set according to the specific situation and needs of the user (such as age, health status, economic conditions, etc.). This selection process may need to consider the user's preferences and priorities to ensure that the selected strategy matches the user's needs; formulate specific intervention measures based on the selected intervention strategy, including dietary advice, exercise plans, mental health guidance, etc. These measures should be specific, feasible, and in line with the user's actual situation; use natural language generation technology to convert intervention measures into easy-to-understand language expressions. This helps users better understand and implement intervention measures and improve the intervention effect; integrate intervention measures and evaluation results into health report and proposal templates to form personalized health reports and proposals. These reports and proposals should contain the user's basic information, evaluation results, intervention measures, and implementation suggestions; send personalized health reports and proposals to users through various means such as email and APP push to ensure that users can obtain and view relevant information in a timely manner; actively collect user feedback on intervention strategies, including satisfaction, effect evaluation, and improvement suggestions. These feedbacks are an important basis for evaluating the effectiveness of intervention strategies and user acceptance. The collected feedbacks are analyzed and processed to identify existing problems and deficiencies. Based on the analysis results, the intervention strategies are optimized and adjusted to improve their relevance and effectiveness. At the same time, these feedbacks also provide valuable references for subsequent research and improvements.

[0076] The effects of the above technical solution are as follows: by defining multiple optimization objectives including anti-aging effect, cost, risk, etc., the technical solution can comprehensively consider all aspects of the anti-aging intervention strategy and avoid the one-sidedness that may be caused by single-objective optimization; the multi-objective optimization algorithm is used to generate the Pareto frontier solution set, providing decision makers with multiple candidate solutions that cannot be completely dominated by other solutions. This allows decision makers to find the best balance between multiple objectives according to actual conditions and needs; the technical solution emphasizes the selection of the most appropriate intervention strategy according to the specific conditions and needs of the user. This personalized selection method can ensure that the intervention strategy matches the actual situation of the user, thereby improving the intervention effect; the intervention measures are converted into easy-to-understand language expressions through natural language generation technology, which helps users better understand and implement the intervention measures. This further enhances the effectiveness of the intervention strategy and user satisfaction; personalized health reports and recommendations are sent to users through various means such as email and APP push, ensuring the timely communication and reception of information. This helps users to understand their health status and intervention suggestions in a timely manner; collect user feedback on the intervention strategy, analyze and process it, and provide important reference for subsequent optimization of the intervention strategy. This feedback loop mechanism helps to continuously improve and optimize intervention strategies, and improve their relevance and effectiveness; the personalized health reports and recommendations provided by this technical solution, as well as the optimization adjustments based on user feedback, provide users with a customized health service experience. This service experience helps to improve user satisfaction and loyalty; by collecting user feedback and continuously optimizing intervention strategies, users are encouraged to actively participate in the health management process. This increased participation helps to enhance users' health awareness and self-management capabilities; based on multi-objective optimization algorithms and big data analysis technology, scientific and objective data support is provided for health management and research. This helps to promote innovation and development in the field of health management and research. ; By continuously collecting user feedback and optimizing intervention strategies, the quality and efficiency of health management and services can be continuously improved. This continuous optimization mechanism helps to promote continuous progress in the field of health management and research.

[0077] In one embodiment of the present invention, the S42 includes:

[0078] Based on medical research and user needs, multiple optimization objectives are defined, and each optimization objective is converted into a mathematical expression to form an objective function set;

[0079] Define intervention strategy space, including multiple dimensions such as diet adjustment, exercise plan, sleep management, psychological intervention, etc.; each dimension can be further subdivided into multiple specific strategies, such as diet adjustment may include increasing protein intake, reducing sugar intake, etc.;

[0080] A genetic algorithm is used to encode candidate intervention strategies into a format that can be processed by the algorithm;

[0081] Use the physiological model to simulate the encoded candidate intervention strategies and evaluate their effects under different user conditions; calculate the objective function value corresponding to each candidate strategy based on the simulation results;

[0082] In the candidate strategy set, non-dominated solutions are screened out according to the Pareto dominance relationship; these non-dominated solutions constitute the Pareto frontier solution set; as the algorithm iterates, newly generated non-dominated solutions are continuously added to the Pareto frontier solution set, and old solutions dominated by new solutions are removed.

[0083] The working principle of the above technical solution is: according to medical research and user needs, multiple optimization goals are clarified and quantified, such as extending healthy life span, improving quality of life, and reducing disease risks. These goals are measured by specific quantitative indicators, such as biomarker levels, daily activity ability scores, mental health scale scores, etc.; these optimization goals are converted into mathematical expressions to form a set of objective functions. This step is the basis of algorithm optimization, ensuring that the algorithm can optimize for these specific goals; considering that there may be conflicts between different optimization goals (such as increasing the amount of exercise to improve health levels may also increase the risk of injury), a reasonable weight allocation mechanism or unweighted method (such as Pareto optimization) is used to deal with these conflicts. Unweighted methods such as Pareto optimization balance multiple goals by finding a non-dominated solution set to avoid the situation where a single goal is optimal at the expense of other goals; based on medical knowledge and practical experience, define a space of intervention strategies containing multiple dimensions, such as diet adjustment, exercise plan, sleep management, psychological intervention, etc. Each dimension is further subdivided into specific strategy options, such as increasing protein intake and reducing sugar intake in dietary adjustments; using evolutionary algorithms such as genetic algorithms, candidate intervention strategies are encoded into a format that can be processed by the algorithm (such as binary strings, real number vectors, etc.). The encoding process must ensure that the strategy space is fully covered, and at the same time facilitate subsequent genetic operations such as crossover and mutation to increase the diversity and optimization potential of the strategy; simulation tools such as physiological models are used to simulate the encoded candidate intervention strategies and evaluate their effects under different user conditions. The simulation process fully considers the influence of individual differences of users (such as age, gender, genetic background, etc.) and environmental factors (such as climate, social support, etc.) to improve the accuracy and personalization of the evaluation; based on the simulation results, the objective function value corresponding to each candidate strategy is calculated. For multi-objective optimization problems, it is necessary to consider the optimization of all objective functions at the same time to form a comprehensive evaluation; in the candidate strategy set, non-dominated solutions are screened out according to the Pareto dominance relationship (that is, no other strategy is better than this strategy in all objectives). These non-dominated solutions constitute the Pareto frontier solution set, which represents the optimal solution set found in the current optimization process; as the algorithm iterates, the newly generated non-dominated solutions are continuously added to the Pareto frontier solution set, and the old solutions dominated by the new solutions are removed. This process gradually approaches the optimal solution set, providing a basis for the final selection of the most appropriate intervention strategy; according to the specific situation and needs of the user (such as preferences, health status, etc.), the most appropriate intervention strategy is selected from the Pareto frontier solution set. This process may involve interaction and feedback mechanisms with users to ensure that the selected strategy meets the user's expectations and needs; the selected intervention strategy is converted into specific intervention measures, and personalized health reports and recommendations are generated through natural language generation technology.These reports and recommendations not only contain the specific content of the intervention measures, but may also include detailed information such as implementation steps and precautions so that users can easily understand and implement them; collect user feedback on the intervention strategy, and analyze and process the feedback. This feedback information can be used to evaluate the effectiveness of the intervention strategy and user satisfaction, and provide a basis for subsequent optimization and improvement; through online learning algorithms and user feedback mechanisms, the aging assessment model and intervention strategy generation algorithm are continuously adjusted and optimized to adapt to changes in user needs and data updates. This process forms a closed-loop continuous optimization mechanism to ensure that the algorithm can continue to provide high-quality personalized services.

[0084] The effect of the above technical solution is: by comprehensively considering medical research, user needs and individual differences, the technical solution can generate highly personalized intervention strategies. Each user can get tailored advice based on their health status, lifestyle and preferences, thereby improving the effectiveness of intervention measures and user acceptance; using multi-objective optimization methods (such as Pareto optimization), the technical solution can simultaneously consider multiple optimization goals, such as extending healthy life span, improving quality of life, reducing disease risk, etc. This method avoids the problem that other goals may be damaged by single-objective optimization, and achieves a more comprehensive and balanced optimization effect; the candidate intervention strategies are simulated and executed using physiological models, and the influence of user individual differences and environmental factors is considered, making the evaluation results more scientific and accurate. This method can predict the effects of different strategies under different user conditions and provide strong support for selecting the optimal strategy; the application of evolutionary algorithms such as genetic algorithms makes the search process of candidate strategies more efficient. Through operations such as encoding, crossover, and mutation, the algorithm can quickly traverse the strategy space and find potential high-quality solutions. At the same time, the generation and update mechanism of the Pareto frontier solution set ensures that the algorithm can gradually approach the optimal solution set, improving the efficiency and effectiveness of the optimization process; the technical solution has the ability of continuous improvement and adaptability. By collecting user feedback and updating medical research results, the aging assessment model and intervention strategy generation algorithm can be continuously optimized to adapt to changes in user needs and data updates. This mechanism ensures the long-term effectiveness and competitiveness of the technical solution; the personalized intervention strategy finally generated can accurately intervene according to the specific situation of the user, helping users to improve their living habits, enhance physical functions, and improve their mental health. These positive intervention effects will significantly improve the overall health level and quality of life of users; the implementation of this technical solution will help promote the popularization and development of health management services. By providing personalized, scientific, and efficient intervention strategies, more users can be attracted to pay attention to their own health and actively participate in health management activities, thus forming a good health trend and social atmosphere.

[0085] One embodiment of the present invention, as Figure 2As shown, a human aging information analysis system based on big data, the system comprises:

[0086] Data collection module: collects multi-source data in real time through a real-time data stream processing system based on Kafka, and performs preliminary processing on the collected multi-source data; the multi-source data includes human physiological data and environmental data;

[0087] Graph construction module: extract multi-dimensional aging-related features from the preliminarily processed multi-source data through deep learning models (such as convolutional neural networks (CNN) and long short-term memory networks (LSTM)), and build an aging feature graph based on the extracted features in combination with graph neural networks (GNN); reveal the complex relationships and potential laws between the features;

[0088] Adjustment and optimization module: Based on the extracted features and the constructed aging feature map, the initial aging assessment model is constructed using machine learning algorithms. The model parameters are continuously adjusted and optimized through online learning algorithms (such as incremental learning, meta-learning) and user feedback mechanisms.

[0089] Report generation module: Based on the user's specific situation and needs, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, and generate personalized health reports and recommendations.

[0090] The working principle of the above technical solution is: using Kafka, a distributed stream processing platform, to collect human physiological data (such as heart rate, blood pressure, number of steps, etc.) and environmental data (such as temperature, humidity, air quality, etc.) in real time from multiple data sources (such as wearable devices, medical testing equipment, environmental sensors, etc.). Kafka's high throughput and fault tolerance ensure that data can be transmitted stably and efficiently; the collected multi-source data is preliminarily processed, including data cleaning (removing noise and outliers), data compression (reducing storage and transmission costs), data format conversion (unifying data formats for subsequent processing), etc. This step aims to improve data quality and provide a reliable data foundation for subsequent analysis; deep learning models (such as CNN, LSTM) are used to conduct in-depth analysis of the preliminarily processed multi-source data to extract multi-dimensional features related to aging. CNN is good at processing the spatial features of images or sequence data, while LSTM is good at capturing long-term dependencies in time series data. These features may include the changing trends of physiological indicators, the periodic changes of environmental factors, etc.; based on the extracted features, a graph neural network (GNN) is used to construct an aging feature map. GNN can process graph-structured data and reveal the complex relationships and potential rules between features through nodes (features) and edges (relationships between features). The aging feature map not only shows the status of each feature, but also reveals the interactions and influences between them, providing comprehensive information support for subsequent aging assessment; the initial aging assessment model is constructed by combining the extracted features and the constructed aging feature map using machine learning algorithms (such as random forests, gradient boosting trees, neural networks, etc.). The model can evaluate the aging status of individuals based on the input feature data; the model is continuously optimized through online learning algorithms (such as incremental learning, meta-learning) and user feedback mechanisms. Incremental learning allows the model to automatically update its parameters and structure when receiving new data to adapt to changes in data. Meta-learning improves the generalization ability and adaptability of the model by learning how to learn. The user feedback mechanism collects user feedback on the evaluation results to guide further optimization of the model; through the above optimization process, adaptive learning of the personalized evaluation model is achieved. The model can conduct customized assessments based on the specific circumstances and needs of users, providing more accurate and personalized aging assessment results; combined with the specific circumstances and needs of users (such as age, gender, health status, economic conditions, etc.), a multi-objective optimization algorithm (such as NSGA-II, MOEA / D, etc.) is used to generate personalized aging intervention strategies. These strategies aim to balance multiple goals such as anti-aging effects, costs, and risks, and provide users with the best intervention plan; natural language generation technology is used to convert complex intervention strategies into easy-to-understand language, and generate personalized health reports and recommendations.These reports and recommendations not only contain specific intervention measures (such as dietary advice, exercise plans, mental health guidance, etc.), but also provide detailed explanations and instructions to help users better understand and implement these measures.

[0091] The effects of the above technical solutions are: using the Kafka real-time data stream processing system, data from multiple sources can be collected and processed in real time to ensure the timeliness and accuracy of the data. This is crucial for capturing the immediate changes in the physiological state of the human body and the immediate impact of environmental factors; extracting multi-dimensional aging-related features through deep learning models (CNN, LSTM, etc.), these features are more refined and comprehensive, and can more accurately reflect the aging state and change trend of the human body; based on the extracted features and aging feature maps, combined with the initial aging assessment model constructed by the machine learning algorithm, it is continuously optimized and adjusted through online learning algorithms (incremental learning, meta-learning) and user feedback mechanisms, realizing the personalized and adaptive learning of the model. This means that the model can be customized according to the specific circumstances and needs of different users, providing more accurate and personalized evaluation results; the personalized aging intervention strategy generated by the multi-objective optimization algorithm not only takes into account the anti-aging effect, but also balances multiple goals such as cost and risk, ensuring the feasibility and practicality of the strategy. At the same time, natural language generation technology is used to convert complex strategies into easy-to-understand language, which improves user acceptance and execution efficiency; the aging feature map constructed by combining graph neural network (GNN) reveals the complex relationship and potential laws between features, providing strong support for the comprehensive evaluation of human aging status. This comprehensive evaluation method helps to understand the aging process of the human body more comprehensively and provide a scientific basis for formulating effective intervention strategies; through continuous learning and optimization, the technical solution also has a certain predictive ability. It can predict future aging trends and potential risks based on historical data and current status, providing possibilities for early prevention and intervention; the generated personalized health reports and recommendations use natural language generation technology, and the content is concise and easy to understand, which improves the user's reading experience and acceptance. At the same time, the user feedback mechanism enhances the interaction between users and the system, enabling users to participate in the evaluation and optimization process, and improving the transparency and credibility of the entire analysis process.

[0092] In one embodiment of the present invention, the data acquisition module includes:

[0093] Type determination module: Determine the required data source types, including wearable devices (such as heart rate monitors, pedometers), medical testing equipment (such as blood pressure monitors, blood glucose meters), environmental sensors (such as temperature and humidity sensors, air quality monitors), negotiate data interface protocols with various data source suppliers, access various data sources, deploy data integration middleware, and perform stress testing and stability verification;

[0094] Architecture building module: builds the Kafka cluster architecture, including the configuration and deployment of Broker nodes, Zookeeper nodes, etc., configures Kafka topics, and divides different topics according to data types and uses;

[0095] Preliminary processing module: Write Kafka consumer and producer codes to collect and send data in real time, and perform preliminary processing on data based on stream processing logic, such as data cleaning and format conversion.

[0096] Status monitoring module: monitors the running status of Kafka clusters and stream processing tasks, and adjusts resource allocation and processing logic in real time based on the monitoring results;

[0097] Normalization module: Perform preliminary inspection on the collected raw data, identify and deal with obvious errors and outliers; handle missing values, fill them with interpolation, mean filling, mode filling or model-based prediction, and standardize and normalize the data.

[0098] Data dimensionality reduction module: Reduce the data dimension through data dimensionality reduction algorithms (such as PCA, LDA), and divide the processed data into training set, validation set and test set.

[0099] The working principle of the above technical solution is: identify and determine the required multi-source data types, including wearable devices (such as heart rate monitors, pedometers), medical testing equipment (such as blood pressure monitors, blood glucose meters) and environmental sensors (such as temperature and humidity sensors, air quality monitors), etc.; negotiate data interface protocols with various data source suppliers to ensure the uniformity of data formats and the stability of transmission. This step is the basis of data integration and helps with the centralized management and scheduling of subsequent data; access various data sources to achieve centralized management and scheduling of data. Deploy data integration middleware to support data exchange and sharing between different data sources; stress test and stability verification of the data integration system to ensure that the system can handle high-concurrency data streams and meet real-time requirements; build a Kafka cluster architecture, including the configuration and deployment of Broker nodes and Zookeeper nodes. Kafka is a distributed stream processing platform that can handle high-throughput data streams; configure Kafka topics and divide different topics according to data types and uses. This helps with the classification management and efficient processing of data; write Kafka consumer and producer code for real-time data collection and transmission. Consumers receive data from data sources, and producers send data to Kafka clusters. Preliminary data processing is performed based on stream processing logic, such as data cleaning and format conversion. This step is a key step to ensure data quality. Preliminary checks are performed on the collected raw data to identify and handle obvious errors and outliers. Missing values ​​are filled by interpolation, mean filling, mode filling, or model-based prediction. Standardization and normalization of data are performed to eliminate the impact of different dimensions and ranges on data analysis. This step helps improve the efficiency of subsequent processing and the accuracy of results. Real-time monitoring of the operating status of Kafka clusters and stream processing tasks is performed, and resource allocation and processing logic are adjusted in real time based on the monitoring results. This helps ensure processing efficiency and stability, and timely discover and solve potential problems. Reduce data dimensions through data dimensionality reduction algorithms (such as PCA and LDA) to improve subsequent processing efficiency. The reduced data is more compact and easy to process. The processed data is divided into training sets, validation sets, and test sets, providing a basis for the construction and evaluation of subsequent machine learning models.

[0100] The effects of the above technical solutions are: by negotiating the data interface protocol with each data source supplier, the uniformity of the data format and the stability of the transmission are ensured. This avoids the processing difficulties and errors caused by inconsistent data formats, and improves the efficiency and accuracy of data processing; it realizes the centralized management and scheduling of multi-source data, simplifies the complexity of data processing, and improves the flexibility and scalability of data processing; it builds a Kafka cluster architecture that can handle high-concurrency data streams and ensure the real-time and high efficiency of data processing. The distributed nature of Kafka enables the system to cope with the challenges of large-scale data processing; through stress testing and stability verification, it ensures the stability and reliability of the system under high load conditions, reducing the risk of system crashes and data loss; writes Kafka consumer and producer codes to realize real-time data collection and transmission. This helps to capture changes in human physiological status and environmental factors in a timely manner, and provide the latest and most accurate data for subsequent analysis; it performs preliminary processing of data based on stream processing logic, such as data cleaning, format conversion, etc. This helps reduce the complexity and error rate of subsequent processing and improve the accuracy and efficiency of data analysis; real-time monitoring of the operating status of the Kafka cluster and stream processing tasks can promptly discover and handle potential problems and bottlenecks; real-time adjustment of resource allocation and processing logic based on monitoring results to ensure processing efficiency and stability. This adaptive adjustment mechanism enables the system to cope with different load conditions and processing requirements, improving the flexibility and response speed of the system; preliminary inspection of the collected raw data to identify and handle obvious errors and outliers. This helps reduce analysis errors and misleading caused by data quality issues; a variety of methods are used to fill missing values, such as interpolation, mean filling, mode filling or model-based prediction. This helps maintain the integrity and consistency of data and improve the reliability of data analysis; standardization and normalization of data eliminates the impact of different dimensions and ranges on data analysis. This helps improve the efficiency of subsequent processing and the comparability of results; reducing data dimensions through data dimensionality reduction algorithms improves the efficiency of subsequent processing. The data after dimensionality reduction is more compact and easier to process, which helps reduce computational complexity and storage costs; the processed data is divided into training sets, validation sets, and test sets, which provides a basis for the construction and evaluation of subsequent machine learning models. This division method helps ensure the generalization ability and accuracy of the model.

[0101] In one embodiment of the present invention, the graph construction module includes:

[0102] Type determination module: Analyze the data characteristics and determine the feature types that need to be extracted based on the analysis results, including time series features, statistical features, image features, etc. Extract features through feature extraction algorithms, such as time series feature extraction based on sliding windows, statistical feature extraction based on histograms, image feature extraction based on CNN, etc., and evaluate the quality of the extracted features to check whether there are redundant or irrelevant features, and perform corresponding screening and optimization;

[0103] Structure definition module: defines the graph structure, taking the extracted aging-related features as nodes and the correlation or influence relationship between the features as edges;

[0104] Model training module: select GNN models (such as GCN, GAT), configure model parameters, write GNN model training code, and train the model using training set data;

[0105] Performance optimization module: After training, the model is verified using the validation set data, and the performance and generalization ability of the model are evaluated; model parameters and structure are adjusted according to the validation results to optimize model performance;

[0106] Data prediction module: Use the optimized model to predict the test set data and generate an aging feature map.

[0107] The working principle of the above technical solution is as follows: first, conduct an in-depth analysis of the data set to understand the type of information it contains, such as time series data, statistical data or image data; based on the characteristics of the data, determine the type of features that need to be extracted, such as time series features, statistical features, image features, etc.; use appropriate feature extraction algorithms, such as time series feature extraction based on sliding windows, statistical feature extraction based on histograms, image feature extraction based on convolutional neural networks (CNNs), etc., to extract features; perform quality assessment on the extracted features to check whether there are redundant or irrelevant features, and perform corresponding screening and optimization to ensure the effectiveness of subsequent analysis; use the extracted aging-related features as nodes in a graph structure, with each node representing a specific feature; and define the edges between nodes based on the correlation or influence relationship between the features. These edges can represent direct connections or potential relationships between features; select appropriate graph neural network (GNN) models, such as graph convolutional networks (GCNs), graph attention networks (GATs), etc., according to task requirements and data characteristics; configure corresponding parameters, such as learning rate, number of iterations, number of layers, etc., according to the requirements of the selected model; write the training code for the GNN model, including data loading, model construction, loss function definition, optimizer selection, etc.; use the training set data to train the model, and adjust the model parameters through the back propagation algorithm so that the model can learn the complex relationship between features; after training, use the validation set data to verify the model and evaluate the performance and generalization ability of the model; according to the verification results, adjust the model parameters and structure to optimize the model performance. This may include changing the model architecture, adjusting the learning rate, adding regularization terms, etc.; use the optimized model to predict the test set data and generate an aging feature map; analyze the prediction results to understand the performance of the model under different conditions, and conduct further research or application based on this.

[0108] The effects of the above technical solutions are as follows: by conducting an in-depth analysis of the data characteristics, determining the appropriate feature type and using a special feature extraction algorithm, more representative and discriminative features can be extracted. This not only improves the effectiveness of subsequent analysis, but also reduces the interference of redundant and irrelevant features on model performance; the quality of the extracted features is evaluated, redundant or irrelevant features are discovered and removed in a timely manner, and the feature set is optimized, so that the model can focus more on factors that have a substantial impact on the aging process; the aging-related features are used as nodes in the graph structure, and the edges are used to represent the correlation or influence relationship between the features. This representation method can intuitively reflect the complex relationship between the features and provide strong support for the subsequent graph neural network model processing; the graph structure not only helps the model learn the interaction between features, but also makes the model results more interpretable. By analyzing the key nodes and edges in the graph structure, we can deeply understand which features have an important impact on the aging process; select appropriate graph neural network models (such as GCN, GAT, etc.) according to task requirements and data characteristics. These models can make full use of the information in the graph structure, learn the complex relationship between features, and generate effective prediction results; through the training, verification and optimization process, the model parameters and structure are continuously adjusted to improve the performance and generalization ability of the model. This enables the model to predict aging characteristics more accurately and maintain good performance on new data sets; the optimized model is used to predict the test set data and generate aging characteristic maps. These maps can intuitively show the changes in individual characteristics at different aging stages, providing strong support for fields such as medical care, health management, and aging research; based on the generated aging characteristic maps, medical professionals can develop more accurate diagnosis and treatment plans, health management companies can provide more personalized health management services, and researchers can explore the aging mechanism and the occurrence and development of related diseases in more depth.

[0109] In one embodiment of the present invention, the adjustment and optimization module includes:

[0110] Performance evaluation module: In-depth analysis of data distribution characteristics, including outlier detection, refinement of missing value filling strategies (such as interpolation based on time series, filling based on similar user groups, etc.), application of feature selection techniques (such as chi-square test, mutual information, recursive feature elimination, etc.), screening out the most influential feature subsets for aging assessment from a large number of candidate features; and encoding and transforming the features to meet the needs of different machine learning algorithms, such as one-hot encoding to process categorical features, polynomial or logarithmic transformation to process numerical features, etc.; comprehensive consideration of the model's predictive performance, interpretability, training efficiency and other factors, taking a variety of machine learning algorithms (such as logistic regression, random forest, gradient boosting tree, deep learning model, etc.) as candidates, and using cross-validation techniques (such as K-fold cross-validation) to evaluate the performance of each candidate model, including accuracy, recall, F1 score, area under the ROC curve (AUC) and other indicators;

[0111] Capability evaluation module: select the optimal model framework based on the evaluation results, preliminarily train the model, and adjust the model parameters using the training set data; test the preliminarily trained model on the validation set to evaluate its generalization ability;

[0112] Case analysis module: Analyze the model's incorrect prediction cases on the validation set to identify possible biases and limitations; adjust the model architecture or parameters based on the validation results, such as adding hidden layers, changing activation functions, adjusting regularization strength, etc.;

[0113] Parameter adjustment module: Combine multiple single models into an integrated model through integrated learning techniques such as bagging, boosting or stacking, evaluate the impact of different integration strategies on model performance, select the optimal integration method, train the integrated model, and adjust the weights and parameters of each base model to maximize the overall performance;

[0114] Relationship capture module: Analyze the interaction between features, identify the feature combinations that have a significant impact on aging assessment, and capture complex feature interactions and temporal dependencies through deep learning models (such as convolutional neural networks (CNN), recurrent neural networks (RNN), or graph neural networks (GNN); design the architecture of deep models, such as a combination of multi-layer perceptrons, convolutional layers, recurrent layers, or graph convolutional layers, and design incremental learning mechanisms that allow the model to automatically update its parameters and structure when receiving new data;

[0115] Data migration module: Through the model monitoring and evaluation system, the performance of the model on new data is regularly evaluated and the incremental learning process is triggered. In the incremental learning process, the transfer learning technology is applied to migrate the knowledge on the old data to the new data.

[0116] Optimization and adjustment module: Analyze the uncertainty of model predictions, estimate the confidence interval of prediction results through Bayesian method, and evaluate the model performance based on the robustness test scheme, including the performance under conditions of noise data, abnormal data or data distribution changes; further optimize and adjust the model according to the uncertainty and robustness evaluation results;

[0117] User feedback module: Based on the user feedback mechanism, collect users' satisfaction, suggestions and improvement opinions on the evaluation results, organize and classify user feedback, identify common problems and personalized needs, analyze the impact of user feedback on model performance, and evaluate the applicability of the model in real scenarios;

[0118] Iterative optimization module: Based on user feedback and model performance evaluation results, an iterative optimization plan is formulated to optimize the model in a targeted manner, such as adjusting feature selection strategies, improving model architecture, optimizing training algorithms, etc., and the model is updated and retrained regularly.

[0119] The working principle of the above technical solution is as follows: first, analyze the distribution characteristics of the data, including outlier detection and missing value processing. By refining the missing value filling strategy (such as time series-based interpolation, filling based on similar user groups, etc.) and feature selection techniques (such as chi-square test, mutual information, recursive feature elimination, etc.), screen out the feature subsets that have the greatest impact on aging assessment; encode and transform the features to meet the needs of different machine learning algorithms. For example, use one-hot encoding to process categorical features, polynomial or logarithmic transformation to process numerical features, etc.; comprehensively consider factors such as the model's predictive performance, interpretability, and training efficiency, select a variety of machine learning algorithms as candidates, and use cross-validation technology to evaluate the performance of each candidate model; select the optimal model framework based on the evaluation results, and use the training set data to preliminarily train the model and adjust the model parameters; test the preliminarily trained model on the validation set to evaluate its generalization ability; analyze the model's incorrect prediction cases on the validation set to identify possible biases and limitations; adjust the model architecture or parameters based on the verification results, such as adding hidden layers, changing activation functions, adjusting regularization strength, etc., to improve model performance; through Combine multiple single models into an integrated model through integrated learning techniques (such as Bagging, Boosting or Stacking); evaluate the impact of different integration strategies on model performance, select the optimal integration method, and adjust the weights and parameters of each base model to maximize the overall performance; capture complex feature interactions and temporal dependencies through deep learning models (such as CNN, RNN or GNN); design the architecture of the deep model to better learn high-level feature representations in the data, and design an incremental learning mechanism to allow the model to automatically update its parameters and structure when receiving new data; regularly evaluate the performance of the model on new data through the model monitoring and evaluation system, and trigger the incremental learning process. In the incremental learning process, transfer learning technology is applied to accelerate the training process of new models; the confidence interval of the prediction results is estimated through the Bayesian method, and the uncertainty of the model prediction is analyzed; based on the robustness test scheme, the performance of the model in the case of noisy data, abnormal data or data distribution changes is evaluated, and the model is further optimized and adjusted according to the evaluation results; through the user feedback mechanism, user satisfaction, suggestions and improvement opinions on the evaluation results are collected; user feedback is sorted and classified, common problems and personalized needs are identified, and their impact on model performance is analyzed; according to user feedback and model performance evaluation results, an iterative optimization plan is formulated to optimize the model in a targeted manner, and the model is regularly updated and retrained.

[0120] The effects of the above technical solutions are as follows: by deeply analyzing the distribution characteristics of the data, implementing outlier detection and missing value filling strategies, the data quality is effectively improved, laying a solid foundation for subsequent analysis; using feature selection technology to screen out the most influential feature subsets for aging assessment, reducing data redundancy and noise, and improving the prediction efficiency and accuracy of the model; encoding and converting features according to the needs of the machine learning algorithm, making the features more suitable for algorithm processing, and further improving the model performance; using cross-validation technology to evaluate the performance of multiple machine learning algorithms to ensure the scientificity and rationality of model selection; selecting the optimal model framework based on the evaluation results, and verifying the generalization ability of the model through preliminary training and verification; continuously optimizing model performance through analysis of erroneous prediction cases and adjustment of model architecture or parameters to ensure the effectiveness of the model in practical applications; combining multiple single models into an integrated model through integrated learning technology, effectively improving the stability and prediction accuracy of the model; using deep learning models to capture features The complex interactions and temporal dependencies between them improve the model's ability to handle complex data; an incremental learning mechanism is designed to enable the model to automatically adapt to new data and maintain the timeliness and accuracy of the model; the performance of the model on new data is regularly evaluated through the model monitoring and evaluation system to ensure real-time monitoring of the model performance; the transfer learning technology is applied in the incremental learning process to accelerate the training process of the new model and improve the efficiency of model updating; the confidence interval of the prediction result is estimated through the Bayesian method, which provides a reliable confidence assessment for the prediction result of the model; the performance of the model in the case of noisy data, abnormal data or data distribution changes is evaluated based on the robustness test scheme, which improves the stability and reliability of the model; the user satisfaction, suggestions and improvement opinions on the evaluation results are collected through the user feedback mechanism, which provides a valuable reference for the optimization of the model; an iterative optimization plan is formulated according to user feedback and model performance evaluation results, and the model is optimized in a targeted manner to ensure the applicability and continuous optimization of the model in real scenarios.

[0121] In one embodiment of the present invention, the report generation module includes:

[0122] Set definition module: defines the optimization target set, including anti-aging effect, cost, risk and other targets, selects multi-objective optimization algorithm (such as NSGA-II, MOEA / D, etc.), and configures algorithm parameters;

[0123] Solution generation module: Through the multi-objective optimization algorithm code, the optimization objectives are converted into a form that can be processed by the algorithm, and candidate intervention strategies are evaluated and optimized based on the algorithm to generate the Pareto frontier solution set;

[0124] Strategy selection module: select the most appropriate intervention strategy from the Pareto frontier solution set according to the user's specific situation and needs;

[0125] Measures conversion module: According to the selected intervention strategy, intervention measures are set up, including dietary advice, exercise plans, mental health guidance, etc., and intervention measures are converted through natural language generation technology;

[0126] Recommendation sending module: Integrate intervention measures and evaluation results into health report and recommendation templates, generate personalized health reports and recommendations, and send them to users through multiple methods (email, APP push);

[0127] Analysis and processing module: collects user feedback on intervention strategies, analyzes and processes the feedback, and provides reference for subsequent optimization of intervention strategies.

[0128] The working principle of the above technical solution is as follows: First, clarify the set of optimization objectives of the anti-aging intervention strategy, which usually includes anti-aging effects, costs, risks and other aspects. These objectives may conflict with each other, and it is necessary to find the best balance point through a multi-objective optimization algorithm; according to the complexity and needs of the problem, select a suitable multi-objective optimization algorithm, such as NSGA-II (non-dominated sorting genetic algorithm II), MOEA / D (multi-objective evolutionary algorithm based on decomposition), etc. At the same time, the algorithm parameters are reasonably configured to ensure the effectiveness and efficiency of the algorithm; by writing or calling the code of the multi-objective optimization algorithm, the optimization objectives are converted into a form that the algorithm can handle. This usually involves converting the objective function, constraints, etc. into mathematical expressions that the algorithm can understand; based on the selected algorithm, the candidate anti-aging intervention strategies are evaluated and optimized. The algorithm generates a series of candidate solutions, and finally generates a Pareto frontier solution set through comparison and screening. The Pareto frontier solution set contains solutions that cannot be completely dominated by other solutions under given conditions, that is, in these solutions, the improvement of any one goal will lead to the deterioration of at least one other goal; select the most appropriate anti-aging intervention strategy from the Pareto frontier solution set according to the specific situation and needs of the user (such as age, health status, economic conditions, etc.). This selection process may need to consider the user's preferences and priorities to ensure that the selected strategy matches the user's needs; formulate specific intervention measures based on the selected intervention strategy, including dietary advice, exercise plans, mental health guidance, etc. These measures should be specific, feasible, and in line with the user's actual situation; use natural language generation technology to convert intervention measures into easy-to-understand language expressions. This helps users better understand and implement intervention measures and improve the intervention effect; integrate intervention measures and evaluation results into health report and proposal templates to form personalized health reports and proposals. These reports and proposals should contain the user's basic information, evaluation results, intervention measures, and implementation suggestions; send personalized health reports and proposals to users through various means such as email and APP push to ensure that users can obtain and view relevant information in a timely manner; actively collect user feedback on intervention strategies, including satisfaction, effect evaluation, and improvement suggestions. These feedbacks are an important basis for evaluating the effectiveness of intervention strategies and user acceptance. The collected feedbacks are analyzed and processed to identify existing problems and deficiencies. Based on the analysis results, the intervention strategies are optimized and adjusted to improve their relevance and effectiveness. At the same time, these feedbacks also provide valuable references for subsequent research and improvements.

[0129] The effects of the above technical solution are as follows: by defining multiple optimization objectives including anti-aging effect, cost, risk, etc., the technical solution can comprehensively consider all aspects of the anti-aging intervention strategy and avoid the one-sidedness that may be caused by single-objective optimization; the multi-objective optimization algorithm is used to generate the Pareto frontier solution set, providing decision makers with multiple candidate solutions that cannot be completely dominated by other solutions. This allows decision makers to find the best balance between multiple objectives according to actual conditions and needs; the technical solution emphasizes the selection of the most appropriate intervention strategy according to the specific conditions and needs of the user. This personalized selection method can ensure that the intervention strategy matches the actual situation of the user, thereby improving the intervention effect; the intervention measures are converted into easy-to-understand language expressions through natural language generation technology, which helps users better understand and implement the intervention measures. This further enhances the effectiveness of the intervention strategy and user satisfaction; personalized health reports and recommendations are sent to users through various means such as email and APP push, ensuring the timely communication and reception of information. This helps users to understand their health status and intervention suggestions in a timely manner; collect user feedback on the intervention strategy, analyze and process it, and provide important reference for subsequent optimization of the intervention strategy. This feedback loop mechanism helps to continuously improve and optimize intervention strategies, and improve their relevance and effectiveness; the personalized health reports and recommendations provided by this technical solution, as well as the optimization adjustments based on user feedback, provide users with a customized health service experience. This service experience helps to improve user satisfaction and loyalty; by collecting user feedback and continuously optimizing intervention strategies, users are encouraged to actively participate in the health management process. This increased participation helps to enhance users' health awareness and self-management capabilities; based on multi-objective optimization algorithms and big data analysis technology, scientific and objective data support is provided for health management and research. This helps to promote innovation and development in the field of health management and research. ; By continuously collecting user feedback and optimizing intervention strategies, the quality and efficiency of health management and services can be continuously improved. This continuous optimization mechanism helps to promote continuous progress in the field of health management and research.

[0130] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for analyzing human aging information based on big data, characterized in that: The method comprises: S1. Through the real-time data stream processing system based on Kafka, multi-source data is collected in real time and the collected multi-source data is preliminarily processed; S2. Through deep learning models, we extract multi-dimensional aging-related features from the preliminarily processed multi-source data, and build an aging feature map based on the extracted features combined with graph neural networks. S3. Based on the extracted features and the constructed aging feature map, the initial aging assessment model is constructed using a machine learning algorithm. The model parameters are continuously adjusted and optimized through an online learning algorithm and user feedback mechanism. S4. Based on the specific situation and needs of the user, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, and generate personalized health reports and recommendations.

2. The human aging information analysis method based on big data according to claim 1 is characterized in that: Said S1 comprises: S11. Determine the required data source types, access various data sources, deploy data integration middleware, and perform stress testing and stability verification; S12. Build the Kafka cluster architecture, configure Kafka topics, and divide different topics according to data types and uses; S13. Write Kafka consumer and producer codes to collect and send data in real time, and perform preliminary data processing based on stream processing logic; S14. Monitor the running status of the Kafka cluster and stream processing tasks, and adjust resource allocation and processing logic in real time based on the monitoring results; S15. Conduct a preliminary check on the collected raw data, identify and handle obvious errors and outliers; handle missing values, and standardize and normalize the data; S16. Reduce the data dimension through the data dimensionality reduction algorithm, and divide the processed data into training set, validation set and test set.

3. The human aging information analysis method based on big data according to claim 1 is characterized in that: The S2 comprises: S21. Analyze the characteristics of the data, determine the type of features to be extracted based on the analysis results, extract the features through the feature extraction algorithm, and evaluate the quality of the extracted features to check whether there are redundant or irrelevant features, and screen and optimize them; S22, defining a graph structure, taking the extracted aging-related features as nodes, and the correlation or influence relationship between the features as edges; S23, select the GNN model, configure the model parameters, write the GNN model training code, and train the model using the training set data; S24. After training, use the validation set data to validate the model and evaluate the performance and generalization ability of the model; adjust the model parameters and structure according to the validation results to optimize the model performance; S25. Use the optimized model to predict the test set data and generate an aging feature map.

4. The human aging information analysis method based on big data according to claim 1 is characterized in that: The S3 includes: S31. Use feature selection technology to select the most influential feature subset for aging assessment from a large number of candidate features; encode and transform the features; use multiple machine learning algorithms as candidates and use cross-validation technology to evaluate the performance of each candidate model; S32. Select the optimal model framework based on the evaluation results, preliminarily train the model, and adjust the model parameters using the training set data; test the preliminarily trained model on the validation set to evaluate its generalization ability; S33. Analyze the model's incorrect prediction cases on the validation set to identify biases and limitations; adjust the model architecture or parameters based on the validation results; S34. Combine multiple single models into an integrated model through ensemble learning technology, evaluate the impact of different ensemble strategies on model performance, select the optimal ensemble method, train the integrated model, and adjust the weights and parameters of each base model; S35. Analyze the interactions between features, identify the feature combinations that have a significant impact on aging assessment, and capture complex feature interactions and temporal dependencies through deep learning models; design the architecture of the deep model and design an incremental learning mechanism that allows the model to automatically update its parameters and structure when receiving new data; S36. The performance of the model on new data is regularly evaluated through the model monitoring and evaluation system, and the incremental learning process is triggered. In the incremental learning process, transfer learning technology is applied to transfer the knowledge on the old data to the new data; S37. Analyze the uncertainty of model predictions, estimate the confidence interval of prediction results through Bayesian method, and evaluate the model performance based on the robustness test scheme; further optimize and adjust the model according to the uncertainty and robustness evaluation results; S38. Based on the user feedback mechanism, collect users’ satisfaction, suggestions and improvement opinions on the evaluation results, organize and classify user feedback, identify common problems and personalized needs, analyze the impact of user feedback on model performance, and evaluate the applicability of the model in real scenarios; S39. Based on user feedback and model performance evaluation results, formulate an iterative optimization plan to optimize the model, and regularly update and retrain the model.

5. The human aging information analysis method based on big data according to claim 1 is characterized in that: The S4 comprises: S41, defining an optimization target set, selecting a multi-objective optimization algorithm, and configuring algorithm parameters; S42. Through the multi-objective optimization algorithm code, the optimization objectives are converted into a form that can be processed by the algorithm, and the candidate intervention strategies are evaluated and optimized based on the algorithm to generate the Pareto frontier solution set; S43. Select the most appropriate intervention strategy from the Pareto frontier solution set based on the user's specific situation and needs; S44. According to the selected intervention strategy, set intervention measures and transform the intervention measures through natural language generation technology; S45. Integrate intervention measures and evaluation results into health report and recommendation templates, generate personalized health reports and recommendations, and send them to users through multiple methods; S46. Collect user feedback on the intervention strategy, and analyze and process the feedback.

6. A human aging information analysis system based on big data, characterized in that: The system comprises: Data collection module: collects multi-source data in real time through the real-time data stream processing system based on Kafka, and performs preliminary processing on the collected multi-source data; Graph construction module: Through deep learning models, multi-dimensional aging-related features are extracted from the preliminarily processed multi-source data. Based on the extracted features, an aging feature graph is constructed in combination with a graph neural network. Adjustment and optimization module: Based on the extracted features and the constructed aging feature map, the initial aging assessment model is constructed using machine learning algorithms. The model parameters are continuously adjusted and optimized through online learning algorithms and user feedback mechanisms. Report generation module: Based on the user's specific situation and needs, a multi-objective optimization algorithm is used to generate personalized aging intervention strategies, and generate personalized health reports and recommendations.

7. The human aging information analysis system based on big data according to claim 6 is characterized in that: The data acquisition module comprises: Type determination module: determines the required data source type, connects to various data sources, deploys data integration middleware, and performs stress testing and stability verification; Architecture building module: build the Kafka cluster architecture, configure Kafka topics, and divide different topics according to data types and uses; Preliminary processing module: Write Kafka consumer and producer codes to collect and send data in real time, and perform preliminary processing on the data based on stream processing logic; Status monitoring module: monitors the running status of Kafka clusters and stream processing tasks, and adjusts resource allocation and processing logic in real time based on the monitoring results; Normalization module: conduct preliminary inspection on the collected raw data, identify and deal with obvious errors and outliers; process missing values, and standardize and normalize the data; Data dimensionality reduction module: Reduce the data dimension through the data dimensionality reduction algorithm, and divide the processed data into training set, validation set and test set.

8. The human aging information analysis system based on big data according to claim 6 is characterized in that: The graph construction module comprises: Type determination module: Analyze the data characteristics, determine the type of features to be extracted based on the analysis results, extract the features through the feature extraction algorithm, and evaluate the quality of the extracted features to check whether there are redundant or irrelevant features, and perform screening and optimization; Structure definition module: defines the graph structure, taking the extracted aging-related features as nodes and the correlation or influence relationship between the features as edges; Model training module: select the GNN model, configure the model parameters, write the GNN model training code, and train the model using the training set data; Performance optimization module: After training, the model is verified using the validation set data, and the performance and generalization ability of the model are evaluated; model parameters and structure are adjusted according to the validation results to optimize model performance; Data prediction module: Use the optimized model to predict the test set data and generate an aging feature map.

9. The human aging information analysis system based on big data according to claim 6 is characterized in that: The adjustment and optimization module comprises: Performance evaluation module: Apply feature selection technology to select the most influential feature subset for aging assessment from a large number of candidate features; encode and transform the features; take multiple machine learning algorithms as candidates and use cross-validation technology to evaluate the performance of each candidate model; Capability evaluation module: select the optimal model framework based on the evaluation results, preliminarily train the model, and adjust the model parameters using the training set data; test the preliminarily trained model on the validation set to evaluate its generalization ability; Case analysis module: Analyze the model's incorrect prediction cases on the validation set to identify biases and limitations; adjust the model architecture or parameters based on the validation results; Parameter adjustment module: Combine multiple single models into an integrated model through integrated learning technology, evaluate the impact of different integration strategies on model performance, select the optimal integration method, train the integrated model, and adjust the weights and parameters of each base model; Relationship Capture Module: Analyze the interaction between features, identify the feature combinations that have a significant impact on aging assessment, and capture complex feature interactions and temporal dependencies through deep learning models; design the architecture of the deep model and design an incremental learning mechanism that allows the model to automatically update its parameters and structure when receiving new data; Data migration module: Through the model monitoring and evaluation system, the performance of the model on new data is regularly evaluated and the incremental learning process is triggered. In the incremental learning process, the transfer learning technology is applied to migrate the knowledge on the old data to the new data. Optimization and adjustment module: Analyze the uncertainty of model prediction, estimate the confidence interval of prediction results through Bayesian method, evaluate the model performance based on the robustness test scheme; further optimize and adjust the model according to the uncertainty and robustness evaluation results; User feedback module: Based on the user feedback mechanism, collect users' satisfaction, suggestions and improvement opinions on the evaluation results, organize and classify user feedback, identify common problems and personalized needs, analyze the impact of user feedback on model performance, and evaluate the applicability of the model in real scenarios; Iterative optimization module: Based on user feedback and model performance evaluation results, an iterative optimization plan is formulated to optimize the model, and the model is updated and retrained regularly.

10. The human aging information analysis system based on big data according to claim 6 is characterized in that: The report generation module comprises: Set definition module: defines the optimization target set, selects the multi-objective optimization algorithm, and configures the algorithm parameters; Solution generation module: Through the multi-objective optimization algorithm code, the optimization objectives are converted into a form that can be processed by the algorithm, and candidate intervention strategies are evaluated and optimized based on the algorithm to generate the Pareto frontier solution set; Strategy selection module: select the most appropriate intervention strategy from the Pareto frontier solution set according to the user's specific situation and needs; Measures conversion module: According to the selected intervention strategy, intervention measures are set up and the intervention measures are converted through natural language generation technology; Recommendation sending module: Integrate intervention measures and evaluation results into health report and recommendation templates, generate personalized health reports and recommendations, and send them to users through multiple methods; Analysis and processing module: collects user feedback on intervention strategies, and analyzes and processes the feedback.

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