Database-based intelligent old-age care remote appointment diagnosis system

Abstract

The invention relates to the technical field of medical appointment, and particularly discloses a database-based intelligent old-age care remote appointment diagnosis system, which comprises a data recording module used for recording operation state data of intelligent wearable equipment in a historical use process and storing the operation state data in a database, the monitoring frequency of the heart rate data of the elderly user is improved through the diagnosis appointment module, so that the variable quantity data of the heart rate data of the elderly user is obtained, the data can reflect the heart rate change condition of the elderly, the misjudgment condition is avoided, data support is provided for whether diagnosis appointment is needed or not, and the user experience is improved. By analyzing the real-time performance of data acquisition of the intelligent wearable device, the timeliness of the data currently used for analysis can be analyzed, so that whether the current data can truly reflect the state of the elderly can be judged, and finally, the reservation priority of the elderly user can be dynamically adjusted by combining the two groups of data. Therefore, clinical risks of old people are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of medical appointment, and in particular to a database-based intelligent elderly care remote appointment diagnosis system. Background Art

[0002] The remote appointment diagnosis system for the elderly is an innovative model that integrates modern information technology with medical resources. It aims to provide convenient, efficient and personalized medical services for the elderly and solve the problems of uneven resource allocation and slow service response in traditional elderly care and medical models.

[0003] The core functions of the elderly remote appointment diagnosis system usually include health monitoring and early warning. The health data and life status information of the elderly are collected in real time through smart wearable devices such as smart bracelets. For example, the cardiac conduction system of the elderly is gradually deteriorating. In order to avoid the deterioration of heart function, the elderly's heart rate data is collected in real time. When the elderly's heart rate is detected to be abnormal, a remote diagnosis appointment is made, and the appointment priority is adjusted according to the risk level of the current heart rate data. Not only can the elderly communicate with doctors based on online consultations, but also by adjusting the priority of appointment diagnosis, the clinical risk of the elderly can be reduced and accidents can be avoided.

[0004] In the prior art, traditional remote appointment diagnosis systems for the elderly generally collect the elderly's heart rate data in real time and determine whether there is an abnormality based on the size of the heart rate data. However, since data collection by smart wearable devices may be delayed, the real-time performance of heart rate data collection is low. Adjusting the elderly's appointment diagnosis priority based on this data may lead to errors in the adjustment results, thereby increasing the clinical risk of the elderly. Summary of the invention

[0005] The purpose of the present invention is to provide a database-based intelligent elderly care remote appointment diagnosis system to solve the following technical problems: How to improve the accuracy of diagnostic priority for elderly people’s appointments.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A database-based intelligent elderly care remote appointment diagnosis system, the system comprising: The data recording module is used to record the operating status data of the smart wearable device during historical use and store it in the database; The status determination module is used to analyze the status of the elderly user in real time by combining the heart rate data of the elderly user collected in real time by the smart wearable device; The diagnosis appointment module is used to increase the monitoring frequency of the elderly user's heart rate data when it is determined that the elderly user's status is abnormal, and to decide whether a diagnosis appointment is needed based on the change in the elderly user's heart rate data; The timeliness analysis module is used to analyze the real-time data collection of the smart wearable device by combining the real-time operation data and historical operation status data of the smart wearable device in the database when it is determined that a diagnosis appointment is needed; The priority adjustment module is used to adjust the appointment priority of the elderly users by combining the change data of the elderly users' heart rate data with the real-time analysis data of the smart wearable devices.

[0007] Furthermore, the determination process of the state determination module includes: The heart rate data of the elderly users is monitored in real time at fixed time intervals through smart wearable devices, and the collected heart rate data is defined as ; Where a is any data collection at a fixed time interval. That is, the heart rate of the smart wearable device during the ath data collection; The heart rate of the smart wearable device during the ath data collection is compared with the preset heart rate threshold Make a comparison; like ,The system determines that the elderly user’s heart rate is normal during the data collection, and continuously monitors it through the smart wearable device; like The system determines that the heart rate of the elderly user is abnormal during the data collection, increases the monitoring frequency of the elderly user's heart rate data, performs abnormal change monitoring operations, and analyzes the change amount of the elderly user's heart rate data.

[0008] Furthermore, the appointment process of the diagnosis appointment module includes: When it is determined that the heart rate of the elderly user has abnormal changes, the monitoring frequency of the elderly user's heart rate data is increased, and the heart rate change curve is established based on the real-time monitored heart rate data. ; And through the formula Calculate the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation ; Among them, t1 is the first heart rate data collection in the i-th abnormal change monitoring operation, The last heart rate data collection in the i-th abnormal change monitoring operation, is the number of heart rate data collections in the i-th abnormal change monitoring operation, The heart rate of the smart wearable device during the ath data collection in the i-th abnormal change monitoring operation, For all The average value of The preset heart rate fluctuation value for abnormal change monitoring operation, To define a function, if , then let , otherwise, let .

[0009] Furthermore, the appointment process of the diagnosis appointment module also includes: By calculating the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation The preset change threshold Make a comparison; like , the system judged that the abnormal changes in the elderly user's heart rate were risky and not a misjudgment, and promptly made a remote appointment diagnosis and adjusted the elderly user's appointment priority; like ,The system judges that there is no risk in the abnormal changes of the elderly user's heart rate, and there is a misjudgment of the abnormal heart rate.

[0010] Furthermore, the analysis process of the aging analysis module includes: By formula Calculate the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation ; in, is the total number of historical data collection times of smart wearable devices before the i-th abnormal change monitoring operation, Any historical data collection for smart wearable devices, The first smart wearable device in history The response time of the data collection, For all The average value of is the preset response time, is the data transmission time of the ath data collection of the smart wearable device in the i-th abnormal change monitoring operation, is the preset data transmission time, for The standard value of is the data processing time of the ath data collection of the smart wearable device in the i-th abnormal change monitoring operation, is the preset data processing time, is the network speed of the smart wearable device during the a-th data collection in the i-th abnormal change monitoring operation, For all The average value of .

[0011] Furthermore, the analysis process of the aging analysis module also includes: By calculating the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation The preset time-effect coefficient threshold Make a comparison; like ,The system determines that the data collected by the smart wearable device in the ith abnormal change monitoring operation is not real-time enough, there is data delay, and the reservation priority needs to be adjusted; like ,The system determines that the data collected by the smart wearable device in the ith abnormal change monitoring operation is in good real-time and there is no data delay, and the appointment diagnosis can be carried out according to the current appointment priority.

[0012] Furthermore, the adjustment process of the priority adjustment module includes: By formula Calculate the change in the corrected heart rate data of the elderly user in the i-th abnormal change monitoring operation; in, is an adjustment coefficient comparison table function, wherein the adjustment coefficient comparison table function The value of The values ​​of are in one-to-one correspondence.

[0013] Furthermore, the adjustment process of the priority adjustment module includes: The reservation priority of the elderly user is adjusted based on the change data of the elderly user's heart rate data corrected in the i-th abnormal change monitoring operation and the pre-set priority judgment criteria.

[0014] Beneficial effects of the present invention: (1) The present invention improves the monitoring frequency of the heart rate data of the elderly user through the diagnosis appointment module, thereby obtaining the change data of the heart rate data of the elderly user. The data can reflect the change of the heart rate of the elderly, avoid misjudgment, and provide data support for whether a diagnosis appointment is needed. After that, by analyzing the real-time performance of data collection of the smart wearable device, the timeliness of the data currently used for analysis can be analyzed, so as to determine whether the current data can truly reflect the status of the elderly. Finally, by combining the two sets of data, the appointment priority of the elderly user can be dynamically adjusted, thereby reducing the clinical risk of the elderly.

[0015] (2) The present invention compares the heart rate of the smart wearable device during the ath data collection with the preset heart rate threshold. By making such a setting, the elderly user's heart rate abnormality can be monitored in real time according to the size of the elderly user's heart rate data, and a preliminary warning can be made when the elderly user's heart rate is judged to be abnormal. By increasing the monitoring frequency of the elderly user's heart rate data to monitor abnormal changes, and analyzing the change in the elderly user's heart rate data, not only can it be analyzed whether the current judgment is a misjudgment, but also the status of the elderly user can be further monitored, thereby providing efficient medical services for the elderly.

[0016] (3) The present invention calculates the change in the heart rate data of the elderly user during the i-th abnormal change monitoring operation. The preset change threshold Through this comparison method, the change in the heart rate data of the elderly user in the monitoring operation can be detected according to the i-th abnormal change. The value of the value is used to analyze the physical condition of the elderly user, thereby eliminating misjudgment due to the influence of the external environment, and then providing accurate data for subsequent decisions on whether a remote diagnosis appointment is needed, thereby improving the accuracy of the decision results.

[0017] (4) The present invention calculates the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation The preset time-effect coefficient threshold Through this comparison method, an accurate judgment can be made on whether there is a data delay in the i-th abnormal change monitoring operation. When the real-time performance of the data collected by the smart wearable device is low, it means that the data cannot reflect the true status of the elderly at the current time point. In order to avoid delays in diagnosis of the disease, it is necessary to adjust the appointment priority to reduce the possibility of risks, thereby avoiding the failure to discover potential risks in a timely manner.

[0018] (5) The present invention adjusts the appointment priority of the elderly user based on the change data of the elderly user's heart rate data corrected in the i-th abnormal change monitoring operation, combined with a pre-set priority judgment standard. Since the data is highly accurate, it can truly reflect the elderly user's heart rate dynamics and heart rate change trends. Adjusting the appointment diagnosis priority based on the data allows doctors to quickly assess the condition and formulate emergency treatment plans, such as guiding patients to take self-help measures or arrange offline medical treatment as soon as possible to avoid worsening of the condition and speed up the process of medical decision-making and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings.

[0020] Figure 1 This is a schematic block diagram of a database-based intelligent elderly care remote appointment diagnosis system in the present invention. DETAILED DESCRIPTION

[0021] 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.

[0022] See also Figure 1 As shown, in one embodiment, the present application provides a database-based smart elderly care remote appointment diagnosis system, the system comprising: The data recording module is used to record the operating status data of the smart wearable device during historical use and store it in the database; The status determination module is used to analyze the status of the elderly user in real time by combining the heart rate data of the elderly user collected in real time by the smart wearable device; The diagnosis appointment module is used to increase the monitoring frequency of the elderly user's heart rate data when it is determined that the elderly user's status is abnormal, and to decide whether a diagnosis appointment is needed based on the change in the elderly user's heart rate data; The timeliness analysis module is used to analyze the real-time data collection of the smart wearable device by combining the real-time operation data and historical operation status data of the smart wearable device in the database when it is determined that a diagnosis appointment is needed; The priority adjustment module is used to adjust the reservation priority of the elderly users by combining the change data of the elderly users' heart rate data with the real-time analysis data of the data analysis of the smart wearable devices; Through the above technical solution, this example provides a data recording module for recording the operating status data of the smart wearable device during historical use and storing it in a database. During daily use, the status determination module can combine the heart rate data of the elderly user collected in real time by the smart wearable device to perform real-time analysis on the status of the elderly user, and when the diagnosis appointment module determines that the status of the elderly user is abnormal, it increases the monitoring frequency of the heart rate data of the elderly user, and combines the change data of the heart rate data of the elderly user to decide whether a diagnosis appointment is needed. When it is determined that a diagnosis appointment is needed, the timeliness analysis module combines the real-time operating data of the smart wearable device in the database with the historical operating status data to analyze the real-time data collection of the smart wearable device, and the priority adjustment module combines the change data of the heart rate data of the elderly user with the real-time analysis data of the data analysis of the smart wearable device to adjust the appointment priority of the elderly user; Through such a setting, during daily use, when it is determined that the status of an elderly user is abnormal, the monitoring frequency of the elderly user's heart rate data is first increased through the diagnosis appointment module, so as to obtain the change data of the elderly user's heart rate data, which can reflect the changes in the elderly's heart rate, avoid misjudgment, and provide data support for whether a diagnosis appointment is needed. After that, by analyzing the real-time data collection of smart wearable devices, the timeliness of the data currently used for analysis can be analyzed, so as to determine whether the current data can truly reflect the status of the elderly, and finally, by combining the two sets of data, the appointment priority of the elderly user can be dynamically adjusted, thereby reducing the clinical risk of the elderly.

[0023] The determination process of the state determination module includes: The heart rate data of the elderly users is monitored in real time at fixed time intervals through smart wearable devices, and the collected heart rate data is defined as ; Where a is any data collection at a fixed time interval. That is, the heart rate of the smart wearable device during the ath data collection; The heart rate of the smart wearable device during the ath data collection is compared with the preset heart rate threshold Make a comparison; like ,The system determines that the elderly user’s heart rate is normal during the data collection, and continuously monitors it through the smart wearable device; like , the system determines that the heart rate of the elderly user is abnormal during the data collection, increases the monitoring frequency of the elderly user's heart rate data to perform abnormal change monitoring operations, and analyzes the change amount of the elderly user's heart rate data; Through the above technical solution, this example compares the heart rate of the smart wearable device during the ath data collection with the preset heart rate threshold By making such a setting, the elderly user's heart rate abnormality can be monitored in real time according to the size of the elderly user's heart rate data, and a preliminary warning can be made when the elderly user's heart rate is judged to be abnormal. By increasing the monitoring frequency of the elderly user's heart rate data to monitor abnormal changes, and analyzing the change in the elderly user's heart rate data, not only can it be analyzed whether the current judgment is a misjudgment, but also the status of the elderly user can be further monitored, thereby providing efficient medical services for the elderly.

[0024] The appointment process of the diagnosis appointment module includes: When it is determined that the heart rate of the elderly user has abnormal changes, the monitoring frequency of the elderly user's heart rate data is increased, and the heart rate change curve is established based on the real-time monitored heart rate data. ; And through the formula Calculate the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation ; Among them, t1 is the first heart rate data collection in the i-th abnormal change monitoring operation, The last heart rate data collection in the i-th abnormal change monitoring operation, is the number of heart rate data collections in the i-th abnormal change monitoring operation, The heart rate of the smart wearable device during the ath data collection in the i-th abnormal change monitoring operation, For all The average value of The preset heart rate fluctuation value for abnormal change monitoring operation, To define a function, if , then let , otherwise, let ; Through the above technical solution, this example provides the change amount of the elderly user's heart rate data in the i-th abnormal change monitoring operation , can be obtained by formula Obtained by calculation, obviously, when the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation is The larger the value is, the more the heart rate data of the elderly user is increasing during the i-th abnormal change monitoring operation, and the trend of the heart rate change is in an upward situation, which means that the elderly may have cardiovascular disease or abnormal heart rate. On the contrary, when the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation is The smaller the value is, the more the user's heart rate data is degrading. If the trend of the heart rate change is in a downward situation, it means that the abnormal heart rate of the elderly is misjudged due to the influence of the external environment. In this case, the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation can be used. The value of the value is used to analyze the physical condition of the elderly user, thereby providing accurate data for subsequent decisions on whether a remote diagnosis appointment is needed, thereby improving the accuracy of the decision results.

[0025] The appointment process of the diagnosis appointment module also includes: By calculating the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation The preset change threshold Make a comparison; like , the system judged that the abnormal changes in the elderly user's heart rate were risky and not a misjudgment, and promptly made a remote appointment diagnosis and adjusted the elderly user's appointment priority; like ,The system judges that there is no risk in the abnormal changes of the elderly user's heart rate, and there is a misjudgment of the abnormal heart rate; Through the above technical solution, this example calculates the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation The preset change threshold Through this comparison method, the change in the heart rate data of the elderly user in the monitoring operation can be detected according to the i-th abnormal change. The value of the value is used to analyze the physical condition of the elderly user, thereby eliminating misjudgment due to the influence of the external environment, and then providing accurate data for subsequent decisions on whether a remote diagnosis appointment is needed, thereby improving the accuracy of the decision results.

[0026] The analysis process of the aging analysis module includes: By formula Calculate the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation ; in, is the total number of historical data collection times of smart wearable devices before the i-th abnormal change monitoring operation, Any historical data collection for smart wearable devices, The first smart wearable device in history The response time of the data collection, For all The average value of is the preset response time, is the data transmission time of the ath data collection of the smart wearable device in the i-th abnormal change monitoring operation, is the preset data transmission time, for The standard value can be selected and set according to the allowable error in the empirical data. is the data processing time of the ath data collection of the smart wearable device in the i-th abnormal change monitoring operation, is the preset data processing time, is the network speed of the smart wearable device during the a-th data collection in the i-th abnormal change monitoring operation, For all The average value of Through the above technical solution, this example provides the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation , can be obtained by formula Calculated, where the formula The data collection response time fluctuation value of the smart wearable device in historical work can be calculated using the formula The network speed fluctuation value in the i-th abnormal change monitoring operation can be calculated. Therefore, it is obvious that when the data collection response time fluctuation value of the smart wearable device in the historical work, the network speed fluctuation value in the i-th abnormal change monitoring operation and the average value of the response time of the historical data collection of the smart wearable device are larger, and the data transmission time and data processing time of the a-th data collection of the smart wearable device in the i-th abnormal change monitoring operation are longer, then the timeliness influence coefficient of the smart wearable device in the i-th abnormal change monitoring operation is The larger the value is, the more likely it is that in the i-th abnormal change monitoring operation, the data of the smart wearable device is delayed and cannot capture the real-time status data of the elderly user in time; On the contrary, when the fluctuation value of the data collection response time of the smart wearable device in the historical work, the network speed fluctuation value in the i-th abnormal change monitoring operation and the average value of the response time of the historical data collection of the smart wearable device are smaller, and the data transmission time and data processing time of the a-th data collection of the smart wearable device in the i-th abnormal change monitoring operation are shorter, then the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation is The smaller it is, it means that in the i-th abnormal change monitoring operation, the data of the smart wearable device has no delay or low delay, and the real-time status data of the elderly user can be captured.

[0027] The analysis process of the aging analysis module also includes: By calculating the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation The preset time-effect coefficient threshold Make a comparison; like ,The system determines that the data collected by the smart wearable device in the ith abnormal change monitoring operation is not real-time enough, there is data delay, and the reservation priority needs to be adjusted; like ,The system determines that the data collected by the smart wearable device in the i-th abnormal change monitoring operation is of good real-time performance and there is no data delay, and the appointment diagnosis can be performed according to the current appointment priority; Through the above technical solution, this example calculates the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation The preset time-effect coefficient threshold Through this comparison method, an accurate judgment can be made on whether there is a data delay in the i-th abnormal change monitoring operation. When the real-time performance of the data collected by the smart wearable device is low, it means that the data cannot reflect the true status of the elderly at the current time point. In order to avoid delays in diagnosis of the disease, it is necessary to adjust the appointment priority to reduce the possibility of risks, thereby avoiding the failure to discover potential risks in a timely manner.

[0028] The adjustment process of the priority adjustment module includes: By formula Calculate the change in the corrected heart rate data of the elderly user in the i-th abnormal change monitoring operation; in, is an adjustment coefficient comparison table function, wherein the adjustment coefficient comparison table function The value of The values ​​of are in one-to-one correspondence. Specifically, The value of can be determined based on empirical data. The influence of the range of values ​​on the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation is obtained based on the test data; Through the above technical solution, this example provides the corrected change in the elderly user's heart rate data in the i-th abnormal change monitoring operation, which can be obtained by the formula Calculated, through the above technical solution, this example combines the timeliness influence coefficient of the smart wearable device in the i-th abnormal change monitoring operation The change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation By making corrections, the real heart rate dynamics and heart rate change trends can be restored, and subtle changes in heart rate can be captured more sensitively, thereby truly reflecting the patient's heart function status. The physical condition of the elderly user can be analyzed based on the real heart function status, and the priority of the elderly user's appointment diagnosis can be adjusted according to the real physical condition, thereby avoiding delays in diagnosis and treatment.

[0029] The adjustment process of the priority adjustment module includes: By adjusting the reservation priority of the elderly user according to the change data of the elderly user's heart rate data corrected in the i-th abnormal change monitoring operation and combining the pre-set priority judgment criteria; Through the above technical scheme, this example provides an adjustment process of the priority adjustment module, which specifically includes adjusting the appointment priority of the elderly user according to the change data of the elderly user's heart rate data corrected in the i-th abnormal change monitoring operation, combined with the pre-set priority judgment criteria. Among them, since the change data of the elderly user's heart rate data corrected in the i-th abnormal change monitoring operation is obtained through diversified data correction, its accuracy is relatively high and can truly reflect the heart rate dynamics and heart rate change trends of the elderly user. Then, adjusting the appointment diagnosis priority based on this data can enable patients to get the doctor's attention as soon as possible, allowing such patients to communicate with the doctor faster, which can not only relieve their anxiety, but also give priority to patients with urgent abnormal heart rate, allowing doctors to quickly assess the condition and formulate emergency treatment plans, such as guiding patients to take self-help measures or arrange offline medical treatment as soon as possible to avoid worsening of the condition and speed up the process of medical decision-making and treatment.

[0030] 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 database-based intelligent elderly care remote appointment diagnosis system, characterized in that: The system comprises: The data recording module is used to record the operating status data of the smart wearable device during historical use and store it in the database; The status determination module is used to analyze the status of the elderly user in real time by combining the heart rate data of the elderly user collected in real time by the smart wearable device; The diagnosis appointment module is used to increase the monitoring frequency of the elderly user's heart rate data when it is determined that the elderly user's status is abnormal, and to decide whether a diagnosis appointment is needed based on the change in the elderly user's heart rate data; The timeliness analysis module is used to analyze the real-time data collection of the smart wearable device by combining the real-time operation data and historical operation status data of the smart wearable device in the database when it is determined that a diagnosis appointment is needed; The priority adjustment module is used to adjust the appointment priority of the elderly users by combining the change data of the elderly users' heart rate data with the real-time analysis data of the smart wearable devices.

2. According to claim 1, a database-based intelligent elderly care remote appointment diagnosis system is characterized in that: The determination process of the state determination module includes: The heart rate data of the elderly users is monitored in real time at fixed time intervals through smart wearable devices, and the collected heart rate data is defined as ; Where a is any data collection at a fixed time interval. That is, the heart rate of the smart wearable device during the ath data collection; The heart rate of the smart wearable device during the ath data collection is compared with the preset heart rate threshold Make a comparison; like ,The system determines that the elderly user’s heart rate is normal during the data collection, and continuously monitors it through the smart wearable device; like The system determines that the heart rate of the elderly user is abnormal during the data collection, increases the monitoring frequency of the elderly user's heart rate data, performs abnormal change monitoring operations, and analyzes the change amount of the elderly user's heart rate data.

3. According to claim 2, a database-based intelligent elderly care remote appointment diagnosis system is characterized in that: The appointment process of the diagnosis appointment module includes: When it is determined that the heart rate of the elderly user has abnormal changes, the monitoring frequency of the elderly user's heart rate data is increased, and the heart rate change curve is established based on the real-time monitored heart rate data. ; And through the formula Calculate the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation ; Among them, t1 is the first heart rate data collection in the i-th abnormal change monitoring operation, The last heart rate data collection in the i-th abnormal change monitoring operation, is the number of heart rate data collections in the i-th abnormal change monitoring operation, The heart rate of the smart wearable device during the ath data collection in the i-th abnormal change monitoring operation, For all The average value of The preset heart rate fluctuation value for abnormal change monitoring operation, To define a function, if , then let , otherwise, let .

4. According to the database-based intelligent elderly care remote appointment diagnosis system of claim 3, it is characterized in that: The appointment process of the diagnosis appointment module also includes: By calculating the change in the heart rate data of the elderly user in the i-th abnormal change monitoring operation The preset change threshold Make a comparison; like , the system judged that the abnormal changes in the elderly user's heart rate were risky and not a misjudgment, and promptly made a remote appointment for diagnosis and adjusted the elderly user's appointment priority; like ,The system judges that there is no risk in the abnormal changes of the elderly user's heart rate, and there is a misjudgment of the abnormal heart rate.

5. According to claim 4, a database-based intelligent elderly care remote appointment diagnosis system is characterized in that: The analysis process of the aging analysis module includes: By formula Calculate the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation ; in, is the total number of historical data collection times of smart wearable devices before the i-th abnormal change monitoring operation, Any historical data collection for smart wearable devices, The first smart wearable device in history The response time of the data collection, For all The average value of is the preset response time, is the data transmission time of the ath data collection of the smart wearable device in the i-th abnormal change monitoring operation, is the preset data transmission time, for The standard value of is the data processing time of the ath data collection of the smart wearable device in the i-th abnormal change monitoring operation, is the preset data processing time, is the network speed of the smart wearable device during the a-th data collection in the i-th abnormal change monitoring operation, For all The average value of .

6. According to claim 5, a database-based intelligent elderly care remote appointment diagnosis system is characterized in that: The analysis process of the aging analysis module also includes: By calculating the timeliness impact coefficient of the smart wearable device in the i-th abnormal change monitoring operation The preset time-effect coefficient threshold Make a comparison; like ,The system determines that the data collected by the smart wearable device in the ith abnormal change monitoring operation is not real-time enough, there is data delay, and the reservation priority needs to be adjusted; like ,The system determines that the data collected by the smart wearable device in the ith abnormal change monitoring operation is in good real-time and there is no data delay, and the appointment diagnosis can be carried out according to the current appointment priority.

7. The database-based intelligent elderly care remote appointment diagnosis system according to claim 6 is characterized in that: The adjustment process of the priority adjustment module includes: By formula Calculate the change in the corrected heart rate data of the elderly user in the i-th abnormal change monitoring operation; in, is an adjustment coefficient comparison table function, wherein the adjustment coefficient comparison table function The value of The values ​​of are in one-to-one correspondence.

8. The database-based intelligent elderly care remote appointment diagnosis system according to claim 7 is characterized in that: The adjustment process of the priority adjustment module includes: The reservation priority of the elderly user is adjusted based on the change data of the elderly user's heart rate data corrected in the i-th abnormal change monitoring operation and the pre-set priority judgment criteria.

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