An elderly cardiovascular health state dynamic monitoring fusion analysis system and method

Abstract

The application discloses an old person cardiovascular health state dynamic monitoring fusion analysis system and method, belongs to the technical field of cardiovascular health monitoring, and comprises the following steps: monitoring and data statistics are carried out on real-time data related to the cardiovascular system of the old people, wherein the real-time data comprises blood pressure, heart rate, pulse wave transmission time and auxiliary data; meanwhile, the real-time data is subjected to validity processing to obtain effective real-time data; old person historical cardiovascular related data are acquired, and the acquired effective real-time data are subjected to identification and analysis based on old person basic information to acquire the current cardiovascular health state of the old people; according to the current cardiovascular health state of the old people, the duration of the corresponding effective data is acquired, a health risk value is calculated, and an early warning signal and an intervention measure are generated according to the health risk value.

Description

Technical Field

[0001] This invention relates to the field of cardiovascular health monitoring technology, specifically to a dynamic monitoring and fusion analysis system and method for cardiovascular health status in the elderly. Background Technology

[0002] With the aging population, the incidence of cardiovascular diseases among the elderly is rising year by year, and the onset of the disease is characterized by suddenness and insidiity. The traditional regular physical examination model is difficult to capture changes in health status in real time, and it is easy to miss the best time for intervention.

[0003] However, current monitoring technologies have significant shortcomings: First, the monitoring parameters are limited, mostly covering only blood pressure and heart rate, without incorporating pulse wave conduction time, which reflects vascular elasticity, as well as auxiliary data such as exercise and emotions, making it difficult to comprehensively capture health signals; second, there is a lack of real-time data validity processing mechanisms, and abnormal data can easily interfere with analysis; third, health status assessments do not combine historical cardiovascular data and basic information of the elderly, relying only on single real-time data, resulting in insufficient accuracy; fourth, risk assessments do not correlate with the duration of valid data, and warning thresholds and intervention measures are generalized, failing to match individual differences. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a dynamic monitoring and fusion analysis system and method for cardiovascular health status in the elderly, so as to solve the problems mentioned in the background art.

[0005] The objective of this invention can be achieved through the following technical solution: a dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly, comprising: The monitoring and acquisition module is used to monitor and statistically analyze cardiovascular data of the elderly. The cardiovascular data includes blood pressure, heart rate, pulse wave conduction time, and auxiliary data, including exercise duration and sleep duration. At the same time, the cardiovascular data is processed for validity. The validity processing includes comparing the timestamp of the cardiovascular data with a preset duration to obtain valid real-time data. The health status fusion analysis module is used to acquire historical cardiovascular data of the elderly. Based on the elderly's basic information and historical cardiovascular data, it identifies and analyzes the acquired effective real-time data. By comparing the monitoring data with the normal threshold range, it determines the normal state. If the detected cardiovascular health status is abnormal, it determines the abnormal deviation index based on the monitoring data and the normal threshold range. Then, based on the abnormal deviation index, it constructs a status assessment model and determines the status assessment value. Based on the status assessment value, it determines the elderly's current cardiovascular health status. The cardiovascular health status includes normal status, potential risk status, and abnormal status. The basic information includes age, weight, allergy history, and medication records. The early warning and intervention module is used to obtain the duration of corresponding effective data based on the current cardiovascular health status of the elderly and the corresponding status assessment value. Based on the status assessment value and the duration, it determines the health risk value and generates early warning signals and intervention measures according to the health risk value. Among them, the early warning signals include normal signals, attention signals and emergency signals.

[0006] As a further aspect of the present invention, the process of performing validity processing on real-time data includes: Timestamps for acquiring cardiovascular data; Based on the timestamp, determine whether there are continuous data gaps in the cardiovascular data that exceed a preset duration; If there are consecutive data gaps in the cardiovascular data that exceed the preset duration, the nearest neighbor mean interpolation method will be used to supplement the cardiovascular data in the gaps in the consecutive data. Compare the timestamp of the real-time data with the current time to calculate the time difference; if the time difference exceeds a preset time threshold, the data is determined to be expired and removed. After the cardiovascular data processing is completed, the collected real-time data is automatically converted into a unified standard format using a preset format conversion algorithm to obtain valid real-time data. The preset format conversion algorithm uses a field name mapping algorithm.

[0007] As a further aspect of the present invention, the method for obtaining the real-time cardiovascular health status of elderly individuals is as follows: Obtain historical cardiovascular data and basic information of elderly individuals; A cardiovascular health baseline specific to the elderly is constructed based on the historical cardiovascular data and the basic information; the basic information includes age, weight, allergy history, and medication records; Among them, the cardiovascular health baseline for the elderly includes: taking the current time as the node and going back six months, the normal threshold range of the corresponding indicators of cardiovascular data for the elderly during this six-month period; The monitoring data corresponding to the effective real-time data is compared with the normal threshold range of the corresponding indicator. If the monitoring data are all within the corresponding normal threshold range, it indicates that the effective real-time data is routine data, and the current cardiovascular health status of the elderly is determined to be normal. If the monitoring data is outside the corresponding normal threshold range, it means that the valid real-time data corresponding to the monitoring data is abnormal data.

[0008] As a further aspect of the present invention, it is characterized by further comprising: Obtain the monitoring data corresponding to all abnormal data, and calculate the abnormal deviation index corresponding to the abnormal data. Obtain a pre-trained state assessment model, take the abnormal deviation index as input data, and output a state assessment value; determine the current real-time cardiovascular health status of the elderly based on the state assessment value.

[0009] As a further aspect of the present invention, the formula for calculating the abnormal deviation index is as follows: ; In the formula, P is the abnormal deviation index, i represents different abnormal data, i=1,2,...,n, and n is the total number of different abnormal data; YCi represents the monitoring data corresponding to different abnormal data, ZBi represents the standard threshold corresponding to different abnormal data; and bi is the proportional coefficient of different abnormal data i.

[0010] As a further aspect of the present invention, the expression of the state evaluation model is as follows: ; In the formula, JK(P) is the state assessment value, A1 and A2 are different cardiovascular health assessment thresholds, and A1 < A2.

[0011] As a further aspect of the present invention, the method for calculating the health risk value is as follows: Obtain the current cardiovascular health status assessment value corresponding to the elderly; when the monitoring data corresponding to the effective real-time data are all within the corresponding normal threshold range, the corresponding cardiovascular health status is normal. The duration of valid data is obtained, and the health risk value R is calculated using the formula R=JK(P)×(1+0.2×T)×K; where T is the duration of valid data and K is the risk adjustment coefficient.

[0012] As a further aspect of the present invention, it also includes: Set the warning thresholds to B1 and B2, respectively, and 0 < B1 < B2; When the health risk value R < B1, a normal signal is generated, indicating that the elderly person's current cardiovascular status is stable and the risk of deviating from the health baseline is low. When the calculated health risk value B1≤R<B2, a concern signal is generated, indicating that the elderly person's current cardiovascular status has deviated from its health status. When the calculated health risk value R ≥ B2, an emergency signal is generated, indicating that the health risk of the elderly is in an emergency response state.

[0013] To address the aforementioned problems, this invention also provides a method for dynamic monitoring and fusion analysis of cardiovascular health status in the elderly, comprising the following steps: Real-time data related to cardiovascular health in the elderly is monitored and statistically analyzed. The real-time data includes blood pressure, heart rate, pulse wave transit time, and auxiliary data. At the same time, the real-time data is processed to obtain effective real-time data. The study aims to acquire historical cardiovascular data of elderly individuals and, based on their basic information, identify and analyze the acquired real-time data to determine their current cardiovascular health status. This status includes normal, potentially risky, and abnormal states. Based on the current cardiovascular health status of the elderly, the duration of obtaining corresponding effective data is used to calculate the health risk value, and warning signals and intervention measures are generated based on the health risk value; among them, the warning signals include normal signals, attention signals and emergency signals.

[0014] Compared to existing solutions, the beneficial effects achieved by this invention are: This invention addresses the problem of single-dimensional monitoring data by simultaneously collecting blood pressure, heart rate, pulse wave transit time, and auxiliary data, achieving multi-dimensional data coverage and avoiding the limitation of single-data monitoring failing to fully reflect cardiovascular status, thus improving the comprehensiveness of monitoring. This invention overcomes the shortcomings of existing technologies that do not perform effective processing on real-time data by effectively filtering out abnormal and interfering data. Furthermore, it combines basic information of the elderly with historical data for integrated analysis, reducing misjudgment of isolated data and significantly improving the accuracy of current cardiovascular health status identification. This invention obtains a quantitative health risk value by calculating the duration of effective data, and then matches it with corresponding early warning signals and intervention measures. This overcomes the shortcomings of existing early warning systems, which are mostly simple threshold alarms, and upgrades from passive alarms to dynamic risk assessment and personalized intervention. This improves the timeliness and pertinence of risk response and is more suitable for the cardiovascular health management needs of the elderly. Attached Figure Description

[0015] The invention will now be further described with reference to the accompanying drawings.

[0016] Figure 1 This is a module structure diagram of a dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly proposed in this invention.

[0017] Figure 2 This is a flowchart of a dynamic monitoring and fusion analysis method for cardiovascular health status in the elderly proposed in this invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] Example 1, as Figure 1 As shown, the present invention is a dynamic monitoring and fusion analysis system for cardiovascular health status of the elderly, including a monitoring and data acquisition module, a health status fusion analysis module, and an early warning and intervention module; The monitoring and acquisition module is used to monitor and statistically analyze real-time cardiovascular data of the elderly, including blood pressure, heart rate, pulse wave transit time, and auxiliary data; at the same time, the real-time data is processed to obtain valid real-time data. The real-time data includes: Heart rate refers to the number of times the heart beats per minute and is a basic indicator reflecting cardiovascular function. Blood pressure refers to the pressure exerted by blood on the walls of blood vessels as blood flows through them. It is divided into systolic pressure (high pressure, when the heart contracts) and diastolic pressure (low pressure, when the heart relaxes). Pulse wave conduction time refers to the time it takes for a pulse wave to travel from the heart to a part of the body (such as from the carotid artery to the femoral artery or radial artery). It is a key indicator for assessing arterial elasticity and can directly reflect the stiffness of blood vessels. The longer the value, the better the arterial elasticity, and the shorter the value, the higher the risk of arteriosclerosis. In this embodiment, pulse wave conduction time is calculated by synchronously collecting data through a wearable device attached to the radial artery of an elderly person. The supplementary data includes exercise duration and sleep duration, which refer to the total exercise time and the total time actually spent in an effective sleep state of the elderly in a day, as monitored by wearable devices, respectively. It should be further explained that the process of validating real-time data includes: The timestamps of real-time data are obtained and their continuity is checked to determine whether there are consecutive data gaps exceeding a preset duration. If there are consecutive data gaps exceeding the preset duration, the nearest neighbor mean interpolation method is used to supplement the data to avoid data gaps. In this embodiment, the preset duration is set to 30 seconds. The time difference is calculated by comparing the timestamp of the real-time data with the current system time. If the time difference exceeds the preset time threshold, the data is determined to be expired and removed to ensure the timeliness of the real-time data. Then, the collected real-time data is automatically converted into a unified standard format through a preset format conversion algorithm to obtain valid real-time data. In this embodiment, the preset format conversion algorithm is a field name mapping algorithm, which is an existing technology and will not be described in detail here.

[0020] The health status fusion analysis module is used to acquire historical cardiovascular-related data of the elderly, and based on the basic information of the elderly, to identify and analyze the acquired effective real-time data to obtain the current cardiovascular health status of the elderly; among which, cardiovascular health status includes normal status, potential risk status and abnormal status; It should be further explained that the method for obtaining the real-time cardiovascular health status of the elderly is as follows: Obtain historical cardiovascular-related data and basic information of the elderly, and construct a cardiovascular health baseline specific to the elderly; the basic information includes age, weight, allergy history and medication records; Among them, the cardiovascular health baseline for the elderly includes the normal threshold range of the corresponding indicators of cardiovascular-related data of the elderly in the past six months, which provides a benchmark for judging the real-time cardiovascular health status of the elderly. The past six months refers to the period of six months before the current time. Obtain the monitoring data corresponding to the valid real-time data and compare it with the normal threshold range of the corresponding indicator. If the monitoring data are all within the corresponding normal threshold range, it means that the valid real-time data is normal data, and the current cardiovascular health status of the elderly is determined to be normal. If any monitoring data exceeds the corresponding normal threshold range, it indicates that the valid real-time data is abnormal. Obtain monitoring data corresponding to various types of abnormal data, and calculate the anomaly deviation index P of the abnormal data; the formula for calculating the anomaly deviation index is: ; In the formula, P is the abnormal deviation index, i represents different abnormal data, i=1,2,...,n, and n is the total number of different abnormal data; YCi represents the monitoring data corresponding to different abnormal data, ZBi represents the standard threshold corresponding to different abnormal data, and the specific value is based on the average value corresponding to the normal historical cardiovascular related data of the elderly; bi is a different proportional coefficient, and the specific value is obtained by those skilled in the art after multiple big data calculation experiments. Furthermore, YCi-ZBi represents the deviation distance of a single data point, calculated by dividing the deviation distance of a single target by a standard threshold. We obtain the relative deviation ratio of a single abnormal data point i. Different cardiovascular indicators have different degrees of influence on health risk (e.g., the risk weights for abnormal heart rate and abnormal blood pressure are different), therefore, a proportionality coefficient bi needs to be introduced for the i-th indicator to weight its deviation, thus obtaining... Let the total number of monitored abnormal indicators be n. By summing the weighted deviations of all indicators, we can obtain the abnormal deviation index P, which reflects the overall degree of abnormality.

[0021] It should be further explained that the Abnormal Deviation Index is a core indicator that comprehensively quantifies the degree to which the real-time cardiovascular health status of the elderly deviates from their own health baseline. It is used to intuitively judge the severity of cardiovascular health abnormalities. The larger the value, the further the elderly person's current cardiovascular status deviates from their own health baseline, and the higher the health risk. Obtain the pre-trained state evaluation model, take the anomaly deviation index as input data, and output the state evaluation value JK(P); the expression of the state evaluation model is as follows: ; In the formula, JK(P) is the state assessment value, A1 and A2 are different cardiovascular health assessment thresholds, and A1 < A2. The specific values ​​are obtained by experts in this field after multiple big data calculation experiments. Based on the status assessment model, the current real-time cardiovascular health status of the elderly is determined; if JK(P)=1, it means that the current cardiovascular health status is normal; if JK(P)=2, it means that the current cardiovascular health status is at potential risk; if JK(P)=3, it means that the current cardiovascular health status is abnormal.

[0022] The early warning and intervention module is used to obtain the duration of corresponding effective data based on the current cardiovascular health status of the elderly, calculate the health risk value, and generate early warning signals and intervention measures based on the health risk value; among them, the early warning signals include normal signals, attention signals, and emergency signals; It should be further explained that the method for calculating the health risk value is as follows: Obtain the current cardiovascular health status assessment value corresponding to the elderly; when the monitoring data corresponding to the effective real-time data are all within the corresponding normal threshold range, the corresponding cardiovascular health status is normal, and its status assessment value is recorded as 0; The duration of valid data is obtained, and the health risk value R is calculated using the formula R=JK(P)×(1+0.2×T)×K. Where T is the duration of valid data, and K is the risk adjustment coefficient, which is used to correct for the impact of individual differences on the risk value. The value range is 0.8-1.2, and it is automatically matched by the system based on the basic information of the elderly or manually adjusted by medical staff. The warning thresholds are set as B1 and B2, with 0 < B1 < B2. The specific values ​​are determined by experts in the field in conjunction with clinical data. The calculated health risk values ​​are compared with the warning thresholds to generate different warning signals, including normal signals, attention signals, and emergency signals; corresponding intervention measures are generated based on the different warning signals. When the calculated health risk value R < B1, a normal signal is generated, indicating that the elderly person's current cardiovascular status is stable, the risk of deviating from the healthy baseline is low, no additional intervention is required, and only the routine monitoring frequency needs to be maintained. When the calculated health risk value B1≤R<B2, a concern signal is generated, indicating that the elderly person's current cardiovascular status has a certain degree of health deviation. It is necessary to strengthen monitoring, implement lifestyle interventions, or contact a health manager to confirm the status and avoid escalation of the risk. When the calculated health risk value R≥B2, an emergency signal is generated, indicating that the health risk of the elderly has reached a level that requires an emergency response. The medical assistance process should be triggered immediately, such as notifying family members and community hospitals, synchronizing health data, and arranging emergency medical treatment if necessary.

[0023] Example 2, as Figure 2 As shown, the present invention also provides a method for dynamic monitoring and fusion analysis of cardiovascular health status in the elderly, comprising the following steps: Real-time data related to cardiovascular health in the elderly is monitored and statistically analyzed. The real-time data includes blood pressure, heart rate, pulse wave transit time, and auxiliary data. At the same time, the real-time data is processed to obtain effective real-time data. The study aims to acquire historical cardiovascular data of elderly individuals and, based on their basic information, identify and analyze the acquired real-time data to determine their current cardiovascular health status. This status includes normal, potentially risky, and abnormal states. Based on the current cardiovascular health status of the elderly, the duration of obtaining corresponding effective data is used to calculate the health risk value, and warning signals and intervention measures are generated based on the health risk value; among them, the warning signals include normal signals, attention signals and emergency signals.

[0024] In the several embodiments provided by this invention, it should be understood that the disclosed system can be implemented in other ways. For example, the embodiments of the invention described above are merely illustrative; for example, the division of modules is only a logical functional division, and there may be other division methods in actual implementation.

[0025] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0026] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0027] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0028] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly, characterized in that, include: The monitoring and acquisition module is used to monitor and statistically analyze cardiovascular data of the elderly. The cardiovascular data includes blood pressure, heart rate, pulse wave conduction time, and auxiliary data, including exercise duration and sleep duration. At the same time, the cardiovascular data is processed for validity. The validity processing includes comparing the timestamps of continuous time with a preset duration based on the timestamps of the cardiovascular data to obtain valid real-time data. The health status fusion analysis module is used to identify and analyze the acquired effective real-time data based on the elderly's basic information and historical cardiovascular data. It generates cardiovascular health status detection results by comparing the monitoring data with normal threshold ranges. If the detection result is abnormal, an abnormal deviation index is determined based on the monitoring data and normal threshold ranges. Then, a status assessment value is determined based on the abnormal deviation index, and the current cardiovascular health status of the elderly is determined based on the status assessment value. The cardiovascular health status includes normal status, potential risk status, and abnormal status. The basic information includes age, weight, allergy history, and medication records. The early warning and intervention module is used to determine the effective data and duration of effective data for each health status based on the current cardiovascular health status of the elderly corresponding to the status assessment value. Based on the elderly person's current cardiovascular health status, corresponding status assessment values, and duration of effective data, health risk values ​​are determined, and early warning signals and intervention measures are generated based on these health risk values. Among these, early warning signals include normal signals, attention signals, and emergency signals.

2. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 1, characterized in that, The process of validating real-time data includes: Timestamps for acquiring cardiovascular data; Based on the timestamp, determine whether there are continuous data gaps in the cardiovascular data that exceed a preset duration; If there are consecutive data gaps in the cardiovascular data that exceed the preset duration, the nearest neighbor mean interpolation method will be used to supplement the cardiovascular data in the gaps in the consecutive data. Compare the timestamp of the real-time data with the current time and calculate the time difference; if the time difference exceeds the preset duration, the cardiovascular data is determined to be outdated and removed. After the cardiovascular data processing is completed, the collected real-time data is automatically converted into a unified standard format through a preset format conversion algorithm to obtain valid real-time data. The preset format conversion algorithm uses a field name mapping algorithm.

3. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 2, characterized in that, The following methods can be used to obtain the real-time cardiovascular health status of older adults: Obtain historical cardiovascular data and basic information of elderly individuals; A cardiovascular health baseline specific to the elderly is constructed based on the historical cardiovascular data and the basic information. The basic information includes age, weight, allergy history, and medication records; Among them, the cardiovascular health baseline for the elderly includes: the normal threshold range of the corresponding indicators of cardiovascular data for the elderly within the target time period, with the current time as the node; Compare the monitoring data corresponding to the effective real-time data with the normal threshold range of the corresponding indicators. If the monitoring data are all within the corresponding normal threshold range, it means that the current cardiovascular health status of the elderly is normal. If the monitoring data is outside the corresponding normal threshold range, it means that the valid real-time data corresponding to the monitoring data is abnormal data.

4. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 3, characterized in that, Also includes: Obtain the monitoring data corresponding to all abnormal data, and calculate the abnormal deviation index corresponding to the abnormal data. Obtain a pre-trained state assessment model, take the abnormal deviation index as input data, and output a state assessment value; determine the current real-time cardiovascular health status of the elderly based on the state assessment value.

5. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 4, characterized in that, The formula for calculating the abnormal deviation index is as follows: ； In the formula, P is the abnormal deviation index, i represents different abnormal data, i=1,2,...,n, and n is the total number of different abnormal data; YCi represents the monitoring data corresponding to different abnormal data, ZBi represents the standard threshold corresponding to different abnormal data; and bi is the proportional coefficient of different abnormal data i.

6. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 5, characterized in that, The expression for the state evaluation model is as follows: ； In the formula, JK(P) is the state assessment value, P is the abnormal deviation index, A1 and A2 are different cardiovascular health assessment thresholds, and A1 < A2.

7. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 6, characterized in that, The method for calculating health risk scores is as follows: Obtain the current cardiovascular health status assessment value corresponding to the elderly; when the monitoring data corresponding to the effective real-time data are all within the corresponding normal threshold range, the corresponding cardiovascular health status is normal. Obtain the duration corresponding to the valid data. Based on the duration corresponding to the valid data and the risk adjustment coefficient, calculate the health risk value using the formula R=JK(P)×(1+0.2×T)×K; where T is the duration corresponding to the valid data and K is the risk adjustment coefficient.

8. The dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly according to claim 7, characterized in that, Also includes: Set the warning thresholds to B1 and B2, respectively, and 0 < B1 < B2; When the health risk value R < B1, a normal signal is generated, indicating that the elderly person's current cardiovascular status is stable and the risk of deviating from the health baseline is low. When the calculated health risk value B1≤R<B2, a concern signal is generated, indicating that the elderly person's current cardiovascular status has deviated from its health status. When the calculated health risk value R ≥ B2, an emergency signal is generated, indicating that the health risk of the elderly is in an emergency response state.

9. A method for dynamic monitoring and fusion analysis of cardiovascular health status in the elderly, using the dynamic monitoring and fusion analysis system for cardiovascular health status in the elderly as described in any one of claims 1-8, characterized in that, include: Real-time data on cardiovascular health in the elderly is monitored and statistically analyzed, including blood pressure, heart rate, pulse wave transit time, and auxiliary data. Simultaneously, real-time data is processed for validity to obtain valid real-time data; The study aims to acquire historical cardiovascular data of elderly individuals and, based on their basic information, identify and analyze the acquired real-time data to determine their current cardiovascular health status. This status includes normal, potentially risky, and abnormal states. Based on the current cardiovascular health status of the elderly, the duration of obtaining corresponding effective data is used to calculate the health risk value, and warning signals and intervention measures are generated based on the health risk value; among them, the warning signals include normal signals, attention signals and emergency signals.

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