Data analysis chronic disease health monitoring method and system based on AI

Abstract

The invention discloses an AI-based data analysis chronic disease health monitoring method and system, and relates to the technical field of computers. The method comprises the following steps: acquiring health data recorded by wearing equipment of each monitored object in a monitoring range; determining a fluctuation time period of chronic disease data in a first time period in the target health data, and determining target equipment which is abnormally used based on behavior data corresponding to the fluctuation time period; determining an abnormally used target time point according to the fluctuation time period, and dividing the first time period into a second time period and a third time period; according to the health data of all the target devices in the second time period and the third time period, redistribution prompt information is output to all the target devices in the monitoring range, and after confirmation information for the redistribution prompt information is received, the health data of all the target devices are corrected. By implementing the technical scheme provided by the invention, the problem of data exception caused by the fact that old people misuse other equipment in a centralized care place is solved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and specifically to an AI-based data analysis method and system for chronic disease health monitoring. Background Art

[0002] As the population ages, the number of people with chronic diseases continues to grow, and the demand for health monitoring is also increasing. Wearable health monitoring devices can record the user's chronic disease data such as blood pressure and heart rate in real time, as well as behavioral data such as daily activities, providing important reference for the prevention and management of chronic diseases.

[0003] Currently, health monitoring systems manage user data using a fixed binding approach. Specifically, during the initial device configuration phase, the system writes basic data such as the user's identity information and device serial number into the device's internal memory. During operation, the device continuously collects physiological indicators and behavioral data from the wearer, packages this data with the stored identity information, and uploads it to a cloud server. Based on the identity information in the data packet, the server assigns the monitoring data to the corresponding user database and then generates a health report through a data analysis engine.

[0004] However, in centralized care facilities like nursing homes, elderly individuals tend to have similar activity patterns, and it's common for them to repeatedly put on and take off their devices for things like naps and baths. Due to the similar appearance of the devices and the general decline in vision among the elderly, it's common for the actual user of the device to not match the pre-set identity. In these cases, the server still assigns data to the user corresponding to the device's built-in identity, resulting in abnormal records in the user database that don't match their actual health data. Summary of the Invention

[0005] This application provides an AI-based data analysis method and system for chronic disease health monitoring, which can solve the problem of data anomalies caused by the elderly misusing other people's equipment in centralized care places such as nursing homes.

[0006] In a first aspect of the present application, a method for chronic disease health monitoring based on AI data analysis is provided, specifically comprising: Obtain health data recorded by a device currently worn by each monitored subject within the monitoring range, the health data including chronic disease data and behavior data recorded by the device during a first time period; Determining a fluctuation time period in which chronic disease data in the first time period fluctuates in target health data, and determining a target device with abnormal usage based on behavioral data corresponding to the fluctuation time period, where the target health data is health data corresponding to any monitored object; Determining a target time point of abnormal use of the target device based on the fluctuation time period, and dividing the first time period into a second time period and a third time period based on the target time point, wherein the second time period is a time period before the abnormal use of the target device occurs, and the third time period is a time period after the abnormal use of the target device occurs; Based on the health data of all target devices in the second time period and the third time period, device reallocation prompt information is output to all target devices within the monitoring range. After receiving confirmation information for the device reallocation prompt information, the health data recorded by all target devices is corrected.

[0007] By adopting the above technical solution, we first obtain the health data recorded by the device currently worn by each monitored object within the monitoring range, determine the abnormal time period by analyzing the fluctuation characteristics of chronic disease data, and perform cross-validation in combination with the changes in behavioral data in this time period to accurately identify the target devices with abnormal use. Then, based on the data fluctuation characteristics, we determine the specific time point of abnormal use, and accurately divide the monitoring time period into two stages before and after abnormal use. Finally, by analyzing the health data characteristics of all target devices in these two time periods, we automatically generate a device reallocation plan, and complete the data correction after manual confirmation. This method based on multidimensional data analysis not only significantly improves the accuracy of abnormal use identification, but also ensures the reliability of data correction through human-computer collaboration.

[0008] Optionally, the determining of a fluctuation time period in which the chronic disease data in the first time period in the target health data fluctuates, and determining a target device with abnormal usage based on the behavior data corresponding to the fluctuation time period, wherein the target health data is health data corresponding to any monitored object, includes: Calculating a first change rate of the chronic disease data in the first time period at each preset time interval; Sort the first change rates from large to small, and select time periods corresponding to a first number of the first change rates as first fluctuation time periods; Calculate the second change rate of the behavioral data within each of the first fluctuation time periods. If there is a second change rate greater than the second preset threshold, select the first fluctuation time period with the largest second change rate as the fluctuation time period, and determine the corresponding device as a target device with abnormal use.

[0009] By employing this technical solution, the rate of change of chronic disease data within a first time period is calculated, and the time period with the highest rate of change is selected as the first fluctuation period. The rate of change of behavioral data within these time periods is then calculated, and the time period with the most significant behavioral data change is selected as the final fluctuation period. This dual rate of change analysis method can accurately identify data fluctuations caused by abnormal device usage.

[0010] Optionally, determining a target time point of abnormal use of the target device according to the fluctuation time period, and dividing the first time period into a second time period and a third time period according to the target time point, includes: During the fluctuation time period, obtaining a first time point at which the first change rate exceeds a first preset threshold, and a second time point at which the second change rate exceeds a second preset threshold; determining the target time point according to the time interval between the first time point and the second time point; Taking the target time point as the dividing point, the time period before the target time point is determined as the second time period, and the time period after the target time point is determined as the third time period.

[0011] By adopting the above technical solution, within the determined fluctuation time period, the time points at which the first change rate of chronic disease data exceeds the first preset threshold value, and the time points at which the second change rate of behavioral data exceeds the second preset threshold value are respectively searched, and these two time points are used as candidate time points. Then, by analyzing the time interval between the two candidate time points, the final target time point is determined. Based on the target time point, the entire monitoring time period is divided into a second time period before abnormal use and a third time period after abnormal use. This time point positioning method that combines the dual characteristics of chronic disease data and behavioral data can not only accurately identify the specific moment of abnormal use of equipment in complex health monitoring scenarios, but also avoids the misjudgment that may be caused by relying solely on a single data feature, provides an accurate time demarcation basis for subsequent data corrections, and significantly improves the reliability of health monitoring data.

[0012] Optionally, determining the target time point according to the time interval between the first time point and the second time point includes: Calculating a time interval between the first time point and the second time point, and when the time interval is less than a preset time threshold, determining an earlier time point between the first time point and the second time point as the target time point; When the time interval is greater than or equal to the preset time threshold, an intermediate time point between the first time point and the second time point is obtained, and the intermediate time point is determined as the target time point.

[0013] By adopting the above technical solution, the time interval between the first time point and the second time point is compared with the preset time threshold, and different time point selection strategies are adopted: when the time interval is small, it indicates that the data changes caused by abnormal equipment use are more concentrated. In this case, selecting an earlier time point as the time of abnormal occurrence can more quickly detect and handle abnormal situations; when the time interval is large, it indicates that the data change process is relatively slow. In this case, selecting the middle time point as the most likely time of abnormal occurrence avoids deviations in time point selection. This adaptive time point selection method based on data change characteristics can not only accurately identify the specific moments of abnormal equipment use in different scenarios, but also reduce the risk of misjudgment through a reasonable time point selection strategy, significantly improve the accuracy and reliability of subsequent data corrections, and provide a strong guarantee for the stable operation of the health monitoring system.

[0014] Optionally, outputting device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and correcting the health data recorded by all target devices after receiving the reallocation confirmation information, includes: calculating health data difference values ​​of all target devices in the second time period and the third time period, and pairing all target devices into at least one target device group according to the health data difference values; According to the target device group, a device reallocation prompt message including a device reallocation plan is generated, and after receiving the reallocation confirmation message corresponding to the device reallocation prompt message, the health data of the third time period is exchanged with the health data of the third time period of the corresponding exchange device in the same target device group.

[0015] By adopting the above technical solution, the difference in health data before and after abnormal use of each target device is calculated. Based on these difference values, the devices are automatically paired to form a target device group. This pairing method ensures accurate matching of exchanged devices. Then, based on the pairing results, a device reallocation plan is generated and the operator is prompted for confirmation. After confirmation, the system automatically completes the data exchange operation between exchanged devices in the same device group. This data correction method that combines automatic analysis and manual confirmation significantly improves the data quality and reliability of health monitoring systems in centralized care facilities, providing a more reliable data foundation for the prevention and management of chronic diseases.

[0016] Optionally, calculating health data difference values ​​of all target devices in the second time period and the third time period, and determining a target device group with abnormal usage according to the health data difference values, includes: respectively calculating the difference between the health data of each target device in the second time period and the health data of other target devices in the third time period; Device pairs with the smallest difference values ​​among the target devices are screened out, and the device pairs with the smallest difference values ​​are divided into the same group to form a target device group.

[0017] By using this technical solution, we compare the differences in health data across devices over different time periods and group the device pairs with the smallest differences, accurately identifying the target device groups that will actually undergo a swap. This data similarity-based grouping method improves the accuracy of device reallocation and effectively solves the identification problem in multi-device swaps.

[0018] Optionally, screening out device pairs with minimum difference values ​​among target devices and grouping the device pairs with minimum difference values ​​into the same group to form a target device group includes: Select any target device as the starting device, and use the other target device with the smallest difference between the health data of the starting device in the second time period and the health data of the other target devices in the third time period as the current device, and use the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period as the next device. The remaining devices are the target devices excluding the starting device and the current device; The next device is used as the current device, and the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is used as the next device, until the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold; When the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold, all the target devices involved are divided into the same target device group; Repeat the above process for the remaining ungrouped target devices until all target devices are grouped or no new target device group can be formed.

[0019] By adopting the above technical solution, starting from any target device, a device chain is constructed by comparing the difference values ​​of the health data between devices, and the complete device interchange loop is identified through cyclic matching. This method can not only handle simple two-by-two interchange situations, but also accurately identify cyclic interchange relationships between multiple devices. By setting a difference value threshold as the termination condition, the reliability of the grouping results is ensured; at the same time, an iterative method is used to process the remaining devices, ensuring that all interchangeable devices can be correctly grouped. This data difference-based cyclic matching grouping method provides accurate device correspondence for subsequent data correction, effectively improving the data accuracy and reliability of the health monitoring system when handling device interchange scenarios.

[0020] The second aspect of the present application provides an AI-based data analysis chronic disease health monitoring system, specifically including: A data acquisition module is used to obtain health data recorded by the device currently worn by each monitored subject within the monitoring range, wherein the health data includes chronic disease data and behavior data recorded by the wearable device in the first time period; an abnormal device determination module, configured to determine a fluctuation time period in which the chronic disease data in the first time period fluctuates in the target health data, and determine a target device with abnormal usage based on the behavior data corresponding to the fluctuation time period, wherein the target health data is health data corresponding to any monitored object; an abnormal time determination module, configured to determine a target time point of abnormal use of the target device based on the fluctuation time period, and divide the first time period into a second time period and a third time period based on the target time point, wherein the second time period is a time period before the abnormal use of the target device occurs, and the third time period is a time period after the abnormal use of the target device occurs; The data correction module is used to output device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and after receiving confirmation information for the device reallocation prompt information, correct the health data recorded by all target devices.

[0021] By adopting the above technical solution, we first obtain the health data recorded by the device currently worn by each monitored object within the monitoring range, determine the abnormal time period by analyzing the fluctuation characteristics of chronic disease data, and perform cross-validation in combination with the changes in behavioral data in this time period to accurately identify the target devices with abnormal use. Then, based on the data fluctuation characteristics, we determine the specific time point of abnormal use, and accurately divide the monitoring time period into two stages before and after abnormal use. Finally, by analyzing the health data characteristics of all target devices in these two time periods, we automatically generate a device reallocation plan, and complete the data correction after manual confirmation. This method based on multidimensional data analysis not only significantly improves the accuracy of abnormal use identification, but also ensures the reliability of data correction through human-computer collaboration.

[0022] In the third aspect of the present application, an electronic device is provided, including a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs any of the methods described above.

[0023] In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions. When the instructions are executed, any one of the methods described above is executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 This is a schematic diagram of the architecture of an AI-based data analysis chronic disease health monitoring system provided in an embodiment of the present application; Figure 2 This is a flowchart of an AI-based data analysis method for chronic disease health monitoring provided in an embodiment of the present application; Figure 3 yes Figure 2 A schematic flow chart of a sub-step of step S102; Figure 4 yes Figure 2 A schematic flow chart of a sub-step of step S103; Figure 5 yes Figure 4 A schematic flow chart of a sub-step of step S1032; Figure 6 yes Figure 2 A schematic flow chart of a sub-step of step S104; Figure 7 yes Figure 6 A schematic flow chart of a sub-step of step S1041; Figure 8 yes Figure 7 A schematic flow chart of a sub-step of step S10412; Figure 9 This is a structural diagram of an AI-based data analysis chronic disease health monitoring system provided in an embodiment of the present application; Figure 10 This is a structural diagram of an electronic device disclosed in an embodiment of the present application.

[0025] Explanation of the accompanying drawings: 11. Data acquisition module; 12. Abnormal device determination module; 13. Abnormal time determination module; 14. Data correction module; 901. Processor; 902. Communication bus; 903. User interface; 904. Network interface; 905. Memory. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

[0027] In the description of the embodiments of this application, words such as "for example" or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "for example" or "for instance" in the embodiments of this application should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "for example" or "for instance" is intended to present the relevant concepts in a concrete manner.

[0028] In the description of the embodiments of the present application, the term "multiple" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0029] Figure 1 An exemplary system architecture 010 of an AI-based data analysis chronic disease health monitoring system is shown.

[0030] like Figure 1 As shown, system architecture 010 may include a wearable device 011, a network 012, and an electronic device 013. Network 012 is used to provide a medium for a communication link between wearable device 011 and electronic device 013. Network 012 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0031] Medical personnel can use electronic devices 013 to analyze wearable devices 011 via network 012 to perform remote health monitoring and data correction for subjects within the monitoring range while protecting privacy. Various monitoring applications can be installed on health monitoring devices 011, such as heart rate monitoring applications, blood pressure monitoring applications, blood sugar monitoring applications, and behavior monitoring applications.

[0032] The wearable device 011 is hardware and can be various types of devices with health monitoring functions, including but not limited to wearable devices with similar shapes, portable monitoring devices, and mobile terminals.

[0033] Electronic device 013 can be a server that provides various health management services, such as an AI server that analyzes data from wearable device 011. The AI ​​server can perform fluctuation analysis, abnormal usage identification, and data correction on the received monitoring data, and can also execute the processing results (device reallocation plan).

[0034] It should be noted that a server can be either hardware or software. When a server is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When a server is software, it can be implemented as multiple software programs or software modules (for example, multiple software programs or software modules used to provide distributed services), or as a single software program or software module. This is not specifically limited here.

[0035] It should be understood that Figure 1 The number of wearable devices 011, networks 012, and electronic devices 013 in the above diagram is merely illustrative. Depending on implementation requirements, any number of wearable devices 011, networks 012, and electronic devices 013 may be present. In particular, if target data does not need to be acquired remotely, the above system architecture may not include network 012, but may only include wearable devices 011 or electronic devices 013.

[0036] Taking the electronic device side as an example, the following describes an AI-based data analysis method for chronic disease health monitoring provided by this application.

[0037] This application provides an AI-based data analysis method for chronic disease health monitoring. Figure 2 , Figure 2 This is a flowchart of an AI-based data analysis method for chronic disease health monitoring provided in an embodiment of the present application, including steps S101 to S104. The above steps are as follows: S101: Obtain health data recorded by a device currently worn by each monitored subject within a monitoring range, where the health data includes chronic disease data and behavior data recorded by the wearable device in a first time period.

[0038] In an embodiment of the present application, the monitoring range represents a specific management area, such as a centralized care place such as a nursing home or a hospital. There are many objects to be monitored within the monitoring range. The monitored objects refer to people whose health status needs to be monitored, usually patients with chronic diseases. Each monitored object will wear a similar wearable device. The wearable device is used to represent wearable health monitoring devices, such as smart bracelets, smart watches, etc. The wearable device will record the health data of the monitored object. Health data refers to various data indicators that reflect the physical condition of the monitored object. The health data contains chronic disease data and behavioral data. Chronic disease data represents physiological indicators related to chronic diseases, such as blood pressure, blood sugar, heart rate, etc. Behavioral data refers to data reflecting the daily activity characteristics of the monitored object, such as exercise volume, sleep status, location information, etc. The wearable device will collect health data in the first time period. The first time period is used to represent a continuous data collection cycle.

[0039] Specifically, the electronic device first determines all objects that need health monitoring in a specific management area and identifies the wearable devices currently being used by these monitored objects. Then, the electronic device continuously collects and records the health data of the monitored objects during the first time period through the various sensors built into these wearable devices. The collected data includes two dimensions: one is the physiological indicator data related to chronic diseases collected by professional medical sensors, and the other is the data reflecting the daily behavioral characteristics of the monitored objects collected by motion sensors, positioning modules, etc.

[0040] S102: Determine a fluctuation time period in which chronic disease data in a first time period in target health data fluctuates, and determine a target device with abnormal usage based on behavioral data corresponding to the fluctuation time period. The target health data is health data corresponding to any monitored object.

[0041] In an embodiment of the present application, the target health data represents the health data record of a single monitored object that needs to be analyzed. The chronic disease data will fluctuate during the fluctuation time period. The fluctuation time period refers to the time interval in which the chronic disease data shows obvious abnormal changes. The device whose behavioral data fluctuates during the fluctuation time period is the target device. At this time, the target device will be marked as abnormal use. The target device refers to a wearable device that is determined to be likely to be used abnormally, and abnormal use indicates that the device may be worn incorrectly or used by other monitored objects.

[0042] Specifically, the electronic device first performs a time-series analysis on each monitored subject's chronic disease data, comparing it with the subject's historical data to identify time periods when the data curve experienced unusual fluctuations. The electronic device then focuses on analyzing the behavioral data characteristics during these periods. If significant changes are detected in the behavioral data (e.g., activity patterns or location trajectories that are inconsistent with the monitored subject's daily routine), the device can be determined to have likely experienced abnormal usage during this period.

[0043] Please refer to Figure 3 , Figure 3 This is a sub-step flow diagram of step S102 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S102: determining a fluctuation time period in which the chronic disease data in the first time period in the target health data fluctuates, and determining a target device with abnormal use based on the behavioral data corresponding to the fluctuation time period, where the target health data is the health data corresponding to any monitored object, can specifically include the following steps: S1021: Calculate a first change rate of chronic disease data in a first time period at each preset time interval.

[0044] In an embodiment of the present application, the preset time interval represents the minimum time unit for data analysis. The chronic disease data will change within the preset time interval, and the corresponding numerical value is the first change rate. The first change rate refers to the amplitude of change in the chronic disease data between the preset time interval time points, which is used to quantify the degree of data fluctuation.

[0045] Specifically, the electronic device first divides the first time period into preset time intervals to ensure data integrity within each time interval. Then, for each time interval, the electronic device obtains the chronic disease data values ​​at the start and end points of the interval and calculates the data change between these two time points. The electronic device then divides the data change by the length of the time interval to obtain the rate of change within that time interval.

[0046] S1022: Sort the first change rates from large to small, and select time periods corresponding to a first number of first change rates as first fluctuation time periods.

[0047] In an embodiment of the present application, the first number is used to represent the number of significant fluctuation intervals that need to be selected. A certain number of time periods need to be selected as the first fluctuation time period. The first fluctuation time period represents a set of time intervals that are determined to have significant fluctuations in chronic disease data.

[0048] Specifically, the electronic device first sorts all calculated first change rates in descending order by numerical value to construct a change rate sequence. Then, based on a predetermined first quantity, the electronic device selects the change rates with the largest number of values ​​from the sorted sequence. The electronic device then obtains the specific time intervals corresponding to these selected change rates and marks these time intervals as first fluctuation time periods.

[0049] S1023: Calculate the second change rate of the behavioral data within each first fluctuation time period. If there is a second change rate greater than the second preset threshold, select the first fluctuation time period with the largest second change rate as the fluctuation time period, and determine the corresponding device as a target device with abnormal use.

[0050] In an embodiment of the present application, the second change rate is used to indicate the magnitude of change in the behavioral data within a time period. The second change rate has a corresponding second preset threshold value. The second preset threshold value indicates a standard value for determining whether the fluctuation in the behavioral data is abnormal. The time period in which the behavioral data fluctuates in the first fluctuation time period is the fluctuation time period. The fluctuation time period refers to the time period in which the abnormality is finally determined. At this time, the device in which these two fluctuations occur is called a target device. The target device indicates a wearable device that is determined to be used abnormally.

[0051] Specifically, the electronic device first iterates through each first fluctuation time period and calculates the rate of change of the behavioral data within each of these time periods. The electronic device then compares the calculated second rate of change with a pre-set threshold, screening out time periods where the behavioral data changes by more than the threshold. Then, among these time periods, the electronic device selects the time period with the most significant change in behavioral data as the final fluctuation time period and marks the wearable device corresponding to this time period as a target device with abnormal usage.

[0052] S103: Determine a target time point of abnormal use of the target device based on the fluctuation time period, and divide the first time period into a second time period and a third time period based on the target time point. The second time period is the time period before the abnormal use of the target device occurs, and the third time period is the time period after the abnormal use of the target device occurs.

[0053] In the embodiment of the present application, the target time point refers to the specific moment when abnormal use of the equipment occurs. The target time point is used as the dividing point. The time period before it is the second time period, and the time period after it is the third time period. The second time period is used to represent the normal use time period before the abnormality occurs, and the third time period refers to the time period after the abnormality occurs.

[0054] Specifically, the electronic device first conducts a detailed analysis of the data within the fluctuation period, identifying the mutation point in the data characteristics to determine the specific target time point of abnormal usage. This time point is when both chronic disease data and behavioral data change significantly. The electronic device then divides the entire first time period into two sub-time periods using this target time point as the dividing point: the second time period before the target time point represents the normal usage phase, and the third time period after the target time point represents the abnormal usage phase.

[0055] Please refer to Figure 4 , Figure 4 This is a schematic diagram of a sub-step flow chart of step S103 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S103: determining the number of stages to be divided between the first operating power and the second operating power based on the first operating power, the second operating power, and historical data, can specifically include the following steps: S1031: Within the fluctuation time period, obtain a first time point at which the first change rate exceeds a first preset threshold, and a second time point at which the second change rate exceeds a second preset threshold.

[0056] Specifically, the electronic device first analyzes the first change rate of the chronic disease data within the fluctuation period. When the change rate exceeds a first preset threshold, the electronic device records the moment as the first time point. Simultaneously, the electronic device performs a similar analysis on the second change rate of the behavioral data. When the change rate exceeds a second preset threshold, the electronic device records the moment as the second time point.

[0057] S1032: Determine a target time point according to the time interval between the first time point and the second time point.

[0058] Specifically, the electronic device first calculates the time difference between the first and second time points, analyzing the order and time span of the two data anomalies. The electronic device then uses different strategies to determine the target time point based on the length of the time interval. If the time interval is small, indicating that the two data anomalies occurred almost simultaneously, the midpoint between the two time points can be selected. If the time interval is large, a more appropriate time point can be determined based on the significance of the data change and business logic.

[0059] Please refer to Figure 5 , Figure 5 This is a schematic diagram of a sub-step flow chart of step S1032 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S1032: determining the target time point based on the time interval between the first time point and the second time point may specifically include the following steps: S10321: Calculate the time interval between the first time point and the second time point, and when the time interval is less than a preset time threshold, determine the earlier time point of the first time point and the second time point as the target time point.

[0060] Specifically, the electronic device first calculates the time difference between the first and second time points to obtain a specific time interval. It then compares this time interval with a pre-set threshold. If the difference is less than the threshold, it indicates that the anomalies in the two data occurred at similar times, indicating a strong correlation. In this case, the electronic device selects the earlier of the two time points as the target time point, as the earlier anomaly is often the source of the subsequent anomaly.

[0061] S10322: When the time interval is greater than or equal to the preset time threshold, obtain an intermediate time point between the first time point and the second time point, and determine the intermediate time point as the target time point.

[0062] Specifically, the electronic device first determines whether the calculated time interval is greater than or equal to a preset time threshold. If this condition is met, it indicates that the anomalies in the two data points occurred far apart in time, possibly indicating a gradual anomaly. The electronic device then calculates the arithmetic mean of the two time points to determine the middle time point located in the middle of the time axis. Finally, this middle time point is determined as the target time point.

[0063] S1033: Taking the target time point as a dividing point, determine the time period before the target time point as the second time period, and determine the time period after the target time point as the third time period.

[0064] Specifically, the electronic device first uses the target time point as a critical time demarcation point, representing the turning point when the device's usage status changes from normal to abnormal. The electronic device then classifies all time before the target time point as a second time period. The data during this time period can reflect the normal usage status of the device and the typical behavioral characteristics of the user. The electronic device then classifies all time after the target time point as a third time period. The data during this time period reflects the change in the device's status after abnormal usage.

[0065] S104: Output device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and after receiving confirmation information for the device reallocation prompt information, correct the health data recorded by all target devices.

[0066] In an embodiment of the present application, the device reallocation prompt information indicates the notification content that recommends the user to adjust the device usage allocation. After the user adjusts the device, the electronic device will receive a confirmation message. The confirmation information refers to the user's acceptance response to the reallocation suggestion. After receiving the confirmation message, the health data of the target device will be corrected. The correction is used to indicate the process of correcting erroneous recorded data.

[0067] Specifically, the electronic device first collects the health data of all target devices marked as abnormally used during normal usage (the second time period) and abnormal usage (the third time period), and through comparative analysis, identifies significant changes in data characteristics. Then, based on the analysis results, the electronic device generates reallocation suggestions for all abnormal devices within the monitoring range. These suggestions may include specific operational instructions such as device swapping and rebinding users. When the electronic device receives the user's confirmation response to these suggestions, it will make targeted corrections to the health data during the abnormal period based on the confirmed reallocation plan to ensure data accuracy and availability.

[0068] Please refer to Figure 6 , Figure 6This is a sub-step flow diagram of step S104 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S104: outputting device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and after receiving confirmation information for the device reallocation prompt information, correcting the health data recorded by all target devices. This step may specifically include the following steps: S1041: Calculate health data difference values ​​of all target devices in the second time period and the third time period, and pair all target devices into at least one target device group according to the health data difference values.

[0069] In an embodiment of the present application, the health data difference value is used to indicate the degree of difference between the data of a device during a normal usage period and the data of other devices during an abnormal usage period. When the difference value is less than a preset threshold, these devices will be paired into a target device group, which represents a set of devices with user interchange.

[0070] Specifically, the electronic device first takes the health data of a target device during its normal usage period (the second time period) as the baseline data, and then compares the baseline data with the health data of all other target devices during the abnormal usage period (the third time period), and calculates the difference between them. Through this cross-comparison method, the electronic device can find out whether the normal usage data of a device is similar to the abnormal usage data of another device. If the difference in health data between the two devices is small, it means that they may have swapped users. Finally, the electronic device pairs devices with significant correlation based on the size of the difference value to form target device groups. Each device group represents a set of devices that may have swapped users.

[0071] Please refer to Figure 7 , Figure 7 This is a sub-step flow diagram of step S1041 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, S1041: calculating the health data difference values ​​of all target devices in the second time period and the third time period, and pairing all target devices into at least one target device group based on the health data difference values ​​can specifically include the following steps: S10411: Calculate the difference between the health data of each target device in the second time period and the health data of other target devices in the third time period.

[0072] Specifically, the electronic device first selects a target device and obtains its health data during normal usage (the second time period) as baseline data. The electronic device then sequentially obtains health data from each target device other than the target device during abnormal usage (the third time period) as comparison data. The electronic device then calculates the difference between each set of baseline data and comparison data, obtaining a corresponding difference value. This process continues across all target devices, ensuring that each device's normal usage data is compared with the abnormal usage data of other devices.

[0073] S10412: Filter out device pairs with the smallest difference values ​​among the target devices, and divide the device pairs with the smallest difference values ​​into the same group to form a target device group.

[0074] In the embodiment of the present application, the target device group refers to a set of devices for which user exchange is confirmed to have occurred.

[0075] Specifically, the electronic device first calculates and ranks the difference values ​​between each device and the other devices, identifying the successor device with the smallest difference value. It then constructs a transfer relationship graph between devices to identify possible device interchange chains. If it finds multiple devices forming a closed-loop transfer relationship (for example, A→B→C→A), and the difference values ​​between adjacent pairs of devices in the chain are small, the electronic device assigns these devices to the same target device group.

[0076] Please refer to Figure 8 , Figure 8 This is a schematic diagram of a sub-step flow chart of step S10412 provided in an embodiment of the present application. Based on the above embodiment, as an optional embodiment, step S10412: screening out device pairs with the smallest difference values ​​among target devices, dividing the device pairs with the smallest difference values ​​into the same group to form a target device group, can specifically include the following steps: S104121: Select any target device as the starting device, and use the other target device with the smallest difference between the health data of the starting device in the second time period and the health data of other target devices in the third time period as the current device, and use the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period as the next device. The remaining devices are the target devices excluding the starting device and the current device.

[0077] In an embodiment of the present application, the starting device refers to the device that is first selected among all target devices to start device interchange matching. During the process of the starting device starting matching, there will be a current device with data similar to the actual device. The current device refers to the device with the data characteristics closest to the starting device during the device matching process.

[0078] Specifically, a target device is selected as the starting device. The health data of this device before the swap (second time period) is compared with the health data of all other devices after the swap (third time period). By calculating the data difference, the device with the closest post-swap data to the starting device is identified. This device is likely the user who received the starting device, so it is marked as the current device. Next, the pre-swap data of the current device is compared with the post-swap data of the remaining devices (excluding the starting and current devices). The device with the smallest difference is selected as the next device.

[0079] S104122: Take the next device as the current device, and execute the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period as the next device, until the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than the preset difference threshold.

[0080] Specifically, the next device determined in the previous round of matching is first updated to the current device, marking the start of processing the next node in the device swap chain. The electronic device then obtains the health data of the current device before the swap (the second time period) and compares it with the data of the remaining unmatched devices after the swap (the third time period). By calculating the difference value and comparing it with a preset threshold, if a device with a difference value less than the threshold is found, the device with the smallest difference value is determined as the new next device, and the matching process continues. If the difference value between all remaining devices and the current device is greater than the preset threshold, it means that no suitable matching device can be found, and the matching process is terminated.

[0081] S104123: When the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold, all the target devices involved are divided into the same target device group.

[0082] Specifically, during each matching round, the electronic device calculates the difference between the health data of the current device in the second time period and the health data of the remaining unmatched devices in the third time period. When all difference values ​​are greater than a preset threshold, it indicates that the current device cannot form a valid matching relationship with any remaining devices, which means that a complete device exchange chain has been found. At this point, the electronic device will classify all devices involved in the exchange chain (including the starting device, the current device in each matching round, and the last current device) into the same target device group, forming a closed-loop exchange relationship between these devices.

[0083] S104124: Repeat the above process for the remaining ungrouped target devices until all target devices are grouped or no new target device group can be formed.

[0084] Specifically, it first identifies which target devices have not yet been assigned to any device group. These devices form the device set for the next round of matching. The electronic device then re-executes the complete device matching and grouping process: selecting a starting device from the remaining devices, searching for matching current and next devices, building a device interchange chain, and ultimately forming a new device group. This process repeats until one of two situations occurs: either all target devices are successfully assigned to a device group, or the data differences between the remaining devices exceed a preset threshold, making it impossible to find a valid interchange relationship.

[0085] S1042: Generate device reallocation prompt information including a device reallocation plan based on the target device group, and after receiving reallocation confirmation information corresponding to the device reallocation prompt information, exchange the health data of the third time period with the health data of the third time period of the corresponding exchange device in the same target device group.

[0086] Specifically, the electronic device first generates a detailed device reallocation plan based on the identified target device group information, including recommended device swap combinations and specific swap operation instructions. It then integrates this information into a user-friendly prompt message and sends it to the relevant user. Once the electronic device receives the user's confirmation of these reallocation plans, it performs the data-level swap operation. Within each target device group, the electronic device swaps the data of the swapped devices during the abnormal usage period to ensure that the data is associated with the correct user.

[0087] refer to Figure 9 , the present application also provides an AI-based data analysis chronic disease health monitoring system 10, specifically including: A data acquisition module 11 is configured to acquire health data recorded by a device currently worn by any monitored subject within the monitoring range, wherein the health data includes chronic disease data and behavioral data continuously recorded by the device during a first time period within a preset time window; an abnormal device determining module 12, configured to determine a fluctuation time period during which the chronic disease data in the first time period fluctuates, and determine a target device with abnormal usage based on the behavior data in the fluctuation time period; an abnormal time determination module 13, configured to determine a target time point of abnormal use of the target device based on the fluctuation time period, and divide the first time period into a second time period and a third time period based on the target time point, wherein the second time period is a time period before the abnormal use of the target device occurs, and the third time period is a time period after the abnormal use of the target device occurs; The data correction module 14 is used to output device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and after receiving the reallocation confirmation information, correct the health data recorded by all target devices.

[0088] Optionally, the abnormal device determination module 12 is specifically configured to: Calculating a first change rate of the chronic disease data in the first time period at each preset time interval; Sort the first change rates from large to small, and select time periods corresponding to a first number of the first change rates as first fluctuation time periods; Calculate the second change rate of the behavioral data within each of the first fluctuation time periods. If there is a second change rate greater than the second preset threshold, select the first fluctuation time period with the largest second change rate as the fluctuation time period, and determine the corresponding device as a target device with abnormal use.

[0089] Optionally, the abnormal time determination module 13 is specifically configured to: During the fluctuation time period, obtaining a first time point at which the first change rate exceeds a first preset threshold, and a second time point at which the second change rate exceeds a second preset threshold; determining the target time point according to the time interval between the first time point and the second time point; Taking the target time point as the dividing point, the time period before the target time point is determined as the second time period, and the time period after the target time point is determined as the third time period.

[0090] Optionally, the abnormal time determination module 13 is further specifically configured to: Calculating a time interval between the first time point and the second time point, and when the time interval is less than a preset time threshold, determining an earlier time point between the first time point and the second time point as the target time point; When the time interval is greater than or equal to the preset time threshold, an intermediate time point between the first time point and the second time point is obtained, and the intermediate time point is determined as the target time point.

[0091] Optionally, the data correction module 14 is specifically configured to: calculating health data difference values ​​of all target devices in the second time period and the third time period, and pairing all target devices into at least one target device group according to the health data difference values; According to the target device group, a device reallocation prompt message including a device reallocation plan is generated, and after receiving the reallocation confirmation message corresponding to the device reallocation prompt message, the health data of the third time period is exchanged with the health data of the third time period of the corresponding exchange device in the same target device group.

[0092] Optionally, the data correction module 14 is further configured to: respectively calculating the difference between the health data of each target device in the second time period and the health data of other target devices in the third time period; Device pairs with the smallest difference values ​​among the target devices are screened out, and the device pairs with the smallest difference values ​​are divided into the same group to form a target device group.

[0093] Optionally, the data correction module 14 is further configured to: Select any target device as the starting device, and use the other target device with the smallest difference between the health data of the starting device in the second time period and the health data of the other target devices in the third time period as the current device, and use the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period as the next device. The remaining devices are the target devices excluding the starting device and the current device; The next device is used as the current device, and the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is used as the next device, until the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold; When the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold, all the target devices involved are divided into the same target device group; Repeat the above process for the remaining ungrouped target devices until all target devices are grouped or no new target device group can be formed.

[0094] It should be noted that the above embodiments provide devices that implement their functions using only the division of the above functional modules as examples. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be repeated here.

[0095] This embodiment also discloses an electronic device, referring to Figure 10 , Figure 10Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present application. The electronic device 013 may include: at least one processor 901, at least one communication bus 902, a user interface 903, a network interface 904, and at least one memory 905.

[0096] The communication bus 902 is used to implement connection and communication between these components.

[0097] The user interface 903 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 903 may also include a standard wired interface and a wireless interface.

[0098] The network interface 904 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

[0099] The processor 901 may include one or more processing cores. Using various interfaces and circuits, the processor 901 connects to various components within the server. It executes instructions, programs, code sets, or instruction sets stored in the memory 905, as well as accesses data stored in the memory 905, to perform various server functions and process data. Optionally, the processor 901 may be implemented using at least one of the following hardware forms: a digital signal processing (DSP), a field-programmable gate array (FPGA), or a programmable logic array (PLA). The processor 901 may integrate one or a combination of a central processing unit (CPU), a graphics processing unit (GPU), and a modem. The CPU primarily processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing content displayed on the display screen; and the modem handles wireless communications. It is understood that the modem may also be implemented independently of the processor 901 and implemented on a separate chip.

[0100] Among them, the memory 905 may include a random access memory (RAM) or a read-only memory (Read-Only Memory). Optionally, the memory 905 includes a non-transitory computer-readable storage medium. The memory 905 can be used to store instructions, programs, codes, code sets or instruction sets. The memory 905 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 905 may also be optionally at least one storage device located away from the aforementioned processor 905. Reference Figure 10 , as a computer storage medium, the memory 905 may include an operating system, a network communication module, a user interface module, and an AI-based data analysis and chronic disease health monitoring application.

[0101] exist Figure 10 In the electronic device shown, the user interface 903 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 901 can be used to call the application program for AI-based data analysis and chronic disease health monitoring stored in the memory 905. When executed by one or more processors 901, the electronic device 013 executes one or more methods in the above embodiments.

[0102] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for this application.

[0103] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely schematic, such as the division of units, which is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interface, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0105] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0106] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0107] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, or the portion that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of this application. The aforementioned memory includes various media that can store program code, such as USB flash drives, mobile hard drives, magnetic disks, or optical disks.

[0108] The above is only an exemplary embodiment of the present disclosure and cannot be used to limit the scope of the present disclosure. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any variations, uses or adaptive changes of the present disclosure, which follow the general principles of the present disclosure and include common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are to be regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for chronic disease health monitoring based on AI data analysis, characterized in that: Applied to electronic equipment, the method includes: Obtain health data recorded by a device currently worn by each monitored subject within the monitoring range, the health data including chronic disease data and behavior data recorded by the device during a first time period; Determining a fluctuation time period in which chronic disease data in the first time period fluctuates in target health data, and determining a target device with abnormal usage based on behavioral data corresponding to the fluctuation time period, where the target health data is health data corresponding to any monitored object; Determining a target time point of abnormal use of the target device based on the fluctuation time period, and dividing the first time period into a second time period and a third time period based on the target time point, wherein the second time period is a time period before the abnormal use of the target device occurs, and the third time period is a time period after the abnormal use of the target device occurs; Based on the health data of all target devices in the second time period and the third time period, device reallocation prompt information is output to all target devices within the monitoring range. After receiving confirmation information for the device reallocation prompt information, the health data recorded by all target devices is corrected.

2. The method according to claim 1, characterized in that The determining of a fluctuation time period in which the chronic disease data in the first time period in the target health data fluctuates, and determining a target device with abnormal usage based on the behavior data corresponding to the fluctuation time period, wherein the target health data is health data corresponding to any monitored object, includes: Calculating a first change rate of the chronic disease data in the first time period at each preset time interval; Sort the first change rates from large to small, and select time periods corresponding to a first number of the first change rates as first fluctuation time periods; Calculate the second change rate of the behavioral data within each of the first fluctuation time periods. If there is a second change rate greater than the second preset threshold, select the first fluctuation time period with the largest second change rate as the fluctuation time period, and determine the corresponding device as a target device with abnormal use.

3. The method according to claim 2, characterized in that The step of determining a target time point of abnormal use of the target device according to the fluctuation time period, and dividing the first time period into a second time period and a third time period according to the target time point, includes: During the fluctuation time period, obtaining a first time point at which the first change rate exceeds a first preset threshold, and a second time point at which the second change rate exceeds a second preset threshold; determining the target time point according to the time interval between the first time point and the second time point; Taking the target time point as the dividing point, the time period before the target time point is determined as the second time period, and the time period after the target time point is determined as the third time period.

4. The method according to claim 3, characterized in that The determining the target time point according to the time interval between the first time point and the second time point includes: Calculating a time interval between the first time point and the second time point, and when the time interval is less than a preset time threshold, determining an earlier time point between the first time point and the second time point as the target time point; When the time interval is greater than or equal to the preset time threshold, an intermediate time point between the first time point and the second time point is obtained, and the intermediate time point is determined as the target time point.

5. The method according to claim 1, characterized in that The step of outputting device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and correcting the health data recorded by all target devices after receiving the reallocation confirmation information, includes: calculating health data difference values ​​of all target devices in the second time period and the third time period, and pairing all target devices into at least one target device group according to the health data difference values; According to the target device group, a device reallocation prompt message including a device reallocation plan is generated, and after receiving the reallocation confirmation message corresponding to the device reallocation prompt message, the health data of the third time period is exchanged with the health data of the third time period of the corresponding exchange device in the same target device group.

6. The method according to claim 5, characterized in that The calculating the health data difference values ​​of all target devices in the second time period and the third time period, and pairing all target devices into at least one target device group according to the health data difference values, includes: respectively calculating the difference between the health data of each target device in the second time period and the health data of other target devices in the third time period; Device pairs with the smallest difference values ​​among the target devices are screened out, and the device pairs with the smallest difference values ​​are divided into the same group to form a target device group.

7. The method according to claim 6, characterized in that The step of screening out device pairs with the smallest difference values ​​among target devices and grouping the device pairs with the smallest difference values ​​into the same group to form a target device group includes: Select any target device as the starting device, and use the other target device with the smallest difference between the health data of the starting device in the second time period and the health data of the other target devices in the third time period as the current device, and use the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period as the next device. The remaining devices are the target devices excluding the starting device and the current device; The next device is used as the current device, and the remaining device with the smallest difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is used as the next device, until the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold; When the difference between the health data of the current device in the second time period and the health data of the remaining devices in the third time period is greater than a preset difference threshold, all the target devices involved are divided into the same target device group; Repeat the above process for the remaining ungrouped target devices until all target devices are grouped or no new target device group can be formed.

8. An AI-based data analysis chronic disease health monitoring system, characterized by: Applied to electronic equipment, the system includes: A data acquisition module is used to acquire health data recorded by a device currently worn by any monitored subject within the monitoring range, wherein the health data includes chronic disease data and behavioral data continuously recorded by the wearable device for a first time period within a preset time window; an abnormal device determining module, configured to determine a fluctuation time period during which the chronic disease data in the first time period fluctuates, and determine a target device with abnormal usage based on the behavior data in the fluctuation time period; an abnormal time determination module, configured to determine a target time point of abnormal use of the target device based on the fluctuation time period, and divide the first time period into a second time period and a third time period based on the target time point, wherein the second time period is a time period before the abnormal use of the target device occurs, and the third time period is a time period after the abnormal use of the target device occurs; The data correction module is used to output device reallocation prompt information to all target devices within the monitoring range based on the health data of all target devices in the second time period and the third time period, and after receiving the reallocation confirmation information, correct the health data recorded by all target devices.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory, a user interface and a network interface, wherein the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.

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