WO2021238460A1

WO2021238460A1 - Risk pre-warning method, risk behavior information acquisition method, and electronic device

Info

Publication number: WO2021238460A1
Application number: PCT/CN2021/086674
Authority: WO
Inventors: 邱凌志; 黄曦; 李宏宝; 吴宙真
Original assignee: 华为技术有限公司
Priority date: 2020-05-29
Filing date: 2021-04-12
Publication date: 2021-12-02
Also published as: CN113744874A

Abstract

A risk pre-warning method, a risk behavior information acquisition method, and an electronic device. The risk pre-warning method comprises: acquiring behavior information of a target user (801); determining whether the behavior information of the target user matches risk behavior information corresponding to specified disease information (802); and if the behavior information of the target user matches the risk behavior information corresponding to the specified disease information, showing, to the target user, information pre-warning against the specified disease information (803). Therefore, a risk behavior that causes a user to suffer from a target disease event can be automatically analyzed and recognized, such that when the user conducts the risk behavior, a pre-warning against specified disease information is issued to the user to help the user with health management.

Description

Risk early warning method, risk behavior information acquisition method and electronic equipment

Technical field

This application relates to the technical field of smart terminals, in particular to risk early warning methods, risk behavior information acquisition methods, and electronic equipment.

Background technique

With the application of wearable devices in the health field, monitoring functions of health-related factors such as sleep, exercise, and heart rate have become important functions of wearable devices and are used by users in their daily lives. At the same time, wearable devices are constantly innovating and making major breakthroughs in blood pressure detection, heart health, and vascular health. For example, wearable devices can already achieve primary screening of atrial fibrillation/premature beats and measurement of arterial elasticity for users. The application of these wearable devices in the health field helps users better understand their own health conditions, thereby realizing active health management and improving the living standards of the whole people.

In order to further identify and manage the user’s disease occurrence information, we need to have a deeper understanding of the essential causes of the user’s disease event. How to analyze the relationship between the aforementioned health-related factors and the user's disease events based on the aforementioned user's health-related factors detected by electronic devices such as wearable devices, and realize the user's health management is a problem that needs to be solved.

Summary of the invention

This application provides a risk early warning method, a risk behavior information acquisition method, and an electronic device, which can provide early warning of factors that induce a user to develop a specified disease, and assist the user in health management.

In the first aspect, an embodiment of the present application provides a risk early warning method applied to electronic equipment, including:

Obtain the behavior information of the target user; the behavior information of the target user is used to describe the behavior of the target user;

Determine whether the target user's behavior information matches the risk behavior information corresponding to the specified disease information; the designated disease information is used to describe the diseases that the target user has occurred, and the risk behavior information is used to describe the behavior that has the risk of inducing the target user to develop the disease;

If the target user's behavior information matches the risk behavior information corresponding to the specified disease information, the target user is shown the early warning information for the specified disease information.

In the above-mentioned method of the embodiment of this application, the target user’s behavior information is identified based on the target user’s behavior information for early warning without intervention and no perception, so as to remind the target user to strengthen the control of his own risk behavior, and better Realize active health management and avoid the occurrence of disease events.

In a possible implementation manner, risk behavior information has corresponding risk parameters; displaying early warning information for specified disease information to target users, including:

According to the risk parameters corresponding to the risk behavior information matching the behavior information of the target user, the target user is shown early warning information for the specified disease information.

In a possible implementation, the risk parameters include risk levels; according to the risk parameters corresponding to the risk behavior information matching the target user's behavior information, the target user is shown early warning information for the specified disease information, including:

According to the risk level corresponding to the risk behavior information matching the behavior information of the target user, the target user is shown the early warning information for the specified disease information according to the early warning strategy corresponding to the risk level.

In a possible implementation, the risk parameter includes a risk value; according to the risk parameter corresponding to the risk behavior information that matches the target user's behavior information, the target user is shown early warning information for the specified disease information, including:

Determine the early warning strategy according to the relationship between the risk value corresponding to the risk behavior information matching the target user's behavior information and the preset risk threshold;

According to the determined early warning strategy, the target users are shown early warning information for the specified disease information.

In a possible implementation manner, obtaining behavior information of the target user includes:

Obtain the usage information of at least one electronic device used by the target user, and obtain the behavior information of the target user according to the usage information; and/or,

Obtain target user behavior information from designated daily behavior monitoring applications.

In a possible implementation, it also includes:

Obtain the risk behavior information corresponding to the specified disease information from an electronic device other than the electronic device; and/or,

Obtain the risk behavior information corresponding to the specified disease information from the specified application of the electronic device other than the electronic device; and/or,

Obtain the risk behavior information corresponding to the specified disease information from the specified application of the electronic device.

In a possible implementation, the method for obtaining risk behavior information corresponding to the specified disease information includes:

Obtain the historical occurrence information corresponding to the specified disease information of the target user;

Determine that the historical occurrence information satisfies the preset conditions, and obtain the historical behavior information of the target user within the specified time range and the historical occurrence information corresponding to the specified disease information of the target user;

According to the historical behavior information within the specified time range and the acquired historical occurrence information corresponding to the specified disease information, risk behavior information that has an association relationship with the specified disease information of the target user is determined.

In a possible implementation, it also includes:

According to the historical behavior information within the designated time range and the historical occurrence information corresponding to the designated disease information, the risk parameter corresponding to the risk behavior information that has an association relationship with the designated disease information of the target user is determined.

In a possible implementation manner, obtaining historical occurrence information corresponding to the specified disease information of the target user within a specified time range includes:

Obtain the medical record information of the target user within the specified time range from the designated medical treatment system, and obtain the historical occurrence information corresponding to the specified disease information of the target user from the acquired medical record information; and/or,

Obtain the historical occurrence information corresponding to the specified disease information of the target user within the specified time range from the specified disease monitoring application; and/or,

Obtain the historical biological data of the target user within the specified time range from the wearable device, and determine the historical occurrence information corresponding to the specified disease information of the target user according to the historical biological data.

In a possible implementation manner, obtaining historical behavior information of the target user within a specified time range includes:

Obtain usage information of at least one electronic device used by the target user within a specified time range, and obtain historical behavior information of the target user according to the usage information; and/or,

Obtain historical behavior information of target users within a specified time range from a designated daily behavior monitoring application.

In the second aspect, an embodiment of the present application provides a method for obtaining risk behavior information, which is applied to electronic equipment, and includes:

This method automatically analyzes and recognizes that the target user’s specified disease occurrence risk is relatively high based on the target user’s historical behavior information and the target user’s specified disease information without the target user’s intervention or perception. Therefore, the risk behavior information corresponding to the target user’s specified disease information is determined based on the target user’s own individual situation, which is closer to the target user’s own individual situation, breaking the universality of disease monitoring in the prior art, and achieving Early warning of diseases based on individual physical differences.

In a possible implementation, after judging that the historical occurrence information satisfies the preset condition, the method further includes:

In a possible implementation manner, before obtaining the historical occurrence information corresponding to the specified disease information of the target user, the method further includes:

Obtain the designated disease information of the target user, and the designated disease information is used to record the disease that the target user has occurred.

In a possible implementation, obtaining the specified disease information of the target user includes:

Obtain the disease information selected by the target user on the preset interface, and use the acquired disease information as the designated disease information; and/or,

Obtain the medical record information of the target user from the designated medical system, and obtain the designated disease information of the target user from the medical record information; and/or,

Obtain the historical disease information of the target user from the designated disease monitoring application, and obtain the user's designated disease information from the historical disease information; and/or,

Obtain historical biological data of the target user, and determine the specified disease information of the target user based on the historical biological data.

In a possible implementation, judging that the historical occurrence information satisfies preset conditions includes:

Determine that the number of times information corresponding to the specified disease information in the historical occurrence information reaches the preset number threshold, and the number of times information is used to record the number of occurrences of the specified disease in the specified disease information record; and/or,

It is determined that the occurrence frequency information corresponding to the specified disease information in the historical occurrence information reaches the preset frequency threshold, and the occurrence frequency information is used to record the occurrence frequency of the specified disease in the specified disease information record.

In a third aspect, an embodiment of the present application provides an electronic device, including:

One or more processors; memory; and one or more computer programs, where one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when the instructions are executed by the device, cause the device to execute the first Any one of the methods on the one hand.

In a fourth aspect, an embodiment of the present application provides an electronic device, including:

One or more processors; memory; and one or more computer programs, where one or more computer programs are stored in the memory, and the one or more computer programs include instructions that, when the instructions are executed by the device, cause the device to execute the first Any one of the two methods.

In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium with a computer program stored in the computer-readable storage medium, and when it runs on a computer, the computer executes the method of any one of the first aspect.

In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium in which a computer program is stored, and when it runs on a computer, the computer executes any of the methods in the second aspect.

In a seventh aspect, the present application provides a computer program, which is used to execute the method of the first aspect or the second aspect when the computer program is executed by a computer.

In a possible design, the program in the seventh aspect may be stored in whole or in part on a storage medium packaged with the processor, or may be stored in part or in a memory not packaged with the processor.

Description of the drawings

In order to explain the technical solutions of the embodiments of the present invention more clearly, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative work, other drawings can be obtained from these drawings.

FIG. 1 is a schematic diagram of the structure of an electronic device according to an embodiment of the application;

2 is a block diagram of the software structure of an electronic device according to an embodiment of the application;

FIG. 3 is an example diagram of an implementation manner for the mobile phone to obtain the daily behavior of the target user according to the embodiment of the application; FIG.

FIG. 4 is an example diagram of a setting interface for the historical occurrence information of a specified disease in an embodiment of the application;

Figure 5A is a schematic diagram of a possible UI interface of the application risk early warning method;

FIG. 5B is a flowchart of an embodiment of the risk early warning method of the application;

FIG. 6A is a schematic diagram of another possible UI interface of the risk early warning method of this application;

FIG. 6B is a flowchart of another embodiment of the risk early warning method of the application;

FIG. 7A is a schematic diagram of another possible UI interface of the risk early warning method of this application;

FIG. 7B is a flowchart of another embodiment of the risk early warning method of the application;

FIG. 8 is a flowchart of another embodiment of the risk early warning method of the application;

FIG. 9 is a flowchart of an embodiment of a method for obtaining risk behavior information according to the application;

FIG. 10 is a structural diagram of an embodiment of the risk early warning device of this application;

Fig. 11 is a structural diagram of an embodiment of an apparatus for acquiring risk behavior information according to the application.

Detailed ways

The terms used in the implementation mode part of this application are only used to explain the specific embodiments of this application, and are not intended to limit this application.

In the existing implementation solutions, wearable devices already have functions for monitoring health-related factors such as sleep, exercise, and heart rate. Moreover, electronic devices used by users such as mobile phones also have functions such as sports health apps, smart scene mode apps, and heart health. App and other functions to monitor user behavior and disease occurrence information. However, the above functions can only monitor specific health factors such as the user's heart rate, and cannot identify user behaviors that adversely affect the specified disease of the target user for a target user.

In a possible embodiment, before and after the user uses a health-related application (App, application) to perform health-related measurements, a health-related App may pop up a questionnaire, and analyze the user’s daily behavior and habits according to the questionnaire filled out by the user. And combined with the user's health-related measurement results to further analyze the risk factors that lead to the user's disease events. However, the content of the questionnaire is designed based on the conventional medical risk factors of related diseases. It has universal applicability in order to be able to face more users. The risk factors of the user’s disease events obtained from the analysis are only the disease events in medicine. The above-mentioned conventional risk factors cannot meet the individual needs of a certain user.

There are many risky behaviors that may cause diseases in daily life. The same disease has common and conventional risk behaviors for most patients in medicine. For example, smoking can cause arterial elasticity to become poor, and drinking alcohol can cause atrial fibrillation. However, specific to each patient, the risk behavior of the same disease is different due to individual differences. For example, some patients will cause premature beats due to poor sleep quality, while some patients will not. Therefore, an electronic device for the user of the electronic device recognizes the behavior of the user’s daily life that may induce the user’s disease, and performs disease prediction and early warning based on this, which will help the user as a patient more accurately. Active health management, stay away from diseases.

To this end, the embodiment of the present application proposes a risk early warning method and electronic device, which can automatically analyze and identify the risk behavior of the target user that induces a disease event in the target user’s behavior, and then give an early warning to the user when the target user engages in the risk behavior again , To assist users in active health management.

The embodiment of the present application also proposes a method and electronic device for acquiring risk behavior information, which can automatically analyze and identify the risk behavior of the target user that induces the occurrence of a disease event in the target user's behavior, so as to provide an early warning basis for the risk early warning method.

The risk early warning method and risk behavior information acquisition method provided by the embodiments of this application can be applied to electronic devices, such as mobile phones, tablet computers (Pad, portable android devices), personal computers (PC, person computer), wearable devices such as smart glasses , Smart watches, smart screens, Intelligent Connected Vehicles (Intelligent Connected Vehicle; hereinafter referred to as ICV), smart/intelligent cars (smart/intelligent cars) or in-vehicle equipment, etc.

Exemplarily, FIG. 1 shows a schematic structural diagram of an electronic device 100. 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, and an antenna 2. , Mobile communication module 150, wireless communication module 160, audio module 170, speaker 170A, receiver 170B, microphone 170C, earphone jack 170D, sensor module 180, buttons 190, motor 191, indicator 192, camera 193, display screen 194, and Subscriber identification module (subscriber identification module, SIM) card interface 195, etc. The sensor module 180 may include a pressure sensor 180A, a gyroscope sensor 180B, an air pressure sensor 180C, a magnetic sensor 180D, an acceleration sensor 180E, a distance sensor 180F, a proximity light sensor 180G, a fingerprint sensor 180H, a temperature sensor 180J, a touch sensor 180K, and ambient light Sensor 180L, bone conduction sensor 180M, etc.

It can be understood that the structure illustrated in the embodiment of the present invention does not constitute a specific limitation on the electronic device 100. In other embodiments of the present application, the electronic device 100 may include more or fewer components than shown, or combine certain components, or split certain components, or arrange different components. The illustrated components can be implemented in hardware, software, or a combination of software and hardware.

The processor 110 may include one or more processing units. For example, the processor 110 may include an application processor (AP), a modem processor, a graphics processing unit (GPU), and an image signal processor. (image signal processor, ISP), controller, video codec, digital signal processor (digital signal processor, DSP), baseband processor, and/or neural-network processing unit (NPU), etc. Among them, the different processing units may be independent devices or integrated in one or more processors.

The controller can generate operation control signals according to the instruction operation code and timing signals to complete the control of fetching and executing instructions.

A memory may also be provided in the processor 110 to store instructions and data. In some embodiments, the memory in the processor 110 is a cache memory. The memory can store instructions or data that have just been used or recycled by the processor 110. If the processor 110 needs to use the instruction or data again, it can be directly called from the memory. Repeated accesses are avoided, the waiting time of the processor 110 is reduced, and the efficiency of the system is improved.

In some embodiments, the processor 110 may include one or more interfaces. The interface can include an integrated circuit (inter-integrated circuit, I2C) interface, an integrated circuit built-in audio (inter-integrated circuit sound, I2S) interface, a pulse code modulation (pulse code modulation, PCM) interface, and a universal asynchronous transmitter (universal asynchronous transmitter) interface. receiver/transmitter, UART) interface, mobile industry processor interface (MIPI), general-purpose input/output (GPIO) interface, subscriber identity module (SIM) interface, and / Or Universal Serial Bus (USB) interface, etc.

The I2C interface is a bidirectional synchronous serial bus, which includes a serial data line (SDA) and a serial clock line (SCL). In some embodiments, the processor 110 may include multiple sets of I2C buses. The processor 110 may couple the touch sensor 180K, charger, flash, camera 193, etc., respectively through different I2C bus interfaces. For example, the processor 110 may couple the touch sensor 180K through an I2C interface, so that the processor 110 and the touch sensor 180K communicate through the I2C bus interface to implement the touch function of the electronic device 100.

The I2S interface can be used for audio communication. In some embodiments, the processor 110 may include multiple sets of I2S buses. The processor 110 may be coupled with the audio module 170 through an I2S bus to implement communication between the processor 110 and the audio module 170. In some embodiments, the audio module 170 may transmit audio signals to the wireless communication module 160 through an I2S interface, so as to realize the function of answering calls through a Bluetooth headset.

The PCM interface can also be used for audio communication to sample, quantize and encode analog signals. In some embodiments, the audio module 170 and the wireless communication module 160 may be coupled through a PCM bus interface. In some embodiments, the audio module 170 may also transmit audio signals to the wireless communication module 160 through the PCM interface, so as to realize the function of answering calls through the Bluetooth headset. Both the I2S interface and the PCM interface can be used for audio communication.

The UART interface is a universal serial data bus used for asynchronous communication. The bus can be a two-way communication bus. It converts the data to be transmitted between serial communication and parallel communication. In some embodiments, the UART interface is generally used to connect the processor 110 and the wireless communication module 160. For example, the processor 110 communicates with the Bluetooth module in the wireless communication module 160 through the UART interface to realize the Bluetooth function. In some embodiments, the audio module 170 may transmit audio signals to the wireless communication module 160 through a UART interface, so as to realize the function of playing music through a Bluetooth headset.

The MIPI interface can be used to connect the processor 110 with the display screen 194, the camera 193 and other peripheral devices. The MIPI interface includes camera serial interface (camera serial interface, CSI), display serial interface (display serial interface, DSI), etc. In some embodiments, the processor 110 and the camera 193 communicate through a CSI interface to implement the shooting function of the electronic device 100. The processor 110 and the display screen 194 communicate through a DSI interface to realize the display function of the electronic device 100.

The GPIO interface can be configured through software. The GPIO interface can be configured as a control signal or as a data signal. In some embodiments, the GPIO interface can be used to connect the processor 110 with the camera 193, the display screen 194, the wireless communication module 160, the audio module 170, the sensor module 180, and so on. The GPIO interface can also be configured as an I2C interface, I2S interface, UART interface, MIPI interface, etc.

The USB interface 130 is an interface that complies with the USB standard specification, and specifically may be a Mini USB interface, a Micro USB interface, a USB Type C interface, and so on. The USB interface 130 can be used to connect a charger to charge the electronic device 100, and can also be used to transfer data between the electronic device 100 and peripheral devices. It can also be used to connect earphones and play audio through earphones. The interface can also be used to connect other electronic devices, such as AR devices.

It can be understood that the interface connection relationship between the modules illustrated in the embodiment of the present invention is merely a schematic description, and does not constitute a structural limitation of the electronic device 100. In other embodiments of the present application, the electronic device 100 may also adopt different interface connection modes in the foregoing embodiments, or a combination of multiple interface connection modes.

The charging management module 140 is used to receive charging input from the charger. Among them, the charger can be a wireless charger or a wired charger. In some wired charging embodiments, the charging management module 140 may receive the charging input of the wired charger through the USB interface 130. In some embodiments of wireless charging, the charging management module 140 may receive the wireless charging input through the wireless charging coil of the electronic device 100. While the charging management module 140 charges the battery 142, the power management module 141 can also supply power to the electronic device.

The power management module 141 is used to connect the battery 142, the charging management module 140 and the processor 110. The power management module 141 receives input from the battery 142 and/or the charging management module 140, and supplies power to the processor 110, the internal memory 121, the display screen 194, the camera 193, and the wireless communication module 160. The power management module 141 can also be used to monitor parameters such as battery capacity, battery cycle times, and battery health status (leakage, impedance). In some other embodiments, the power management module 141 may also be provided in the processor 110. In other embodiments, the power management module 141 and the charging management module 140 may also be provided in the same device.

The wireless communication function of the electronic device 100 can be implemented by the antenna 1, the antenna 2, the mobile communication module 150, the wireless communication module 160, the modem processor, and the baseband processor.

The antenna 1 and the antenna 2 are used to transmit and receive electromagnetic wave signals. Each antenna in the electronic device 100 can be used to cover a single or multiple communication frequency bands. Different antennas can also be reused to improve antenna utilization. For example, antenna 1 can be multiplexed as a diversity antenna of a wireless local area network. In other embodiments, the antenna can be used in combination with a tuning switch.

The mobile communication module 150 may provide a wireless communication solution including 2G/3G/4G/5G and the like applied to the electronic device 100. The mobile communication module 150 may include at least one filter, a switch, a power amplifier, a low noise amplifier (LNA), and the like. The mobile communication module 150 can receive electromagnetic waves by the antenna 1, and perform processing such as filtering, amplifying and transmitting the received electromagnetic waves to the modem processor for demodulation. The mobile communication module 150 can also amplify the signal modulated by the modem processor, and convert it into electromagnetic wave radiation via the antenna 1. In some embodiments, at least part of the functional modules of the mobile communication module 150 may be provided in the processor 110. In some embodiments, at least part of the functional modules of the mobile communication module 150 and at least part of the modules of the processor 110 may be provided in the same device.

The modem processor may include a modulator and a demodulator. Among them, the modulator is used to modulate the low frequency baseband signal to be sent into a medium and high frequency signal. The demodulator is used to demodulate the received electromagnetic wave signal into a low-frequency baseband signal. The demodulator then transmits the demodulated low-frequency baseband signal to the baseband processor for processing. The low-frequency baseband signal is processed by the baseband processor and then passed to the application processor. The application processor outputs a sound signal through an audio device (not limited to the speaker 170A, the receiver 170B, etc.), or displays an image or video through the display screen 194. In some embodiments, the modem processor may be an independent device. In other embodiments, the modem processor may be independent of the processor 110 and be provided in the same device as the mobile communication module 150 or other functional modules.

The wireless communication module 160 can provide applications on the electronic device 100 including wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), bluetooth (BT), and global navigation satellites. System (global navigation satellite system, GNSS), frequency modulation (frequency modulation, FM), near field communication technology (near field communication, NFC), infrared technology (infrared, IR) and other wireless communication solutions. The wireless communication module 160 may be one or more devices integrating at least one communication processing module. The wireless communication module 160 receives electromagnetic waves via the antenna 2, frequency modulates and filters the electromagnetic wave signals, and sends the processed signals to the processor 110. The wireless communication module 160 may also receive a signal to be sent from the processor 110, perform frequency modulation, amplify it, and convert it into electromagnetic waves to radiate through the antenna 2.

In some embodiments, the antenna 1 of the electronic device 100 is coupled with the mobile communication module 150, and the antenna 2 is coupled with the wireless communication module 160, so that the electronic device 100 can communicate with the network and other devices through 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), broadband Code division multiple access (wideband code division multiple access, WCDMA), time-division code division multiple access (time-division code division multiple access, TD-SCDMA), long term evolution (LTE), BT, GNSS, WLAN, NFC , FM, and/or IR technology, etc. The GNSS may include the global positioning system (GPS), the global navigation satellite system (GLONASS), the Beidou navigation satellite system (BDS), and the quasi-zenith satellite system (quasi). -zenith satellite system, QZSS) and/or satellite-based augmentation systems (SBAS).

The electronic device 100 implements a display function through a GPU, a display screen 194, an application processor, and the like. The GPU is a microprocessor for image processing, connected to the display 194 and the application processor. The GPU is used to perform mathematical and geometric calculations and is used for graphics rendering. The processor 110 may include one or more GPUs that execute program instructions to generate or change display information.

The display screen 194 is used to display images, videos, and the like. The display screen 194 includes a display panel. The display panel can adopt liquid crystal display (LCD), organic light-emitting diode (OLED), active matrix organic light-emitting diode or active-matrix organic light-emitting diode (active-matrix organic light-emitting diode). AMOLED, flexible light-emitting diode (FLED), Miniled, MicroLed, Micro-oLed, quantum dot light-emitting diode (QLED), etc. In some embodiments, the electronic device 100 may include one or N display screens 194, and N is a positive integer greater than one.

The electronic device 100 can realize a shooting function through an ISP, a camera 193, a video codec, a GPU, a display screen 194, and an application processor.

The ISP is used to process the data fed back from the camera 193. For example, when taking a picture, the shutter is opened, the light is transmitted to the photosensitive element of the camera through the lens, the light signal is converted into an electrical signal, and the photosensitive element of the camera transfers the electrical signal to the ISP for processing and is converted into an image visible to the naked eye. ISP can also optimize the image noise, brightness, and skin color. ISP can also optimize the exposure, color temperature and other parameters of the shooting scene. In some embodiments, the ISP may be provided in the camera 193.

The camera 193 is used to capture still images or videos. The object generates an optical image through the lens and is projected to the photosensitive element. The photosensitive element may be a charge coupled device (CCD) or a complementary metal-oxide-semiconductor (CMOS) phototransistor. The photosensitive element converts the optical signal into an electrical signal, and then transfers the electrical signal to the ISP to convert it into a digital image signal. ISP outputs digital image signals to DSP for processing. DSP converts digital image signals into standard RGB, YUV and other formats of image signals. In some embodiments, the electronic device 100 may include one or N cameras 193, and N is a positive integer greater than one.

Digital signal processors are used to process digital signals. In addition to digital image signals, they can also process other digital signals. For example, when the electronic device 100 selects a frequency point, the digital signal processor is used to perform Fourier transform on the energy of the frequency point.

Video codecs are used to compress or decompress digital video. The electronic device 100 may support one or more video codecs. In this way, the electronic device 100 can play or record videos in multiple encoding formats, such as: moving picture experts group (MPEG) 1, MPEG2, MPEG3, MPEG4, and so on.

NPU is a neural-network (NN) computing processor. By drawing on the structure of biological neural networks, for example, the transfer mode between human brain neurons, it can quickly process input information, and it can also continuously self-learn. The NPU can realize applications such as intelligent cognition of the electronic device 100, such as image recognition, face recognition, voice recognition, text understanding, and so on.

The external memory interface 120 may be used to connect an external memory card, such as a Micro SD card, to expand the storage capacity of the electronic device 100. The external memory card communicates with the processor 110 through the external memory interface 120 to realize the data storage function. For example, save music, video and other files in an external memory card.

The internal memory 121 may be used to store computer executable program code, where the executable program code includes instructions. The internal memory 121 may include a storage program area and a storage data area. Among them, the storage program area can store an operating system, at least one application program (such as a sound playback function, an image playback function, etc.) required by at least one function. The data storage area can store data (such as audio data, phone book, etc.) created during the use of the electronic device 100. In addition, the internal memory 121 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, a flash memory device, a universal flash storage (UFS), and the like. The processor 110 executes various functional applications and data processing of the electronic device 100 by running instructions stored in the internal memory 121 and/or instructions stored in a memory provided in the processor.

The electronic device 100 can implement audio functions through the audio module 170, the speaker 170A, the receiver 170B, the microphone 170C, the earphone interface 170D, and the application processor. For example, music playback, recording, etc.

The audio module 170 is used to convert digital audio information into an analog audio signal for output, and is also used to convert an analog audio input into a digital audio signal. The audio module 170 can also be used to encode and decode audio signals. In some embodiments, the audio module 170 may be provided in the processor 110, or part of the functional modules of the audio module 170 may be provided in the processor 110.

The speaker 170A, also called "speaker", is used to convert audio electrical signals into sound signals. The electronic device 100 can listen to music through the speaker 170A, or listen to a hands-free call.

The receiver 170B, also called a "handset", is used to convert audio electrical signals into sound signals. When the electronic device 100 answers a call or voice message, it can receive the voice by bringing the receiver 170B close to the human ear.

The microphone 170C, also called "microphone", "microphone", is used to convert sound signals into electrical signals. When making a call or sending a voice message, the user can make a sound by approaching the microphone 170C through the human mouth, and input the sound signal into the microphone 170C. The electronic device 100 may be provided with at least one microphone 170C. In other embodiments, the electronic device 100 may be provided with two microphones 170C, which can implement noise reduction functions in addition to collecting sound signals. In other embodiments, the electronic device 100 may also be provided with three, four or more microphones 170C to collect sound signals, reduce noise, identify sound sources, and realize directional recording functions.

The earphone interface 170D is used to connect wired earphones. The earphone interface 170D may be a USB interface 130, or a 3.5mm open mobile terminal platform (OMTP) standard interface, or a cellular telecommunications industry association (cellular telecommunications industry association of the USA, CTIA) standard interface.

The pressure sensor 180A is used to sense the pressure signal and can convert the pressure signal into an electrical signal. In some embodiments, the pressure sensor 180A may be provided on the display screen 194. Pressure sensor 180A

There are many types, such as resistive pressure sensors, inductive pressure sensors, capacitive pressure sensors and so on. The capacitive pressure sensor may include at least two parallel plates with conductive materials. When a force is applied to the pressure sensor 180A, the capacitance between the electrodes changes. The electronic device 100 determines the intensity of the pressure according to the change in capacitance. When a touch operation acts on the display screen 194, the electronic device 100 detects the intensity of the touch operation according to the pressure sensor 180A. The electronic device 100 may also calculate the touched position according to the detection signal of the pressure sensor 180A. In some embodiments, touch operations that act on the same touch position but have different touch operation intensities can correspond to different operation instructions. For example: when a touch operation whose intensity is less than the first pressure threshold is applied to the short message application icon, an instruction to view the short message is executed. When a touch operation with a touch operation intensity greater than or equal to the first pressure threshold acts on the short message application icon, an instruction to create a new short message is executed.

The gyro sensor 180B may be used to determine the movement posture of the electronic device 100. In some embodiments, the angular velocity of the electronic device 100 around three axes (ie, x, y, and z axes) can be determined by the gyroscope sensor 180B. The gyro sensor 180B can be used for image stabilization. Exemplarily, when the shutter is pressed, the gyro sensor 180B detects the shake angle of the electronic device 100, calculates the distance that the lens module needs to compensate according to the angle, and allows the lens to counteract the shake of the electronic device 100 through reverse movement to achieve anti-shake. The gyro sensor 180B can also be used for navigation and somatosensory game scenes.

The air pressure sensor 180C is used to measure air pressure. In some embodiments, the electronic device 100 calculates the altitude based on the air pressure value measured by the air pressure sensor 180C to assist positioning and navigation.

The magnetic sensor 180D includes a Hall sensor. The electronic device 100 can use the magnetic sensor 180D to detect the opening and closing of the flip holster. In some embodiments, when the electronic device 100 is a flip machine, the electronic device 100 can detect the opening and closing of the flip according to the magnetic sensor 180D. Then, according to the detected opening and closing state of the leather case or the opening and closing state of the flip cover, features such as automatic unlocking of the flip cover are set.

The acceleration sensor 180E can detect the magnitude of the acceleration of the electronic device 100 in various directions (generally three axes). When the electronic device 100 is stationary, the magnitude and direction of gravity can be detected. It can also be used to identify the posture of electronic devices, and be used in applications such as horizontal and vertical screen switching, pedometers and so on.

Distance sensor 180F, used to measure distance. The electronic device 100 can measure the distance by infrared or laser. In some embodiments, when shooting a scene, the electronic device 100 may use the distance sensor 180F to measure the distance to achieve fast focusing.

The proximity light sensor 180G may include, for example, a light emitting diode (LED) and a light detector such as a photodiode. The light emitting diode may be an infrared light emitting diode. The electronic device 100 emits infrared light to the outside through the light emitting diode. The electronic device 100 uses a photodiode to detect infrared reflected light from nearby objects. When sufficient reflected light is detected, it can be determined that there is an object near the electronic device 100. When insufficient reflected light is detected, the electronic device 100 can determine that there is no object near the electronic device 100. The electronic device 100 can use the proximity light sensor 180G to detect that the user holds the electronic device 100 close to the ear to talk, so as to automatically turn off the screen to save power. The proximity light sensor 180G can also be used in leather case mode, and the pocket mode will automatically unlock and lock the screen.

The ambient light sensor 180L is used to sense the brightness of the ambient light. The electronic device 100 can adaptively adjust the brightness of the display screen 194 according to the perceived brightness of the ambient light. The 180L ambient light sensor can also be used to automatically adjust the white balance when taking pictures. The ambient light sensor 180L can also cooperate with the proximity light sensor 180G to detect whether the electronic device 100 is in the pocket to prevent accidental touch.

The fingerprint sensor 180H is used to collect fingerprints. The electronic device 100 can use the collected fingerprint characteristics to implement fingerprint unlocking, access application locks, fingerprint photographs, fingerprint answering calls, and so on.

The temperature sensor 180J is used to detect temperature. In some embodiments, the electronic device 100 uses the temperature detected by the temperature sensor 180J to execute a temperature processing strategy. For example, when the temperature reported by the temperature sensor 180J exceeds a threshold value, the electronic device 100 reduces the performance of the processor located near the temperature sensor 180J, so as to reduce power consumption and implement thermal protection. In other embodiments, when the temperature is lower than another threshold, the electronic device 100 heats the battery 142 to avoid abnormal shutdown of the electronic device 100 due to low temperature. In some other embodiments, when the temperature is lower than another threshold, the electronic device 100 boosts the output voltage of the battery 142 to avoid abnormal shutdown caused by low temperature.

Touch sensor 180K, also called "touch device". The touch sensor 180K may be provided on the display screen 194, and the touch screen is composed of the touch sensor 180K and the display screen 194, which is also called a “touch screen”. The touch sensor 180K is used to detect touch operations acting on or near it. The touch sensor can pass the detected touch operation to the application processor to determine the type of touch event. The visual output related to the touch operation can be provided through the display screen 194. In other embodiments, the touch sensor 180K may also be disposed on the surface of the electronic device 100, which is different from the position of the display screen 194.

The bone conduction sensor 180M can acquire vibration signals. In some embodiments, the bone conduction sensor 180M can acquire the vibration signal of the vibrating bone mass of the human voice. The bone conduction sensor 180M can also contact the human pulse and receive the blood pressure pulse signal. In some embodiments, the bone conduction sensor 180M may also be provided in the earphone, combined with the bone conduction earphone. The audio module 170 can parse the voice signal based on the vibration signal of the vibrating bone block of the voice obtained by the bone conduction sensor 180M, and realize the voice function. The application processor may analyze the heart rate information based on the blood pressure beating signal obtained by the bone conduction sensor 180M, and realize the heart rate detection function.

The button 190 includes a power-on button, a volume button, and so on. The button 190 may be a mechanical button. It can also be a touch button. The electronic device 100 may receive key input, and generate key signal input related to user settings and function control of the electronic device 100.

The motor 191 can generate vibration prompts. The motor 191 can be used for incoming call vibration notification, and can also be used for touch vibration feedback. For example, touch operations that act on different applications (such as taking pictures, audio playback, etc.) can correspond to different vibration feedback effects. Acting on touch operations in different areas of the display screen 194, the motor 191 can also correspond to different vibration feedback effects. Different application scenarios (for example: time reminding, receiving information, alarm clock, games, etc.) can also correspond to different vibration feedback effects. The touch vibration feedback effect can also support customization.

The indicator 192 may be an indicator light, which may be used to indicate the charging status, power change, or to indicate messages, missed calls, notifications, and so on.

The SIM card interface 195 is used to connect to the SIM card. The SIM card can be inserted into the SIM card interface 195 or pulled out from the SIM card interface 195 to achieve contact and separation with the electronic device 100. The electronic device 100 may support 1 or N SIM card interfaces, and N is a positive integer greater than 1. The SIM card interface 195 can support Nano SIM cards, Micro SIM cards, SIM cards, etc. The same SIM card interface 195 can insert multiple cards at the same time. The types of the multiple cards can be the same or different. The SIM card interface 195 can also be compatible with different types of SIM cards. The SIM card interface 195 can also be compatible with external memory cards. The electronic device 100 interacts with the network through the SIM card to implement functions such as call and data communication. In some embodiments, the electronic device 100 adopts an eSIM, that is, an embedded SIM card. The eSIM card can be embedded in the electronic device 100 and cannot be separated from the electronic device 100.

The software system of the electronic device 100 may adopt a layered architecture, an event-driven architecture, a microkernel architecture, a microservice architecture, or a cloud architecture. The embodiment of the present invention takes an Android system with a layered architecture as an example to illustrate the software structure of the electronic device 100 by way of example.

FIG. 2 is a software structure block diagram of an electronic device 100 according to an embodiment of the present invention.

The layered architecture divides the software into several layers, and each layer has a clear role and division of labor. Communication between layers through software interface. In some embodiments, the Android system is divided into four layers, from top to bottom, the application layer, the application framework layer, the Android runtime and system library, and the kernel layer.

The application layer can include a series of application packages.

As shown in Figure 2, the application package can include applications such as camera, gallery, calendar, call, map, navigation, WLAN, Bluetooth, music, video, short message, etc.

The application framework layer provides an application programming interface (application programming interface, API) and a programming framework for applications in the application layer. The application framework layer includes some predefined functions.

As shown in Figure 2, the application framework layer can include a window manager, a content provider, a view system, a phone manager, a resource manager, and a notification manager.

The window manager is used to manage window programs. The window manager can obtain the size of the display screen, determine whether there is a status bar, lock the screen, take a screenshot, etc.

The content provider is used to store and retrieve data and make these data accessible to applications. The data may include video, image, audio, phone calls made and received, browsing history and bookmarks, phone book, etc.

The view system includes visual controls, such as controls that display text, controls that display pictures, and so on. The view system can be used to build applications. The display interface can be composed of one or more views. For example, a display interface that includes a short message notification icon may include a view that displays text and a view that displays pictures.

The phone manager is used to provide the communication function of the electronic device 100. For example, the management of the call status (including connecting, hanging up, etc.).

The resource manager provides various resources for the application, such as localized strings, icons, pictures, layout files, video files, and so on.

The notification manager enables the application to display notification information in the status bar, which can be used to convey notification-type messages, and it can automatically disappear after a short stay without user interaction. For example, the notification manager is used to notify download completion, message reminders, and so on. The notification manager can also be a notification that appears in the status bar at the top of the system in the form of a chart or a scroll bar text, such as a notification of an application running in the background, or a notification that appears on the screen in the form of a dialog window. For example, prompt text information in the status bar, sound a prompt sound, electronic device vibration, flashing indicator light, etc.

Android Runtime includes core libraries and virtual machines. Android runtime is responsible for the scheduling and management of the Android system.

The core library consists of two parts: one part is the function function that the java language needs to call, and the other part is the core library of Android.

The application layer and the application framework layer run in a virtual machine. The virtual machine executes the java files of the application layer and the application framework layer as binary files. The virtual machine is used to perform functions such as object life cycle management, stack management, thread management, security and exception management, and garbage collection.

The system library can include multiple functional modules. For example: surface manager (surface manager), media library (Media Libraries), three-dimensional graphics processing library (for example: OpenGL ES), 2D graphics engine (for example: SGL), etc.

The surface manager is used to manage the display subsystem and provides a combination of 2D and 3D layers for multiple applications.

The media library supports playback and recording of a variety of commonly used audio and video formats, as well as still image files. The media library can support a variety of audio and video encoding formats, such as: MPEG4, H.264, MP3, AAC, AMR, JPG, PNG, etc.

The 3D graphics processing library is used to realize 3D graphics drawing, image rendering, synthesis, and layer processing.

The 2D graphics engine is a graphics engine for 2D drawing.

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

For ease of understanding, the following embodiments of the present application will take the electronic device with the structure shown in FIG. 1 and FIG. 2 as an example, combined with the accompanying drawings and application scenarios, to specifically describe the risk early warning method and risk behavior information acquisition method provided by the embodiments of the present application. illustrate.

In the risk behavior information acquisition method of the embodiment of the present application, based on the historical behavior information of the target user, combined with the historical occurrence information of the specified disease information of the target user, determine the relative risk of inducing the target user to develop the specified disease in the historical behavior information of the target user For higher behaviors, this behavior is identified as the risk behavior corresponding to the specified disease; further, risk parameters such as risk level or risk value can be set for each risk behavior corresponding to the specified disease to identify each risk behavior. This kind of risk behavior may lead to the degree of risk of the specified disease. In the following, the electronic device that executes the above method for obtaining risk behavior information is a mobile phone as an example for description. It should be noted that the mobile phone in the following embodiments can be extended to other electronic devices, which will not be repeated here.

The target user refers to the user for whom the method of the embodiment of the present application performs risk behavior information acquisition of the specified disease and the risk early warning, and is generally the owner of the electronic device that executes the above method of the embodiment of the present application. In the embodiments of this application, targeted and personalized risk behavior information acquisition and risk warning can be performed for target users, which is closer to the target user's own individual situation, breaks the universality of disease monitoring in the prior art, and realizes that according to the individual's body Differences in disease warning.

The specified disease of the target user is a disease that has occurred in the target user. The ways to obtain information about the specified disease of the target user can include but are not limited to the following:

According to the user’s specified disease setting operation, for example, the mobile phone provides the user with a designated disease setting interface, and the user can enter information such as the name of the disease that has occurred in the interface. Accordingly, the mobile phone obtains the target user’s information based on the user’s operation in the interface Information about the specified disease; and/or,

Obtain from the disease information set in the electronic device of the target user. The electronic device of the target user can be a mobile phone that executes the above-mentioned method for acquiring risk behavior information, or other electronic devices of the target user, such as wearable devices, tablet computers (Pad , Portable android device), personal computer (PC, person computer), etc., which are not limited in the embodiments of this application; and/or,

Obtained from a disease monitoring application (App, application) installed in the target user’s electronic device. The target user’s electronic device can be a mobile phone that executes the aforementioned risk behavior information acquisition method, or it can be other electronic devices of the target user, such as wearables. Device, Pad, PC, etc., the embodiments of this application are not limited; and/or,

Obtain from the medical record information of the target user recorded by the hospital system. Specifically, the mobile phone can be connected to the hospital system to obtain the medical record information of the target user from the hospital system, and then obtain the information of the specified disease of the target user.

The specified disease information of the target user may include information about one or more diseases. Since the method in the embodiment of this application has the same processing process for the information of each specified disease, the specified disease information of the target user is used in the embodiment of this application. The disease information includes information about a specific disease as an example.

The daily behavior of the target user can be based on the global positioning system (GPS) positioning of the electronic device used by the target user, the movement posture detected by the gyroscope sensor, and the wireless fidelity (WiFi, wireless fidelity)/Bluetooth short-range positioning , The screen on time, the usage of various apps, the usage of cameras and microphones and other electronic equipment are comprehensively analyzed. The daily behavior of the target user can include but is not limited to: staying up late and working overtime, playing with mobile phones for a long time, Time to listen to music at loud volume, stay in a bar, etc. When analyzing the daily behavior of the target user, a comprehensive analysis can be made based on the usage information of one or more electronic devices used by the target user. The electronic devices used by the user may include but are not limited to: mobile phones, Pads, PCs, wearable devices such as Smart glasses and smart watches, household appliances such as TVs and large screens, etc. As shown in Figure 3, other electronic devices used by the user can send their own usage information to the mobile phone, and the mobile phone performs comprehensive analysis on the usage information of the mobile phone itself and other electronic devices of the target user to obtain the daily behavior of the target user. The communication mode between the electronic devices used by the target user is not limited in the embodiment of the present application. For example, it may be wired communication or wireless communication. The wireless communication mode may include, but is not limited to, Bluetooth, WiFi, and the like. The mobile phone can record the daily behaviors of the target user obtained by the analysis and become historical behavior information.

If an application capable of monitoring user behavior (hereinafter referred to as daily behavior monitoring application) is installed in the target user’s electronic device such as mobile phone, Pad, PC, etc., such as a smart scene mode App, the mobile phone can also be installed from a target user’s electronic device. Or obtain historical behavior information of target users from multiple daily behavior monitoring applications.

The historical occurrence information of the specified disease of the target user can be determined according to the historical biological data of the target user, and/or obtained from the disease occurrence information set by the target user in the electronic device of one or more target users of the target user, and/ Or, obtain from the historical medical records of the target user in the hospital system, and/or from the application that can monitor the target user's disease (hereinafter referred to as the disease monitoring application) installed on the target user's electronic device (hereinafter referred to as the disease monitoring application), such as the heart health App, Get it from the Sports Health App, etc. The historical occurrence information of the specified disease of the target user may include: each time the specified disease occurs in the target user.

The biological data of the target user can be obtained from the wearable device of the target user, and the mobile phone can obtain and record the biological data of the target user from the wearable device, thereby obtaining the historical biological data of the target user. For example, in the daily use of the wearable device by the target user, the wearable device can use various biological information sensors such as photoplethysmograph (PPG), electrocardiogram (ECG), acceleration (ACC, acceleration), etc. The target user's pulse, ECG, vibration, sound and other biological information are collected in a timely manner, and the biological information is analyzed through various related algorithms to obtain various biological data such as the target user's heart rate, sleep, and exercise. The wearable device can upload the above-mentioned biological data of the target user to the mobile phone through wireless communication methods such as Bluetooth and WIFI. Correspondingly, the mobile phone can detect whether the target user has a designated disease according to the above-mentioned biological data of the target user, so that the mobile phone can determine the historical occurrence information of the designated disease of the target user according to the historical biological data of the target user.

The historical occurrence information of the specified disease of the target user can be obtained by the mobile phone from the disease information set by the target user in the target user’s electronic device (such as mobile phone, wearable device, Pad, etc.), or the mobile phone can also provide the target user with the specified disease information The setting interface of disease history occurrence information, as shown in Figure 4, the target user enters the time when the specified disease occurred on himself into the text control. Correspondingly, the mobile phone obtains the content entered by the user in the text control to obtain the occurrence time of the user’s specified disease. .

The method for acquiring risk behavior information in the embodiments of the present application can be implemented as a system function of a mobile phone, or as a new App in the mobile phone, or as a part of an existing App, such as the aforementioned daily behavior monitoring application or disease monitoring application.

The foregoing risk behavior information acquisition method can be used as a part of the risk early warning method of the embodiment of the present application, and provides a data basis for the risk early warning method. The risk early warning method of the embodiment of the present application may be based on the risk behavior of the specified disease determined by the above-mentioned risk behavior information acquisition method, monitor whether the risk behavior occurs in the daily behavior of the target user, and provide the user with the specified disease when the risk behavior occurs. Early warning. Further, different early warning strategies can be set according to the risk parameters of the risk behavior, and early warning can be performed according to the early warning strategy corresponding to the risk parameters of the risk behavior. For example, for different risk behavior information, you can set corresponding relevant medical knowledge and expert advice. When the user is performing or about to perform a risk behavior, the behavior advice and health guidelines can be displayed in the warning to the user, such as early rest, prohibition Drinking and other tips. Such targeted, intuitive and actionable prompts help users avoid disease events more easily and effectively, better realize active health management, and enhance user experience. It should be noted that the specific electronic device, such as a mobile phone, implements an early warning method in the embodiment of the present application, which is not limited. For example, it may be a pop-up prompt dialog box, and/or a mobile phone playing preset prompt music, and so on.

In the following, the electronic device implementing the risk early warning method is a mobile phone as an example for description. It should be noted that the mobile phone in the following embodiments can be extended to other electronic devices, which will not be repeated here.

When the mobile phone monitors the daily behavior of the target user, it can obtain usage information from at least one electronic device used by the target user, and determine the behavior information of the target user based on the usage information; and/or, the mobile phone can also obtain information from the target user’s electronic device. The installed daily behavior monitoring application obtains the behavior information of the target user.

The risk early warning method of the embodiment of the present application can be implemented as a function of a mobile phone, or as a new App in the mobile phone, or as a part of an existing App, such as the aforementioned daily behavior monitoring application or disease monitoring application. The steps included in the risk early warning method of the embodiment of the application can be implemented on the same electronic device. For example, the steps included in the risk early warning method are all implemented on the mobile phone of the target user; or, the risk early warning method of the embodiment of the application is The steps included can also be implemented on different electronic devices. For example, the steps related to obtaining risk behavior information in the risk early warning method can be implemented on the target user’s wearable devices such as smart watches, and the related steps of early warning based on risk behavior information It can be implemented on the target user's mobile phone.

The risk early warning method of the embodiments of this application can be implemented in the same App on the same electronic device, or can be implemented in different apps on the same electronic device, or can be implemented in the same App on different electronic devices, or , Can be implemented in different App of different electronic devices. For example, steps related to obtaining risk behavior information and early warning steps are implemented in the health app of the target user’s mobile phone; alternatively, steps related to obtaining risk behavior information are realized in the health app of the target user’s mobile phone, and early warning steps are implemented in the target user’s health app. It is implemented in the smart scene mode app of the mobile phone; or, the steps related to obtaining risk behavior information are implemented in the health app in the target user’s Pad, and the early warning steps are implemented in the health app in the target user’s mobile phone; or, the risk behavior information acquisition is The relevant steps are implemented in the health app in the target user's Pad, and the early warning steps are implemented in the smart scene mode App in the target user's mobile phone.

In the following, taking the electronic device that executes the disease risk early warning method according to the embodiment of the present application is the mobile phone of the target user as an example, the risk early warning method according to the embodiment of the present application is described through examples.

FIG. 5A shows a schematic diagram of a user interface (UI, user interface) and data interaction relationship of a possible embodiment of the risk early warning method according to the embodiment of the present application, and FIG. 5B shows the corresponding flow chart. In this embodiment, this The risk early warning method of the application embodiment exists as a system function "disease risk behavior early warning" of the target user's mobile phone. Specifically:

In part 51 of Fig. 5A, the user opens the disease risk behavior warning setting interface through operation. Accordingly, the mobile phone can display the disease risk behavior warning setting interface shown in Fig. 5A to the target user. The target user selects "On" in the interface. Control, correspondingly, the mobile phone activates the disease risk behavior warning function; after that,

In part 52 of Figure 5A, the mobile phone displays the designated disease setting interface to the target user. The target user selects at least one of the diseases listed according to his own disease. Yes, the designated disease information obtained by the mobile phone is "atrial fibrillation"; it should be noted that if the target user selects multiple diseases listed in it, the designated disease information obtained by the mobile phone records multiple specified diseases, and the mobile phone is The processing process of the information of the designated disease is the same. In the embodiment of the application, only one type of designated disease is recorded in the designated disease information as an example.

In part 53 of FIG. 5A, the mobile phone obtains the historical behavior information of the target user and the historical occurrence information of the specified disease information of the target user, and determines the risk behavior information of the specified disease information. The historical behavior information may include: the name of the user behavior and the time when the user behavior occurred. The name of the user behavior is only an example, and it can also be replaced by information describing the user behavior such as a user behavior identifier, which is not limited in the embodiment of the present application.

Optionally, the mobile phone may also determine the risk parameter corresponding to the risk behavior according to the acquired historical behavior information and the historical occurrence information. The historical occurrence information of the designated disease information may include: the name of the designated disease and the occurrence time of the designated disease. The designated disease name is only an example, and it can also be replaced by information describing the user's behavior such as designated disease identification, which is not limited in the embodiment of the present application. The risk parameter of the risk behavior is used to identify the risk degree of the risk behavior to induce the target user to develop the specified disease. The risk parameter may include but is not limited to the risk level and/or the risk value. The higher the risk level, the higher the risk level or the risk value.

Among them, the mobile phone can periodically or based on certain trigger conditions trigger the acquisition of information and determine the execution of the risky behavior step. In order to avoid the mobile phone from repetitive processing of the same historical behavior information or historical occurrence information, the mobile phone obtains the historical behavior information of the target user and When specifying the historical occurrence information of a disease, a specified time range can be set to ensure that the historical behavior information of the target user processed by the mobile phone each time and the historical occurrence information of the specified disease are not duplicated.

As shown in part 53 of Figure 5A, when the mobile phone obtains the historical occurrence information corresponding to the specified disease information of the target user within the specified time range: the mobile phone can obtain the target user's history within the specified time range from the wearable device of the target user, such as a smart watch Biological data, based on historical biological data, determine whether the target user has atrial fibrillation within a specified time range and record the time when the atrial fibrillation occurred, so as to obtain the historical occurrence information of the target user's "atrial fibrillation"; and/or the mobile phone can be obtained from the connected hospital The system obtains the medical record information of the target user within the specified time range, and obtains the historical occurrence information of the target user's "atrial fibrillation" from the medical record information; and/or the mobile phone can be obtained from the mobile phone and/or other electronic equipment of the target user ( In Figure 5A, Pad is taken as an example) The disease monitoring App has been installed to obtain the historical occurrence information of "atrial fibrillation" of the target user.

As shown in part 53 of Figure 5A, when the mobile phone obtains the historical behavior information of the target user within the specified time range: the mobile phone can obtain the target user within the specified time range from the electronic device of the target user such as wearable devices, PCs, Pads, and TVs. The historical usage information of these electronic devices can be comprehensively analyzed to obtain the historical behavior information of the target user. In Figure 5A, wearable devices, PCs, and TVs are taken as examples; and/or the mobile phone can be obtained from mobile phones and/or other target users’ The daily behavior monitoring App that has been installed on the electronic device (Pad as an example in FIG. 5A) obtains historical behavior information of the target user.

In part 54 of Fig. 5A, the mobile phone obtains the behavior information of the target user.

The behavior information of the target user acquired by the mobile phone in this step is generally information about the behavior currently occurring to the user. If the occurrence time of the behavior is relatively short, it may also be information about the behavior that has just occurred. When the mobile phone judges the behavior that has just occurred, a time threshold can be set. If the time interval between the end of the behavior and the execution time of this step is less than the time threshold, it can be considered as the behavior that has just occurred.

As shown in part 54 of FIG. 5A, the mobile phone can obtain the usage information of at least one electronic device used by the target user from the wearable device, and obtain the behavior information of the target user according to the usage information; and/or, the mobile phone can obtain the usage information of the target user from the mobile phone and/or other The daily behavior monitoring App that has been installed on the target user's electronic device (Pad as an example in FIG. 5A) obtains behavior information of the target user.

In part 55 of FIG. 5A, the mobile phone warns the user when the risk behavior information of the specified disease appears in the behavior information of the target user.

Among them, one or more early warning strategies can be preset, and the early warning strategy corresponds to the risk parameter corresponding to the risk behavior information, and the mobile phone can perform early warning according to the early warning strategy corresponding to the risk parameter. For example, in section 55, the risky behaviors of atrial fibrillation have two risk levels: high-risk and low-risk. Assuming that the high-risk risk behaviors of atrial fibrillation include: staying up late, when the mobile phone detects that the user is staying up late, you can use a pop-up reminder The box reminds the user that “behavior of staying up late may cause atrial fibrillation”. Assuming that the low-risk risk behaviors of atrial fibrillation include: running, when the mobile phone detects that the user is running, the user can prompt the user to pay attention to measuring the electrocardiogram through a pop-up reminder box. Through different early warning strategies, the purpose of early warning of designated diseases is achieved.

It should be noted that the warning method of the pop-up prompt box shown in FIG. 5A is only an example, and other warning methods that may be perceived by the user such as playing designated music for the mobile phone can also be used, which are not limited in the embodiment of the present application.

FIG. 5B is a flow chart of a possible embodiment of the risk early warning method according to the embodiment of the present application. As shown in FIG. 5B, it may include the following steps:

Step 501: The mobile phone obtains the designated disease information of the target user.

This step can correspond to part 52 in FIG. 5A, and will not be repeated here.

Step 502: The mobile phone obtains the historical occurrence information corresponding to the specified disease information.

Among them, the mobile phone can periodically or based on a certain time rule to obtain historical occurrence information within a period of time before the current time. The specific duration of the above period of time is not limited in this embodiment of the application, in order to prevent the mobile phone from responding to the specified disease information corresponding to the same historical occurrence. The information is processed repeatedly. For example, the aforementioned period of time may be the period of time until the current time after the step of determining risk behavior information in the risk early warning method of the embodiment of the present application was executed by the mobile phone.

Step 503: The mobile phone determines that the acquired historical occurrence information satisfies preset conditions, and acquires historical behavior information of the target user within a specified time range and historical occurrence information corresponding to the specified disease information of the target user within the specified time range.

Among them, the mobile phone may determine that the number of occurrences of "atrial fibrillation" of the target user exceeds a preset threshold of times or the frequency of occurrence exceeds a preset frequency threshold to realize that the aforementioned historical occurrence information satisfies the preset condition.

Step 504: The mobile phone determines the risk behavior information associated with the specified disease information of the target user and the risk parameters corresponding to the risk behavior information according to the historical occurrence information and the historical behavior information within the specified time range.

Optionally, the mobile phone may not determine the risk parameters corresponding to the risk behavior information, and at this time, there may be no difference in the degree of risk between the risk behaviors recorded in the risk behavior information.

Among them, the risk parameters corresponding to the risk behavior information may include, but are not limited to, risk value and/or risk level. The specific number of risk levels is not limited in the embodiment of the present application. For example, it may include high-risk behaviors with relatively high risks, and risk relative The two risk levels of the lower low-risk behavior may also include, for example, 3, 4, or more risk levels.

It should be noted that the risk early warning method of the embodiment of the present application may be triggered multiple times to execute

steps

503 and 504 based on the judgment in step 503 that the historical occurrence information satisfies the preset conditions, that is, the step of determining risk behavior information is executed multiple times, correspondingly Yes, the risk behaviors corresponding to atrial fibrillation and the risk parameters of risk behaviors can be changed and updated as the steps of determining risk behavior information are executed multiple times. As the user's behavior is different each time before the user has atrial fibrillation, The risk behaviors and risk parameters of the risk behaviors recorded in the mobile phone may also change and update, that is, a certain behavior of the user has not been recorded as a risk behavior before, as the number of occurrences of the behavior before the occurrence of atrial fibrillation Increase, the behavior may be recorded as a risk behavior by the mobile phone, a certain risk behavior of atrial fibrillation that has been recorded in the mobile phone may also be deleted from the risk behavior of atrial fibrillation, and the risk parameters of the same risk behavior may also change. No more examples here.

In a possible implementation manner, the mobile phone can maintain the risk behavior set corresponding to the specified disease information, and the elements included in the risk behavior set may be: (risk behavior name, risk parameter). Correspondingly, the mobile phone can determine the risk behavior information associated with the target user's designated disease information and the risk parameters corresponding to the risk behavior information by updating the above risk behavior set. Specifically, examples of risk behavior information that the mobile phone determines to have an association relationship with the target user's designated disease information are as follows:

Assuming that the specified disease information includes atrial fibrillation, the specified time range is 72 hours before the time of the most recent atrial fibrillation, and the risk parameter is the risk value. The set of risk behaviors before the update includes: (stay up late, 90), (running, 60), and The 12-hour interval is divided into a specified time range of 72 hours, and 6 time periods are obtained. Each time period corresponds to a certain risk value update strategy, such as the risk value of risk behaviors that occurred within 12 hours before the time of the most recent atrial fibrillation. Increase by 10% from the original value. The risk value of risky behaviors within 12 hours to 24 hours before the time of the most recent atrial fibrillation is increased by 5% from the original value and decreases in sequence; the same risk occurs in multiple time periods The behavior can update the risk value multiple times, or update the risk value once based on the most recent one.

It should be noted that the risk behavior set can be established when the mobile phone executes the risk behavior information acquisition method for the first time, or it can be set in the mobile phone in advance. If it is set in the mobile phone in advance, the risk behavior set can be empty, or it can be preset in medical routines. For risk behavior, the risk value corresponding to the risk behavior may be set according to the risk level of the risk behavior, or may be uniformly set to a certain fixed value, which is not limited in the embodiment of the present application. For example, user behaviors such as insufficient sleep and staying up late can increase the probability of occurrence of atrial fibrillation. Therefore, (staying up late, 60) (lack of sleep, 60) can be preset in the mobile phone in combination with risk behaviors corresponding to atrial fibrillation.

It should be noted that if there is a behavior that is not included in the risk behavior set in the user behavior that occurs within 72 hours, the behavior can be added to the risk behavior set as a risk behavior, and the initial risk value corresponding to the interval at which the behavior is located Set the risk value of the risky behavior, or set the risk value of the risky behavior to a certain fixed value; for the risky behaviors included in the risky behavior set but not occurred within 72 hours, the risk of the risky behavior can be reduced according to the preset strategy Value, and when the risk value is less than the removal threshold, the risk behavior is removed from the risk behavior set.

Step 505: The mobile phone obtains the behavior information of the target user.

This step can correspond to part 54 in FIG. 5A, and will not be repeated here.

Step 506: When the mobile phone judges that risk behavior information corresponding to the specified disease information of the target user appears in the behavior information of the target user, an early warning is given to the target user.

This step can correspond to part 55 in FIG. 5A, and will not be repeated here.

It should be noted that the execution order between steps 502 to 504 and steps 505 to 506 is not limited.

6A shows a schematic diagram of the UI interface and data interaction relationship of a possible embodiment of the risk early warning method of the embodiment of the present application. In this embodiment, the risk early warning method of the embodiment of the present application is used as an application of the target user's mobile phone. "Disease risk warning" is realized, specifically:

In Parts 61 and 62, the target user opens the application "disease risk warning" in the mobile phone through operation, and the mobile phone displays the disease setting interface in the application to the target user; the target user selects at least from the diseases listed in the disease setting interface according to their own disease A disease, for example, in Figure 6A, it is assumed that the target user selects the disease "atrial fibrillation". Accordingly, the mobile phone uses the disease risk warning application to determine the designated disease information as "atrial fibrillation". Among them, data transmission between applications of the same device or between applications of different devices can be realized through data authorization sharing or data communication.

For the implementation of parts 63-65 in FIG. 6A, please refer to the related descriptions in parts 53-55 in FIG. 5A. The main difference is that the mobile phone implements the processing process in FIG. 5A through the application of "disease risk warning".

The processing steps included in the risk early warning method of the embodiment of this application corresponding to the embodiment of Fig. 6A can be referred to Fig. 5B. The main difference lies in the following: "Step 501 to step 506 are implemented.

For example, as shown in FIG. 6B, the following steps may be included:

In steps 601 to 602, the disease risk warning application detects that the number of occurrences of atrial fibrillation exceeds the number threshold P, and obtains the target user's historical behavior information and the historical occurrence information of atrial fibrillation within a specified time range before the time of the latest atrial fibrillation. ；

In step 603, the disease risk early warning application updates the risk behaviors and risk values in the preset risk behavior set of atrial fibrillation according to the acquired information;

In step 604, the disease risk early warning application judges whether the risk value of the risk behavior in the risk behavior set reaches the preset risk value threshold, and if it reaches, sets the risk behavior as high risk;

In step 605, the disease risk early warning application obtains the user's behavior information, and if high-risk risk behavior information appears in the behavior information, the warning prompt box in FIG. 5A and FIG. 6A pops up for early warning.

For example, if the target user stays up late until the morning before falling asleep, the disease risk warning application counts the number of times the target user has atrial fibrillation throughout the night. If the number of atrial fibrillation occurrences exceeds the preset number threshold multiple times, the risk behavior set is triggered Update of the risk value of risky behaviors, the disease risk warning application updates the risk value of the risky behavior of "staying up late (lack of sleep)" multiple times, so that the risk value of the risky behavior of "staying up late (lack of sleep)" exceeds the preset threshold , Is set as high risk. After that, if the target user does not fall asleep beyond 11 o'clock in the evening, the disease risk warning application can pop up a notification to remind the target user that staying up late (lack of sleep) will cause the target user's atrial fibrillation to worsen, and attention needs to be paid to ensure adequate sleep.

It should be noted that the disease risk early warning application can be a disease monitoring application and an application other than the daily behavior monitoring application, or it can be a disease monitoring application or a daily behavior monitoring application. For example, if the disease risk warning application is a disease monitoring application, the disease risk warning application can not only obtain the historical occurrence information of the specified disease of the target user from other disease monitoring applications, other electronic devices, hospital systems, etc., the disease risk warning application itself It is also possible to monitor and record the historical occurrence information of the specified disease of the target user.

FIG. 7A shows a schematic diagram of a UI interface of another possible embodiment of the risk warning method according to the embodiment of the present application. In FIG. The second application in, such as a daily behavior monitoring application, provides early warning of the risky behavior of the target user in the daily behavior according to the risky behavior information obtained by the first application. The implementation of FIG. 7A is similar to that of FIG. 6A, with the main difference being: since the risk early warning method is dispersed and executed in two applications, the second application needs to obtain risk behavior information from the first application in order to judge whether the daily behavior is a risk behavior.

The processing steps included in the risk early warning method of the embodiment of the present application corresponding to the embodiment of FIG. 7A can be referred to FIG. 5B. The main difference lies in: in the risk early warning method of the embodiment of the present application corresponding to the embodiment of FIG. Step 501-Step 504, implement Step 505-Step 506 through the second application.

The following uses examples to illustrate the risk early warning method shown in FIG. 7A. As shown in FIG. 7B, it is assumed that the first application is a disease monitoring application in the mobile phone of the target user, and the second application is a daily behavior monitoring application in the mobile phone of the target user. Compared with the method flow shown in FIG. 6B, this method differs mainly in that steps 601 to 604 are executed by the disease monitoring application to obtain risk behavior information, and the daily behavior monitoring application performs risk warning of the specified disease based on the risk behavior information. For example, in Figure 7B, the daily behavior monitoring application performs early warnings for high-risk risky behaviors and low-risk risky behaviors in the behavior information according to different early warning strategies, which will not be repeated here.

It should be noted that the embodiment shown in FIG. 7A can also extend the first application of the mobile phone to the first application of the first electronic device of the target user, and extend the second application of the mobile phone to the second electronic device of the target user. For the second application, the first electronic device and the second electronic device can be the same electronic device, such as the mobile phone in the above embodiment, or different electronic devices. In this case, it is necessary to ensure that the first electronic device and the second electronic device are It can communicate with each other, and the communication mode is not limited in the embodiment of this application.

Based on the above embodiments, FIG. 8 is a flowchart of an embodiment of the risk behavior warning method for diseases according to the application. As shown in FIG. 8, the method may include:

Step 801: Obtain the behavior information of the target user; the behavior information of the target user is used to describe the behavior of the target user;

Step 802: Determine whether the behavior information of the target user matches the risk behavior information corresponding to the designated disease information; the designated disease information is used to describe the disease that the target user has experienced, and the risk behavior information is used to describe the behavior that has the risk of inducing the target user to develop the disease ；

Step 803: If the behavior information of the target user matches the risk behavior information corresponding to the designated disease information, the target user is shown the early warning information for the designated disease information.

Optionally, if the behavior information of the target user does not match the risk behavior information corresponding to the specified disease information, the early warning processing may not be performed based on the behavior information of the target user, and the new behavior information of the target user may continue to be obtained.

Optionally, the risk behavior information has corresponding risk parameters; displaying early warning information for the specified disease information to the target user, including:

According to the risk parameters corresponding to the risk behavior information matching the behavior information of the target user, the target user is shown the early warning information for the specified disease information.

Optionally, the risk parameter includes a risk level; according to the risk parameter corresponding to the risk behavior information that matches the behavior information of the target user, displaying the early warning information for the specified disease information to the target user, including:

Optionally, the risk parameter includes a risk value; according to the risk parameter corresponding to the risk behavior information matching the behavior information of the target user, displaying the early warning information for the specified disease information to the target user, including:

Optionally, acquiring behavior information of the target user includes:

Optionally, the method may further include:

Optionally, the method for obtaining risk behavior information corresponding to the specified disease information includes:

Optionally, it also includes:

Optionally, obtaining historical occurrence information corresponding to the specified disease information of the target user within a specified time range includes:

Optionally, obtaining historical behavior information of the target user within a specified time range includes:

The above method of the embodiment of the present application automatically analyzes and identifies the specified disease that induces the target user based on the historical behavior information of the target user and the historical occurrence information corresponding to the specified disease information of the target user without the target user's intervention or perception. In the event of relatively risky risk behavior information, the user’s daily behavior information is obtained, and the risk behavior information is identified for early warning, so as to remind the target user to strengthen the control of their own risk behaviors, to better implement active health management, and to avoid disease events. Occurrence; Moreover, the risk behavior information corresponding to the target user’s designated disease information is determined based on the target user’s own individual situation, which is closer to the target user’s own individual situation, breaking the universality of disease monitoring in the prior art, and realizing the individual Early warning of diseases based on physical differences.

FIG. 9 is a flowchart of an embodiment of a method for obtaining risk behavior information of a disease according to the application. As shown in FIG. 9, the method may include:

Step 901: Obtain historical occurrence information corresponding to the specified disease information of the target user.

Step 902: Determine that the historical occurrence information satisfies a preset condition, and obtain historical behavior information of the target user within a specified time range and historical occurrence information corresponding to the specified disease information of the target user.

Wherein, the specified time range may be: a time period of a preset duration before the most recent occurrence time of the specified disease, and the specific length of the preset duration is not limited in this embodiment of the application. For example, assuming that the current time is 20:58 on May 20, 2020, the latest occurrence time of the specified disease is 20:55 on May 20, 2020, and the length of the preset time period is 72 hours, the specified time range can be : From 20:55 on May 17, 2020 to 20:55 on May 20, 2020.

Step 903: According to the historical behavior information within the designated time range and the acquired historical occurrence information corresponding to the designated disease information, determine risk behavior information that has an association relationship with the designated disease information of the target user.

Optionally, after judging that the historical occurrence information satisfies a preset condition, it further includes:

Optionally, before obtaining the historical occurrence information corresponding to the specified disease information of the target user, the method further includes:

Optionally, obtaining the specified disease information of the target user includes:

Obtain the medical record information of the target user from the designated medical treatment system, and obtain the designated disease information of the target user from the medical record information; and/or,

Optionally, determining that the historical occurrence information satisfies preset conditions includes:

Take an example to illustrate the method of obtaining risk behavior information shown in Figure 9. Specifically,

The set of risk behaviors corresponding to the specified disease information can be preset in the electronic device.

Among them, the risk behavior set can be initially empty, or one or more risk behavior information and risk parameters of the risk behavior information can be preset. The risk behavior information can include the name of the risk behavior; it is assumed that the risk parameter is the risk value.

The risk behavior corresponding to the risk behavior information included in the risk behavior set of the specified disease information is the behavior that may cause the occurrence of the specified disease. The preset risk behavior information may be description information of behaviors that have a general and routine influence on a specified disease in medicine. The risk value of the risk behavior information identifies the risk degree of the risk behavior inducing a specified disease. The risk value may be a numerical value, and the higher the risk of the risk behavior corresponding to the risk behavior information that induces the specified disease, the greater the risk value.

For example, for premature cardiac beats, common risk behaviors may include drinking, staying up late, etc., and the risk behavior set of premature cardiac beats may preset the following risk behavior information: drinking, staying up late. Optionally, the risk behaviors in the risk behavior set can also be added by the user voluntarily according to their own circumstances, which is not limited in the embodiment of the present application.

The risk values of the preset risk behavior information may be the same, or different values may be set according to the degree of risk, which is not limited in the embodiment of the present application.

In addition, one or more risk value thresholds can be preset in the electronic device to classify the risk behavior information in the risk behavior set into risk levels. For example, if a risk value threshold is set, the risk behavior information whose risk value exceeds the risk value threshold corresponds to high Risk level, the risk behavior information in the risk behavior set whose risk value does not exceed the risk value threshold corresponds to the low risk level.

In the case of a preset risk behavior set, the risk behavior information determined to have an association relationship with the specified disease information of the target user in step 903 may be an updated risk behavior set. It can include:

For each user behavior information obtained: if the user behavior information is not included in the risk behavior set, add the user behavior information to the risk behavior set, according to the time when the user behavior information occurs and the time after the user behavior information occurs Specify the time interval between the latest occurrence of the disease to determine the risk value of the user behavior information; if the user behavior information is included in the risk behavior set, according to the occurrence time of the user behavior information and the time after the occurrence time of the user behavior information Specify the time interval between the latest occurrence of the disease to update the risk value of the user's behavior information. Optionally, if the same user behavior occurs multiple times in the acquired historical behavior information of the target user, that is, there are multiple pieces of the same user behavior information, the above operations can be performed on each user behavior information. Optionally, for risk behavior information in the risk behavior set that does not appear in the historical behavior information of the target user, the risk value of the risk behavior information is reduced according to a preset rule.

The above method of the embodiment of the application monitors the target user's daily behavior without intervention or perception, automatically analyzes and identifies risky behaviors that induce the target user to have a relatively high risk of occurrence of the specified disease, and provides early warning to remind the target Users strengthen the control of their own risk behaviors, better realize active health management, and avoid the occurrence of disease events.

It can be understood that some or all of the steps or operations in the above-mentioned embodiments are merely examples, and the embodiments of the present application may also perform other operations or various operation modifications. In addition, each step may be performed in a different order presented in the foregoing embodiment, and it may not be necessary to perform all operations in the foregoing embodiment.

FIG. 10 is a schematic structural diagram of an embodiment of the risk early warning device of this application. As shown in FIG. 10, the device 1000 may include:

The obtaining unit 1010 is used to obtain behavior information of the target user; the behavior information of the target user is used to describe the behavior of the target user;

The judging unit 1020 is used to judge whether the behavior information of the target user matches the risk behavior information corresponding to the designated disease information; the designated disease information is used to describe the disease that the target user has experienced, and the risk behavior information is used to describe the disease that has induced the target user to develop the disease Risky behavior

The display unit 1030 is configured to display the early warning information for the designated disease information to the target user if the behavior information of the target user matches the risk behavior information corresponding to the designated disease information.

Optionally, the risk behavior information has corresponding risk parameters; the display unit 1030 may be specifically used to: display the target user an early warning for the specified disease information according to the risk parameter corresponding to the risk behavior information matching the behavior information of the target user information.

Optionally, the risk parameter includes a risk level; the display unit 1030 may be specifically used to: according to the risk level corresponding to the risk behavior information matching the behavior information of the target user, according to the risk level corresponding early warning strategy to show the target user that the disease is specific to the specified disease Information warning information.

Optionally, the risk parameter includes a risk value; the display unit 1030 may be specifically used to determine an early warning strategy according to the relationship between the risk value corresponding to the risk behavior information matching the target user's behavior information and the preset risk threshold;

Optionally, the acquiring unit 1010 may be specifically used for:

Optionally, the acquiring unit 1010 may also be used to:

Optionally, the obtaining unit 1010 may also be used to: obtain historical occurrence information corresponding to the specified disease information of the target user;

The judging unit 1020 can also be used for judging that the historical occurrence information satisfies a preset condition, and obtaining the historical behavior information of the target user within a specified time range and the historical occurrence information corresponding to the specified disease information of the target user;

The device 1000 may further include: a determining unit for determining risk behavior information associated with the specified disease information of the target user based on the historical behavior information within a specified time range and the acquired historical occurrence information corresponding to the specified disease information.

Optionally, the determining unit may also be used to determine the risk parameter corresponding to the risk behavior information associated with the specified disease information of the target user based on the historical behavior information within the specified time range and the historical occurrence information corresponding to the specified disease information.

Optionally, the acquiring unit 1010 may be specifically used for:

FIG. 11 is a schematic structural diagram of an embodiment of an apparatus for acquiring risk behavior information according to this application. As shown in FIG. 11, the apparatus 1100 may include:

The acquiring unit 1110 is used to acquire the historical occurrence information corresponding to the specified disease information of the target user; determine that the historical occurrence information satisfies preset conditions, and obtain the historical behavior information of the target user within the specified time range and the historical occurrence corresponding to the specified disease information of the target user information;

The determining unit 1120 is configured to determine the risk behavior information associated with the specified disease information of the target user based on the historical behavior information within the specified time range and the acquired historical occurrence information corresponding to the specified disease information.

Optionally, the determining unit 1120 may be further configured to: determine the risk parameter corresponding to the risk behavior information associated with the specified disease information of the target user based on the historical behavior information within the specified time range and the historical occurrence information corresponding to the specified disease information .

Optionally, the obtaining unit 1110 may be specifically used for:

Optionally, the acquiring unit 1110 may be specifically used for:

Optionally, the obtaining unit 1110 may also be used to:

Optionally, the acquiring unit 1110 may be specifically used for:

Optionally, the obtaining unit 1110 may be specifically used for:

The devices provided in the embodiments shown in FIGS. 10 to 11 can be used to implement the technical solutions of the method embodiments shown in FIGS. 5A to 9 of this application. For the implementation principles and technical effects, you may further refer to the related descriptions in the method embodiments.

It should be understood that the division of the various units of the devices shown in Figures 10 to 11 above is only a division of logical functions, and may be fully or partially integrated into one physical entity during actual implementation, or may be physically separated. And these units can all be implemented in the form of software invocation through processing elements; they can also be implemented in the form of hardware; part of the units can also be implemented in the form of software invocation through processing elements, and some of the units can be implemented in the form of hardware. For example, the acquisition unit may be a separately established processing element, or it may be integrated in a certain chip of the electronic device. The implementation of other units is similar. In addition, all or part of these units can be integrated together or implemented independently. In the implementation process, each step of the above method or each of the above units can be completed by an integrated logic circuit of hardware in the processor element or instructions in the form of software.

An embodiment of the present application also provides an electronic device, including: one or more processors; a memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one The or multiple computer programs include instructions, and when the instructions are executed by the device, the device executes the methods shown in FIGS. 5A to 9.

The embodiment of the present application also provides a computer-readable storage medium that stores a computer program, and when it runs on a computer, the computer executes the functions provided by the embodiments shown in Figs. 5A to 9 of the present application. method.

The embodiments of the present application also provide a computer program product. The computer program product includes a computer program that, when running on a computer, causes the computer to execute the method provided in the embodiments shown in FIG. 5A to FIG. 9 of the present application.

In the embodiments of the present application, "at least one" refers to one or more, and "multiple" refers to two or more. "And/or" describes the association relationship of the associated objects, indicating that there can be three types of relationships, for example, A and/or B, which can mean that A exists alone, A and B exist at the same time, and B exists alone. Among them, A and B can be singular or plural. The character "/" generally indicates that the associated objects before and after are in an "or" relationship. "The following at least one item" and similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single, or There can be more than one.

A person of ordinary skill in the art may be aware that the units and algorithm steps described in the embodiments disclosed in this document can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professionals and technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered as going beyond the scope of this application.

Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the above-described system, device, and unit can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.

In the several embodiments provided in this application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solution of this application essentially or the part that contributes to the existing technology or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including Several instructions are used to make a computer device (which may be a personal computer, a server, or a network device, etc.) execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (Read-Only Memory; hereinafter referred to as ROM), random access memory (Random Access Memory; hereinafter referred to as RAM), magnetic disks or optical disks, etc. A medium that can store program codes.

The above are only specific implementations of this application. Any person skilled in the art can easily conceive of changes or substitutions within the technical scope disclosed in this application, and they should all be covered by the protection scope of this application. The protection scope of this application shall be subject to the protection scope of the claims.

Claims

A risk early warning method applied to electronic equipment, characterized in that it includes:

Acquiring behavior information of a target user; the behavior information of the target user is used to describe the behavior of the target user;

Determine whether the behavior information of the target user matches the risk behavior information corresponding to the designated disease information; the designated disease information is used to describe the disease that the target user has occurred, and the risk behavior information is used to describe the target user The behavior of the user at risk of the disease;

If the behavior information of the target user matches the risk behavior information corresponding to the designated disease information, the target user is shown early warning information for the designated disease information.
The method according to claim 1, wherein the risk behavior information has corresponding risk parameters; and the displaying early warning information for the specified disease information to the target user comprises:

According to the risk parameter corresponding to the risk behavior information that matches the behavior information of the target user, the target user is shown early warning information for the specified disease information.
The method according to claim 2, wherein the risk parameter includes a risk level; the risk parameter corresponding to the risk behavior information that matches the behavior information of the target user is reported to the target user Display early warning information for the specified disease information, including:

According to the risk level corresponding to the risk behavior information that matches the behavior information of the target user, the early warning information for the specified disease information is displayed to the target user according to the early warning strategy corresponding to the risk level.
The method according to claim 2, wherein the risk parameter comprises a risk value; the risk parameter corresponding to the risk behavior information matching the behavior information of the target user is reported to the target user Display early warning information for the specified disease information, including:

Determining an early warning strategy according to the relationship between the risk value corresponding to the risk behavior information matching the target user's behavior information and the preset risk threshold;

According to the determined early warning strategy, the early warning information for the specified disease information is displayed to the target user.
The method according to any one of claims 1 to 4, wherein the acquiring behavior information of the target user comprises:

Acquire usage information of at least one electronic device used by the target user, and acquire behavior information of the target user according to the usage information; and/or,

Obtain the behavior information of the target user from a designated daily behavior monitoring application.
The method according to any one of claims 1 to 4, further comprising:

Obtain the risk behavior information corresponding to the specified disease information from an electronic device other than the electronic device; and/or,

Obtain risk behavior information corresponding to the specified disease information from a specified application of an electronic device other than the electronic device; and/or,

Obtain the risk behavior information corresponding to the specified disease information from the specified application of the electronic device.
The method according to any one of claims 1 to 4, wherein the method for obtaining risk behavior information corresponding to the specified disease information comprises:

Obtain the historical occurrence information corresponding to the specified disease information of the target user;

Judging that the historical occurrence information satisfies a preset condition, and obtaining historical behavior information of the target user within a specified time range and historical occurrence information corresponding to the specified disease information of the target user;

According to the historical behavior information within the designated time range and the acquired historical occurrence information corresponding to the designated disease information, risk behavior information that has an association relationship with the designated disease information of the target user is determined.
The method according to claim 7, further comprising:

According to the historical behavior information within the specified time range and the historical occurrence information corresponding to the specified disease information, the risk parameter corresponding to the risk behavior information having an association relationship with the specified disease information of the target user is determined.
The method according to claim 7, wherein said obtaining historical occurrence information corresponding to the specified disease information of the target user within a specified time range comprises:

Obtain the medical visit record information of the target user within the specified time range from the designated medical visit system, and obtain historical occurrence information corresponding to the specified disease information of the target user from the acquired medical visit record information; and/or,

Obtain the historical occurrence information corresponding to the specified disease information of the target user within the specified time range from the specified disease monitoring application; and/or,

Acquire historical biological data of the target user within the specified time range from a wearable device, and determine historical occurrence information corresponding to the specified disease information of the target user according to the historical biological data.
The method according to claim 7, wherein said acquiring historical behavior information of said target user within a specified time range comprises:

Acquiring usage information of at least one electronic device used by the target user within the specified time range, and acquiring historical behavior information of the target user according to the usage information; and/or,

Obtain the historical behavior information of the target user within the designated time range from a designated daily behavior monitoring application.
A method for acquiring risk behavior information, applied to electronic equipment, characterized in that it includes:

Obtain the historical occurrence information corresponding to the specified disease information of the target user;

Judging that the historical occurrence information satisfies a preset condition, and obtaining historical behavior information of the target user within a specified time range and historical occurrence information corresponding to the specified disease information of the target user;

According to the historical behavior information within the designated time range and the acquired historical occurrence information corresponding to the designated disease information, risk behavior information that has an association relationship with the designated disease information of the target user is determined.
The method according to claim 11, wherein after determining that the historical occurrence information satisfies a preset condition, the method further comprises:

According to the historical behavior information within the designated time range and the historical occurrence information corresponding to the designated disease information, the risk parameter corresponding to the risk behavior information associated with the designated disease information of the target user is determined.
The method according to claim 11 or 12, wherein the obtaining historical occurrence information corresponding to the specified disease information of the target user within a specified time range comprises:

Obtain the medical visit record information of the target user within the specified time range from the designated medical visit system, and obtain historical occurrence information corresponding to the specified disease information of the target user from the acquired medical visit record information; and/or,

Obtain the historical occurrence information corresponding to the specified disease information of the target user within the specified time range from the specified disease monitoring application; and/or,

Acquire historical biological data of the target user within the specified time range from a wearable device, and determine historical occurrence information corresponding to the specified disease information of the target user according to the historical biological data.
The method according to claim 11 or 12, wherein the obtaining historical behavior information of the target user within a specified time range comprises:

Acquiring usage information of at least one electronic device used by the target user within the specified time range, and acquiring historical behavior information of the target user according to the usage information; and/or,

Obtain the historical behavior information of the target user within the designated time range from a designated daily behavior monitoring application.
The method according to claim 11 or 12, wherein before acquiring the historical occurrence information corresponding to the specified disease information of the target user, the method further comprises:

Obtain designated disease information of the target user, where the designated disease information is used to record the disease that the target user has occurred.
The method according to claim 15, wherein said acquiring designated disease information of the target user comprises:

Acquire disease information selected by the target user on a preset interface, and use the acquired disease information as the designated disease information; and/or,

Obtain the medical record information of the target user from the designated medical treatment system, and obtain the designated disease information of the target user from the medical record information; and/or,

Obtain historical disease information of the target user from a designated disease monitoring application, and obtain designated disease information of the user from the historical disease information; and/or,

The historical biological data of the target user is acquired, and the designated disease information of the target user is determined according to the historical biological data.
The method according to claim 11 or 12, wherein the judging that the historical occurrence information satisfies a preset condition comprises:

Determine that the frequency information corresponding to the specified disease information in the historical occurrence information reaches a preset frequency threshold, and the frequency information is used to record the occurrence frequency of the specified disease recorded in the specified disease information; and/or,

It is determined that the occurrence frequency information corresponding to the specified disease information in the historical occurrence information reaches a preset frequency threshold, and the occurrence frequency information is used to record the occurrence frequency of the specified disease in the specified disease information record.
An electronic device, characterized in that it comprises:

One or more processors; memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, when the instructions are When the device is executed, the device is caused to execute the method according to any one of claims 1 to 10.
An electronic device, characterized in that it comprises:

One or more processors; memory; and one or more computer programs, wherein the one or more computer programs are stored in the memory, and the one or more computer programs include instructions, when the instructions are When the device is executed, the device is caused to execute the method according to any one of claims 11 to 17.
A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which when running on a computer, causes the computer to execute the method according to any one of claims 1 to 10.
A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which when running on a computer, causes the computer to execute the method according to any one of claims 11 to 17.