Cardiovascular remote monitoring and emergency response system

Abstract

The invention discloses a cardiovascular remote monitoring and emergency response system, and belongs to the technical field of medical health monitoring, and the system comprises a data obtaining module which is used for obtaining historical data of a preset patient, and the historical data comprises physiological parameter data of the preset patient during historical onset; the physiological parameter data comprises heart rate, blood pressure and oxyhemoglobin saturation; the data processing module is used for processing the physiological parameter data of the preset patient during the historical morbidity, including dividing the physiological parameter data, and the dividing mode includes selecting the physiological parameter data during the latest three morbidity, and dividing the physiological parameter data into a # imgabs0 # parameter, a # imgabs1 # parameter and a # imgabs2 # parameter; obtaining a minimum parameter and a maximum parameter from the divided parameters; by comprehensively analyzing the historical data and real-time physiological parameter changes of the cardiovascular disease patient, medical staff can be helped to better manage the illness state of the patient, the illness state stability of the patient can be evaluated more comprehensively, and a basis is provided for adjustment of a treatment scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of medical and health monitoring, and particularly to a cardiovascular remote monitoring and emergency response system. Background Art

[0002] Remote monitoring technology, by means of electronic technology and communication means, transmits the patient's heart-related data (such as electrocardiogram, blood pressure, heart rate, etc.) to a medical institution or a professional doctor's end in real time to achieve remote analysis and monitoring.

[0003] Regarding the development of remote monitoring technology in medical and health monitoring, the application document with the application number CN202011338427.4 provides a cloud computing cardiovascular health monitoring system and method based on a network monitoring camera. The technical solution includes a front-end monitoring camera for collecting facial videos, a cloud server for remotely obtaining the uploaded video data stream, selecting the facial ROI of the monitored user for pulse wave extraction, pulse wave noise reduction processing, and estimating three cardiovascular parameters of heart rate, blood oxygen saturation, and blood viscosity using corresponding algorithms. This technical solution uses an existing network camera to transmit remote video stream images, and the cloud server provides the algorithm support required by the IPPG technology, solving the problem that non-contact heart rate detection only supports local operations and is not convenient for continuous real-time monitoring, enabling non-contact heart rate detection to achieve long-term continuous detection in real scenarios.

[0004] Another technical application document with the application number CN202010069159.4 provides a remote medical monitoring and rescue platform system. The technical solution includes a user server for monitoring the user's cardiovascular function and feeding back the monitoring data to a remote monitoring center; a doctor server for receiving the data transmitted by the remote monitoring center and feeding back suggestions to the user end through the remote monitoring center; and a remote monitoring center for connecting the user server and the doctor server to realize information interaction between the two. This technical solution realizes the connection between the user server and the doctor server, and can give guiding suggestions after the doctor server checks the data.

[0005] However, the monitoring of cardiovascular diseases requires the integration of multiple physiological parameters (such as electrocardiogram, blood pressure, blood oxygen saturation, etc.), but the current technology still has deficiencies in multi-modal data fusion and comprehensive analysis, resulting in a decrease in data accuracy and affecting the timeliness and accuracy of emergency response. Summary of the Invention

[0006] In view of the above problems existing in the current technical field of medical and health monitoring, the present invention is proposed.

[0007] Therefore, one of the objectives of the present invention is to provide a cardiovascular remote monitoring and emergency response system, which provides an accurate and efficient remote monitoring and early warning system by comprehensively analyzing the historical data and real-time physiological parameter changes of cardiovascular disease patients. It can not only help medical staff better manage the patient's condition, but also more comprehensively evaluate the patient's condition stability, providing a basis for the adjustment of treatment plans.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: The present invention provides a cardiovascular remote monitoring and emergency response system, including: A data acquisition module for acquiring the historical data of a preset patient, where the historical data includes the physiological parameter data of the preset patient during historical onset; the physiological parameter data includes heart rate, blood pressure, and blood oxygen saturation; A data processing module for processing the physiological parameter data of the preset patient during historical onset, including dividing the physiological parameter data. The division method includes selecting the physiological parameter data of the last 3 onsets and dividing the physiological parameter data into parameters, parameters, and parameters; and obtaining the minimum parameter and the maximum parameter from each divided parameter; A fusion analysis module. The fusion analysis module responds to the minimum parameter and the maximum parameter and is used to monitor the change rules of the minimum parameter and the maximum parameter; the fusion analysis module includes an analysis unit, a calculation unit, a processing unit, and a judgment unit; The analysis unit responds to the change rules and is used to preset a monitoring period according to the change rules. During the monitoring period, a set of physiological parameter data is collected based on the minimum parameter, and the trend change of this set of physiological parameter data is analyzed; the set of physiological parameter data includes at least 30 to 50 data; The calculation unit responds to the trend change and is used to calculate the difference change of each physiological parameter in the set of physiological parameter data. The calculation method includes calculating the difference of each physiological parameter at 30 minutes, 1 hour, and 2 hours after a meal of the preset patient during the monitoring period, and the monitoring period is at least one natural month; The processing unit is used to process the physiological parameters according to different degrees of change intervals based on the calculated difference change.

[0009] As a preferred solution of the present invention, among them: in the data processing module, when processing the physiological parameter data of the preset patient during historical onset, the processing method further includes correcting the collected physiological parameter data to ensure the accuracy and consistency of the data; Or, process the physiological parameter data in a statistical analysis manner, including calculating the mean, median, and / or percentile of the physiological parameters to obtain the distribution characteristics of the data.

[0010] As a preferred embodiment of the present invention, in the processing unit, physiological parameters are processed according to different degrees of change intervals, including presetting a safety threshold based on the change in difference, and obtaining the degree change intervals where the physiological parameters are greater than and / or less than the safety threshold. The degree intervals include the normal degree change intervals of heart rate, blood pressure, and blood oxygen saturation, as follows: The normal degree change interval of the heart rate is 60 - 100 beats per minute; The normal degree change interval of the blood pressure is systolic blood pressure 90 - 140 mmHg and diastolic blood pressure 60 - 90 mmHg; The normal degree change interval of the blood oxygen saturation is ≥95%; If the physiological parameters collected for the preset patient in the future period are lower than the normal degree change interval by 1% - 5%, the corresponding physiological parameters will be marked as the low degree change interval; If the physiological parameters collected for the preset patient in the future period are higher than the normal degree change interval by 1% - 5%, the corresponding physiological parameters will be marked as the high degree change interval.

[0011] As a preferred embodiment of the present invention, the judgment unit responds to different degree change intervals and is used to judge the condition status of the preset patient according to the low degree change interval and the high degree change interval. If it is the low degree change interval and / or the high degree change interval, the system determines that the physiological parameters all exceed the safety threshold, and determines that the condition status of the preset patient is in an abnormal change state, and issues an emergency response warning; otherwise, it does not judge.

[0012] As a preferred embodiment of the present invention, if the system determines that the physiological parameters do not exceed the safety threshold, the physiological parameters corresponding to the situation where they do not exceed the safety threshold are obtained, and the level of the physiological parameters in the normal degree change interval is calculated, including calculating the differences between the physiological parameters and the heart rate, blood pressure, and blood oxygen saturation in the normal degree change interval, and predicting the condition status of the preset patient based on the differences.

[0013] As a preferred embodiment of the present invention, when predicting the condition status of the preset patient based on the differences, a prediction period is given. The prediction period is a natural month. In the prediction period, among the heart rate, blood pressure, and / or blood oxygen saturation obtained for the preset patient, if only the change of one of the physiological parameters shows an increasing trend compared with the differences between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change interval, the system determines that the condition status of the preset patient is in a low degree fluctuating change state; if the changes of two of the physiological parameters show an increasing trend compared with the differences between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change interval, the system determines that the condition status of the preset patient is in a high degree fluctuating change state, and issues an emergency response warning.

[0014] As a preferred embodiment of the present invention, the following applies: When only the change in one of the physiological parameters shows an increasing trend compared to the differences in heart rate, blood pressure, and blood oxygen saturation within the normal change range, then each natural day is used as a verification period to verify the trend changes in the differences between the remaining two physiological parameters and the heart rate, blood pressure, or blood oxygen saturation within the normal change range. If no change occurs in the trend within 3 to 6 consecutive verification periods, the system determines that the disease state of the preset patient is stable; otherwise, it does not make a determination.

[0015] As a preferred embodiment of the present invention, the following applies: If the system determines that the disease state of the preset patient is stable, then the physiological parameters collected from the preset patient 30 minutes, 1 hour, and 2 hours after a meal are differentiated, including differentiating the physiological parameters collected 30 minutes, 1 hour, and 2 hours after a meal into early-stage parameters, mid-stage parameters, and late-stage parameters. If both the late-stage parameters and the mid-stage parameters are greater than the early-stage parameters, and the late-stage parameters are greater than the mid-stage parameters, then the system cancels the determination that the disease state of the preset patient is stable; otherwise, the corresponding determination is maintained.

[0016] As a preferred embodiment of the present invention, the following applies: Among the mid-stage parameters and the late-stage parameters, the mid-stage parameters and the late-stage parameters are differentiated into mid-stage parameters corresponding to lunch and late-stage parameters corresponding to dinner, and the average values of the mid-stage parameters and the late-stage parameters are calculated with a two-day calculation period within the monitoring cycle. If, within the monitoring cycle, the change in the average value is within the normal change range, the system determines that the disease state of the preset patient is stable; otherwise, it does not make a determination. Beneficial Effects

[0017] 1. By comprehensively analyzing the change trends of physiological parameters such as heart rate, blood pressure, and blood oxygen saturation, the disease state of cardiovascular disease patients can be monitored more accurately; and by analyzing the historical disease data of patients, personalized disease assessment and prediction can be provided for each patient, improving the accuracy and pertinence of diagnosis. 2. Through the data processing module and the fusion analysis module, operations such as data correction, classification, and trend analysis can be performed on the data, reducing the complexity and errors of manual operations and improving the diagnostic efficiency; and through long-term (such as one natural month) monitoring and analysis of physiological parameters, the disease stability of patients can be evaluated more comprehensively, providing a basis for adjusting treatment plans. 3. By monitoring the changes in physiological parameters at different time periods after a meal (such as 30 minutes, 1 hour, 2 hours), it is possible to help identify the impact of short-term fluctuations on the disease condition, and then the monitoring strategy can be dynamically adjusted according to real-time data to adapt to the changes in the patient's disease condition, providing more accurate health management services for patients. Description of the Drawings

[0018] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them: Figure 1 It is a schematic modular structure diagram of the cardiovascular remote monitoring and emergency response system according to the embodiment of the present invention; Figure 2 It is a schematic flow structure diagram of the embodiment of the present invention; Reference numerals in the figure: 110 - data acquisition module; 120 - data processing module; 130 - fusion analysis module; 1301 - analysis unit; 1302 - calculation unit; 1303 - processing unit; 1304 - judgment unit. Detailed implementation manners

[0019] To make the objectives, technical solutions and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions of the embodiments of the present invention in conjunction with the drawings of the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the described embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope of protection of the present invention.

[0020] Since the monitoring of cardiovascular diseases requires comprehensive consideration of multiple physiological parameters (such as electrocardiogram, blood pressure, blood oxygen saturation, etc.), but the current technology still has deficiencies in multi-modal data fusion and comprehensive analysis, resulting in a decline in data accuracy and affecting the timeliness and accuracy of emergency response.

[0021] Based on this, the present invention proposes a cardiovascular remote monitoring and emergency response system, which provides a precise and efficient remote monitoring and early warning system by comprehensively analyzing the historical data and real-time physiological parameter changes of cardiovascular disease patients. It can not only help medical staff better manage the patient's condition, but also more comprehensively evaluate the patient's condition stability and provide a basis for adjusting the treatment plan.

[0022] The following further specifically describes this solution through embodiments and in conjunction with the drawings.

[0023] Refer to Figures 1 to 2 , which is an embodiment of the present invention. This embodiment provides a cardiovascular remote monitoring and emergency response system, including: A data acquisition module 110, configured to acquire historical data of a preset patient, where the historical data includes physiological parameter data of the preset patient during historical onset; the physiological parameter data includes heart rate, blood pressure, and blood oxygen saturation; The data processing module 120 is used to process the physiological parameter data of a preset patient during historical onset, including dividing the physiological parameter data. The dividing method includes selecting the physiological parameter data during the last 3 onsets and dividing the physiological parameter data into parameters, parameters and parameters; and obtaining the minimum parameter and the maximum parameter from each divided parameter; The fusion analysis module 130 responds to the minimum parameter and the maximum parameter and is used to monitor the variation rules of the minimum parameter and the maximum parameter; the fusion analysis module 130 includes an analysis unit 1301, a calculation unit 1302, a processing unit 1303 and a judgment unit 1304; The analysis unit 1301 responds to the variation rules and is used to preset a monitoring period according to the variation rules. During the monitoring period, a set of physiological parameter data is collected based on the minimum parameter, and the trend variation of this set of physiological parameter data is analyzed; the set of physiological parameter data includes at least 30 to 50 data; The calculation unit 1302 responds to the trend variation and is used to calculate the difference variation of each physiological parameter in the set of physiological parameter data. The calculation method includes calculating the difference of each physiological parameter at 30 minutes, 1 hour and 2 hours after a preset patient has a meal during the monitoring period, and the monitoring period is at least one natural month; In this embodiment, according to the research experience on cardiovascular diseases, collecting the cardiovascular data of cardiovascular disease patients after a meal is of great significance for judging the development of the disease, including evaluating blood glucose control, reflecting the stress response of the cardiovascular system, early detecting potential risks, guiding diet and lifestyle adjustments, and optimizing drug treatment plans, etc.; for example, the rapid increase and fluctuation of postprandial blood glucose may lead to increased oxidative stress, endothelial dysfunction, etc., and further aggravate cardiovascular lesions; by monitoring blood glucose after a meal, the blood glucose control situation can be better evaluated, the treatment plan can be adjusted in time, and the risk of cardiovascular complications can be reduced; At the same time, after eating, the digestive system of the human body needs more blood supply to complete the digestion and absorption process, which will lead to physiological changes such as increased heart burden, increased heart rate, and increased blood pressure. For cardiovascular disease patients, these changes may be more obvious and may even induce acute events such as angina pectoris and myocardial ischemia; And by monitoring data such as heart rate, blood pressure, and electrocardiogram after a meal, the response ability of the heart to this physiological stress can be evaluated; for example, if the heart rate of a patient increases significantly and is difficult to recover after a meal, it may indicate insufficient cardiac reserve function and a potential risk of arrhythmia; The processing unit 1303 is used to process the physiological parameters according to different degrees of variation ranges according to the calculated difference variation; In this embodiment, the system integrates data acquisition, processing, and fusion analysis modules, capable of comprehensively monitoring the historical and real-time physiological parameter data of cardiovascular disease patients, providing a comprehensive basis for condition assessment; It also covers multi-parameter analysis, including key physiological parameters such as heart rate, blood pressure, and blood oxygen saturation, ensuring the assessment of the patient's cardiovascular health status from multiple dimensions; Through the collaborative work of the analysis unit, calculation unit, processing unit, and judgment unit, the system can dynamically adjust the monitoring strategy to adapt to the changes in the patient's condition; In the data processing module 120, the physiological parameter data of a preset patient during historical disease onset is processed. The processing method also includes correcting the collected physiological parameter data to ensure the accuracy and consistency of the data; Alternatively, the physiological parameter data is processed in a statistical analysis manner, including calculating the mean, median, and / or percentile of the physiological parameters to obtain the distribution characteristics of the data; In this embodiment, the uncorrected physiological parameter data may be affected by device errors, environmental factors, individual differences, etc., resulting in data deviation. Through correction, these deviations can be eliminated, making the data closer to the true value, thereby reducing the possibility of misdiagnosis and missed diagnosis; The corrected data can more accurately reflect the patient's true physiological state, helping doctors more precisely identify the early signs and abnormal conditions of cardiovascular diseases; Moreover, the corrected data can be used for long-term trend analysis to help doctors observe the change trend of the condition of cardiovascular disease patients; for example, through the corrected blood pressure, heart rate, etc. data, it can be more accurately evaluated whether the disease is deteriorating or improving; At the same time, the corrected data can more truly reflect the physiological changes of the patient in different states, such as the cardiovascular responses in states such as exercise, rest, and after meals; this kind of dynamic monitoring helps to comprehensively understand the progress of the condition; By calculating the mean, median, and percentile of the physiological parameters, the distribution characteristics of the data are obtained, providing a more reliable statistical basis for subsequent analysis; this also provides more accurate data support for medical staff to help them make more scientific diagnosis and treatment decisions; In the processing unit 1303, the physiological parameters are processed according to different degrees of change intervals, including presetting safety thresholds based on the difference changes, obtaining the degree change intervals where the physiological parameters are greater than and / or less than the safety thresholds. The degree intervals include the normal degree change intervals of heart rate, blood pressure, and blood oxygen saturation, as follows: The normal degree change interval of heart rate is 60 - 100 beats per minute; The normal degree change interval of blood pressure is systolic pressure 90 - 140 mmHg, diastolic pressure 60 - 90 mmHg; The normal range of blood oxygen saturation is ≥95%; If the physiological parameters collected for a preset patient in a future period are 1% - 5% lower than the normal range, the corresponding physiological parameters will be marked as the low - range; If the physiological parameters collected for a preset patient in a future period are 1% - 5% higher than the normal range, the corresponding physiological parameters will be marked as the high - range; In this embodiment, by presetting the normal ranges of heart rate, blood pressure, and blood oxygen saturation, a clear standard is provided for judging the condition; And dividing the changes in physiological parameters into low - range and high - range facilitates a more detailed assessment of the severity of the condition; On this basis, the judgment unit 1304 responds to different ranges of changes and is used to judge the condition status of a preset patient according to the low - range and high - range. If it is the low - range and / or high - range, the system determines that the physiological parameters all exceed the safety threshold, determines that the condition status of the preset patient is in an abnormal change state, and issues an emergency response warning; otherwise, it does not judge; In this embodiment, clinically, a continuously increasing heart rate may indicate myocardial ischemia, arrhythmia, or worsening heart failure; however, an increasing heart rate may also be a physiological reaction (such as exercise, anxiety) or a drug side effect (such as insufficient β - blocker); And a continuously increasing blood pressure may indicate a hypertensive crisis or worsening heart failure; too low blood pressure may indicate insufficient cardiac output or shock; At the same time, a continuously decreasing blood oxygen saturation may indicate respiratory failure or worsening heart failure; Therefore, dividing the values including these three data into different ranges of changes is of reference significance for judging the condition status of the patient; In this embodiment, further, if the system determines that the physiological parameters do not exceed the safety threshold, it obtains the physiological parameters corresponding to when they do not exceed the safety threshold, and calculates the level of the physiological parameters in the normal range, including calculating the differences between the physiological parameters and heart rate, blood pressure, and blood oxygen saturation in the normal range, and predicting the condition status of the preset patient based on the differences; In this embodiment, the system not only judges the current state but also can predict the future condition status based on the difference changes, providing forward - looking information; At the same time, it can dynamically adjust the prediction result according to real - time data to ensure the timeliness and accuracy of the prediction; This can help medical staff formulate intervention measures in advance and optimize the treatment plan; Further, when predicting the disease state of a preset patient based on the difference, given a prediction period which is a natural month, among the heart rate, blood pressure, and / or blood oxygen saturation obtained for the preset patient during the prediction period, if only the change of one of the physiological parameters shows an increasing trend compared to the difference between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change interval, the system determines that the disease state of the preset patient is a low-degree fluctuating change state; if the changes of two of the physiological parameters show an increasing trend compared to the difference between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change interval, the system determines that the disease state of the preset patient is a high-degree fluctuating change state and issues an emergency response warning. In this embodiment, the change trends of the heart rate, blood pressure, and blood oxygen saturation are comprehensively considered to provide a more comprehensive disease assessment. At the same time, warnings are issued at different levels according to the degree of fluctuating changes, which is convenient for medical staff to take different levels of response measures. It should be emphasized in this embodiment that when only the change of one of the physiological parameters shows an increasing trend compared to the difference between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change interval, each natural day is used as a verification period to verify the trend changes of the differences between the other two physiological parameters and the heart rate, blood pressure, or blood oxygen saturation in the normal degree change interval. If the trend changes do not occur in 3 to 6 consecutive verification periods, the system determines that the disease state of the preset patient is stable; otherwise, it is not determined. In this embodiment, the stability of the disease is evaluated through a verification period of 3 to 6 consecutive natural days to avoid misjudgment of short-term fluctuations. By dynamically monitoring the change trends of physiological parameters, the judgment of disease stability is ensured to be more accurate. Specifically in this embodiment, if the system determines that the disease state of the preset patient is stable, the physiological parameters collected from the preset patient 30 minutes, 1 hour, and 2 hours after a meal are distinguished, including classifying the physiological parameters collected 30 minutes, 1 hour, and 2 hours after a meal into early-stage parameters, mid-stage parameters, and late-stage parameters. If both the late-stage parameters and the mid-stage parameters are greater than the early-stage parameters, and the late-stage parameters are greater than the mid-stage parameters, the system cancels the determination that the disease state of the preset patient is stable; otherwise, the corresponding determination is maintained. In this embodiment, according to research on cardiovascular diseases, the heart rate, blood pressure, and blood oxygen saturation two hours after a meal are usually lower than those one hour after a meal because the cardiovascular system reaches its maximum load one hour after a meal and then gradually recovers; therefore, in this embodiment, if both the late-stage parameters and the mid-stage parameters are greater than the early-stage parameters, and the late-stage parameters are greater than the mid-stage parameters, the system cancels the determination that the disease state of the preset patient is stable, which has practical reference significance. However, in actual situations, this trend may vary due to individual differences, dietary composition, and cardiovascular regulatory abilities. Distinguish and analyze physiological parameters at different time periods after meals to more meticulously evaluate the postprandial cardiovascular response; And dynamically adjust the determination of the disease state according to the changes in the parameters after meals to ensure the accuracy of the determination result; This can more accurately evaluate the cardiovascular status of patients in different life scenarios; On the above basis, in this embodiment, further, among the mid-term parameters and the late-term parameters, the mid-term parameters and the late-term parameters are divided into mid-term parameters corresponding to lunch and late-term parameters corresponding to dinner, and the average values of the mid-term parameters and the late-term parameters are calculated with a two-day period as a calculation cycle within the monitoring period. If, within the monitoring period, the change in the average value is within the normal change range, the system determines that the disease state of the preset patient is stable; otherwise, it does not determine; In this embodiment, with a two-day period as a calculation cycle, analyze the average values of the mid-term and late-term parameters to provide a more stable disease assessment; And evaluate the long-term trend of the disease through the change in the average value within the monitoring period to avoid the influence of short-term fluctuations.

[0024] In summary, through comprehensive analysis of the historical data and real-time physiological parameter changes of cardiovascular disease patients, the present invention provides a precise and efficient remote monitoring and warning system, which can not only help medical staff better manage the patient's condition, but also more comprehensively evaluate the disease stability of the patient, providing a basis for the adjustment of the treatment plan.

[0025] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A cardiovascular remote monitoring and emergency response system, characterized in that, Including: A data acquisition module, which is used to acquire the historical data of a preset patient, and the historical data includes the physiological parameter data of the preset patient during historical onset; The physiological parameter data includes heart rate, blood pressure and blood oxygen saturation; A data processing module for processing physiological parameter data at the time of the preset patient's historical onset, including dividing the physiological parameter data, and the dividing method includes selecting the physiological parameter data at the time of the last 3 onsets, and dividing the physiological parameter data into parameters, parameters and parameters; and obtaining the minimum parameter and the maximum parameter from each divided parameter; A fusion analysis module, which responds to the minimum parameter and the maximum parameter and is used to monitor the change rules of the minimum parameter and the maximum parameter; the fusion analysis module includes an analysis unit, a calculation unit, a processing unit and a judgment unit; The analysis unit responds to the change rule and is used to preset a monitoring period according to the change rule. During the monitoring period, a set of physiological parameter data is collected based on the minimum parameter, and the trend change of this set of physiological parameter data is analyzed; The set of physiological parameter data includes at least 30 to 50 data; The calculation unit responds to the trend change and is used to calculate the difference change of each physiological parameter in the set of physiological parameter data. The calculation method includes calculating the difference of each physiological parameter at 30 minutes, 1 hour and 2 hours after a meal of the preset patient during the monitoring period, and the monitoring period is at least one natural month; The processing unit is used to process the physiological parameters according to different degrees of change intervals according to the calculated difference change.

2. A cardiovascular remote monitoring and emergency response system according to claim 1, characterized in that, In the data processing module, the physiological parameter data of the preset patient during historical onset is processed, and the processing method also includes correcting the collected physiological parameter data to ensure the accuracy and consistency of the data; Or, the physiological parameter data is processed in a statistical analysis manner, including calculating the mean, median and / or percentile of the physiological parameters to obtain the distribution characteristics of the data.

3. A cardiovascular remote monitoring and emergency response system according to claim 1, characterized in that, In the processing unit, the physiological parameters are processed according to different degrees of change intervals, including presetting a safety threshold based on the difference change, and obtaining the degree change intervals where the physiological parameters are greater than and / or less than the safety threshold. The degree intervals include the normal degree change intervals of heart rate, blood pressure and blood oxygen saturation, as follows: The normal degree change interval of heart rate is 60 - 100 beats per minute; The normal degree change interval of blood pressure is systolic blood pressure 90 - 140 mmHg, diastolic blood pressure 60 - 90 mmHg; The normal degree change interval of blood oxygen saturation is ≥95%; If the physiological parameters collected for the preset patient in the future period are 1% - 5% lower than the normal degree change interval, the corresponding physiological parameters will be marked as the low degree change interval; If the physiological parameters collected for the preset patient in the future period are 1% - 5% higher than the normal degree change interval, the corresponding physiological parameters will be marked as the high degree change interval.

4. A cardiovascular remote monitoring and emergency response system according to claim 3, characterized in that, The judgment unit responds to different degree change intervals and is used to judge the disease state of the preset patient according to the low degree change interval and the high degree change interval. If it is the low degree change interval and / or the high degree change interval, the system determines that the physiological parameters all exceed the safety threshold, and determines that the disease state of the preset patient is in an abnormal change state, and issues an emergency response warning; Otherwise, no judgment is made.

5. A cardiovascular remote monitoring and emergency response system according to claim 4, characterized in that, If the system determines that the physiological parameters do not exceed the safety threshold, obtain the physiological parameters corresponding to the situation where the safety threshold is not exceeded, and calculate the level of the physiological parameters within the normal degree change range, including calculating the differences between the physiological parameters and heart rate, blood pressure, and blood oxygen saturation within the normal degree change range, and predicting the disease state of the preset patient based on the differences.

6. A cardiovascular remote monitoring and emergency response system according to claim 5, characterized in that, When predicting the disease state of the preset patient based on the differences, a prediction period is given. The prediction period is a natural month. During the prediction period, among the heart rate, blood pressure, and / or blood oxygen saturation obtained for the preset patient, if only the change of one of the physiological parameters shows an increasing trend compared to the differences between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change range, the system determines that the disease state of the preset patient is in a low-degree fluctuation change state; If the changes of two of the physiological parameters show an increasing trend compared to the differences between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change range, the system determines that the disease state of the preset patient is in a high-degree fluctuation change state and issues an emergency response warning.

7. A cardiovascular remote monitoring and emergency response system according to claim 6, characterized in that, When only the change of one of the physiological parameters shows an increasing trend compared to the differences between the heart rate, blood pressure, and blood oxygen saturation in the normal degree change range, take each natural day as a verification period to verify the trend changes of the differences between the other two physiological parameters and the heart rate, blood pressure, or blood oxygen saturation in the normal degree change range. If the trend changes do not occur in 3 to 6 consecutive verification periods, the system determines that the disease state of the preset patient is stable; Otherwise, no determination is made.

8. A cardiovascular remote monitoring and emergency response system according to claim 7, characterized in that, If the system determines that the disease state of the preset patient is stable, distinguish the physiological parameters collected from the preset patient 30 minutes, 1 hour, and 2 hours after meals, including classifying the physiological parameters collected 30 minutes, 1 hour, and 2 hours after meals as early-stage parameters, mid-stage parameters, and late-stage parameters. If both the late-stage parameters and the mid-stage parameters are greater than the early-stage parameters, and the late-stage parameters are greater than the mid-stage parameters, the system cancels the determination that the disease state of the preset patient is stable; otherwise, maintain the corresponding determination.

9. A cardiovascular remote monitoring and emergency response system according to claim 8, wherein, Among the mid-stage parameters and the late-stage parameters, classify the mid-stage parameters and the late-stage parameters into mid-stage parameters corresponding to lunch and late-stage parameters corresponding to dinner, and calculate the average values of the mid-stage parameters and the late-stage parameters with a two-day calculation period within the monitoring period. If, within the monitoring period, the change of the average value is within the normal degree change range, the system determines that the disease state of the preset patient is stable; otherwise, no determination is made.

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