Data abnormity early warning system of vital sign monitor

Abstract

The invention relates to the technical field of vital sign monitoring, and particularly discloses a data abnormity early warning system of a vital sign monitor, which comprises a body data recording module used for recording body data of different patients and recording the data, the data calculation module is combined with patient data recorded in the body data recording module to calculate the body state influence indexes of different patients, so that whether the patients need to adjust the threshold interval of heart rate monitoring or not can be judged; then the heart rate monitoring threshold value intervals of different patients can be adjusted through the dynamic adjusting module in combination with the calculation result of the data calculation module and the recorded data in the heart rate monitoring module, and the process is carried out based on diversified data support, so that the accuracy of the adjusting result can be improved; therefore, the sensitivity of heart rate monitoring is improved, and the accuracy of the subsequent judgment result of the body state of the patient is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vital sign monitoring, and in particular to a data abnormality early warning system for a vital sign monitor. Background Art

[0002] A vital signs monitor is a medical device used to monitor a patient's vital signs in real time. It can help medical staff understand the patient's health status in real time, identify abnormalities in a timely manner and take relevant treatment measures, thereby improving treatment effectiveness and safety.

[0003] Vital signs monitors are generally used to monitor a patient's heart rate, blood pressure, respiratory rate, blood oxygen saturation and other data. Heart rate monitoring of patients generally uses special sensors or devices such as bracelets to directly contact the skin to capture electrocardiogram signals, display heart rate data in real time, and compare it with the preset heart rate monitoring threshold range to evaluate heart activity through real-time monitoring. The preset heart rate monitoring threshold range is generally set in combination with the patient's disease.

[0004] In the prior art, the heart rate monitoring threshold range of traditional vital signs monitors is generally set in combination with the patient's disease type. However, due to differences in physical data of different patients, if the heart rate monitoring threshold range is set only according to the patient's disease type, there may be errors in the threshold range, resulting in reduced sensitivity of heart rate monitoring, thereby affecting the accuracy of the heart activity assessment results. Summary of the invention

[0005] The purpose of the present invention is to provide a data abnormality warning system for a vital sign monitor to solve the following technical problems: How to improve the sensitivity of heart rate monitoring to improve the accuracy of cardiac activity assessment results.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A data abnormality warning system for a vital sign monitor, the system comprising: A physical data recording module is used to record the physical data of different patients and record the data; A heart rate monitoring module, including a vital signs monitor, is used to continuously monitor changes in heart rates of different patients and record the data; A data calculation module, used to calculate the physical condition impact index of different patients in combination with the patient data recorded in the physical data recording module; A data analysis module, used to analyze whether the patient needs to adjust the threshold interval of heart rate monitoring based on the calculation results of the data calculation module; A dynamic adjustment module, for adjusting the heart rate monitoring threshold interval of different patients by combining the calculation result of the data calculation module with the recorded data in the heart rate monitoring module when it is determined that the patient needs to adjust the heart rate monitoring threshold interval; The monitoring and early warning module is used to combine the real-time heart rate data of different patients monitored in the new heart rate monitoring module, compare it with the patient's adjusted heart rate monitoring threshold range, and judge the patient's physical condition based on the comparison results, and issue a timely warning when it is judged that the patient's physical condition is abnormal.

[0007] Furthermore, the data recorded by the body data recording module includes: Age, gender, basal metabolism and historical blood pressure data of different patients.

[0008] Furthermore, the calculation process in the data calculation module includes: By formula Calculate the influence coefficient of the body data of the ath patient ; Among them, a is any patient whose heart rate needs to be monitored. is the age of the ath patient, is the gender influence coefficient, which is set based on empirical fitting. is the proportionality coefficient, which is set according to empirical fitting. for The standard value of is the basal metabolic value of the ath patient, is the preset basal metabolic value, i is any blood pressure monitoring after the patient receives treatment, and n is the total number of blood pressure monitoring after the patient receives treatment. is the low pressure value of the a-th patient during the i-th blood pressure monitoring, is the preset low pressure value. is the high pressure value of the a-th patient during the i-th blood pressure monitoring, is the preset high pressure value. for The standard value of and is the first weight coefficient, where According to empirical data The influence of the range of numerical values ​​on the size of the coefficient of influence of body data is set. According to empirical data The range of numerical values ​​affects the fitting setting of the coefficient of influence of body data.

[0009] Furthermore, the analysis process of the data analysis module includes: By taking the influence coefficient of the physical data of the ath patient The influence coefficient threshold interval is Make a comparison; like , the system determines that the basic physical condition of the ath patient is good, which means that the patient has a high level of physical activity and needs to increase the narrowness of the threshold interval of heart rate monitoring; like , the system determines that the basic physical condition of the patient a is moderate, and there is no need to adjust the threshold interval of heart rate monitoring; like , the system determines that the basic physical condition of the ath patient is poor, which means that the patient's physical activity level is low, and the narrowness of the threshold interval of heart rate monitoring needs to be reduced.

[0010] Furthermore, the adjustment process of the dynamic adjustment module includes: By formula Calculate the dispersion coefficient of the heart rate change of the ath patient in the past period of time ; Among them, c is a heart rate collection at a fixed time interval in the past period of time, m is the total number of heart rate collections in the past period of time, is the heart rate of the ath patient at the cth heart rate collection in the past period of time, For all The average value of For all The maximum value in For all The minimum value in .

[0011] Furthermore, the adjustment process of the dynamic adjustment module also includes: By calculating the dispersion coefficient of the heart rate change of the ath patient in the past period of time The preset coefficient of dispersion threshold Make a comparison; when , the system determines that the heart rate fluctuation of patient a is small, and the narrowness of the heart rate monitoring threshold interval needs to be reduced; like , the system determines that the heart rate fluctuation of patient a is large, and the narrowness of the heart rate monitoring threshold range needs to be increased.

[0012] Furthermore, the adjustment process of the dynamic adjustment module includes: By formula Calculate the minimum value of the heart rate monitoring threshold interval after the adjustment of the ath patient ; And through the formula Calculate the maximum value of the heart rate monitoring threshold interval after adjustment for the ath patient ; in, is the minimum value in the preset heart rate monitoring threshold range. is the maximum value in the preset heart rate monitoring threshold range. and is the second weight coefficient, where According to empirical data The influence of the range of values ​​on the heart rate monitoring threshold is set by fitting. According to empirical data The influence of the range of values ​​on the heart rate monitoring threshold is set by fitting. It is the adjustment coefficient comparison table function, because according to the empirical data The degree of influence of the range of values ​​on the heart rate monitoring threshold is obtained based on test data.

[0013] Furthermore, the monitoring process of the monitoring and early warning module includes: The heart rate monitoring module is used to obtain the heart rate of the ath patient at the current time point. and the corrected heart rate monitoring threshold interval Make a comparison; like , the system determines that the heart rate of the ath patient at the current time point is low, which means that the patient has bradycardia, and immediately issues a warning; like , the system determines that the heart rate of the ath patient at the current time point is within the normal range and does not issue a warning; like , the system determines that the heart rate of the ath patient at the current time point is high, which means that the patient has tachycardia, and immediately issues a warning.

[0014] Beneficial effects of the present invention: (1) The present invention calculates the physical condition influence index of different patients by combining the data calculation module with the patient data recorded in the physical data recording module, and can determine whether the patient needs to adjust the threshold interval of heart rate monitoring. After that, the heart rate monitoring threshold interval of different patients can be adjusted by combining the calculation result of the data calculation module with the recorded data in the heart rate monitoring module through the dynamic adjustment module. Since this process is carried out based on diversified data support, the accuracy of the adjustment result can be improved, thereby improving the sensitivity of heart rate monitoring, and further ensuring the accuracy of the subsequent judgment results of the patient's physical condition.

[0015] (2) The present invention uses the influence coefficient of the physical data of the ath patient as The influence coefficient threshold interval is Through this comparison method, the basic physical condition and physical activity level of the ath patient can be analyzed, and then a decision can be made whether the threshold interval of heart rate monitoring needs to be adjusted based on the analysis results. Through such a setting, after evaluating the patient's health status in combination with the data, the accuracy of the subsequent heart rate monitoring threshold interval adjustment results can be guaranteed, thereby ensuring the accuracy of the heart activity assessment results.

[0016] (3) The present invention calculates the discrete coefficient of the heart rate change of the ath patient over a period of time. The preset coefficient of dispersion threshold By comparing, the fluctuation of the heart rate of the ath patient can be accurately judged, and the judgment result can be used to further decide whether to reduce or increase the narrowness of the heart rate monitoring threshold interval. By setting it in this way, the influence coefficient of the physical data of the ath patient can be further determined. The influence coefficient threshold interval is The judgment results can provide diversified data support for the subsequent adjustment of the heart rate monitoring threshold intervals for different patients to ensure the accuracy of the adjustment results.

[0017] (4) The present invention obtains the heart rate of the ath patient at the current time point through the heart rate monitoring module and the corrected heart rate monitoring threshold interval For comparison, the data is based on diversified data support and combined with calculations, and is calculated in combination with multi-faceted judgment results, so the accuracy and reliability of the data are relatively high, thereby improving the sensitivity of heart rate monitoring. In this case, through this comparison method, an accurate judgment can be made on the heart rate of the ath patient at the current time point, and a warning can be issued in time when the patient is judged to have an abnormal heart rate, thereby ensuring the patient's physical safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings.

[0019] Figure 1 The present invention provides a schematic block diagram of a data anomaly warning system for a vital sign monitor. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1 As shown, in one embodiment, the present application provides a data abnormality warning system for a vital sign monitor, the system comprising: A physical data recording module is used to record the physical data of different patients and record the data; A heart rate monitoring module, including a vital signs monitor, is used to continuously monitor changes in heart rates of different patients and record the data; A data calculation module, used to calculate the physical condition impact index of different patients in combination with the patient data recorded in the physical data recording module; A data analysis module, used to analyze whether the patient needs to adjust the threshold interval of heart rate monitoring based on the calculation results of the data calculation module; A dynamic adjustment module is used to adjust the heart rate monitoring threshold interval of different patients by combining the calculation result of the data calculation module and the recorded data in the heart rate monitoring module when it is determined that the patient needs to adjust the heart rate monitoring threshold interval; The monitoring and early warning module is used to combine the real-time heart rate data of different patients monitored in the new heart rate monitoring module, compare it with the adjusted heart rate monitoring threshold interval of the patient, and judge the patient's physical condition according to the comparison result, and issue an early warning in time when it is judged that the patient's physical condition is abnormal; Through the above technical scheme, this example provides a body data recording module and a heart rate monitoring module, which are respectively used to record the body data of different patients and continuously monitor the heart rate changes of different patients, and record the data. When adjusting the heart rate monitoring threshold interval of the patient, firstly, the data calculation module is combined with the patient data recorded in the body data recording module to calculate the physical state impact index of different patients, and then the data analysis module is combined with the calculation result of the data calculation module to analyze whether the patient needs to adjust the heart rate monitoring threshold interval, and when the dynamic adjustment module is judged that the patient needs to adjust the heart rate monitoring threshold interval, the heart rate monitoring threshold interval of different patients is adjusted in combination with the calculation result of the data calculation module and the recorded data in the heart rate monitoring module. Finally, the monitoring and early warning module can be combined with the real-time heart rate data of different patients monitored in the new heart rate monitoring module, and compared with the adjusted heart rate monitoring threshold interval of the patient, and the patient's physical state is judged according to the comparison result, and when it is judged that the patient's physical state is abnormal, a warning is issued in time; Through such a setting, when adjusting the patient's heart rate monitoring threshold interval, the data calculation module is combined with the patient data recorded in the body data recording module to calculate the physical condition impact index of different patients, so as to determine whether the patient needs to adjust the heart rate monitoring threshold interval. After that, the heart rate monitoring threshold interval of different patients can be adjusted by the dynamic adjustment module in combination with the calculation results of the data calculation module and the recorded data in the heart rate monitoring module. And because this process is based on diversified data support, it can improve the accuracy of the adjustment results, thereby improving the sensitivity of heart rate monitoring, and then ensuring the accuracy of subsequent judgment results on the patient's physical condition.

[0022] The data recorded by the body data recording module includes: Age, gender, basal metabolism and historical blood pressure data of different patients; Through the above technical solution, this example provides data recorded by the body data recording module, including the age, gender, basal metabolism and historical blood pressure data of different patients. There must be differences between the body data of different patients. Since this data will affect the patient's basal heart rate, and the traditional heart rate monitoring threshold interval is generally set in combination with the patient's disease type and does not involve the patient's body data, so by combining this data to correct the preset heart rate monitoring interval for different patients, it can be ensured that the corrected threshold interval is more suitable for the patient, thereby improving the sensitivity of heart rate monitoring and avoiding false alarms.

[0023] The calculation process in the data calculation module includes: By formula Calculate the influence coefficient of the body data of the ath patient ; Among them, a is any patient whose heart rate needs to be monitored. is the age of the ath patient, is the gender influence coefficient, which is set based on empirical fitting. is the proportionality coefficient, which is set according to empirical fitting. for The standard value can be selected and set according to the allowable error in the empirical data. is the basal metabolic value of the ath patient, is the preset basal metabolic value, i is any blood pressure monitoring after the patient receives treatment, and n is the total number of blood pressure monitoring after the patient receives treatment. is the low pressure value of the a-th patient during the i-th blood pressure monitoring, is the preset low pressure value. is the high pressure value of the a-th patient during the i-th blood pressure monitoring, is the preset high pressure value. for The standard value can be selected and set according to the allowable error in the empirical data. and is the first weight coefficient, where According to empirical data The influence of the range of numerical values ​​on the size of the coefficient of influence of body data is set. According to empirical data The influence of the range of values ​​on the size of the coefficient of influence of body data is set in the fitting; Through the above technical solution, this example provides the influence coefficient of the body data of the ath patient , can be obtained by formula It is obvious that the older the a-th patient is, the lower his basal metabolism is, and the greater the difference between the high and low blood pressure values ​​of the a-th patient and the preset values ​​during the i-th blood pressure monitoring, the greater the influence coefficient of the a-th patient's physical data is. The larger the value, the worse the basic physical condition of the a-th patient is. This is because as people age, their heart rate will gradually slow down. The higher the basal metabolic rate, the slower the heart rate. When the basal metabolic rate decreases, the heart rate will increase. When the blood pressure value is too low or too high, the heart rate will increase. Therefore, when the age of the a-th patient is younger, the basal metabolic rate is higher, and the difference between the high and low pressure values ​​of the a-th patient and the preset values ​​during the i-th blood pressure monitoring is smaller, the physical data influence coefficient of the a-th patient is The smaller it is, the better the basic physical condition of the patient a is. Through this calculation method, accurate data can be provided for subsequent judgment on whether it is necessary to adjust the heart rate monitoring threshold interval according to the patient's physical data, thereby improving the accuracy of the judgment results.

[0024] The analysis process of the data analysis module includes: By taking the influence coefficient of the physical data of the ath patient The influence coefficient threshold interval is Make a comparison; like , the system determines that the basic physical condition of the ath patient is good, which means that the patient has a high level of physical activity and needs to increase the narrowness of the threshold interval of heart rate monitoring; like , the system determines that the basic physical condition of the patient a is moderate, and there is no need to adjust the threshold interval of heart rate monitoring; like , the system determines that the basic physical condition of the ath patient is poor, which means that the patient's physical activity level is low, and the narrowness of the threshold interval of heart rate monitoring needs to be reduced; Through the above technical solution, this example calculates the influence coefficient of the body data of the ath patient The influence coefficient threshold interval is By comparing, the basic physical condition and physical activity level of the a-th patient can be analyzed, and then the threshold interval of the heart rate monitoring needs to be adjusted according to the analysis results. By setting it in this way, after the health status of the patient is evaluated in combination with the data, the accuracy of the subsequent heart rate monitoring threshold interval adjustment result can be guaranteed, thereby ensuring the accuracy of the heart activity evaluation result; The judgment basis is that when it is judged that the basic physical condition of the a-th patient is poor, it means that the patient's heart load is high, so the heart rate monitoring threshold interval needs to be lowered to maintain the sensitivity of heart rate monitoring, so as to timely detect slight changes in heart rate; when it is judged that the basic physical condition of the a-th patient is good, it means that the patient's heart load is low, and a wider alarm range can be appropriately set to reduce false alarms triggered by normal fluctuations.

[0025] The adjustment process of the dynamic adjustment module includes: By formula Calculate the dispersion coefficient of the heart rate change of the ath patient in the past period of time ; Among them, c is a heart rate collection at a fixed time interval in the past period of time, m is the total number of heart rate collections in the past period of time, is the heart rate of the ath patient at the cth heart rate collection in the past period of time, For all The average value of For all The maximum value in For all The minimum value in ; Through the above technical solution, this example provides the heart rate variation dispersion coefficient of the ath patient in the past period of time , can be obtained by formula It is calculated that the data can reflect the fluctuation value of the heart rate of the a-th patient in the past period of time. When the data is larger, it means that the heart rate value of the a-th patient has fluctuated greatly in the past period of time. In this case, it is necessary to increase the narrowness of the heart rate monitoring threshold interval to avoid false alarms. When the data is smaller, it means that the heart rate value of the a-th patient has fluctuated less in the past period of time. In this case, it is necessary to reduce the narrowness of the heart rate monitoring threshold interval to improve the monitoring sensitivity, thereby realizing dynamic adjustment of the heart rate monitoring threshold interval for different patients and ensuring the accuracy of the patient's heart rate monitoring results.

[0026] The adjustment process of the dynamic adjustment module also includes: By calculating the dispersion coefficient of the heart rate change of the ath patient in the past period of time The preset coefficient of dispersion threshold Make a comparison; when , the system determines that the heart rate fluctuation of patient a is small, and the narrowness of the heart rate monitoring threshold interval needs to be reduced; like , the system determines that the heart rate fluctuation of patient a is large, and the narrowness of the heart rate monitoring threshold interval needs to be increased; Through the above technical solution, this example calculates the discrete coefficient of the heart rate change of the ath patient in the past period of time The preset coefficient of dispersion threshold By comparing, the fluctuation of the heart rate of the ath patient can be accurately judged, and the judgment result can be used to further decide whether to reduce or increase the narrowness of the heart rate monitoring threshold interval. By setting it in this way, the influence coefficient of the physical data of the ath patient can be further determined. The influence coefficient threshold interval is The judgment results can provide diversified data support for the subsequent adjustment of the heart rate monitoring threshold intervals for different patients to ensure the accuracy of the adjustment results.

[0027] The adjustment process of the dynamic adjustment module includes: By formula Calculate the minimum value of the heart rate monitoring threshold interval after the adjustment of the ath patient ; And through the formula Calculate the maximum value of the heart rate monitoring threshold interval after adjustment for the ath patient ; in, is the minimum value in the preset heart rate monitoring threshold range. is the maximum value in the preset heart rate monitoring threshold range. and is the second weight coefficient, where According to empirical data The influence of the range of values ​​on the heart rate monitoring threshold is set by fitting. According to empirical data The influence of the range of values ​​on the heart rate monitoring threshold is set by fitting. It is the adjustment coefficient comparison table function, because according to the empirical data The influence of the range of values ​​on the heart rate monitoring threshold is obtained based on test data; Through the above technical solution, this example provides the minimum value of the heart rate monitoring threshold interval after the adjustment of the ath patient is completed. The maximum value of the heart rate monitoring threshold interval after adjustment for the ath patient , since the calculation result of this value is based on diversified data support and combined with the heart rate variation dispersion coefficient of the ath patient in the past period of time The preset coefficient of dispersion threshold The judgment result and the influence coefficient of the physical data of the ath patient The influence coefficient threshold interval is judgment results, so the accuracy and reliability of the data can be improved, so that the adjusted heart rate monitoring threshold interval of the ath patient is more suitable for the patient's physical condition, thereby improving the sensitivity and accuracy of subsequent monitoring results, and then improving the accuracy of the heart activity assessment results.

[0028] The monitoring process of the monitoring and early warning module includes: The heart rate monitoring module is used to obtain the heart rate of the ath patient at the current time point. and the corrected heart rate monitoring threshold interval Make a comparison; like , the system determines that the heart rate of the ath patient at the current time point is low, which means that the patient has bradycardia, and immediately issues a warning; like , the system determines that the heart rate of the ath patient at the current time point is within the normal range and does not issue a warning; like , the system determines that the heart rate of the ath patient at the current time point is high, which means that the patient has tachycardia, and immediately issues a warning; Through the above technical solution, this example obtains the heart rate of the ath patient at the current time point through the heart rate monitoring module. and the corrected heart rate monitoring threshold interval For comparison, the data is based on diversified data support and combined with calculations, and is calculated in combination with multi-faceted judgment results, so the accuracy and reliability of the data are relatively high, thereby improving the sensitivity of heart rate monitoring. In this case, through this comparison method, an accurate judgment can be made on the heart rate of the ath patient at the current time point, and a warning can be issued in time when the patient is judged to have an abnormal heart rate, thereby ensuring the patient's physical safety.

[0029] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A data abnormality warning system for a vital sign monitor, characterized in that: The system comprises: A physical data recording module is used to record the physical data of different patients and record the data; A heart rate monitoring module, including a vital signs monitor, is used to continuously monitor changes in heart rates of different patients and record the data; A data calculation module, used to calculate the physical condition impact index of different patients in combination with the patient data recorded in the physical data recording module; A data analysis module, used to analyze whether the patient needs to adjust the threshold interval of heart rate monitoring based on the calculation results of the data calculation module; A dynamic adjustment module is used to adjust the heart rate monitoring threshold interval of different patients by combining the calculation result of the data calculation module and the recorded data in the heart rate monitoring module when it is determined that the patient needs to adjust the heart rate monitoring threshold interval; The monitoring and early warning module is used to combine the real-time heart rate data of different patients monitored in the new heart rate monitoring module, compare it with the patient's adjusted heart rate monitoring threshold range, and judge the patient's physical condition based on the comparison results, and issue a timely warning when it is judged that the patient's physical condition is abnormal.

2. The data abnormality warning system of a vital sign monitor according to claim 1 is characterized in that: The data recorded by the body data recording module includes: Age, gender, basal metabolism and historical blood pressure data of different patients.

3. The data abnormality warning system of a vital sign monitor according to claim 1, characterized in that: The calculation process in the data calculation module includes: By formula Calculate the influence coefficient of the body data of the ath patient ; Among them, a is any patient whose heart rate needs to be monitored. is the age of the ath patient, is the gender influence coefficient, which is set based on empirical fitting. is the proportionality coefficient, which is set according to empirical fitting. for The standard value of is the basal metabolic value of the ath patient, is the preset basal metabolic value, i is any blood pressure monitoring after the patient receives treatment, and n is the total number of blood pressure monitoring after the patient receives treatment. is the low pressure value of the a-th patient during the i-th blood pressure monitoring, is the preset low pressure value. is the high pressure value of the a-th patient during the i-th blood pressure monitoring, is the preset high pressure value. for The standard value of and is the first weight coefficient, where According to empirical data The influence of the range of numerical values ​​on the size of the coefficient of influence of body data is set. According to empirical data The range of numerical values ​​affects the fitting setting of the coefficient of influence of body data.

4. The data abnormality warning system of a vital sign monitor according to claim 3 is characterized in that: The analysis process of the data analysis module includes: By taking the influence coefficient of the physical data of the ath patient The influence coefficient threshold interval is Make a comparison; like , the system determines that the basic physical condition of the ath patient is good, which means that the patient has a high level of physical activity and needs to increase the narrowness of the threshold interval of heart rate monitoring; like , the system determines that the basic physical condition of the patient a is moderate, and there is no need to adjust the threshold interval of heart rate monitoring; like , the system determines that the basic physical condition of the ath patient is poor, which means that the patient's physical activity level is low, and the narrowness of the threshold interval of heart rate monitoring needs to be reduced.

5. The data abnormality warning system of a vital sign monitor according to claim 1, characterized in that: The adjustment process of the dynamic adjustment module includes: By formula Calculate the dispersion coefficient of the heart rate change of the ath patient in the past period of time ; Among them, c is a heart rate collection at a fixed time interval in the past period of time, m is the total number of heart rate collections in the past period of time, is the heart rate of the ath patient at the cth heart rate collection in the past period of time, For all The average value of For all The maximum value in For all The minimum value in .

6. The data abnormality warning system of a vital sign monitor according to claim 5, characterized in that: The adjustment process of the dynamic adjustment module also includes: By calculating the dispersion coefficient of the heart rate change of the ath patient in the past period of time The preset coefficient of dispersion threshold Make a comparison; when , the system determines that the heart rate fluctuation of patient a is small, and the narrowness of the heart rate monitoring threshold interval needs to be reduced; like , the system determines that the heart rate fluctuation of patient a is large, and the narrowness of the heart rate monitoring threshold range needs to be increased.

7. The data abnormality warning system of a vital sign monitor according to claim 6, characterized in that: The adjustment process of the dynamic adjustment module includes: By formula Calculate the minimum value of the heart rate monitoring threshold interval after the adjustment of the ath patient ; And through the formula Calculate the maximum value of the heart rate monitoring threshold interval after adjustment for the ath patient ; in, is the minimum value in the preset heart rate monitoring threshold range. is the maximum value in the preset heart rate monitoring threshold range. and is the second weight coefficient, where According to empirical data The influence of the range of values ​​on the heart rate monitoring threshold is set by fitting. According to empirical data The influence of the range of values ​​on the heart rate monitoring threshold is set by fitting. It is the adjustment coefficient comparison table function, because according to the empirical data The degree of influence of the range of values ​​on the heart rate monitoring threshold is obtained based on test data.

8. The data abnormality warning system for a vital sign monitor according to claim 7, characterized in that: The monitoring process of the monitoring and early warning module includes: The heart rate monitoring module is used to obtain the heart rate of the ath patient at the current time point. and the corrected heart rate monitoring threshold interval Make a comparison; like , the system determines that the heart rate of the ath patient at the current time point is low, which means that the patient has bradycardia, and immediately issues a warning; like , the system determines that the heart rate of the ath patient at the current time point is within the normal range and does not issue a warning; like , the system determines that the heart rate of the ath patient at the current time point is high, which means that the patient has tachycardia, and immediately issues a warning.

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