Asthma management system combined with air quality analysis

Abstract

The invention discloses an asthma management system combined with air quality analysis, and belongs to the technical field of asthma management systems, and the system comprises a personalized health record module which is used for collecting and storing the medical information of each patient, and obtaining the medical information of each patient through a machine learning algorithm; the personalized health archive module can establish a personalized health archive for each patient according to the collected medical information and is used for providing a basis for subsequent decision making, and the real-time air quality monitoring module is used for being connected with an external air quality monitor, collecting and analyzing pollutant information in air in real time and providing a real-time air quality monitoring result. The real-time air quality monitoring module can convert collected pollutant information into indexes having practical significance for asthma patients. The problems that an existing asthma management system is complex in operation, long in data processing and analysis process and inscriptional in system decision making are solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of asthma management systems, and in particular to an asthma management system combined with air quality analysis. Background Art

[0002] Asthma is a chronic inflammatory respiratory disease characterized by airway hyperresponsiveness and reversible airway obstruction, manifested by recurrent episodes of wheezing, dyspnea, chest tightness and cough. These symptoms usually worsen at night or after exercise and may be triggered by a variety of factors, such as allergens, respiratory infections, cold air, exercise and mood changes. The pathophysiology of asthma involves airway inflammation, smooth muscle spasm, increased mucus secretion and airway remodeling. Long-term management includes avoiding triggering factors, using medications and, in some cases, immunotherapy.

[0003] The existing asthma management systems that combine air quality analysis still have problems mainly in the following aspects:

[0004] Decision-making is relatively rigid. The existing asthma management system cannot fully adapt to the patient's actual situation and past medical history when making decisions. It is often based on preset algorithms and models, and performs simple correlation analysis between air quality data and asthma symptoms, but ignores key factors such as individual differences among patients, the complexity of medical history, and dynamic changes in the disease. As a result, the recommendations and treatment plans provided by the system cannot be applicable to all patients.

[0005] The system operation is relatively complicated. The existing asthma management system cannot achieve fast operation of the system and often requires professional personnel to operate and maintain the system. In addition, the data in the existing asthma management system takes a long time to process and analyze, which cannot meet the immediate needs of asthma patients for air quality information in emergency situations.

[0006] Therefore, a new asthma management system combined with air quality analysis is needed to solve the above problems. Summary of the Invention

[0007] In response to the problems existing in the existing technology, the present invention provides an asthma management system combined with air quality analysis, which has the advantages of relatively convenient system operation, fast data processing and analysis efficiency, and the ability to formulate corresponding treatment plans based on the actual situation of the patient. It solves the problems of existing asthma management systems that are relatively complex to operate, time-consuming in data processing and analysis, and relatively rigid system decision-making.

[0008] The present invention is implemented as follows: an asthma management system combined with air quality analysis includes a personalized health record module for collecting and storing medical information of each patient. Through a machine learning algorithm, the personalized health record module can establish a personalized health record for each patient based on the collected medical information to provide a basis for subsequent decision-making, wherein the medical information includes but is not limited to medical history, personal medical history, allergen information, living habits and environmental factors; a real-time air quality monitoring module for connecting to an external air quality monitor and collecting and analyzing pollutant information in the air in real time; the real-time air quality monitoring module will convert the collected pollutant information into indicators that are of practical significance to asthma patients; a symptom tracking and analysis module for tracking the patient's asthma symptoms and activity level in real time through mobile devices and wearable technology, and monitoring the frequency and severity of the patient's symptoms in real time based on sensor data and patient input; a decision support and adaptive algorithm module for In combination with the health record established by the personalized health record module and the real-time air quality data monitored by the real-time air quality monitoring module, the decision support and adaptive algorithm module can use advanced data analysis and machine learning technologies to provide patients with customized decision support and dynamically adjust management strategies according to the patient's real-time symptoms and environmental changes. The emergency response and notification module is used to immediately issue an alarm to the patient and medical staff when the real-time air quality monitoring module and the symptom tracking and analysis module detect a sharp deterioration in air quality and a sudden worsening of the patient's symptoms, and provide emergency treatment suggestions. The user interaction interface module is used to provide a convenient user operation interface so that non-professionals can quickly access system information, where access to system information includes but is not limited to viewing health advice, recording symptoms and receiving notifications. The data processing and analysis acceleration module is used to use cloud computing and edge computing technologies to quickly process and analyze system data, and respond to patients' inquiries and emergency operations in a timely manner.

[0009] As preferred embodiment of the present invention, the personalized health record module includes an information collection unit for obtaining the patient's medical history, personal medical history, allergen information, living habits and environmental information from different channels, a data storage unit for storing and backing up the collected medical data, and protecting the stored data through encryption technology, a data processing unit for cleaning, converting and integrating the stored data to make the stored data format consistent, a machine learning analysis unit for conducting in-depth analysis of the data based on advanced machine learning algorithms, identifying patterns and associations in the data, and providing a basis for the subsequent construction of health records, and a health record construction unit for generating and updating personalized health records for different patients based on the analysis results of the machine learning analysis unit, specifically including the patient's specific health status, risk factors, disease tendencies and treatment recommendations.

[0010] As a preferred embodiment of the present invention, the real-time air quality monitoring module includes a data capture unit for connecting to an external air quality monitoring device and continuously collecting concentration data of pollutants in the air, wherein the pollutants include but are not limited to PM2.5, PM10, carbon monoxide, sulfur dioxide, ozone and nitrogen oxides; a monitoring data processing unit for receiving the original data transmitted by the data capture unit, and performing data cleaning, calibration and format conversion on the original data to make the data format consistent; an analysis and conversion unit for performing a comprehensive analysis on the processed data, calculating the air quality index, and converting it into a health assessment parameter that has direct guiding significance for asthma patients; a user display unit for transmitting the analysis results to the user interaction interface module and displaying them intuitively to the user, and the display method is not limited to graphical display, color warning and text description.

[0011] As a preferred embodiment of the present invention, the symptom tracking and analysis module includes a physiological information acquisition unit for collecting physiological data of the patient through mobile devices and wearable devices, wherein the physiological data includes but is not limited to heart rate, respiratory rate, activity level and sleep quality; a real-time monitoring unit for receiving real-time data streams from the physiological information acquisition unit and performing preliminary analysis on the received data, wherein the real-time monitoring unit is capable of tracking the patient's activity level and physiological state in real time and automatically identifying abnormal patterns that may cause asthma symptom attacks; a patient input unit for providing a dedicated input interface in the user interaction interface module so that the patient can manually input relevant symptom information, wherein the symptom information includes but is not limited to attack frequency, severity and triggering factors; a data analysis and processing unit for processing and analyzing the data collected from the physiological information acquisition unit and the patient input unit, and automatically identifying the pattern of symptom attacks and assessing the severity of symptoms; a historical data storage and management unit for storing the patient's long-term symptom records, treatment responses and environmental factors, and managing data access rights; and a symptom feedback unit for generating feedback and customized management suggestions based on the patient's actual situation, and transmitting the feedback and management suggestions to the user interaction interface module for display to the patient.

[0012] As a preferred embodiment of the present invention, the decision support and adaptive algorithm module includes a data integration unit for collecting and integrating patient health data from the personalized health record module and environmental data from the real-time air quality monitoring module, and synchronizing and storing the data; an analysis engine unit for processing the integrated data using advanced data analysis technology, and automatically identifying trends, associations and anomalies in the data, providing a basis for subsequent decision-making, wherein the data analysis technology includes but is not limited to statistical analysis, pattern recognition and data mining; a machine learning algorithm unit for establishing a prediction model using machine learning technology, and predicting the impact of health risks and air quality changes on patients based on historical data and real-time data; a decision support unit for providing patients with customized health advice and management strategies based on the output data of the analysis engine unit and the machine learning algorithm unit, wherein the health advice and management strategies include but are not limited to drug dosage adjustments, lifestyle change recommendations and preventive measures; an adaptive management strategy unit for dynamically adjusting the management strategy according to the patient's real-time symptoms and environmental changes, and monitoring the patient's response to the strategy through a feedback mechanism, thereby further updating the management strategy.

[0013] As a preferred embodiment of the present invention, the emergency response and notification module includes a real-time monitoring data receiving unit for receiving data from the real-time air quality monitoring module and continuously monitoring the air quality index, a symptom tracking data receiving unit for obtaining data from the symptom tracking and analysis module and analyzing the patient's health status, a decision logic unit for judging the deterioration of air quality and the aggravation of patient symptoms, an alarm notification unit for immediately sending a notification to the patient and medical staff after the decision logic unit judges that the air quality and patient symptoms are abnormal, and the notification methods include but are not limited to SMS, email, application push and telephone, an emergency treatment suggestion unit for providing treatment suggestions in emergency situations based on current air quality data and patient symptoms, and the treatment suggestions include but are not limited to giving corresponding first aid measures and suggesting that patients seek medical treatment immediately, a recording and feedback unit for recording all issued alarms, notifications and user responses, and further adjusting the system based on the collected feedback information.

[0014] As a preferred embodiment of the present invention, the data processing and analysis acceleration module includes a receiving processing unit, which is used to receive and integrate all data within the system and pre-process the integrated data; an edge cloud computing processing unit, which is used to directly obtain environmental and patient data at the specific point where the data is generated, and use the powerful computing power of cloud computing to instantly process the data pre-processed by the receiving processing unit and make decisions; a data analysis acceleration unit, which is used to adopt optimization analysis technology to accelerate the data analysis process and realize rapid transmission of data between different modules, wherein the optimization analysis technology includes but is not limited to parallel computing and distributed processing.

[0015] As a preferred embodiment of the present invention, it also includes an environment and symptom learning module, which is used to extract the current data of the real-time air quality monitoring module and the symptom tracking and analysis module after at least one of the real-time air quality monitoring module and the symptom tracking and analysis module monitors patient abnormality, and relies on advanced learning algorithms to analyze the correlation between the environment and patient symptoms. The analyzed correlation results will be transmitted to the decision support and adaptive algorithm module for further optimization of treatment decisions, and a file special information extraction module is used to extract important information from the medical information collected from the patient in the personalized health file module, and transmit the extracted information in batches to the data processing and analysis acceleration module for accelerated processing according to the importance of the information, so as to provide a decision basis for the decision support and adaptive algorithm module.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] The setting of the personalized health record module can provide the system with a comprehensive overview of the patient's health, helping the system to make more accurate diagnosis and treatment decisions. Secondly, through personalized health records, the system can better understand the patient's specific needs and potential risks, thereby providing more personalized medical services. The real-time air quality monitoring module can provide asthma patients with timely and accurate air quality information, thereby helping patients to better manage their health status, avoid activities in heavily polluted environments, and thus reduce the risk of asthma attacks. The symptom tracking and analysis module can provide patients and related personnel with continuous, real-time health monitoring, so as to detect early signs of asthma symptoms in time, and allow patients or related personnel to take preventive measures to avoid worsening of symptoms. At the same time, through long-term tracking, the symptom tracking and analysis module can reveal the relationship between symptoms and specific activities and environmental factors. The module can identify the relationship between factors or lifestyle habits, and then customize more personalized treatment plans for patients. The decision support and adaptive algorithm module can provide patients with more accurate and personalized health management suggestions, and dynamically adjust the treatment plan according to the real-time changes in the environment and patient symptoms, thereby ensuring the real-time and targeted nature of the treatment plan. At the same time, by setting up the emergency response and notification module, it can respond quickly to potential health risks, ensuring that relevant personnel can understand the patient's condition in a timely manner and quickly take appropriate medical intervention measures. The user interaction interface module can ensure that the system operation is relatively fast and convenient, avoiding obstruction of operation by patients or related personnel and affecting patient treatment. Finally, the setting of the data processing and analysis acceleration module can ensure the efficient transmission of system data and ensure that the system can respond immediately after detecting abnormalities in patients or the environment, thereby further ensuring patient safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a principle block diagram provided by an embodiment of the present invention;

[0019] Figure 2 This is a block diagram of the principle of providing a personalized health record module according to an embodiment of the present invention;

[0020] Figure 3 This is a block diagram of the principle of a real-time air quality monitoring module provided by an embodiment of the present invention;

[0021] Figure 4 is a functional block diagram of a symptom tracking and analysis module provided by an embodiment of the present invention;

[0022] Figure 5 This is a principle block diagram of the decision support and adaptive algorithm module provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to further understand the content, features and effects of the present invention, the following embodiments are given as examples and described in detail with reference to the accompanying drawings.

[0024] The structure of the present invention is described in detail below with reference to the accompanying drawings.

[0025] refer to Figures 1 to 5 The asthma management system combined with air quality analysis provided by an embodiment of the present invention includes a personalized health record module for collecting and storing each patient's medical information. Through a machine learning algorithm, the personalized health record module can establish a personalized health record for each patient based on the collected medical information to provide a basis for subsequent decision-making. The medical information includes but is not limited to medical history, personal medical history, allergen information, lifestyle habits, and environmental factors.

[0026] A real-time air quality monitoring module, which is used to connect to an external air quality monitor and collect and analyze air pollutant information in real time. The real-time air quality monitoring module converts the collected pollutant information into indicators that are practical for asthma patients;

[0027] A symptom tracking and analysis module, which tracks patients' asthma symptoms and activity levels in real time through mobile devices and wearable technology, and monitors the frequency and severity of patients' symptoms in real time based on sensor data and patient input;

[0028] A decision support and adaptive algorithm module, which is used to combine the health records established by the personalized health record module and the real-time air quality data monitored by the real-time air quality monitoring module. The decision support and adaptive algorithm module can use advanced data analysis and machine learning technologies to provide customized decision support for patients and dynamically adjust management strategies based on the patient's real-time symptoms and environmental changes;

[0029] An emergency response and notification module, configured to immediately alert patients and medical personnel when the real-time air quality monitoring module and the symptom tracking and analysis module detect a sharp deterioration in air quality and a sudden worsening of patient symptoms, and provide emergency treatment suggestions;

[0030] A user interaction interface module is used to provide a convenient user operation interface so that non-professionals can quickly access system information, where accessing system information includes but is not limited to viewing health advice, recording symptoms, and receiving notifications;

[0031] The data processing and analysis acceleration module is used to quickly process and analyze system data using cloud computing and edge computing technologies, and respond to patient inquiries and emergency operations in a timely manner.

[0032] When the system is in operation, the personalized health profile module collects and integrates the patient's medical information to create a comprehensive personal health profile. It uses machine learning algorithms to analyze the patient's historical medical records, personal medical history, allergen information, lifestyle habits, and environmental factors. Based on the analysis results, it automatically identifies patterns and trends in the patient's health status and creates a customized health profile for each patient. This profile includes not only static data such as past diagnosis and treatment records, but also dynamic data such as the impact of lifestyle habits and environmental changes on health. The real-time air quality monitoring module collects real-time air pollutant data through external air quality monitors, including but not limited to PM2.5. The concentration of common air pollutants such as PM10, carbon monoxide, sulfur dioxide, and ozone. The built-in analysis algorithm of the real-time air quality monitoring module will process the monitored data and convert it into indicators that are of practical significance to asthma patients, thereby helping asthma patients understand the current air quality conditions and take corresponding preventive measures. The symptom tracking and analysis module is used to collect patients' physiological data and activity information in real time through sensors integrated in mobile devices and wearable devices. Sensors include heart rate monitors, accelerometers, environmental sensors, etc., which can monitor patients' heart rate, activity level, respiratory rate, and surrounding air quality data. In addition, the symptom tracking and analysis module also allows patients to enter their own symptom information, such as difficulty breathing, coughing, or chest tightness. By analyzing these sensor data and patient input, the system can monitor the frequency and severity of asthma symptoms in real time and provide a comprehensive overview of health status. The decision support and adaptive algorithm module will integrate and analyze the health record data established by the personalized health record module and the air quality data provided by the real-time air quality monitoring module, and use advanced data analysis technologies, including but not limited to statistical analysis, pattern recognition, and machine learning algorithms, to process and interpret these data. , thereby identifying key information in health records, such as the patient's medical history, allergens, and previous health events, as well as key indicators such as pollution levels and particulate matter concentrations in real-time air quality data, and providing customized decision support for patients based on this data. The Emergency Response and Notification Module is used to monitor data from the Air Quality Monitoring Module and the Symptom Tracking and Analysis Module in real time. When either of these modules detects an abnormality, the Emergency Response and Notification Module immediately triggers an alarm mechanism and provides emergency treatment recommendations to patients and medical staff based on pre-set emergency procedures. The User Interaction Interface Module is used to provide an intuitive and easy-to-understand interface that allows users to easily interact with the system. The interface typically includes graphical elements, buttons, menus, and navigation tools, which are organized into a logical structure so that users can quickly find the functions they need. Finally, the Data Processing and Analysis Acceleration Module relies on cloud computing and edge computing technologies to achieve efficient data processing and analysis during system operation, thereby reducing data transmission delays and achieving faster response times. At the same time, through a distributed computing architecture, the Data Processing and Analysis Acceleration Module can quickly process and analyze data in the system, ensuring real-time and efficient data processing.

[0033] The setting of the personalized health record module can provide the system with a comprehensive overview of the patient's health, helping the system to make more accurate diagnosis and treatment decisions. Secondly, through personalized health records, the system can better understand the patient's specific needs and potential risks, thereby providing more personalized medical services. The real-time air quality monitoring module can provide asthma patients with timely and accurate air quality information, thereby helping patients to better manage their health status, avoid activities in heavily polluted environments, and thus reduce the risk of asthma attacks. The symptom tracking and analysis module can provide patients and related personnel with continuous, real-time health monitoring, so as to detect early signs of asthma symptoms in time, and allow patients or related personnel to take preventive measures to avoid worsening of symptoms. At the same time, through long-term tracking, the symptom tracking and analysis module can reveal the relationship between symptoms and specific activities and environmental factors. The module can identify the relationship between factors or lifestyle habits, and then customize more personalized treatment plans for patients. The decision support and adaptive algorithm module can provide patients with more accurate and personalized health management suggestions, and dynamically adjust the treatment plan according to the real-time changes in the environment and patient symptoms, thereby ensuring the real-time and targeted nature of the treatment plan. At the same time, by setting up the emergency response and notification module, it can respond quickly to potential health risks, ensuring that relevant personnel can understand the patient's condition in a timely manner and quickly take appropriate medical intervention measures. The user interaction interface module can ensure that the system operation is relatively fast and convenient, avoiding obstruction of operation by patients or related personnel and affecting patient treatment. Finally, the setting of the data processing and analysis acceleration module can ensure the efficient transmission of system data and ensure that the system can respond immediately after detecting abnormalities in patients or the environment, thereby further ensuring patient safety.

[0034] Furthermore, the personalized health record module includes an information collection unit for obtaining the patient's medical history, personal medical history, allergen information, living habits and environmental information from different channels;

[0035] Data storage unit, used to store and back up the collected medical data and protect the stored data through encryption technology;

[0036] The data processing unit is used to clean, convert and integrate the stored data to make the stored data format consistent;

[0037] Machine learning analysis unit, which uses advanced machine learning algorithms to conduct in-depth data analysis, identify patterns and associations in the data, and provide a basis for the subsequent construction of health records;

[0038] The health record construction unit is used to generate and update personalized health records for different patients based on the analysis results of the machine learning analysis unit, specifically including the patient's specific health status, risk factors, disease tendencies and treatment recommendations.

[0039] When the system is operating, it first obtains the patient's medical history, personal medical history, allergen information, living habits and environmental information from multiple channels through the information collection unit. These data are then transmitted to the data storage unit for storage and backup, and protected by encryption technology to ensure data security. The data processing unit will then clean, convert and integrate the stored data to ensure the consistency of the data format, and the machine learning analysis unit will use advanced algorithms to conduct in-depth analysis of the processed data, identify the patterns and associations therein, and provide the necessary information for building a health record. Finally, the health record construction unit will generate and update the patient's personalized health record based on the analysis results of the machine learning analysis unit, specifically including the patient's specific health status, risk factors, disease tendencies and treatment recommendations, thereby providing a basis for subsequent decision-making.

[0040] By integrating and analyzing the patient's multi-dimensional health information, the system can provide patients and related personnel with comprehensive and accurate patient health records, thereby helping doctors better understand the patient's health status and potential risks. Through in-depth analysis of machine learning algorithms, the system can reveal subtle patterns and associations in the patient's health data, thereby identifying disease risks in advance and formulating personalized treatment plans. In addition, due to the encrypted storage and processing of data, patient privacy can be protected, thereby effectively enhancing data security. Finally, personalized health records are generated based on the actual information of different patients, which can provide a basis for subsequent patient treatment decisions, ensuring that the system can be applied to different patients and environments, thereby effectively improving the use effect of the asthma management system.

[0041] Furthermore, the real-time air quality monitoring module includes a data capture unit for connecting to an external air quality monitoring device and continuously collecting concentration data of pollutants in the air, wherein the pollutants include but are not limited to PM2.5, PM10, carbon monoxide, sulfur dioxide, ozone and nitrogen oxides;

[0042] A monitoring data processing unit, configured to receive the raw data transmitted by the data capture unit, and perform data cleaning, calibration, and format conversion on the raw data to make the data format consistent;

[0043] The analysis and conversion unit is used to conduct a comprehensive analysis of the processed data, calculate the air quality index, and convert it into health assessment parameters that have direct guiding significance for asthma patients;

[0044] The user display unit is used to transmit the analysis results to the user interaction interface module and display them intuitively to the user, and the display methods include but are not limited to graphic display, color warning and text description.

[0045] When the system is operating, the data capture unit will connect to various peripherals, such as sensors, to continuously collect concentration data of different pollutants in the air, including PM2.5, PM10, carbon monoxide, sulfur dioxide, ozone and nitrogen oxides. Subsequently, the monitoring data processing unit will receive the raw data from the data capture unit and perform data cleaning, calibration and format conversion to ensure data consistency and accuracy. The processed data will be passed to the analysis and conversion unit, which will conduct a comprehensive analysis of the data, calculate the air quality indicators, and convert them into health assessment parameters that have direct guiding significance for asthma patients. Finally, the user display unit will transmit the analysis results to the user interaction interface module, and intuitively display them to the user in a variety of ways such as graphical display, color warnings and text descriptions, so that users can easily understand the air quality status and its impact on health.

[0046] By continuously monitoring and analyzing the concentration of pollutants in the air, it can provide users with real-time and accurate air quality information to help users understand the current environmental conditions. Through data processing and analysis, the device can convert complex data into easy-to-understand health assessment parameters. Especially for asthma patients, these parameters can provide direct health guidance to help patients take appropriate preventive measures to avoid the impact of pollutants on their health. In addition, through intuitive user display, users can quickly obtain air quality information and make corresponding action decisions, such as adjusting outdoor activity plans or taking indoor air purification measures, thereby improving the quality of life and reducing health risks.

[0047] Furthermore, the symptom tracking and analysis module includes a physiological information acquisition unit for collecting physiological data of the patient through mobile devices and wearable devices, wherein the physiological data includes but is not limited to heart rate, respiratory rate, activity level and sleep quality;

[0048] a real-time monitoring unit for receiving the real-time data stream from the physiological information acquisition unit and performing preliminary analysis on the received data, wherein the real-time monitoring unit is capable of tracking the patient's activity level and physiological state in real time and automatically identifying abnormal patterns that may cause asthma symptoms to flare up;

[0049] a patient input unit, configured to provide a dedicated input interface within the user interface module, enabling the patient to manually input relevant symptom information, wherein the symptom information includes but is not limited to attack frequency, severity, and triggering factors;

[0050] a data analysis and processing unit, configured to process and analyze the data collected from the physiological information acquisition unit and the patient input unit, and automatically identify the pattern of symptom onset and assess the severity of symptoms;

[0051] A historical data storage and management unit, used to store patients' long-term symptom records, treatment responses, and environmental factors, and manage access rights to the data;

[0052] The symptom feedback unit is used to generate feedback and customized management suggestions based on the patient's actual situation, and transmit the feedback and management suggestions to the user interaction interface module for display to the patient.

[0053] When the system is operating, the physiological information collection unit uses advanced mobile device and wearable device technology to continuously and accurately collect multiple physiological data of the patient. These data include but are not limited to key indicators such as heart rate, respiratory rate, daily activity level and sleep quality. The real-time monitoring unit, as one of the core components of the system, will receive the data stream from the physiological information collection unit and immediately perform preliminary analysis and processing. It can track the patient's activity level and physiological state in real time, detect any abnormal physiological changes in time, and automatically identify abnormal patterns that may indicate an imminent onset of asthma symptoms, such as a sudden increase in heart rate or a significant increase in respiratory rate, thereby providing early warning and buying valuable response time for patients and medical professionals. The patient input unit provides patients with an intuitive and easy-to-use interface, allowing patients to manually enter their symptom information, including the frequency of asthma attacks, the severity of symptoms, and various factors that may trigger symptoms, such as specific foods, environmental factors or mood swings. , medical professionals can obtain a more comprehensive and personalized health portrait of patients, providing strong support for the formulation of precise treatment plans, and the data analysis and processing unit will receive a large amount of data from the physiological information acquisition unit and the patient input unit, and use advanced algorithms and technologies to conduct in-depth analysis and processing, so as to automatically identify the pattern of symptom onset, assess the severity of symptoms, and explore potential health risks. The historical data storage and management unit will securely store the patient's long-term symptom records, treatment responses, environmental factors and other data, and manage data access rights, thereby providing a basis for subsequent evaluation of treatment effects, tracking changes in the disease and predicting future onset trends. The symptom feedback unit will generate customized management suggestions and feedback based on the patient's actual situation. Suggestions include adjusting lifestyle habits, optimizing treatment plans or taking specific preventive measures, etc., and present the information to patients and related personnel in a clear and easy-to-understand manner through the user interaction interface module, helping patients and related personnel to better manage and formulate treatment plans.

[0054] Through comprehensive physiological data monitoring and analysis, patients and medical professionals can obtain real-time and historical data on patients' health status, thereby gaining a deeper understanding of the patterns and triggers of asthma symptoms, helping to promptly identify and respond to possible asthma attacks, improving patients' self-management capabilities, and providing doctors with important information to develop more effective treatment plans. In addition, the system can also provide personalized feedback and management recommendations to help patients improve their lifestyle habits and treatment plans, thereby improving their quality of life and reducing the risk of asthma attacks.

[0055] Furthermore, the decision support and adaptive algorithm module includes a data integration unit for collecting and integrating the patient health data from the personalized health profile module and the environmental data from the real-time air quality monitoring module, and synchronizing and storing the data;

[0056] An analysis engine unit, which is used to process the integrated data using advanced data analysis techniques and automatically identify trends, associations, and anomalies in the data, providing a basis for subsequent decision-making. Data analysis techniques include but are not limited to statistical analysis, pattern recognition, and data mining;

[0057] A machine learning algorithm unit, which is used to establish a prediction model using machine learning technology and predict the impact of health risks and air quality changes on patients based on historical and real-time data;

[0058] a decision support unit, configured to provide customized health advice and management strategies for the patient based on the output data of the analysis engine unit and the machine learning algorithm unit, wherein the health advice and management strategies include but are not limited to drug dosage adjustments, lifestyle change suggestions, and preventive measures;

[0059] The adaptive management strategy unit is used to dynamically adjust the management strategy according to the patient's real-time symptoms and environmental changes, and monitor the patient's response to the strategy through a feedback mechanism to further update the management strategy.

[0060] When the system is operating, the data integration unit will comprehensively collect and integrate the patient health data recorded in detail in the personalized health record module and the accurate environmental data provided by the real-time air quality monitoring module, and ensure that the data provides a basis for subsequent decision-making through efficient data synchronization and storage mechanisms. The analysis engine unit makes full use of cutting-edge data analysis technologies such as statistical analysis, pattern recognition and data mining to deeply process the integrated data. It can automatically identify potential trends, correlations and outliers in the data, reveal the laws and patterns hidden behind the data, and thus provide strong support for decision-making. The machine learning algorithm unit further exerts the powerful capabilities of machine learning to build accurate prediction models based on historical data and real-time data. These models can Predicting the possible impact of health risks and air quality changes on patients provides a scientific basis for formulating targeted management strategies. The decision support unit will provide patients with customized health advice and management strategies based on the output data of the analysis engine and machine learning algorithm. The suggestions include adjusting drug dosages, changing lifestyles, and taking preventive measures, etc., to help patients better manage their own health. The adaptive management strategy unit pays more attention to the flexibility and dynamism of the strategy. It will adjust the management strategy in time according to the patient's real-time symptom changes and fluctuations in environmental factors. At the same time, it will monitor the patient's response to the strategy through an effective feedback mechanism to further optimize and update the management strategy to ensure that patients can obtain the best health management results.

[0061] By integrating and analyzing patients' health data and environmental data, more accurate and personalized health management plans can be provided. Using advanced data analysis technology and machine learning algorithms, the system can predict the impact of potential health risks and environmental changes on patients, so as to take preventive measures in advance and reduce health risks. The customized suggestions and management strategies provided by the decision support unit help patients better manage their health status and improve their quality of life. The dynamic adjustment function of the adaptive management strategy unit ensures that the management strategy can be updated in time according to the patient's actual situation and environmental changes, thereby providing a continuous and effective health management strategy.

[0062] Furthermore, the emergency response and notification module includes a real-time monitoring data receiving unit for receiving data from the real-time air quality monitoring module and continuously monitoring the air quality index;

[0063] a symptom tracking data receiving unit, configured to obtain data from the symptom tracking and analysis module and analyze the patient's health status;

[0064] A decision logic unit, used to determine the deterioration of air quality and the aggravation of patient symptoms;

[0065] An alarm notification unit, configured to immediately send notifications to patients and medical personnel after the decision logic unit determines that the air quality and patient symptoms are abnormal, including but not limited to text messages, emails, app push notifications, and phone calls;

[0066] Emergency treatment suggestion unit, used to provide emergency treatment suggestions based on current air quality data and patient symptoms, including but not limited to providing appropriate first aid measures and advising patients to seek medical treatment immediately;

[0067] The recording and feedback unit is used to record all issued alarms, notifications and user responses, and further adjust the system based on the collected feedback information.

[0068] When the system is operating, the real-time monitoring data receiving unit obtains the air quality index from the real-time air quality monitoring module, and the symptom tracking data receiving unit obtains the patient's health status data from the symptom tracking and analysis module. The decision logic unit will comprehensively analyze the obtained data to determine whether the current air quality has deteriorated and whether the patient's symptoms have worsened. Once an abnormal situation is detected, the alarm notification unit will be activated immediately and send alarm notifications to patients and medical staff through various means such as SMS, email, application push and phone. At the same time, the emergency treatment suggestion unit will provide emergency treatment suggestions based on the current environment and health data, such as first aid measures and medical advice. The recording and feedback unit is responsible for recording all alarms, notifications and user responses, and adjusting the system based on the collected feedback information to optimize its performance and response efficiency.

[0069] By responding promptly to deteriorating air quality and worsening patient symptoms, patients and medical staff can be quickly notified, reducing health risks. Multiple notification methods can ensure that information is conveyed to relevant personnel in a timely manner, thereby further ensuring patient safety. The advice provided by the emergency treatment advice unit can help patients take appropriate measures to alleviate symptoms or prevent the situation from worsening while waiting for medical assistance. In addition, the continuous recording and adjustment functions of the recording and feedback unit help the system to continuously learn and improve, improve the accuracy and efficiency of future responses, and provide patients with more personalized and effective health monitoring and management services.

[0070] Furthermore, the data processing and analysis acceleration module includes a receiving and processing unit for receiving and integrating all data within the system and preprocessing the integrated data;

[0071] An edge cloud computing processing unit is used to directly obtain environmental and patient data at the specific point where the data is generated, and to use the powerful computing power of cloud computing to instantly process the data pre-processed by the receiving processing unit and make decisions;

[0072] The data analysis acceleration unit is used to accelerate the data analysis process by using optimized analysis technology and realize the rapid transmission of data between different modules. Among them, the optimized analysis technology includes but is not limited to parallel computing and distributed processing.

[0073] When the system is operating, the receiving and processing unit will first receive and integrate all the data within the system, and perform preliminary processing on the integrated data to ensure the availability and consistency of the data. Subsequently, the edge cloud computing processing unit will directly collect environmental and patient data at the location where the data is generated, and use the powerful computing resources of cloud computing to perform real-time analysis and processing on the pre-processed data, so as to make decisions quickly. The data analysis acceleration unit will improve the speed of data analysis by applying optimized analysis technologies, such as parallel computing and distributed processing, while ensuring that data can be transmitted quickly between different modules of the system, thereby improving overall processing efficiency.

[0074] Through a multi-level data processing architecture, the system can achieve rapid response and efficient data analysis. The pre-processing function of the receiving processing unit ensures data quality and lays a solid foundation for subsequent analysis. The instant processing capability of the edge cloud computing processing unit reduces the delay in data transmission to the central server, making decisions faster and more accurate. The optimization technology of the data analysis acceleration unit further shortens the analysis time and improves the data processing speed, thereby providing feedback and results faster, ensuring higher data processing efficiency, and effectively enhancing the reliability and response speed of the system.

[0075] Furthermore, the system further includes an environment and symptom learning module, which is used to extract current data from the real-time air quality monitoring module and the symptom tracking and analysis module after at least one of the real-time air quality monitoring module and the symptom tracking and analysis module detects an abnormality in the patient, and analyze the correlation between the environment and the patient's symptoms using an advanced learning algorithm. The analyzed correlation results will be transmitted to the decision support and adaptive algorithm module for further optimization of treatment decisions;

[0076] The archive special information extraction module is used to extract important information from the medical information collected by the personalized health record module from the patient, and transmit the extracted information in batches to the data processing and analysis acceleration module for accelerated processing according to the importance of the information, so as to provide a decision basis for the decision support and adaptive algorithm module.

[0077] When the system is operating, the environment and symptom learning module will immediately collect the latest data from the real-time air quality monitoring module and the symptom tracking and analysis module when the real-time air quality monitoring module or the symptom tracking and analysis module detects an abnormality in the patient. Then, it will use advanced learning algorithms, such as machine learning or deep learning technology, to analyze these data to identify and understand the potential correlation between environmental factors and patient symptoms, thereby revealing how environmental changes affect the patient's health status, thereby providing a scientific basis for medical decision-making. The archive special information extraction module will first receive medical information from the personalized health record module, and analyze and filter this information through built-in algorithms and logic to identify key and important information. Once these key information are identified, the archive special information extraction module will sort the information according to its importance and transmit the information in batches to the data processing and analysis acceleration module. This process ensures the efficiency and accuracy of data processing.

[0078] By analyzing the relationship between environmental factors and patient symptoms, medical professionals can better understand which environmental conditions may aggravate the patient's condition and take preventive measures or adjust treatment plans. This learning module helps optimize treatment plans, reduce unnecessary medical interventions, improve treatment outcomes, and potentially reduce medical costs. In addition, it can help the medical system respond more effectively to environmental health risks and provide patients with more accurate and timely medical services. At the same time, by extracting and prioritizing key information, it ensures that the data processing and analysis acceleration module can quickly obtain the most important data, thereby speeding up the entire decision-making process. This not only improves the performance of the decision support and adaptive algorithm modules, but also enables medical professionals to obtain key information more quickly to make more timely and accurate medical decisions. In addition, by effectively managing information flow, the archive special information extraction module can also reduce the burden on the data processing system, thereby improving the overall stability and reliability of the system.

[0079] Working principle of the present invention:

[0080] When the system is in operation, the personalized health profile module collects and integrates the patient's medical information to create a comprehensive personal health profile. It uses machine learning algorithms to analyze the patient's historical medical records, personal medical history, allergen information, lifestyle habits, and environmental factors. Based on the analysis results, it automatically identifies patterns and trends in the patient's health status and creates a customized health profile for each patient. This profile includes not only static data such as past diagnosis and treatment records, but also dynamic data such as the impact of lifestyle habits and environmental changes on health. The real-time air quality monitoring module collects real-time air pollutant data through external air quality monitors, including but not limited to PM2.5. The concentration of common air pollutants such as PM10, carbon monoxide, sulfur dioxide, and ozone. The built-in analysis algorithm of the real-time air quality monitoring module will process the monitored data and convert it into indicators that are of practical significance to asthma patients, thereby helping asthma patients understand the current air quality conditions and take corresponding preventive measures. The symptom tracking and analysis module is used to collect patients' physiological data and activity information in real time through sensors integrated in mobile devices and wearable devices. Sensors include heart rate monitors, accelerometers, environmental sensors, etc., which can monitor patients' heart rate, activity level, respiratory rate, and surrounding air quality data. In addition, the symptom tracking and analysis module also allows patients to enter their own symptom information, such as difficulty breathing, coughing, or chest tightness. By analyzing these sensor data and patient input, the system can monitor the frequency and severity of asthma symptoms in real time and provide a comprehensive overview of health status. The decision support and adaptive algorithm module will integrate and analyze the health record data established by the personalized health record module and the air quality data provided by the real-time air quality monitoring module, and use advanced data analysis technologies, including but not limited to statistical analysis, pattern recognition, and machine learning algorithms, to process and interpret these data. , thereby identifying key information in health records, such as the patient's medical history, allergens, and previous health events, as well as key indicators such as pollution levels and particulate matter concentrations in real-time air quality data, and providing customized decision support for patients based on this data. The Emergency Response and Notification Module is used to monitor data from the Air Quality Monitoring Module and the Symptom Tracking and Analysis Module in real time. When either of these modules detects an abnormality, the Emergency Response and Notification Module immediately triggers an alarm mechanism and provides emergency treatment recommendations to patients and medical staff based on pre-set emergency procedures. The User Interaction Interface Module is used to provide an intuitive and easy-to-understand interface that allows users to easily interact with the system. The interface typically includes graphical elements, buttons, menus, and navigation tools, which are organized into a logical structure so that users can quickly find the functions they need. Finally, the Data Processing and Analysis Acceleration Module relies on cloud computing and edge computing technologies to achieve efficient data processing and analysis during system operation, thereby reducing data transmission delays and achieving faster response times. At the same time, through a distributed computing architecture, the Data Processing and Analysis Acceleration Module can quickly process and analyze data in the system, ensuring real-time and efficient data processing.

[0081] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0082] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An asthma management system combined with air quality analysis, characterized by: A personalized health profile module is included to collect and store each patient's medical information. Through machine learning algorithms, the personalized health profile module can create a personalized health profile for each patient based on the collected medical information to provide a basis for subsequent decision-making. The medical information includes but is not limited to medical history, personal medical history, allergen information, lifestyle habits and environmental factors; A real-time air quality monitoring module, which is used to connect to an external air quality monitor and collect and analyze air pollutant information in real time. The real-time air quality monitoring module converts the collected pollutant information into indicators that are practical for asthma patients; A symptom tracking and analysis module, which tracks patients' asthma symptoms and activity levels in real time through mobile devices and wearable technology, and monitors the frequency and severity of patients' symptoms in real time based on sensor data and patient input; A decision support and adaptive algorithm module, which is used to combine the health records established by the personalized health record module and the real-time air quality data monitored by the real-time air quality monitoring module. The decision support and adaptive algorithm module can use advanced data analysis and machine learning technologies to provide customized decision support for patients and dynamically adjust management strategies based on the patient's real-time symptoms and environmental changes; An emergency response and notification module, configured to immediately alert patients and medical personnel when the real-time air quality monitoring module and the symptom tracking and analysis module detect a sharp deterioration in air quality and a sudden worsening of patient symptoms, and provide emergency treatment suggestions; A user interaction interface module is used to provide a convenient user operation interface so that non-professionals can quickly access system information, where accessing system information includes but is not limited to viewing health advice, recording symptoms, and receiving notifications; The data processing and analysis acceleration module is used to quickly process and analyze system data using cloud computing and edge computing technologies, and respond to patient inquiries and emergency operations in a timely manner.

2. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The personalized health record module includes an information collection unit for obtaining the patient's medical history, personal medical history, allergen information, living habits and environmental information from different channels; A data storage unit, used to store and back up the collected medical data and protect the stored data through encryption technology; The data processing unit is used to clean, convert and integrate the stored data to make the stored data format consistent; Machine learning analysis unit, which uses advanced machine learning algorithms to conduct in-depth data analysis, identify patterns and associations in the data, and provide a basis for the subsequent construction of health records; The health record construction unit is used to generate and update personalized health records for different patients based on the analysis results of the machine learning analysis unit, specifically including the patient's specific health status, risk factors, disease tendencies and treatment recommendations.

3. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The real-time air quality monitoring module includes a data capture unit for connecting to an external air quality monitoring device and continuously collecting concentration data of pollutants in the air, wherein the pollutants include but are not limited to PM2.5, PM10, carbon monoxide, sulfur dioxide, ozone and nitrogen oxides; A monitoring data processing unit, configured to receive the raw data transmitted by the data capture unit, and perform data cleaning, calibration, and format conversion on the raw data to make the data format consistent; The analysis and conversion unit is used to conduct a comprehensive analysis of the processed data, calculate the air quality index, and convert it into health assessment parameters that have direct guiding significance for asthma patients; The user display unit is used to transmit the analysis results to the user interaction interface module and display them intuitively to the user, and the display methods include but are not limited to graphic display, color warning and text description.

4. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The symptom tracking and analysis module includes a physiological information acquisition unit for collecting physiological data of the patient through mobile devices and wearable devices, wherein the physiological data includes but is not limited to heart rate, respiratory rate, activity level and sleep quality; a real-time monitoring unit for receiving the real-time data stream from the physiological information acquisition unit and performing preliminary analysis on the received data, wherein the real-time monitoring unit is capable of tracking the patient's activity level and physiological state in real time and automatically identifying abnormal patterns that may cause asthma symptoms to flare up; a patient input unit, configured to provide a dedicated input interface within the user interface module, enabling the patient to manually input relevant symptom information, wherein the symptom information includes but is not limited to attack frequency, severity, and triggering factors; a data analysis and processing unit, configured to process and analyze the data collected from the physiological information acquisition unit and the patient input unit, and automatically identify the pattern of symptom onset and assess the severity of symptoms; A historical data storage and management unit, which is used to store patients' long-term symptom records, treatment responses, and environmental factors, and manage access rights to the data; The symptom feedback unit is used to generate feedback and customized management suggestions based on the patient's actual situation, and transmit the feedback and management suggestions to the user interaction interface module for display to the patient.

5. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The decision support and adaptive algorithm module includes a data integration unit for collecting and integrating the patient health data from the personalized health record module and the environmental data from the real-time air quality monitoring module, and synchronizing and storing the data; An analysis engine unit, which is used to process the integrated data using advanced data analysis techniques and automatically identify trends, associations, and anomalies in the data, providing a basis for subsequent decision-making. Data analysis techniques include but are not limited to statistical analysis, pattern recognition, and data mining; A machine learning algorithm unit, which is used to establish a prediction model using machine learning technology and predict the impact of health risks and air quality changes on patients based on historical and real-time data; a decision support unit, configured to provide customized health advice and management strategies for the patient based on the output data of the analysis engine unit and the machine learning algorithm unit, wherein the health advice and management strategies include but are not limited to drug dosage adjustments, lifestyle change suggestions, and preventive measures; The adaptive management strategy unit is used to dynamically adjust the management strategy according to the patient's real-time symptoms and environmental changes, and monitor the patient's response to the strategy through a feedback mechanism to further update the management strategy.

6. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The emergency response and notification module includes a real-time monitoring data receiving unit for receiving data from the real-time air quality monitoring module and continuously monitoring the air quality index; a symptom tracking data receiving unit, configured to obtain data from the symptom tracking and analysis module and analyze the patient's health status; A decision logic unit, used to determine the deterioration of air quality and the aggravation of patient symptoms; An alarm notification unit, configured to immediately send notifications to patients and medical personnel after the decision logic unit determines that the air quality and patient symptoms are abnormal, including but not limited to text messages, emails, app push notifications, and phone calls; Emergency treatment suggestion unit, used to provide emergency treatment suggestions based on current air quality data and patient symptoms, including but not limited to providing appropriate first aid measures and advising patients to seek medical treatment immediately; The recording and feedback unit is used to record all issued alarms, notifications and user responses, and further adjust the system based on the collected feedback information.

7. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The data processing and analysis acceleration module includes a receiving and processing unit for receiving and integrating all data within the system and preprocessing the integrated data; An edge cloud computing processing unit is used to directly obtain environmental and patient data at the specific point where the data is generated, and to use the powerful computing power of cloud computing to instantly process the data pre-processed by the receiving processing unit and make decisions; The data analysis acceleration unit is used to accelerate the data analysis process by using optimized analysis technology and realize the rapid transmission of data between different modules. Among them, the optimized analysis technology includes but is not limited to parallel computing and distributed processing.

8. The asthma management system combined with air quality analysis according to claim 1, characterized in that: The system further includes an environment and symptom learning module for extracting current data from the real-time air quality monitoring module and the symptom tracking and analysis module after at least one of the real-time air quality monitoring module and the symptom tracking and analysis module detects an abnormality in the patient, and analyzing the correlation between the environment and the patient's symptoms using an advanced learning algorithm. The analyzed correlation results are then transmitted to the decision support and adaptive algorithm module for further optimizing treatment decisions. The archive special information extraction module is used to extract important information from the medical information collected by the personalized health record module from the patient, and transmit the extracted information in batches to the data processing and analysis acceleration module for accelerated processing according to the importance of the information, so as to provide a decision basis for the decision support and adaptive algorithm module.

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