Multifunctional vital sign monitoring method and system

Abstract

The embodiment of the invention provides a multifunctional vital sign monitoring method and system. The method comprises the following steps: acquiring real-time vital sign signals of a user in different physiological states, and forming a multi-dimensional monitoring data set in combination with environmental parameters; dynamically adjusting the monitoring frequency and precision of real-time vital sign signals by applying an intelligent regulation and control algorithm according to the multi-dimensional monitoring data set, generating a data collection strategy, and collecting vital sign data based on the data collection strategy; based on the vital sign data, an instant evaluation engine is used for analyzing and processing monitored vital sign abnormal conditions, and a health condition evaluation result and personalized health suggestions are generated; and safely synchronizing the health condition evaluation result and the personalized suggestion to a medical monitoring system of the user to realize remote medical service and continuous health management. According to the technical scheme provided by the embodiment of the invention, the accuracy and efficiency of health monitoring are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of intelligent health monitoring and telemedicine technology, and in particular to a multifunctional vital signs monitoring method and system. Background Art

[0002] With the aging population and the increasing incidence of chronic diseases, there is a growing demand for continuous health monitoring and telemedicine services. In scenarios such as home care, hospital wards, and rehabilitation centers, real-time acquisition of users' multi-dimensional vital signs data and environmental parameters is essential for accurately assessing the health status of users. In addition, modern health management not only requires timely warning of abnormal conditions, but also expects to provide personalized health advice to help users better manage their own health. Therefore, a multifunctional vital signs monitoring method is needed that can combine multi-dimensional data, dynamically adjust monitoring strategies, and achieve remote synchronization.

[0003] Currently, there are a variety of vital signs monitoring devices and technologies on the market, including wearable devices, home medical instruments, and hospital-level monitoring systems. These devices are usually able to collect basic vital signs data and collect and transmit data at a preset fixed frequency. Some advanced devices are also equipped with preliminary data analysis functions that can identify common abnormal situations and issue alarms. However, most existing solutions focus on single-dimensional data collection, lack comprehensive consideration of environmental parameters, and have fixed data collection strategies that cannot be flexibly adjusted according to the user's specific status and needs.

[0004] However, although existing technologies have met the basic needs of vital sign monitoring to a certain extent, there are still some shortcomings. Fixed-frequency data collection may lead to waste of resources in low-risk conditions and fail to capture key changes in a timely manner in high-risk conditions. Most existing devices perform data analysis based on general algorithms, which fail to fully consider the impact of individual differences and environmental factors, resulting in health recommendations that are not personalized and targeted enough. Many devices do not have secure synchronization functions, making it difficult to achieve efficient telemedicine and continuous health management, limiting doctors' real-time understanding of patients' health conditions and their ability to intervene.

[0005] In summary, the existing vital signs monitoring technology and equipment still have room for improvement in terms of flexibility, personalization and remote services. The present invention aims to achieve more accurate, efficient and personalized vital signs monitoring by introducing intelligent control algorithms and instant evaluation engines, combined with multi-dimensional data sets, thereby improving user experience and the quality of telemedicine services. Summary of the invention

[0006] The embodiments of the present application provide a multifunctional vital signs monitoring method and system to solve the problems of low accuracy and efficiency of health monitoring in the prior art.

[0007] In a first aspect, an embodiment of the present application provides a multifunctional vital sign monitoring method, comprising:

[0008] Obtain real-time vital sign signals of users in different physiological states and combine them with environmental parameters to form a multi-dimensional monitoring data set;

[0009] According to the multi-dimensional monitoring data set, an intelligent control algorithm is applied to dynamically adjust the monitoring frequency and accuracy of real-time vital sign signals, generate a data collection strategy, and collect vital sign data based on the data collection strategy;

[0010] Based on the vital sign data, the monitored abnormal vital signs are analyzed and processed using an instant assessment engine, and health status assessment results and personalized health recommendations are generated in combination with information on gender, height, weight, genetic disease history, and past medical history;

[0011] The health status assessment results and personalized recommendations are securely synchronized to the user's medical monitoring system to achieve remote medical services and continuous health management.

[0012] Optionally, the method of dynamically adjusting the monitoring frequency and accuracy of real-time vital sign signals by applying an intelligent control algorithm according to the multi-dimensional monitoring data set, generating a data collection strategy, and collecting vital sign data based on the data collection strategy includes:

[0013] Using data analysis technology and the multi-dimensional monitoring data set, the user's behavior pattern, activity level, and changes in the environment are identified to obtain information reflecting the user's current status;

[0014] According to the information reflecting the current state of the user, the requirements for monitoring frequency and accuracy in the current state are evaluated, and a preliminary monitoring requirements evaluation result is generated;

[0015] Combining historical vital signs data, developing personalized monitoring frequency and accuracy strategies, optimizing the preliminary monitoring needs assessment results, and obtaining optimized monitoring strategies;

[0016] The optimized monitoring strategy is used to dynamically control the monitoring frequency and accuracy of the vital sign signal acquisition device to obtain vital sign data.

[0017] Optionally, the step of evaluating the requirements for monitoring frequency and accuracy in the current state according to the information reflecting the current state of the user, and generating a preliminary monitoring requirement evaluation result includes:

[0018] Using a behavior pattern recognition algorithm, based on the information reflecting the current state of the user, the user's activity type is classified to obtain a classification result of the user's activity type;

[0019] According to the classification result of the user activity type and the environmental parameters, evaluating the impact of the environmental parameters on the user's health status, and obtaining an environmental adaptability evaluation result;

[0020] Based on the environmental adaptability assessment results, a pre-trained context awareness model is applied to predict the potential health risk level of the user in the current state and generate a health risk prediction report;

[0021] According to the health risk prediction report, determine the requirements for monitoring frequency and accuracy in the current state, and obtain a monitoring requirement level;

[0022] Based on the specific monitoring needs level, a preliminary monitoring needs assessment result is generated.

[0023] Optionally, the combining of historical vital signs data to formulate a personalized monitoring frequency and accuracy strategy, optimizing the preliminary monitoring needs assessment results to obtain an optimized monitoring strategy, includes:

[0024] Using the preliminary monitoring needs assessment results, the monitoring needs of users in different scenarios are refined to generate specific monitoring needs descriptions;

[0025] According to the specific monitoring requirements, integrate the user's long-term behavior patterns, health status change trends and vital signs data to build a historical model of user behavior and health;

[0026] Based on the historical model of user behavior and health, a machine learning algorithm is applied to build a personalized prediction model suitable for the user, wherein the personalized prediction model can predict the user's future behavior pattern and its impact on health;

[0027] Utilizing the personalized prediction model and the specific monitoring requirements, strategies for monitoring frequency and accuracy under different scenarios are formulated to generate optimized monitoring strategies.

[0028] Optionally, based on the vital sign data, the real-time assessment engine is used to analyze and process the abnormal vital sign conditions monitored, and health status assessment results and personalized health recommendations are generated in combination with information on gender, height, weight, genetic disease history and past medical history, including:

[0029] Using an instant assessment engine, the vital signs data is analyzed and processed in real time to obtain a vital signs abnormality detection report;

[0030] Determine the vital sign abnormality detection report based on the fuzzy logic reasoning mechanism and generate a vital sign abnormality confirmation result;

[0031] Based on the abnormal vital sign confirmation result, by comparing the user's long-term health data with similar cases, the user's current health status is evaluated using a pre-trained health risk prediction model to generate a health status evaluation result;

[0032] The health status assessment results are used in combination with the user's gender, height, weight, genetic history, lifestyle habits, activity level and past medical history to provide the user with customized health advice and generate personalized health advice.

[0033] Optionally, the determining the vital sign abnormality detection report based on the fuzzy logic reasoning mechanism and generating the vital sign abnormality confirmation result includes:

[0034] Analyze the abnormal conditions in the vital sign abnormality detection report by using a fuzzy logic reasoning mechanism to obtain a preliminary abnormal parameter list;

[0035] Extracting key vital sign parameters corresponding to the preliminary abnormal parameter list, and comparing them with preset standard ranges, identifying parameter values ​​beyond the normal range, and generating an abnormal parameter set, wherein the key vital sign parameters include heart rate, blood pressure and respiratory rate;

[0036] Based on the abnormal parameter set exceeding the standard range, combined with the fuzzy logic rule base, each abnormal parameter is weighted to generate a weighted abnormal parameter set;

[0037] The weighted abnormal parameter set is comprehensively evaluated by using a fuzzy reasoning algorithm, and the score of each abnormal parameter is calculated through a fuzzy membership function and reasoning rules, and combined with the overall health status, a vital sign abnormality confirmation result is obtained.

[0038] Optionally, based on the abnormal vital sign confirmation result, by comparing the user's long-term health data with similar cases, a pre-trained health risk prediction model is used to comprehensively evaluate the user's current health status, and a health status evaluation result is generated, including:

[0039] Using the pre-trained health risk prediction model, based on the abnormal vital sign confirmation results, a preliminary assessment of the user's current health status is performed to obtain a preliminary health risk assessment report;

[0040] Based on the preliminary health risk assessment report, combined with the user's long-term health data, an in-depth analysis of the user's health status is performed to generate personalized health data analysis results, wherein the long-term health data includes past medical history, living habits, and activity level;

[0041] Based on the personalized health data analysis results, similar cases in the medical database are searched for comparative analysis and processing to generate similar case comparative analysis results;

[0042] Using the similar case comparative analysis results, adjusting the parameters of the health risk prediction model, optimizing the health risk prediction model, and obtaining an optimized health risk prediction model;

[0043] According to the optimized health risk prediction model, the user's current health status is evaluated and a health status evaluation result is generated by comprehensively considering abnormal confirmation results, long-term health data and similar case information.

[0044] In a second aspect, an embodiment of the present application provides a multifunctional vital signs monitoring system, comprising:

[0045] The acquisition module is used to obtain the real-time vital sign signals of the user in different physiological states and combine them with environmental parameters to form a multi-dimensional monitoring data set;

[0046] An adjustment module, for dynamically adjusting the monitoring frequency and accuracy using an intelligent control algorithm according to the multi-dimensional monitoring data set, generating a data collection strategy, while minimizing energy consumption, and obtaining vital sign data;

[0047] An analysis module is used to analyze and process abnormal vital signs detected based on the vital signs data using an instant assessment engine, and generate health status assessment results and personalized health recommendations based on information such as gender, height, weight, genetic disease history, and past medical history;

[0048] The synchronization module is used to securely synchronize the health status assessment results and personalized recommendations to the user's medical monitoring system, support telemedicine services and achieve continuous health management.

[0049] In a third aspect, an embodiment of the present application provides a computing device, comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multifunctional vital signs monitoring method as described in the first aspect above.

[0050] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program, which, when executed by a computer, implements a multifunctional vital sign monitoring method as described in the first aspect.

[0051] In an embodiment of the present application, real-time vital sign signals of a user under different physiological states are obtained, and combined with environmental parameters to form a multi-dimensional monitoring data set; based on the multi-dimensional monitoring data set, an intelligent control algorithm is applied to dynamically adjust the monitoring frequency and accuracy of the real-time vital sign signals, generate a data collection strategy, and based on the data collection strategy, collect vital sign data; based on the vital sign data, an instant assessment engine is used to analyze and process the monitored vital sign abnormalities, and health status assessment results and personalized health recommendations are generated in combination with information on gender, height, weight, genetic history and previous medical history; the health status assessment results and personalized recommendations are securely synchronized to the user's medical monitoring system to achieve remote medical services and continuous health management.

[0052] The technical solution of this application has the following beneficial effects:

[0053] This application can more comprehensively reflect the user's health status and reduce false alarms and missed alarms by combining multi-dimensional real-time vital sign signals and environmental parameters. The intelligent control algorithm dynamically adjusts the monitoring frequency and accuracy according to actual conditions, avoiding unnecessary high-frequency data collection, thereby extending the service life of the equipment and saving energy. The instant assessment engine provides personalized health advice to help users better understand and manage their health status and improve user satisfaction. Securely synchronize health status assessment results and recommendations to the medical monitoring system, so that doctors can obtain the latest health information of patients in a timely manner and achieve efficient remote diagnosis and treatment. Through continuous monitoring and data analysis, users are provided with long-term health trend analysis, which helps to detect potential health problems early and take preventive measures.

[0054] Furthermore, the embodiments of the present application also involve the application of intelligent control algorithms and instant assessment engines to achieve dynamic monitoring of user vital signs data and real-time assessment of health status. Specifically, by using data analysis technology and multi-dimensional monitoring data sets, the user's behavior patterns, activity levels and environmental changes are identified to obtain information reflecting the user's current state. Based on this information, the demand for monitoring frequency and accuracy in the current state is evaluated, and a preliminary monitoring demand assessment result is generated. In combination with the historical vital signs data optimization strategy, a personalized monitoring frequency and accuracy strategy is formulated to dynamically control the monitoring parameters of the acquisition equipment. In addition, the vital signs data is analyzed and processed in real time using an instant assessment engine to generate an anomaly detection report, and the anomaly is confirmed through a fuzzy logic reasoning mechanism. Based on the anomaly confirmation results, the user's long-term health data and similar cases are compared, and the user's current health status is evaluated using a pre-trained health risk prediction model, and ultimately a health status assessment result and customized health recommendations are generated.

[0055] Through the above method, this application significantly improves the accuracy and flexibility of vital signs monitoring. First, the intelligent control algorithm dynamically adjusts the monitoring frequency and accuracy according to the user's real-time status, ensuring that key health information can be captured under different physiological states while avoiding waste of resources. Secondly, the introduction of the instant assessment engine enables abnormal situations to be quickly identified and confirmed, enhancing the timeliness of early warning. Finally, by combining the user's historical health data and similar cases, personalized health recommendations are generated, which improves the pertinence and effectiveness of health management. Overall, this method not only optimizes resource utilization, but also significantly improves the quality of telemedicine services and user experience, and realizes efficient, accurate and personalized continuous health management.

[0056] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flow chart of a multifunctional vital sign monitoring method provided by the present application is shown;

[0059] Figure 2 A schematic diagram of the structure of a multifunctional vital signs monitoring system provided by the present application is shown;

[0060] Figure 3 A schematic diagram of the structure of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0062] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0063] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0064] Figure 1 A flowchart of a multifunctional vital signs monitoring method is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0065] 101. Obtain real-time vital signs signals of users in different physiological states and combine them with environmental parameters to form a multi-dimensional monitoring data set;

[0066] This step involves collecting real-time vital signs signals from multiple sensors and data sources, including but not limited to heart rate, blood pressure, blood oxygen saturation, respiratory rate, etc. These physiological parameters can reflect the user's current physical condition. At the same time, environmental parameters such as temperature, humidity, air quality, light intensity, etc. are also combined to provide more comprehensive background information. By integrating these physiological and environmental data, a multi-dimensional monitoring data set is formed for subsequent analysis and decision-making.

[0067] First, the system collects the user's vital signs data in real time through wearable devices (such as smart watches and bracelets) and other medical instruments (such as blood pressure monitors and pulse oximeters). At the same time, environmental sensors (such as temperature and humidity sensors and air quality monitors) also synchronously record the surrounding environmental conditions. All of this data is transmitted to the central processing unit or cloud platform for preliminary data cleaning and formatting to ensure data integrity and consistency. Then, these multi-dimensional data are stored in a structured database, providing a basis for subsequent intelligent control algorithms and real-time evaluation engines.

[0068] Suppose in a home care scenario, an elderly person with chronic obstructive pulmonary disease wears a multifunctional health monitoring bracelet. The bracelet can not only continuously monitor heart rate, blood oxygen saturation and respiratory rate, but also record indoor temperature and air quality through built-in environmental sensors. When the elderly person is resting at home, the system automatically collects this data and uploads it to the cloud platform. By combining these physiological and environmental data, the system can better understand the health status of the elderly. For example, in the case of poor air quality, changes in respiratory rate may indicate the risk of worsening of the disease.

[0069] 102. According to the multi-dimensional monitoring data set, an intelligent control algorithm is applied to dynamically adjust the monitoring frequency and accuracy of real-time vital sign signals, generate a data collection strategy, and collect vital sign data based on the data collection strategy;

[0070] This step aims to use intelligent control algorithms to analyze multi-dimensional monitoring data sets, identify user behavior patterns, activity levels, and changes in the environment, and dynamically adjust monitoring frequency and accuracy. This not only improves the flexibility and pertinence of data collection, but also avoids unnecessary waste of resources. By learning users' historical data and behavioral habits, intelligent control algorithms can predict and adapt to different users' needs, ensuring that key information is captured at critical moments.

[0071] The intelligent control algorithm first conducts an in-depth analysis of the multi-dimensional monitoring data set to identify the user's daily behavior patterns and changes in activity levels. For example, it distinguishes between resting and moving states, or identifies different stages in the sleep cycle. Then, based on this information, the algorithm evaluates the demand for monitoring frequency and accuracy in the current state and generates a preliminary monitoring demand assessment result. Next, combined with the user's historical vital signs data, the preliminary assessment results are optimized to develop a personalized monitoring frequency and accuracy strategy. Finally, according to the optimized strategy, the working mode of the vital signs signal acquisition device is dynamically controlled to ensure efficient and accurate data collection.

[0072] Continuing with the above example, in the home care scenario, the intelligent control algorithm learns from the elderly's historical data and understands that his heart rate usually rises briefly after getting up in the morning, while it is relatively stable when sleeping at night. Therefore, the system will appropriately increase the monitoring frequency of heart rate and blood oxygen saturation in the morning to capture potential abnormalities; at night, it will reduce the monitoring frequency to save power and reduce interference. In addition, when the indoor air quality decreases, the system will automatically increase the monitoring accuracy of the respiratory rate to detect any abnormal changes in time and ensure the safety of the elderly.

[0073] 103. Based on the vital sign data, the monitored abnormal vital signs are analyzed and processed using an instant assessment engine, and health status assessment results and personalized health recommendations are generated in combination with information on gender, height, weight, genetic disease history, and past medical history;

[0074] This step uses the instant assessment engine to analyze and process vital sign data in real time to identify and confirm abnormal situations. The instant assessment engine, combined with the fuzzy logic reasoning mechanism, can quickly and accurately determine abnormal events and generate detailed abnormality detection reports. Based on these reports, the system further compares the user's long-term health data with similar cases, uses pre-trained health risk prediction models to assess the user's current health status, and ultimately generates health status assessment results and customized health recommendations. This process not only improves the timeliness of early warnings, but also provides personalized health management guidance.

[0075] The instant assessment engine receives vital sign data from the collection device and performs data analysis and anomaly detection in real time. Once an abnormality is detected, the engine will confirm the type and severity of the abnormality through a fuzzy logic reasoning mechanism and generate an abnormality detection report. Subsequently, the system compares the abnormality detection results with the user's long-term health data (such as gender, height, weight, genetic history, past medical history, and lifestyle habits), and searches for similar cases in the medical database to find matching reference cases. The pre-trained health risk prediction model evaluates the user's current health status based on this information and generates detailed health status assessment results. Finally, the system combines the user's lifestyle habits, activity level, and past medical history to provide users with customized health advice to help them take appropriate preventive and treatment measures.

[0076] Continuing with the above home care scenario as an example, when the instant assessment engine detects a sudden drop in the elderly's blood oxygen saturation, the system immediately starts the fuzzy logic reasoning mechanism and confirms that this is an acute hypoxia event. The system then retrieves the elderly's historical health data and finds that he has had similar hypoxia symptoms in the past, and consults similar cases to conclude that it may be due to poor indoor air quality. After evaluation, the health risk prediction model believes that there is a high health risk in this case, and recommends that the elderly immediately improve indoor ventilation and contact a doctor. The system will also remind family members to pay attention to the elderly's situation and provide specific response measures, such as adjusting the room ventilation time and preparing oxygen tanks.

[0077] 104. The health status assessment results and personalized recommendations are securely synchronized to the user's medical monitoring system to achieve remote medical services and continuous health management.

[0078] This step is responsible for securely synchronizing health assessment results and personalized recommendations to the user's medical monitoring system, ensuring that this information can be efficiently shared between doctors, patients and their families. Encrypted communication technology and security protocols are used to ensure the security and privacy of data transmission. Telemedicine services enable doctors to understand the patient's latest health status in real time, conduct remote diagnosis and intervention, while continuous health management helps track the patient's health trends over the long term and provide continuous personalized support.

[0079] The system transmits the generated health status assessment results and personalized recommendations to the user's medical monitoring system through a secure channel. In this process, encryption technologies such as the Advanced Encryption Standard are used to ensure that the data will not be stolen or tampered with during transmission. After receiving the data, the medical monitoring system automatically updates the patient's electronic health record and notifies relevant medical staff. Doctors can view the patient's latest health information at any time through mobile applications or web pages, and conduct remote diagnosis and treatment recommendations. In addition, the system will regularly send health reports and recommendations to patients and their families to keep communication open and ensure that patients receive continuous attention and support.

[0080] In the above home care scenario, the system synchronizes the elderly's health assessment results and personalized recommendations to the mobile app of their attending physician through an encrypted channel. Doctors can check the latest health data of the elderly at any time during outpatient visits, understand the reasons for the decline in their blood oxygen saturation and the recommendations provided by the system. At the same time, doctors can also communicate directly with the elderly and their families through the application, guide them on how to improve indoor air quality, and arrange necessary medical examinations. In addition, the system regularly sends health reports to the elderly and their families to help them better manage their health and ensure that their quality of life is continuously improved.

[0081] Through the implementation of steps 101 to 104, this application realizes the intelligent management of the entire process from data collection to telemedicine services. First, the construction of a multi-dimensional monitoring data set ensures the comprehensiveness and accuracy of the data; secondly, the intelligent control algorithm dynamically adjusts the monitoring strategy to improve resource utilization efficiency and the pertinence of data collection; thirdly, the introduction of the real-time evaluation engine significantly enhances the ability to identify and warn of abnormal situations; finally, the security synchronization mechanism ensures the effective transmission of information and privacy protection, and realizes efficient telemedicine services and continuous health management. Overall, this method not only optimizes each aspect of health monitoring, but also significantly improves the user experience and the quality of medical services, providing users with a comprehensive, intelligent and efficient health management solution.

[0082] Optionally, the method in step 102 dynamically adjusts the monitoring frequency and accuracy of real-time vital signs signals based on the multi-dimensional monitoring data set by applying an intelligent control algorithm, generates a data collection strategy, and collects vital signs data based on the data collection strategy, including: using data analysis technology and the multi-dimensional monitoring data set to identify changes in the user's behavior patterns, activity levels, and environment, and obtain information reflecting the user's current state; based on the information reflecting the user's current state, evaluate the demand for monitoring frequency and accuracy in the current state, and generate a preliminary monitoring demand assessment result; in combination with historical vital signs data, formulate a personalized monitoring frequency and accuracy strategy, optimize the preliminary monitoring demand assessment result, and obtain an optimized monitoring strategy; and use the optimized monitoring strategy to dynamically control the monitoring frequency and accuracy of the vital signs signal acquisition equipment to obtain vital signs data.

[0083] In order to solve the problem that the monitoring strategy is not flexible and personalized enough, in some embodiments, the step 102 evaluates the requirements for monitoring frequency and accuracy in the current state according to the information reflecting the current state of the user, and generates a preliminary monitoring demand evaluation result, including:

[0084] Using a behavioral pattern recognition algorithm, based on the information reflecting the user's current state, the user's activity type is classified to obtain a classification result of the user's activity type; based on the classification result of the user's activity type and the environmental parameters, the impact of the environmental parameters on the user's health status is evaluated to obtain an environmental adaptability assessment result; based on the environmental adaptability assessment result, a pre-trained situational awareness model is applied to predict the user's potential health risk level in the current state, and a health risk prediction report is generated; based on the health risk prediction report, the requirements for monitoring frequency and accuracy in the current state are determined to obtain a monitoring requirement level; based on the specific monitoring requirement level, a preliminary monitoring requirement assessment result is generated.

[0085] In this embodiment, the behavior pattern recognition algorithm is used to analyze the user's activity type, such as resting, walking, running, or sleeping. By combining data from sensors (such as accelerometers and gyroscopes), the user's activities can be classified. These data not only reflect the user's daily behavior patterns, but also provide important information about their physical state. Environmental parameters include temperature, humidity, air quality, light intensity, etc., which can affect the user's health status. For example, high humidity may aggravate breathing problems, while low air quality may increase the risk of cardiovascular disease. Therefore, environmental parameters are an important part of assessing the user's health risks. The context-aware model is a pre-trained machine learning model that can predict the potential health risk level based on the user's behavior patterns and environmental parameters. It uses a large amount of historical data and similar cases for training, can provide accurate health risk predictions, and guide the adjustment of monitoring strategies. The health risk prediction report is based on the output of the context-aware model and describes in detail the user's potential health risks in the current state, including risk levels, possible influencing factors, and recommended measures. This report is the basis for formulating specific monitoring demand levels. The monitoring demand level is based on the health risk prediction report. The system determines the specific needs for monitoring frequency and accuracy in the current state, which are divided into high demand (frequent and accurate), medium demand (moderate) and low demand (lower frequency and accuracy). This helps optimize resource utilization and ensure that key health information can be captured in different situations.

[0086] In an embodiment of the present application, first, the algorithm identifies the user's activity pattern, such as resting, walking, or exercising, by analyzing sensor data (such as accelerometers and gyroscopes). Secondly, the system considers the environmental conditions of the user (such as temperature, humidity, air quality), and combines the type of activity to evaluate the potential impact of these environmental factors on the user's health. Next, the model uses historical data and similar cases to predict the user's health risk level under current activities and environmental conditions. Furthermore, based on the content of the health risk prediction report, the system decides whether more frequent and more accurate data collection is needed, or whether the monitoring intensity can be appropriately reduced to save energy. Finally, the system integrates all evaluation information to form a final preliminary monitoring needs assessment result to guide subsequent data collection strategies.

[0087] Here is a specific example:

[0088] In a home care scenario, suppose a child with asthma wears a smart bracelet with a built-in accelerometer, gyroscope, and environmental sensor. When the child is playing at home, the behavior pattern recognition algorithm recognizes that he is doing strenuous exercise. At the same time, the environmental sensor records that the indoor air quality is poor and the PM2.5 index is high. Based on this information, the system evaluates the impact of environmental parameters on the child's health and concludes that poor air quality may aggravate asthma symptoms.

[0089] Next, the situational awareness model predicts that the child's health risk level is high under current activities and environmental conditions based on the child's historical health data and similar cases, and generates a detailed health risk prediction report. Based on this report, the system determines that the monitoring frequency and accuracy need to be increased in the current state to better capture any abnormal changes. As a result, the system sets the monitoring demand level to "high demand" and increases the monitoring frequency of heart rate, respiratory rate, and blood oxygen saturation over the next period of time.

[0090] Finally, the system generates preliminary monitoring needs assessment results to guide the subsequent collection of vital signs data. This dynamic adjustment not only improves the pertinence of monitoring, but also ensures that key health information can be captured in a timely manner at critical moments, helping parents and doctors better manage children's health conditions.

[0091] In order to solve the problem that the preliminary monitoring needs assessment results are not personalized enough, in some embodiments, the step 102 combines historical vital signs data to formulate a personalized monitoring frequency and accuracy strategy, optimizes the preliminary monitoring needs assessment results, and obtains an optimized monitoring strategy, including:

[0092] Utilizing the preliminary monitoring needs assessment results, the user's monitoring needs in different scenarios are refined to generate specific monitoring needs descriptions; based on the specific monitoring needs descriptions, the user's long-term behavior patterns, health status change trends, and vital signs data are integrated to build a historical model of user behavior and health; based on the historical model of user behavior and health, a machine learning algorithm is used to build a personalized prediction model suitable for the user, wherein the personalized prediction model can predict the user's future behavior patterns and impact on health; utilizing the personalized prediction model and the specific monitoring needs descriptions, monitoring frequency and accuracy strategies for different scenarios are formulated to generate an optimized monitoring strategy.

[0093] In this embodiment, the specific monitoring requirements description is a detailed description of the user's monitoring requirements in different situations, including specific activity types (such as resting, exercising), environmental conditions (such as temperature, humidity) and corresponding health risk levels. These instructions are used to guide the subsequent monitoring strategy formulation. The historical model of user behavior and health integrates the user's long-term behavior patterns, health status change trends and vital signs data, and can fully reflect the relationship between the user's daily activities and health status. By analyzing these data, potential factors affecting health can be identified, and future behavior patterns and health risks can be predicted. The personalized prediction model is a model built based on a machine learning algorithm, which is trained using the user's historical data and can predict the user's future behavior patterns and their impact on health. This model can provide highly personalized health advice and monitoring strategies based on individual differences of users. The optimized monitoring strategy is based on the personalized prediction model and the specific monitoring requirements description, and the system formulates monitoring frequency and accuracy strategies for different situations. These strategies not only take into account the current health status, but also foresee possible changes in the future to ensure the flexibility and pertinence of monitoring.

[0094] In an embodiment of the present application, first, the system describes in detail the user's monitoring needs under different activity types and environmental conditions based on the preliminary evaluation results, such as the need for more frequent heart rate monitoring when exercising in a high humidity environment. Secondly, the system analyzes the user's long-term data to identify the association between behavior patterns and health conditions, such as finding that a user's increased blood pressure during a specific time period is related to work pressure. Next, the system uses machine learning technology to train a personalized prediction model based on the data of the historical model, so that it can accurately predict the user's future behavior patterns and health effects. Finally, the system dynamically adjusts the monitoring strategy based on the output of the personalized prediction model and the specific monitoring needs description to ensure that key health information can be captured in different situations while avoiding waste of resources.

[0095] Here is a specific example:

[0096] In a home care scenario, suppose an elderly patient with hypertension wears a smart health monitoring bracelet. The bracelet collects data such as heart rate, blood pressure, activity level, etc. in real time, and combines it with environmental parameters (such as temperature and humidity). When the system finds that the patient's blood pressure usually rises within an hour after getting up in the morning based on the preliminary monitoring needs assessment results, it will refine the monitoring needs in this situation and generate a specific monitoring needs description, indicating that the frequency of blood pressure monitoring needs to be increased during this period.

[0097] Next, the system integrates the patient's long-term behavior patterns (such as the habit of taking a morning walk every day), health status trends (such as large fluctuations in blood pressure in the morning), and vital signs data to build a historical model of user behavior and health. By analyzing this data, the system identifies that morning walks may be one of the causes of high blood pressure.

[0098] The system then applies machine learning algorithms to build a personalized prediction model for that patient based on the historical model. This model not only predicts the patient's future behavior patterns (such as whether he will continue to take a morning walk) but also assesses its impact on health (such as the specific impact of morning walks on blood pressure).

[0099] Finally, the system uses personalized prediction models and specific monitoring requirements to develop monitoring frequency and accuracy strategies for morning walks. For example, blood pressure monitoring frequency is increased half an hour before a morning walk, and high-frequency monitoring is maintained during the walk. This optimized monitoring strategy not only improves the pertinence of monitoring, but also helps doctors better understand the health status of patients in daily life, thereby providing more personalized health management recommendations.

[0100] In this way, the system not only solves the problem of insufficient personalization of preliminary monitoring needs assessment results, but also realizes forward-looking and flexible management of user health status, significantly improving the quality of telemedicine services and user experience.

[0101] In order to solve the problem of inaccurate detection of vital sign abnormalities and health recommendations, in some embodiments, the step 103 uses an instant assessment engine to analyze and process the monitored vital sign abnormalities based on the vital sign data, and generates health status assessment results and personalized health recommendations based on information such as gender, height, weight, genetic history, and past medical history, including

[0102] The instant assessment engine is used to perform real-time analysis and processing on the vital signs data to obtain a vital signs abnormality detection report; based on a fuzzy logic reasoning mechanism, the vital signs abnormality detection report is determined to generate a vital signs abnormality confirmation result; based on the vital signs abnormality confirmation result, by comparing the user's long-term health data with similar cases, a pre-trained health risk prediction model is used to evaluate the user's current health status to generate a health status assessment result; using the health status assessment result, combined with the user's living habits, activity level and previous medical history, customized health advice is provided to the user to generate personalized health advice.

[0103] In this embodiment, the instant assessment engine is a real-time processing system that can quickly analyze vital sign data (such as heart rate, blood pressure, blood oxygen saturation, etc.) from sensors and identify potential abnormalities. The engine can generate a detailed vital sign abnormality detection report in a short time through complex algorithms and rule sets. The vital sign abnormality detection report records in detail any abnormalities found during the monitoring process, including the specific values ​​of the abnormal parameters, the time of occurrence and their severity. It not only provides a snapshot of the current state, but also lays the foundation for subsequent further analysis. The fuzzy logic reasoning mechanism is a mathematical method for processing uncertainty and fuzzy information. In this solution, the fuzzy logic reasoning mechanism is used to confirm the abnormalities in the vital sign abnormality detection report, considering multiple factors (such as data fluctuations, environmental impact) to determine the authenticity and severity of the abnormality. The vital sign abnormality confirmation result is the final confirmed vital sign abnormality after being processed by the fuzzy logic reasoning mechanism. These confirmation results are the basis for the next step of health assessment, ensuring the accuracy and reliability of abnormality detection. The health risk prediction model is a pre-trained machine learning model that can predict the risk level of the current health status based on the user's long-term health data and similar cases. The model is trained using a large amount of historical data and similar cases to improve the accuracy of predictions. Personalized health recommendations are based on health status assessment results, combined with the user's lifestyle, activity level and medical history, and the system provides customized health recommendations to users. These recommendations are designed to help users take appropriate preventive and treatment measures to improve their health.

[0104] In the embodiment of the present application, first, the instant assessment engine receives and processes data from sensors, identifies any abnormalities, and generates a detailed abnormality detection report. Secondly, the system applies a fuzzy logic reasoning mechanism, comprehensively considers multiple factors (such as data fluctuations, environmental impacts), confirms the authenticity and severity of the abnormal situation, and generates the final vital sign abnormality confirmation result. Then, the system uses a health risk prediction model, combined with the user's historical health data and similar cases, to comprehensively assess the current health status and generate detailed health status assessment results. Finally, the system provides specific health advice based on the health status assessment results to help users take appropriate preventive and treatment measures to improve their health.

[0105] Here is a specific example:

[0106] In a home care scenario, suppose an elderly person with chronic obstructive pulmonary disease wears a multi-function health monitoring bracelet. The bracelet continuously monitors heart rate, blood oxygen saturation, and respiratory rate, and transmits data in real time to the instant assessment engine.

[0107] When the elderly man was resting at home, the instant assessment engine detected that his blood oxygen saturation suddenly dropped to 85%, which is below the normal range. The system immediately generated a vital sign abnormality detection report, recording the specific value and time of the abnormality.

[0108] Next, the system applies the fuzzy logic reasoning mechanism, comprehensively considers the elderly’s daily activity patterns and the current indoor air quality (low humidity, high PM2.5 index), confirms that the drop in blood oxygen saturation is an acute hypoxic event, and generates a vital sign abnormality confirmation result.

[0109] The system then uses a pre-trained health risk prediction model, combined with the elderly's historical health data (such as past medical history, lifestyle habits) and similar cases, to assess their current health status. The model predicts that there is a high health risk in this case, especially considering that the elderly have had similar hypoxia symptoms in the past.

[0110] Finally, the system provides customized health advice to the elderly based on the health status assessment results. For example, it is recommended that he immediately improve indoor ventilation, increase air humidity, and contact a doctor for further examination. At the same time, the system reminds family members to pay attention to the elderly's condition and prepare necessary medical equipment (such as oxygen tanks). In addition, the system will regularly send health reports to the elderly and their families to help them better manage their health.

[0111] In this way, the system not only solves the problem of inaccurate detection of abnormal vital signs and health recommendations, but also realizes real-time monitoring and personalized management of user health status, significantly improving the quality of telemedicine services and user experience.

[0112] In order to solve the problem of inaccurate confirmation of abnormal conditions in vital sign abnormality detection reports, in some embodiments, the fuzzy logic reasoning mechanism is used to determine the vital sign abnormality detection report and generate a vital sign abnormality confirmation result in step 103, including:

[0113] The fuzzy logic reasoning mechanism is used to analyze the abnormal conditions in the vital signs abnormality detection report to obtain a preliminary abnormal parameter list; the key vital signs parameters corresponding to the preliminary abnormal parameter list are extracted and compared with the preset standard range, the parameter values ​​beyond the normal range are identified, and an abnormal parameter set is generated, wherein the key vital signs parameters include heart rate, blood pressure and respiratory rate; based on the abnormal parameter set beyond the standard range, each abnormal parameter is weighted in combination with a fuzzy logic rule base to generate a weighted abnormal parameter set; the fuzzy reasoning algorithm is used to comprehensively evaluate the weighted abnormal parameter set, and the score of each abnormal parameter is calculated through fuzzy membership functions and reasoning rules, and combined with the overall health status, the vital signs abnormality confirmation result is obtained.

[0114] In this embodiment, the fuzzy logic reasoning mechanism is a mathematical method for processing uncertainty and fuzzy information, which is suitable for describing and processing imprecise data. In this scheme, the fuzzy logic reasoning mechanism is used to analyze the abnormal conditions in the vital signs abnormal detection report, considering multiple factors (such as data fluctuations, environmental impact) to determine the authenticity and severity of the abnormality. The preliminary abnormal parameter list is a list obtained by preliminary screening of all abnormal conditions in the vital signs abnormal detection report. The list contains all possible abnormal parameters and their corresponding values, providing a basis for further analysis. Key vital sign parameters are vital sign parameters closely related to health status, including heart rate, blood pressure and respiratory rate. They have important reference value in assessing the user's health status and can provide key clues about potential health problems. The abnormal parameter set is a set generated by extracting key vital sign parameters from the preliminary abnormal parameter list and comparing them with the preset standard range. It only contains those parameter values ​​that are beyond the normal range for subsequent weight assignment and comprehensive evaluation. The weighted abnormal parameter set is a set generated after weight assignment of each abnormal parameter according to the fuzzy logic rule base. The weight reflects the degree of influence of different parameters on the overall health status, ensuring the accuracy and personalization of the evaluation. A fuzzy membership function is a mathematical tool used to map input variables to membership values ​​in the interval [0, 1], which represents the degree or likelihood of a condition. In this solution, the fuzzy membership function is used to evaluate the severity of each abnormal parameter. Fuzzy inference rules are a set of predefined rules that guide the fuzzy inference algorithm on how to calculate the output results based on the input data. In this solution, the fuzzy inference rules help the system combine the overall health status of multiple abnormal parameters to obtain the final vital sign abnormality confirmation result.

[0115] In the embodiment of the present application, first, the system identifies and lists all possible abnormal parameters and their corresponding values ​​as the basis for further analysis. Secondly, the system only retains key vital sign parameters (such as heart rate, blood pressure, and respiratory rate), and compares them with the preset standard range to find parameter values ​​that exceed the normal range. Then, the system assigns appropriate weights to each abnormal parameter based on the fuzzy logic rule base to reflect the degree of its impact on overall health. Finally, the system uses a fuzzy reasoning algorithm to comprehensively evaluate the scores of all abnormal parameters based on the fuzzy membership function and reasoning rules, and finally generates a vital sign abnormality confirmation result to ensure the accuracy and reliability of the evaluation.

[0116] Here is a specific example:

[0117] In a home care scenario, suppose an elderly person with chronic obstructive pulmonary disease wears a multi-function health monitoring bracelet. The bracelet continuously monitors heart rate, blood oxygen saturation, and respiratory rate, and transmits data in real time to the instant assessment engine.

[0118] When the elderly man was resting at home, the instant assessment engine detected that his blood oxygen saturation suddenly dropped to 85%, which is below the normal range. The system immediately generated a vital sign abnormality detection report, recording the specific value and time of the abnormality.

[0119] Next, the system uses the fuzzy logic reasoning mechanism to conduct a preliminary analysis of all abnormal conditions in this abnormality detection report and generates a preliminary list of abnormal parameters, including parameters such as blood oxygen saturation, heart rate and respiratory rate.

[0120] The system then extracted key vital sign parameters (heart rate, blood pressure, respiratory rate) and compared them with the preset standard range. The results showed that the blood oxygen saturation (85%) was significantly lower than the normal range (90%-100%), while the heart rate and respiratory rate were also in the marginal range. The system generated an abnormal parameter set that only included the blood oxygen saturation parameter that was out of the normal range.

[0121] Next, the system assigns weights to blood oxygen saturation based on the abnormal parameter set and the fuzzy logic rule base. According to the rule base, blood oxygen saturation has a higher weight because it has a direct impact on the health status of patients with chronic obstructive pulmonary disease. The system generates a weighted abnormal parameter set in which blood oxygen saturation is assigned a higher weight value.

[0122] Finally, the system uses fuzzy reasoning algorithms to comprehensively evaluate the weighted abnormal parameter set. Through fuzzy membership functions and reasoning rules, the system calculates the blood oxygen saturation score, and combines the overall health status of the elderly (such as past medical history and living habits) to obtain the final abnormal vital sign confirmation result - acute hypoxia event. The system confirms the authenticity and severity of this abnormal situation and generates a detailed abnormality confirmation report.

[0123] In this way, the system not only solves the problem of inaccurate confirmation of abnormal conditions in vital sign abnormality detection reports, but also achieves rapid and accurate identification of abnormal conditions, significantly improving the quality of telemedicine services and user experience. This meticulous evaluation method helps doctors better understand the patient's current health status and take timely and effective intervention measures.

[0124] In order to solve the problem that the health status assessment is not comprehensive and personalized, in some embodiments, the abnormal vital sign confirmation result in step 103 is based on the comparison of the user's long-term health data and similar cases, and the pre-trained health risk prediction model is used to comprehensively assess the user's current health status, and generate a health status assessment result, including:

[0125] Using the pre-trained health risk prediction model, based on the abnormal vital sign confirmation results, the user's current health status is preliminarily evaluated and processed to obtain a preliminary health risk assessment report; based on the preliminary health risk assessment report, combined with the user's long-term health data, the user's health status is deeply analyzed to generate personalized health data analysis results, and the long-term health data includes past medical history, living habits, and activity levels; based on the personalized health data analysis results, similar cases in the medical database are searched for comparative analysis and processing to generate similar case comparative analysis results; using the similar case comparative analysis results, the parameters of the health risk prediction model are adjusted, the health risk prediction model is optimized, and an optimized health risk prediction model is obtained; according to the optimized health risk prediction model, the abnormal confirmation results, long-term health data and similar case information are comprehensively considered to evaluate the user's current health status and generate a health status assessment result.

[0126] In this embodiment, the pre-trained health risk prediction model is a machine learning model trained with a large amount of historical data and similar cases, which can quickly assess the user's preliminary health risk based on the input vital sign abnormality confirmation results. The model is trained using advanced algorithms (such as neural networks, decision trees, etc.) to ensure the accuracy and reliability of the assessment. The preliminary health risk assessment report is the first assessment report generated based on the vital sign abnormality confirmation results, which describes in detail the preliminary risk level of the user under the current health status. This report provides basic information for subsequent in-depth analysis. Long-term health data includes the user's medical history, living habits, and activity level, which can provide long-term background information about the user's health status. Long-term health data is essential for identifying potential health problems and formulating personalized health management strategies. The personalized health data analysis result is a detailed personalized health data analysis result generated by the system through in-depth analysis of the user's long-term health data. This result not only reflects the user's current health status, but also reveals its health trends and potential risk factors. The similar case comparison analysis result is the result generated by searching for similar cases in the medical database and comparing them with the user's situation. Similar cases provide additional reference information, which helps to more accurately assess the user's health risks. The optimized health risk prediction model is based on the combination of personalized health data analysis results and similar case comparative analysis results. The system adjusts and optimizes the pre-trained health risk prediction model to make it more adaptable to the individual differences of users. The optimized model improves the accuracy of prediction and can better reflect the actual health status of users.

[0127] In an embodiment of the present application, first, the system inputs the abnormal confirmation result into the pre-trained health risk prediction model to generate a preliminary health risk assessment report to record the user's current health risk level. Secondly, the system deeply analyzes the user's long-term health data, identifies potential factors that may affect health, and generates detailed personalized health data analysis results. Next, the system searches for cases similar to the user's situation, conducts comparative analysis, identifies common problems and solutions in similar cases, and generates similar case comparative analysis results. Furthermore, the system adjusts the parameters of the health risk prediction model based on the information of similar cases to make it more adaptable to the individual differences of users and improve the accuracy of the prediction. Finally, the system uses the optimized health risk prediction model, combined with all relevant information, to generate the final health status assessment results to ensure the comprehensiveness and personalization of the assessment.

[0128] Here is a specific example:

[0129] In a home care scenario, suppose an elderly person with chronic obstructive pulmonary disease wears a multi-function health monitoring bracelet. The bracelet continuously monitors heart rate, blood oxygen saturation, and respiratory rate, and transmits data in real time to the instant assessment engine.

[0130] When the elderly man was resting at home, the system detected that his blood oxygen saturation suddenly dropped to 85%, which was below the normal range. The system immediately generated a vital sign abnormality detection report and confirmed that this was an acute hypoxic event through the fuzzy logic reasoning mechanism, generating a vital sign abnormality confirmation result.

[0131] Next, the system used the pre-trained health risk prediction model to conduct a preliminary assessment of the elderly person’s current health status based on this acute hypoxia event, and generated a preliminary health risk assessment report, pointing out the existence of a high acute health risk.

[0132] The system then conducts an in-depth analysis of the elderly person's health status, combined with their long-term health data (such as medical history, lifestyle habits, and activity levels). The system found that the elderly person had a history of chronic obstructive pulmonary disease and had recently increased their outdoor activity time, which may have exposed them to poor air quality. The system generated a detailed personalized health data analysis that revealed the impact of these factors on the elderly person's health.

[0133] Next, the system searched for similar cases in the medical database and found several patients who also suffered from COPD and experienced acute hypoxia in similar environments. Through comparative analysis, the system found that the common characteristics of these patients were poor indoor air quality and excessive exercise, and generated similar case comparative analysis results.

[0134] Subsequently, the system used the results of similar case comparison analysis to adjust the parameters of the health risk prediction model and optimize the model to make it more suitable for the individual differences of the elderly. The optimized model improves the accuracy of predicting acute hypoxia events.

[0135] Finally, the system comprehensively evaluates the current health status of the elderly based on the optimized health risk prediction model, taking into account the abnormal confirmation results, long-term health data and similar case information, and generates a detailed health status assessment result. The system recommends that the elderly immediately improve indoor ventilation, reduce outdoor activities, and contact a doctor for further examination. In addition, the system will regularly send health reports to the elderly and their families to help them better manage their health status.

[0136] In this way, the system not only solves the problem of incomplete and individuated health status assessment, but also realizes accurate assessment and personalized management of users' health status, significantly improving the quality of telemedicine services and user experience. This meticulous assessment method helps doctors better understand the patient's current health status and take timely and effective intervention measures.

[0137] This application takes into account that with the increasing attention paid to health management, especially the needs of patients with chronic diseases and the elderly, accurate and personalized health monitoring has become particularly important. Traditional vital signs monitoring methods often use a fixed-frequency data collection method, which cannot be flexibly adjusted according to the user's real-time status, resulting in waste of resources or missing key health information. To this end, a dynamic monitoring strategy based on an intelligent control algorithm has been developed, aiming to achieve accurate assessment of the user's monitoring frequency and accuracy requirements in the current state through behavioral pattern recognition, environmental parameter evaluation and situational awareness models. Therefore, a new optional solution is proposed, which includes:

[0138] Based on the information reflecting the current state of the user, the requirements for monitoring frequency and accuracy in the current state are evaluated, and a preliminary monitoring requirements evaluation result is generated, including:

[0139] Using a behavior pattern recognition algorithm, based on the information reflecting the current state of the user, the user's activity type is classified to obtain a classification result of the user's activity type;

[0140] The weighted activity type classification accuracy A is calculated using the following formula: acc :

[0141]

[0142] Among them, A acc represents the weighted activity type classification accuracy, N represents the total number of samples, C i represents the classification result of the i-th sample, Ti represents the true label of the i-th sample, is an indicator function that returns 1 if the condition is true, otherwise it returns 0. act，i represents the activity weight of the i-th sample, which is used to emphasize the importance of different activity types;

[0143] Based on the classification results of user activity types and combined with environmental parameters, a comprehensive assessment is conducted on how the user's health status is affected to obtain the environmental adaptability assessment results;

[0144] The environmental adaptability score E is calculated using the following formula: score :

[0145]

[0146] Among them, E score represents the environmental adaptability score, M represents the number of environmental parameters, and w j represents the weight of the jth environmental parameter, E j represents the impact value of the jth environmental parameter (such as temperature, humidity, etc.), β j is the exponential factor of the jth environmental parameter, which is used to adjust the influence intensity of each parameter;

[0147] Based on the environmental adaptability assessment results, the pre-trained situational awareness model is used to predict the user's potential health risk level in the current state and generate a health risk prediction report;

[0148] The health risk probability P is calculated by the following formula: risk :

[0149] P risk =σ(W T x+b+λ·log(E score ))

[0150] Among them, P risk represents the probability of health risk, σ is the Sigmoid function, which is used to map the result of the linear combination to the interval [0, 1], indicating the possibility of health risk, W is the weight vector of the situation-aware model, x is the input feature vector, b is the bias term, and λ is the adjustment coefficient, which is used to adjust the impact of the environmental adaptability score on health risk prediction;

[0151] According to the health risk prediction report, determine the specific requirements for monitoring frequency and accuracy in the current state. In high-risk situations, more frequent and more accurate data collection is required; while in low-risk or stable conditions, the monitoring intensity can be appropriately reduced to save energy and obtain a specific monitoring demand level;

[0152] The specific monitoring requirement level D is obtained through the following formula:level :

[0153]

[0154] Among them, D level Indicates the monitoring requirement level, P threshold is the preset risk threshold used to distinguish between high-demand and low-demand states, δ is the time sensitivity coefficient, and Δt is the time interval since the last assessment, which is used to consider the increase in risk over time;

[0155] Integrate the specific monitoring needs levels to generate preliminary monitoring needs assessment results.

[0156] The following is a detailed explanation of each parameter:

[0157] N: Total number of samples. This refers to the total number of samples in the dataset used to evaluate the behavior pattern recognition algorithm, usually collected through data collection devices (such as smart bracelets, sensors) to collect user activity data over a period of time.

[0158] C i : The classification result of the ith sample. This is the result of the behavior pattern recognition algorithm classifying each sample, usually obtained through a machine learning model or rule engine.

[0159] T i : The true label of the i-th sample. This is the actual activity type label of each sample, usually provided by manual annotation or known standard data.

[0160] I(C i =T i ): Indicator function. This is a binary function that returns 1 when the condition is true and 0 otherwise. It is used to determine whether the classification result is correct.

[0161] w act，i : The activity weight of the ith sample. Different activity types have different health importance and are therefore assigned different weights. These weights can be determined through expert knowledge or historical data analysis.

[0162] M: Number of environmental parameters. This refers to the number of environmental factors considered, such as temperature, humidity, air quality, etc., which are usually collected in real time through environmental sensors.

[0163] w j : The weight of the jth environmental parameter. Different environmental parameters have different impacts on health and are therefore assigned different weights. These weights can be determined through expert knowledge or historical data analysis.

[0164] E j: The impact value of the jth environmental parameter. This is the specific measured value of each environmental parameter, such as temperature is 22℃, humidity is 60%, etc.

[0165] α j : Exponential factor for the jth environmental parameter. Used to adjust the impact strength of each environmental parameter so that some parameters (such as air quality) have a greater impact in specific situations. The exponential factor can be set based on experimental data or expert opinion.

[0166] σ: Sigmoid function. Used to map the result of the linear combination to the interval [0, 1] to indicate the possibility of health risk. The Sigmoid function ensures that the output value is within a reasonable range, which is easy to interpret and apply.

[0167] W: The weight vector of the context-aware model. These are the parameters of the pre-trained context-aware model, which are usually learned from a large amount of historical data through a machine learning algorithm.

[0168] x: Input feature vector. This is a feature vector containing information about the user’s current state, such as vital sign data, activity type classification results, etc.

[0169] b: Bias term. This is a constant term in the context-aware model that is used to adjust the model’s baseline predictions.

[0170] λ: Adjustment coefficient. Used to adjust the environmental adaptability score E score Impact on health risk prediction. This coefficient can be adjusted according to actual conditions to balance the importance of environmental factors and other characteristics.

[0171] log(E score ): The logarithmic value of the environmental adaptability score. The logarithmic form is used to smooth the impact of environmental parameters and prevent extreme values ​​from excessively affecting the prediction results.

[0172] P threshold : Preset risk threshold. This is the benchmark value for distinguishing high-demand and low-demand states, usually set based on clinical experience and historical data.

[0173] δ: Time sensitivity coefficient. It is used to adjust the risk increase over time and reflect the impact of time on health risks. This coefficient can be set according to the user's health status and living habits.

[0174] Δt: The time interval since the last evaluation. This is the length of time since the last evaluation, usually in hours or days.

[0175] Here is a specific example:

[0176] Suppose in a home care scenario, an elderly patient with hypertension wears a multifunctional health monitoring bracelet. The bracelet continuously monitors vital signs such as heart rate, blood pressure, and respiratory rate, and combines environmental sensors to record environmental parameters such as temperature and humidity. The following are the specific implementation steps:

[0177] The system first uses the behavioral pattern recognition algorithm to classify the user's activity type based on information reflecting the user's current state (such as accelerometer and gyroscope data). For example, over a period of time, the system collected N = 100 sample data, which contained classification results and true labels for activity types such as resting, walking, and running.

[0178] The weighted activity type classification accuracy A is calculated by the following formula: acc :

[0179]

[0180] Assume that C i and T i Represent the classification result and true label of the i-th sample, I(C i =T i ) is an indicator function that returns 1 when the classification is correct and 0 otherwise. act，i is the activity weight of the ith sample.

[0181] Substitute the numerical calculation:

[0182]

[0183] That is, the weighted activity type classification accuracy is 95%, indicating that the behavior pattern recognition algorithm has a good classification effect in this time period;

[0184] Next, the system will conduct a comprehensive assessment of how the user's health status is affected based on the classification results of the user's activity type and combined with environmental parameters (such as temperature and humidity) to obtain the environmental adaptability assessment results. For example, the indoor temperature is 22°C and the humidity is 60%;

[0185] The environmental adaptability score E is calculated by the following formula score :

[0186]

[0187] Assume that M = 2, indicating that there are two environmental parameters (temperature and humidity), w1 = 0.6, w2 = 0.4 are the weights of temperature and humidity respectively, E1 = 22, E2 = 60 are the influence values ​​of temperature and humidity respectively, α1 = 1.2, α2 = 1.5 are the exponential factors of temperature and humidity respectively;

[0188] Substitute the numerical calculation:

[0189] E score =0.6·22 1.2 +0.4 60 1.5 ≈0.6·74.86+0.4·288.44≈44.92+115.38=160.30The system applies the pre-trained context-aware model to predict the potential health risk level of the user in the current state and generate a health risk prediction report. Assuming that the input feature vector x includes the user's historical health data and current vital signs data, the Sigmoid function is used to map the result of the linear combination to the [0, 1] interval to represent the possibility of health risk;

[0190] The health risk probability P is calculated by the following formula risk :

[0191] P risk =σ(W T x+b+λ·log(E score ))

[0192] Assume that W T x + b = -0.5, which is the output of the situational awareness model, λ = 0.01, the adjustment coefficient is used to adjust the impact of environmental adaptability score on health risk prediction;

[0193] Substitute the numerical calculation:

[0194] P risk =σ(-0.5+0.01·log(160.30))≈σ(-0.5+0.01·2.20)≈σ(-0.478)≈0.38

[0195] That is, the health risk probability is about 38%, indicating that there is a certain health risk in the current state, but it has not yet reached a high risk level;

[0196] Based on the health risk prediction report, the system determines the specific requirements for monitoring frequency and accuracy in the current state. Assuming the preset risk threshold P threshold =0.5, time sensitivity coefficient δ=0.05, time interval since the last evaluation Δt=2 hours.

[0197] The specific monitoring requirement level D is obtained by the following formula level :

[0198]

[0199] Substitute the numerical calculation:

[0200] P threshold·(1+δ·Δt)=0.5·(1+0.05·2)=0.5·1.1=0.55

[0201] Because P risk =0.38<0.55, so the monitoring demand level of the system is "low demand", which means that the monitoring intensity can be appropriately reduced in the current state to save energy.

[0202] Through the practical application of the above formula, the system can not only accurately identify the user's activity type, but also comprehensively evaluate the impact of environmental parameters on health status, and predict potential health risks through contextual awareness models. Finally, the system determines the current monitoring demand level based on the health risk probability and achieves a reasonable allocation of resources. This refined monitoring strategy helps to improve the efficiency of health management and user experience, ensuring that key health information can be captured in different situations while avoiding unnecessary waste of resources.

[0203] This application takes into account that with the development of medical technology and the increasing attention people pay to health management, accurate and personalized health assessments have become particularly important. Traditional health assessment methods often rely on doctors' experience and limited data, and it is difficult to fully consider users' individual differences and long-term health data. To this end, a comprehensive assessment system based on pre-trained health risk prediction models and similar case comparative analysis has been developed, aiming to achieve a comprehensive assessment of the user's current health status through multi-level data analysis. Therefore, a new optional solution is proposed, which includes:

[0204] Based on the abnormal vital sign confirmation result, by comparing the user's long-term health data with similar cases, a pre-trained health risk prediction model is used to comprehensively evaluate the user's current health status and generate a health status evaluation result, including:

[0205] Using the pre-trained health risk prediction model, based on the abnormal vital sign confirmation results, a preliminary assessment of the user's current health status is performed to obtain a preliminary health risk score;

[0206] The preliminary health risk score R is calculated by the following formula: initial :

[0207]

[0208] Among them, R initial represents the preliminary health risk score, N represents the number of abnormal parameters, and w risk，i represents the risk weight of the ith abnormal parameter, A i represents the risk score of the i-th abnormal parameter, β i is the exponential factor of the i-th abnormal parameter, which is used to adjust the influence intensity of each parameter;

[0209] Based on the preliminary health risk assessment report and combined with the user's long-term health data, the user's health status is deeply analyzed to generate a personalized health score;

[0210] The personalized health score S is calculated by the following formula: personal :

[0211]

[0212] Among them, S personal represents the personalized health score, α, β, γ are the weight coefficients of each factor, H represents the medical history score, L represents the lifestyle score, A represents the activity level score, and γ H , γ L , γ A They are the exponential factors of each factor, which are used to adjust the importance of different factors;

[0213] Based on the results of personalized health data analysis, search for similar cases in the medical database, find cases that match the user's situation, conduct comparative analysis, generate a comprehensive evaluation process, and generate a final health status assessment score;

[0214] The similar case score C is calculated by the following formula: similarity :

[0215]

[0216] Among them, C similarity Represents the similarity score, S user represents the user's personalized health score, S case represents the health score of similar cases, σ is the standard deviation, which is used to adjust the sensitivity of the similarity function;

[0217] Using the results of the comparative analysis of similar cases, adjusting the parameters of the health risk prediction model, optimizing the model to better adapt to the individual differences of users, and obtaining an optimized health risk prediction model;

[0218] Based on the optimized health risk prediction model, the user's current health status is comprehensively evaluated and processed by comprehensively considering abnormal confirmation results, long-term health data and similar case information to generate a health status assessment score;

[0219] The health status assessment score F is calculated using the following formula: final :

[0220]

[0221] Among them, F finalrepresents the health status assessment score, θ1, θ2, θ3 are the weight coefficients of each part, which respectively represent the influence of the preliminary health risk score, personalized health score and similar case score, δ R , δ S , δ C It is the exponential factor of each part, which is used to adjust the influence intensity of different factors.

[0222] The following is a detailed explanation of each parameter:

[0223] N: The number of abnormal parameters. This refers to the number of parameters that are confirmed to be abnormal in the vital signs data, such as heart rate, blood pressure, etc. These parameters are usually obtained through real-time monitoring devices (such as smart bracelets, sensors).

[0224] w risk，i : Risk weight of the ith abnormal parameter. Different parameters have different impacts on health risks and are therefore assigned different weights. These weights can be determined through expert knowledge or historical data analysis.

[0225] A i : Risk score of the ith abnormal parameter. This is a risk score calculated based on the specific value of each abnormal parameter, usually obtained through a pre-set scoring rule or machine learning model.

[0226] β i : Exponential factor for the ith abnormal parameter. Used to adjust the impact strength of each abnormal parameter so that some parameters (such as heart problems) have a greater impact in specific situations. The exponential factor can be set based on experimental data or expert opinion.

[0227] α, β, γ: weight coefficients of each factor. Different factors (past medical history, lifestyle habits, activity level) have different impacts on health status, so they are given different weights. These weights can be determined through expert knowledge or historical data analysis.

[0228] H: Past medical history score. This is a score calculated based on the user's past medical history, usually derived from medical records or information provided by the user.

[0229] L: Lifestyle score. This is a score calculated based on the user's daily living habits (such as diet, exercise, smoking, etc.), usually obtained through questionnaires or user input data.

[0230] A: Activity level score. This score is calculated based on the user's daily activity (such as walking steps, exercise time, etc.), usually obtained through smart bracelets or other monitoring devices.

[0231] γ H , γ L , γA : Exponential factors for each factor. Used to adjust the importance of different factors so that some factors (such as previous medical history) have a greater impact in specific situations. Exponential factors can be set based on experimental data or expert opinion.

[0232] S user : The user's personalized health score. This is the score of the user's current health status calculated by the above formula.

[0233] S case :Health scores of similar cases. This is the health score of cases matching the user's situation found in the medical database, usually obtained through search algorithms and comparison analysis.

[0234] σ: Standard deviation. Used to adjust the sensitivity of the similarity function to ensure that the similarity evaluation is neither too loose nor too strict. The standard deviation can be adjusted according to actual conditions.

[0235] θ1, θ2, θ3: weight coefficients of each part. Different assessment parts (preliminary health risk score, personalized health score, similar case score) have different influences on the final health status assessment, so they are given different weights. These weights can be determined by expert knowledge or historical data analysis.

[0236] δ R , δ S , δ C : Exponential factors for each component. Used to adjust the strength of the impact of different assessment components so that some components (such as the preliminary health risk score) have a greater impact in specific situations. The exponential factors can be set based on experimental data or expert opinion.

[0237] The following is an introduction to the design reasons of each sub-item:

[0238] Consider the impact of past medical history on current health status. The weight α emphasizes the importance of past medical history, while the exponential factor γ H Flexibly adjust the degree of impact.

[0239] Consider the impact of lifestyle habits on current health status. The weight β emphasizes the importance of lifestyle habits, while the exponential factor γ L Flexibly adjust the degree of impact.

[0240] Consider the impact of activity level on current health status. The weight γ emphasizes the importance of activity level, while the exponential factor γ A Flexibly adjust the degree of impact.

[0241] The weighted index of each factor is added together to generate a comprehensive personalized health score that takes into account the contribution of all factors to health status. This approach ensures that each factor is appropriately reflected in the final score while maintaining the interpretability of the assessment results.

[0242] Consider the impact of the initial health risk score on the current health status. The weight θ1 emphasizes the importance of the initial health risk score, while the exponential factor δ R Flexibly adjust the degree of impact.

[0243] Consider the impact of personalized health score on current health status. The weight θ2 emphasizes the importance of personalized health score, while the exponential factor δ S Flexibly adjust the degree of impact.

[0244] Consider the impact of similar case scores on current health status. The weight θ3 emphasizes the importance of similar case scores, while the exponential factor δ C Flexibly adjust the degree of impact.

[0245] The weighted index of each part is added together to comprehensively consider the contribution of all assessment parts to health status and generate a comprehensive health status assessment score. This approach ensures that each assessment part is appropriately reflected in the final assessment result while maintaining the interpretability and accuracy of the assessment result.

[0246] The overall design of this set of formulas is to achieve a comprehensive assessment of the user's current health status. By introducing a multi-dimensional evaluation method, the system can comprehensively consider the user's current status, long-term health data and similar case information to generate personalized health status assessment results. Specifically, through a comprehensive evaluation of three dimensions: preliminary health risk score, personalized health score and similar case score, the system can fully capture the user's health status and ensure the comprehensiveness and accuracy of the evaluation results. The introduction of weight coefficients and exponential factors enables the system to flexibly adjust the importance of different factors and evaluation parts to ensure that the evaluation results meet the needs of different users. The health risk prediction model is optimized using the results of similar case comparative analysis, so that the system can continuously improve and adapt to the individual differences of users and improve the accuracy of the evaluation. Through comprehensive evaluation, the system can dynamically adjust the monitoring strategy to reasonably allocate resources and avoid unnecessary waste while ensuring the effectiveness of health monitoring.

[0247] This approach not only improves the efficiency and accuracy of health management, but also enhances the user experience, ensuring that key health information is captured in different situations, helping users better manage their health conditions.

[0248] Here is a specific example:

[0249] Suppose in a home care scenario, an elderly patient with hypertension wears a multifunctional health monitoring bracelet. The bracelet continuously monitors vital signs such as heart rate, blood pressure, and respiratory rate, and combines environmental sensors to record environmental parameters such as temperature and humidity. The following are the specific implementation steps:

[0250] The system first uses the pre-trained health risk prediction model to conduct a preliminary assessment of the user's current health status based on the abnormal vital sign confirmation results to obtain a preliminary health risk score;

[0251] The preliminary health risk score R is calculated by the following formula initial :

[0252]

[0253] Assume that N = 3, indicating that there are three abnormal parameters (heart rate, blood pressure, blood oxygen saturation), w risk，1 =0.4, w risk，2 =0.3, w risk，3 =0.3 are the risk weights of each abnormal parameter, A1=8, A2=7, A3=6 are the risk scores of each abnormal parameter, β1=1.2, β2=1.1, β3=1.0 are the exponential factors of each abnormal parameter;

[0254] Substitute the numerical calculation:

[0255]

[0256] Next, the system combines the user's long-term health data (such as medical history, lifestyle habits, and activity level) to conduct an in-depth analysis of the user's health status and generate a personalized health score;

[0257] The personalized health score S is calculated by the following formula personal :

[0258]

[0259] Assume that α=0.4, β=0.3, γ=0.3 are the weight coefficients of each factor, H=7, L=6, A=8 are the medical history score, lifestyle score, activity level score, γ H =1.2,γ L =1.1,γ A =1.0 are the exponential factors of each factor;

[0260] Substitute the numerical calculation:

[0261] S personal =0.4·7 1.2 +0.3 61.1 +0.3·8 1.0 ≈0.4·12.58+0.3·6.6+0.3·8

[0262] ≈5.03+1.98+2.4≈9.41

[0263] Based on the results of personalized health data analysis, the system searches for similar cases in the medical database, finds cases that match the user's situation, performs comparative analysis, and generates a similar case score.

[0264] The similarity case score C is calculated by the following formula similarity :

[0265]

[0266] Assume that S user =9.41, the user's personalized health score, S case =9.3, health score of similar cases, σ = 0.5, standard deviation used to adjust the sensitivity of the similarity function;

[0267] Substitute the numerical calculation:

[0268]

[0269] Finally, based on the optimized health risk prediction model, the system comprehensively considers the abnormal confirmation results, long-term health data and similar case information, conducts a comprehensive assessment of the user's current health status and generates a health status assessment score.

[0270] The health status assessment score F is calculated by the following formula final :

[0271]

[0272] Assume that θ1=0.4, θ2=0.3, θ3=0.3 are the weight coefficients of each part, δ R =1.2,δ S =1.1,δ C =1.0 are the exponential factors of each part;

[0273] Substitute the numerical calculation:

[0274] F final =0.4·9.17 1.2 +0.3 9.41 1.1 +0.3 0.976 1.0

[0275] ≈0.4·13.25+0.3·10.24+0.3·0.976

[0276] ≈5.3+3.07+0.29≈8.66

[0277] Through the practical application of the above formula, the system can not only accurately evaluate the impact of abnormal vital sign confirmation results on the current health status, but also conduct in-depth analysis based on the user's long-term health data and similar cases to generate a personalized health score. Finally, by comprehensively considering all factors, the system generates a health status assessment score F final =8.66, indicating that the user's current health condition has certain risks, but is generally within the controllable range.

[0278] This refined assessment method helps doctors and family members better understand the patient's health status and take timely and effective intervention measures. For example, the system recommends that the elderly pay attention to controlling their blood pressure, maintain good living habits, and undergo regular health checks in their daily lives. In addition, the system will regularly send health reports to the elderly and their families to help them better manage their health status. In this way, the system achieves a comprehensive assessment and personalized management of the user's health status, significantly improving the quality of telemedicine services and user experience.

[0279] Figure 2 A structural diagram of a multifunctional vital signs monitoring system is provided for an embodiment of the present application, such as Figure 2 As shown, the system includes:

[0280] The acquisition module 21 is used to acquire the real-time vital sign signals of the user in different physiological states and combine them with environmental parameters to form a multi-dimensional monitoring data set;

[0281] An adjustment module 22 is used to dynamically adjust the monitoring frequency and accuracy according to the multi-dimensional monitoring data set by applying an intelligent control algorithm, generate a data collection strategy, minimize energy consumption, and obtain vital sign data;

[0282] The analysis module 23 is used to analyze and process the abnormal vital signs monitored based on the vital signs data using the real-time assessment engine, and generate health status assessment results and personalized health recommendations in combination with information on gender, height, weight, genetic disease history and past medical history;

[0283] The synchronization module 24 is used to securely synchronize the health status assessment results and personalized recommendations to the user's medical monitoring system, support remote medical services and achieve continuous health management.

[0284] Figure 2 The multifunctional vital signs monitoring system can perform Figure 1The implementation principle and technical effect of the multifunctional vital signs monitoring method described in the embodiment are not described in detail. The specific manner in which each module and unit performs operations in the multifunctional vital signs monitoring system in the above embodiment has been described in detail in the embodiment of the method, and will not be described in detail here.

[0285] In one possible design, Figure 2 The multifunctional vital sign monitoring system of the illustrated embodiment may be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0286] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0287] The processing component 32 is used for the above Figure 1 The multifunctional vital signs monitoring method of the embodiment.

[0288] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0289] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0290] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0291] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0292] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0293] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0294] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The multifunctional vital signs monitoring method of the illustrated embodiment.

[0295] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0296] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0297] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0298] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multifunctional vital signs monitoring method, characterized in that: include: Obtain real-time vital sign signals of users in different physiological states and combine them with environmental parameters to form a multi-dimensional monitoring data set; According to the multi-dimensional monitoring data set, an intelligent control algorithm is applied to dynamically adjust the monitoring frequency and accuracy of real-time vital sign signals, generate a data collection strategy, and collect vital sign data based on the data collection strategy; Based on the vital sign data, the monitored abnormal vital signs are analyzed and processed using an instant assessment engine, and health status assessment results and personalized health recommendations are generated in combination with information on gender, height, weight, genetic disease history, and past medical history; The health status assessment results and personalized recommendations are securely synchronized to the user's medical monitoring system to achieve remote medical services and continuous health management.

2. The method according to claim 1, characterized in that According to the multi-dimensional monitoring data set, the monitoring frequency and accuracy of the real-time vital sign signals are dynamically adjusted by applying an intelligent control algorithm, a data collection strategy is generated, and based on the data collection strategy, vital sign data is collected, including: Using data analysis technology and the multi-dimensional monitoring data set, the user's behavior pattern, activity level, and changes in the environment are identified to obtain information reflecting the user's current status; According to the information reflecting the current state of the user, the requirements for monitoring frequency and accuracy in the current state are evaluated, and a preliminary monitoring requirements evaluation result is generated; Combining historical vital signs data, developing personalized monitoring frequency and accuracy strategies, optimizing the preliminary monitoring needs assessment results, and obtaining optimized monitoring strategies; The optimized monitoring strategy is used to dynamically control the monitoring frequency and accuracy of the vital sign signal acquisition device to obtain vital sign data.

3. The method according to claim 2, characterized in that The step of evaluating the requirements for monitoring frequency and accuracy in the current state based on the information reflecting the current state of the user, and generating a preliminary monitoring requirements evaluation result, includes: Using a behavior pattern recognition algorithm, based on the information reflecting the current state of the user, the user's activity type is classified to obtain a classification result of the user's activity type; According to the classification result of the user activity type and the environmental parameters, evaluating the impact of the environmental parameters on the user's health status, and obtaining an environmental adaptability evaluation result; Based on the environmental adaptability assessment results, a pre-trained context awareness model is applied to predict the potential health risk level of the user in the current state and generate a health risk prediction report; According to the health risk prediction report, determine the requirements for monitoring frequency and accuracy in the current state, and obtain a monitoring requirement level; Based on the specific monitoring needs level, a preliminary monitoring needs assessment result is generated.

4. The method according to claim 2, characterized in that: The method of combining historical vital signs data to formulate a personalized monitoring frequency and accuracy strategy, optimizing the preliminary monitoring needs assessment results, and obtaining an optimized monitoring strategy includes: Using the preliminary monitoring needs assessment results, the monitoring needs of users in different scenarios are refined to generate specific monitoring needs descriptions; According to the specific monitoring requirements, integrate the user's long-term behavior patterns, health status change trends and vital signs data to build a historical model of user behavior and health; Based on the historical model of user behavior and health, a machine learning algorithm is applied to build a personalized prediction model suitable for the user, wherein the personalized prediction model can predict the user's future behavior pattern and its impact on health; Utilizing the personalized prediction model and the specific monitoring requirements, strategies for monitoring frequency and accuracy under different scenarios are formulated to generate optimized monitoring strategies.

5. The method according to claim 1, characterized in that Based on the vital sign data, the real-time assessment engine is used to analyze and process the abnormal vital signs monitored, and the health status assessment results and personalized health suggestions are generated by combining the information of gender, height, weight, genetic history and past medical history, including: Using an instant assessment engine, the vital signs data is analyzed and processed in real time to obtain a vital signs abnormality detection report; Determine the vital sign abnormality detection report based on the fuzzy logic reasoning mechanism and generate a vital sign abnormality confirmation result; Based on the abnormal vital sign confirmation result, by comparing the user's long-term health data with similar cases, the user's current health status is evaluated using a pre-trained health risk prediction model to generate a health status evaluation result; The health status assessment results are used in combination with the user's gender, height, weight, genetic history, lifestyle habits, activity level and past medical history to provide the user with customized health advice and generate personalized health advice.

6. The method according to claim 5, characterized in that The method of determining the abnormal vital sign detection report based on the fuzzy logic reasoning mechanism and generating the abnormal vital sign confirmation result includes: Analyze the abnormal conditions in the vital sign abnormality detection report by using a fuzzy logic reasoning mechanism to obtain a preliminary abnormal parameter list; Extracting key vital sign parameters corresponding to the preliminary abnormal parameter list, and comparing them with preset standard ranges, identifying parameter values ​​beyond the normal range, and generating an abnormal parameter set, wherein the key vital sign parameters include heart rate, blood pressure and respiratory rate; Based on the abnormal parameter set exceeding the standard range, combined with the fuzzy logic rule base, each abnormal parameter is weighted to generate a weighted abnormal parameter set; The weighted abnormal parameter set is comprehensively evaluated by using a fuzzy reasoning algorithm, and the score of each abnormal parameter is calculated through a fuzzy membership function and reasoning rules, and combined with the overall health status, a vital sign abnormality confirmation result is obtained.

7. The method according to claim 5, characterized in that Based on the abnormal vital sign confirmation result, by comparing the user's long-term health data with similar cases, a pre-trained health risk prediction model is used to comprehensively evaluate the user's current health status and generate a health status evaluation result, including: Using the pre-trained health risk prediction model, based on the abnormal vital sign confirmation results, a preliminary assessment of the user's current health status is performed to obtain a preliminary health risk assessment report; Based on the preliminary health risk assessment report, combined with the user's long-term health data, an in-depth analysis of the user's health status is performed to generate personalized health data analysis results, wherein the long-term health data includes past medical history, living habits, and activity level; Based on the personalized health data analysis results, similar cases in the medical database are searched for comparative analysis and processing to generate similar case comparative analysis results; Using the similar case comparative analysis results, adjusting the parameters of the health risk prediction model, optimizing the health risk prediction model, and obtaining an optimized health risk prediction model; According to the optimized health risk prediction model, the user's current health status is evaluated and a health status evaluation result is generated by comprehensively considering abnormal confirmation results, long-term health data and similar case information.

8. A multifunctional vital signs monitoring system, characterized in that: include: The acquisition module is used to obtain the real-time vital sign signals of the user in different physiological states and combine them with environmental parameters to form a multi-dimensional monitoring data set; An adjustment module, for dynamically adjusting the monitoring frequency and accuracy using an intelligent control algorithm according to the multi-dimensional monitoring data set, generating a data collection strategy, while minimizing energy consumption, and obtaining vital sign data; An analysis module is used to analyze and process abnormal vital signs detected based on the vital sign data using an instant assessment engine, and generate health status assessment results and personalized health recommendations based on information such as gender, height, weight, genetics, and past medical history; The synchronization module is used to securely synchronize the health status assessment results and personalized recommendations to the user's medical monitoring system, support telemedicine services and achieve continuous health management.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multifunctional vital sign monitoring method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a multifunctional vital sign monitoring method as claimed in any one of claims 1 to 7 is implemented.

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