An adaptive health monitoring algorithm and dynamic risk assessment management system

Abstract

This invention discloses an adaptive health monitoring algorithm and a dynamic risk assessment management system, belonging to the field of health management technology. It includes a data processing module comprising an acquisition unit and a processing unit. The acquisition unit acquires relevant data of the target patient when their condition worsens, including heart rate, which is then marked as a reference heart rate. The processing unit performs relevant processing based on the reference heart rate, including constructing a heart rate dataset for the target patient. The construction steps are as follows: two data values ​​are set based on the reference heart rate, with the two values ​​set to be 10% and 15% less than the reference heart rate, respectively. The heart rate corresponding to a value less than 15% of the reference heart rate is considered a warning heart rate. This invention achieves early detection, accurate judgment, and low false alarms of abnormal heart rates in patients through a complete technical chain of "multi-dimensional feature extraction → three-level fusion judgment → progressive early warning," demonstrating significant clinical application value.

Description

Technical Field

[0001] This invention relates to the field of health management technology, and in particular to an adaptive health monitoring algorithm and a dynamic risk assessment and management system. Background Technology

[0002] Health monitoring is an important part of the healthcare field, designed to track an individual's health status and identify potential health risks.

[0003] Regarding this research, application CN202510053725.5 provides a dynamic adaptive health monitoring method and system utilizing artificial intelligence. The technical solution includes: using multi-source heterogeneous health data, employing a hierarchical diffusion probability model for noise reduction and combining it with an improved attention autoencoder to extract preliminary features; then learning discriminative features through a contrastive learning network; inputting the discriminative features into an adaptive wavelet neural network for multi-scale feature decomposition; inputting this into a dynamic graph neural network to obtain a health feature tensor; utilizing a hierarchical memory enhancement network to obtain temporal features; and combining a dynamic routing capsule network and a causal inference network to generate a health knowledge graph. This technical solution combines a multi-agent collaborative optimization system and a deep deterministic policy gradient network, and obtains an adaptive monitoring scheme through a model predictive control network.

[0004] Another application, CN202511636295.6, provides a real-time physiological parameter monitoring and health risk assessment system based on AI algorithms. This technical solution includes: continuously collecting multimodal physiological data and contextual information; generating predicted physiological data based on a personalized physiological digital twin (PDT) model; calculating the original difference signal between the real physiological data and the predicted data; analyzing contextual information using a context-aware attention network to generate attention weights and weight the original difference signal to obtain a weighted conflict signal; performing nonlinear dynamic analysis on the weighted conflict signal to extract deep features; and finally outputting a quantified health risk index through a risk discrimination model, achieving early warning and attribution. This technical solution, by combining PDT, context-aware weighting, and nonlinear analysis, can improve the sensitivity and accuracy of identifying sub-health states and early disease risks.

[0005] In real life, heart rate is one of the most important single indicators in health monitoring. It is the only real-time data that can simultaneously reflect the state of the heart, circulation, respiration, metabolism, infection, and emotions. For diseases that are "unstable or at risk of worsening, and where early detection can change the prognosis," heart rate monitoring can obtain most early signals of acute deterioration. However, the above-mentioned technical solutions are difficult to dynamically adjust according to changes in the patient's state (e.g., distinguishing heart rate based on exercise and resting states), resulting in a lack of predictability (no early warning), inability to use time-series data to predict future risk trends, and a high risk of missed or hidden cases. Summary of the Invention

[0006] In view of the problems existing in the field of existing health management technology, the present invention is proposed.

[0007] Therefore, one of the objectives of this invention is to provide an adaptive health monitoring algorithm and a dynamic risk assessment and management system, which achieves early detection, accurate judgment, and low false alarm of abnormal heart rate in patients through a complete technical chain of "multi-dimensional feature extraction → three-level fusion judgment → progressive early warning", and has significant clinical application value.

[0008] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides an adaptive health monitoring algorithm and a dynamic risk assessment and management system, comprising: The data processing module includes an acquisition unit and a processing unit; The acquisition unit is used to acquire relevant data of the target patient when the condition worsens, including heart rate, and the heart rate is marked as a reference heart rate. The processing unit is used to perform correlation processing based on the reference heart rate. The correlation processing includes constructing a heart rate dataset for the target patient, and the construction steps are as follows: Based on the reference heart rate, two data values ​​are set, which are respectively set to be 10% and 15% less than the reference heart rate. The heart rate corresponding to being less than 15% of the reference heart rate is regarded as the warning heart rate. Acquire the heart rate of the target patient and analyze the heart rate, including: An initial heart rate is set from the acquired heart rate, wherein the initial heart rate is the heart rate with a fluctuation of less than 1% acquired at a relevant time in any natural day, wherein the relevant time is the morning period; A comparison value is obtained based on the initial heart rate, wherein the comparison value is the highest heart rate obtained within the same natural day; Analyze the pattern of change in the initial heart rate toward the comparison value, and extract relevant features based on this pattern. These relevant features include: Set at least three step values, which are the highest heart rates monitored in the 30 minutes, 1 hour, and 2 hours before the comparison value appears; The highest heart rate monitored within 30 minutes is considered as heart rate I, and the highest heart rate monitored within 1 hour is considered as heart rate II. The characteristic value of the heart rate II changing towards heart rate I is obtained. The characteristic value includes the difference in heart rate every 10 seconds during the process of heart rate II changing towards heart rate I. The difference is regarded as the reference difference. A data fusion evaluation module, comprising a comparison unit, a calculation unit, and an evaluation unit; The comparison unit is used to compare the difference when the target patient has the same heart rate as the II heart rate at a future time. During the process of the heart rate changing to the same heart rate as the I heart rate, if the difference in heart rate every 10 seconds is greater than the reference difference, relevant calculations are performed. The computing unit responds to the relevant calculation to calculate the time required for the heart rate to change to the same heart rate as I.

[0009] In a preferred embodiment of the present invention, the processing unit acquires the difference in heart rate every 10 seconds during the process of the heart rate changing from II to I, and calculates it according to the following formula: ; In the formula, Indicates the first The difference in heart rate between intervals; Indicates the first Heart rate at each sampling time; Indicates the first Heart rate at +1 sampling time; Indicates the sampling sequence number. .

[0010] In a preferred embodiment of the present invention, the following formula is also included: ; In the formula, The standard deviation of the sequence of differences; Indicates the first The difference in heart rate between intervals; Indicates the total number of intervals; This represents the average change every 10 seconds throughout the entire process.

[0011] In a preferred embodiment of the present invention, the cumulative change in heart rate is obtained based on the calculation results, as shown below: ; In the formula, This indicates the total fluctuation in heart rate throughout the entire process.

[0012] In a preferred embodiment of the present invention, the cumulative change is regarded as a reference cumulative change based on the calculation results, and the reference cumulative change is divided equally, as shown in the following steps: Obtain the total number of intervals, and divide the reference cumulative change equally into halves of the total number of intervals; In the segmented reference cumulative change, the first half of the change is regarded as change I, and the time taken to reach change I is obtained, and the time taken is regarded as reference time. When a heart rate identical to the heart rate described in II is detected at a future time, if the change in this heart rate reaches the same amount as the change in I while being below the reference time, the system determines that the target patient's heart rate is in an abnormal state and issues an early warning; otherwise, it does not determine that the target patient's heart rate is in an abnormal state.

[0013] In a preferred embodiment of the present invention, the calculation unit calculates the time required for the heart rate to change to the same level as heart rate I, based on the following formula: ; In the formula, This represents the total time required for the heart rate to change from level II to level I. This represents the total number of 10-second intervals that meet the condition; This indicates the interval between each sample.

[0014] In a preferred embodiment of the present invention, the evaluation unit responds to the calculated time and uses it to preset a safety threshold based on the time. When a heart rate with the same heart rate as the II heart rate is detected at a future time, if this heart rate is still lower than the I heart rate even though it exceeds the safety threshold, the system determines that the target patient's heart rate is in a normal state; otherwise, it does not determine this.

[0015] In a preferred embodiment of the present invention, if the system determines that the target patient's heart rate is normal, then associated information is obtained. This associated information is obtained based on the warning heart rate and the initial heart rate, as shown below: While calculating the time required for the heart rate to change to the same heart rate as I, the rate of heart rate change during this time is obtained, including the number of heart rate changes per minute, and this rate is regarded as the reference rate. When a heart rate identical to the first heart rate is detected at a future time, the rate is obtained based on this heart rate, where the rate is the rate at which the first heart rate changes toward the warning heart rate. During the process of the heart rate changing towards the warning heart rate, if the rate is greater than the reference rate, the system determines that the target patient's heart rate is in an abnormal state; otherwise, it does not determine that the heart rate is in an abnormal state.

[0016] In a preferred embodiment of the present invention, when the system determines that the heart rate of the target patient is in an abnormal state, it acquires associated data, including the heart rate that appears most frequently before its rate exceeds the reference rate. The heart rate is regarded as a critical value. When a heart rate with the same heart rate as the I heart rate is detected at a future time, if the critical value occurs during the process of the I heart rate changing towards the warning heart rate, the system determines that the rate will exceed the reference rate and issues a warning; otherwise, no warning is issued. Beneficial effects

[0017] This invention uses the reference heart rate of the target patient when the condition deteriorates as a benchmark (each patient has independent characteristics, avoiding false alarms or false negatives caused by a universal threshold), and sets an alert heart rate based on this reference heart rate. At the same time, this invention sets at least 3 step values ​​(the highest heart rate in the 30 minutes, 1 hour, and 2 hours before the comparison value appears), and further extracts the heart rate difference every 10 seconds during the change from heart rate II to heart rate I as a reference difference. This multi-time window plus high-frequency sampling enables the system to capture the rate and acceleration characteristics of heart rate changes, and enables the system to identify the hidden risk of "the heart rate seems not to have reached the danger value, but the rate of rise is abnormally fast". This invention, through the establishment of a three-level linkage data fusion architecture consisting of a comparison unit, a calculation unit, and an evaluation unit, can realize difference comparison, time calculation, and safety threshold determination. This three-level linkage avoids misjudgment triggered by a single time point or a single value. An anomaly is only determined when the three conditions of change rate, change time, and safety threshold simultaneously meet the abnormal characteristics, thereby improving the reliability and specificity of monitoring. This invention divides the cumulative change in heart rate from heart rate II to heart rate I into equal parts and takes the time corresponding to the change in the first half (change in heart rate I) as a reference time. This enables the system to establish an early warning line for "accelerated change". That is, if the patient's heart rate changes faster than their "fastest record" in history, the system can issue an early warning before the heart rate reaches a dangerous absolute value, thus achieving true early warning. Even when the patient is determined to be in a normal state, the system further obtains the rate at which the heart rate changes towards the warning heart rate (reference rate) for secondary verification. At the same time, the system also extracts the threshold value (the heart rate that occurs most frequently before the rate exceeds the reference rate). This is a progressive risk assessment chain of "normal → re-verification → still normal → continued tracking". By predicting the threshold value, the system can issue an early warning before the heart rate accelerates, achieving predictive warning rather than post-event alarm. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein: Figure 1 This is a schematic diagram of the modular structure of the adaptive health monitoring algorithm and dynamic risk assessment management system according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the process structure of an embodiment of the present invention; The diagram is labeled as follows: 110 - Data processing module; 1101 - Acquisition unit; 1102 - Processing unit; 120 - Data fusion and evaluation module; 1201 - Comparison unit; 1202 - Calculation unit; 1203 - Evaluation unit. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0020] Because existing technologies are difficult to dynamically adjust to changes in a patient's condition (e.g., distinguishing heart rate based on exercise and resting states), they lack predictability (no early warning) and cannot use time-series data to predict risk trends in the future, making it easy to miss or conceal risks.

[0021] Based on this, the present invention proposes an adaptive health monitoring algorithm and a dynamic risk assessment and management system. Through a complete technical chain of "multi-dimensional feature extraction → three-level fusion judgment → progressive early warning", it realizes early detection, accurate judgment and low false alarm of patients' heart rate abnormalities, and has significant clinical application value.

[0022] The present solution will be further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0023] Reference Figures 1 to 2 This is one embodiment of the present invention, which provides an adaptive health monitoring algorithm and a dynamic risk assessment and management system, including: Data processing module 110, which includes acquisition unit 1101 and processing unit 1102; The acquisition unit 1101 is used to acquire relevant data of the target patient when the condition worsens, including heart rate, and to mark the heart rate as a reference heart rate. How to obtain a patient's heart rate is well known to those skilled in the art, and the applicant will not elaborate on it here.

[0024] Processing unit 1102 is used to perform relevant processing based on a reference heart rate. The relevant processing includes constructing a heart rate dataset for the target patient. The construction steps are as follows: Two data values ​​are set based on the reference heart rate. The two data values ​​are set to be 10% and 15% less than the reference heart rate, respectively. The heart rate corresponding to the heart rate that is 15% less than the reference heart rate is regarded as the warning heart rate. Obtain the target patient's heart rate and analyze it, including: An initial heart rate is set from the acquired heart rates. The initial heart rate is the heart rate with fluctuations of less than 1% at a relevant time during any natural day. The relevant time is the morning period (6:00-7:30, the resting heart rate measured within 5-10 minutes of lying flat after waking up, medically known as the "true resting heart rate," which is the data point with the smallest fluctuations and the most valuable reference throughout the day. Therefore, using this as the initial heart rate has practical significance). A comparison value is obtained based on the initial heart rate; the comparison value is the highest heart rate obtained within the same natural day. Analyze the pattern of initial heart rate change towards the baseline value, and extract relevant features based on this pattern. These relevant features include: Set at least 3 step values, which are the highest heart rates monitored in the 30 minutes, 1 hour and 2 hours before the comparison value appears; The highest heart rate monitored within 30 minutes is considered as heart rate I, and the highest heart rate monitored within 1 hour is considered as heart rate II. The characteristic value of the change from heart rate II to heart rate I is obtained. The characteristic value includes the difference in heart rate every 10 seconds during the change from heart rate II to heart rate I. The difference is regarded as the reference difference. The data fusion evaluation module 120 includes a comparison unit 1201, a calculation unit 1202, and an evaluation unit 1203. The comparison unit 1201 is used to compare the difference when the target patient has the same heart rate as the II heart rate at a future time. During the process of the heart rate (the same heart rate as the II heart rate) changing to the same heart rate as the I heart rate, if the difference in heart rate every 10 seconds is greater than the reference difference, relevant calculations are performed. The calculation unit 1202 performs related calculations to calculate the time required for the heart rate to change to the same heart rate as I.

[0025] When the patient's heart rate reaches level II at some point in the future, and the difference is greater than the reference difference every 10 seconds thereafter, the system starts timing until the heart rate rises to level I.

[0026] In summary, using heart rate as a reference benchmark when the condition worsens can enable personalized patient modeling and avoid false alarms / false negatives caused by a general threshold. Setting three-step values ​​of 30min / 1h / 2h allows for multi-time window sampling, capturing the phased characteristics of heart rate changes rather than a single snapshot; Using a 10-second interval difference as a reference difference allows for high-frequency sampling to capture the rate of change and acceleration, thus identifying hidden acceleration risks.

[0027] In the processing unit, the difference in heart rate every 10 seconds during the change from heart rate II to heart rate I is obtained and calculated according to the following formula: ; In the formula, Indicates the first The difference in heart rate at intervals (10 seconds); Indicates the first Heart rate at each sampling time; Indicates the first Heart rate at +1 sampling time (i.e., 10 seconds later); Indicates the sampling sequence number. ; In this embodiment, interval difference calculation is performed to accurately quantify the heart rate change within each 10-second window, providing basic data for subsequent statistical analysis.

[0028] It also includes calculations based on the following formula: ; In the formula, The standard deviation of the series of differences (reflecting the stability of heart rate changes); Indicates the first The difference in heart rate between intervals; Indicates the total number of intervals; This represents the average change every 10 seconds throughout the entire process; as shown below: ; in, The heart rate that marks the starting point of the second heart rate variability; The heart rate at the end of heart rate I.

[0029] like If the heart rate is low, it is considered that the heart rate transitions smoothly from II to I, and the change is controllable; Conversely, if A large heart rate is considered a severe fluctuation in heart rate, indicating an unstable heart rhythm.

[0030] In this embodiment, a standard deviation is introduced to measure the dispersion of the difference and determine whether the heart rate change is stable. If the standard deviation is large, it indicates that the change is uneven and there is a risk of sudden change.

[0031] At the same time, by combining it with the average change, the magnitude and consistency of change can be considered, thereby improving the robustness and noise resistance of the assessment.

[0032] The cumulative change in heart rate is obtained based on the calculation results, as shown below: ; In the formula, This indicates the total fluctuation in heart rate throughout the entire process.

[0033] If the heart rate is 72→75→73→78, then =3+2+5=10.

[0034] Comparison: 72→78 is a direct route, therefore... =6.

[0035] In this case, the former is more volatile, and even if the endpoint is the same, the risk characteristics are different.

[0036] This reflects the total range of change from heart rate II to heart rate I, providing a global perspective and avoiding the one-sidedness of only looking at local differences.

[0037] Furthermore, both increases and decreases in heart rate are included in the total fluctuation, ensuring that no change information is missed.

[0038] Based on the calculation results, the cumulative change is regarded as the reference cumulative change, and the reference cumulative change is divided equally, as shown in the following steps: Obtain the total number of intervals, and divide the reference cumulative change equally by half of the total number of intervals; In the segmented reference cumulative change, the change in the first half is regarded as change I, and the time taken to reach change I is obtained and regarded as reference time. When a heart rate identical to that of heart rate II is detected at a future time, if the change in this heart rate reaches the same level as the change in heart rate I within a time frame shorter than the reference time, the system determines that the target patient's heart rate is in an abnormal state and issues an alert; otherwise, it does not determine that the target patient's heart rate is in an abnormal state.

[0039] This equidistant division, taking only the first half, can establish an early warning line for "accelerated change." An early warning is triggered whenever the rate of change exceeds half the historical fastest recorded rate.

[0040] Using time rather than absolute value as the criterion can enable anomaly detection in the time dimension, providing early warnings before the heart rate reaches a dangerous level; Meanwhile, if the time taken is less than the reference time, it is considered abnormal, which is equivalent to "if it is faster than your fastest time, there is a problem". This provides personalized monitoring and has a low false alarm rate.

[0041] In the calculation unit, the time required for the heart rate to change to the same level as heart rate I is calculated using the following formula: ; In the formula, This represents the total time required for the heart rate to change from level II to level I. This represents the total number of 10-second intervals that meet the conditions (i.e., how many 10-second intervals have elapsed from the first sampling point to heart rate I). This indicates the interval between each sample (fixed at 10 seconds).

[0042] In this embodiment, the number of intervals that "meet the conditions" can be counted only for valid change intervals, eliminating invalid data interference and improving calculation accuracy.

[0043] The evaluation unit 1203 responds to the calculated time, which is used to preset a safety threshold based on the time. When a heart rate with the same heart rate as II is detected in the future, if this heart rate is still lower than the heart rate of I even though it exceeds the safety threshold, the system determines that the target patient's heart rate is in a normal state. Otherwise, no determination is made (no determination is made).

[0044] For heart rates exceeding the threshold but still below I (normal), this allows for a reasonable buffer time for heart rate changes, preventing misjudgments of abnormalities due to minor delays and reducing the false alarm rate.

[0045] Conversely, if the change is too slow (reaching heart rate I before exceeding the threshold), it indicates an abnormally rapid change. This is a kind of reverse logic.

[0046] If the system determines that the target patient's heart rate is normal, it retrieves relevant information based on the alert heart rate and the first-order heart rate, as shown below: While calculating the time required for the heart rate to change to the same heart rate as I (as described in claim 6), the rate of heart rate change during this time is obtained, including the number of heart rate changes per minute, and the rate is regarded as the reference rate. When a heart rate identical to heart rate I is detected at a future time, the rate is obtained based on this heart rate. The rate is the rate at which heart rate I changes toward the warning heart rate. During the process of the heart rate changing towards the warning heart rate, if the rate is greater than the reference rate, the system determines that the target patient's heart rate is in an abnormal state; otherwise, it does not determine that the heart rate is in an abnormal state.

[0047] The secondary verification mechanism in this embodiment ensures that the first layer of normality does not equate to absolute safety. This progressive evaluation chain significantly improves the detection rate.

[0048] The reference rate is the rate of change from historical heart rate II to heart rate I. This can be compared with the patient's own historical data, making it more accurate than a fixed threshold.

[0049] For a rate greater than the reference rate → abnormality, the trend of "accelerated deterioration" can be captured, and the abnormality can be detected before the absolute value reaches the warning heart rate.

[0050] When the system determines that the target patient's heart rate is abnormal, it acquires related data, including the heart rate that appears most frequently before its rate exceeds the reference rate. This heart rate is considered a critical value. When a heart rate identical to the I heart rate is detected in the future, if the critical value is reached during the process of the I heart rate changing towards the warning heart rate, the system determines that the rate will exceed the reference rate and issues a warning; otherwise, no warning is issued.

[0051] In this embodiment, the threshold value is the heart rate that appears most frequently. This is because statistical regularity is used to find the most predictive heart rate value, which has strong predictive power.

[0052] The warning is triggered when a critical value is reached, without waiting for the rate to actually exceed the limit. The warning is issued as soon as the trend of change appears, thus achieving true predictive warning.

[0053] At the same time, if this critical value is not reached, it indicates that the current trend of change may not accelerate, thus avoiding excessive alarms.

[0054] In summary, this application achieves early detection, accurate diagnosis, low false alarm rate, and predictability of abnormal heart rates in patients through a six-layer progressive architecture consisting of feature extraction, statistical enhancement, three-level judgment, progressive early warning, and critical prediction, and has significant clinical application value.

[0055] 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, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An adaptive health monitoring algorithm and dynamic risk assessment management system, characterized in that, include: The data processing module includes an acquisition unit and a processing unit; The acquisition unit is used to acquire relevant data of the target patient when the condition worsens, including heart rate, and the heart rate is marked as a reference heart rate. The processing unit is used to perform correlation processing based on the reference heart rate. The correlation processing includes constructing a heart rate dataset for the target patient, and the construction steps are as follows: Based on the reference heart rate, two data values ​​are set, which are respectively set to be 10% and 15% less than the reference heart rate. The heart rate corresponding to being less than 15% of the reference heart rate is regarded as the warning heart rate. Acquire the heart rate of the target patient and analyze the heart rate, including: An initial heart rate is set from the acquired heart rate. The initial heart rate is the heart rate with a fluctuation of less than 1% acquired at a relevant time in any natural day, where the relevant time is the morning period. A comparison value is obtained based on the initial heart rate, wherein the comparison value is the highest heart rate obtained within the same natural day; Analyze the pattern of change in the initial heart rate toward the comparison value, and extract relevant features based on this pattern. These relevant features include: Set at least three step values, which are the highest heart rates monitored in the 30 minutes, 1 hour, and 2 hours before the comparison value appears; The highest heart rate monitored within 30 minutes is considered as heart rate I, and the highest heart rate monitored within 1 hour is considered as heart rate II. The characteristic value of the heart rate II changing towards heart rate I is obtained. The characteristic value includes the difference in heart rate every 10 seconds during the process of heart rate II changing towards heart rate I. The difference is regarded as the reference difference. A data fusion evaluation module, comprising a comparison unit, a calculation unit, and an evaluation unit; The comparison unit is used to compare the difference when the target patient has the same heart rate as the II heart rate at a future time. During the process of the heart rate changing to the same heart rate as the I heart rate, if the difference in heart rate every 10 seconds is greater than the reference difference, relevant calculations are performed. The computing unit responds to the relevant calculation to calculate the time required for the heart rate to change to the same heart rate as I.

2. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 1, characterized in that, In the processing unit, the difference in heart rate every 10 seconds during the process of the heart rate changing from II to I is obtained and calculated according to the following formula: ； In the formula, Indicates the first The difference in heart rate between intervals; Indicates the first Heart rate at each sampling time; Indicates the first Heart rate at +1 sampling time; Indicates the sampling sequence number. .

3. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 2, characterized in that, It also includes calculations based on the following formula: ； In the formula, The standard deviation of the sequence of differences; Indicates the first The difference in heart rate between intervals; Indicates the total number of intervals; This represents the average change every 10 seconds throughout the entire process.

4. An adaptive health monitoring algorithm and dynamic risk assessment management system as described in any one of claims 2 to 3, characterized in that, The cumulative change in heart rate is obtained based on the calculation results, as shown below: ； In the formula, This indicates the total fluctuation in heart rate throughout the entire process.

5. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 4, characterized in that, Based on the calculation results, the cumulative change is regarded as a reference cumulative change, and the reference cumulative change is divided equally, as shown in the following steps: Obtain the total number of intervals, and divide the reference cumulative change equally into halves of the total number of intervals; In the segmented reference cumulative change, the first half of the change is regarded as change I, and the time taken to reach change I is obtained, and the time taken is regarded as reference time. When a heart rate identical to the heart rate described in II is detected at a future time, if the change in this heart rate reaches the same amount as the change in I while being below the reference time, the system determines that the target patient's heart rate is in an abnormal state and issues an early warning. Conversely, the heart rate of the target patient is not determined to be abnormal.

6. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 1, characterized in that, In the calculation unit, the time required for the heart rate to change to the same level as heart rate I is calculated according to the following formula: ； In the formula, This represents the total time required for the heart rate to change from level II to level I. This represents the total number of 10-second intervals that meet the condition; This indicates the interval between each sample.

7. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 6, characterized in that, The evaluation unit responds to the calculated time, which is used to preset a safety threshold based on the time. When a heart rate with the same heart rate as the II heart rate is detected at a future time, if this heart rate is still lower than the I heart rate even though it exceeds the safety threshold, the system determines that the target patient's heart rate is in a normal state; otherwise, it does not make a determination.

8. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 7, characterized in that, If the system determines that the target patient's heart rate is normal, it acquires related information based on the warning heart rate and the I heart rate, as shown below: While calculating the time required for the heart rate to change to the same heart rate as I, the rate of heart rate change during this time is obtained, including the number of heart rate changes per minute, and this rate is regarded as the reference rate. When a heart rate identical to the first heart rate is detected at a future time, the rate is obtained based on this heart rate, where the rate is the rate at which the first heart rate changes toward the warning heart rate. During the process of the heart rate changing towards the warning heart rate, if the rate is greater than the reference rate, the system determines that the target patient's heart rate is in an abnormal state; otherwise, it does not determine that the heart rate is in an abnormal state.

9. The adaptive health monitoring algorithm and dynamic risk assessment management system as described in claim 8, characterized in that, When the system determines that the target patient's heart rate is in an abnormal state, it acquires related data. The related data includes the heart rate that appears most frequently before its rate exceeds the reference rate. The heart rate is regarded as a critical value. When a heart rate with the same heart rate as the reference rate is detected in the future, if the critical value is reached during the process of the heart rate changing towards the warning heart rate, the system determines that the rate will exceed the reference rate and issues a warning. Conversely, no warning will be issued.

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