Digital one-stop health education platform based on artificial intelligence and construction method thereof

Abstract

The invention relates to the technical field of health education, and particularly discloses a digital one-stop health education platform based on artificial intelligence and a construction method thereof, and the platform comprises a health data management module which is used for collecting health data of a user from a plurality of data sources, and automatically generating a health file of the user in combination with personal health information inputted by the user; the health portrait generation module is used for generating a personalized health portrait based on the health file, and performing personalized demand analysis according to the health portrait in combination with a target set by a user; according to the invention, based on the established health portrait, customized health education content is obtained according to personal requirements, and a health scheme is adjusted at any time according to health data changes, so that individuation and dynamics of health management are ensured; the user can ask questions at any time to obtain real-time health guidance and professional suggestions; by means of artificial intelligence and big data analysis, the health trend can be predicted, early warning can be given out when potential health problems are found, and effective health intervention measures are provided.

Description

Technical Field

[0001] The present invention belongs to the technical field of health education, and specifically relates to a digital one-stop health education platform based on artificial intelligence and a construction method thereof. Background Art

[0002] With the rapid development of modern society, people's health awareness has gradually increased, and health management has gradually become an important part of people's daily life. The traditional health education model usually relies on offline lectures, health books and fixed courses, which makes it difficult to achieve personalized, real-time and targeted guidance.

[0003] With the continuous development of digital technology, Internet-based health management platforms have gradually become mainstream. Especially in the field of health education, the rapid development of the Internet and artificial intelligence technology has provided new solutions for the emergence of digital health management and education platforms. These platforms use digital means and intelligent algorithms to provide users with personalized health education and management services. However, most of the existing health education platforms have the following problems: (1) Insufficient personalization: Traditional health education platforms mostly provide unified and standardized educational content, and lack the function of personalized push based on the health status, living habits, medical history and other information of different users. (2) Poor interactivity: Most platforms only provide one-way information transmission and cannot effectively interact with users. Especially when users ask questions and provide feedback, they cannot answer users' questions in real time, nor can they make dynamic adjustments based on user feedback, resulting in low user participation and satisfaction. (3) Information fragmentation: Health data mostly comes from different channels and devices, and often has problems such as repeated content, incomplete information, and chaotic structure. The platform cannot effectively integrate this data, which makes it difficult for users to obtain effective health information.

[0004] Therefore, it is necessary to propose a digital one-stop health education platform based on artificial intelligence and its construction method to solve the problems existing in the existing technology, such as the lack of personalized health education content push, poor interactivity, and inability to achieve personalized health advice and behavioral intervention.

[0005] The above information disclosed in this background technology is only used to increase the understanding of the background technology of the present invention and therefore, it may include information that does not constitute the prior art known to ordinary technicians in this field. Summary of the invention

[0006] The purpose of the present invention is to provide a digital one-stop health education platform based on artificial intelligence and a construction method thereof to solve the problems raised in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A digital one-stop health education platform based on artificial intelligence, including: A health data management module is used to collect the user's health data from multiple data sources and automatically generate the user's health profile based on the personal health information input by the user; The health profile generation module is used to generate personalized health profiles based on health records, and conduct personalized demand analysis based on the health profiles and the goals set by the user; The health education recommendation module is used to generate personalized health education recommendation content based on health profiles and demand analysis using deep learning algorithms; The health guidance question-and-answer module is used to achieve real-time interaction with users, answer health questions raised by users based on natural language processing technology, and provide users with health status and personalized health advice; Dynamic optimization module, which is used to collect users' real-time feedback, behavior data and health changes, and dynamically adjust health advice and recommended content; The health prediction and early warning module is used to analyze health profiles using artificial intelligence algorithms, predict the user's health trends within a preset time period, and generate early warnings based on health trends; The health behavior intervention module is used to analyze behavior data through artificial intelligence algorithms based on early warning, and combine psychological principles to generate health behavior improvement suggestions that meet the user's personal preferences; The community interaction and health activity module is used to provide a platform for health communication and community interaction, share experiences and health progress, and organize participation in online and offline activities.

[0008] Preferably, the health data management module is further used to use a data fusion algorithm to fuse health data from multiple data sources to obtain multi-source fusion data; integrate the input personal health information with the multi-source fusion data through a synchronous update mechanism to generate a user's health record; Based on the health records, the machine learning model can be used to identify the current health status and potential health risks, and generate a personalized health report for the user; the user's health status can be monitored in real time, and the health record can be dynamically updated according to changes in the user's health data.

[0009] Preferably, the health portrait generation module is also used to extract health-related features from health records through PCA, and use neural networks to integrate multi-dimensional health records; generate a dynamic health portrait based on the extracted features combined with health records and real-time health data; use a decision tree model to compare the gap between the current health status and the set goal, and predict the user's health needs.

[0010] Preferably, the health education recommendation module is further used to generate personalized recommendation content based on the features extracted by the deep learning algorithm processing of the health portrait and the time series data in the health data; combined with the content-based recommendation algorithm, the recommended content is adjusted according to the user's current health status and set goals, and recommended to the user for viewing; Use collaborative filtering to recommend other user behaviors with similar health profiles and set goals to users. The calculation formula is as follows: ; In the formula, Indicates that the user project Rating, Is with the user Similar user groups, Is a user With users The similarity of Is a user About Project Ratings; Determine the user's interest preferences based on demand analysis, and adjust the display format of recommended content according to their interest preferences; use reinforcement learning to optimize the recommendation method used, monitor the user's learning progress in real time, and adjust the recommendation strategy according to the learning progress.

[0011] Preferably, the health guidance question-and-answer module is also used to communicate and provide feedback with the platform in real time through a graphical interface, voice interaction or real-time data upload; the text of the health questions raised by the user is cut into words using HanLP, and the relationship between the words is understood based on dependency grammar analysis; Use the Word2Vec model to map words into a high-dimensional space to capture the semantic similarity between words. Use BERT to process health issues based on the relationship and semantic similarity between words to generate personalized health recommendations. Integrate the medical knowledge base, match health questions to questions in the existing health knowledge base through question-answer matching algorithms, and generate personalized answers; provide online expert consultation functions, and obtain professional health consultation services through various forms of communication; An integrated expert matching system recommends suitable experts based on health problems through artificial intelligence algorithms to provide health consultation services; an integrated virtual health assistant is used for real-time interaction with experts to assist in health consultations, while also recording consultation history for follow-up tracking and advice.

[0012] Preferably, the dynamic optimization module is also used to collect real-time health data through IoT devices, and collect user behavior data and feedback information on the platform; based on Flink, the real-time health data is compared with the health data in the user portrait to obtain the user's health changes; Use sentiment analysis technology to mine the feedback information, and combine K-means clustering to divide users into different groups. Use reinforcement learning based on group division to evaluate the effects of recommended content and health advice in real time, and make dynamic adjustments based on users' health changes and behavior data. A multimodal learning algorithm is introduced to analyze the user portrait in combination with the image data and voice data input by the user to further adjust the recommended content and health advice. During each interaction, a multi-armed bandit algorithm is used to try various forms of expression of health advice, and the recommendation strategy is adjusted based on user feedback.

[0013] Preferably, the health prediction and early warning module is also used to use LSTM or ARIMA to predict based on the health portrait to obtain the health trend within a preset time period; identify complex patterns in health data through deep neural networks to predict the relationship between health changes and health factors; With the help of transfer learning, the health data of new users can be predicted and warned based on the existing health data; by integrating health trends and prediction relationships, a conditional dependency network of health risks is constructed to calculate the risks of real-time health data and generate early warnings; ; In the formula, is the health risk probability under given data conditions, is the conditional probability of the data, is the joint probability distribution of the data, The probability of a health risk event occurring; combined with anomaly detection algorithms, abnormal fluctuations in real-time health data are detected and early warnings are triggered.

[0014] Preferably, the health behavior intervention module is further used to use RNN to identify and learn complex features and patterns in behavior data, and generate personalized intervention suggestions for users based on the complex features; and use a stage change model to provide staged intervention suggestions based on behavior data. The stage change model formula is as follows: 1. Preparation stage (preparing for behavior change): ; In the formula, For the user's intrinsic motivation, for users’ confidence in successful behavior change; 2. Action phase (implementing behavioral changes): ; In the formula, For intervention recommendations, Inform users about their preferences for intervention recommendations; combine reward mechanisms and behavioral habit tracking to encourage users to adhere to healthy behaviors and increase platform stickiness through social means.

[0015] The construction method of a digital one-stop health education platform based on artificial intelligence includes: Collect the user's health data from multiple data sources and automatically generate the user's health profile based on the personal health information entered by the user; Generate personalized health profiles based on health records, and conduct personalized needs analysis based on the health profiles and the goals set by the user; Based on health profiles and demand analysis, deep learning algorithms are used to generate personalized health education recommendations; Realize real-time interaction with users through natural language processing technology, answer health questions raised by users, provide health status assessment and personalized health advice; Collect users' real-time feedback, behavioral data, and health changes, and dynamically adjust health advice and recommendations; Use artificial intelligence algorithms to analyze health profiles, predict users' health trends within a preset time period, and generate early warnings based on health trends; Based on early warning, AI algorithms analyze user behavior data and combine psychology principles to generate health behavior improvement suggestions that meet the user's personal preferences; Provide a platform for health communication and community interaction where users can share their experiences and health progress, and participate in online and offline health activities.

[0016] Preferably, the big data analysis is combined with a machine learning algorithm to evaluate the user's health status, lifestyle and risk factors, and generate a dynamically updated health portrait; the user's behavior data, feedback information and health changes are tracked in real time through an artificial intelligence algorithm, and personalized health advice and recommended content are optimized and adjusted based on the feedback information.

[0017] Compared with the prior art, the present invention has the following beneficial effects: The present invention is based on the established health portrait, obtains customized health education content according to personal needs, and adjusts the health plan at any time according to changes in health data to ensure the personalization and dynamism of health management; users can ask questions at any time to obtain real-time health guidance and professional advice, avoiding the shortcomings of the traditional health education model that cannot interact and answer questions; with the help of artificial intelligence and big data analysis, it can not only predict health trends, but also issue early warnings when potential health problems are discovered, and provide effective health intervention measures.

[0018] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a framework diagram of the digital one-stop health education platform based on artificial intelligence of the present invention; Figure 2 The figure is a flow chart of the construction method of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Embodiment 1:

[0021] See also Figure 1 As shown in the figure, the digital one-stop health education platform based on artificial intelligence includes: A health data management module is used to collect the user's health data from multiple data sources and automatically generate the user's health profile based on the personal health information input by the user; The health profile generation module is used to generate personalized health profiles based on health records, and conduct personalized demand analysis based on the health profiles and the goals set by the user; The health education recommendation module is used to generate personalized health education recommendation content based on health profiles and demand analysis using deep learning algorithms; The health guidance question-and-answer module is used to achieve real-time interaction with users, answer health questions raised by users based on natural language processing technology, and provide users with health status and personalized health advice; Dynamic optimization module, which is used to collect users' real-time feedback, behavior data and health changes, and dynamically adjust health advice and recommended content; The health prediction and early warning module is used to analyze health profiles using artificial intelligence algorithms, predict the user's health trends within a preset time period, and generate early warnings based on health trends; The health behavior intervention module is used to analyze behavior data through artificial intelligence algorithms based on early warning, and combine psychological principles to generate health behavior improvement suggestions that meet the user's personal preferences; The community interaction and health activity module is used to provide a platform for health communication and community interaction, share experiences and health progress, and organize participation in online and offline activities.

[0022] The health data management module is also used to fuse health data from multiple data sources using a data fusion algorithm to obtain multi-source fused data; The input personal health information is integrated with multi-source fusion data through a synchronous update mechanism to generate the user's health record; Identify the current health status and potential health risks based on health records through machine learning models, and generate personalized health reports for users; Monitor the user's health status in real time and dynamically update the health record based on changes in the user's health data.

[0023] Furthermore, the health data management module uses multi-source data fusion and machine learning algorithms to achieve intelligent collection and dynamic update of health data. By real-time monitoring of the user's health status and dynamically updating health records, it provides high-quality data support for personalized health advice and education recommendations.

[0024] The health profile generation module is also used to extract health-related features from health records through PCA and synthesize multi-dimensional health records using neural networks; Generate dynamic health profiles based on extracted features combined with health records and real-time health data; Use the decision tree model to compare the gap between the current health status and the set goals and predict the user's health needs.

[0025] Furthermore, the health portrait generation module generates dynamic health portraits based on health records and real-time health data through PCA, neural networks, decision tree models and other technologies. This health portrait can reflect the user's health changes in real time and predict health needs according to set goals, further improving the accuracy of health management.

[0026] The health education recommendation module is also used to generate personalized recommendation content based on the health profile by processing the extracted features and time series data in the health data through deep learning algorithms; Combined with the content-based recommendation algorithm, the recommended content is adjusted according to the user's current health status and set goals, and recommended to the user for viewing; Use collaborative filtering to recommend other user behaviors with similar health profiles and set goals to users; Determine the user's interest preferences based on demand analysis and adjust the display format of recommended content according to their interest preferences; Reinforcement learning is used to optimize the recommendation method used, monitor the user's learning progress in real time, and adjust the recommendation strategy based on the learning progress.

[0027] Furthermore, the health education recommendation module processes the time series data in the health data and the user's health profile through deep learning algorithms, and can make accurate personalized recommendations based on the user's health needs. In addition, combined with collaborative filtering and reinforcement learning, the module can continuously optimize the recommendation strategy and make real-time adjustments based on user feedback and learning progress.

[0028] The health guidance question and answer module is also used to communicate and provide feedback with the platform in real time through a graphical interface, voice interaction, or real-time data upload; Use HanLP to segment the text of the health questions raised by users into words, and understand the relationship between words based on dependency grammar analysis; Use the Word2Vec model to map words into a high-dimensional space to capture the semantic similarity between words; The relationship and semantic similarity between words are integrated to process health problems through BERT to generate personalized health recommendations; Integrate the medical knowledge base, match health questions to questions in the existing health knowledge base through the question-answer matching algorithm, and generate personalized answers; Provide online expert consultation function, and obtain professional health consultation services through various forms of communication; Integrated expert matching system, recommending suitable experts based on health problems through artificial intelligence algorithms to provide health consultation services; Integrated virtual health assistant for real-time interaction with experts to assist with health consultations, while recording consultation history for follow-up tracking and advice.

[0029] Furthermore, the health guidance question-and-answer module integrates natural language processing technology and a knowledge base in the medical field, which can answer users' health questions in real time and provide health advice based on their personalized needs. Through the expert matching system, users can get more professional health consulting services.

[0030] The dynamic optimization module is also used to collect real-time health data through IoT devices, while collecting user behavior data and feedback information on the platform; Based on Flink, the user's health changes are obtained by comparing the real-time health data with the health data in the user profile. Use sentiment analysis technology to mine the feedback information, and combine K-means clustering to divide users into different groups; Use reinforcement learning based on group segmentation to evaluate the effectiveness of recommended content and health advice in real time, and make dynamic adjustments based on users' health changes and behavior data; Introducing a multimodal learning algorithm to analyze user portraits combined with image data and voice data input by users, and further adjust recommended content and health advice; During each interaction, a multi-armed bandit algorithm is used to try multiple forms of health advice and adjust the recommendation strategy based on user feedback.

[0031] Furthermore, the dynamic optimization module uses IoT devices and sentiment analysis technology to track users' health data and behavioral feedback in real time, and performs dynamic optimization based on K-means clustering and reinforcement learning algorithms. The module can adjust recommended content and health suggestions based on changes in users' health status and behavioral data, thereby improving the effectiveness of health interventions.

[0032] The health prediction and early warning module is also used to predict the health trend within a preset time period using LSTM or ARIMA based on the health profile; Identify complex patterns in health data using deep neural networks to predict the relationship between health changes and health factors; With the help of transfer learning, the health data of new users can be predicted and warned based on the existing health data; Integrate health trends and prediction relationships, calculate risks on real-time health data, and generate early warnings by building a conditional dependency network of health risks; Combined with anomaly detection algorithms, abnormal fluctuations in real-time health data can be discovered and trigger early warnings.

[0033] Furthermore, the health prediction and early warning module predicts the user's health trend through algorithms such as LSTM, ARIMA and deep neural networks, and identifies potential health risks in advance. Combined with transfer learning, it can provide health warnings for new users and help users take health intervention measures in a timely manner.

[0034] The health behavior intervention module is also used to use RNN to identify and learn complex features and patterns in behavioral data, and generate personalized intervention recommendations for users based on the complex features; Use the stage change model to provide phased intervention recommendations based on behavioral data; Combining reward mechanisms and behavioral habit tracking, users are encouraged to maintain healthy behaviors and the platform's stickiness is increased through social means.

[0035] Furthermore, the health behavior intervention module uses the RNN algorithm to identify complex patterns in behavior data and combines psychological principles to generate personalized health behavior intervention suggestions based on the user's health needs. The module provides staged guidance based on the user's health changes and behavioral habits to promote the formation of healthy behaviors. Embodiment 2:

[0036] See also Figure 2 As shown, the construction method of a digital one-stop health education platform based on artificial intelligence includes: Collect the user's health data from multiple data sources and automatically generate the user's health profile based on the personal health information entered by the user; Generate personalized health profiles based on health records, and conduct personalized needs analysis based on the health profiles and the goals set by the user; Based on health profiles and demand analysis, deep learning algorithms are used to generate personalized health education recommendations; Realize real-time interaction with users through natural language processing technology, answer health questions raised by users, provide health status assessment and personalized health advice; Collect users' real-time feedback, behavioral data, and health changes, and dynamically adjust health advice and recommendations; Use artificial intelligence algorithms to analyze health profiles, predict users' health trends within a preset time period, and generate early warnings based on health trends; Based on early warning, AI algorithms analyze user behavior data and combine psychology principles to generate health behavior improvement suggestions that meet the user's personal preferences; Provide a platform for health communication and community interaction where users can share their experiences and health progress, and participate in online and offline health activities.

[0037] Big data analysis combined with machine learning algorithms evaluates the user's health status, lifestyle and risk factors, and generates a dynamically updated health profile; Through artificial intelligence algorithms, users’ behavioral data, feedback information and health changes are tracked in real time, and personalized health advice and recommendations are optimized and adjusted based on the feedback information.

[0038] Example scenario: Mr. Zhang is a 30-year-old office worker who is usually busy with work, has an irregular diet, and lacks exercise. Recently, he feels that his physical strength has declined, and he occasionally experiences symptoms such as dizziness and fatigue. He decided to use this digital one-stop health education platform based on artificial intelligence for health management.

[0039] Mr. Zhang entered his basic personal information (such as age, height, weight, etc.) through the health platform, and bound a smart bracelet and health monitoring equipment. The platform automatically collected and integrated information from the smart bracelet (such as steps, heart rate, sleep quality), health checkup reports (such as blood pressure, blood sugar level), and Mr. Zhang's living habits information filled out on the platform (such as diet and exercise). Based on the collected multi-source data, the platform generated Mr. Zhang's health profile, recording his current health status, potential health risks, and historical health data.

[0040] Based on Mr. Zhang's health records, the platform used PCA and neural network algorithms to extract features from multi-dimensional health data and generated a dynamic health portrait that depicts Mr. Zhang's current health status. Combined with Mr. Zhang's set goals (for example, weight loss, physical fitness improvement, etc.), it analyzed his personalized needs in health management and set clear health goals for him.

[0041] The platform uses deep learning algorithms to analyze Mr. Zhang's health profile and provides him with personalized health education recommendations on diet, exercise, and daily routines based on the user's goals (for example, losing 5 kg). The recommended content is adjusted in real time based on the content of the demand analysis and time series data. If Mr. Zhang does not exercise enough, the platform will add recommended content for related exercises to ensure that the health management plan is continuously optimized.

[0042] When Mr. Zhang was using the platform, he encountered a question about dizziness and immediately asked a question through the platform's health question-and-answer module. The platform answered Mr. Zhang's question based on natural language processing technology (such as BERT, Word2Vec, etc.) and generated personalized health advice based on the medical knowledge base. Furthermore, the platform will recommend appropriate experts for online consultation based on the complexity of the question, providing Mr. Zhang with more professional advice.

[0043] As Mr. Zhang's health changes (for example, increased exercise and changes in weight after dietary adjustments), the platform collects new health data and dynamically optimizes health recommendations to ensure that he can track and adjust his health goals. The platform uses the LSTM model to predict Mr. Zhang's health trends in the coming months, such as changes in weight and potential chronic disease risks, and generates health warnings based on the prediction results. If the platform finds that Mr. Zhang's health has abnormal fluctuations (for example, high blood pressure), the system will promptly remind him and give advice on how to deal with it. In addition, by analyzing Mr. Zhang's behavioral data, the platform combines psychological principles to generate health behavior intervention recommendations that meet his personal preferences. For example, Mr. Zhang is encouraged to exercise for 30 minutes a day, and a reward points system is used to motivate him to complete the task.

[0044] From the above, it can be seen that the present invention generates a health record by collecting the user's health data and combining it with the input personal health information, and further analyzes the user's health status to generate a personalized health portrait, providing a scientific basis for subsequent needs analysis and education recommendations; using deep learning algorithms to push customized health education content based on the user's health portrait and needs analysis, to ensure that each user obtains resources that match their health goals; at the same time, based on natural language processing technology, it interacts with users in real time, answers health questions, provides personalized health advice, and enhances user participation and satisfaction.

[0045] The present invention continuously adjusts health advice and recommended content based on the user's real-time feedback, behavioral data and health changes to maintain their timeliness and effectiveness; uses artificial intelligence algorithms to analyze user health trends, identify potential health problems in advance, provide timely warnings, and help users take early intervention measures; at the same time, combines psychological principles with behavioral data analysis to provide users with personalized health behavior improvement suggestions to help users make positive changes in psychology and behavior. Implementation 3:

[0046] The embodiment of the present invention also provides a computer-readable storage medium, on which a program of a digital one-stop health education platform based on artificial intelligence as described above is stored, and when the program is executed by a processor, each process of the health education platform embodiment described above is implemented, and the same technical effect can be achieved. To avoid repetition, it is not repeated here. Among them, the computer-readable storage medium, such as read-only memory (ROM), random access memory (RAM), disk, etc.

[0047] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0048] In the drawings of the embodiments disclosed in the present invention, only the structures involved in the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0049] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0050] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A digital one-stop health education platform based on artificial intelligence, characterized by: include: A health data management module is used to collect the user's health data from multiple data sources and automatically generate the user's health profile based on the personal health information input by the user; A health profile generation module, used to generate a personalized health profile based on the health profile, and to perform personalized needs analysis based on the health profile combined with the goals set by the user; A health education recommendation module, for generating personalized health education recommendation content using a deep learning algorithm based on the health portrait and the demand analysis; The health guidance question-and-answer module is used to achieve real-time interaction with users, answer health questions raised by users based on natural language processing technology, and provide users with health status and personalized health advice; A dynamic optimization module, which is used to collect real-time feedback, behavioral data and health changes of users, and dynamically adjust the health advice and the recommended content; A health prediction and early warning module, which is used to analyze the health portrait using an artificial intelligence algorithm, predict the user's health trend within a preset time period, and generate an early warning based on the health trend; A health behavior intervention module is used to analyze the behavior data through an artificial intelligence algorithm based on the early warning, and generate health behavior improvement suggestions that meet the user's personal preferences in combination with psychological principles; The community interaction and health activity module is used to provide a platform for health communication and community interaction, share experiences and health progress, and organize participation in online and offline activities.

2. The digital one-stop health education platform based on artificial intelligence according to claim 1 is characterized in that: The health data management module is also used for: Using a data fusion algorithm to fuse the health data from multiple data sources to obtain multi-source fused data; The input personal health information is integrated with the multi-source fusion data through a synchronous update mechanism to generate the health record of the user; Identify the current health status and potential health risks through a machine learning model based on the health record, and generate a personalized health report for the user; Monitor the user's health status in real time and dynamically update the health record according to changes in the user's health data.

3. The digital one-stop health education platform based on artificial intelligence according to claim 2 is characterized in that: The health portrait generation module is also used for: Extracting health-related features from the health records by PCA, and synthesizing the health records in multiple dimensions using a neural network; Generate the dynamic health portrait based on the extracted features combined with the health record and the real-time health data; A decision tree model is used to compare the gap between the current health status and the set goals, and to predict the user's health needs.

4. The one-stop digital health education platform based on artificial intelligence according to claim 3 is characterized in that: The health education recommendation module is also used to: Generate personalized recommended content based on the features extracted by deep learning algorithm processing of the health portrait and the time series data in the health data; In combination with a content-based recommendation algorithm, the recommended content is adjusted according to the user's current health status and set goals, and recommended to the user for viewing; Use collaborative filtering to recommend other user behaviors with similar health profiles and set goals to users. The calculation formula is as follows: ； In the formula, Indicates that the user project Ratings, Is with the user Similar user groups, Is a user With users The similarity of Is a user About Project Ratings; Determining the user's interest preference based on the demand analysis, and adjusting the display form of the recommended content according to the interest preference; The recommendation method used is optimized using reinforcement learning, the user's learning progress is monitored in real time, and the recommendation strategy is adjusted according to the learning progress.

5. The digital one-stop health education platform based on artificial intelligence according to claim 4 is characterized in that: The health guidance question and answer module is also used for: Communicate and provide feedback with the platform in real time through graphical interface, voice interaction or real-time data upload; Using HanLP to segment the text of the health question raised by the user into words, and understanding the relationship between the words based on dependency grammar analysis; Use the Word2Vec model to map the words into a high-dimensional space to capture the semantic similarity between words; The relationship between the words and the semantic similarity are integrated to process the health problem through BERT to generate personalized health advice; Integrate the medical field knowledge base, match the health question to the questions in the existing health knowledge base through the question-answer matching algorithm, and generate personalized answers; Provide online expert consultation function, and obtain professional health consultation services through various forms of communication; An integrated expert matching system recommends appropriate experts through artificial intelligence algorithms based on the health issues and provides health consultation services; Integrated virtual health assistant for real-time interaction with experts to assist with health consultations, while recording consultation history for follow-up tracking and advice.

6. The one-stop digital health education platform based on artificial intelligence according to claim 5 is characterized in that: The dynamic optimization module is also used for: Collect real-time health data through IoT devices, and collect user behavior data and feedback information on the platform; Based on Flink, the health changes of the user are obtained by comparing the real-time health data with the health data in the user portrait; Use sentiment analysis technology to mine the feedback information, and combine K-means clustering to divide users into different groups; Using reinforcement learning based on group division to evaluate the effects of the recommended content and the health advice in real time, and dynamically adjust according to the health changes and behavior data of the user; Introducing a multimodal learning algorithm to analyze the user portrait in combination with the image data and voice data input by the user, and further adjusting the recommended content and the health advice; During each interaction, a multi-armed bandit algorithm is used to try multiple expressions of the health advice, and the recommendation strategy is adjusted according to the feedback information of the user.

7. The one-stop digital health education platform based on artificial intelligence according to claim 6 is characterized in that: The health prediction and early warning module is also used to: Use LSTM or ARIMA to predict the health profile and obtain the health trend within a preset time period; Recognizing complex patterns in the health data through deep neural networks and predicting the relationship between the health changes and health factors; Using transfer learning to predict and warn the health data of new users based on the existing health data; Combining the health trend and the prediction relationship, by constructing a conditional dependency network of health risks, performing risk calculation on the real-time health data, and generating early warnings; ； In the formula, is the health risk probability under given data conditions, is the conditional probability of the data, is the joint probability distribution of the data, The probability of a health risk event occurring; Combined with anomaly detection algorithms, abnormal fluctuations in real-time health data can be detected and early warnings can be triggered.

8. The one-stop digital health education platform based on artificial intelligence according to claim 7 is characterized in that: The health behavior intervention module is also used to: Using RNN to identify and learn complex features and patterns in the behavioral data, and generating personalized intervention suggestions for the user based on the complex features; Using a stage change model to provide phased intervention suggestions based on the behavioral data; Combining reward mechanisms and behavioral habit tracking, users are encouraged to maintain healthy behaviors and the platform's stickiness is increased through social means.

9. A method for constructing a digital one-stop health education platform based on artificial intelligence, characterized in that: include: Collect the user's health data from multiple data sources and automatically generate the user's health profile based on the personal health information entered by the user; Generate a personalized health profile based on the health profile, and perform a personalized needs analysis based on the health profile and the goals set by the user; Generate personalized health education recommendation content using a deep learning algorithm based on the health portrait and the needs analysis; Realize real-time interaction with users through natural language processing technology, answer health questions raised by users, provide health status assessment and personalized health advice; Collect users' real-time feedback, behavior data, and health changes, and dynamically adjust the health advice and recommended content; Analyze the health profile using an artificial intelligence algorithm, predict the user's health trend within a preset time period, and generate early warnings based on the health trend; Based on the early warning, the user behavior data is analyzed through artificial intelligence algorithms, and health behavior improvement suggestions that meet the user's personal preferences are generated in combination with psychological principles; Provide a platform for health communication and community interaction where users can share their experiences and health progress, and participate in online and offline health activities.

10. The construction method according to claim 9, characterized in that: Generating a personalized health profile based on the health record, and performing a personalized needs analysis based on the health profile combined with the goals set by the user, includes: Big data analysis combined with machine learning algorithms evaluates the user's health status, lifestyle and risk factors, and generates a dynamically updated health profile; The collecting of real-time feedback, behavior data and health changes of users and the dynamic adjustment of the health advice and the recommended content include: Through artificial intelligence algorithms, the user's behavior data, feedback information and health changes are tracked in real time, and the personalized health advice and recommended content are optimized and adjusted according to the feedback information.

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