Method for determining physiological parameter control index and electronic device

Abstract

Provided in the present application are a method for determining a physiological parameter control index and an electronic device. In the method, the electronic device acquires physiological parameter data of a user within a preset time period; the electronic device acquires exercise data of the user, and determines, according to the exercise data, target physiological parameter data of a non-exercise stage from the physiological parameter data; the electronic device determines, according to the exercise data of the user, an improvement index, and determines, according to the target physiological parameter data, a physiological parameter control index of the non-exercise stage; and the electronic device determines, according to the physiological parameter control index of the non-exercise stage and the improvement index, a first physiological parameter control index. By means of the solution, the electronic device can calculate the physiological parameter control index of the user in combination with the beneficial control effect brought by exercise to the user, thereby improving the calculation accuracy of the physiological parameter control index, so that the user can better monitor the health level.

Description

A method and electronic device for determining the control index of physiological parameters

[0001] Cross-references to related applications

[0002] This application claims priority to Chinese Patent Application No. 202510339827.3, filed on March 19, 2025, entitled "A Method for Determining Physiological Parameter Control Index and Electronic Device", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This application relates to the field of terminal technology, and in particular to a method for determining physiological parameter control indices and an electronic device. Background Technology

[0004] With the continuous advancement of technology and people's increasing emphasis on health management, portable physiological parameter measurement devices have gradually become an important tool in modern health management. These devices allow users to conveniently monitor key physiological indicators such as blood sugar, blood pressure, and heart rate anytime, anywhere, thereby providing important data support for personal health management and disease prevention.

[0005] Commonly used electronic devices such as mobile phones and smartwatches can integrate physiological parameter measurement functions or acquire measurement data from other measuring devices. The acquired physiological parameter measurement data can be processed to determine the control index of each physiological parameter. The physiological parameter control index can be used to represent the fluctuation of the user's physiological parameters, so that the user can better monitor their health level.

[0006] However, many factors influence users' physiological parameters, and some of the collected data may not be accurate enough. The physiological parameter control index determined based on all collected physiological parameter measurement data may not be accurate enough.

[0007] Some physiological parameter control index determination schemes support users to manually delete some data, and the electronic device then calculates the physiological parameter control index based on the remaining data after the user deletes some data. However, this scheme requires manual operation by the user, and the user needs to accurately judge which data needs to be deleted. If data is deleted by mistake, it will also affect the accuracy of the physiological parameter control index. Summary of the Invention

[0008] This application provides a method and electronic device for determining physiological parameter control indices, which can improve the accuracy of physiological parameter control index calculation.

[0009] Firstly, this application provides a method for determining a physiological parameter control index, which can be executed by an electronic device. In this method, the electronic device acquires physiological parameter data of a user within a preset time period; the electronic device acquires the user's exercise data, and determines target physiological parameter data for a non-exercise phase from the physiological parameter data based on the exercise data; the electronic device determines an improvement index based on the user's exercise data, and determines a physiological parameter control index for the non-exercise phase based on the target physiological parameter data; the electronic device determines a first physiological parameter control index based on the physiological parameter control index for the non-exercise phase and the improvement index.

[0010] In the above methods, the fluctuations in physiological parameters caused by user exercise are beneficial to the user. Therefore, when calculating the physiological parameter control index, the electronic device can determine the physiological parameter data in the non-exercise phase based on the user's exercise data, calculate the physiological parameter control index in the non-exercise phase, and then calculate the beneficial improvement index of exercise on the user's physiological parameters. Finally, based on the physiological parameter control index and improvement index in the non-exercise phase, the user's first physiological parameter control index is determined, thereby fully considering the beneficial control effect of exercise on the user, improving the accuracy of the physiological parameter control index calculation, and enabling users to better monitor their health level.

[0011] In one possible design, the physiological parameter data is blood glucose data, the target physiological data is target blood glucose data, the first physiological parameter control index is a blood glucose fluctuation index, and the physiological parameter control index for the non-exercise phase is a blood glucose fluctuation index for the non-exercise phase. Through this design, the physiological parameter control index method provided in this application can be used to determine a user's blood glucose fluctuation index, thereby accurately indicating the user's blood glucose fluctuations and facilitating blood glucose level monitoring.

[0012] In one possible design, determining the physiological parameter control index for the non-exercise phase based on the target physiological parameter data includes: calculating the user's blood glucose baseline based on the target blood glucose data; calculating the user's blood glucose fluctuation based on the blood glucose change curve corresponding to the target blood glucose data and the blood glucose baseline; calculating the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and blood glucose fluctuation parameters based on the target blood glucose data; and determining the blood glucose fluctuation index for the non-exercise phase based on the blood glucose fluctuation, the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and the blood glucose fluctuation parameters. Through this design, the electronic device can determine multiple data points—including the blood glucose baseline, blood glucose fluctuation, cumulative rate of blood glucose change, maximum amplitude of blood glucose fluctuation, and blood glucose fluctuation parameters—based on the acquired target blood glucose data for the non-exercise phase, thereby determining the user's blood glucose fluctuation index for the non-exercise phase and ensuring the accuracy of the blood glucose fluctuation index.

[0013] In one possible design, the user's exercise data includes the user's exercise time, and the step of determining the target physiological parameter data for the non-exercise phase from the physiological parameter data based on the exercise data includes: marking the blood glucose data according to the user's exercise time, and dividing the blood glucose data into exercise phase blood glucose data and the target blood glucose data.

[0014] In one possible design, determining the improvement index based on the user's exercise data includes: determining the blood glucose fluctuation improvement index corresponding to the user's exercise data based on a pre-stored correspondence between exercise data and improvement indices; or, determining the blood glucose fluctuation improvement index based on the user's exercise data using an improvement index model. Through this design, the electronic device can determine the improvement effect of exercise on blood glucose fluctuations based on exercise data, thereby considering the positive effects of exercise when calculating the blood glucose fluctuation index and further improving the accuracy of the determined blood glucose fluctuation index.

[0015] In one possible design, determining the improvement index based on the user's exercise data includes: determining a blood glucose fluctuation improvement index based on the user's exercise data and the user's physiological data; wherein the user's physiological data includes at least one of the user's basal metabolic rate, skeletal muscle mass, visceral fat level, and body fat percentage. Through this design, the electronic device can also determine a blood glucose fluctuation improvement indicator based on the user's exercise data and physiological data, thereby generating a personalized blood glucose fluctuation index for different users' physiological conditions, further ensuring the accuracy of the determined blood glucose fluctuation index.

[0016] In one possible design, the method further includes: displaying a notification message when the user's blood glucose fluctuation index exceeds a preset threshold, the notification message being used to notify the user of abnormal blood glucose fluctuations. This design allows for timely alerts when a user's blood glucose levels fluctuate abnormally, enabling the user to better control their blood glucose levels.

[0017] In one possible design, the method further includes: displaying an exercise reminder message; wherein the exercise reminder message includes at least one of the following: exercise type, exercise time, and exercise intensity, determined based on the user's blood glucose fluctuation index, and the exercise reminder message is used to remind the user to exercise in accordance with the exercise reminder message. Through this design, the electronic device can display an exercise reminder message, which may include recommended exercise strategies for the user, allowing the user to exercise according to the recommended strategies to improve their blood glucose control level.

[0018] In one possible design, the preset threshold is an ideal value of the blood glucose fluctuation index generated based on population health data, or the preset threshold is a user-defined target value for blood glucose fluctuation index management.

[0019] In one possible design, the physiological parameter data is blood pressure data, and the physiological parameter control index is a blood pressure control index. With this design, the physiological parameter control index determination method provided in this application can also be applied to determine the blood pressure control index, enabling users to better monitor their blood pressure levels.

[0020] Secondly, this application provides a method for determining a physiological parameter control index, which can be executed by an electronic device. In this method, the electronic device acquires physiological parameter data of a user within a preset time period; the electronic device acquires the user's movement data, and determines, based on the movement data, first physiological parameter data for the movement phase and second physiological parameter data for the non-movement phase from the physiological parameter data; the electronic device determines abnormal and normal data in the first physiological parameter data based on a first preset threshold, and determines abnormal and normal data in the second physiological parameter data based on a second preset threshold, wherein the first preset threshold is greater than the second preset threshold; the electronic device determines the user's physiological parameter control index based on the abnormal and normal data in the first and second physiological parameter data.

[0021] In the above methods, when determining the user's physiological parameter control index, the electronic device can set different abnormal data thresholds for the exercise phase and the non-exercise phase respectively, so as to adapt to the changes in the user's physiological parameters under different scenarios and ensure the accuracy of the calculated physiological parameter control index.

[0022] In one possible design, the physiological parameter data is blood pressure data, and the physiological parameter control index is a blood pressure control index. With this design, the electronic device can set different blood pressure thresholds for exercise and non-exercise phases when determining the user's blood pressure control status, adapting to changes in the user's blood pressure in different scenarios and ensuring the accuracy of the calculated blood pressure control index.

[0023] In one possible design, the blood pressure control index is the time within the target blood pressure range; determining the user's physiological parameter control index based on abnormal and normal data in the first physiological parameter data and abnormal and normal data in the second physiological parameter data includes: determining the sum of the number of normal data in the first physiological parameter data and the number of normal data in the second physiological parameter data as the total number of normal blood pressure data; and using the ratio of the total number of normal blood pressure data to the total number of blood pressure data as the blood pressure control index.

[0024] In one possible design, the physiological parameter data is blood glucose data, and the physiological parameter control index is a blood glucose fluctuation index. With this design, the physiological parameter control indicator determination method provided in this application can be used to determine a user's blood glucose fluctuation index, enabling the user to detect blood glucose levels promptly and accurately.

[0025] Thirdly, this application provides a method for determining a physiological parameter control index, which can be executed by an electronic device. In this method, the electronic device acquires physiological parameter data of a user within a preset time period, acquires the user's movement data, determines target physiological parameter data for a non-movement phase from the physiological parameter data based on the movement data, and determines the user's physiological parameter control index based on the target physiological parameter data.

[0026] In the above method, the electronic device can determine the physiological parameter control index based on the physiological parameter data of the non-exercise phase in the collected physiological parameter data of the user, so as to avoid the impact of data fluctuations caused by the user's exercise on the physiological parameter control index and improve the accuracy of the determined physiological parameter control index.

[0027] In one possible design, the physiological parameter data is blood glucose data, the target physiological data is target blood glucose data, and the physiological parameter control index is blood glucose fluctuation index.

[0028] Fourthly, this application provides an electronic device comprising a plurality of functional modules; the plurality of functional modules interact to implement the methods performed by the electronic device in any of the above aspects and their respective embodiments. The plurality of functional modules can be implemented based on software, hardware, or a combination of software and hardware, and the plurality of functional modules can be arbitrarily combined or divided based on specific implementations.

[0029] Fifthly, this application provides an electronic device including at least one processor and at least one memory, wherein the at least one memory stores computer program instructions, and when the electronic device is running, the at least one processor executes any of the above aspects and the methods executed by the electronic device in its various embodiments.

[0030] Sixthly, this application also provides a computer program product containing instructions that, when the computer program product is run on a computer, cause the computer to perform the method executed by the electronic device in any of the above aspects and embodiments.

[0031] In a seventh aspect, this application also provides a computer-readable storage medium storing a computer program that, when executed by a computer, causes the computer to perform the method executed by the electronic device in any of the above aspects and embodiments.

[0032] Eighthly, this application also provides a chip for reading a computer program stored in a memory and executing the method executed by the electronic device in any of the above aspects and embodiments.

[0033] Ninthly, this application also provides a chip system including a processor for supporting a computer device in implementing the methods executed by electronic devices in any of the above aspects and their embodiments. In one possible design, the chip system further includes a memory for storing programs and data necessary for the computer device. The chip system may be composed of chips or may include chips and other discrete devices. Attached Figure Description

[0034] Figure 1 is a schematic diagram of the architecture of a blood glucose monitoring system provided in an embodiment of this application;

[0035] Figure 2 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0036] Figure 3 is a software structure block diagram of an electronic device provided in an embodiment of this application;

[0037] Figure 4 is a schematic diagram of a user's blood glucose data provided in an embodiment of this application;

[0038] Figure 5 is a schematic diagram of a blood glucose monitoring interface provided in an embodiment of this application;

[0039] Figure 6 is a schematic diagram of a blood glucose monitoring interface provided in an embodiment of this application;

[0040] Figure 7 is a schematic diagram of a blood glucose monitoring interface provided in an embodiment of this application;

[0041] Figure 8 is a flowchart illustrating a method for determining a physiological parameter control index according to an embodiment of this application;

[0042] Figure 9 is a schematic diagram of a physiological parameter control index determination device provided in an embodiment of this application;

[0043] Figure 10 is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the embodiments of this application will be further described in detail below with reference to the accompanying drawings. In the description of the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" and "second" may explicitly or implicitly include one or more of that feature.

[0045] It should be understood that in the embodiments of this application, "at least one" means one or more, and "more than one" means two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, a and b, a and c, b and c, or a, b, and c, where a, b, and c can be single or multiple.

[0046] Commonly used electronic devices such as mobile phones and smartwatches can integrate physiological parameter measurement functions or acquire measurement data from other devices. This acquired physiological parameter data can then be processed to determine control indices for each physiological parameter. These control indices can represent fluctuations in a user's physiological parameters, allowing for better monitoring of their health. For example, when the physiological parameter is blood glucose, the control index can be the blood glucose fluctuation index; when the physiological parameter is blood pressure, the control index can be the blood pressure control index.

[0047] Blood glucose fluctuations have multifaceted effects on diabetic patients. These fluctuations can lead to oxidative stress, which in turn can cause microvascular and macrovascular complications in diabetes. Controlling blood glucose fluctuations is crucial for preventing and delaying these complications. The glucose variability index (GVI) quantifies changes in blood glucose levels over a period of time. It is very important for diabetic patients or others who need to monitor their blood glucose levels, as persistent blood glucose fluctuations can increase the risk of complications.

[0048] A continuous glucose monitoring system (CGMS) monitors a user's blood glucose levels in real time using a subcutaneous sensor and displays the user's blood glucose fluctuations throughout the day as a dynamic curve. CGMS can record detailed dynamic changes in blood glucose, helping doctors and patients understand the patterns and frequency of blood glucose fluctuations. When CGMS continuously monitors a user's blood glucose, all acquired blood glucose data are included in the calculation of the glycemic variability index (GVIA). However, some blood glucose fluctuations may be caused by other factors, and including all blood glucose data in the calculation of the GVIA may affect the accuracy of the GVIA.

[0049] Some glycemic index (GI) determination schemes allow users to manually delete some blood glucose data. The electronic device then calculates the GI based on the remaining blood glucose data after the user deletes some data. However, this scheme requires manual operation by the user, who must accurately determine which blood glucose data needs to be deleted. If blood glucose data is deleted by mistake, it will affect the accuracy of the GI.

[0050] To address the aforementioned issues, this application provides a method for determining a physiological parameter control index, thereby improving the accuracy of its calculation. Taking blood glucose as an example, Figure 1 is a schematic diagram of the architecture of a blood glucose monitoring system provided in this application. Referring to Figure 1, the blood glucose monitoring system includes an electronic device and a CGMS device. The CGMS device can be, for example, a CGMS patch, used to continuously record the user's blood glucose data. In some examples, the CGMS device can also be called a continuous glucose monitoring (CGM) device. The electronic device can be, for example, the user's mobile phone or wearable device, used to record the user's exercise data and to calculate the user's blood glucose fluctuation index. In this application embodiment, the electronic device and the CGMS device can establish a communication connection. After the CGMS device collects the user's blood glucose data, it can send the user's blood glucose data to the electronic device. Alternatively, the blood glucose monitoring system can also include a server. After the CGMS device collects the user's blood glucose data, it can upload the user's blood glucose data to the server, and the electronic device can obtain the user's blood glucose data from the server. Optionally, the electronic device can install an application for managing the user's blood glucose. This application can be a system application on the electronic device or a third-party application. The electronic device can display blood glucose data and the blood glucose fluctuation index for different time periods to the user in this application.

[0051] In the physiological parameter control index determination method provided in this application embodiment, the electronic device acquires the user's physiological parameter data within a preset time period, acquires the user's exercise data, and determines the target physiological parameter data for the non-exercise phase from the physiological parameter data based on the exercise data. The electronic device determines an improvement index based on the user's exercise data, determines a physiological parameter control index for the non-exercise phase based on the target physiological parameter data, and finally determines the user's physiological parameter control index based on the physiological parameter control index and improvement index for the non-exercise phase. Through this scheme, fluctuations in physiological parameters caused by user exercise are beneficial fluctuations for the user. Therefore, when calculating the physiological parameter control index, the electronic device can determine the physiological parameter data for the non-exercise phase based on the user's exercise data, calculate the physiological parameter control index for the non-exercise phase, calculate the beneficial improvement index brought by exercise to the user's physiological parameters, and finally determine the user's physiological parameter control index based on the physiological parameter control index and improvement index for the non-exercise phase. This fully considers the beneficial control effect brought by exercise to the user, improves the accuracy of the physiological parameter control index calculation, and enables users to better monitor their health level.

[0052] The following describes an electronic device and embodiments for using such an electronic device. The electronic device in this application embodiment can be a tablet computer, mobile phone, wearable device, in-vehicle device, augmented reality (AR) / virtual reality (VR) device, laptop computer, ultra-mobile personal computer (UMPC), netbook, personal digital assistant (PDA), etc. This application embodiment does not limit the specific type of electronic device.

[0053] In some embodiments of this application, the electronic device may also be a portable terminal device that includes other functions such as a personal digital assistant and / or a music player. Exemplary embodiments of the portable terminal device include, but are not limited to, devices equipped with... Or portable terminal devices with other operating systems.

[0054] Figure 2 is a schematic diagram of the structure of an electronic device 100 provided in an embodiment of this application. As shown in Figure 2, the electronic device 100 may include a processor 110, an external memory interface 120, an internal memory 121, a universal serial bus (USB) interface 130, a charging management module 140, a power management module 141, a battery 142, an antenna 1, an antenna 2, a mobile communication module 150, a wireless communication module 160, an audio module 170, a speaker 170A, a receiver 170B, a microphone 170C, a headphone jack 170D, a sensor module 180, buttons 190, a motor 191, an indicator 192, a camera 193, a display screen 194, and a subscriber identification module (SIM) card interface 195, etc.

[0055] Processor 110 may include one or more processing units, such as an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural network processing unit (NPU). Different processing units may be independent devices or integrated into one or more processors. The controller may serve as the central nervous system and command center of the electronic device 100. The controller can generate operation control signals based on instruction opcodes and timing signals to control instruction fetching and execution. Processor 110 may also include memory for storing instructions and data. In some embodiments, the memory in processor 110 is a cache memory. This memory can store instructions or data that processor 110 has recently used or is repeatedly used. If processor 110 needs to reuse an instruction or data, it can directly retrieve it from the memory. This avoids repeated access, reduces the waiting time of processor 110, and thus improves system efficiency.

[0056] USB interface 130 is a USB standard compliant interface, specifically a Mini USB interface, Micro USB interface, USB Type-C interface, etc. USB interface 130 can be used to connect a charger to charge electronic device 100, and can also be used for data transfer between electronic device 100 and peripheral devices. Charging management module 140 receives charging input from the charger. Power management module 141 connects battery 142, charging management module 140, and processor 110. Power management module 141 receives input from battery 142 and / or charging management module 140, providing power to processor 110, internal memory 121, external memory, display 194, camera 193, and wireless communication module 160, etc.

[0057] The wireless communication function of electronic device 100 can be implemented through antenna 1, antenna 2, mobile communication module 150, wireless communication module 160, modem processor, and baseband processor. Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in electronic device 100 can be used to cover one or more communication frequency bands. Different antennas can also be multiplexed to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna for a wireless local area network. In some other embodiments, the antennas can be used in conjunction with tuning switches.

[0058] The mobile communication module 150 can provide solutions for wireless communication, including 2G / 3G / 4G / 5G, applied to the electronic device 100. The mobile communication module 150 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 150 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 150 may be housed in the processor 110. In some embodiments, at least some functional modules of the mobile communication module 150 and at least some modules of the processor 110 may be housed in the same device.

[0059] The wireless communication module 160 can provide solutions for wireless communication applications on the electronic device 100, including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC), and infrared (IR) technologies. The wireless communication module 160 can be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 110. The wireless communication module 160 can also receive signals to be transmitted from processor 110, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0060] In some embodiments, antenna 1 of electronic device 100 is coupled to mobile communication module 150, and antenna 2 is coupled to wireless communication module 160, enabling electronic device 100 to communicate with networks and other devices via wireless communication technology. The wireless communication technology may include Global System for Mobile Communications (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Time-Division Code Division Multiple Access (TD-SCDMA), Long Term Evolution (LTE), BT, GNSS, WLAN, NFC, FM, and / or IR technologies, etc. The GNSS may include the Global Positioning System (GPS), the Global Navigation Satellite System (GLONASS), the BeiDou Navigation Satellite System (BDS), the Quasi-Zenith Satellite System (QZSS), and / or satellite-based augmentation systems (SBAS).

[0061] The display screen 194 is used to display the display interface of an application, such as the display page of an application installed on the electronic device 100. The display screen 194 includes a display panel. The display panel can be a liquid crystal display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (AMOLED), a flexible light-emitting diode (FLED), a Miniled LED, a MicroLED, a Micro-OLED, a quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 100 may include one or N display screens 194, where N is a positive integer greater than 1.

[0062] Camera 193 is used to capture still images or videos. An object is projected onto a photosensitive element by generating an optical image through the lens. The photosensitive element can be a charge-coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the light signal into an electrical signal, which is then passed to an ISP for conversion into a digital image signal. The ISP outputs the digital image signal to a DSP for processing. The DSP converts the digital image signal into image signals in standard RGB, YUV, or other formats. In some embodiments, the electronic device 100 may include one or N cameras 193, where N is a positive integer greater than 1.

[0063] Internal memory 121 can be used to store computer executable program code, which includes instructions. Processor 110 executes various functional applications and data processing of electronic device 100 by running the instructions stored in internal memory 121. Internal memory 121 may include a program storage area and a data storage area. The program storage area may store the operating system and software code for at least one application program. The data storage area may store data generated during the use of electronic device 100 (e.g., captured images, recorded videos, etc.). Furthermore, internal memory 121 may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, universal flash storage (UFS), etc.

[0064] The external storage interface 120 can be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device. The external memory card communicates with the processor 110 through the external storage interface 120 to perform data storage functions. For example, images, videos, and other files can be saved on the external memory card.

[0065] Electronic device 100 can implement audio functions, such as music playback and recording, through audio module 170, speaker 170A, receiver 170B, microphone 170C, headphone jack 170D, and application processor.

[0066] The sensor module 180 may include a pressure sensor 180A, an acceleration sensor 180B, a touch sensor 180C, etc.

[0067] The pressure sensor 180A is used to sense pressure signals and can convert the pressure signals into electrical signals. In some embodiments, the pressure sensor 180A may be disposed on the display screen 194.

[0068] Touch sensor 180C, also known as a "touch panel," can be located on display screen 194. The touch sensor 180C and display screen 194 together form a touchscreen, also known as a "touch screen." Touch sensor 180C detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 194. In other embodiments, touch sensor 180C may also be located on the surface of electronic device 100, in a different position than display screen 194.

[0069] Buttons 190 include a power button, volume buttons, etc. Buttons 190 can be mechanical buttons or touch buttons. Electronic device 100 can receive button inputs and generate key signal inputs related to user settings and function control. Motor 191 can generate vibration alerts. Motor 191 can be used for incoming call vibration alerts or for touch vibration feedback. For example, touch operations applied to different applications (such as taking photos, audio playback, etc.) can correspond to different vibration feedback effects. Touch vibration feedback effects can also be customized. Indicator 192 can be an indicator light, used to indicate charging status, battery level changes, or to indicate messages, missed calls, notifications, etc. SIM card interface 195 is used to connect a SIM card. The SIM card can be inserted into or removed from the SIM card interface 195 to achieve contact and separation with electronic device 100.

[0070] It is understood that the components shown in Figure 2 do not constitute a specific limitation on the electronic device 100. The electronic device may include more or fewer components than shown, or combine some components, or separate some components, or have different component arrangements. Furthermore, the combination / connection relationships between the components in Figure 2 can also be adjusted and modified.

[0071] Figure 3 is a software structure block diagram of an electronic device provided in an embodiment of this application. As shown in Figure 3, the software structure of the electronic device can be a layered architecture. For example, the software can be divided into several layers, each with a clear role and division of labor. The layers communicate with each other through software interfaces. In some embodiments, the operating system is divided into four layers, from top to bottom: the application layer, the application framework layer (framework, FWK), the runtime and system libraries, and the kernel layer.

[0072] The application layer can include a series of application packages. As shown in Figure 3, the application layer can include camera, settings, skin modules, user interface (UI), third-party applications, etc. Third-party applications can include gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, SMS, etc.

[0073] The application framework layer provides application programming interfaces (APIs) and a programming framework for applications in the application layer. The application framework layer can include some predefined functions. As shown in Figure 3, the application framework layer can include a window manager, content provider, view system, phone manager, resource manager, and notification manager.

[0074] The window manager is used to manage windowed applications. It can obtain the screen size, determine if a status bar is present, lock the screen, and capture screenshots. The content provider stores and retrieves data, making this data accessible to applications. This data may include videos, images, audio, made and received phone calls, browsing history and bookmarks, phone books, etc.

[0075] A view system includes visual controls, such as controls for displaying text and controls for displaying images. View systems can be used to build applications. A display interface can consist of one or more views. For example, a display interface including a text notification icon could include views for displaying text and views for displaying images.

[0076] A phone manager is used to provide communication functions for electronic devices. For example, it manages call status (including connection and disconnection).

[0077] The file explorer provides applications with various resources, such as localized strings, icons, images, layout files, video files, and more.

[0078] The notification manager allows applications to display notifications in the status bar. These notifications can be used to deliver informational messages and can disappear automatically after a short pause, requiring no user interaction. For example, the notification manager can be used to notify users of completed downloads or message alerts. The notification manager can also display notifications as icons or scrolling text in the top status bar, such as notifications from background applications, or as dialog boxes on the screen. Examples include displaying text messages in the status bar, emitting sounds, vibrating electronic devices, and flashing indicator lights.

[0079] The runtime includes the core libraries and the virtual machine. The runtime is responsible for the scheduling and management of the operating system.

[0080] The core library consists of two parts: one part contains the functionalities that the Java language needs to call, and the other part contains the core libraries of the operating system. The application layer and application framework layer run in the virtual machine. The virtual machine executes the Java files of the application layer and application framework layer as binary files. The virtual machine is used to perform functions such as object lifecycle management, stack management, thread management, security and exception management, and garbage collection.

[0081] System libraries can include multiple functional modules. For example: surface manager, media libraries, 3D graphics processing libraries (e.g., OpenGL ES), 2D graphics engines (e.g., SGL), image processing libraries, etc.

[0082] The Surface Manager is used to manage the display subsystem and provides the blending of 2D and 3D layers for multiple applications.

[0083] The media library supports playback and recording of various common audio and video formats, as well as still image files. It supports multiple audio and video encoding formats, such as MPEG4, H.264, MP3, AAC, AMR, JPG, and PNG.

[0084] The 3D graphics processing library is used to implement 3D graphics drawing, image rendering, compositing, and layer processing.

[0085] A 2D graphics engine is a graphics engine for 2D drawing.

[0086] The kernel layer is the layer between hardware and software. The kernel layer contains at least the display driver, camera driver, audio driver, and sensor driver.

[0087] The hardware layer can include various types of sensors, such as accelerometers, gyroscopes, and touch sensors.

[0088] It should be noted that the structures shown in Figures 2 and 3 are merely examples of electronic devices provided in the embodiments of this application, and cannot be used to limit the electronic devices provided in the embodiments of this application. In specific implementations, electronic devices may have more or fewer devices or modules than those shown in Figures 2 or 3.

[0089] The method for determining the physiological parameter control index provided in the embodiments of this application is described below.

[0090] In this embodiment of the application, physiological parameters may include blood glucose, blood pressure, heart rate, blood oxygen, etc. When the physiological parameter is blood glucose, the physiological parameter control index is the blood glucose fluctuation index. The following describes the implementation method of the physiological parameter control index determination method provided in this embodiment of the application when it is applied to the calculation of the blood glucose fluctuation index, taking blood glucose as an example.

[0091] In this embodiment, the electronic device can acquire blood glucose data within a preset time period. This preset time period can be a time interval for calculating the blood glucose fluctuation index (GVI), such as calculating the GVI every 12 hours or every 24 hours. Alternatively, the preset time period can be a user-defined time period, such as the user setting the calculation of the GVI between 8:00 and 23:00. The blood glucose data acquired by the electronic device within the preset time period can be blood glucose data collected by a user-worn CGMS device. The user-worn CGMS device can be used to continuously monitor the user's blood glucose. The CGMS device can send the collected blood glucose data to the electronic device. For example, the CGMS device can send the blood glucose data to the electronic device through a communication connection between the CGMS device and the electronic device, or the CGMS device can upload the blood glucose data to a server, and the electronic device can obtain the user's blood glucose data from the server.

[0092] The electronic device can also acquire the user's exercise data, which may include exercise time, including start and end times. The exercise data may also include at least one of the following: exercise type, exercise intensity, calories burned, and average heart rate. The electronic device can label blood glucose data based on the exercise data. For example, it can divide blood glucose data into exercise-phase and non-exercise-phase data based on exercise time, and use the non-exercise-phase blood glucose data as the target blood glucose fluctuation index. For instance, Figure 4 is a schematic diagram of a user's blood glucose data provided in an embodiment of this application. Referring to Figure 4, the electronic device can acquire the user's blood glucose data between 0:00 and 24:00. The electronic device acquires the user's exercise data and determines that the user exercised between 15:00 and 16:00 based on the exercise time in the exercise data. Therefore, the electronic device can determine that the blood glucose data between 0:00-15:00 and 16:00-24:00 are non-exercise-phase blood glucose data.

[0093] In one optional implementation, the electronic device can determine the user's blood glucose variability index during the non-exercise phase based on target blood glucose data during the non-exercise phase, and use this index as the user's blood glucose variability index. Optionally, the electronic device can generate a blood glucose change curve for the user based on the target blood glucose data, calculate the user's blood glucose baseline, and calculate the user's blood glucose fluctuation based on the blood glucose change curve corresponding to the baseline and target blood glucose data. The electronic device can also calculate the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and blood glucose fluctuation parameters based on the target blood glucose data. Finally, the electronic device can determine the blood glucose variability index during the non-exercise phase based on the blood glucose fluctuation, the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and the blood glucose fluctuation parameters.

[0094] For example, an electronic device can determine a user's blood glucose baseline based on target blood glucose data. For instance, the electronic device can sort multiple blood glucose data points in the target blood glucose data in ascending order to obtain a blood glucose sequence, and use the blood glucose data point at a preset position in this sequence as the blood glucose baseline. For example, the electronic device can select the blood glucose data point at the 25th percentile in the blood glucose sequence as the user's blood glucose baseline. Alternatively, the electronic device can calculate the average value of blood glucose data points within a preset range in the blood glucose sequence and use this average value as the user's blood glucose baseline. For example, the electronic device can calculate the average value of blood glucose data points within the 20%-30% range in the blood glucose sequence and use the calculated average value as the user's blood glucose baseline. Optionally, the electronic device can also determine a segment of blood glucose fluctuation data from the target blood glucose data and determine the user's blood glucose baseline based on this segment of blood glucose fluctuation data. For example, the electronic device can determine multiple blood glucose data points within a preset time range from multiple blood glucose data points in the target blood glucose data, determine the maximum value of these multiple blood glucose data points within the preset time range, and search backwards from the acquisition time corresponding to the maximum value for the minimum value among the multiple blood glucose data points. If the minimum value is the smallest blood glucose data point within a preset time period before and after the acquisition time corresponding to the minimum value, then the segment of blood glucose data between the minimum and maximum values ​​is a segment of blood glucose fluctuation data, and the electronic device can determine this minimum value as the user's blood glucose baseline. Optionally, the electronic device can also set a preset value for the user's fasting blood glucose level. This preset value can be any value within the healthy blood glucose range, or the user's fasting blood glucose level can be a blood glucose control target value selected by the user from the healthy blood glucose range, such as a healthy blood glucose range of 3.9 mmol / L to 6.1 mmol / L.

[0095] After determining the user's blood glucose baseline, the electronic device can consider the first collection point where the blood glucose value exceeds the baseline as the start time of blood glucose fluctuation, denoted as t_start, and the first collection point where the blood glucose value falls below the baseline as the end time of blood glucose fluctuation, denoted as t_end. Taking a CGMS device that collects blood glucose data every minute as an example, the user's blood glucose fluctuation Auc can satisfy the following relationship:

[0096] Among them, G i For from t start to t end The CGMS device collects blood glucose values ​​at multiple acquisition times between these points. FastingGlucose represents the user's blood glucose baseline, and AUC is measured in mmol / L*min.

[0097] Optionally, if the CGMS device does not collect blood glucose data every minute, the electronic device can first use an interpolation method to interpolate the target blood glucose data to a blood glucose value corresponding to each minute, and then calculate Auc. The Auc calculation method is related to the interpolation method, and the interpolation methods that can be selected include linear interpolation, spline interpolation, etc.

[0098] Taking a CGMS device that collects blood glucose data every minute as an example, the cumulative value of the blood glucose rate of change (Roc) can satisfy the following relationship:

[0099] Among them, G i For from t start to t end-1 Glucose levels collected by the CGMS device at multiple acquisition times between [times]. i+1 For G i The blood glucose value collected at the next sampling time.

[0100] If the CGMS device does not collect blood glucose data every minute, the electronic device can first use an interpolation method to interpolate the target blood glucose data to a blood glucose value corresponding to each minute, and then calculate the Roc. The Roc calculation method is related to the interpolation method, and the interpolation methods that can be selected include linear interpolation, spline interpolation, etc.

[0101] The maximum fluctuation range of blood glucose, Lage, can satisfy the following relationship: Lage = G max -G min

[0102] Among them, G max G represents the maximum blood glucose level within the target blood glucose data fluctuation. min The minimum blood glucose value within the blood glucose fluctuation of the target blood glucose data.

[0103] Blood glucose fluctuation parameters can include standard deviation (SD) and coefficient of variation (CV), where SD and CV can satisfy the following relationship:

[0104] in, This represents the average of multiple data points in the target blood glucose dataset, where Gi is the blood glucose value collected by CGMS at multiple collection times in the target blood glucose dataset, and n is the number of collection times.

[0105] The user's glycemic index during non-exercise phases can be expressed by the following formula: GV n =w1*Auc+w2*Roc+w3*Lage+w4*SD+w5*CV+w0

[0106] Among them, GV nThis is the blood glucose fluctuation index for users during non-exercise periods. w1, w2, w3, w4, and w5 are the weight values ​​for Auc, Roc, Lage, SD, and VC, respectively. w1, w2, w3, w4, and w5 can be empirical values ​​set by technical personnel. w0 is the intercept or bias.

[0107] In another optional implementation, the electronic device can determine a blood glucose fluctuation improvement index based on exercise data. This index indicates the improvement effect of exercise on blood glucose fluctuations. The electronic device can determine the user's blood glucose fluctuation index based on the blood glucose fluctuation improvement index and the blood glucose fluctuation index during non-exercise phases. Optionally, the electronic device can record the user's exercise data. The electronic device can acquire the user's continuous exercise data and intermittent exercise data. Continuous exercise data may include at least one of exercise type, exercise intensity, exercise calories burned, exercise time, and average heart rate. Intermittent exercise data may include at least one of standing times, steps, sedentary duration, and exercise intensity.

[0108] In some examples, after acquiring exercise data, the electronic device can determine the blood glucose fluctuation improvement index corresponding to the user's exercise data based on the pre-stored correspondence between exercise data and improvement index. The pre-stored correspondence between exercise data and improvement index can be a mapping table of exercise data and improvement index generated based on multi-sample data. For example, when the exercise data is a 30-minute slow walk, the electronic device can look up the corresponding improvement index of 5 from the mapping table of exercise data and improvement index.

[0109] In other instances, electronic devices can determine the blood glucose fluctuation improvement index of exercise data based on an improvement index model trained on sample data including exercise data and the corresponding improvement index. The electronic device can input exercise data into the improvement index model and obtain the blood glucose fluctuation improvement index output by the improvement index model.

[0110] After determining the blood glucose variability improvement index and the blood glucose variability index during non-exercise phases, electronic devices can determine the user's blood glucose variability index based on these two indices. For example, the user's blood glucose variability index can satisfy the following relationship: GV = G n -I

[0111] Where GV is the user's glycemic variability index, GV n I represents the glycemic fluctuation index during the user's non-exercise phase, and I represents the glycemic fluctuation improvement index.

[0112] Optionally, the electronic device can also acquire the user's physiological data, which may include at least one of the user's body mass index (BMI), basal metabolic rate (BMR), skeletal muscle mass (SMM), visceral adipose tissue (VAT), and body fat percentage (BF). The electronic device can determine the blood glucose fluctuation improvement index based on the user's physiological data and the user's exercise data.

[0113] In some examples, the electronic device can determine the improvement coefficient based on the user's physiological data, and determine the blood glucose fluctuation improvement index based on the improvement coefficient and the user's exercise data. Optionally, the electronic device can calculate the initial value of the blood glucose fluctuation improvement index based on the method for determining the blood glucose fluctuation improvement index based on exercise data provided in the above embodiments, and then use the product of the improvement coefficient and the initial value of the blood glucose fluctuation improvement index as the user's blood glucose fluctuation improvement index. Alternatively, when determining the user's blood glucose fluctuation improvement index based on the improvement index model, the electronic device can use the improvement coefficient and exercise data as inputs to the improvement index model, and obtain the user's blood glucose fluctuation improvement index output by the improvement index model.

[0114] For example, the improvement coefficient of electronic devices can be calculated using multiple linear regression, and the improvement coefficient can satisfy the following relationship: Ic=p1*BMR+p2*SMM-n1*CGM-n2*VAT-n3*BMI-n4*BF

[0115] Where Ic is the improvement coefficient, CGM is the average blood glucose value obtained based on user blood glucose data, and p1, p2, n1, n2, n3, and n4 are preset weight values, with "+" indicating positive correlation and "-" indicating negative correlation.

[0116] For example, electronic devices can determine improvement coefficients based on machine learning models (such as decision trees, support vector machines, logistic regression, etc.) or deep learning models. Electronic devices can use the user's average blood glucose level, BMI, BMR, SMM, VAT, and BF as inputs to the model and obtain the improvement coefficients output by the model.

[0117] In the blood glucose fluctuation index determination method provided in this application embodiment, after determining the user's blood glucose fluctuation index, the electronic device can display the user's blood glucose fluctuation index, allowing the user to view the index at any time to control blood glucose levels. When the user's blood glucose fluctuation index exceeds a preset threshold, the electronic device can display a notification message to notify the user of abnormal blood glucose fluctuations. The preset threshold can be an ideal value for the blood glucose fluctuation index set by the system, which can be generated based on population health data; or the preset threshold can be a target value for blood glucose fluctuation index management set by the user. For example, Figure 5 is a schematic diagram of a blood glucose monitoring interface provided in this application embodiment. Referring to Figure 5, when the electronic device detects that the user's blood glucose fluctuation index exceeds the preset threshold within a preset time period, the electronic device displays a notification message, as shown in Figure 5, which reads "Abnormal blood glucose fluctuations, please pay attention to blood glucose control."

[0118] Optionally, if the electronic device detects that the user's blood glucose fluctuation index has not exceeded a preset threshold for a relatively long period of time (such as within a week or a month), and the preset threshold is greater than the blood glucose fluctuation index value corresponding to healthy people, the electronic device can also remind the user to set the preset threshold to a smaller value to gradually achieve further control over blood glucose fluctuations, thereby helping the user to better control blood glucose fluctuations.

[0119] In some embodiments, after acquiring target blood glucose data and exercise data, and determining the user's blood glucose fluctuation index, the electronic device can display the user's blood glucose fluctuation index in real time. Alternatively, after acquiring the target blood glucose data, the electronic device can update the user's blood glucose fluctuation index. When the user's blood glucose fluctuation index changes, the electronic device displays a reminder message, which may include the user's blood glucose fluctuation index. When the electronic device can detect the user's eating status, emotional fluctuations, or exercise status, the reminder message can also be used to remind the user of blood glucose fluctuations after eating, emotional fluctuations, or exercise. For example, Figure 6 is a schematic diagram of a blood glucose monitoring interface provided in an embodiment of this application. Referring to Figure 6, after the electronic device determines the user's blood glucose fluctuation index and determines that the user's blood glucose fluctuation index has changed, it can display a reminder message, such as "Your blood glucose fluctuation index in the past eight hours after this meal is 45" as shown in Figure 6. In this way, the user can obtain the blood glucose fluctuation index in real time, which facilitates the user to better monitor and control blood glucose levels.

[0120] When a user's blood glucose fluctuation index (GVI) exceeds a preset threshold, the electronic device can also display an exercise reminder message. This reminder message can include at least one of the following: exercise type, exercise duration, and exercise intensity, determined based on the user's GVI. The reminder message serves to prompt the user to exercise according to the information provided. For example, the electronic device can pre-store the correspondence between exercise data and improvement indices, or it can determine this correspondence based on the user's historical data. The electronic device can determine a recommended exercise strategy for the user based on the GVI exceeding the preset threshold and the correspondence between the exercise data and the improvement index. This strategy can include at least one of the following: exercise type, exercise duration, and exercise intensity. For instance, if the user sets a preset threshold of 60, and the electronic device determines the user's GVI to be 65, and the improvement index from exercise is greater than 5, the user's blood glucose management goal is achieved. The electronic device can then generate at least one exercise strategy based on the correspondence between the exercise data and the improvement index, such as: 90 minutes of moderate-intensity exercise, running 2km, etc. The electronic device can also display the blood glucose fluctuation control effect of the exercise strategy, such as the beneficial effect of 90 minutes of moderate-intensity exercise alleviating the harm caused by a GVI of 25. Optionally, when a user's blood glucose fluctuation index is less than or equal to a preset threshold, the electronic device may also display an exercise reminder message. This reminder message may include a recommended exercise strategy for the user, who can then exercise according to the recommended strategy to improve their blood glucose control.

[0121] For example, Figure 7 is a schematic diagram of a blood glucose monitoring interface provided in an embodiment of this application. Referring to Figure 7, the electronic device determines that the user's blood glucose fluctuation index is 65 and the user's preset threshold is 60. The electronic device can display a recommended exercise strategy for the user to perform. As shown in Figure 7, the electronic device displays "It is recommended that you perform moderate-intensity exercise for 90 minutes. It is expected that the blood glucose fluctuation index will be reduced by 25 after exercise, so the blood glucose fluctuation index can be reduced to 40 after exercise."

[0122] In this way, electronic devices can not only accurately monitor users' blood sugar fluctuations, but also recommend exercise strategies to users when their blood sugar fluctuations are abnormal, thereby helping users to better control their blood sugar levels and improve user experience.

[0123] Similarly, the physiological parameters in the embodiments of this application can also be other parameters, such as blood pressure, heart rate, and blood oxygen. The implementation method of the physiological parameter control index determination method provided in the embodiments of this application for calculating the control index of other physiological parameters can be found in the above embodiments for the description of calculating the blood glucose fluctuation index. Repeated descriptions will not be repeated here.

[0124] This application also provides a method for determining a blood pressure control index. An electronic device can acquire a user's blood pressure data within a preset time period. The electronic device acquires the user's exercise data and marks the blood pressure data based on the exercise data. It then determines a first blood pressure data point for the exercise phase and a second blood pressure data point for the non-exercise phase from the blood pressure data. Since exercise leads to an increase in blood pressure, but an appropriately high level of blood pressure increase is beneficial to the user, the electronic device sets a first preset blood pressure threshold for the exercise phase that is greater than a second preset blood pressure threshold for the non-exercise phase when determining whether the blood pressure data is abnormal. The electronic device can determine abnormal and normal blood pressure data in the first blood pressure data based on the first preset blood pressure threshold, and the electronic device can determine abnormal and normal blood pressure data in the second blood pressure data based on the second preset blood pressure threshold. The electronic device determines the user's blood pressure control index based on the abnormal and normal blood pressure data in the first and second blood pressure data. The user's blood pressure control index can be, for example, time in target range (TTR). TTR refers to the proportion of time within a certain time period during which the patient's blood pressure measurement value is within a preset target range, used to assess the stability and effectiveness of blood pressure control in hypertensive patients during treatment. In this embodiment of the application, the electronic device can calculate the ratio of the sum of the number of normal blood pressure data in the first blood pressure data and the number of normal blood pressure data in the second blood pressure data to the total number of the first blood pressure data and the second blood pressure data, and use this ratio as the user's blood pressure control index.

[0125] For example, Table 1 below is an example of blood pressure data provided in an embodiment of this application.

[0126] Table 1. Example of blood pressure data

[0127] In the "Abnormal" category, "1" indicates abnormal blood pressure, and "0" indicates normal blood pressure.

[0128] Referring to Table 1, the blood pressure data in serial numbers 1-5 and 11-20 are blood pressure data during non-exercise periods. When determining whether the blood pressure data is abnormal, the electronic device can make a judgment based on a second preset threshold (e.g., 140). Blood pressure data greater than or equal to the second preset threshold are identified as abnormal blood pressure data. The blood pressure data in serial numbers 6-10 are blood pressure data during exercise periods. When determining whether the blood pressure data is abnormal, the electronic device can make a judgment based on a first preset threshold (e.g., 210). Blood pressure data greater than or equal to the first preset threshold are identified as abnormal blood pressure data. Therefore, out of the 20 blood pressure data in Table 1, there are 6 abnormal blood pressure data and 14 normal blood pressure data. Thus, the user's blood pressure control index TTR = 14 / 20 = 70%.

[0129] In this way, electronic devices can set different blood pressure thresholds for exercise and non-exercise phases when determining a user's blood pressure control status, in order to adapt to changes in the user's blood pressure in different scenarios and ensure the accuracy of the calculated blood pressure control index.

[0130] In some implementations, the blood pressure control index determination method described above can also be used to calculate the blood glucose fluctuation index. For example, when determining the blood glucose fluctuation index, the electronic device can set different fluctuation amplitude thresholds for blood glucose data during exercise and non-exercise phases to determine whether the user's blood glucose fluctuation is normal or abnormal, thereby determining the user's blood glucose fluctuation index. Alternatively, the blood pressure control index determination method can also be used to calculate the control index corresponding to physiological parameters such as heart rate and blood oxygen. In implementation, please refer to the description of the blood pressure control index determination method in the above embodiments, and repeated details will not be elaborated here.

[0131] Based on the above embodiments, Figure 8 is a flowchart illustrating a method for determining a physiological parameter control index provided in this application. This method can be executed by an electronic device. Referring to Figure 8, the method includes the following steps:

[0132] S801: Electronic device acquires physiological parameter data of user within a preset time period.

[0133] For example, in the embodiments of this application, the user's physiological parameter data can be blood glucose data or blood pressure data.

[0134] S802: The electronic device acquires the user's motion data and determines the target physiological parameter data for the non-motion phase from the physiological parameter data based on the motion data.

[0135] In one optional implementation, taking blood glucose data as an example, the electronic device can mark the blood glucose data according to the exercise time in the exercise data, and divide the blood glucose data into exercise stage blood glucose data and target blood glucose data.

[0136] S803: The electronic device determines the improvement index based on the user's exercise data and the physiological parameter control index for the non-exercise phase based on the target physiological parameter data.

[0137] Optionally, the electronic device can pre-store the correspondence between exercise data and improvement index, and then determine the corresponding blood glucose fluctuation improvement index based on the user's exercise data, or the electronic device can determine the blood glucose fluctuation improvement index based on the user's exercise data based on the improvement index model.

[0138] In some implementations, the electronic device may also determine a glycemic fluctuation improvement index based on the user's exercise data and the user's physiological data; wherein the user's physiological data includes at least one of the user's basal metabolic rate, skeletal muscle mass, visceral fat level, and body fat percentage.

[0139] For example, when calculating the glycemic variability index during non-exercise periods, the electronic device can calculate the user's blood glucose baseline based on target blood glucose data. The electronic device then calculates the user's blood glucose fluctuation based on the blood glucose change curve corresponding to the target blood glucose data and the blood glucose baseline. Finally, the electronic device calculates the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and blood glucose fluctuation parameters based on the target blood glucose data. The electronic device then determines the glycemic variability index during non-exercise periods based on the amount of blood glucose fluctuation, the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and the blood glucose fluctuation parameters.

[0140] S804: The electronic device determines the first physiological parameter control index based on the physiological parameter control index and improvement index during the non-motor phase.

[0141] In some implementations, when a user's blood glucose fluctuation index exceeds a preset threshold, the electronic device can also display a notification message to inform the user of the abnormal blood glucose fluctuation. For example, the electronic device can display the reminder message shown in Figure 5.

[0142] In other embodiments, the electronic device may also display an exercise reminder message; wherein the exercise reminder message includes at least one of the following: exercise type, exercise time, and exercise intensity, determined based on the user's blood glucose fluctuation index, and is used to remind the user to exercise in accordance with the exercise reminder message. For example, the electronic device may display the exercise reminder message shown in Figure 7.

[0143] It should be noted that the physiological parameter control index method in the embodiment shown in Figure 8 can be implemented in the aforementioned embodiments, and repeated parts will not be described again.

[0144] It is understood that, in order to implement the functions of the electronic device in the above embodiments, the electronic device includes hardware and / or software structures corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and method steps of the various examples described in conjunction with the embodiments disclosed in this application, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application scenario and design constraints of the technical solution.

[0145] Figure 9 is a schematic diagram of a physiological parameter control index determination device provided in an embodiment of this application. Referring to Figure 9, the device may include a data monitoring module 901, a motion monitoring module 902, and a calculation module 903. When the physiological parameter in this embodiment is blood glucose, the data monitoring module 901 may be a module with continuous glucose monitoring function to collect the user's blood glucose data, or the data monitoring module 901 may be used to obtain blood glucose data collected by other CGMS devices. The calculation module 903 may also be called a blood glucose fluctuation estimation module. This physiological parameter control index determination device can be used to implement the method executed by the electronic device in the above embodiments. The physiological parameter control index determination device may be the electronic device itself, or it may be a chip or chipset in the electronic device, or a part of the chip used to execute the relevant method function.

[0146] In one embodiment, the physiological parameter control index determination device shown in FIG9 can be used to implement the method executed by the electronic device in the embodiments of this application. The data monitoring module 901 is used to acquire the user's physiological parameter data within a preset time period; the motion monitoring module 902 is used to acquire the user's motion data; and the calculation module 903 is used to determine the target physiological parameter data for the non-motor phase from the physiological parameter data based on the motion data; determine an improvement index based on the user's motion data; determine a physiological parameter control index for the non-motor phase based on the target physiological parameter data; and determine a first physiological parameter control index based on the physiological parameter control index and the improvement index for the non-motor phase.

[0147] For example, the physiological parameter data is blood glucose data, the target physiological data is target blood glucose data, and the physiological parameter control index is blood glucose fluctuation index.

[0148] Optionally, the calculation module 903 is specifically used to calculate the user's blood glucose baseline based on the target blood glucose data; calculate the user's blood glucose fluctuation based on the blood glucose change curve and blood glucose baseline corresponding to the target blood glucose data; calculate the cumulative value of blood glucose change rate, the maximum value of blood glucose fluctuation, and blood glucose fluctuation parameters based on the target blood glucose data; and determine the blood glucose fluctuation index during the non-exercise phase based on the blood glucose fluctuation, the cumulative value of blood glucose change rate, the maximum value of blood glucose fluctuation, and the blood glucose fluctuation parameters.

[0149] Optionally, the calculation module 903 is specifically used to mark blood glucose data according to the user's exercise time, and to divide the blood glucose data into exercise phase blood glucose data and target blood glucose data.

[0150] Optionally, the calculation module 903 is specifically used to determine the blood glucose fluctuation improvement index corresponding to the user's exercise data based on the pre-stored correspondence between exercise data and improvement index; or, to determine the blood glucose fluctuation improvement index based on the user's exercise data based on the improvement index model.

[0151] Optionally, the calculation module 903 is specifically used to determine the blood glucose fluctuation improvement index based on the user's exercise data and the user's physiological data; wherein, the user's physiological data includes at least one of the user's basal metabolic rate, skeletal muscle mass, visceral fat level and body fat percentage.

[0152] Optionally, the physiological parameter control index determination device may further include a display module 904, which is used to display a notification message when the user's blood glucose fluctuation index is greater than a preset threshold. The notification message is used to notify the user of abnormal blood glucose fluctuation.

[0153] Optionally, the display module 904 is also used to display an exercise reminder message; wherein the exercise reminder message includes at least one of the exercise type, exercise time, and exercise intensity determined based on the user's blood glucose fluctuation index, and the exercise reminder message is used to remind the user to exercise in accordance with the exercise reminder message.

[0154] For example, the preset threshold is either an ideal value of the blood glucose fluctuation index generated based on population health data, or a user-defined target value for blood glucose fluctuation index management.

[0155] For example, the physiological parameter data is blood pressure data, and the physiological parameter control index is the blood pressure control index.

[0156] In another embodiment provided in this application, a data monitoring module 901 is used to acquire physiological parameter data of a user within a preset time period; a motion monitoring module 902 is used to acquire the user's motion data; and a calculation module 903 is used to determine, based on the motion data, first physiological parameter data of the motion phase and second physiological parameter data of the non-motion phase from the physiological parameter data; to determine abnormal and normal data in the first physiological parameter data based on a first preset threshold, and to determine abnormal and normal data in the second physiological parameter data based on a second preset threshold, wherein the first preset threshold is greater than the second preset threshold; and to determine the user's physiological parameter control index based on the abnormal and normal data in the first and second physiological parameter data.

[0157] For example, the physiological parameter data is blood pressure data, and the physiological parameter control index is the blood pressure control index.

[0158] Optionally, the blood pressure control index is calculated within the target blood pressure range for the time specified by the calculation module 903. Specifically, it is used to determine the total number of normal blood pressure data as the sum of the number of normal data in the first physiological parameter data and the number of normal data in the second physiological parameter data; and to use the ratio of the total number of normal blood pressure data to the sum of the number of blood pressure data as the blood pressure control index.

[0159] For example, the physiological parameter data is blood glucose data, and the physiological parameter control index is the blood glucose fluctuation index.

[0160] This application also provides an electronic device. As shown in FIG10, the electronic device 1000 includes: a bus 1002, a processor 1004, a memory 1006, and a communication interface 1008. The processor 1004, the memory 1006, and the communication interface 1008 communicate with each other via the bus 1002. It should be understood that this application does not limit the number of processors and memories in the electronic device 1000.

[0161] Bus 1002 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, only one line is used in Figure 10, but this does not imply that there is only one bus or one type of bus. Bus 1002 can include pathways for transmitting information between various components of electronic device 1000 (e.g., memory 1006, processor 1004, communication interface 1008).

[0162] The processor 1004 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0163] The memory 1006 may include volatile memory, such as random access memory (RAM). The processor 1004 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0164] The memory 1006 stores executable program code, and the processor 1004 executes the executable program code to implement the functions performed by the electronic device in the aforementioned embodiments, thereby realizing the physiological parameter control index determination method provided in this application embodiment. That is, the memory 1006 stores instructions for executing the physiological parameter control index determination method.

[0165] The communication interface 1008 uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable the virtual machine to receive or send data.

[0166] Based on the above embodiments, this application also provides a computer program product containing instructions, which, when run on a computer, causes the computer to perform the methods described in the embodiments of this application.

[0167] Based on the above embodiments, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a computer, causes the computer to perform the methods described in the embodiments of this application.

[0168] Based on the above embodiments, this application also provides a chip for reading computer programs stored in a memory to implement the methods described in the embodiments of this application.

[0169] Based on the above embodiments, this application provides a chip system including a processor for supporting a computer device in implementing the methods described in the embodiments of this application. In one possible design, the chip system further includes a memory for storing necessary programs and data of the computer device. This chip system may be composed of chips or may include chips and other discrete devices.

[0170] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0171] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in one or more blocks of the flowchart illustrations and / or one or more blocks of the block diagrams.

[0172] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in one or more flowcharts and / or one or more block diagrams.

[0173] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in one or more flowcharts and / or one or more block diagrams.

[0174] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope of protection of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for determining a physiological parameter control index, characterized in that, Applied to electronic devices, the method includes: Obtain the user's physiological parameter data within a preset time period; Acquire the user's motion data, and determine the target physiological parameter data for the non-motor phase from the physiological parameter data based on the motion data; An improvement index is determined based on the user's exercise data, and a physiological parameter control index for the non-exercise phase is determined based on the target physiological parameter data. The first physiological parameter control index is determined based on the physiological parameter control index during the non-exercise phase and the improvement index.

2. The method as described in claim 1, characterized in that, The physiological parameter data is blood glucose data, the target physiological data is target blood glucose data, and the physiological parameter control index is blood glucose fluctuation index.

3. The method as described in claim 2, characterized in that, The step of determining the physiological parameter control index for the non-exercise phase based on the target physiological parameter data includes: Calculate the user's blood glucose baseline based on the target blood glucose data; The blood glucose fluctuation of the user is calculated based on the blood glucose change curve corresponding to the target blood glucose data and the blood glucose baseline. Calculate the cumulative rate of blood glucose change, the maximum amplitude of blood glucose fluctuation, and blood glucose fluctuation parameters based on the target blood glucose data; The blood glucose fluctuation index during the non-exercise phase is determined based on the blood glucose fluctuation amount, the cumulative value of the blood glucose change rate, the maximum amplitude of the blood glucose fluctuation, and the blood glucose fluctuation parameters.

4. The method as described in claim 2 or 3, characterized in that, The user's exercise data includes the user's exercise time. Determining target physiological parameter data for the non-exercise phase from the physiological parameter data based on the exercise data includes: The blood glucose data is labeled according to the user's exercise time, and the exercise phase blood glucose data and the target blood glucose data are obtained from the blood glucose data.

5. The method according to any one of claims 2-4, characterized in that, The step of determining the improvement index based on the user's exercise data includes: Based on the correspondence between pre-stored exercise data and improvement indices, determine the blood glucose fluctuation improvement index corresponding to the user's exercise data; or, The blood glucose fluctuation improvement index is determined based on the user's exercise data using an improvement index model.

6. The method according to any one of claims 2-4, characterized in that, The step of determining the improvement index based on the user's exercise data includes: A blood glucose fluctuation improvement index is determined based on the user's exercise data and physiological data. The user's physiological data includes at least one of the user's basal metabolic rate, skeletal muscle mass, visceral fat level, and body fat percentage.

7. The method according to any one of claims 2-6, characterized in that, The method further includes: When the user's blood glucose fluctuation index exceeds a preset threshold, a notification message is displayed to notify the user of the abnormal blood glucose fluctuation.

8. The method according to any one of claims 2-7, characterized in that, The method further includes: Displays exercise reminder messages; The exercise reminder message includes at least one of the following: exercise type, exercise time, and exercise intensity, determined based on the user's blood glucose fluctuation index. The exercise reminder message is used to remind the user to exercise in accordance with the exercise reminder message.

9. The method as described in claim 7, characterized in that, The preset threshold is either an ideal value for the blood glucose fluctuation index generated based on population health data, or a user-defined target value for blood glucose fluctuation index management.

10. The method as described in claim 1, characterized in that, The physiological parameter data is blood pressure data, and the physiological parameter control index is the blood pressure control index.

11. A method for determining a physiological parameter control index, characterized in that, The method includes: Obtain the user's physiological parameter data within a preset time period; Acquire the user's exercise data, and determine the first physiological parameter data for the exercise phase and the second physiological parameter data for the non-exercise phase from the physiological parameter data based on the exercise data; Abnormal and normal data in the first physiological parameter data are determined according to a first preset threshold, and abnormal and normal data in the second physiological parameter data are determined according to a second preset threshold, wherein the first preset threshold is greater than the second preset threshold; The user's physiological parameter control index is determined based on the abnormal and normal data in the first physiological parameter data and the abnormal and normal data in the second physiological parameter data.

12. The method as described in claim 11, characterized in that, The physiological parameter data is blood pressure data, and the physiological parameter control index is the blood pressure control index.

13. The method as described in claim 12, characterized in that, The blood pressure control index is the time within the target blood pressure range; determining the user's physiological parameter control index based on abnormal and normal data in the first physiological parameter data and abnormal and normal data in the second physiological parameter data includes: The total number of normal blood pressure data is determined by summing the number of normal data in the first physiological parameter data and the number of normal data in the second physiological parameter data. The ratio of the total number of normal blood pressure data to the sum of the total number of blood pressure data is used as the blood pressure control index.

14. The method as described in claim 11, characterized in that, The physiological parameter data is blood glucose data, and the physiological parameter control index is the blood glucose fluctuation index.

15. An electronic device, characterized in that, It includes at least one processor coupled to at least one memory, the at least one processor being configured to read a program stored in the at least one memory to perform the method as claimed in any one of claims 1-10, or to perform the method as claimed in any one of claims 11-14.

16. A readable storage medium, characterized in that, The readable storage medium stores instructions that, when executed on an electronic device, cause the electronic device to perform the method as described in any one of claims 1-10, or the method as described in any one of claims 11-14.

17. A computer program product containing instructions, characterized in that, When the computer program product is run on a computer, it causes the computer to perform the method as described in any one of claims 1-10, or the method as described in any one of claims 11-14.

18. A chip, characterized in that, The chip is used to read a computer program stored in a memory to execute the method as described in any one of claims 1-10, or to execute the method as described in any one of claims 11-14.

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