Evaluation method and system of child active health system, electronic equipment and medium

Abstract

The invention provides an evaluation method and system of a child active health system, electronic equipment and a medium. The method comprises the following steps: acquiring current health assessment data of a target child through a physiological sensor cluster, and sending the current health assessment data to an active health assessment platform, so that the active health assessment platform acquires a historical health assessment data sequence of the target child according to identity information; the method comprises the following steps: acquiring current health assessment data, determining a prediction feature value by using a child active health assessment model, determining a data state of the current health assessment data according to the prediction feature value and the current health assessment data, and when the data state is a to-be-confirmed data state, sending a health assessment data to-be-confirmed instruction to at least one monitoring terminal in a monitoring terminal cluster, according to the invention, abnormal conditions can be found in time, and monitoring terminals of related medical personnel are notified, so that the abnormal conditions of the health assessment data can be confirmed in time, and the efficiency and accuracy of child health management can be improved.

Description

Technical Field

[0001] The present application relates to data processing technology, and more particularly to an evaluation method, system, electronic device and medium for a child's active health system. Background Art

[0002] In the existing child health assessment system, the assessment of health status mainly relies on regular physical examinations and manually recorded data. This approach has many shortcomings: first, the frequency of data collection is low, making it difficult to capture real-time changes in children's health status; second, data analysis methods are limited, making it difficult to extract valuable health trend information from large amounts of data; third, there is a lack of automated and intelligent assessment tools, and the assessment process is cumbersome and prone to errors. In addition, medical staff often need to discover children's health problems after the fact, making it difficult to intervene and deal with them in a timely manner. Summary of the invention

[0003] The present application provides an evaluation method, system, electronic device and medium for a children's active health system, which is used to promptly detect abnormal conditions and notify the monitoring terminals of relevant medical staff, thereby promptly confirming abnormalities in health assessment data, which helps to improve the efficiency and accuracy of children's health management.

[0004] In a first aspect, the present application provides an evaluation method for a child's active health system, which is applied to an evaluation system for a child's active health system, wherein the evaluation system for the child's active health system comprises a physiological sensor cluster, an active health evaluation platform, and a monitoring terminal cluster, wherein each physiological sensor in the physiological sensor cluster and each monitoring terminal in the monitoring terminal cluster are respectively connected to the active health evaluation platform in communication; the method comprises: Acquire current health assessment data of the target child through the physiological sensor cluster, and send the current health assessment data to the active health assessment platform, wherein the current health assessment data includes identity information of the target child and heart rate data within a preset monitoring period; The active health assessment platform obtains a historical health assessment data sequence of the target child according to the identity information; The active health assessment platform uses a preset children's active health assessment model and determines a prediction feature value based on the historical health assessment data sequence, wherein the prediction feature value includes a first prediction feature value and a second prediction feature value, wherein the first prediction feature value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second prediction feature value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence; The active health assessment platform determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster, wherein the health assessment data to-be-confirmed instruction includes the current health assessment data, the historical health assessment data sequence, and the predicted characteristic value.

[0005] In the above scheme, the current health assessment data of the target child is obtained through a physiological sensor cluster, and the current health assessment data is sent to an active health assessment platform, so that the active health assessment platform obtains the historical health assessment data sequence of the target child according to the identity information, and uses a preset child active health assessment model to determine the predicted characteristic value according to the historical health assessment data sequence, and then determines the data status of the current health assessment data according to the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster, so as to realize timely detection of abnormal situations, and notify the monitoring terminals of relevant medical staff, so as to perform timely abnormal confirmation of the health assessment data, which helps to improve the efficiency and accuracy of children's health management.

[0006] In addition, children's health data is obtained in real time through a physiological sensor cluster to ensure the timeliness and accuracy of the data. The active health assessment platform uses historical health data to provide a basis for prediction and improve the accuracy of predictions. Through the preset children's active health assessment model, future health assessment data and its rate of change are predicted to provide a basis for health intervention. Then, abnormal data is processed for confirmation to ensure the reliability and accuracy of the data and avoid misjudgment, so that abnormal situations can be discovered in time and relevant personnel can be notified to take corresponding measures.

[0007] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, including: The active health assessment platform determines a first estimate value based on the historical health assessment data sequence; The active health assessment platform determines a second estimated value based on the historical health assessment data sequence and the first estimated value; The active health assessment platform determines a third estimated value based on the historical health assessment data sequence and the second estimated value; The active health assessment platform determines a fourth estimated value based on the historical health assessment data sequence and the third estimated value; The active health assessment platform determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value.

[0008] In the above scheme, the final prediction characteristic value is determined by iterative calculation of multiple estimated values, and the true value is approached by using multiple stages of step-by-step estimation (first estimated value to fourth estimated value) to reduce errors, thereby more accurately reflecting the trend of children's health data and improving prediction accuracy.

[0009] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, including: The active health assessment platform uses formula 1 and according to the historical health assessment data sequence Determine the first prediction feature value, where Formula 1 is: in, is the first prediction feature value, are respectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient; Based on The first estimate of Based on and The second estimate of Based on and The third estimate of Based on and The fourth estimate of .

[0010] In the above scheme, the calculation method of the first prediction characteristic value is clarified, making the prediction process more standardized and reproducible. Through the above formula 1, using a specific weight coefficient combined with historical data for prediction can improve the accuracy and reliability of the prediction.

[0011] In the above formula 1, by combining multiple estimates and their corresponding weight coefficients, the impact of multiple factors on health assessment data is comprehensively considered, thus achieving a comprehensive and integrated consideration of historical health assessment data. This multi-factor comprehensive approach can improve the accuracy and robustness of predictions and reduce the deviations or errors that may be caused by a single factor. The weight coefficients in the formula are adjustable, which means that the weights can be optimized according to actual conditions or data characteristics to better adapt to different prediction scenarios and needs. The adjustability of the weight coefficients increases the flexibility and adaptability of the model. In addition, with the addition of new health assessment data, the weight coefficients can also be updated through a certain mechanism to ensure that the model can adapt to changes in data and maintain a high level of prediction accuracy.

[0012] Optionally, before the active health assessment platform determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, the further comprising: The active health assessment platform determines a maximum health assessment value and a median health assessment value based on the historical health assessment data sequence, and the maximum health assessment value and the median health assessment value are used to input into a preset estimation characteristic function to determine the first estimation value, the second estimation value, the third estimation value and the fourth estimation value.

[0013] In the above scheme, by determining the maximum and median values ​​of historical data as input, the model can consider the characteristics of historical data more comprehensively, which helps the model to capture the changing patterns of data more accurately, enhance the robustness of the model and make it unaffected by outliers, and improve prediction accuracy.

[0014] Optionally, in determining the first prediction feature value according to the historical health assessment data sequence Previously, it also included: The active health assessment platform uses formula 2 and determines the first estimated value based on the historical health assessment data sequence The second estimated value , the third estimated value and the fourth estimated value , wherein the formula 2 is: in, is the maximum health assessment value in the historical health assessment data sequence, is the median health assessment value in the historical health assessment data sequence, is the preset offset value, is the preset monitoring time step, The historical health assessment data sequence is Monitoring time nodes, To estimate the characteristic function.

[0015] In the above scheme, by specifically calculating each estimated value, it can be ensured that the prediction results of the model are more accurate and reliable. In addition, in the above formula 2, full use is made of the statistical information (such as maximum and median values) in the historical health assessment data series. This information can reflect the overall distribution and changing trend of the data and provide an important reference for prediction. Then, by introducing the estimated characteristic function, different function forms can be designed according to specific needs and data characteristics to calculate the estimated value. This flexibility enables the model to adapt to different prediction scenarios and data characteristics. Therefore, by comprehensively considering multiple aspects of historical data (such as maximum value, median value, etc.) and combining the estimated characteristic function for detailed calculation, it is helpful to improve the accuracy and reliability of the predicted characteristic value, thereby improving the performance of the entire health assessment system.

[0016] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and further includes: The active health assessment determines the second predicted characteristic value according to the historical health assessment data sequence and the preset estimated characteristic function.

[0017] In the above scheme, by calculating the second predictive characteristic value (predicted rate of change of health assessment data), more dimensional information is provided for health assessment. Predicting the rate of change of health assessment data helps to detect potential health problems in advance and provide guidance for health intervention.

[0018] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and further includes: The active health assessment platform uses formula 3 and according to the historical health assessment data sequence Determine the second prediction characteristic value, the formula 3 is: in, is the second predicted feature value.

[0019] In the above scheme, the rate of change of health assessment data can be predicted by formula 3, which is crucial for evaluating the changing trend of children's health status and conducting early intervention. By understanding the rate of change of health assessment data, doctors, parents or guardians can more quickly identify potential health problems or signs of improvement, so as to make more timely decisions. Then, taking the rate of change of health assessment data into consideration can make health assessment more comprehensive and accurate, help the system to detect abnormal change trends in time, and promptly notify guardians or medical staff to take necessary measures. It not only helps to detect the changing trend of children's health status in time, but also supports more accurate risk assessment and ensures more effective health management of children.

[0020] Optionally, the active health assessment platform determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform determines the change rate of the current health assessment data according to the current health assessment data and the historical health assessment data sequence; The active health assessment platform determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform determines that the first deviation characteristic value is greater than a preset first deviation threshold or the second deviation characteristic value is greater than a preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

[0021] In the above scheme, by comparing the deviation between the predicted value and the actual value, it can be determined whether the current data needs further confirmation to verify the reliability of the current health assessment data, so as to perform confirmation processing on the abnormal data to avoid misjudgment and misoperation.

[0022] Optionally, the active health assessment platform determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform uses Formula 4 and determines the current health assessment data change rate according to the current health assessment data and the historical health assessment data sequence. Formula 4 is: in, The rate of change of the current health assessment data; The active health assessment platform determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform determines that the first deviation characteristic value is greater than a preset first deviation threshold or the second deviation characteristic value is greater than a preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

[0023] In the above scheme, the change rate of the current health assessment data is calculated to provide a basis for determining the data state, and then the state of the current health assessment data is more accurately judged by combining the first deviation characteristic value and the second deviation characteristic value.

[0024] Optionally, after at least one monitoring terminal in the monitoring terminal cluster sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction; The active health assessment platform updates the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient according to the updated health assessment data sequence to generate a first updated weight coefficient, a second updated weight coefficient, a third updated weight coefficient and a fourth updated weight coefficient, wherein the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are used to determine the first predicted characteristic value, and the first updated weight coefficient, the second updated weight coefficient, the third updated weight coefficient and the fourth updated weight coefficient are used to determine the predicted characteristic value of the next time node.

[0025] In the above scheme, the data sequence is updated according to the confirmed health assessment data to ensure the timeliness and accuracy of the data. In addition, by updating the weight coefficient, the system can continuously improve itself as new data is added. While improving the accuracy of subsequent predictions, it can also improve the adaptability and long-term stability of the prediction model and achieve continuous optimization of the model.

[0026] Optionally, after at least one monitoring terminal in the monitoring terminal cluster sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction. ; The active health assessment platform uses formula 5 and updates the health assessment data sequence according to the The first weight coefficient The second weight coefficient , the third weight coefficient and the fourth weight coefficient Update to generate the first updated weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth update weight coefficient , wherein the first update weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth updated weight coefficient For determining the predicted characteristic value of the next time node, the formula 5 is: in, is the preset adjustment factor.

[0027] In the above scheme, Formula 5 allows the weight coefficient to be dynamically adjusted according to the latest feedback of the new health assessment data, thereby improving the adaptability of the prediction model to the new data. This dynamic adaptability is particularly important for dealing with scenarios such as children's health assessments, which change significantly over time and may be affected by multiple factors. With the continuous accumulation and updating of health assessment data, the prediction model can be gradually optimized through the dynamic adjustment of the weight coefficient by Formula 5, thereby improving the prediction accuracy of future health assessment data. It can be seen that this weight coefficient update mechanism helps to ensure the stability and effectiveness of the model and ensure that the prediction model can adapt to changes in data. In addition, the above weights can be adjusted to adapt to the prediction of health assessment data for different individuals.

[0028] Optionally, the evaluation system of the children's active health system includes a user terminal cluster, each user terminal in the user terminal cluster is communicatively connected to the active health evaluation platform; after at least one monitoring terminal in the monitoring terminal cluster sends a health evaluation data to be confirmed instruction, it also includes: The monitoring terminal sends a health assessment data abnormality instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform sends a health assessment data exception report to the target user terminal corresponding to the identity information in the user terminal cluster in response to the health assessment data exception instruction, and the health assessment data exception report includes the updated health assessment data sequence.

[0029] In the above scheme, by adding the function of user terminal cluster, health abnormality reports can be sent to the corresponding users in a timely manner, thereby ensuring that guardians or relevant responsible persons can quickly understand and deal with children's health problems, thereby improving the responsiveness and practicality of the entire system.

[0030] In a second aspect, the present application provides an evaluation system for a child's active health system, comprising: a physiological sensor cluster, an active health evaluation platform, and a monitoring terminal cluster, wherein each physiological sensor in the physiological sensor cluster and each monitoring terminal in the monitoring terminal cluster are respectively connected to the active health evaluation platform for communication; Acquire current health assessment data of the target child through the physiological sensor cluster, and send the current health assessment data to the active health assessment platform, wherein the current health assessment data includes identity information of the target child and heart rate data within a preset monitoring period; The active health assessment platform obtains a historical health assessment data sequence of the target child according to the identity information; The active health assessment platform uses a preset children's active health assessment model and determines a prediction feature value based on the historical health assessment data sequence, wherein the prediction feature value includes a first prediction feature value and a second prediction feature value, wherein the first prediction feature value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second prediction feature value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence; The active health assessment platform determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data confirmation instruction to at least one monitoring terminal in the monitoring terminal cluster, and the health assessment data confirmation instruction includes the current health assessment data, the historical health assessment data sequence and the predicted characteristic value.

[0031] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, including: The active health assessment platform determines a first estimate value based on the historical health assessment data sequence; The active health assessment platform determines a second estimated value based on the historical health assessment data sequence and the first estimated value; The active health assessment platform determines a third estimated value based on the historical health assessment data sequence and the second estimated value; The active health assessment platform determines a fourth estimated value based on the historical health assessment data sequence and the third estimated value; The active health assessment platform determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value.

[0032] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, including: The active health assessment platform uses formula 1 and according to the historical health assessment data sequence Determine the first prediction feature value, where Formula 1 is: in, is the first prediction feature value, are respectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient; Based on The first estimate of Based on and The second estimate of Based on and The third estimate of Based on and The fourth estimate of .

[0033] Optionally, before the active health assessment platform determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, the further comprising: The active health assessment platform determines a maximum health assessment value and a median health assessment value based on the historical health assessment data sequence, and the maximum health assessment value and the median health assessment value are used to input into a preset estimation characteristic function to determine the first estimation value, the second estimation value, the third estimation value and the fourth estimation value.

[0034] Optionally, before determining the first prediction feature value according to the historical health assessment data sequence, the method further includes: The active health assessment platform uses formula 2 and determines the first estimated value based on the historical health assessment data sequence The second estimated value , the third estimated value and the fourth estimated value , wherein the formula 2 is: in, is the maximum health assessment value in the historical health assessment data sequence, is the median health assessment value in the historical health assessment data sequence, is the preset offset value, is the preset monitoring time step, The historical health assessment data sequence is Monitoring time nodes, To estimate the characteristic function.

[0035] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and further includes: The active health assessment determines the second predicted characteristic value according to the historical health assessment data sequence and the preset estimated characteristic function.

[0036] Optionally, the active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and further includes: The active health assessment platform uses formula 3 and according to the historical health assessment data sequence Determine the second prediction characteristic value, the formula 3 is: in, is the second predicted feature value.

[0037] Optionally, the active health assessment platform determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform determines the change rate of the current health assessment data according to the current health assessment data and the historical health assessment data sequence; The active health assessment platform determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform determines that the first deviation characteristic value is greater than a preset first deviation threshold or the second deviation characteristic value is greater than a preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

[0038] Optionally, the active health assessment platform determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform uses Formula 4 and determines the current health assessment data change rate according to the current health assessment data and the historical health assessment data sequence. Formula 4 is: in, The rate of change of the current health assessment data; The active health assessment platform determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform determines that the first deviation characteristic value is greater than a preset first deviation threshold or the second deviation characteristic value is greater than a preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

[0039] Optionally, after at least one monitoring terminal in the monitoring terminal cluster sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction; The active health assessment platform updates the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient according to the updated health assessment data sequence to generate a first updated weight coefficient, a second updated weight coefficient, a third updated weight coefficient and a fourth updated weight coefficient, wherein the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are used to determine the first predicted characteristic value, and the first updated weight coefficient, the second updated weight coefficient, the third updated weight coefficient and the fourth updated weight coefficient are used to determine the predicted characteristic value of the next time node.

[0040] Optionally, after at least one monitoring terminal in the monitoring terminal cluster sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction. ; The active health assessment platform uses formula 5 and updates the health assessment data sequence according to the The first weight coefficient The second weight coefficient , the third weight coefficient and the fourth weight coefficient Update to generate the first updated weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth update weight coefficient , wherein the first update weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth updated weight coefficient For determining the predicted characteristic value of the next time node, the formula 5 is: in, is the preset adjustment factor.

[0041] Optionally, the evaluation system of the children's active health system includes a user terminal cluster, each user terminal in the user terminal cluster is communicatively connected to the active health evaluation platform; after at least one monitoring terminal in the monitoring terminal cluster sends a health evaluation data to be confirmed instruction, it also includes: The monitoring terminal sends a health assessment data abnormality instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform sends a health assessment data exception report to the target user terminal corresponding to the identity information in the user terminal cluster in response to the health assessment data exception instruction, and the health assessment data exception report includes the updated health assessment data sequence.

[0042] In a third aspect, the present application provides an electronic device, including: processor; and, A memory, configured to store executable instructions of the processor; The processor is configured to perform any possible method described in the first aspect by executing the executable instructions.

[0043] In a fourth aspect, the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, they are used to implement any possible method described in the first aspect.

[0044] The evaluation method, system, electronic device and medium of the children's active health system provided in the present application obtain the current health assessment data of the target child through a physiological sensor cluster, and send the current health assessment data to the active health assessment platform, so that the active health assessment platform obtains the historical health assessment data sequence of the target child according to the identity information, and uses the preset children's active health assessment model to determine the predicted characteristic value according to the historical health assessment data sequence, and then determines the data status of the current health assessment data according to the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster, so as to realize timely detection of abnormal situations, and notify the monitoring terminals of relevant medical staff, so as to perform timely abnormal confirmation of the health assessment data, which helps to improve the efficiency and accuracy of children's health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0046] Figure 1 This is a flowchart of a method for evaluating a child's active health system according to an exemplary embodiment of the present application; Figure 2 This is a flowchart of a method for evaluating a child's active health system according to another exemplary embodiment of the present application; Figure 3 is a structural schematic diagram of an evaluation system for a children's active health system according to an exemplary embodiment of the present application; Figure 4 It is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application.

[0047] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0048] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0049] In order to solve the above problems, the embodiments provided in this application integrate physiological sensor clusters, active health assessment platforms and monitoring terminal clusters to achieve comprehensive collection, intelligent analysis and timely feedback of children's health data, thereby effectively improving the level and efficiency of children's health management.

[0050] In order to achieve the above objectives, the technical solution conceived by the present invention mainly includes the following aspects: Data Acquisition System: A physiological sensor cluster, including heart rate sensors, body temperature sensors and other types of sensors, is used to collect the current health assessment data of the target child in real time, including identity information and physiological indicators such as heart rate within the preset monitoring time. These sensors communicate with each monitoring terminal in the monitoring terminal cluster to ensure timely upload and storage of data.

[0051] Data processing and analysis platform: As the core of data processing and analysis, the active health assessment platform first retrieves the historical health assessment data sequence from the historical database based on the identity information of the target child. Then, the historical data is analyzed and modeled using the preset child active health assessment model to determine the predicted characteristic values. These predicted characteristic values ​​include the first predicted characteristic value (characterizing the predicted health assessment data based on historical data) and the second predicted characteristic value (characterizing the change rate of the predicted health assessment data based on historical data).

[0052] Anomaly detection and feedback mechanism: The active health assessment platform compares and analyzes the predicted characteristic values ​​with the current health assessment data collected in real time, and determines whether the current data is abnormal by calculating indicators such as deviation characteristic values ​​and change rates. Once abnormal data is found (i.e., the data status is pending confirmation), the platform will immediately send a health assessment data pending confirmation instruction to at least one monitoring terminal in the monitoring terminal cluster, and provide current data, historical data sequences and predicted characteristic values ​​as reference information. After receiving the instruction, the monitoring terminal can further verify the data and send a confirmation or abnormal instruction to the platform.

[0053] Model optimization and adaptive adjustment: After the data is confirmed, the platform will generate an updated health assessment data sequence based on the historical health assessment data sequence and the current health assessment data. At the same time, the weight coefficients in the model are dynamically adjusted using a specific update formula to adapt to the changing trend of the data and ensure the accuracy and stability of the prediction model. In addition, the system also supports the access of user terminal clusters, so that abnormal reports of health assessment data can be sent to target users in real time, realizing remote monitoring and timely intervention.

[0054] By implementing the technical solution of this application, the following significant technical effects can be achieved: Real-time monitoring and accurate assessment: Real-time monitoring and accurate assessment of children's health data can be achieved to promptly identify potential health problems and provide strong support for medical intervention.

[0055] Improve management efficiency: Automated and intelligent data processing and analysis processes have significantly improved the efficiency of child health management and reduced the workload of medical staff.

[0056] Enhanced data reliability: Through anomaly detection and feedback mechanisms, the reliability of health assessment data is effectively improved, avoiding misjudgments and misoperations.

[0057] Model adaptive optimization: Dynamically adjust the weight coefficient of the prediction model according to the new health assessment data, achieving adaptive optimization and long-term stability of the model.

[0058] In summary, the inventive concept of this application is to provide a comprehensive, efficient and accurate evaluation method and system for children's active health system, which is of great significance for improving the level of children's health management.

[0059] Figure 1 FIG. 1 is a flow chart of a method for evaluating a child's active health system according to an exemplary embodiment of the present application. Figure 1 As shown, the evaluation method of the children's active health system provided in this embodiment includes: S101. Acquire current health assessment data of the target child through a physiological sensor cluster, and send the current health assessment data to an active health assessment platform.

[0060] The method provided in this embodiment can be applied to the evaluation system of the active health system of children. The evaluation system of the active health system of children includes a physiological sensor cluster, an active health evaluation platform and a monitoring terminal cluster. Each physiological sensor in the physiological sensor cluster and each monitoring terminal in the monitoring terminal cluster are respectively connected to the active health evaluation platform in communication. These physiological sensors include but are not limited to heart rate sensors, body temperature sensors, etc., which can monitor and record key physiological indicators such as heart rate and body temperature of children in real time. The collected data includes the identity information of the target child (such as name, age, gender, etc.) and heart rate data within the preset monitoring time. These data are sent to the active health evaluation platform via wireless communication to ensure the real-time and accuracy of the data.

[0061] In this step, the current health assessment data of the target child is obtained through the physiological sensor cluster and sent to the active health assessment platform. The current health assessment data includes the identity information of the target child and the heart rate data within the preset monitoring time.

[0062] Specifically, each sensor in the physiological sensor cluster has a built-in wireless communication module that can automatically establish a connection with a specific monitoring terminal in the monitoring terminal cluster. The monitoring terminal acts as a transfer station for data transmission, forwarding the collected sensor data to the active health assessment platform through a more stable network connection (such as Wi-Fi, 4G / 5G, etc.). In this process, the security and integrity of data transmission are guaranteed by encryption technology and verification mechanisms.

[0063] S102. The active health assessment platform obtains the historical health assessment data sequence of the target child based on the identity information.

[0064] In this step, after the active health assessment platform receives the current health assessment data, it first retrieves the corresponding historical health assessment data sequence from the database based on the identity information contained in the data (such as the child's ID number). These historical data record the changes in the child's physiological indicators at different time points, providing a basis for subsequent data analysis and prediction.

[0065] The active health assessment platform has an efficient data retrieval mechanism that can quickly locate and extract the historical health records of the target children. At the same time, the platform also has data cleaning and preprocessing functions that can automatically identify and remove outliers or missing data to ensure the quality of data used for analysis.

[0066] S103. The active health assessment platform uses a preset children's active health assessment model and determines the predicted characteristic value based on the historical health assessment data sequence.

[0067] In this step, the active health assessment platform uses a preset children's active health assessment model and determines a predicted characteristic value based on a historical health assessment data sequence. The predicted characteristic value includes a first predicted characteristic value and a second predicted characteristic value. The first predicted characteristic value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second predicted characteristic value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence.

[0068] Optionally, the above-mentioned preset child active health assessment model can be constructed by using dynamic differential equations to form the child digital twin health profile.

[0069] Specifically, the preset children's active health assessment model can be used, and the active health assessment platform can perform data analysis and prediction based on the retrieved historical health assessment data sequence. The model uses complex algorithms and machine learning techniques to identify the patterns and trends in the data and then determine the predicted characteristic values. These predicted characteristic values ​​include the first predicted characteristic value (characterizing the predicted health assessment data based on historical data) and the second predicted characteristic value (characterizing the change rate of the predicted health assessment data based on historical data).

[0070] In terms of specific implementation, the model first analyzes and models historical data, extracts key features and builds a prediction model. Then, the model calculates the first prediction feature value and the second prediction feature value based on the latest historical data input. These prediction values ​​not only reflect the current and future health status expectations of children, but also provide important information on the trend of health status changes.

[0071] In one possible implementation, the active health assessment platform may determine a first estimate based on a historical health assessment data sequence; the active health assessment platform may determine a second estimate based on the historical health assessment data sequence and the first estimate; the active health assessment platform may determine a third estimate based on the historical health assessment data sequence and the second estimate; the active health assessment platform may determine a fourth estimate based on the historical health assessment data sequence and the third estimate; the active health assessment platform may determine a first predictive feature value based on the first estimate, the second estimate, the third estimate and the fourth estimate.

[0072] Furthermore, before the active health assessment platform determines the first prediction characteristic value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, the following steps may also be included: The active health assessment platform determines the maximum health assessment value and the median health assessment value based on the historical health assessment data sequence. The maximum health assessment value and the median health assessment value are used to input into a preset estimation characteristic function to determine a first estimation value, a second estimation value, a third estimation value and a fourth estimation value.

[0073] The final prediction characteristic value is determined through iterative calculation of multiple estimated values, and the true value is approached by using multiple stages of stepwise estimation (first estimated value to fourth estimated value), thereby reducing errors, thereby more accurately reflecting the trend of children's health data and improving prediction accuracy.

[0074] In addition, active health assessment can also determine the second predicted characteristic value based on the historical health assessment data sequence and the preset estimated characteristic function. Thus, the second predicted characteristic value (predicted health assessment data change rate) provides more dimensional information for health assessment. Predicted health assessment data change rate helps to discover potential health problems in advance and provide guidance for health intervention.

[0075] S104. The active health assessment platform determines the data status of the current health assessment data according to the predicted characteristic value and the current health assessment data.

[0076] In this step, the active health assessment platform determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data state to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster. The health assessment data to-be-confirmed instruction includes the current health assessment data, the historical health assessment data sequence, and the predicted characteristic value.

[0077] After obtaining the predicted characteristic value, the active health assessment platform compares and analyzes it with the current health assessment data collected in real time. By calculating indicators such as deviation characteristic value and change rate, the platform can determine whether the current data is abnormal. If the data status is determined to be pending confirmation (that is, there is a significant deviation or abnormal trend), the platform will immediately send a health assessment data pending confirmation instruction to at least one monitoring terminal in the monitoring terminal cluster. The instruction contains key information such as current health assessment data, historical health assessment data sequence, and predicted characteristic value, so that the operator of the monitoring terminal can quickly understand the data background and abnormal situation. After receiving the instruction, the monitoring terminal will immediately start the data verification process and confirm the authenticity and accuracy of the data through communication with medical staff. Once the data is confirmed or new problems are found, the monitoring terminal will send corresponding feedback instructions to the active health assessment platform.

[0078] In one possible implementation, the active health assessment platform determines a first deviation characteristic value based on a first predicted characteristic value and current health assessment data; the active health assessment platform determines the rate of change of current health assessment data based on current health assessment data and a historical health assessment data sequence; the active health assessment platform determines a second deviation characteristic value based on a second predicted characteristic value and the rate of change of current health assessment data; if the active health assessment platform determines that the first deviation characteristic value is greater than a preset first deviation threshold or the second deviation characteristic value is greater than a preset second deviation threshold, then the data state of the current health assessment data is determined to be a data state to be confirmed.

[0079] In this embodiment, the current health assessment data of the target child is obtained through a physiological sensor cluster, and the current health assessment data is sent to an active health assessment platform, so that the active health assessment platform obtains the historical health assessment data sequence of the target child according to the identity information, and uses a preset child active health assessment model to determine the predicted characteristic value according to the historical health assessment data sequence, and then determines the data status of the current health assessment data according to the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster, so as to realize timely detection of abnormal situations, and notify the monitoring terminals of relevant medical staff, so as to perform timely abnormal confirmation of the health assessment data, which helps to improve the efficiency and accuracy of children's health management.

[0080] Figure 2 FIG. 1 is a flow chart of an evaluation method for a child's active health system according to another exemplary embodiment of the present application. Figure 2 As shown, the method provided in this embodiment includes: S201. Acquire current health assessment data of the target child through a physiological sensor cluster, and send the current health assessment data to an active health assessment platform.

[0081] In this step, the current health assessment data of the target child is obtained through the physiological sensor cluster and sent to the active health assessment platform. The current health assessment data includes the identity information of the target child and the heart rate data within the preset monitoring time.

[0082] Specifically, each sensor in the physiological sensor cluster has a built-in wireless communication module that can automatically establish a connection with a specific monitoring terminal in the monitoring terminal cluster. The monitoring terminal acts as a transfer station for data transmission, forwarding the collected sensor data to the active health assessment platform through a more stable network connection (such as Wi-Fi, 4G / 5G, etc.). In this process, the security and integrity of data transmission are guaranteed by encryption technology and verification mechanisms.

[0083] S202. The active health assessment platform obtains the historical health assessment data sequence of the target child based on the identity information.

[0084] In this step, after the active health assessment platform receives the current health assessment data, it first retrieves the corresponding historical health assessment data sequence from the database based on the identity information contained in the data (such as the child's ID number). These historical data record the changes in the child's physiological indicators at different time points, providing a basis for subsequent data analysis and prediction.

[0085] The active health assessment platform has an efficient data retrieval mechanism that can quickly locate and extract the historical health records of the target children. At the same time, the platform also has data cleaning and preprocessing functions that can automatically identify and remove outliers or missing data to ensure the quality of data used for analysis.

[0086] S203. The active health assessment platform uses a preset children's active health assessment model and determines a prediction feature value based on a historical health assessment data sequence.

[0087] In this step, the active health assessment platform uses a preset children's active health assessment model and determines a predicted characteristic value based on a historical health assessment data sequence. The predicted characteristic value includes a first predicted characteristic value and a second predicted characteristic value. The first predicted characteristic value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second predicted characteristic value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence.

[0088] In one possible implementation, the active health assessment platform uses Formula 1 and based on the historical health assessment data series Determine the first prediction feature value, where Formula 1 is: in, is the first predicted feature value, are respectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient; Based on The first estimate of Based on and The second estimate of Based on and The third estimate of Based on and The fourth estimate of .

[0089] In the above formula 1, by combining multiple estimates and their corresponding weight coefficients, the impact of multiple factors on health assessment data is comprehensively considered, thus achieving a comprehensive and integrated consideration of historical health assessment data. This multi-factor comprehensive approach can improve the accuracy and robustness of predictions and reduce the deviations or errors that may be caused by a single factor. The weight coefficients in the formula are adjustable, which means that the weights can be optimized according to actual conditions or data characteristics to better adapt to different prediction scenarios and needs. The adjustability of the weight coefficients increases the flexibility and adaptability of the model. In addition, with the addition of new health assessment data, the weight coefficients can also be updated through a certain mechanism to ensure that the model can adapt to changes in data and maintain a high level of prediction accuracy.

[0090] Determine the first prediction feature value based on the historical health assessment data series Previously, it also included: The active health assessment platform uses formula 2 and determines the first estimate based on the historical health assessment data series , the second estimate , the third estimate and the fourth estimate , where Formula 2 is: in, is the maximum value of health assessment in the historical health assessment data series, is the median health assessment value in the historical health assessment data series, is the preset offset value, is the preset monitoring time step, The historical health assessment data series Monitoring time nodes, To estimate the characteristic function.

[0091] In the above scheme, by specifically calculating each estimated value, it can be ensured that the prediction results of the model are more accurate and reliable. In addition, in the above formula 2, full use is made of the statistical information (such as maximum and median values) in the historical health assessment data series. This information can reflect the overall distribution and changing trend of the data and provide an important reference for prediction. Then, by introducing the estimated characteristic function, different function forms can be designed according to specific needs and data characteristics to calculate the estimated value. This flexibility enables the model to adapt to different prediction scenarios and data characteristics. Therefore, by comprehensively considering multiple aspects of historical data (such as maximum value, median value, etc.) and combining the estimated characteristic function for detailed calculation, it is helpful to improve the accuracy and reliability of the predicted characteristic value, thereby improving the performance of the entire health assessment system.

[0092] In addition, the active health assessment platform uses a preset children's active health assessment model and determines the predictive feature value based on the historical health assessment data series, and also includes: The active health assessment platform uses formula 3 and based on the historical health assessment data series Determine the second prediction feature value, formula 3 is: in, is the second predicted feature value.

[0093] In the above scheme, the rate of change of health assessment data can be predicted by formula 3, which is crucial for evaluating the changing trend of children's health status and conducting early intervention. By understanding the rate of change of health assessment data, doctors, parents or guardians can more quickly identify potential health problems or signs of improvement, so as to make more timely decisions. Then, taking the rate of change of health assessment data into consideration can make health assessment more comprehensive and accurate, help the system to detect abnormal change trends in time, and promptly notify guardians or medical staff to take necessary measures. It not only helps to detect the changing trend of children's health status in time, but also supports more accurate risk assessment and ensures more effective health management of children.

[0094] S204: The active health assessment platform determines the data status of the current health assessment data according to the predicted characteristic value and the current health assessment data.

[0095] In this step, the active health assessment platform determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data state to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster. The health assessment data to-be-confirmed instruction includes the current health assessment data, the historical health assessment data sequence, and the predicted characteristic value.

[0096] In one possible implementation, the active health assessment platform determines a first deviation characteristic value based on the first predicted characteristic value and the current health assessment data; The active health assessment platform uses formula 4.1 and determines the current health assessment data change rate based on the current health assessment data and the historical health assessment data sequence. Formula 4.1 is: in, Assess the rate of change of data for current health; The active health assessment platform determines a second deviation characteristic value according to the second predicted characteristic value and the current health assessment data change rate; If the active health assessment platform determines that the first deviation characteristic value is greater than the preset first deviation threshold or the second deviation characteristic value is greater than the preset second deviation threshold, the data state of the current health assessment data is determined to be a data state to be confirmed.

[0097] Optionally, the active health assessment platform can also use formula 4.2 and determine the change rate of the current health assessment data using a dynamic differential equation based on the current health assessment data and the historical health assessment data sequence. Formula 4.2 is: in, x(t) is the state variable vector of the system, which changes with time t. In this embodiment, it can be S i ; dx / dt is the derivative of the state variable vector with respect to time t, describing the rate of change of the state variable; f is a vector function that defines the relationship between the rate of change of the state variable and the current state, input, time, and parameters; f for Dynamically changing functional relationships; u(t) is an external input vector, which may change over time and affect the dynamic behavior of the system, and is calculated using actual data; p It is a parameter vector, which contains constant or variable parameters that affect the dynamic behavior of the system and can be determined by experience.

[0098] S205: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform in response to the health assessment data confirmation instruction.

[0099] The monitoring terminal responds to the health assessment data to be confirmed instruction and sends a health assessment data confirmation instruction to the active health assessment platform. After the active health assessment platform sends the health assessment data to be confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster, the monitoring terminal will receive the instruction. The instruction contains the current health assessment data, the historical health assessment data sequence, and the predicted feature value. The operator of the monitoring terminal will review these data to evaluate the authenticity and accuracy of the current health assessment data.

[0100] After the operator at the monitoring terminal confirms that the current health assessment data is correct or has been adjusted as necessary, a health assessment data confirmation instruction will be sent to the active health assessment platform through the monitoring terminal. This step confirms the accuracy and validity of the data.

[0101] S206: The active health assessment platform generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction.

[0102] In this step, after receiving the health assessment data confirmation instruction sent by the monitoring terminal, the active health assessment platform will generate an updated health assessment data sequence based on the historical health assessment data sequence and the currently confirmed health assessment data. This updated sequence will include all confirmed historical data and newly confirmed current data, providing comprehensive data support for subsequent health assessments.

[0103] Specifically, the active health assessment platform responds to the health assessment data to be confirmed instruction and generates an updated health assessment data sequence based on the historical health assessment data sequence and the current health assessment data. .

[0104] S207. The active health assessment platform updates the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient according to the updated health assessment data sequence to generate a first updated weight coefficient, a second updated weight coefficient, a third updated weight coefficient and a fourth updated weight coefficient.

[0105] In this step, the active health assessment platform updates the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient according to the updated health assessment data sequence to generate the first updated weight coefficient, the second updated weight coefficient, the third updated weight coefficient and the fourth updated weight coefficient, wherein the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are used to determine the first predicted characteristic value, and the first updated weight coefficient, the second updated weight coefficient, the third updated weight coefficient and the fourth updated weight coefficient are used to determine the predicted characteristic value of the next time node.

[0106] In a possible implementation, the active health assessment platform uses Formula 5 and updates the health assessment data sequence according to For the first weight coefficient , the second weight coefficient , the third weight coefficient and the fourth weight coefficient Update to generate the first updated weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth update weight coefficient , where the first update weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth update weight coefficient The predicted feature value used to determine the next time node is: in, is the preset adjustment factor.

[0107] On the basis of the above embodiment, the evaluation system of the children's active health system may further include a user terminal cluster, each user terminal in the user terminal cluster is communicatively connected to the active health evaluation platform; after at least one monitoring terminal in the monitoring terminal cluster sends a health evaluation data to be confirmed instruction, it also includes: The monitoring terminal sends a health assessment data abnormality instruction to the active health assessment platform in response to the health assessment data pending confirmation instruction.

[0108] The active health assessment platform responds to the health assessment data abnormality instruction and sends a health assessment data abnormality report to the target user terminal corresponding to the identity information in the user terminal cluster, and the health assessment data abnormality report includes updating the health assessment data sequence.

[0109] Specifically, after the active health assessment platform sends a health assessment data confirmation instruction to at least one monitoring terminal in the monitoring terminal cluster, if the operator of the monitoring terminal finds that the current health assessment data is abnormal (such as obviously deviating from the normal range or inconsistent with the historical data trend), the monitoring terminal will send a health assessment data abnormal instruction to the active health assessment platform. The instruction marks the abnormal state of the current data and requests further processing.

[0110] After receiving the abnormal health assessment data instruction sent by the monitoring terminal, the active health assessment platform will immediately parse the instruction to confirm the specific content and situation of the abnormal data. Based on the information in the abnormal instruction, the platform will further analyze the difference between the current health assessment data and the historical data to verify the authenticity and severity of the abnormality.

[0111] After confirming the data anomaly, the active health assessment platform will generate a detailed health assessment data anomaly report based on the currently confirmed abnormal data, the historical health assessment data sequence, and the updated health assessment data sequence (if any). The report will include the specific situation of the abnormal data, the time when the anomaly occurred, the possible cause analysis, and the suggestions for subsequent processing.

[0112] The active health assessment platform sends health assessment data abnormality reports to target user terminals (such as children's parents or guardians) corresponding to abnormal data through user terminal clusters. The reports will be presented in an easy-to-understand manner to ensure that users can quickly understand the abnormal status of children's health data and take appropriate measures.

[0113] After receiving the abnormal health assessment data report, users will be informed of changes in their children's health status in a timely manner and take appropriate measures based on the recommendations in the report. If necessary, users can contact medical institutions or professionals for further consultation and treatment.

[0114] Figure 3 FIG. 1 is a schematic diagram of a structure of an evaluation system for a child's active health system according to an exemplary embodiment of the present application. Figure 3 As shown, the evaluation system 300 of the children's active health system provided in this embodiment includes: A physiological sensor cluster 310, an active health assessment platform 320 and a monitoring terminal cluster 330, wherein each physiological sensor in the physiological sensor cluster 310 and each monitoring terminal in the monitoring terminal cluster 330 are respectively connected to the active health assessment platform 320 for communication; Acquire current health assessment data of the target child through the physiological sensor cluster 310, and send the current health assessment data to the active health assessment platform 320, wherein the current health assessment data includes the identity information of the target child and the heart rate data within a preset monitoring period; The active health assessment platform 320 obtains the historical health assessment data sequence of the target child according to the identity information; The active health assessment platform 320 uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, wherein the prediction feature value includes a first prediction feature value and a second prediction feature value, wherein the first prediction feature value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second prediction feature value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence; The active health assessment platform 320 determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data confirmation instruction to at least one monitoring terminal in the monitoring terminal cluster 330, and the health assessment data confirmation instruction includes the current health assessment data, the historical health assessment data sequence and the predicted characteristic value.

[0115] Optionally, the active health assessment platform 320 uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, including: The active health assessment platform 320 determines a first estimate value based on the historical health assessment data sequence; The active health assessment platform 320 determines a second estimated value based on the historical health assessment data sequence and the first estimated value; The active health assessment platform 320 determines a third estimated value based on the historical health assessment data sequence and the second estimated value; The active health assessment platform 320 determines a fourth estimated value based on the historical health assessment data sequence and the third estimated value; The active health assessment platform 320 determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value.

[0116] Optionally, the active health assessment platform 320 uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, including: The active health assessment platform 320 uses formula 1 and according to the historical health assessment data sequence Determine the first prediction feature value, where Formula 1 is: in, is the first prediction feature value, are respectively a first weight coefficient, a second weight coefficient, a third weight coefficient and a fourth weight coefficient; Based on The first estimate of Based on and The second estimate of Based on and The third estimate of Based on and The fourth estimate of .

[0117] Optionally, before the active health assessment platform 320 determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, the further step includes: The active health assessment platform 320 determines a maximum health assessment value and a median health assessment value based on the historical health assessment data sequence, and the maximum health assessment value and the median health assessment value are used to input into a preset estimation characteristic function to determine the first estimation value, the second estimation value, the third estimation value and the fourth estimation value.

[0118] Optionally, in determining the first prediction feature value according to the historical health assessment data sequence Previously, it also included: The active health assessment platform 320 uses Formula 2 and determines the first estimated value based on the historical health assessment data sequence The second estimated value , the third estimated value and the fourth estimated value , wherein the formula 2 is: in, is the maximum health assessment value in the historical health assessment data sequence, is the median health assessment value in the historical health assessment data sequence, is the preset offset value, is the preset monitoring time step, The historical health assessment data sequence is Monitoring time nodes, To estimate the characteristic function.

[0119] Optionally, the active health assessment platform 320 uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and further includes: The active health assessment determines the second predicted characteristic value according to the historical health assessment data sequence and the preset estimated characteristic function.

[0120] Optionally, the active health assessment platform 320 uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and further includes: The active health assessment platform 320 uses formula 3 and according to the historical health assessment data sequence Determine the second prediction characteristic value, the formula 3 is: in, is the second predicted feature value.

[0121] Optionally, the active health assessment platform 320 determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform 320 determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform 320 determines the current health assessment data change rate based on the current health assessment data and the historical health assessment data sequence; The active health assessment platform 320 determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform 320 determines that the first deviation characteristic value is greater than the preset first deviation threshold or the second deviation characteristic value is greater than the preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

[0122] Optionally, the active health assessment platform 320 determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform 320 determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform 320 uses Formula 4 and determines the current health assessment data change rate according to the current health assessment data and the historical health assessment data sequence. Formula 4 is: in, The rate of change of the current health assessment data; The active health assessment platform 320 determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform 320 determines that the first deviation characteristic value is greater than the preset first deviation threshold or the second deviation characteristic value is greater than the preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

[0123] Optionally, after at least one monitoring terminal in the monitoring terminal cluster 330 sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform 320 in response to the health assessment data to be confirmed instruction; The active health assessment platform 320 generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction; The active health assessment platform 320 updates the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient according to the updated health assessment data sequence to generate a first updated weight coefficient, a second updated weight coefficient, a third updated weight coefficient and a fourth updated weight coefficient, wherein the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are used to determine the first predicted characteristic value, and the first updated weight coefficient, the second updated weight coefficient, the third updated weight coefficient and the fourth updated weight coefficient are used to determine the predicted characteristic value of the next time node.

[0124] Optionally, after at least one monitoring terminal in the monitoring terminal cluster 330 sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform 320 in response to the health assessment data to be confirmed instruction; The active health assessment platform 320 generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction. ; The active health assessment platform 320 uses formula 5 and updates the health assessment data sequence according to the The first weight coefficient The second weight coefficient , the third weight coefficient and the fourth weight coefficient Update to generate the first updated weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth update weight coefficient , wherein the first update weight coefficient , the second update weight coefficient , the third update weight coefficient And the fourth updated weight coefficient For determining the predicted characteristic value of the next time node, the formula 5 is: in, is the preset adjustment factor.

[0125] Optionally, the evaluation system of the children's active health system includes a user terminal cluster 340, each user terminal in the user terminal cluster is communicatively connected to the active health evaluation platform 320; after at least one monitoring terminal in the monitoring terminal cluster 330 sends a health evaluation data to be confirmed instruction, it also includes: The monitoring terminal sends a health assessment data abnormality instruction to the active health assessment platform 320 in response to the health assessment data to be confirmed instruction; The active health assessment platform 320 sends a health assessment data exception report to the target user terminal corresponding to the identity information in the user terminal cluster 340 in response to the health assessment data exception instruction, and the health assessment data exception report includes the updated health assessment data sequence.

[0126] Figure 4 is a schematic diagram of the structure of an electronic device according to an exemplary embodiment of the present application. Figure 4As shown, an electronic device 400 provided in this embodiment includes: a processor 401 and a memory 402; wherein: The memory 402 is used to store computer programs, and the memory may also be a flash memory.

[0127] The processor 401 is used to execute the execution instructions stored in the memory to implement each step in the above method. For details, please refer to the relevant description in the above method embodiment.

[0128] Optionally, the memory 402 may be independent or integrated with the processor 401 .

[0129] When the memory 402 is a device independent of the processor 401, the electronic device 400 may further include: The bus 403 is used to connect the memory 402 and the processor 401 .

[0130] This embodiment further provides a readable storage medium, in which a computer program is stored. When at least one processor of an electronic device executes the computer program, the electronic device executes the methods provided in the above-mentioned various implementation modes.

[0131] This embodiment also provides a program product, which includes a computer program stored in a readable storage medium. At least one processor of the electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device implements the methods provided in the above various embodiments.

[0132] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. The present application is intended to cover any modification, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary techniques in the art that are not disclosed in the present application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present application are indicated by the claims.

[0133] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for evaluating a child's active health system, characterized in that: An evaluation system for an active health system for children, the evaluation system for the active health system for children comprising a physiological sensor cluster, an active health evaluation platform and a monitoring terminal cluster, wherein each physiological sensor in the physiological sensor cluster and each monitoring terminal in the monitoring terminal cluster are respectively connected to the active health evaluation platform in communication; The method comprises: Acquiring current health assessment data of the target child through the physiological sensor cluster, and sending the current health assessment data to the active health assessment platform, wherein the current health assessment data includes identity information of the target child; The active health assessment platform obtains a historical health assessment data sequence of the target child according to the identity information; The active health assessment platform uses a preset children's active health assessment model and determines a prediction feature value based on the historical health assessment data sequence, wherein the prediction feature value includes a first prediction feature value and a second prediction feature value, wherein the first prediction feature value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second prediction feature value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence; The active health assessment platform determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data to-be-confirmed instruction to at least one monitoring terminal in the monitoring terminal cluster, wherein the health assessment data to-be-confirmed instruction includes the current health assessment data, the historical health assessment data sequence, and the predicted characteristic value.

2. The method for evaluating the children's active health system according to claim 1, characterized in that: The active health assessment platform uses a preset child active health assessment model and determines a prediction feature value based on the historical health assessment data sequence, including: The active health assessment platform determines a first estimate value based on the historical health assessment data sequence; The active health assessment platform determines a second estimated value based on the historical health assessment data sequence and the first estimated value; The active health assessment platform determines a third estimated value based on the historical health assessment data sequence and the second estimated value; The active health assessment platform determines a fourth estimated value based on the historical health assessment data sequence and the third estimated value; The active health assessment platform determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value.

3. The method for evaluating the children's active health system according to claim 2, characterized in that: Before the active health assessment platform determines the first prediction feature value according to the first estimated value, the second estimated value, the third estimated value, and the fourth estimated value, the further step includes: The active health assessment platform determines a maximum health assessment value and a median health assessment value based on the historical health assessment data sequence, and the maximum health assessment value and the median health assessment value are used to input into a preset estimation characteristic function to determine the first estimation value, the second estimation value, the third estimation value and the fourth estimation value.

4. The method for evaluating the children's active health system according to claim 3, characterized in that: The active health assessment platform uses a preset child active health assessment model and determines a prediction feature value according to the historical health assessment data sequence, and also includes: The active health assessment determines the second predicted characteristic value according to the historical health assessment data sequence and the preset estimated characteristic function.

5. The method for evaluating the children's active health system according to claim 4, characterized in that: The active health assessment platform determines the data state of the current health assessment data according to the predicted characteristic value and the current health assessment data, including: The active health assessment platform determines a first deviation characteristic value according to the first predicted characteristic value and the current health assessment data; The active health assessment platform determines the change rate of the current health assessment data according to the current health assessment data and the historical health assessment data sequence; The active health assessment platform determines a second deviation characteristic value according to the second prediction characteristic value and the current health assessment data change rate; If the active health assessment platform determines that the first deviation characteristic value is greater than the preset first deviation threshold, or the second deviation characteristic value is greater than the preset second deviation threshold, the data state of the current health assessment data is determined to be the data state to be confirmed.

6. The method for evaluating the active health system for children according to any one of claims 2 to 5, characterized in that: After at least one monitoring terminal in the monitoring terminal cluster sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data confirmation instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform generates an updated health assessment data sequence according to the historical health assessment data sequence and the current health assessment data in response to the health assessment data to be confirmed instruction; The active health assessment platform updates the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient according to the updated health assessment data sequence to generate a first updated weight coefficient, a second updated weight coefficient, a third updated weight coefficient and a fourth updated weight coefficient, wherein the first weight coefficient, the second weight coefficient, the third weight coefficient and the fourth weight coefficient are used to determine the first predicted characteristic value, and the first updated weight coefficient, the second updated weight coefficient, the third updated weight coefficient and the fourth updated weight coefficient are used to determine the predicted characteristic value of the next time node.

7. The method for evaluating the children's active health system according to claim 6, characterized in that: The evaluation system of the children's active health system includes a user terminal cluster, each user terminal in the user terminal cluster is communicatively connected with the active health evaluation platform; After at least one monitoring terminal in the monitoring terminal cluster sends a health assessment data to be confirmed instruction, the method further includes: The monitoring terminal sends a health assessment data abnormality instruction to the active health assessment platform in response to the health assessment data to be confirmed instruction; The active health assessment platform sends a health assessment data exception report to the target user terminal corresponding to the identity information in the user terminal cluster in response to the health assessment data exception instruction, and the health assessment data exception report includes the updated health assessment data sequence.

8. An evaluation system for children's active health system, characterized in that: include: A physiological sensor cluster, an active health assessment platform and a monitoring terminal cluster, wherein each physiological sensor in the physiological sensor cluster and each monitoring terminal in the monitoring terminal cluster are respectively connected to the active health assessment platform for communication; Acquiring current health assessment data of the target child through the physiological sensor cluster, and sending the current health assessment data to the active health assessment platform, wherein the current health assessment data includes identity information of the target child; The active health assessment platform obtains a historical health assessment data sequence of the target child according to the identity information; The active health assessment platform uses a preset children's active health assessment model and determines a prediction feature value based on the historical health assessment data sequence, wherein the prediction feature value includes a first prediction feature value and a second prediction feature value, wherein the first prediction feature value is used to characterize the predicted health assessment data based on the historical health assessment data sequence, and the second prediction feature value is used to characterize the change rate of the predicted health assessment data based on the historical health assessment data sequence; The active health assessment platform determines the data status of the current health assessment data based on the predicted characteristic value and the current health assessment data, and when the data status is a data status to be confirmed, sends a health assessment data confirmation instruction to at least one monitoring terminal in the monitoring terminal cluster, and the health assessment data confirmation instruction includes the current health assessment data, the historical health assessment data sequence and the predicted characteristic value.

9. An electronic device, characterized in that: include: processor; as well as, A memory, configured to store executable instructions of the processor; The processor is configured to perform the method of any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 7 when executed by a processor.

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