Medical health intelligent monitoring management method and system

Abstract

The invention belongs to the technical field of medical monitoring and management, and provides a medical health intelligent monitoring and management method and system.The method comprises the steps that whether the heart rate and the blood oxygen saturation degree of a user need to be deeply calculated and analyzed or not is judged, and if yes, the heart rate and the blood oxygen saturation degree are judged; according to the method, the operation of medical monitoring analysis is optimized by calculating and analyzing the heart rate and the oxyhemoglobin saturation of the user close to the endpoint value of the normal threshold range, and after calculation and analysis, if the heart rate or the oxyhemoglobin saturation of the user is close to the endpoint value of the normal threshold range, the heart rate or the oxyhemoglobin saturation of the user is predicted and analyzed; therefore, whether the heart rate or the oxyhemoglobin saturation of the user deviates from the normal range or not in the monitoring period is judged, a corresponding early warning prompt is generated according to the judgment result, medical safety of the user is guaranteed, and finally, whether the user falls or not is judged by conducting fluctuation analysis on the acceleration amplitude of the user. And comprehensive medical monitoring is realized from the aspects of the physiological state and the posture of the user.

Description

Technical Field

[0001] The present invention belongs to the technical field of medical monitoring management, and specifically relates to a medical health intelligent monitoring management method and system. Background Art

[0002] With the help of advanced information technology, smart medical monitoring plays an important role in optimizing medical service processes, improving medical quality, and promoting the rational allocation of medical resources. However, in reality, there are still some shortcomings. For example, the efficiency of smart medical monitoring is low. Therefore, it is of great significance to study a medical health smart monitoring management method and system.

[0003] In the prior art, smart medical care achieves medical monitoring of users by combining advanced information technology. However, during the monitoring process, there are often many monitoring and analysis operations, resulting in unnecessary waste of monitoring resources. For example, when performing physiological monitoring of user heart rate and blood oxygen, there is a lack of consideration of first conducting a preliminary analysis of the physiological data and judging whether in-depth analysis or predictive analysis is needed based on the analysis results. This results in the inability to improve monitoring efficiency while increasing the burden on medical resources. In addition, when conducting in-depth analysis of medical monitoring, there is also a lack of consideration of accurately grasping the timing of medical analysis when predicting the user's physiological state. For example, when predicting the user's physiological state, the closeness of the physiological data to the normal physiological data range or the trend of deviating from the normal physiological data range can be used to determine whether to predict the user's physiological state, which further leads to a waste of medical monitoring resources.

[0004] To this end, the present invention provides a medical health intelligent monitoring management method and system. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve its technical problem is: In a first aspect, the present invention provides a medical health intelligent monitoring management method, comprising the following steps: Monitor the user's blood oxygen saturation and heart rate in real time and process and analyze them to determine whether in-depth analysis of the user's physiological state is needed. If necessary, generate an analysis signal; According to the type of the analysis signal, the heart rate approximate value and the blood oxygen saturation approximate value are calculated, and according to the physiological approximate data, it is determined whether it is necessary to make a targeted prediction of the user's physiological state. If necessary, a prediction signal is generated; According to the type of prediction signal, the user's physiological state is predicted in a targeted manner, and according to the prediction result, it is determined whether to generate an early warning prompt of the user's state within the monitoring period. If so, an early warning prompt is generated; Monitor the acceleration components of the user in three axes, calculate the acceleration amplitude, analyze the amplitude change of the acceleration amplitude, and determine whether the user falls. If so, generate a fall alarm prompt.

[0007] In a second aspect, the present invention provides a medical health intelligent monitoring and management system, including the following modules: In-depth analysis module: monitors the user's blood oxygen saturation and heart rate in real time and performs processing and analysis to determine whether in-depth analysis of the user's physiological state is needed. If necessary, an analysis signal is generated; Prediction need analysis module: according to the type of analysis signal, calculate the heart rate approximate value and blood oxygen saturation approximate value, and judge whether it is necessary to make a targeted prediction of the user's physiological state according to the physiological approximate data. If necessary, generate a prediction signal; Physiological prediction module: According to the type of prediction signal, the user's physiological state is predicted in a targeted manner, and based on the prediction result, it is determined whether to generate an early warning prompt of the user's state within the monitoring period. If so, an early warning prompt is generated; Posture analysis module: monitors the acceleration components of the user in three axes, calculates the acceleration amplitude, analyzes the amplitude change of the acceleration amplitude, determines whether the user falls, and generates a fall alarm if so.

[0008] The beneficial effects of the present invention are as follows: when analyzing the user's heart rate and blood oxygen saturation, firstly, through comparative analysis, it is determined whether it is necessary to perform in-depth calculation and analysis on the user's heart rate and blood oxygen saturation. The advantage is that medical monitoring and analysis operations are reduced and the timing of medical monitoring is accurately grasped. When it is necessary to perform in-depth calculation and analysis on the user's heart rate and blood oxygen saturation, the operation of medical monitoring and analysis is optimized by performing calculation and analysis on the user's heart rate and blood oxygen saturation close to the endpoint value of the normal threshold range, and the timing of medical monitoring is further accurately grasped. After the calculation and analysis, if the user's heart rate or blood oxygen saturation is close to the endpoint value of the normal threshold range, a predictive analysis of the user's heart rate or blood oxygen saturation is performed to determine whether the user's heart rate or blood oxygen saturation will deviate from the normal range during the monitoring period, and a corresponding early warning prompt is generated according to the judgment result, thereby ensuring the user's medical safety. Finally, by performing a fluctuation analysis on the user's acceleration amplitude, it is determined whether the user has fallen. This solution realizes comprehensive medical monitoring from the user's physiological state and posture. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The present invention will be further described below in conjunction with the accompanying drawings.

[0010] Figure 1 It is a flowchart of the steps of a medical health intelligent monitoring and management method according to an embodiment of the present invention; Figure 2is a sampling circuit diagram of the sensor chip MAX30102 for monitoring blood oxygen saturation and heart rate as described in an embodiment of the present invention; Figure 3 is a sampling circuit diagram of the acceleration sensor MPU6050 described in an embodiment of the present invention; Figure 4 is a simulation diagram of the acceleration amplitude described in an embodiment of the present invention; Figure 5 It is a flowchart of a medical health intelligent monitoring and management system described in an embodiment of the present invention. DETAILED DESCRIPTION

[0011] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods. Example

[0012] See also Figure 1 As shown, a medical health intelligent monitoring management method according to an embodiment of the present invention includes the following steps: S1, by using a sensor chip integrating blood oxygen saturation monitoring and heart rate monitoring to monitor the physiological data of the user (elderly) in real time, wherein the physiological data includes blood oxygen saturation and heart rate, and by processing and analyzing the physiological data of the user, it is determined whether it is necessary to conduct an in-depth analysis of the physiological state of the user, and if necessary, an analysis signal is generated, and the analysis signal includes a heart rate analysis signal and a blood oxygen analysis signal; The specific method of using the sensor chip integrated with blood oxygen saturation monitoring and heart rate monitoring in the S1 to monitor the user's physiological data in real time includes: See also Figure 2 As shown, during the monitoring cycle, the sensor chip MAX30102 with integrated blood oxygen saturation monitoring and heart rate monitoring is used and set in the ring worn by the user. The chip contains LED, photodetector, ambient light suppression circuit and other circuits. The heart rate monitoring sensor is driven by 0.7μA current to achieve fast heart rate sampling. The blood oxygen saturation monitoring is driven by the LED driver to illuminate the user's finger through the cover glass, and then the reflected light and ambient light are packaged together and returned to the photodiode. The diode filters the ambient light and converts it into a digital signal through the ADC converter, and then performs digital noise elimination, thereby achieving fast sampling of blood oxygen saturation. The process of processing and analyzing the physiological data of the user in S1 to determine whether it is necessary to predict the physiological state of the user includes: Monitor the user's heart rate in real time, and compare it with a first heart rate warning value, a second heart rate warning value, a first heart rate normal threshold, and a second heart rate normal threshold; If the second heart rate warning value ≤ the user's heart rate ≤ the first heart rate warning value, a normal heart rate signal is generated, and no operation is performed, and real-time monitoring continues; If the first heart rate warning value is less than the user's heart rate and less than the first heart rate normal threshold or the second heart rate normal threshold is less than the user's heart rate and less than the second heart rate warning value, a heart rate analysis signal is generated; It should be noted that the first heart rate normal threshold value < the first heart rate warning value < the second heart rate warning value < the second heart rate normal threshold value, wherein the first heart rate normal threshold value and the second heart rate normal threshold value are obtained by two end point values ​​of the normal heart rate range, the normal heart rate range is [60-100], and the first heart rate warning value and the second heart rate warning value are set by those skilled in the art, and are warning values ​​set to prevent the user's heart rate from exceeding the normal heart rate range; It should also be noted that the heart rate analysis signal means that the heart rate of the user currently monitored in real time has exceeded the warning range limited by the first heart rate warning value and the second heart rate warning value. In order to prevent the user's heart rate from exceeding the normal heart rate range, the heart rate of the current user is analyzed to ensure the safety of the user. Monitor the user's blood oxygen saturation in real time, and compare it with the blood oxygen saturation warning value, the first blood oxygen saturation threshold, and the second blood oxygen saturation threshold; If the blood oxygen saturation warning value ≤ blood oxygen saturation < the first blood oxygen saturation threshold, a normal blood oxygen saturation signal is generated, and no operation is performed, and real-time monitoring continues; If the second blood oxygen saturation threshold value is less than the blood oxygen saturation value and less than the blood oxygen saturation warning value, a blood oxygen analysis signal is generated; It should be noted that the second blood oxygen saturation threshold value is less than the blood oxygen saturation warning value and less than the first blood oxygen saturation threshold value, wherein the first blood oxygen saturation threshold value and the second blood oxygen saturation threshold value are obtained by two end point values ​​of the normal range of blood oxygen saturation, and the normal range of blood oxygen saturation is [97%-100%]. It should also be noted that the blood oxygen saturation warning value is closer to the second blood oxygen saturation threshold value, because the user's blood oxygen saturation is normal when it reaches 100%, and physiologically the user's blood oxygen saturation will not exceed 100%, and the blood oxygen saturation warning value is set by those skilled in the art; S2, based on the analysis signal and according to the type of the analysis signal, calculating physiological proximity data, wherein the physiological proximity data includes a heart rate proximity value and a blood oxygen saturation proximity value, and judging whether it is necessary to perform targeted prediction of the user's physiological state according to the physiological proximity data, and if necessary, generating a prediction signal, wherein the prediction signal includes a heart rate prediction signal and a blood oxygen prediction signal; The process of calculating and obtaining the physiological proximity data in S2 according to the type of the analysis signal is as follows: If the analysis signal is a heart rate analysis signal, the user's heart rate is subjected to difference processing with the approximate endpoint value of the normal heart rate range, and the absolute value is taken and then ratio processing is performed with the endpoint value of the normal heart rate range to obtain the approximate heart rate value; If the analysis signal is a blood oxygen analysis signal, the blood oxygen saturation is subjected to difference processing with the second blood oxygen saturation threshold, and the absolute value is taken and then subjected to ratio processing with the second blood oxygen saturation threshold to obtain a blood oxygen saturation approximate value; It should be noted that the heart rate approximation value and the blood oxygen saturation approximation value reflect the degree of proximity between the user's heart rate and the edge of the normal heart rate range after leaving the warning range, and the degree of proximity between the user's blood oxygen saturation and the edge of the normal blood oxygen saturation range after leaving the warning range. The smaller the degree of proximity, the greater the need for targeted prediction of the user's physiological state. The process of determining whether it is necessary to perform targeted prediction of the user's physiological state according to the physiological proximity data in S2 includes: Compare the heart rate approximate value and the blood oxygen saturation approximate value with corresponding preset thresholds respectively; If the heart rate approximate value and the blood oxygen saturation approximate value are both greater than or equal to the corresponding preset thresholds, no operation is performed; If at least one of the heart rate approximate value and the blood oxygen saturation approximate value is less than the corresponding preset threshold, a prediction signal is generated, wherein if the heart rate approximate value is less than the corresponding preset threshold, a heart rate prediction signal is generated, and if the blood oxygen saturation approximate value is less than the corresponding preset threshold, a blood oxygen prediction signal is generated; S3, according to the type of prediction signal, make a targeted prediction of the user's physiological state, and determine whether to generate a warning prompt of the user's state within the monitoring period according to the prediction result, and if so, generate a warning prompt; Warning prompts include heart rate warning prompts and blood oxygen warning prompts; The process of performing targeted prediction of the user's physiological state according to the type of the prediction signal in S3 includes: If the type of the prediction signal is a heart rate prediction signal, the time point when the user's heart rate deviates from the heart rate warning value and the time point when the heart rate prediction signal is generated are obtained, and a difference is performed to obtain a prediction analysis period; The heart rate warning value refers to the first heart rate warning value or the second heart rate warning value; According to the forecast analysis period, a user heart rate change curve is constructed with the Y axis being the user heart rate and the X axis being time; Connect the two end points of the user's heart rate change curve to obtain an endpoint connection line; divide the prediction analysis period into several time nodes, obtain the user's heart rate corresponding to each time node on the user's heart rate change curve and the user's heart rate corresponding to the endpoint connection line, and integrate them into a heart rate data group (a n , bn ), where a n represents the user's heart rate corresponding to the user's heart rate change curve at the nth time node, b n Indicates the user's heart rate corresponding to the nth time node on the endpoint connection line, where n represents the number of time nodes; The heart rate data set (a i , b i ) for processing and analysis, specifically: By formula: Calculate the data similarity value ED of the heart rate data group, where b i represents the user's heart rate corresponding to the endpoint connection line at the i-th time node, a i Indicates the user's heart rate corresponding to the user's heart rate change curve at the i-th time node; Compare the data similarity value ED with the data similarity threshold; If the data similarity value ED is greater than the data similarity threshold, it means that the user's heart rate shows nonlinear changes during the prediction analysis period; If the data similarity value ED is less than or equal to the data similarity threshold, it means that the user's heart rate changes linearly during the prediction analysis period; If the user's heart rate changes linearly, then in the coordinate system where the user's heart rate change curve is located, a straight line parallel to the X-axis is drawn with the normal heart rate threshold value that the user's heart rate is close to when the heart rate prediction signal is generated, i.e., the heart rate threshold line, where the normal heart rate threshold value refers to the first normal heart rate threshold value or the second normal heart rate threshold value; Extend the endpoint connection line and intersect it with the heart rate threshold line, obtain the time point corresponding to the intersection on the X-axis, and mark it as the heart rate critical time point; It should be noted that the critical heart rate time point refers to the time point at which the user's heart rate reaches the normal heart rate threshold according to the prediction results; Compare the critical heart rate time point with the end time point of the monitoring period; If the heart rate critical time point is earlier than the monitoring period end time point, it means that the user's heart rate will reach the normal heart rate threshold before the end of the monitoring period, and it is necessary to generate an early warning prompt of the user status, that is, a heart rate early warning prompt; If the heart rate critical time point is later than the monitoring period end time point, it means that the user's heart rate will not reach the normal heart rate threshold before the end of the monitoring period, and there is no need to generate a warning prompt for the user status; If the user's heart rate shows nonlinear changes, the user's heart rate corresponding to each time node on the user's heart rate change curve is integrated into the user's actual heart rate group (a1, a2, a3...a n ); The moving average method is used to predict the user's actual heart rate group, specifically including: A1, get the user's actual heart rate group (a1, a2, a3...a n ); A2, set rolling window k, rolling window k=3; A3, as the rolling window rolls, calculates the mean of the data in the rolling window each time it rolls, that is, the rolling average MAt. The specific calculation formula is: ; A4, using the rolling average as the predicted value to expand the user's actual heart rate group, and obtain the expanded user's actual heart rate group (a1, a2, a3...a n 、a n+1 ......a n+P ), where a n+P represents the pth user's heart rate after expansion, and p represents the number of new user heart rate data after the user's actual heart rate group is expanded according to the predicted value; The user's heart rate change curve is extended by the expanded user actual heart rate group. If the extended user's heart rate change curve exceeds or reaches the heart rate threshold line during the monitoring period, it means that the user's heart rate will reach the normal heart rate threshold before the end of the monitoring period, and it is necessary to generate an early warning prompt of the user status, that is, a heart rate early warning prompt. If the extended user's heart rate change curve does not reach the heart rate threshold line during the monitoring period, it means that the user's heart rate will not reach the normal heart rate threshold before the end of the monitoring period, and it is not necessary to generate an early warning prompt of the user status; It should be noted that the user heart rate change curve is extended by the expanded user actual heart rate group in the following manner: the user heart rate corresponding to the user heart rate change curve includes the user actual heart rate group (a1, a2, a3...a n ), when the user's heart rate change curve is extended, by adding a n+1 ......a n+P Mark and connect the user's heart rate change curve to extend the user's heart rate change curve; It should be noted that if the prediction signal is a blood oxygen prediction signal, the prediction processing analysis of the user's blood oxygen saturation is the same as the prediction processing analysis of the user's heart rate, and will not be described here; If the blood oxygen saturation reaches the second normal threshold of blood oxygen saturation before the end of the monitoring period, it is necessary to generate a warning prompt of the user status, that is, a blood oxygen warning prompt; If the blood oxygen saturation does not reach the second normal threshold of blood oxygen saturation before the end of the monitoring period, there is no need to generate an early warning prompt of the user status; Example

[0013] See also Figure 1 As shown, a medical health intelligent monitoring management method according to an embodiment of the present invention includes the following steps: S4, using an acceleration monitoring sensor to monitor the acceleration components of the user in three axes, and calculating the acceleration amplitude SVM, performing amplitude change analysis on the acceleration amplitude SVM to determine whether the user falls, and if so, generating a fall alarm prompt; In S4, the acceleration monitoring sensor is used to monitor the acceleration components of the user in three axes, and the process of calculating and obtaining the acceleration amplitude SVM includes: See also Figure 3 As shown, the MPU6050 sensor is used to obtain acceleration data, the collected raw acceleration data is filtered to remove noise and interference signals, and the acceleration components ax, ay, and az on three axes (usually x, y, and z) are obtained from the MPU6050 sensor; according to the formula: The acceleration amplitude SVM is calculated; See also Figure 4 As shown in the figure, through the simulation analysis of the acceleration amplitude SVM, it can be known that when a fall occurs, the acceleration amplitude SVM of the human body will have a sudden change phenomenon, and the acceleration amplitude SVM changes greatly. Therefore, through the amplitude change analysis of the acceleration amplitude SVM, it is judged whether the user falls, specifically including: Set the time window T to analyze the changes of the acceleration amplitude SVM. The time window T can be 50ms or 100ms. For each time window T, obtain the acceleration amplitude SVM sequence (SVM1, SVM2, SVM3...SVMz) within the time window T; where z represents the number of acceleration amplitudes; According to the acceleration amplitude SVM sequence in the time window T, the acceleration amplitude standard deviation BZ in the time window T is calculated by the standard deviation formula. The specific calculation formula is: ,in, represents the average value of the acceleration amplitude SVM series; represents the i-th acceleration amplitude SVM in the acceleration amplitude SVM sequence; Calculate the ratio of the standard deviation of the acceleration amplitude in two adjacent time windows T, that is, the acceleration floating value FD; Compare the acceleration floating value FD with a preset acceleration floating threshold; If the acceleration floating value FD is greater than or equal to the preset acceleration floating threshold, it means that the acceleration amplitude changes greatly within the adjacent time window T, and the user is judged to have fallen, and a fall alarm prompt is generated; If the acceleration floating value FD is less than the preset acceleration floating threshold, no operation is performed; Example

[0014] See also Figure 5 As shown, a medical health intelligent monitoring and management system according to an embodiment of the present invention includes: In-depth analysis module: The sensor chip with integrated blood oxygen saturation monitoring and heart rate monitoring is used to monitor the user's (elderly) physiological data in real time. The physiological data includes blood oxygen saturation and heart rate. By processing and analyzing the user's physiological data, it is determined whether an in-depth analysis of the user's physiological state is needed. If necessary, an analysis signal is generated. The analysis signal includes a heart rate analysis signal and a blood oxygen analysis signal. Prediction need analysis module: based on the analysis signal, according to the type of the analysis signal, calculate the physiological proximity data, wherein the physiological proximity data includes the heart rate proximity value and the blood oxygen saturation proximity value, and judge whether it is necessary to make a targeted prediction of the user's physiological state according to the physiological proximity data, and if necessary, generate a prediction signal, wherein the prediction signal includes the heart rate prediction signal and the blood oxygen prediction signal; Physiological prediction module: According to the type of prediction signal, the user's physiological state is predicted in a targeted manner, and based on the prediction result, it is determined whether to generate an early warning prompt of the user's state within the monitoring period. If so, an early warning prompt is generated; Posture analysis module: The acceleration components of the user in three axes are monitored by using acceleration monitoring sensors, and the acceleration amplitude SVM is calculated. The amplitude change analysis of the acceleration amplitude SVM is performed to determine whether the user has fallen. If so, a fall alarm is generated.

[0015] The technical solution of the present application is: by processing and analyzing the user's blood oxygen saturation and heart rate, it is determined whether it is necessary to conduct an in-depth analysis of the user's physiological state. If necessary, an analysis signal is generated, and the analysis signal includes a heart rate analysis signal and a blood oxygen analysis signal. According to the type of the analysis signal, physiological proximity data is calculated, wherein the physiological proximity data includes a heart rate proximity value and a blood oxygen saturation proximity value. It is determined whether it is necessary to conduct a targeted prediction of the user's physiological state. If necessary, a prediction signal is generated, wherein the prediction signal includes a heart rate prediction signal and a blood oxygen prediction signal. According to the type of the prediction signal, a targeted prediction of the user's physiological state is made. According to the prediction result, it is determined whether to generate an early warning prompt of the user's state within the monitoring period. The acceleration components of the user in three axes are monitored by using an acceleration monitoring sensor, and the acceleration amplitude SVM is calculated. The acceleration amplitude SVM is analyzed for amplitude changes to determine whether the user has fallen. If so, a fall alarm prompt is generated. The present application is for the user. When analyzing the user's heart rate and blood oxygen saturation, firstly, through comparative analysis, it is determined whether it is necessary to perform in-depth calculation and analysis on the user's heart rate and blood oxygen saturation. The advantage is that it reduces the medical monitoring and analysis operations and accurately grasps the timing of medical monitoring. When it is necessary to perform in-depth calculation and analysis on the user's heart rate and blood oxygen saturation, the operation of medical monitoring and analysis is optimized by performing close calculation and analysis on the user's heart rate and blood oxygen saturation to the endpoint value of the normal threshold range, and the timing of medical monitoring is further accurately grasped. After the calculation and analysis, if the user's heart rate or blood oxygen saturation is close to the endpoint value of the normal threshold range, a predictive analysis of the user's heart rate or blood oxygen saturation is performed to determine whether the user's heart rate or blood oxygen saturation will deviate from the normal range during the monitoring period, and generate corresponding early warning prompts based on the judgment results, thereby ensuring the user's medical safety. Finally, by analyzing the fluctuation of the user's acceleration amplitude, it is determined whether the user has fallen. This solution realizes comprehensive medical monitoring from the user's physiological state and posture.

[0016] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A medical health intelligent monitoring and management method, characterized by: include: Monitor the user's blood oxygen saturation and heart rate in real time and process and analyze them to determine whether in-depth analysis of the user's physiological state is needed. If necessary, generate an analysis signal; According to the type of the analysis signal, the heart rate approximate value and the blood oxygen saturation approximate value are calculated, and it is determined whether it is necessary to make a targeted prediction of the user's physiological state. If necessary, a prediction signal is generated; According to the type of prediction signal, the user's physiological state is predicted in a targeted manner, and according to the prediction result, it is determined whether to generate an early warning prompt of the user's state within the monitoring period. If so, an early warning prompt is generated; Monitor the acceleration components of the user in three axes, calculate the acceleration amplitude, analyze the amplitude change of the acceleration amplitude, and determine whether the user falls. If so, generate a fall alarm prompt.

2. A medical health intelligent monitoring and management method according to claim 1, characterized in that: The analysis signal includes a heart rate analysis signal and a blood oxygen analysis signal; If the first heart rate warning value is less than the user's heart rate and less than the first heart rate normal threshold or the second heart rate normal threshold is less than the user's heart rate and less than the second heart rate warning value, a heart rate analysis signal is generated; If the second blood oxygen saturation threshold value is less than the blood oxygen saturation and less than the blood oxygen saturation warning value, a blood oxygen analysis signal is generated.

3. A medical health intelligent monitoring and management method according to claim 1, characterized in that: The heart rate approximate value and the blood oxygen saturation approximate value are obtained in the following manner: If the analysis signal is a heart rate analysis signal, the user's heart rate is subjected to difference processing with the approximate endpoint value of the normal heart rate range, and the absolute value is taken and then ratio processing is performed with the endpoint value of the normal heart rate range to obtain the approximate heart rate value; If the analysis signal is a blood oxygen analysis signal, the blood oxygen saturation is subjected to difference processing with the second blood oxygen saturation threshold, and the absolute value is taken and then ratio processing is performed with the second blood oxygen saturation threshold to obtain an approximate value of the blood oxygen saturation.

4. A medical health intelligent monitoring and management method according to claim 1, characterized in that: The prediction signal includes a heart rate prediction signal and a blood oxygen prediction signal; If at least one of the heart rate approximation value and the blood oxygen saturation approximation value is less than the corresponding preset threshold, a prediction signal is generated, wherein if the heart rate approximation value is less than the corresponding preset threshold, a heart rate prediction signal is generated, and if the blood oxygen saturation approximation value is less than the corresponding preset threshold, a blood oxygen prediction signal is generated.

5. A medical health intelligent monitoring and management method according to claim 1, characterized in that: The warning prompts include heart rate warning prompts and blood oxygen warning prompts; The heart rate warning prompt is generated in the following manner: By processing and analyzing the constructed user heart rate change curve, the change type of the user's heart rate is determined; If the user's heart rate changes linearly, a heart rate threshold line is constructed according to the normal heart rate threshold, and the critical heart rate time point is determined by the time point on the X-axis corresponding to the intersection of the constructed endpoint connecting line and the heart rate threshold line after extension; If the heart rate critical time point is earlier than the end time point of the monitoring period, a heart rate warning prompt is generated; If the user's heart rate shows nonlinear changes, the user's actual heart rate group is obtained through the user's heart rate change curve; The user's actual heart rate group is predicted using a moving average method to obtain an expanded user's actual heart rate group; The user's heart rate change curve is extended by the expanded user's actual heart rate group. If the extended user's heart rate change curve exceeds or reaches the heart rate threshold line during the monitoring period, a heart rate warning prompt is generated.

6. A medical health intelligent monitoring and management method according to claim 5, characterized in that: The user's heart rate change curve is constructed in the following manner: The time point when the user's heart rate deviates from the heart rate warning value and the time point when the heart rate prediction signal is generated are obtained, and the difference is processed to obtain the prediction analysis period; according to the prediction analysis period, a user heart rate change curve is constructed with the Y-axis as the user's heart rate and the X-axis as time.

7. A medical health intelligent monitoring and management method according to claim 5, characterized in that: The process of determining the type of change in the user's heart rate includes: Connect the two end points of the user's heart rate change curve to obtain an end point connection line; divide the prediction analysis period into several time nodes, obtain the user's heart rate corresponding to each time node on the user's heart rate change curve and the user's heart rate corresponding to the end point connection line, and integrate them into a heart rate data group; The heart rate data group is processed and analyzed using the Euclidean distance method to obtain the data similarity value of the heart rate data group; If the data similarity value is greater than the data similarity threshold, it means that the user's heart rate shows nonlinear changes during the prediction analysis period; If the data similarity value is less than or equal to the data similarity threshold, it means that the user's heart rate shows a linear change during the prediction analysis period.

8. A medical health intelligent monitoring and management method according to claim 5, characterized in that: The blood oxygen warning prompt is generated in the following manner: If the blood oxygen saturation reaches the second normal threshold of blood oxygen saturation before the end of the monitoring period, a blood oxygen warning prompt will be generated.

9. A medical health intelligent monitoring and management method according to claim 1, characterized in that: The fall alarm prompt is generated in the following manner: Get the acceleration amplitude sequence within the set time window, calculate the acceleration amplitude standard deviation within the time window using the standard deviation formula, and calculate the ratio of the acceleration amplitude standard deviations within two adjacent time windows, that is, the acceleration floating value; If the acceleration floating value is greater than or equal to the preset acceleration floating threshold, a fall alarm prompt is generated.

10. A medical health intelligent monitoring and management system, characterized by: include: In-depth analysis module: monitors the user's blood oxygen saturation and heart rate in real time and performs processing and analysis to determine whether in-depth analysis of the user's physiological state is needed. If necessary, an analysis signal is generated; Prediction need analysis module: according to the type of analysis signal, calculate the approximate value of heart rate and blood oxygen saturation, and determine whether it is necessary to make a targeted prediction of the user's physiological state. If necessary, generate a prediction signal; Physiological prediction module: According to the type of prediction signal, the user's physiological state is predicted in a targeted manner, and based on the prediction result, it is determined whether to generate an early warning prompt of the user's state within the monitoring period. If so, an early warning prompt is generated; Posture analysis module: monitors the acceleration components of the user in three axes, calculates the acceleration amplitude, analyzes the amplitude change of the acceleration amplitude, determines whether the user falls, and generates a fall alarm if so.

Images