Blood pressure data processing method and device and storage medium

Abstract

The embodiment of the invention discloses a blood pressure data processing method and device, equipment and a storage medium, the method and device are applied to electronic equipment, the electronic equipment comprises a first system and a second system, and the method comprises the steps that under the condition that monitoring data of multiple target sampling periods are obtained, the monitoring data of the multiple target sampling periods are obtained through the first system; the monitoring data of the one or more target sampling time periods are evaluated, a blood pressure evaluation result corresponding to each target sampling time period is obtained, and the monitoring data comprise data used for reflecting the blood pressure condition of the user; and processing the blood pressure evaluation results corresponding to the plurality of target sampling periods through the second system to obtain a blood pressure processing result. The power consumption of the electronic equipment can be reduced, the standby time of the electronic equipment is prolonged, and the accuracy of a blood pressure processing result is improved.

Description

Technical Field

[0001] Embodiments of the present application relate to information processing technologies, including but not limited to a blood pressure data processing method, device, and storage medium. Background Art

[0002] With the continuous development of technologies, people are paying more and more attention to their own health. As an example, people can use electronic devices to monitor their physiological data and process the physiological data to know their health status.

[0003] Therefore, it is of great significance to provide a blood pressure data processing method with low power consumption and accurate measurement. Summary of the Invention

[0004] In view of this, the blood pressure data processing method, device, equipment, and storage medium provided by the embodiments of the present application can reduce the power consumption of electronic devices, increase the standby time of electronic devices, and improve the accuracy of blood pressure processing results. The blood pressure data processing method, device, equipment, and storage medium provided by the embodiments of the present application are implemented as follows:

[0005] In a first aspect, an embodiment of the present application provides a blood pressure data processing method, which is applied to an electronic device. The method includes:

[0006] In the case of obtaining monitoring data for multiple target sampling periods, through the first system, evaluate and process the monitoring data for one or more of the target sampling periods to obtain a blood pressure evaluation result corresponding to each target sampling period, where the monitoring data includes data for reflecting the user's blood pressure situation;

[0007] Through the second system, process the blood pressure evaluation results corresponding to multiple target sampling periods to obtain a blood pressure processing result.

[0008] In a second aspect, an embodiment of the present application provides an electronic device, which includes a first system and a second system. The device includes:

[0009] In the case of obtaining monitoring data for multiple target sampling periods, through the first system, evaluate and process the monitoring data for one or more of the target sampling periods to obtain a blood pressure evaluation result corresponding to each target sampling period, where the monitoring data includes data for reflecting the user's blood pressure situation;

[0010] Through the second system, process the blood pressure evaluation results corresponding to multiple target sampling periods to obtain a blood pressure processing result.

[0011] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method provided by the embodiment of the present application is implemented.

[0012] The blood pressure data processing method, device, electronic device, and computer-readable storage medium provided by the embodiments of the present application, in the case of obtaining monitoring data of a plurality of target sampling periods, evaluate and process the monitoring data of one or more target sampling periods through a first system in the electronic device to obtain a blood pressure evaluation result corresponding to each target sampling period; then, through a second system in the electronic device, process the blood pressure evaluation results corresponding to the plurality of target sampling periods to obtain a final blood pressure processing result.

[0013] In the embodiments of the present application, on the one hand, by collecting monitoring data for reflecting the user's blood pressure in a plurality of target sampling periods and comprehensively determining the blood pressure processing result of the user based on the monitoring data of multiple sampling periods, the accuracy of the blood pressure processing result of the user can be improved; on the second hand, it is realized by the cooperation of a first system and a second system with different power consumptions in the electronic device. In this way, compared with using only one system to determine the blood pressure processing result, the power consumption of the electronic device can be effectively reduced, and the standby time of the electronic device can be increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The drawings here are incorporated into the specification and form a part of this specification. These drawings show embodiments consistent with the present application and are used together with the specification to explain the technical solutions of the present application.

[0015] Figure 1 It is a schematic structural diagram of the electronic device provided by the embodiment of the present application;

[0016] Figure 2 It is a schematic implementation flowchart of the blood pressure data processing method provided by the embodiment of the present application;

[0017] Figure 3 It is a schematic implementation flowchart of another blood pressure data processing method provided by the embodiment of the present application;

[0018] Figure 4 It is a schematic implementation flowchart of extracting the characteristic value corresponding to the monitoring data of the target sampling period provided by the embodiment of the present application;

[0019] Figure 5 It is a schematic diagram of the heart beat cycle provided by the embodiment of the present application;

[0020] Figure 6 It is a schematic implementation flowchart of determining the blood pressure processing result provided by the embodiment of the present application;

[0021] Figure 7Schematic diagram of the implementation process of another blood pressure data processing method provided by an embodiment of the present application;

[0022] Figure 8 Schematic diagram of the implementation process of presenting information related to monitoring data and blood pressure conditions provided by an embodiment of the present application;

[0023] Figure 9 Schematic diagram of a display interface provided by an embodiment of the present application;

[0024] Figure 10 Schematic diagram of another display interface provided by an embodiment of the present application;

[0025] Figure 11 Schematic diagram of the structure of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the following will further describe the specific technical solutions of the present application in detail with reference to the accompanying drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application but are not intended to limit the scope of the present application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0028] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments. However, it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with each other without conflict.

[0029] It should be noted that the terms "first / second / third" involved in the embodiments of the present application are used to distinguish similar or different objects and do not represent a specific order for the objects. It can be understood that "first / second / third" can be interchanged with a specific order or sequence when permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0030] With the continuous development of technology, people's attention to their own health is also increasing. As an example, people can monitor their physiological data through an electronic device and process the physiological data to obtain their own health status.

[0031] In related technologies, when measuring a user's blood pressure, a non-invasive blood pressure data processing method is mostly used. The non-invasive blood pressure data processing method can be divided into two types: cuff type and cuffless type. Among them, the cuff type blood pressure data processing method has the advantage of high measurement accuracy. However, since blood pressure measurement needs to be performed by inflating and deflating the cuff, continuous blood pressure detection cannot be achieved and it is not convenient for long-term use. The cuffless blood pressure data processing method requires continuous collection of blood pressure data, which has relatively high requirements for the power consumption of electronic devices.

[0032] Therefore, it is of great significance to provide a blood pressure data processing method with low power consumption and accurate measurement.

[0033] In view of this, the embodiment of the present application provides a blood pressure data processing method, which is applied to an electronic device. Figure 1 FIG. is an application scenario diagram of the blood pressure data processing method provided in an embodiment. As Figure 1 shown, the user can carry, wear or use the electronic device 10.

[0034] The electronic device involved in the embodiment of the present invention may include a general handheld electronic terminal, such as a mobile phone, a smart phone, a portable terminal, a terminal, a personal digital assistant (Personal Digital Assistant, PDA), a portable multimedia player (Personal Media Player, PMP) device, a notebook computer, a notebook (Note Pad), a wireless broadband (Wireless Broadband, Wibro) terminal, a tablet computer (personal computer, PC), a smart PC, a point of sales (Point of Sales, POS), and an in-vehicle computer, etc.

[0035] The electronic device may also include a wearable device. A wearable device is a portable electronic device that can be directly worn on the user's body or integrated into the user's clothes or accessories. A wearable device is not only a hardware device, but can also achieve powerful intelligent functions through software support, data interaction, and cloud interaction, such as: computing function, positioning function, alarm function, and can also be connected to mobile phones and various terminals. Wearable devices may include, but are not limited to, watch-type devices supported by the wrist (such as watches, wrists, etc.), shoes-type devices supported by the feet (such as shoes, socks, or other products worn on the legs), Glass-type devices supported by the head (such as glasses, helmets, headbands, etc.), and smart clothing, schoolbags, crutches, accessories, and other various non-mainstream product forms.

[0036] As Figure 1 shown, the electronic device provided in the embodiment of the present application includes a first system and a second system.

[0037] In some embodiments, the power consumptions of the first system and the second system are different. That is to say, when corresponding processing is performed on the same content, the power consumption of the first system is different from that of the second system.

[0038] In some embodiments, the computing capabilities of the first system and the second system are different. For example, the first system has a relatively weak computing capability, while the second system has a relatively strong computing capability. The strength of the computing capability can also be understood as the strength of the processing capability.

[0039] In some embodiments, the first system may be a first processor, and the second system may be a second processor. Both the first processor and the second processor can be microprocessors.

[0040] In some embodiments, the first processor operating in the first system is an MCU, and the second processor operating in the second system is a CPU.

[0041] In the case where the power consumption of the first system is higher than that of the second system, the first processor corresponding to the first system may be the core processor; in the case where the power consumption of the first system is less than that of the second system, the second processor corresponding to the second system may be the core processor. The core processor can be understood as the processor corresponding to the system with high power consumption, or the core processor can also be understood as the processor with stronger computing power among the first processor and the second processor.

[0042] In the embodiments of the present application, there is no limitation on the running timing of the first system and the second system. For example, the first system and the second system can run simultaneously, that is, when the second system runs, the first system also runs; or, the first system and the second system can also have a running sequence, such as the first system runs first and the second system runs later.

[0043] In some embodiments, when the first system runs, the second system may not run, but when the second system runs, the first system must run simultaneously. That is to say, when the first system controls, it can be that the first system runs alone and the second system does not run, which may save power consumption; when the second system controls, both the first system and the second system run, and in this case, the power consumption will be higher, but the processing ability will also be stronger.

[0044] Here, the first processor and the second processor may include microprocessors configured according to actual applications, and there is no limitation on the operating systems of the first processor and the second processor. For example, the operating system can be an Android system, a Linux system, a Windows system, an IOS system, an RTOS (Real Time Operating System), etc.

[0045] In some embodiments, the first system running on the first processor is an RTOS system, and the second system running on the second processor is an Android system or an IOS system or a Windows system.

[0046] A communication connection can be established between the first processor and the second processor through SPI (Serial Peripheral Interface), so that the first system and the second system can transmit communication data through the SPI bus.

[0047] Figure 2 This is a schematic flowchart of the implementation of the blood pressure data processing method provided by the embodiments of the present application. This method is applied to an electronic device as shown in Figure 1 the electronic device includes a first system and a second system. As shown in Figure 2 the method may include the following steps 201 to step 202:

[0048] Step 201, in the case of obtaining monitoring data for a plurality of target sampling periods, through the first system of the electronic device, evaluate and process the monitoring data for one or more target sampling periods to obtain a blood pressure evaluation result corresponding to each target sampling period. The monitoring data includes data for reflecting the user's blood pressure situation.

[0049] In some embodiments, the monitoring data obtained for a plurality of target sampling periods may be data for reflecting the user's blood pressure situation under a plurality of target sampling periods.

[0050] For example, the monitoring data may be volume pulse wave data, which is PPG data measured by photoplethysmography (PPG). Of course, the monitoring data may also be or may further be motion data, electrocardiogram (ECG) data, etc. The monitoring data can be obtained through sensors corresponding to the data type.

[0051] In some embodiments, the target sampling periods are obtained by screening the monitoring data for a plurality of initial sampling periods. The number of monitoring data for the plurality of initial sampling periods is greater than or equal to the number of monitoring data for the plurality of target sampling periods.

[0052] It can be understood that after obtaining the monitoring data for a plurality of initial sampling periods, not all the monitoring data for each initial sampling period meet the usage requirements. Therefore, in the embodiments of the present application, in order to increase the availability of samples, reduce abnormal samples, and improve the accuracy of blood pressure processing results, the monitoring data for the obtained plurality of initial sampling periods can be screened first to obtain the monitoring data for a plurality of target sampling periods.

[0053] In the embodiments of the present application, the screening process is not limited. For example, the screening process can be a process of screening the monitoring data of multiple initial sampling periods according to the time intervals between multiple initial sampling periods. Or, the screening process can be a process of filtering out abnormal data.

[0054] When the screening process is to screen the monitoring data of multiple initial sampling periods according to the time intervals between multiple initial sampling periods, one or more of the monitoring data of the initial sampling periods with relatively close time intervals can be screened out. For example, when the time interval is 0.5 seconds, the initial sampling period 1 is obtained at the 1st second, the initial sampling period 2 is obtained at the 1.5th second, the initial sampling period 3 is obtained at the 2nd second, and so on. To avoid excessive computational complexity caused by too much collected data, some of the data of the initial sampling periods can be removed. For example, any one or more of the initial sampling period 1 obtained at the 1st second, the initial sampling period 2 obtained at the 1.5th second, or the initial sampling period 3 obtained at the 2nd second can be removed.

[0055] Among them, to ensure that monitoring data can be obtained within a time period, such as this time period is 1 second, the time interval can be set to 0.5 seconds, so as to ensure that multiple groups of monitoring data can be obtained within this time period. At the same time, to avoid excessive computational complexity caused by too much collected data, the foregoing method can be used to remove the monitoring data of some sampling periods.

[0056] In some embodiments, to avoid redundant collected data, when the monitoring data of multiple obtained initial sampling periods are similar, some of the monitoring data of the initial sampling periods can also be removed.

[0057] When the screening process is a process of filtering out abnormal data, the embodiments of the present application do not limit the method of filtering out abnormal data. For example, the screening process can be to filter out abnormal values in the monitoring data of each initial sampling period; or, filter out the monitoring data of the initial sampling periods with abnormalities among multiple initial sampling periods; or, according to data such as gyroscopes and acceleration sensors in the electronic device, judge the hand movement state of the user, and thus filter out the data with large jitters in the monitoring data of multiple initial sampling periods based on the hand movement state; or, the monitoring data of multiple initial sampling periods can also be subjected to mean filtering, baseline drift removal, or high-frequency noise removal, etc.

[0058] Among them, the mean filtering method can adopt filtering methods such as band-pass filters or moving average filters. The methods of removing baseline drift and removing high-frequency noise can be processed by methods such as Gaussian filtering, frequency domain filtering, and wavelet transform.

[0059] By screening and processing the monitoring data of multiple initial sampling periods, the influence of the surrounding environment, measuring instruments, etc. can be minimized, thereby improving the accuracy of subsequent blood pressure processing results.

[0060] In the embodiments of the present application, the execution subject for obtaining the monitoring data of multiple target sampling periods is not limited.

[0061] For example, in some embodiments, the monitoring data of multiple target sampling periods can be obtained through the data acquisition unit in the electronic device. In other embodiments, the monitoring data of multiple target sampling periods can also be obtained through the first system or the second system. The data acquisition unit and the first system or the second system can be different.

[0062] In some embodiments, when the first system evaluates and processes the monitoring data of one or more target sampling periods to obtain the blood pressure evaluation result corresponding to each target sampling period, it may include, for each target sampling period, evaluating and processing it through its own corresponding monitoring data to obtain the corresponding blood pressure evaluation result.

[0063] In some embodiments, when the first system evaluates and processes the monitoring data of one or more target sampling periods to obtain the blood pressure evaluation result corresponding to each target sampling period, it may include, for each target sampling period, obtaining the blood pressure evaluation result corresponding to the target sampling period through its own corresponding monitoring data and the monitoring data of one or more target sampling periods before the target sampling period. For example, for the target sampling period 3, the blood pressure evaluation result corresponding to the target sampling period 3 can be jointly determined through its own corresponding monitoring data and the monitoring data corresponding to the target sampling period 2 and the target sampling period 1 before the target sampling period 3.

[0064] In the embodiments of the present application, the manner in which the first system of the electronic device evaluates and processes the monitoring data of one or more target sampling periods to obtain the blood pressure evaluation result corresponding to each target sampling period is not limited.

[0065] In some embodiments, the blood pressure evaluation result can be in the form of a blood pressure evaluation score or in the form of other parameters, and can be the blood pressure evaluation situation characterized by the blood pressure-related features in this time period.

[0066] For example, in some embodiments, the characteristic value corresponding to the monitoring data of each target sampling period can be extracted, and based on this characteristic value, the blood pressure evaluation result corresponding to each target sampling period can be determined. The implementation process can be realized with reference to steps 301 to 302 in the following embodiments.

[0067] In some other embodiments, the blood pressure evaluation result corresponding to each target sampling period can be determined jointly by extracting the eigenvalue corresponding to the monitoring data of each target sampling period and the user status corresponding to each obtained target sampling period. The implementation process can be realized with reference to steps 701 to 703 in the following embodiments.

[0068] Step 202, process the blood pressure evaluation results corresponding to multiple target sampling periods through the second system of the electronic device to obtain a blood pressure processing result.

[0069] In the embodiments of the present application, there is no limitation on the manner of triggering the processing of the blood pressure evaluation results corresponding to multiple target sampling periods through the second system to obtain a blood pressure processing result.

[0070] For example, in some embodiments, the step of obtaining a blood pressure processing result through the second system can be executed when the number of monitoring data of multiple obtained target sampling periods meets a preset number.

[0071] In some other embodiments, the step of obtaining a blood pressure processing result through the second system can also be executed when the processing period of obtaining a blood pressure processing result through the second system meets a preset period.

[0072] The so-called processing period meeting a preset period can include that the time interval between the last time of obtaining a blood pressure processing result through the second system and the current time of obtaining a blood pressure processing result through the second system meets a preset period.

[0073] For example, if the preset period is 2 days and the last execution of obtaining a blood pressure processing result through the second system was on November 12th, then the current execution of obtaining a blood pressure processing result through the second system needs to be satisfied on November 14th.

[0074] In still some other embodiments, the step of obtaining a blood pressure processing result through the second system can also be executed when the current time meets a preset time.

[0075] Among them, there is no limitation on the preset time. For example, if the preset time is 10 o'clock, then at 10 o'clock every day, the step of obtaining a blood pressure processing result through the second system is executed.

[0076] In some embodiments, the step of obtaining a blood pressure processing result through the second system can also be executed when one or more blood pressure evaluation results meet a preset result.

[0077] Among them, there is no limitation on the setting of the preset result, and it can be set according to actual needs.

[0078] In some embodiments, a preset result may be set to greater than 70 points, which indicates that the user has high blood pressure and may be at risk; and, a preset result may be set to less than 40 points, which indicates that the user has low blood pressure and is also at risk. In this way, when one or more of the multiple blood pressure assessment results obtained meet the preset result, indicating that the user may be at health risk, the second system can be used to process the blood pressure assessment result corresponding to each target sampling period obtained to obtain the final blood pressure processing result.

[0079] In some embodiments, when the blood pressure assessment result processed by the first system indicates a risk, the user can be prompted to alert the risk, and / or the second system can be activated to perform more detailed calculations to give a more comprehensive and accurate result for the user's reference.

[0080] In some embodiments, to obtain the blood pressure processing result, it can be achieved by performing the method described in step 303 of the following embodiments.

[0081] In the embodiments of the present application, on the one hand, by collecting monitoring data for reflecting the user's blood pressure at multiple target sampling periods and comprehensively determining the blood pressure processing result of the user based on the monitoring data of multiple sampling periods, the accuracy of the blood pressure processing result for the user can be improved; on the other hand, it is achieved by the cooperation of the first system and the second system with different power consumptions in the electronic device. In this way, compared with using only one system to determine the blood pressure processing result, the power consumption of the electronic device can be effectively reduced, and the standby time of the electronic device can be increased.

[0082] Figure 3 This is a schematic flowchart of the implementation process of another blood pressure data processing method provided by the embodiments of the present application. This method is applied to an electronic device as shown in Figure 1 The electronic device includes a first system and a second system.

[0083] As shown in Figure 3 The method may include the following steps:

[0084] Step 301, in the case of obtaining monitoring data for multiple target sampling periods, through the first system of the electronic device, obtain the characteristic value corresponding to the monitoring data for each target sampling period.

[0085] In the embodiments of the present application, the multiple target sampling periods are obtained by screening the monitoring data of multiple initial sampling periods, and the number of monitoring data of the multiple initial sampling periods is greater than or equal to the number of monitoring data of the multiple target sampling periods.

[0086] Among them, the screening process may include screening the multiple initial sampling periods according to the time interval between the multiple initial sampling periods or filtering out abnormal data.

[0087] Understandably, the monitored data obtained for the target sampling period is generally periodic data. For each target sampling period, it can be divided into multiple heartbeat cycles to obtain monitored sub-data corresponding to multiple heartbeat cycles. Considering that the consecutive heartbeats of a normal person generally do not change particularly drastically, it can be assumed that for any characteristic dimension, all the characteristic values within a target sampling period satisfy a Gaussian distribution. Thus, an optimization method (such as the least squares method) can be used to fit a Gaussian distribution. In this way, for the monitored data within a target sampling period, a Gaussian distribution can be obtained for each characteristic dimension. Each sampling of these Gaussian distributions can generate a set of features, and thus any number of sets of data can be sampled to more comprehensively characterize the possible inherent characteristics of the user during this period, thereby enhancing the diversity of the monitored data obtained for the target sampling period and improving the accuracy of the blood pressure processing result.

[0088] For example, in some embodiments, the characteristic values corresponding to the monitored data of the target sampling period can be extracted by performing steps 401 to 403 in the following embodiments:

[0089] Step 401: Perform segmentation processing on the monitored data of each target sampling period to obtain monitored sub-data of multiple heartbeat cycles.

[0090] Here, the monitored data may include one or more of PPG data and ECG data, and the above data are all data that can reflect the user's heart fluctuations. The cardiac cycle can reflect the wave velocity of the pulse wave in the blood vessel to a certain extent, thereby indirectly reflecting the change in the magnitude of blood pressure and realizing the measurement of the user's blood pressure.

[0091] In some embodiments, the monitored data of a target sampling period includes at least one heartbeat cycle. Here, the monitored data of each target sampling period can be segmented to obtain monitored sub-data of multiple heartbeat cycles.

[0092] In some embodiments, the heartbeat cycle can be the cardiac cycle, which refers to the periodic change from the start of atrial contraction to the end of ventricular contraction during one heartbeat of the heart.

[0093] In some embodiments, after segmenting the monitored data of each target sampling period to obtain monitored sub-data of multiple heartbeat cycles, in order to improve the usability of the monitored sub-data, denoising processing can also be performed on the monitored sub-data of multiple heartbeat cycles.

[0094] In some embodiments, a template matching method may be used to detect noise data in the monitoring sub-data of multiple heartbeat cycles. By calculating the similarity between the monitoring sub-data of each heartbeat cycle and the template data, the data with a similarity greater than or equal to the threshold is determined as non-noise data and retained; the data with a similarity less than the threshold is determined as noise data and removed. Among them, the template data can be obtained by taking the average of the monitoring sub-data of multiple heartbeat cycles, or the template data can also be the maximum or minimum value of the monitoring sub-data of multiple heartbeat cycles, and there is no limitation on this.

[0095] Step 402: Determine the characteristic value of each heartbeat cycle according to the peak value and trough value of the monitoring sub-data of each heartbeat cycle.

[0096] It can be understood that, as Figure 5 shown, a schematic diagram of a heartbeat cycle is provided. A heartbeat cycle may include multiple peak values and trough values. To a certain extent, the peak value and trough value reflect the waveform change of the heartbeat cycle, and thus can reflect the magnitude of the user's blood pressure.

[0097] Therefore, in the embodiments of the present application, after the monitoring sub-data of multiple heartbeat cycles corresponding to each target sampling period is segmented, the peak value and trough value in the monitoring sub-data of each heartbeat cycle in the target sampling period can be extracted, and thus the characteristic value of each heartbeat cycle can be determined according to the peak value and trough value in each heartbeat cycle.

[0098] In some embodiments, multiple peak values and trough values in each monitoring sub-signal may be used as the first key points of the cardiac cycle; and the maximum value among the first key points, the peak values and trough values in the first key points whose acquisition order is greater than the threshold may be used as the second key points of the cardiac cycle.

[0099] Among them, there is no limitation on the setting of the acquisition order greater than the threshold. For example, the first three peak values and the first three trough values can be acquired and used as the second key points.

[0100] Subsequently, through the above key points, the Pulsewave analysis (PWA) characteristic value corresponding to each cardiac cycle is extracted.

[0101] Step 403: Obtain the characteristic value corresponding to the monitoring data of the corresponding target sampling period according to the characteristic values of multiple heartbeat cycles included in each target sampling period.

[0102] Here, there is no limitation on the implementation manner of obtaining the characteristic value corresponding to the monitoring data of the corresponding target sampling period according to the characteristic values of multiple heartbeat cycles included in each target sampling period.

[0103] For each target sampling period, the median of the characteristic values of the multiple heartbeat cycles included therein can be calculated, and this median can be used as the characteristic value corresponding to the monitoring data of this target sampling period. Alternatively, for each target sampling period, the average value of the characteristic values of the multiple heartbeat cycles included therein can also be used as the characteristic value corresponding to the monitoring data of this target sampling period, etc.

[0104] Step 302: Input the characteristic value corresponding to the monitoring data of each target sampling period into a pre-trained first result evaluation model to obtain the blood pressure evaluation result corresponding to each target sampling period.

[0105] In some embodiments, after obtaining the characteristic value corresponding to the monitoring data of each target sampling period, it can be input into a pre-trained first result evaluation model to train and obtain the blood pressure evaluation result corresponding to this target sampling period.

[0106] Among them, the pre-trained first result evaluation model is obtained by training a preset result evaluation model according to the historical monitoring data collected within a historical period and the historical blood pressure evaluation results corresponding to the historical monitoring data.

[0107] In the embodiments of the present application, the type of the preset result evaluation model is not limited. For example, in some embodiments, considering the algorithm interpretability and the resource consumption on the electronic device, a machine learning model based on a tree algorithm can be selected as the preset result evaluation model.

[0108] Step 303: Through the second system of the electronic device, determine the blood pressure processing result according to the blood pressure evaluation result of each target sampling period and the corresponding target weight value.

[0109] It should be noted that the monitoring data of multiple target sampling periods obtained in some embodiments may include the monitoring data of multiple target sampling periods within multiple target durations of each day within N days. In some embodiments, the blood pressure evaluation result can be in the form of a blood pressure evaluation score.

[0110] The multiple target sampling periods include a first part of target sampling periods and a second part of target sampling periods. The first part of target sampling periods are the multiple sampling periods included in the first target duration, and the second part of target sampling periods are the multiple sampling periods included in the second target duration.

[0111] For example, the monitoring data of the multiple target sampling periods may include the monitoring data of multiple initial sampling periods within multiple target time periods such as from 10:00 to 12:00 in the morning, from 14:00 to 16:00 in the afternoon, and from 20:00 to 22:00 in the evening for each of the three days obtained. Moreover, the number of initial sampling periods within each target time period is not limited, and the number of initial sampling periods within different target time periods may be the same or different.

[0112] Further, in some embodiments, to obtain a more accurate blood pressure processing result, the time interval between different target time periods may be set to be greater than a certain time interval threshold, and the value range of the time interval threshold may include 4 hours to 10 hours. For example, it may be 4 hours, 6 hours, 6.5 hours, or 8 hours, 10 hours, etc., which can be set as needed.

[0113] In the embodiments of the present application, there is no limitation on the way of assigning corresponding target weight values to the blood pressure evaluation results of each target sampling period.

[0114] For example, in some embodiments, the target weight values corresponding to the blood pressure evaluation results of each target sampling period may be set to be the same.

[0115] In some other embodiments, it may be set that the sum of the multiple target weight values corresponding to the first part of the target sampling periods is the same as the sum of the multiple target weight values corresponding to the second part of the target sampling periods.

[0116] That is to say, assuming that the first part of the target sampling periods included in the first target time period is 100, and the second part of the target sampling periods included in the second target time period is 50, it can be set that the sum of the target weight values set for the 100 first part of the target sampling periods in the first target time period is the same as the sum of the target weight values of the 50 second part of the target sampling periods in the second target time period.

[0117] In this way, it is possible to avoid overemphasizing the monitoring data of the target sampling periods within a certain target time period, but to consider the monitoring data of the target sampling periods within each target time period in a balanced manner, thereby improving the accuracy of the obtained blood pressure evaluation result, and further improving the accuracy of the blood pressure processing result.

[0118] In the embodiments of the present application, there is no limitation on the type of the blood pressure processing result.

[0119] For example, in some embodiments, the blood pressure processing result may include the blood pressure evaluation result calculated according to the blood pressure evaluation result of each target sampling period and the corresponding target weight value.

[0120] In some other embodiments, the blood pressure processing result may include a blood pressure grading result. In this case, the blood pressure processing result can be determined by performing steps 601 to 603 in the following embodiments:

[0121] Step 601: Determine a candidate evaluation result corresponding to each target sampling period according to the blood pressure evaluation result corresponding to the monitoring data of each target sampling period and the corresponding target weight value.

[0122] Here, the candidate evaluation result may include the product of the blood pressure evaluation result corresponding to the monitoring data of each target sampling period and the corresponding target weight value.

[0123] Step 602: Synthesize the candidate evaluation results corresponding to multiple target sampling periods to obtain a target evaluation result.

[0124] Here, the target evaluation result may include results such as the weighted average value, weighted median value, and bisecting line of the candidate evaluation results corresponding to multiple target sampling periods.

[0125] Step 603: Determine the blood pressure classification result according to the target evaluation result and the evaluation threshold, and different blood pressure classification results correspond to different evaluation thresholds.

[0126] Here, the evaluation threshold can be divided into multiple intervals, and the evaluation thresholds in different intervals correspond to different blood pressure classification results. The value of each interval can be set according to the actual situation, and no limitation is made thereto.

[0127] For example, in some embodiments, the evaluation threshold may include a threshold obtained based on actual experience. Alternatively, in some other embodiments, the evaluation threshold may be determined according to physiological data, and the physiological data includes at least one of age, weight, gender, and body mass index (BMI).

[0128] In some embodiments, the target evaluation result may be in the form of a target evaluation score, and of course, it may also be in the form of other features, and no limitation is made thereto.

[0129] It can be understood that by synthesizing the physiological data of the user to determine the value of the evaluation threshold, the blood pressure data processing method has a relatively balanced performance for different populations and high stability.

[0130] The blood pressure processing result can be divided into multiple levels of processing results. For example, it may include three levels of processing results, five levels of processing results, or more levels of processing results.

[0131] As an example, the multiple levels of processing results include five levels of processing results, such as low blood pressure, normal blood pressure, normal high value, grade 1 hypertension, and grade 2 hypertension. The so-called normal high value may refer to a value that is on the high side within the normal range value. The so-called low blood pressure, normal blood pressure, normal high value, grade 1 hypertension, and grade 2 hypertension are the comparison results of the obtained blood pressure processing results with the reference threshold.

[0132] In this way, by dividing the blood pressure stratification results into more detailed ones, it can assist users to pay more attention to their own blood pressure health in a more detailed manner, which is convenient for improvement through lifestyle intervention.

[0133] Understandably, if the measured blood pressure of the user is normal, it can indicate that the health risk degree of the user during this period is relatively low. Therefore, to reduce the device power consumption, the sampling frequency of its monitoring data can be selected to be reduced; if the measured blood pressure of the user is abnormal, it can indicate that the health risk degree of the user is relatively high. Therefore, to better monitor the health condition of the user, the sampling frequency of the monitoring data can be selected to be increased.

[0134] Based on this, in some embodiments, when the blood pressure processing result indicates normal blood pressure, the sampling frequency of the monitoring data is reduced; when the blood pressure processing result indicates abnormal blood pressure, the sampling frequency of the monitoring data is increased.

[0135] Here, the blood pressure processing result can include a result obtained after processing the blood pressure evaluation results corresponding to multiple target sampling periods; it can also include multiple results obtained after processing the blood pressure evaluation results corresponding to multiple target sampling periods. For example, the blood pressure evaluation results can be processed every 10, and a blood pressure processing result corresponding to the 10 blood pressure evaluation results can be obtained, so as to obtain multiple blood pressure processing results.

[0136] Of course, when the blood pressure processing result includes multiple ones, the blood pressure processing result indicating abnormal blood pressure can include that one of the multiple blood pressure processing results is abnormal, which can indicate that the blood pressure processing result is abnormal; or, when the measurement results indicating abnormal blood pressure in the multiple blood pressure processing results are greater than the threshold, it is determined that the blood pressure processing result is abnormal.

[0137] Or, in some other embodiments, when the number of blood pressure evaluation results indicating normal blood pressure is greater than or equal to the preset number threshold, the sampling frequency of the monitoring data is reduced; when the number of blood pressure evaluation results indicating abnormal blood pressure is less than the preset number threshold, the sampling frequency of the monitoring data is increased.

[0138] Here, the value of the preset number threshold is not limited, and it can be set according to actual needs.

[0139] Implementing this embodiment, by adjusting the sampling frequency in a timely manner according to the user's blood pressure situation, the sampling frequency of the monitoring data is reduced when the user's health risk degree is relatively low, thereby reducing the device power consumption; when the user's health risk degree is relatively high, the sampling frequency of the monitoring data is increased, so as to be able to strictly pay attention to the user's health condition and reduce the user's risk.

[0140] In some embodiments, the blood pressure assessment result can characterize the situation of a single blood pressure measurement or the blood pressure situation over a short-term time period, such as the blood pressure situation on the same day, while the blood pressure processing result can characterize the blood pressure situation over a relatively long period of time, such as reflecting the long-term blood pressure situation. Thus, it provides comprehensive monitoring and early warning for the user's blood pressure from short-term to long-term.

[0141] In the embodiments of the present application, on the one hand, by collecting monitoring data for reflecting the user's blood pressure situation at multiple target sampling time periods and synthesizing the monitoring data of multiple sampling time periods to determine the blood pressure processing result for the user, the accuracy of the blood pressure processing result for the user can be improved; on the other hand, it is achieved by the coordinated use of a first system and a second system with different power consumptions in the electronic device. In this way, compared with using only one system to determine the blood pressure processing result, the power consumption of the electronic device can be effectively reduced, and the standby time of the electronic device can be increased.

[0142] Figure 7 It is a schematic flowchart of the implementation process of another blood pressure data processing method provided by the embodiments of the present application. This method is applied to an electronic device as shown in Figure 1 shown, and the electronic device includes a first system and a second system with different power consumptions. As shown in Figure 7 shown, this method may include the following steps:

[0143] Step 701, obtain the monitoring data of multiple target sampling time periods, and obtain the user status corresponding to each target sampling time period, where the user status includes one or more of a motion state and a sleep state.

[0144] It can be understood that when the user carries or wears the electronic device, he may be in a normal state, a motion state, a sleep state, etc. When the user is in different states, the corresponding blood pressure values may also be different. For example, when the user is in a motion state, the measured blood pressure value is a bit higher than that measured in the sleep state, but this does not mean that the user has high blood pressure in the motion state.

[0145] Therefore, in the embodiments of the present application, when obtaining the monitoring data of multiple target sampling time periods, the user status of the electronic device at the time of collecting the monitoring data of each target sampling time period can also be obtained, and the user status at least includes a motion state and a sleep state.

[0146] Similarly, there is no limitation on the execution subject for obtaining the monitoring data of multiple target sampling time periods and the user status corresponding to each target sampling time period.

[0147] In some embodiments, the monitoring data for multiple target sampling periods and the user state corresponding to each target sampling period can be obtained through a data acquisition unit in an electronic device. In other embodiments, the monitoring data for multiple target sampling periods and the user state corresponding to each target sampling period can also be obtained through a first system or a second system. The data acquisition unit is different from the first system or the second system.

[0148] In some embodiments, the user state corresponding to a target sampling period can also be a type of data in the monitoring data. In this way, when obtaining the monitoring data for each target sampling period, the user state corresponding to the target sampling period can be obtained synchronously.

[0149] Step 702: Obtain the eigenvalue corresponding to the monitoring data for each target sampling period through the first system of the electronic device.

[0150] In the embodiments of the present application, the implementation manner of step 702 is the same as the implementation manner of obtaining the eigenvalue corresponding to the monitoring data for each target sampling period recorded in step 302 in the above embodiments, and will not be elaborated here.

[0151] Step 703: Input the eigenvalue corresponding to the monitoring data for each target sampling period and the user state corresponding to each target sampling period into a pre-trained second result evaluation model to obtain the blood pressure evaluation result corresponding to each target sampling period.

[0152] It can be understood that when evaluating the blood pressure evaluation result corresponding to each target sampling period, the detection data and their user states of multiple previous target sampling periods can also be used.

[0153] In some embodiments, after obtaining the eigenvalue corresponding to the monitoring data for each target sampling period and the corresponding user state, they can be input into a pre-trained first result evaluation model to train and obtain the blood pressure evaluation result corresponding to the target sampling period.

[0154] Among them, the pre-trained second result evaluation model is obtained by training a preset result evaluation model according to the historical monitoring data collected during the historical period, the user state of the electronic device when collecting the historical monitoring data, and the historical blood pressure evaluation result corresponding to the historical monitoring data.

[0155] In the embodiments of the present application, the type of the second result evaluation model is not limited. For example, in some embodiments, considering the algorithm interpretability and the resource consumption on the electronic device, a machine learning model based on a tree-based algorithm can be selected as the second result evaluation model.

[0156] Step 704: Through the second system of the electronic device, determine the blood pressure processing result according to the blood pressure evaluation result of each target sampling period and the corresponding target weight value.

[0157] Here, the implementation manner of determining the blood pressure processing result may be the same as that described in step 303 of the above embodiment, and will not be elaborated here.

[0158] However, it should be noted that in some embodiments, when the user status corresponding to each target sampling period is obtained, the target weight value corresponding to each target sampling period is related to the user status of the corresponding target sampling period.

[0159] For example, it can be set that the target weight value of the blood pressure evaluation result at the target sampling moment in the exercise state is less than the target weight value of the blood pressure evaluation result at the target sampling moment when the user is in the normal state.

[0160] In some embodiments, the weight of the monitoring data corresponding to the target sampling period can be determined according to the user status corresponding to the target sampling period, and then the monitoring data and weights of one or more target sampling periods are input into a pre-trained second result evaluation model to obtain the blood pressure evaluation result of the current target sampling period.

[0161] It can be understood that the user status may affect the effect of the monitoring data. In some states, the monitoring data may not be suitable as evaluation parameters, and in some states, its weight needs to be adjusted. In addition, in some embodiments, calculating the blood pressure evaluation result of the current target sampling period requires using the monitoring data of multiple previous target sampling periods, and further, the data of each target sampling period also needs to be adjusted in weight in combination with its user status. Thus, the blood pressure evaluation is made more accurate. That is to say, the user status can be considered when the blood pressure is evaluated in the first system.

[0162] In some embodiments, on the one hand, by collecting the monitoring data for reflecting the user's blood pressure in multiple target sampling periods and comprehensively determining the blood pressure processing result of the user based on the monitoring data of multiple sampling periods, the accuracy of the blood pressure processing result of the user can be improved; on the other hand, it is achieved by the coordinated use of the first system and the second system with different power consumptions in the electronic device. In this way, compared with using only one system to determine the blood pressure processing result, the power consumption of the electronic device can be effectively reduced, and the standby time of the electronic device can be increased.

[0163] It should be noted that for the convenience of the user to perceive the collection of the monitoring data and to manage their own blood pressure situation, in the embodiments of the present application, the monitoring data or the blood pressure processing result can also be displayed on the electronic device.

[0164] In some embodiments, the electronic device may further include a display screen, which can establish a communication connection with the first system and the second system and display the data output by the first system or the second system.

[0165] Here, to display information about the monitoring data and blood pressure conditions, the following steps 801 to 802 may be executed:

[0166] Step 801, output target information, where the target information includes at least one of the total number of all acquired monitoring data, the number of invalid monitoring data, the number of valid monitoring data, the ratio of invalid monitoring data to all monitoring data, and the ratio of valid monitoring data to all monitoring data. The valid monitoring data is the monitoring data of multiple target sampling periods.

[0167] Here, the valid monitoring data may include the monitoring data of multiple target sampling periods determined from all the monitoring data.

[0168] When classifying all the monitoring data, there is no limitation on the classification method.

[0169] In some embodiments, for all the acquired monitoring data, the valid monitoring data and the invalid monitoring data therein may be determined based on the acquisition time and quantity of all the monitoring data.

[0170] For example, if an effective time range K (such as 30 days) is set, then when measuring blood pressure with all the acquired monitoring data, to avoid the situation where the user's blood pressure status has changed due to the sampling time being too far from the present, resulting in inaccurate measurement results, the data with sampling periods outside the effective time range in all the monitoring data may be regarded as invalid monitoring data.

[0171] Alternatively, it may also be set that the quantity of monitoring data acquired within one day is not less than M. Then, when the quantity of monitoring data acquired within one day is less than M, the monitoring data acquired on that day is regarded as invalid monitoring data.

[0172] Figure 9 A schematic diagram of the display interface is given. As Figure 9 shown, the total number of all the monitoring data, the number of invalid monitoring data, and the number of valid monitoring data can be displayed on the display interface of the electronic device.

[0173] That is, the quantity M of valid monitoring data acquired every day is displayed on the display interface, and it is also displayed which days are invalid due to the quantity of valid monitoring data being less than M.

[0174] The above quantities can be displayed using a calendar diagram or a time series diagram, and there is no limitation on this.

[0175] Figure 10 Another schematic diagram of the display interface is given, such as Figure 10 shown, and the proportion of invalid monitoring data to all monitoring data and the proportion of valid monitoring data to all monitoring data can also be displayed on the display interface of the electronic device.

[0176] The above data can be presented in ways such as numerical values, progress bars, circular rings, etc., and there is no limitation on this.

[0177] Step 802, when the target information does not meet the corresponding quantity condition, output information for prompting to increase the wearing duration.

[0178] Here, there is no limitation on the situation where the target information does not meet the corresponding quantity condition. For example, when the quantity of valid monitoring data collected within a day is less than the quantity threshold, output information for prompting to increase the wearing duration.

[0179] In this way, the user can perceive which days the wearing situation is too poor, such as forgetting to wear the watch on weekends or not wearing it at some nights. By outputting information for prompting to increase the wearing duration, the user is reminded to wear for a longer time or wear at night, so as to accelerate the collection of monitoring data, and thus accelerate the speed of obtaining the blood pressure processing result.

[0180] In some embodiments, when the number of target sampling periods is small, it will affect the calculation of blood pressure. Therefore, when the number of target sampling periods is less than the preset quantity, the user can be reminded to increase the wearing time or pay attention to the wearing accuracy. The target sampling period can be understood as the period during which valid monitoring data is obtained.

[0181] It should be understood that although each step in the above flowcharts is shown in sequence according to the arrow indication, these steps do not necessarily need to be executed in the order indicated by the arrow. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the above flowcharts may include multiple sub-steps or multiple stages. These sub-steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments. The execution order of these sub-steps or stages is not necessarily sequential either, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0182] It should be noted that in the embodiments of the present application, if the above method is implemented in the form of software function modules and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present application, in essence, or the part that contributes to the related technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions to enable an electronic device to execute all or part of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), magnetic disks, or optical discs that can store program codes. In this way, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0183] Embodiments of the present application provide an electronic device, which can be a server, and its internal structure diagram can be as Figure 11 shown. The electronic device includes a first system and a second system, and the device includes:

[0184] In the case of obtaining monitoring data for a plurality of target sampling periods, through the first system, the monitoring data for one or more of the target sampling periods is evaluated and processed to obtain a blood pressure evaluation result corresponding to each target sampling period. The monitoring data includes data for reflecting the user's blood pressure situation;

[0185] Through the second system, the blood pressure evaluation results corresponding to a plurality of target sampling periods are processed to obtain a blood pressure processing result.

[0186] In some embodiments, the first system is further configured to obtain a characteristic value corresponding to the monitoring data for each target sampling period; input the characteristic value corresponding to the monitoring data for each target sampling period into a pre-trained first result evaluation model to obtain a blood pressure evaluation result corresponding to each target sampling period; the first pre-trained result evaluation model is obtained by training a preset result evaluation model according to historical monitoring data collected within a historical period, the user state of the electronic device when the historical monitoring data is collected, and the historical blood pressure evaluation results corresponding to the historical monitoring data.

[0187] In some embodiments, the electronic device further obtains the user state corresponding to each target sampling period, and the user state includes one or more of a motion state and a sleep state;

[0188] The first system is further configured to obtain a characteristic value corresponding to the monitoring data for each target sampling period;

[0189] Input the eigenvalue corresponding to the monitoring data of each of the target sampling periods and the user status corresponding to each of the target sampling periods into a pre-trained second result evaluation model to obtain the blood pressure evaluation result corresponding to each of the target sampling periods; the pre-trained second result evaluation model is obtained by training a preset result evaluation model according to the historical monitoring data collected during the historical period, the user status of the electronic device when the historical monitoring data is collected, and the historical blood pressure evaluation result corresponding to the historical monitoring data.

[0190] In some embodiments, the target sampling periods are obtained by screening the monitoring data of multiple initial sampling periods, the number of the monitoring data of the multiple initial sampling periods is greater than or equal to the number of the monitoring data of the multiple target sampling periods, and the screening process includes screening the multiple initial sampling periods according to the time intervals between the multiple initial sampling periods or filtering abnormal data.

[0191] In some embodiments, the first system is further configured to perform segmentation processing on the monitoring data of each of the target sampling periods to obtain monitoring sub-data of multiple heartbeat cycles;

[0192] Determine the eigenvalue of each heartbeat cycle according to the peak value and the trough value of the monitoring sub-data of each heartbeat cycle;

[0193] Obtain the eigenvalue corresponding to the monitoring data of the corresponding target sampling period according to the eigenvalues of the multiple heartbeat cycles included in each target sampling period.

[0194] In some embodiments, the second system is further configured to determine the blood pressure processing result according to the blood pressure evaluation result of each of the target sampling periods and the corresponding target weight value.

[0195] In some embodiments, the blood pressure processing result includes a blood pressure grading result, and the second system is further configured to determine a candidate evaluation result corresponding to each target sampling period according to the blood pressure evaluation result corresponding to the monitoring data of each target sampling period and the corresponding target weight value;

[0196] Integrate the candidate evaluation results corresponding to multiple target sampling periods to obtain a target evaluation result;

[0197] Determine the blood pressure grading result according to the target evaluation result and the evaluation threshold, and different blood pressure gradings correspond to different evaluation thresholds.

[0198] In some embodiments, when the user status corresponding to each target sampling period is obtained, the target weight value corresponding to each target sampling period is related to the user status of the corresponding target sampling period.

[0199] In some embodiments, the multiple target sampling periods include a first part of target sampling periods and a second part of target sampling periods. The first part of target sampling periods are multiple sampling periods included in a first target duration, and the second part of target sampling periods are multiple sampling periods included in a second target duration. The sum of the multiple target weight values corresponding to the first part of target sampling periods is the same as the sum of the multiple target weight values corresponding to the second part of target sampling periods.

[0200] In some embodiments, the evaluation threshold is determined according to physiological data, and the physiological data includes at least one of age, weight, gender, and body mass index (BMI).

[0201] In some embodiments, the monitoring data of the multiple target sampling periods is obtained through the first system and / or the second system; the power consumption of the first system and the second system is different, or the computing capabilities of the first system and the second system are different, or the first system runs on a first processor and the second system runs on a second processor, or the first system runs simultaneously when the second system runs.

[0202] In some embodiments, when the blood pressure processing result indicates normal blood pressure, the sampling frequency of the monitoring data is reduced; or,

[0203] when the blood pressure processing result indicates abnormal blood pressure, the sampling frequency of the monitoring data is increased; or,

[0204] when the number of the blood pressure evaluation results indicating normal blood pressure is greater than or equal to a preset number threshold, the sampling frequency of the monitoring data is reduced; or,

[0205] when the number of the blood pressure evaluation results indicating abnormal blood pressure is less than the preset number threshold, the sampling frequency of the monitoring data is increased.

[0206] In some embodiments, the second system is further configured to process the blood pressure evaluation results corresponding to multiple target sampling periods to obtain a blood pressure processing result when a preset condition is met; wherein, the meeting of the preset condition includes one or more of the following: the number of the monitoring data meets a preset number;

[0207] the processing period for obtaining the blood pressure processing result through the second system meets a preset time limit;

[0208] the current moment meets a preset moment;

[0209] one or more of the blood pressure evaluation results meet a preset result.

[0210] Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0211] An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps in the method provided in the above embodiment are implemented.

[0212] An embodiment of the present application provides a computer program product containing instructions. When it runs on a computer, it causes the computer to execute the steps in the method provided in the above method embodiment.

[0213] Those skilled in the art can understand that Figure 11 the structure shown in is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0214] It should be noted here that the descriptions of the above storage medium and device embodiments are similar to those of the above method embodiments and have beneficial effects similar to those of the method embodiments. For the technical details not disclosed in the storage medium, storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0215] It should be understood that "one embodiment" or "an embodiment" or "some embodiments" mentioned throughout the specification means that specific features, structures, or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, the appearances of "in one embodiment" or "in an embodiment" or "in some embodiments" throughout the specification do not necessarily refer to the same embodiment. In addition, these specific features, structures, or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present application, the magnitudes of the serial numbers of the above processes do not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The serial numbers of the embodiments of the present application above are only for description and do not represent the advantages or disadvantages of the embodiments. The above descriptions of each embodiment tend to emphasize the differences between the embodiments, and the same or similar parts can be referred to each other. For the sake of brevity, they are not repeated herein.

[0216] As used herein, the term "and / or" is merely a description of an associated relationship between associated objects, indicating that there can be three relationships. For example, object A and / or object B can represent three situations: object A exists alone, both object A and object B exist simultaneously, and object B exists alone.

[0217] It should be noted that, as used herein, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising such element.

[0218] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there can be other division methods in actual implementation. For example, multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling or communication connection between the components shown or discussed with each other can be through some interfaces, and the indirect coupling or communication connection of devices or modules can be electrical, mechanical or other forms.

[0219] The modules described above as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules; they can be located in one place or distributed to multiple network units; some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0220] In addition, each functional module in the embodiments of the present application can be all integrated in a processing unit, or each module can be separately used as a unit, or two or more modules can be integrated in a unit; the above integrated modules can be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0221] Those of ordinary skill in the art will understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: various media such as removable storage devices, read-only memory (ROM), magnetic disks, or optical discs that can store program codes.

[0222] Alternatively, if the above integrated units of the present application are implemented in the form of software function modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solutions of the embodiments of the present application, in essence or the part that contributes to the related art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing an electronic device to execute all or part of the methods described in the various embodiments of the present application. And the foregoing storage medium includes: various media such as removable storage devices, ROM, magnetic disks, or optical discs that can store program codes.

[0223] The methods disclosed in several method embodiments provided by the present application can be arbitrarily combined without conflict to obtain new method embodiments.

[0224] The features disclosed in several product embodiments provided by the present application can be arbitrarily combined without conflict to obtain new product embodiments.

[0225] The features disclosed in several method or device embodiments provided by the present application can be arbitrarily combined without conflict to obtain new method embodiments or device embodiments.

[0226] The above is only the implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A blood pressure data processing method, characterized in that, The method is applied to an electronic device, which includes a first system and a second system. The method includes: When monitoring data of a plurality of target sampling periods is obtained, through the first system, the monitoring data of one or more of the target sampling periods is evaluated and processed to obtain a blood pressure evaluation result corresponding to each target sampling period. The monitoring data includes data for reflecting the user's blood pressure condition. Through the second system, the blood pressure evaluation results corresponding to a plurality of target sampling periods are processed to obtain a blood pressure processing result.

2. The method according to claim 1, characterized in that The evaluating and processing the monitoring data of the plurality of target sampling periods to obtain a blood pressure evaluation result corresponding to each target sampling period includes: Obtaining a characteristic value corresponding to the monitoring data of each target sampling period; Inputting the characteristic value corresponding to the monitoring data of each target sampling period into a pre-trained first result evaluation model to obtain a blood pressure evaluation result corresponding to each target sampling period. The pre-trained first result evaluation model is obtained by training a preset result evaluation model according to historical monitoring data collected within a historical period and historical blood pressure evaluation results corresponding to the historical monitoring data.

3. The method according to claim 1, wherein The method further includes: Obtaining the user state corresponding to each target sampling period, where the user state includes one or more of a motion state and a sleep state; The evaluating and processing the monitoring data of one or more of the target sampling periods to obtain a blood pressure evaluation result corresponding to each target sampling period includes: Obtaining a characteristic value corresponding to the monitoring data of each target sampling period; Inputting the characteristic value corresponding to the monitoring data of each target sampling period and the user state corresponding to each target sampling period into a pre-trained second result evaluation model to obtain a blood pressure evaluation result corresponding to each target sampling period. The pre-trained second result evaluation model is obtained by training a preset result evaluation model according to historical monitoring data collected within a historical period, the user state of the electronic device when the historical monitoring data is collected, and historical blood pressure evaluation results corresponding to the historical monitoring data.

4. The method according to any one of claims 1 to 3, characterized in that, The target sampling periods are obtained by screening the monitoring data of a plurality of initial sampling periods. The number of the monitoring data of the plurality of initial sampling periods is greater than or equal to the number of the monitoring data of the plurality of target sampling periods. The screening process includes screening the monitoring data of the plurality of initial sampling periods according to the time intervals between the plurality of initial sampling periods or filtering abnormal data.

5. The method according to claim 2 or 3, characterized in that, The obtaining a characteristic value corresponding to the monitoring data of each target sampling period includes: Performing segmentation processing on the monitoring data of each target sampling period to obtain monitoring sub-data of a plurality of heartbeat cycles; Determining a characteristic value of each heartbeat cycle according to the peak value and the trough value of the monitoring sub-data of each heartbeat cycle; Obtaining a characteristic value corresponding to the monitoring data of the corresponding target sampling period according to the characteristic values of the plurality of heartbeat cycles included in each target sampling period.

6. The method according to claim 1, wherein Processing the blood pressure evaluation results corresponding to multiple target sampling periods to obtain a blood pressure processing result, including: Determining the blood pressure processing result according to the blood pressure evaluation result of each target sampling period and the corresponding target weight value.

7. The method according to claim 6, characterized in that, The blood pressure processing result includes a blood pressure grading result. The determining the blood pressure processing result according to the blood pressure evaluation result of each target sampling period and the corresponding target weight value includes: Determining a candidate evaluation result corresponding to each target sampling period according to the blood pressure evaluation result corresponding to the monitoring data of each target sampling period and the corresponding target weight value; Combining the candidate evaluation results corresponding to multiple target sampling periods to obtain a target evaluation result; Determining the blood pressure grading result according to the target evaluation result and an evaluation threshold, and different blood pressure grading results correspond to different evaluation thresholds.

8. The method according to claim 6, wherein When the user state corresponding to each target sampling period is obtained, the target weight value corresponding to each target sampling period is related to the user state of the corresponding target sampling period.

9. The method according to claim 6, wherein The multiple target sampling periods include a first part of target sampling periods and a second part of target sampling periods. The first part of target sampling periods are multiple sampling periods included in a first target duration, and the second part of target sampling periods are multiple sampling periods included in a second target duration. The sum of the multiple target weight values corresponding to the first part of target sampling periods is the same as the sum of the multiple target weight values corresponding to the second part of target sampling periods.

10. The method according to claim 7, wherein The evaluation threshold is determined according to physiological data, and the physiological data includes at least one of age, weight, gender, and body mass index BIM.

11. The method according to claim 1, wherein The method further includes: Obtaining the monitoring data of the multiple target sampling periods through the first system and / or the second system; the power consumption of the first system and the second system is different, or the computing capabilities of the first system and the second system are different, or the first system runs on a first processor and the second system runs on a second processor, or the first system runs simultaneously while the second system runs.

12. The method according to claim 1, wherein The method further includes: When the blood pressure processing result indicates normal blood pressure, reducing the sampling frequency of the monitoring data; or When the blood pressure processing result indicates abnormal blood pressure, increasing the sampling frequency of the monitoring data; or When the number of blood pressure evaluation results indicating normal blood pressure is greater than or equal to a preset number threshold, reducing the sampling frequency of the monitoring data; or When the number of blood pressure evaluation results indicating abnormal blood pressure is less than the preset number threshold, increasing the sampling frequency of the monitoring data.

13. The method according to claim 1, characterized in that, The processing the blood pressure evaluation results corresponding to multiple target sampling periods through the second system to obtain a blood pressure processing result includes: When a preset condition is satisfied, processing the blood pressure evaluation results corresponding to multiple target sampling periods through the second system to obtain a blood pressure processing result; wherein, the satisfaction of the preset condition includes one or more of the following: the number of the monitoring data satisfies a preset number; The processing cycle for obtaining the blood pressure processing result through the second system meets a preset time limit; The current moment meets a preset moment; One or more of the blood pressure assessment results meet a preset result.

14. An electronic device, characterized in that, The electronic device includes a first system and a second system, and the device includes: In the case of obtaining monitoring data for a plurality of target sampling periods, through the first system, the monitoring data for one or more of the target sampling periods is evaluated and processed to obtain a blood pressure assessment result corresponding to each target sampling period, where the monitoring data includes data for reflecting the user's blood pressure condition; Through the second system, the blood pressure assessment results corresponding to a plurality of target sampling periods are processed to obtain a blood pressure processing result.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method according to any one of claims 1 to 13.

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