User self-management system embedded with seasonal allergic rhinitis expert database

Abstract

The invention discloses a user self-management system embedded with a seasonal allergic rhinitis expert database, and belongs to the technical field of big data health management, and the system comprises an expert database construction module which is used for constructing an expert database; the model supervised training module is used for supervising and training the IMB intervention model; the engine connection establishing module is used for establishing engine connection, and the role permission and the index matrix have a corresponding relation; the data formatting module is used for receiving the rhinitis data, identifying and carrying out semantic analysis, and converting and determining formatted data; the guidance information determination module is used for user guidance information; and the user self-management module is used for carrying out terminal visual display on the user guidance information and carrying out user self-management. According to the method, the problem of how to provide targeted guidance according to the seasonal environment variable and the user state to realize effective self-management of the user is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of big data health management, and in particular to a user self-management system with an embedded seasonal allergic rhinitis expert database. Background Art

[0002] With the development of information technology, big data and artificial intelligence have been widely used in the medical field, especially in disease management and the formulation of personalized treatment plans. The combination of data mining, machine learning and intelligent intervention models makes medical and health management more accurate and personalized, helping patients achieve better treatment effects and self-management.

[0003] Seasonal allergic rhinitis (AR) is a chronic allergic disease. Patients are often affected by seasonal allergens and suffer from recurrent symptoms. Due to the strong environmental variability of seasonal allergic rhinitis and the variability of user status, it is difficult for users to pay effective attention and respond to it in daily life, which aggravates the user's reaction state. Therefore, how to provide targeted guidance based on seasonal environmental variables and user status to achieve effective self-management of users is a technical problem to be solved. Summary of the invention

[0004] This application provides a user self-management system with an embedded seasonal allergic rhinitis expert database, aiming to solve the problem of how to provide targeted guidance based on seasonal environmental variables and user status to achieve effective self-management of users.

[0005] In view of the above problems, the present application provides a user self-management system with an embedded seasonal allergic rhinitis expert database.

[0006] The present application discloses a user self-management system with an embedded seasonal allergic rhinitis expert database, the system comprising: performing big data retrieval and calling rhinitis data, performing data mining based on seasonal environmental variables-first allergic characteristics-guidance information, and constructing an expert database; mining the second allergic relationship of information-motivation-behavior, and supervising the training of an IMB intervention model, wherein the IMB intervention model is embedded with the expert database; setting role permissions and an index matrix, target users accessing and assigning role permissions, and establishing an engine connection, wherein there is a corresponding relationship between the role permissions and the index matrix, and the index matrix contains scanning engines in multiple ranges; receiving rhinitis data, identifying and performing semantic analysis, and converting and determining formatted data; for the formatted data, performing database retrieval and intervention guidance decision-making under permission constraints based on the IMB intervention model, and determining user guidance information; performing terminal visualization display of the user guidance information, and performing user self-management.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] By adopting the technical solutions of embedded seasonal allergic rhinitis expert database, personalized intervention decision-making based on IMB intervention model, data mining and user self-management, the problem of how to provide targeted guidance according to seasonal environmental variables and user status to achieve effective self-management of users is solved, and the technical effect of improving patients' self-management ability, optimizing treatment effects and reducing the recurrence and aggravation of rhinitis is achieved through precise intervention, real-time guidance and personalized management.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A structural schematic diagram of a user self-management system with a seasonal allergic rhinitis expert database embedded therein is provided for the embodiment of the present application.

[0011] Figure 2 A flow chart of constructing an expert database in a user self-management system with a seasonal allergic rhinitis expert database embedded therein is provided for the embodiment of the present application.

[0012] Explanation of the accompanying drawings: expert database construction module 11, model supervision training module 12, engine connection establishment module 13, data formatting module 14, guidance information determination module 15, user self-management module 16. DETAILED DESCRIPTION

[0013] The overall idea of ​​the technical solution provided by this application is as follows:

[0014] The embodiment of the present application provides a user self-management system with an embedded seasonal allergic rhinitis expert database. By constructing a user self-management system based on the seasonal allergic rhinitis expert database, combined with big data retrieval, data mining and IMB intervention model, a personalized and comprehensive intervention plan is provided. The system determines guidance information by analyzing rhinitis data, environmental variables and user characteristics, and provides dynamic and real-time management guidance through authority control and formatted data display, helping users to effectively manage the disease, optimize the treatment effect, and improve their self-management ability, reducing the risk of disease recurrence and aggravation.

[0015] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0016] Examples, such as Figure 1 As shown, the embodiment of the present application provides a user self-management system with an embedded seasonal allergic rhinitis expert database, the system comprising:

[0017] The expert database construction module 11 is used to perform big data retrieval and call rhinitis data, conduct data mining based on seasonal environmental variables - first allergic characteristics - guidance information, and construct an expert database.

[0018] Specifically, rhinitis data refers to various types of data related to allergic rhinitis (AR), including patient symptom data, treatment data, environmental factor data (such as seasonal changes, air quality, etc.), immune response data, etc. Seasonal environmental variables refer to environmental factors related to seasonal changes, such as temperature, humidity, and the concentration of allergens in the air. These factors affect the frequency and severity of allergic rhinitis. The first allergic characteristic refers to the characteristics of the immune system response that causes seasonal rhinitis attacks, mainly including immune responses to specific allergens. Guidance information is personalized advice or treatment strategies for users generated based on data mining results to help patients better manage the disease.

[0019] First, "big data retrieval" is carried out, that is, data related to seasonal allergic rhinitis is collected from major health databases, literature and clinical data through data collection tools (such as API interfaces, crawlers, etc.). These data include patient symptoms, environmental changes, immune responses, etc. For example, climate change data in a certain area can be obtained through environmental monitoring stations, as well as rhinitis symptom data recorded by patients through health management apps.

[0020] Next, data mining techniques were used to process these data and extract seasonal environmental variables with predictive significance, such as spring pollen concentration and autumn humidity. Combining these environmental factors with the symptoms of rhinitis patients, the first allergic characteristics, such as the intensity of allergic reactions in specific seasons, were further analyzed.

[0021] Subsequently, data mining was performed using machine learning algorithms (such as decision trees and support vector machines) to construct an expert database containing different seasonal environmental variables and allergic characteristics. The database contains information such as the impact of environmental changes on rhinitis and the changing patterns of rhinitis symptoms in different seasons.

[0022] Through big data retrieval and data mining, the system can identify the impact of seasonal environmental changes on rhinitis symptoms and build an expert database. This database not only provides doctors with a scientific basis to help them make more accurate treatment decisions, but also provides patients with personalized guidance information.

[0023] The model supervision training module 12 is used to mine the second allergic relationship of information-motivation-behavior and supervise the training of the IMB intervention model, wherein the IMB intervention model is embedded with the expert database.

[0024] Specifically, information-motivation-behavior (IMB) is the three core components of the model.

[0025] Information refers to the knowledge and information that individuals obtain about health problems, which helps them understand the seriousness of the problem and how to take action. Motivation refers to the internal driving force for individuals to take action, including the importance of health and the willingness to take certain actions. Behavior refers to actual health behaviors, such as following treatment plans and changing lifestyles. The second allergic relationship refers to the interaction between an individual's understanding of information and his or her motivation under the framework of the IMB model.

[0026] The first thing to achieve is the application of the information-motivation-behavior (IMB) intervention model. This model works together through three dimensions (information, motivation, and behavior) to help individuals change their health behaviors. Specifically, the information part is provided to patients through medical knowledge and treatment recommendations stored in the expert database. This information involves knowledge of the disease, treatment methods, environmental control techniques, etc. The motivation part is mainly to stimulate the motivation of patients to participate in health management by analyzing their historical data, behavioral responses, and individualized health conditions.

[0027] Next, the mining of the second allergic relationship is carried out by analyzing the interactive relationship between the patient's personal motivation and health information. For example, some patients still lack the motivation to change their lifestyle even if they know that spring pollen aggravates rhinitis symptoms. Therefore, the system will dynamically adjust the intervention strategy and optimize the motivation part according to the patient's acceptance of information, changes in the condition, and treatment feedback.

[0028] In this process, the role of supervised training is revealed. The system monitors the patient's health behaviors in real life (such as medication compliance, environmental control implementation, etc.) and gives personalized feedback. This feedback is generated through information and intervention strategies in the expert database.

[0029] By mining the secondary allergic relationship of information-motivation-behavior and combining it with the supervised training IMB intervention model, the system can more accurately identify and influence the patient's behavioral patterns in rhinitis management, helping patients better control seasonal rhinitis, alleviate symptoms, and improve their quality of life.

[0030] The engine connection establishment module 13 is used to set role permissions and index matrices, target users access and allocate role permissions, and establish engine connections, wherein the role permissions correspond to the index matrix, and the index matrix includes multiple ranges of scanning engines.

[0031] Specifically, role-based permissions refer to granting different access rights to users based on their roles (such as administrator, doctor, patient, etc.). Each role has specific operating permissions that control which functions or data the user can access in the system. Role permissions are usually used to ensure the security of the system and the allocation of functions. Index Matrix is ​​a two-dimensional or multi-dimensional data structure used to index and quickly find specific information. It enables users or systems to efficiently locate and access data by classifying and arranging data. In this step, the index matrix is ​​used to store and map the correspondence between role permissions and related functions. Scanning engine is an automated tool for quickly scanning and identifying data. It is usually used for data classification, search, verification and other operations. In this system, the scanning engine can be used to traverse and retrieve different databases or data tables to meet specific query requirements. Engine connection refers to the process of connecting various modules or databases in the system with the processing engine. Through engine connection, the system can realize rapid processing, analysis and query of data.

[0032] First, you need to set role permissions and establish an index matrix to ensure that different users can access corresponding data and functions according to their roles. Specifically, the system will first define different roles for different user groups (such as patients, doctors, and administrators) and set different permissions based on these roles. For example, doctors can access medical records and treatment plans, while patients can only view their own health data and guidance information.

[0033] Next, the target user accesses and assigns role permissions. After the user registers or logs in, the system confirms the user's identity and assigns corresponding role permissions based on their identity. For each role, the system has a set of preset permission lists that determine what operations the role can perform (such as querying rhinitis data, modifying health records, viewing intervention recommendations, etc.).

[0034] In order to efficiently manage and retrieve data, the system uses an index matrix to establish a mapping relationship between role permissions and system functions. The index matrix is ​​a two-dimensional structure, the first dimension represents different roles, and the second dimension represents the functional modules or data items available in the system. Each element in the matrix indicates whether a role has access to a certain function. For example, the patient role can only access his own health data, while the doctor role has access to all patient data.

[0035] On this basis, the system establishes a scanning engine and specifies different scanning engine ranges for each role. The function of the scanning engine is to quickly scan the data source and retrieve specific data or functions. The scanning engine quickly finds the scope corresponding to the role permissions by accessing the index matrix, thereby efficiently extracting the information required by the user.

[0036] Finally, the engine connection is established to efficiently connect these modules (role permissions, index matrix, scanning engine) so that the system can flexibly perform permission control and data access. When a user logs in, the system interacts with the corresponding scanning engine through the engine connection, quickly retrieves the user's permissions from the index matrix, and matches the permissions with the data access request in the system to ensure that users can only access the content they are authorized to.

[0037] By setting the mapping relationship between role permissions and index matrices, and using the scanning engine for efficient data retrieval, the system can achieve precise permission control and fast data access. At the same time, the combination of the index matrix and the scanning engine greatly improves the efficiency of the system in processing large amounts of user data, reduces query time and data processing burden, and improves the system's response speed and stability.

[0038] The data formatting module 14 is used to receive rhinitis data, identify and perform semantic analysis, and convert and determine formatted data.

[0039] Specifically, semantic parsing is a natural language processing technology used to understand the meaning and structure in text. Its goal is to convert natural language input into semantic representations that can be understood by computers, usually structured data or concepts. For example, convert the patient's description of "I feel itchy nose and sneeze" into the entities of the two symptoms "itchy nose" and "sneezes". Formatted data: Formatted data refers to converting raw data into a data structure that conforms to specific rules or standards. The purpose of formatting is to enable data to be processed, analyzed, and stored in different systems.

[0040] First, rhinitis data from different sources are received, including patients' self-reported symptoms, doctors' diagnoses, laboratory test results, etc. These data are usually stored in unstructured forms, such as free text, pictures or PDF reports, which are difficult to use directly for subsequent processing and analysis.

[0041] Next, the system uses semantic parsing technology to process this data. Specifically, the process of semantic parsing includes tasks such as text segmentation, entity recognition, relationship extraction, and sentiment analysis. Through these technologies, the system can extract key information from the text and identify specific symptoms, causes, or treatment recommendations. For example, when parsing the patient's description of "severe symptoms of nasal congestion and runny nose", the semantic parsing engine will identify "nasal congestion" and "runny nose" as symptom entities.

[0042] After that, the system formats the parsed information, that is, converts the information into structured data according to certain rules. These structured data are usually stored and represented based on standard data formats (such as JSON, XML or database tables) for subsequent operations. For example, the system converts the patient's symptoms of "nasal congestion" and "runny nose" into structured data records: "Symptoms: nasal congestion" "Symptoms: runny nose". These formatted data can not only be further processed by the system, but also integrated with other data sources. Natural language processing (NLP) libraries (such as spaCy, NLTK, BERT, etc.) are usually used for semantic parsing, database management systems (such as MySQL, MongoDB, etc.) are used for data storage, and API interfaces are used for data interaction and format conversion.

[0043] Through semantic parsing and formatting of data, the system can automatically extract and structure the originally scattered and unstructured rhinitis data. This process significantly improves the efficiency of data utilization, facilitating subsequent analysis, model training, and personalized health management recommendations. Specific technical effects include: reducing manual intervention, improving the speed and accuracy of data processing; ensuring that the system can quickly retrieve and analyze based on structured data; and providing accurate support for further intelligent intervention and personalized medicine.

[0044] The guidance information determination module 15 is used to perform database retrieval and intervention guidance decision under authority constraints based on the IMB intervention model for the formatted data, and determine user guidance information.

[0045] Specifically, the IMB intervention model is the Information-Motivation-Behavioral Skills model, which is a psychological model that aims to change individual behavior by providing information, motivating motivation, and cultivating behavioral skills. This model is used to guide individual behavior change and health management. Permission constraints refer to setting different data access scopes and operation permissions in the system according to user roles and permissions to ensure the security and compliance of information and operations. Database retrieval queries and extracts relevant data based on the formatted data provided by the user, and uses the information in the stored rhinitis expert database to support decision-making. Intervention-guided decision-making means that under the guidance of the IMB intervention model, the system provides users with personalized health management suggestions or intervention measures based on the queried data.

[0046] First, the received formatted data is used to analyze the user's rhinitis condition, medical history, seasonal factors and other information. Next, based on the IMB intervention model, the system guides the user's self-management behavior according to the three main elements in the model: information, motivation, and behavioral skills. For example, in the "information" link, the system provides seasonal triggers and symptom management information related to rhinitis; in the "motivation" link, the system encourages users to adhere to health management behaviors, such as enhancing motivation through goal setting and improving self-efficacy; and in the "behavioral skills" link, the system provides specific behavioral guidance, such as daily nasal cleaning and guidance on medication use.

[0047] In order to ensure the security and compliance of data, the system adopts a permission constraint mechanism to assign different data access rights to users of different roles. When performing database retrieval, the system compares the formatted data with the storage content in the expert database, queries the rhinitis data most relevant to the user's health status, and generates corresponding guidance decisions. Ultimately, the system will provide users with specific guidance information based on the IMB intervention model. This information may include health advice, symptom management methods, drug recommendations, etc., to help users effectively manage and relieve rhinitis symptoms.

[0048] Through this system based on the IMB intervention model, users can better manage their rhinitis symptoms, improve treatment compliance, and reduce recurrence of the disease under personalized health guidance. At the same time, permission constraints ensure data security and user privacy protection.

[0049] The user self-management module 16 is used to perform terminal visualization display of the user guidance information and perform user self-management.

[0050] Specifically, terminal visualization refers to the display of user guidance information in various ways such as graphics, text, and video through terminal devices (such as smartphones, computers, tablets, etc.). Terminal visualization makes information easier to understand and use, helping users quickly obtain health management advice. User self-management refers to the user's active participation in the health management process and adjusting and managing it according to their own conditions. Self-management includes taking medication on time, recording symptoms, adjusting lifestyle habits, and responding to health challenges, with the aim of improving health levels and quality of life.

[0051] First, guidance information is generated based on the user's health data, history, and personalized analysis based on the IMB intervention model. The core goal of terminal visualization display is to enable users to clearly and intuitively understand the actions they need to take through graphics, text, and even voice. In this process, the system will use graphical interface design tools (such as Android Studio, Xcode, React Native, etc.) to develop terminal applications and use data visualization libraries (such as D3.js, Highcharts, etc.) to display health management information. Through these tools, the system can transform complex health data and management recommendations into easy-to-understand charts, flow charts, and dynamic displays to help users easily track and manage their health status.

[0052] Through the terminal visualization display, users can intuitively view the personalized guidance information generated by the system. This approach not only helps users better understand and implement health management recommendations, but also improves user participation and satisfaction. The visual interface makes complex health management data clearer and easier to operate, reducing the user's cognitive burden. At the same time, the real-time interactivity of the terminal device makes self-management more flexible and efficient, which helps to improve user compliance and achieve better health management results.

[0053] Furthermore, the expert database construction module 11 is also used to perform the following steps: determine the seasonal rhinitis triggering node in combination with the rhinitis data, determine the initial and terminal node time zones one by one, and determine multiple seasonal time zones; traverse the multiple seasonal time zones to determine the time zone environmental variables, wherein the time zone environmental variables are environmental factors that affect the rhinitis state; use the environmental variables as independent variables and the rhinitis characteristics as dependent variables to mine the first allergic characteristics.

[0054] Specifically, seasonal rhinitis trigger nodes refer to the key moments or time periods that cause seasonal allergic rhinitis symptoms to worsen or occur. Node time zones refer to the triggering time range of rhinitis symptoms within a specific season or time period. For example, the spring pollen season is a "node time zone", which includes the specific time period from the increase in pollen concentration to the onset of allergic symptoms. Seasonal time zones refer to different time periods divided according to seasonal changes. These time periods are related to specific environmental factors (such as temperature, humidity, pollen concentration, etc.) and can affect the occurrence and aggravation of rhinitis symptoms. Time zone environmental variables refer to environmental factors within a specific season or time period, such as temperature, humidity, pollen concentration in the air, etc., which have a direct impact on the onset of rhinitis and symptom changes. Environmental variables as independent variables and rhinitis characteristics as dependent variables refer to using seasonal environmental factors (such as pollen concentration, temperature, etc.) as independent variables and rhinitis symptoms (such as nasal congestion, sneezing) as dependent variables, and using statistical methods or machine learning algorithms to analyze the causal relationship between the two.

[0055] First, the rhinitis data is analyzed to identify seasonal rhinitis trigger nodes. These trigger nodes are usually critical time points when rhinitis symptoms intensify in a specific season or under specific environmental conditions. For example, pollen season, periods of significant humidity changes, and sharp temperature fluctuations are all seasonal trigger nodes. By performing statistical analysis on a large number of historical data on patient symptoms, the system can identify these trigger nodes.

[0056] Next, the system determines the initial and final time zones for each trigger node. For example, in the spring pollen season, the time zone of the trigger node is from the time when the pollen concentration begins to rise (such as early February) to the time when the pollen concentration decreases (such as mid-May). By determining these time zones, the system can more accurately divide each seasonal time zone and identify relevant environmental variables for each time zone.

[0057] Then, the system traverses multiple seasonal time zones and analyzes environmental variables in each time zone (such as temperature, humidity, concentration of allergens in the air, etc.). For example, in spring, warm and humid environments promote the release of certain pollens, while in autumn, it is a high incidence period for mold and dust mites. By analyzing environmental variables in these time zones, the system finds their impact on rhinitis symptoms (such as nasal congestion, sneezing, etc.).

[0058] Finally, using statistical and machine learning methods (such as regression analysis, decision trees or neural networks, etc.), environmental variables are used as independent variables, and rhinitis characteristics (such as nasal congestion, runny nose and other symptoms) are used as dependent variables to model causal relationships and conduct data mining, thereby refining the first allergic characteristics. For example, through analysis, it was found that when the spring pollen concentration reached a certain level, the incidence of nasal congestion symptoms increased significantly, and this pattern became part of the first allergic characteristics.

[0059] Through this step, the system can accurately identify the triggering nodes and time zone environmental variables of seasonal rhinitis, and analyze how environmental factors affect rhinitis symptoms. Using data mining technology, the system can not only reveal the relationship between rhinitis symptoms and environmental factors, but also provide patients with personalized treatment plans.

[0060] Further, such as Figure 2 As shown, the expert database construction module 11 is also used to perform the following steps: based on the seasonal rhinitis triggering node, perform a primary data clustering integration to determine the first data grouping; based on the multi-level authority, perform a secondary data clustering integration to determine the second data grouping; and identify the expert database according to the first data grouping and the second data grouping.

[0061] Specifically, the trigger node of seasonal rhinitis refers to the key time point or period of rhinitis symptom onset during seasonal changes, which is often closely related to changes in environmental factors (such as temperature, humidity, pollen concentration, etc.). For example, the arrival of the spring pollen season is a trigger node. The first clustering and integration of data refers to the first round of clustering analysis of the initially collected rhinitis-related data, with the aim of dividing the data into several data groups with similarities. Clustering is usually performed based on certain characteristics (such as season, environmental factors, patient type, etc.). The first data grouping refers to the preliminary grouping obtained based on the first clustering and integration of data. Multi-level permissions refer to setting different access rights levels in a database or system. Through these permissions, the system can perform different access control and management on data according to different roles. The second data clustering and integration refers to a more detailed clustering analysis based on the first data clustering and integration, so as to further refine the data grouping according to other characteristics (such as the severity of the patient's condition, treatment response, etc.). The second data grouping refers to the refined grouping obtained after the second clustering and integration. These groups can be defined based on factors such as the patient's specific treatment needs and different stages of the disease. Expert database identification refers to labeling and classifying each data in the expert database to make it easier to retrieve, analyze and use. Database identification can be defined based on data category, source, treatment effect and other information.

[0062] First, the rhinitis data will be preliminarily clustered according to the seasonal rhinitis triggering nodes (for example, spring pollen season, autumn mold season, etc.). This process is called "data one-time clustering integration". In this step, the data is assigned to different groups according to specific seasonal environmental factors or allergen types. For example, the data of the spring pollen season and the rhinitis data triggered by summer humidity changes are divided into two separate groups. The purpose of this grouping is to integrate the rhinitis data in a roughly similar seasonal environment for subsequent more in-depth analysis.

[0063] Next, based on multi-level permissions, the system will conduct a more sophisticated "secondary data clustering integration". Multi-level permissions refer to assigning data access and management permissions to different users based on the user's role or the importance of the data. In secondary clustering, the data will be grouped more finely based on the severity of the patient's condition, the intensity of the allergic reaction, the effectiveness of the treatment plan, etc. For example, the data of patients with mild and severe seasonal rhinitis can be divided into different groups, which is convenient for subsequent precise intervention.

[0064] Finally, based on the results of the first data grouping and the second data grouping, the system will perform expert database identification on these data, that is, label or mark each data item to facilitate future data retrieval and analysis.

[0065] Through this process, the system can not only accurately classify and identify the relevant data of patients with seasonal allergic rhinitis, but also provide more personalized and targeted management plans for different patients based on data grouping.

[0066] Furthermore, a normalized standard data format is set, and the index matrix is ​​initialized based on the standard data format.

[0067] Specifically, normalized data format is to convert data from different sources and types into a standardized structure to facilitate processing and analysis in the system. The purpose of normalization is to eliminate the heterogeneity of data so that different data sources and formats can be seamlessly connected in the same system.

[0068] First, a normalized standard data format needs to be set. The key to this process is to convert rhinitis data from different sources (such as patients' chief complaints, medical test reports, environmental variables, allergen information, etc.) into a standardized data structure. Specifically, it includes mapping text-described symptoms to standard symptom codes and unifying laboratory results in different formats (such as IgE levels in blood tests) into standard data fields. The normalized standard data format uses formats such as JSON, XML, or is represented by rows and columns in a database table.

[0069] Once the data is normalized, the next step is to initialize the index matrix based on the standard data format. The role of the index matrix is ​​to quickly locate and query the stored data. For example, if we need to quickly find all patient records that have experienced nasal congestion symptoms in a certain season, the index matrix can help the system quickly find relevant entries in massive data by using "seasonality" and "symptom type" as index conditions. Usually, the design of the index matrix sets multiple dimensions according to actual needs, such as symptoms, time, environment, allergens, etc. The construction of the index matrix usually uses indexing technology in database management systems (such as MySQL, MongoDB), or uses data processing frameworks (such as Pandas, NumPy) to create multidimensional matrices and arrays. Through the unified management of these standard data formats, the system can efficiently store, update and retrieve data.

[0070] By setting a normalized standard data format and initializing the index matrix, the entire system can achieve efficient data storage, management, and retrieval. The consistency and operability of the data are significantly improved, ensuring that the system can process heterogeneous data from different sources and find case information related to the target in a short time. Especially in the case of large amounts of data, the use of the index matrix can greatly improve the speed and efficiency of data query.

[0071] Furthermore, the data formatting module 14 is also used to perform the following steps: construct a large language model, wherein the large language model includes a semantic parsing unit and a format conversion unit; by receiving the rhinitis data, performing semantic recognition based on the semantic parsing unit to determine the semantic elements; based on the standard data format, in combination with the format conversion unit, converting the semantic elements to determine the formatted data.

[0072] Specifically, large language models (such as GPT-4, BERT, etc.) are a type of natural language processing model based on deep learning, which is used to process and understand grammar, semantics, context and other information in human language. Large language models can learn large-scale text data through pre-training and generate text with strong reasoning and generation capabilities. The semantic parsing unit is a key component in the large language model, which is responsible for extracting meaningful content from natural language text. This includes identifying and extracting entities (such as symptoms, time, place, etc.), relationships (such as the relationship between allergic reactions and allergens), intentions (such as "patients hope to relieve nasal congestion") and other information in the text. The format conversion unit is used to convert the semantically parsed information into a standardized, computer-recognizable data format. This is a key step in converting unstructured natural language text into a format that can be used for storage, retrieval and analysis (such as JSON, XML, database tables, etc.).

[0073] First, a large language model needs to be built. Specifically, first, a large amount of text data is collected to ensure that the data contains rich rhinitis-related content, such as medical literature, patient feedback, clinical records, etc. Then, the data is preprocessed, including noise removal, labeling, and word segmentation, to ensure the quality of input data. Next, a deep learning framework (such as TensorFlow or PyTorch) is used to select a suitable model architecture (such as Transformer, BERT, GPT, etc.), and train it on large-scale data, and optimize the model parameters through back propagation. During the training process, the model performance is improved by adjusting the hyperparameters (such as learning rate and batch size). After the training is completed, the accuracy and generalization ability of the model are verified, and fine-tuned as needed (for example, specific adjustments are made to the field of rhinitis). Finally, the trained large language model is integrated into the system to handle rhinitis-related semantic parsing tasks.

[0074] Once the large language model is trained, the next step is for the semantic parsing unit. The semantic parsing unit processes the received rhinitis data, first extracting key information from the text through natural language processing technology. After identifying these semantic elements, the next task is to convert them into a standard data format through the format conversion unit. The format conversion unit structures the parsed semantic elements based on a predefined standard data format (such as JSON or a database table structure).

[0075] This process greatly improves the efficiency and accuracy of data processing. Through semantic parsing with a large language model, effective information can be automatically extracted from free text, avoiding the limitations of manual intervention and traditional rule matching. The format conversion unit ensures that this information can be uniformly stored and managed, thus providing a standardized data foundation for subsequent data mining, analysis and decision-making.

[0076] Furthermore, the guidance information determination module 15 is also used to perform the following steps: transmitting the formatted data to the IMB intervention model, determining the user motivation based on the second allergic relationship; and performing database retrieval and guidance decision-making based on the user motivation to determine the user guidance information.

[0077] Specifically, user motivation refers to the driving force for users to change their behavior when facing health problems. In the IMB intervention model, the enhancement of motivation is the key factor in promoting users to change their health behaviors.

[0078] First, the user's health information obtained through formatted data is transmitted to the IMB intervention model for subsequent analysis and intervention decision-making. First, the system will analyze the user's motivation through the second allergy interrelationship model based on the rhinitis data input by the user.

[0079] After motivation identification, the system conducts a database search based on the user's motivation. The purpose of the database search is to extract personalized health guidance information for the user's specific symptoms and needs from the expert database.

[0080] Ultimately, the system will combine the user's motivation, the expert database's recommendations, and permission constraints (for example, patients can only view personal data, while doctors can access more clinical resources) to generate final health management guidance information for users. This guidance information can be displayed to users through terminal devices (such as mobile phone apps or smart health devices) to help them manage themselves.

[0081] Through this step, not only can the user's motivation be identified, but also personalized intervention decisions can be provided in combination with the expert database. This approach helps users manage their health problems more effectively, improve treatment compliance, and reduce disease recurrence, while ensuring data security and privacy protection.

[0082] Furthermore, the guidance information determination module 15 is also used to perform the following steps: introducing user historical guidance information, wherein the user historical guidance information is called based on historical management records; based on the user historical guidance information, initializing the second allergy relationship, wherein the second allergy relationship is a universal relationship.

[0083] Specifically, historical management records refer to the historical data of all treatment and health management operations of users in the system, including the treatment methods accepted by users, drug use, symptom changes, etc. Universal relationships refer to a universal health management model for most users, which can be applied to different users but can still be personalized in specific applications.

[0084] First, the user's historical guidance information is introduced, that is, the user's long-term health status is understood by calling the user's historical management records (such as past rhinitis symptoms, treatment feedback, medication use, etc.). For example, if the user reported nasal congestion and runny nose many times in the past spring allergy season and used antihistamines and nasal hormone drugs to control the symptoms, the system will record this information as historical guidance information.

[0085] Next, the system initializes the second allergic relationship based on these user historical guidance information. This relationship is a universal model designed to help the system better provide treatment decisions for users. The key to this step is that the system adjusts and optimizes the user's health management through the input of historical data combined with a universal relationship model (i.e. a universal model that can adapt to most people). The system converts this data into applicable intervention decisions and makes corresponding adjustments based on the unique situation of each user.

[0086] For example, suppose user Zhang San has seasonal allergic rhinitis. In the past three years, he has had symptoms of nasal congestion, runny nose and itchy eyes every spring. The system records Zhang San's use of antihistamines and nasal hormones every spring, as well as measures to avoid outdoor activities during the peak pollen season. Based on these historical records, the system builds a personalized "second allergic correlation" for Zhang San. The model believes that Zhang San's symptoms are related to the high pollen concentration in spring and that he responds well to antihistamines. Through this model, the system can automatically provide Zhang San with new health advice, such as early prevention when pollen concentrations are high and the rational use of drugs.

[0087] By introducing the user's historical health records and treatment feedback, the system can intelligently initialize and optimize the "second allergic relationship" to provide users with more accurate and personalized health management suggestions. It not only helps users to intervene immediately when symptoms occur, but also provides a basis for long-term health management, which can significantly improve treatment compliance and patient satisfaction.

[0088] Furthermore, after user self-management, it includes: setting a preset update cycle; based on periodic nodes, uploading new theoretical data, updating the expert database, calling periodic management records, and updating and learning the IMB intervention model, wherein the periodic management records include output guidance records and input feedback records.

[0089] Specifically, the preset update cycle refers to the time interval or frequency of regular updates pre-set in the system. Periodic nodes refer to specific time points or event nodes within the update cycle, which are used to trigger system data updates. New theoretical data refers to relevant theoretical research results, treatment methods, drug information or case analysis newly added to the expert database. Periodic management records refer to periodic data recorded for user health management activities during the operation of the system.

[0090] After the user completes self-management, the system will set a preset update cycle to ensure that the expert database and intervention model always maintain the latest data and academic achievements. For example, if the system is set to update once a quarter, the periodic node can be the end of each quarter. At this time, the system will trigger new data upload and update through the periodic node. The newly added theoretical data includes the latest medical research results, treatment methods, clinical case analysis, etc., to supplement and improve the expert database.

[0091] At the same time, the system will upload periodic management records, which reflect the user's health management behavior in the past period of time, including output guidance records (such as the system's personalized health management suggestions to users) and input feedback records (such as users' feedback, reactions and implementation of these suggestions). Through these records, the system can analyze the implementation of users' health management and update intervention strategies based on user feedback.

[0092] The IMB intervention model will be updated and learned based on these management records. By combining the output guidance records with the input feedback records, the IMB model can optimize the user's health management intervention strategy. For example, if many users give poor feedback on a certain treatment method in a certain cycle, the system will make adjustments in the next cycle and recommend other more suitable solutions to users.

[0093] By setting preset update cycles and periodic nodes, the system can keep pace with the latest medical research and practice, and timely optimize and update the expert database and intervention model. This regular update ensures that the system can flexibly adapt to changes in user needs and improve the accuracy and effectiveness of interventions.

[0094] In summary, the user self-management system with a built-in seasonal allergic rhinitis expert database provided in the embodiment of the present application has the following technical effects:

[0095] 1. The present invention realizes personalized self-management for seasonal rhinitis patients by constructing a seasonal allergic rhinitis expert database. By embedding the expert database, it can provide accurate intervention decisions and guidance, enhance users' cognition of rhinitis, promote self-management behavior, and thus improve rhinitis symptoms.

[0096] 2. By mining the first allergic characteristics of seasonal rhinitis and combining it with the analysis of environmental variables, the triggers of rhinitis attacks can be effectively identified. Through data mining and feature analysis, scientific prevention and intervention measures can be provided to patients to reduce the risk of symptom recurrence and aggravation.

[0097] 3. By introducing user historical guidance information and universal second allergy relationships, the present invention optimizes the personalization and universality of user health management. The call and analysis of historical data improves the accuracy and adaptability of the model, making the intervention plan more in line with the specific needs of each user.

[0098] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0099] Furthermore, the first or second mentioned above not only represents an order relationship, but also represents a specific concept, and / or refers to the selection of multiple elements individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application intends to include these modifications and variations.

Claims

1. A user self-management system with a built-in seasonal allergic rhinitis expert database, characterized by: The system comprises: Expert database construction module, used to retrieve big data and call rhinitis data, conduct data mining based on seasonal environmental variables-first allergic characteristics-guidance information, and build an expert database; A model supervision training module, used for mining the secondary allergic relationship of information-motivation-behavior, and supervising the training of the IMB intervention model, wherein the IMB intervention model is embedded with the expert database; The engine connection establishment module is used to set the role authority and the index matrix, the target user accesses and allocates the role authority, and establishes the engine connection, wherein the role authority and the index matrix have a corresponding relationship, and the index matrix includes multiple ranges of scanning engines; A data formatting module is used to receive rhinitis data, identify and perform semantic analysis, and convert and determine formatted data; A guidance information determination module, for determining user guidance information by performing database retrieval and intervention guidance decision under authority constraints based on the IMB intervention model for the formatted data; The user self-management module is used to perform terminal visual display of the user guidance information and perform user self-management.

2. The user self-management system with a seasonal allergic rhinitis expert database embedded therein as claimed in claim 1, characterized in that: Mining first allergic features, including: In combination with the rhinitis data, seasonal rhinitis triggering nodes are determined, initial and terminal defined node time zones are determined node by node, and multiple seasonal time zones are determined; Traversing the plurality of seasonal time zones to determine time zone environmental variables, wherein the time zone environmental variables are environmental factors that affect the state of rhinitis; Taking environmental variables as independent variables and rhinitis characteristics as dependent variables, the first allergic characteristics were explored.

3. The user self-management system with a built-in seasonal allergic rhinitis expert database as claimed in claim 2, characterized in that: The constructing of the expert database comprises: Based on the seasonal rhinitis triggering nodes, data is clustered and integrated to determine the first data grouping; Based on multi-level permissions, perform secondary data clustering and integration to determine the second data grouping; The expert database is identified according to the first data group and the second data group.

4. The user self-management system with a built-in seasonal allergic rhinitis expert database as claimed in claim 1, characterized in that: A normalized standard data format is set, and the index matrix is ​​initialized based on the standard data format.

5. The user self-management system with a built-in seasonal allergic rhinitis expert database as claimed in claim 4, characterized in that: Receive rhinitis data, identify and perform semantic analysis, and convert and determine the formatted data, including: Constructing a large language model, wherein the large language model includes a semantic parsing unit and a format conversion unit; By receiving the rhinitis data, performing semantic recognition based on the semantic parsing unit to determine semantic elements; Based on the standard data format and in combination with the format conversion unit, the semantic elements are converted to determine the formatted data.

6. The user self-management system with a built-in seasonal allergic rhinitis expert database as claimed in claim 1, characterized in that: Based on the IMB intervention model, database retrieval and intervention guidance decision-making under authority constraints are performed to determine user guidance information, including: transmitting the formatted data to the IMB intervention model to determine user motivation based on the second allergy interrelationship; Based on the user motivation, database retrieval and guidance decision are performed to determine the user guidance information.

7. The user self-management system with a built-in seasonal allergic rhinitis expert database as claimed in claim 6, characterized in that: Guided by the second allergic relationship, including: Introducing user history guidance information, wherein the user history guidance information is called based on history management records; Based on the user historical guidance information, the second allergic relationship is initialized, wherein the second allergic relationship is a universal relationship.

8. The user self-management system with a built-in seasonal allergic rhinitis expert database as claimed in claim 1, characterized in that: After user self-management, including: Set a preset update cycle; Based on the periodic nodes, newly added theoretical data are uploaded, the expert database is updated, the periodic management records are called, and the IMB intervention model is updated and learned, wherein the periodic management records include output guidance records and input feedback records.

Images