Early warning device, method and terminal equipment for respiratory diseases

Abstract

The application provides a respiratory disease early warning device, method and terminal equipment, and relates to the technical field of terminals, wherein the device comprises: an acquisition module configured to acquire at least one of heartbeat data, respiration data and posture data of a living body; a detection module configured to detect a target event corresponding to a respiratory system disease based on at least one of the acquired heartbeat data, respiration data and posture data; and a determination module configured to determine first risk data based on the target event, wherein the first risk data is used to indicate the risk of the living body suffering from the respiratory system disease. The technical scheme provided by the application can realize early warning of respiratory diseases.

Description

Technical Field

[0001] This application relates to the field of terminal technology, and in particular to an early warning device, method and terminal equipment for respiratory diseases. Background Technology

[0002] Respiratory diseases are caused by pathogenic microorganisms, including bacteria, viruses, mycoplasma, fungi, and parasites, that invade and multiply in the respiratory tract of a living organism. Based on their location, they are divided into upper respiratory tract infections and lower respiratory tract infections. The former refers to the general term for acute inflammation from the nasal cavity to the larynx, including influenza, rhinitis, pharyngitis, and laryngitis; the latter includes tracheitis, bronchitis, and pneumonia.

[0003] In existing technologies, medical diagnosis of living organisms can usually be performed using specialized medical equipment, including serological diagnosis, pathogen (nasal swab, sputum) examination, and chest X-ray detection. However, this detection method requires a specific location (such as a hospital) to perform the test on a living organism. Early clinical symptoms of respiratory diseases are often not obvious, making it difficult for users to detect them in time and proactively go to that specific location for testing. This can easily lead to a worsening of the respiratory disease. Summary of the Invention

[0004] In view of this, this application provides a respiratory disease early warning device, method and terminal equipment, which can realize early warning of respiratory diseases.

[0005] To achieve the above objectives, in a first aspect, embodiments of this application provide a method for early warning of respiratory diseases, comprising:

[0006] Acquire at least one of the following: heart rate data, respiratory data, and posture data of a living organism;

[0007] Based on at least one of the acquired heart rate data, breathing data, and posture data, a target event corresponding to a respiratory disease is detected;

[0008] Based on the target event, first risk data is determined, which is used to describe the risk that the living organism may have the respiratory disease.

[0009] In this embodiment, at least one of heartbeat data, breathing data, and posture data of a living organism can be acquired. Based on at least one of the heartbeat data, breathing data, and posture data, target events corresponding to respiratory diseases can be detected. That is, based on changes in the heartbeat, breathing, and posture of a living organism, target events corresponding to respiratory diseases that may occur in the living organism can be captured. Then, based on these target events, first risk data used to describe the risk of the living organism having a respiratory disease can be determined, thereby achieving early warning of respiratory diseases.

[0010] Among them, heart rate data can be used to describe the state of a living animal's heartbeat, breathing data can be used to describe the state of a living animal's breathing, and posture data can be used to describe the movement posture of a living animal.

[0011] It should be noted that the more types and the higher the frequency of the detected target events, the greater the risk of the living organism having respiratory diseases.

[0012] Optionally, the heartbeat data includes at least one of heart rate data, electrocardiography (ECG) data, photoplethysmograph (PPG) data, and heart sound data.

[0013] Optionally, the respiratory data includes at least one of respiratory rate data and respiratory sound data.

[0014] Optionally, respiratory rate data can be determined based on heart rate data.

[0015] Optionally, the attitude data may include at least one of acceleration and angular velocity.

[0016] Optionally, the target event is determined using a first network model based on at least one of heartbeat data, breathing data, and posture data.

[0017] Optionally, the method further includes:

[0018] Acquire the physiological sound data of the living organism;

[0019] The determination of the first risk data based on the target event includes:

[0020] Determine the frequency at which the target event is detected within the first time period;

[0021] If the frequency of the target event determines that the living organism has symptoms corresponding to the respiratory disease, then the first risk data is determined based on the physiological sound data of the living organism.

[0022] Since a living organism may indeed be infected with a respiratory disease when it exhibits symptoms corresponding to a respiratory illness, it may also be infected with other diseases. Therefore, if it is determined that a living organism has symptoms of a respiratory disease, further testing can be conducted.

[0023] Optionally, determining that the living organism has symptoms corresponding to the respiratory disease based on the frequency of the target event includes:

[0024] Based on weather data and the frequency of the target events, a second risk data is determined;

[0025] If the second risk data indicates that the living organism has symptoms corresponding to the respiratory disease, then it is determined that the living organism has symptoms corresponding to the respiratory disease.

[0026] Since different weather conditions can have different effects on the incidence of respiratory diseases, such as the increased incidence of respiratory diseases in hot and cold weather, it is possible to more accurately determine whether a living person has symptoms of respiratory diseases based on the occurrence of a target event in the body and the interference of external weather.

[0027] Optionally, weather data, the frequency of deep breathing events, the frequency of coughing events, and the frequency of nose blowing events are input into the second network model to obtain the second risk data output by the second network model.

[0028] Optionally, determining that the living organism has symptoms corresponding to the respiratory disease based on the frequency of the target event includes:

[0029] If the frequency of the target event is greater than or equal to a first frequency threshold, a first alert message is issued. The first alert message is used to alert the living organism of the risk of developing symptoms corresponding to the respiratory disease.

[0030] If a first feedback message is received based on the first prompt message, and the first feedback message is a message confirming that the living organism has symptoms corresponding to the respiratory disease, then it is determined that the living organism has symptoms corresponding to the respiratory disease.

[0031] Optionally, determining the first risk data based on the physiological sound data of the living organism includes:

[0032] The first risk data is determined based on the physiological sound data, weather data, and the frequency of each of the target events.

[0033] Optionally, the detection results of physiological sound data, weather data, and the frequency of target events are input into the fourth network model to obtain the first risk data output by the fourth network model.

[0034] Optionally, the first risk data can be determined based on physiological sound data, weather data, and the frequency of the target event.

[0035] Optionally, the risk of a living organism having a respiratory disease can be determined based on physiological sound data (or the detection results of physiological sound data) and the frequency of the target event.

[0036] Optionally, the risk level of a living organism having a respiratory disease can be determined based on physiological sound data (or the detection results of physiological sound data), weather data, the frequency of the target event, and the first feedback message.

[0037] Optionally, the risk level of a living organism having a respiratory disease can be determined based on physiological sound data (or the detection results of physiological sound data), the frequency of the target event, and the first feedback message.

[0038] Optionally, the method further includes:

[0039] When it is determined that the living organism has the symptoms corresponding to the respiratory disease, a second prompt message is issued, which is used to indicate that the living organism has the symptoms corresponding to the respiratory disease.

[0040] If a second feedback message is received based on the second prompt message, and the second feedback message is a message agreeing to determine the first risk data, then the first risk data is determined based on the physiological sound data of the living organism.

[0041] Optionally, the method further includes:

[0042] A third notification message is issued to alert the living individual to the risk of having a respiratory disease, so that the user can further investigate whether the individual has a respiratory disease through more professional means such as going to the hospital for testing.

[0043] Optionally, the physiological sound data includes at least one of respiratory sound data, speech sound data, and cough sound data.

[0044] Optionally, the method further includes:

[0045] The physiological sound data is collected using a microphone.

[0046] Optionally, the target event includes at least one of a deep breathing event, a coughing event, and a nose blowing event.

[0047] Secondly, embodiments of this application provide an early warning device for respiratory diseases, comprising:

[0048] The acquisition module is used to acquire at least one of the following: heartbeat data, respiratory data, and posture data of a living organism;

[0049] The detection module is used to detect target events corresponding to respiratory diseases based on at least one of the acquired heartbeat data, breathing data, and posture data.

[0050] A determination module is configured to determine first risk data based on the target event, the first risk data being used to describe the risk that the living organism has the respiratory disease.

[0051] Optionally, the acquisition module is further configured to acquire the physiological sound data of the living organism;

[0052] The determining module is specifically used to determine the frequency of the target event detected within a first time period; if it is determined based on the frequency of the target event that the living organism has symptoms corresponding to the respiratory disease, then the first risk data is determined based on the physiological sound data of the living organism.

[0053] Optionally, the determining module is further configured to:

[0054] Based on weather data and the frequency of the target events, a second risk data is determined;

[0055] If the second risk data indicates that the living organism has symptoms corresponding to the respiratory disease, then it is determined that the living organism has symptoms corresponding to the respiratory disease.

[0056] Optionally, the device further includes:

[0057] The first prompting module is used to issue a first prompting message if the frequency of the target event is greater than or equal to a first frequency threshold. The first prompting message is used to indicate the risk that the living organism will develop symptoms corresponding to the respiratory disease.

[0058] The determining module is further configured to: if a first feedback message is received based on the first prompt message, and the first feedback message is a message confirming that the living body has symptoms corresponding to the respiratory system disease, then determine that the living body has symptoms corresponding to the respiratory system disease.

[0059] Optionally, the determining module is specifically used for:

[0060] The first risk data is determined based on the physiological sound data, weather data, and the frequency of the target event.

[0061] Optionally, the physiological sound data includes at least one of respiratory sound data, speech sound data, and cough sound data.

[0062] Optionally, the target event includes at least one of a deep breathing event, a coughing event, and a nose blowing event.

[0063] Optionally, the device further includes a second prompting module for:

[0064] When it is determined that the living organism has the symptoms corresponding to the respiratory disease, a second prompt message is issued, which is used to indicate that the living organism has the symptoms corresponding to the respiratory disease.

[0065] The determining module is specifically used to determine the first risk data based on the physiological sound data of the living organism if a second feedback message is received based on the second prompt message, and the second feedback message is a message agreeing to determine the first risk data.

[0066] Optionally, the device further includes a third prompting module for:

[0067] A third alert message is issued, which is used to alert the living organism to the risk of having a respiratory disease.

[0068] Thirdly, embodiments of this application provide a terminal device, including: a memory and a processor, wherein the memory is used to store a computer program; and the processor is used to execute the method described in any one of the first aspects when the computer program is invoked.

[0069] Fourthly, embodiments of this application provide a chip system including a processor coupled to a memory, the processor executing a computer program stored in the memory to implement the method described in any one of the first aspects above.

[0070] The chip system can be a single chip or a chip module composed of multiple chips.

[0071] Fifthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in any one of the first aspects above.

[0072] Sixthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the method described in any one of the first aspects above.

[0073] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0074] Figure 1 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application;

[0075] Figure 2 This is a flowchart illustrating a method for early warning of respiratory diseases provided in an embodiment of this application;

[0076] Figure 3 A schematic diagram of heart rate provided for an embodiment of this application;

[0077] Figure 4 A schematic diagram of another heart rate provided for an embodiment of this application;

[0078] Figure 5 A schematic diagram of another heart rate provided for an embodiment of this application;

[0079] Figure 6 A schematic diagram of acceleration provided for an embodiment of this application;

[0080] Figure 7 A schematic diagram of another acceleration provided for an embodiment of this application;

[0081] Figure 8 A schematic diagram illustrating the principle of detecting target events provided in an embodiment of this application;

[0082] Figure 9 A flowchart illustrating a method for determining first risk data provided in an embodiment of this application;

[0083] Figure 10 A schematic diagram of a reminder interface provided in an embodiment of this application;

[0084] Figure 11 A schematic diagram of another notification interface provided in an embodiment of this application;

[0085] Figure 12 A schematic diagram of another notification interface provided in an embodiment of this application;

[0086] Figure 13 A schematic diagram illustrating the principle of determining second risk data provided in an embodiment of this application;

[0087] Figure 14 A schematic diagram of another notification interface provided in an embodiment of this application;

[0088] Figure 15 A schematic diagram of another notification interface provided in an embodiment of this application;

[0089] Figure 16 This application provides a schematic diagram illustrating the principle of determining first risk data in an embodiment of the present application.

[0090] Figure 17 A schematic diagram of another notification interface provided in an embodiment of this application;

[0091] Figure 18 This is a schematic diagram of the structure of a respiratory disease early warning device provided in an embodiment of this application;

[0092] Figure 19 This is a schematic diagram of the structure of another terminal device provided in an embodiment of this application. Detailed Implementation

[0093] The respiratory disease early warning method provided in this application can be applied to terminal devices such as mobile phones, tablets, wearable devices, in-vehicle devices, augmented reality (AR) / virtual reality (VR) devices, laptops, ultra-mobile personal computers (UMPCs), netbooks, and personal digital assistants (PDAs). This application does not impose any restrictions on the specific type of terminal device.

[0094] Figure 1 This is a schematic diagram of the structure of a terminal device 100 provided in an embodiment of this application. The terminal device 100 may include a processor 110, a memory 120, and a communication module 130, etc.

[0095] The processor 110 may include one or more processing units, and the memory 120 is used to store program code and data. In this embodiment, the processor 110 can execute computer execution instructions stored in the memory 120 to control and manage the actions of the terminal device 100.

[0096] The communication module 130 can be used for communication between various internal modules of the terminal device 100, or for communication between the terminal device 100 and other external terminal devices. For example, if the terminal device 100 communicates with other terminal devices via a wired connection, the communication module 130 may include an interface, such as a USB interface. The USB interface can be an interface conforming to the USB standard specification, specifically a Mini USB interface, a Micro USB interface, a USB Type-C interface, etc. The USB interface can be used to connect a charger to charge the terminal device 100, or to transfer data between the terminal device 100 and peripheral devices. It can also be used to connect headphones for audio playback. This interface can also be used to connect other terminal devices, such as AR devices.

[0097] Alternatively, the communication module 130 may include audio devices, radio frequency circuits, Bluetooth chips, wireless fidelity (Wi-Fi) chips, near-field communication (NFC) modules, etc., which can enable the terminal device 100 to interact with other terminal devices in a variety of different ways.

[0098] Optionally, the terminal device 100 may also include a display screen 140, which can display images or videos in the human-computer interaction interface.

[0099] Optionally, the terminal device 100 may also include peripheral devices 150, such as a mouse, keyboard, speaker, microphone, etc. For example, the user's voice can be captured by the microphone, and voice messages can be played to the user through the speaker.

[0100] Optionally, the terminal device 100 may also include a sensor 160. In some embodiments, the sensor 160 may include at least one of a pressure sensor, a motion sensor, a touch sensor, a bone conduction sensor, and an optical heart rate sensor.

[0101] A pressure sensor is used to sense pressure signals and convert them into electrical signals. In some embodiments, the pressure sensor may be located on the display screen 140. There are many types of pressure sensors, such as resistive pressure sensors, inductive pressure sensors, and capacitive pressure sensors. A capacitive pressure sensor may include at least two parallel plates with conductive material. When force is applied to the pressure sensor, the capacitance between the electrodes changes. The terminal device 100 determines the pressure intensity based on the change in capacitance. When a touch operation is applied to the display screen 140, the terminal device 100 detects the intensity of the touch operation based on the pressure sensor. The terminal device 100 may also calculate the touch position based on the detection signal from the pressure sensor. In some embodiments, touch operations applied to the same touch position but with different intensities may correspond to different operation commands. For example, when a touch operation with an intensity less than a first pressure threshold is applied to the SMS application icon, a command to view an SMS message is executed. When a touch operation with an intensity greater than or equal to the first pressure threshold is applied to the SMS application icon, a command to create a new SMS message is executed.

[0102] Motion sensors can detect user movement and include at least one of a gyroscope sensor and an accelerometer sensor. The gyroscope sensor can be used to determine the motion posture of the terminal device 100. In some embodiments, the angular velocity of the terminal device 100 about three axes (i.e., the x, y, and z axes) can be determined by the gyroscope sensor. The accelerometer sensor can detect the magnitude of the acceleration of the terminal device 100 in various directions (generally three axes). When the terminal device 100 is stationary, the magnitude and direction of gravity can be detected. In some embodiments, the terminal device 100 can detect user actions, such as deep breathing, coughing, blowing its nose, sneezing, or drinking water, using motion sensors.

[0103] A touch sensor, also known as a "touch panel," can be located on the display screen 140. The touch sensor and the display screen 140 together form a touchscreen, also called a "touch display." The touch sensor detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through the display screen 140. In some embodiments, the touch sensor may also be located on the surface of the terminal device 100, in a different position than the display screen 140.

[0104] Bone conduction sensors can acquire vibration signals. In some embodiments, a bone conduction sensor can acquire vibration signals from vibrating bone fragments in a living body. A bone conduction sensor can also contact a living pulse to receive blood pressure signals. In some embodiments, the blood pressure signals acquired by a bone conduction sensor can be used to analyze heart rate information to achieve heart rate detection, i.e., it can function as a heart rate sensor.

[0105] Optical heart rate sensors can use PPG to measure heart rate. The measurement principle is that a capacitive lamp is shone onto the skin, and the light reflected back through the skin tissue is received by a photosensitive sensor and converted into an electrical signal. This electrical signal is then converted into a digital signal, and the heart rate is calculated based on the light absorbance of the blood.

[0106] It should be understood that, in addition to Figure 1 In addition to the various components or modules listed, the embodiments of this application do not specifically limit the structure of the terminal device 100. In other embodiments of this application, the terminal device 100 may also include more or fewer components than those shown in the figures, or combine some components, or split some components, or have different component arrangements. The components shown in the figures may be implemented in hardware, software, or a combination of software and hardware.

[0107] To facilitate understanding of the technical solutions in the embodiments of this application, the application scenarios of the embodiments of this application will be introduced first below.

[0108] Humans and other living organisms, including those with respiratory systems, can contract respiratory diseases. Current diagnostic methods require specific locations (such as hospitals) and specialized medical equipment to perform tests on living individuals. However, early clinical symptoms of respiratory diseases are often not obvious, making it difficult for users to detect them in time and proactively seek testing at these specific locations. This can easily lead to the aggravation of respiratory diseases, such as an upper respiratory tract infection developing into a more severe lower respiratory tract infection.

[0109] To at least address some of the aforementioned technical problems and achieve early screening and warning of respiratory diseases, this application provides a method for early warning of respiratory diseases. In this application embodiment, at least one of a living organism's heartbeat data, respiratory data, and posture data can be acquired. Based on at least one of these data, target events corresponding to respiratory diseases are detected. That is, based on changes in the living organism's heartbeat, respiratory changes, and posture, potential target events corresponding to respiratory diseases are captured. Then, based on these target events, first risk data is determined to illustrate the risk of the living organism having a respiratory disease, thereby achieving early warning of respiratory diseases.

[0110] It should be noted that the living body in the embodiments of this application can be a living body including a respiratory system, such as a human or other mammal.

[0111] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0112] Please refer to Figure 2 This is a flowchart illustrating a method for early warning of respiratory diseases provided in an embodiment of this application. It should be noted that this method does not rely on... Figure 2 The specific order described below is a limitation. It should be understood that in other embodiments, the order of some steps in the method can be interchanged according to actual needs, or some steps can be omitted or deleted. The method includes the following steps:

[0113] S201, The terminal device acquires at least one of the following: heartbeat data, breathing data, and posture data of a living organism.

[0114] Heartbeat data can be used to describe the state of a living heartbeat. In some embodiments, heartbeat data may include at least one of heart rate data, ECG data, PPG data, and heart sound data. Among these, heart rate data can be used to indicate the heart rate of a living organism; ECG data or PPG data can more comprehensively indicate multiple features related to the heartbeat; and heart sound data can be used to describe the acoustic characteristics of a living heartbeat.

[0115] Respiratory data can be used to describe the state of respiration in a living organism. In some embodiments, respiratory data may include at least one of respiratory rate data and respiratory sound data. The respiratory rate data can be used to indicate the respiratory frequency of a living organism, and the respiratory sound data can indicate the acoustic characteristics of respiration in a living organism.

[0116] Attitude data can be used to describe the motion posture of a living organism. In some embodiments, attitude data may include at least one of acceleration and angular velocity.

[0117] In some embodiments, the terminal device is provided with a sensor for acquiring heartbeat data, breathing data, or posture data, so the terminal device can directly acquire the heartbeat data, breathing data, or posture data collected by the sensor.

[0118] For example, the terminal device is a wearable device such as a smartwatch or smart bracelet. This wearable device is equipped with three sensors for collecting heart rate data, breathing data, and posture data, respectively. Therefore, the wearable device can collect heart rate data, breathing data, or posture data through these three sensors.

[0119] In some embodiments, if the terminal device does not have a sensor for acquiring heartbeat data, breathing data, or posture data, then the terminal device can acquire and store heartbeat data, breathing data, or posture data from other devices.

[0120] For example, the terminal device is a mobile phone, which can connect to a network of multiple wearable devices such as smartwatches and smart bracelets. Each wearable device can be equipped with sensors for collecting at least one of the following data: heart rate data, respiratory data, and posture data. In this case, the mobile phone can obtain heart rate data, respiratory data, or posture data from at least one wearable device. Alternatively, the terminal device is a mobile phone connected to a storage device (such as a portable hard drive or data server). In this case, the mobile phone can obtain heart rate data, respiratory data, and posture data from the storage device.

[0121] In some embodiments, since a living organism exchanges external air with internal air through the respiratory system during respiration, including replacing carbon dioxide in the blood with oxygen, and since a living organism delivers oxygen to various parts of the body through blood circulation during a heartbeat, the respiratory system and the heart are closely related. Therefore, respiratory data and heartbeat data are also closely correlated. Taking the human body as an example, during respiration, the heart rate increases during inhalation and decreases during exhalation. Therefore, the terminal device can acquire heartbeat data and process it to obtain respiratory data. In some embodiments, the terminal device can determine respiratory rate data based on heart rate data.

[0122] For example, living heart rate can be like Figure 3 As shown. By Figure 3 It is known that the heart rate of a living organism fluctuates regularly in a shape similar to a sine wave, with a period of approximately 10 seconds. Taking the period from 0 to 10 seconds as an example, the heart rate of a living organism rises from a lower 60 beats per minute to a higher 85 beats per minute (corresponding to inhalation), and then decreases again to 60 beats per minute (corresponding to exhalation). That is, within the period from 0 to 10 seconds, the living organism takes one breath. Therefore, for... Figure 3Overall analysis shows that the living organism breathed 5 times between 0s and 60s, which is a respiratory rate of 5.

[0123] Of course, in practical applications, terminal devices can also obtain at least one of heart rate data, breathing data, and posture data through other means, and the methods by which the terminal obtains heart rate data, breathing data, and posture data can be different or the same.

[0124] For example, the terminal device is a mobile phone, which may also be equipped with a motion sensor. Therefore, the mobile phone can obtain posture data through the motion sensor in the phone, obtain heart rate data from the heart rate sensor of other wearable devices such as smartwatches, and determine breathing data based on the heart rate data.

[0125] S202, the terminal device detects a target event corresponding to a respiratory disease based on at least one of heartbeat data, breathing data, and posture data.

[0126] When a living organism suffers from a respiratory disease, it may experience various symptoms such as difficulty breathing, dry and itchy throat, and runny nose. In order to eliminate or improve these symptoms, the living organism may perform more deep breathing, coughing, and blowing its nose. When the living organism performs different events, the actions performed are different, and the burden on the cardiopulmonary system is also different. Accordingly, the heart rate data, breathing data, and posture data will also have different data characteristics. Therefore, the terminal device can detect whether the acquired heart rate data, breathing data, and posture data have data characteristics corresponding to a certain target event. If so, it can be determined that the target event has been detected (or captured).

[0127] Among them, the target events and the data characteristics corresponding to each target event can be determined in advance by relevant personnel based on respiratory diseases, and the terminal device can receive the target events and the data characteristics corresponding to each target event submitted by relevant technical personnel.

[0128] In some embodiments, the target event may include at least one of a deep breathing event, a coughing event, and a nose-blowing event. It should be noted that in practical applications, the target event may also include other events, such as a drinking water event.

[0129] When a living organism takes a deep breath at rest, compared to normal breathing (i.e., short breaths), the breathing is relatively stable, the amplitude of heart rate rises and falls is greater, the frequency of short breaths is lower, and body movements are minimal. Please continue reading. Figure 3 and Figure 4 , Figure 3 This shows the changes in heart rate in a living organism when taking 5 deep breaths per minute. Figure 4 The heart rate changes in a living organism with a normal respiratory rate of 13 breaths per minute are shown. Figure 3 and Figure 4 The comparison shows that during deep breathing in a living organism, the magnitude of the increase and decrease in heart rate varies from [previous data]. Figure 3 The value of 10 (approximately) increases to Figure 4 30 (approximate value) Figure 4 The heart rate curve in the middle Figure 3 The heart rate curve is smoother and more regular, meaning breathing is more stable.

[0130] When a living organism coughs, it will emit a sound similar to "achoo," its respiratory rate will increase, and its body will tremble during the cough. Following the cough, its heart rate will briefly rise before gradually decreasing to normal. Please refer to [link / reference needed]. Figure 5 Under normal conditions, a living organism's heart rate remains between 70 and 75 beats per minute. Between the 5th and 10th second, the organism coughed, causing its heart rate to rapidly rise to 95 beats per minute, before gradually returning to around 70-75 beats per minute over the next 15 seconds. (Please also refer to...) Figure 6 Under normal conditions, the acceleration amplitude of a living organism is between 4000 and 5000. At the 2nd and 6th second, the living organism coughed. Due to the body tremors, the acceleration fluctuated significantly, with the amplitude fluctuation even exceeding 2500.

[0131] When a living person blows their nose, it affects normal breathing, thus reducing the respiratory rate and causing corresponding fluctuations in heart rate. This is accompanied by the raising of the hand and the sound of air vibration in the nasal cavity. The hand-raising motion causes changes in posture data sensed by motion sensors. Please refer to [link / reference needed]. Figure 7 When the living organism is in a normal state, its triaxial acceleration is relatively stable. Between the 1st and 6th second, the living organism blew its nose, and the triaxial acceleration fluctuated significantly. After the nose-blowing action at the 6th second, it returned to stability.

[0132] In some embodiments, the terminal device can determine at least one target event based on at least one of heartbeat data, breathing data, and posture data using a first network model. The first network model can be a network model such as a machine learning model or a deep learning model. The terminal device can, for example... Figure 8 As shown, at least one of heart rate data, breathing data, and posture data is used as input to the first network model 810, and the detection result of the first network model 810 is obtained. The detection result can be used to indicate the detected target event.

[0133] In some embodiments, each first network model can be used to detect a target event. Accordingly, the terminal device can input at least one of heart rate data, breathing data, and posture data into at least one first network model to obtain the detection result of at least one first network model. The detection result of each first network model can be used to indicate whether a target event corresponding to the first network model has been detected. For example, the first network model may include a deep breathing event model, a cough event model, and a nose-blowing event model, wherein the deep breathing event model can be used to detect deep breathing events, the cough event model can be used to detect cough events, and the nose-blowing event model can be used to detect nose-blowing events. The terminal device inputs heart rate data, breathing data, and posture data into the deep breathing event model, the cough event model, and the nose-blowing event model to obtain the detection results of the deep breathing event model, the cough event model, and the nose-blowing event model, respectively. The detection result of the deep breathing event model can be used to indicate whether a deep breathing event has been detected, the detection result of the cough event model can be used to indicate whether a cough event has been detected, and the detection result of the nose-blowing event model can be used to indicate whether a nose-blowing event has been detected.

[0134] In some embodiments, a first network model can be used to detect multiple target events. Accordingly, the terminal device can input at least one of heartbeat data, breathing data, and posture data into the first network model to obtain a detection result from the first network model. This detection result can be used to indicate whether any target event has been detected. For example, the terminal device can input heartbeat data, breathing data, and posture data into the first network model to obtain a detection result from the first network model. This detection result can be used to indicate whether a deep breathing event, a coughing event, or a nose-blowing event has been detected.

[0135] It should be noted that the first network model can be obtained by the terminal device in advance. In some embodiments, the terminal device can obtain a first sample set, wherein each sample in the first sample set includes at least one of heartbeat data, breathing data, and posture data, and each sample is labeled based on real target events. The terminal device trains and obtains the first network model through the first sample set.

[0136] It should also be noted that in practical applications, terminal devices can also detect target events corresponding to respiratory diseases based on at least one of heart rate data, respiratory data, and posture data, using methods other than network models. Alternatively, terminal devices can determine target events through other means. For example, in some embodiments, the terminal device can receive target event records submitted by the user. Or, in other embodiments, since living beings emit sounds when performing certain events, and these sounds have sound characteristics corresponding to the event, such as the sound of blowing a nose or coughing, the terminal device can acquire sound data through a microphone and detect target events based on this sound data. Alternatively, it can detect target events based on the sound data and at least one of heart rate data, respiratory data, and posture data. In some embodiments, taking the sound of blowing a nose as an example, the terminal device can turn on the microphone to acquire the sound of blowing a nose when it determines that the user is raising their hand through a motion sensor.

[0137] S203, The terminal device determines first risk data based on the target event, the first risk data being used to describe the risk of a living person having a respiratory disease.

[0138] As mentioned above, when a living organism suffers from a respiratory disease, the organism may experience corresponding target events. When the terminal device detects these target events, it can determine the risk of the living organism suffering from a respiratory disease based on the occurrence of these target events, thereby achieving early warning of respiratory diseases.

[0139] Terminal devices can determine the number and type of target events detected. The more types and the higher the number of target events detected, the greater the risk of a living organism having respiratory diseases.

[0140] For example, target events corresponding to respiratory diseases include deep breathing events, coughing events, and nose blowing events. If the terminal device detects only deep breathing events through S202, and the number of deep breathing events is low (e.g., the number of deep breathings per day is less than a certain preset value), then it can be determined that the risk of the living person having a respiratory disease is low; or, if the terminal device detects deep breathing events, coughing events, and nose blowing events through S202, and the number of all three events is high (e.g., the number of these three events per day is greater than a certain preset value), then it can be determined that the risk of the living person having a respiratory disease is high.

[0141] In addition, the method by which terminal devices determine the first risk data based on the target event can also be referenced below. Figure 9 As shown.

[0142] In this embodiment, at least one of heartbeat data, breathing data, and posture data of a living organism can be acquired. Based on at least one of the heartbeat data, breathing data, and posture data, target events corresponding to respiratory diseases can be detected. That is, based on changes in the heartbeat, breathing, and posture of a living organism, target events corresponding to respiratory diseases that may occur in the living organism can be captured. Then, based on these target events, first risk data used to describe the risk of the living organism having a respiratory disease can be determined, thereby achieving early warning of respiratory diseases.

[0143] Please refer to Figure 9 This is a flowchart illustrating a method for determining first risk data provided in an embodiment of this application. It should be noted that this method does not rely on... Figure 9 The specific order described below is a limitation. It should be understood that in other embodiments, the order of some steps in the method can be interchanged according to actual needs, or some steps can be omitted or deleted. The method includes the following steps:

[0144] S901, the terminal device determines the frequency of the target event detected within the first time period.

[0145] It should be noted that the first duration can be determined in advance by the terminal device. For example, the first duration can be 1 minute, 1 hour, 4 hours or 1 day. This application embodiment does not limit the way the terminal device determines the first duration or the length of the first duration.

[0146] In some embodiments, when the frequency of the target event is greater than or equal to a preset first frequency threshold, a first notification message is issued. This first notification message can be used to indicate the risk of a living person exhibiting symptoms corresponding to a respiratory disease. In other embodiments, the terminal device can also receive a first feedback message submitted by the user in response to the first notification message. This first feedback message can be a message from the user confirming or denying the presence of symptoms corresponding to a respiratory disease in the living person.

[0147] It should be noted that the first frequency threshold can be determined in advance by the terminal device, and the value of the first frequency threshold can be different or the same for different target events. This application embodiment does not limit the terminal device's determination of the first frequency threshold or the size of the first frequency threshold.

[0148] For example, the terminal device is a smartwatch, the target event is a deep breathing event, the first duration is the most recent 4 hours before the current moment, and the first frequency threshold is 35. If the terminal device detects that the frequency of deep breathing events occurring in a living person within the most recent 4 hours before the current moment is 50, then since 50 is greater than 35, the terminal device can display something like this: Figure 10The reminder interface shown includes a first prompt message: "In the past four hours, your deep breathing rate at rest has been relatively high, reaching 50 breaths per hour. Do you experience symptoms such as nasal congestion or difficulty breathing?" It also includes "Yes" and "No" buttons. If the terminal device receives the user's click action based on "Yes," it determines that the first feedback message received is a confirmation that the user has symptoms such as nasal congestion or difficulty breathing; if the terminal device receives the user's click action based on "No," it determines that the first feedback message received is a denial that the user has symptoms such as nasal congestion or difficulty breathing.

[0149] For example, the terminal device is a smartwatch, the target event is a coughing event, the first duration is the most recent 4 hours before the current moment, and the first frequency threshold is 10. If the terminal device detects that the frequency of coughing events occurring in a living person within the most recent 4 hours before the current moment is 20, then since 20 is greater than 10, the terminal device can display something like this: Figure 11 The reminder interface shown includes a first prompt message: "You have coughed more than 10 times in the past four hours. Do you have any symptoms such as respiratory inflammation?", and two buttons: "Yes" and "No". If the terminal device receives the user's click action based on "Yes", it determines that the first feedback message received is a confirmation that the user has symptoms such as respiratory inflammation; if the terminal device receives the user's click action based on "No", it determines that the first feedback message received is a denial that the user has symptoms such as respiratory inflammation.

[0150] For example, the terminal device is a smartwatch, the target event is a nose-blowing event, the first duration is the most recent 4 hours before the current moment, and the first frequency threshold is 3. If the terminal device detects that the frequency of live coughing events in the most recent 4 hours before the current moment is 5, then since 5 is greater than 3, the terminal device can display something like this: Figure 12 The reminder interface shown includes a first prompt message: "In the past four hours, you have blown your nose approximately five times. Do you have any cold symptoms?", and two buttons: "Yes" and "No". If the terminal device receives the user's click action based on "Yes", it determines that the first feedback message received is a confirmation that the user has cold symptoms; if the terminal device receives the user's click action based on "No", it determines that the first feedback message received is a denial that the user has cold symptoms.

[0151] In some embodiments, the terminal device may determine the sum of the frequencies of multiple target events, i.e., the total frequency of the multiple target events, and issue a first prompt message when the total frequency is greater than or equal to a preset second frequency threshold. Similarly, the terminal device may also receive a first feedback message submitted by the user in response to the first prompt message.

[0152] It should be noted that the second frequency threshold can be greater than the first frequency threshold, and the way the terminal device determines the second frequency threshold can be the same as or similar to the way it determines the first frequency threshold.

[0153] S902, the terminal device determines second risk data based on weather data and the frequency of target events. The second risk data is used to indicate whether the living person has symptoms corresponding to respiratory diseases.

[0154] Since different weather conditions can have different effects on the incidence of respiratory diseases, such as the increased incidence of respiratory diseases in hot and cold weather, terminal devices can determine secondary risk data based on weather data and the frequency of target events. This allows for a more accurate assessment of whether a living person has symptoms of respiratory diseases, based on the occurrence of target events and external weather interference.

[0155] In some embodiments, the terminal device can input weather data and the frequency of target events into a second network model, and obtain second risk data output by the second network model. For example, Figure 13 As shown, the terminal device can input weather data, the frequency of deep breathing events, the frequency of coughing events, and the frequency of nose blowing events into the second network model 1310, and obtain the second risk data output by the second network model 1310.

[0156] The second network model can be used to determine whether a living organism exhibits symptoms of a systemic respiratory disease based on weather data and the frequency of target events. In some embodiments, the second network model may include a machine learning model or a deep learning model.

[0157] It should be noted that the second network model can be obtained by the terminal device in advance. In some embodiments, the terminal device can obtain a second sample set, in which each sample includes weather data and the frequency of the target event, and each sample is labeled based on the actual results of whether a living person has symptoms of respiratory diseases. The terminal device trains and obtains the second network model through the second sample set.

[0158] It should also be noted that the terminal device can determine the second risk data in other ways, or the terminal device can omit step S902. For example, in some embodiments, the terminal device can determine whether the living person has symptoms corresponding to respiratory diseases through the aforementioned first feedback message, so step S902 can be omitted.

[0159] When a living organism exhibits symptoms corresponding to a respiratory disease, it may indeed be infected with a respiratory disease, but it may also be infected with other diseases. Therefore, if the terminal device determines that the living organism has symptoms of a respiratory disease (including second risk data indicating that the living organism has symptoms of a respiratory disease, or the aforementioned first feedback message confirming that the living organism has symptoms corresponding to a respiratory disease), it can continue to execute S903 below for further detection; if the terminal device determines that the living organism does not have symptoms of a respiratory disease (including second risk data indicating that the living organism does not have symptoms of a respiratory disease, or the aforementioned first feedback message denying that the living organism has symptoms corresponding to a respiratory disease), it can stop executing subsequent steps.

[0160] In some embodiments, when the terminal device determines that a living person has symptoms corresponding to a respiratory disease, it can issue a second prompt message. The second prompt message indicates that the living person has symptoms corresponding to a respiratory disease, allowing the user to determine whether further testing is necessary. In other embodiments, the terminal device can also receive a second feedback message from the user based on the second prompt message. The second feedback message can be a message indicating that the user agrees or refuses to determine the first risk data (i.e., further testing to determine whether the living person has a respiratory disease). When the second feedback message indicates that the user agrees to determine the first risk data, step S903 can continue to be executed; when the second feedback message indicates that the user refuses to determine the first risk data, step S903 can be skipped.

[0161] For example, if the terminal device is a mobile phone and the living being is the user, when the terminal device determines that the user has symptoms corresponding to a respiratory disease, it can display something like this on the mobile phone. Figure 14 The reminder interface shown, and / or, instructs the smartwatch to display something like... Figure 15 The reminder interface shown. Figure 14 and Figure 15 The displayed alerts all include phrases such as "You have symptoms of a respiratory illness" and "Further screening." Additionally, in Figure 14 The displayed reminder interface also includes a prompt to the user to "open the microphone to record physiological sounds (such as speaking, breathing, and coughing sounds)" and three buttons for "Speaking," "Coughing," and "Breathing." If the user clicks any of these buttons, the received second feedback message is determined to be a message indicating that the user agrees to confirm the first risk data. If the user exits the current interface, the received second feedback message is determined to be a message indicating that the user refuses to confirm the first risk data. Similarly, in Figure 15The reminder interface also includes the words "It is recommended that you record physiological sounds" and two buttons, "Yes" and "No". If the user clicks the "Yes" button, it is determined that the received second feedback message is a message that the user agrees to determine the first risk data. If the user clicks the "No" button, it is determined that the received second feedback message is a message that the user refuses to determine the first risk data.

[0162] S903, the terminal device determines the first risk data based on the physiological sound data of a living organism.

[0163] Physiological sound data can include at least one of respiratory sound data, speech sound data, and cough sound data. Since living organisms rely on the respiratory system to produce physiological sounds such as breathing, speaking, or coughing, and the physiological sounds produced when the respiratory system is diseased will have different sound characteristics, the primary risk data can be accurately determined based on the physiological sound data of living organisms.

[0164] In some embodiments, the terminal device may acquire the physiological sound data of a living organism when it determines to execute S903 (e.g., the received second feedback message is a message in which the user agrees to determine the first risk data).

[0165] For example, in such Figure 14 In the displayed notification interface, if the user clicks any of the three buttons—"Speaking," "Coughing," and "Breathing"—then recording of the selected physiological sound data can begin. Alternatively, in... Figure 15 In the reminder interface shown, if the user clicks the "Yes" button, recording physiological sound data can begin.

[0166] In some embodiments, the terminal device may input physiological sound data into a third network model and use the detection results output by the third network model as the first risk data.

[0167] The third network model can be used to determine the risk level of a living organism having a systemic respiratory disease based on physiological sound data. In some embodiments, the third network model may include a machine learning model or a deep learning model.

[0168] It should be noted that the third network model can be obtained by the terminal device in advance. In some embodiments, the terminal device can obtain a third sample set, in which each sample includes physiological sound data, and each sample is labeled based on the true risk level of a living organism suffering from a systemic respiratory disease. The terminal device trains and obtains the third network model through the third sample set.

[0169] In some embodiments, the terminal device may determine first risk data based on physiological sound data (or the detection results of physiological sound data by a third network model, etc.), weather data, and the frequency of the target event.

[0170] In some embodiments, the terminal device can input physiological sound data (or the detection results of physiological sound data), weather data, and the frequency of the target event into the fourth network model, and obtain the first risk data output by the fourth network model. For example, as shown... Figure 16 As shown, the terminal device can input the detection results of physiological sound data, weather data, frequency of deep breathing events, frequency of coughing events and frequency of nose blowing events into the fourth network model 1610, and obtain the first risk data output by the fourth network model 1610.

[0171] The fourth network model can be used to determine the risk of a living organism having respiratory diseases based on physiological sound data (or the detection results of physiological sound data), weather data, and the frequency of target events.

[0172] It should be noted that the fourth network model can be obtained by the terminal device in advance. In some embodiments, the terminal device can obtain a fourth sample set, in which each sample includes physiological sound data (or detection results of physiological sound data), weather data, and the frequency of the target event. Each sample is labeled based on the true risk level of a living organism suffering from a systemic respiratory disease. The terminal device trains and obtains the fourth network model through the fourth sample set.

[0173] In some embodiments, the terminal device can determine the risk level of a living organism having a respiratory disease based on physiological sound data (or the detection results of physiological sound data) and the frequency of the target event.

[0174] In some embodiments, the terminal device may determine the risk level of a living person having a respiratory disease based on physiological sound data (or the detection results of physiological sound data), weather data, the frequency of the target event, and a first feedback message.

[0175] In some embodiments, the terminal device may determine the risk level of a living organism having a respiratory disease based on physiological sound data (or the detection results of physiological sound data), the frequency of the target event, and a first feedback message.

[0176] In some embodiments, the terminal device may issue a third notification message to alert the user of the risk of having a respiratory disease, so that the user can further test whether they have a respiratory disease through more professional means such as going to the hospital for testing.

[0177] For example, the terminal device is a smartwatch, which can display things like... Figure 17The reminder interface shown includes a third prompt message: "Hello, according to the test, you are at high risk of respiratory infection. It is recommended that you seek medical attention in time." This timely warning will alert the user so that they can seek medical attention promptly when they have a respiratory disease.

[0178] It should be noted that in practical applications, terminal devices can also use other multi-parameter fusion algorithms to determine the first risk data based on weather data, relevant data of the target event, physiological sound data, and one or more of the first feedback messages.

[0179] Based on the same inventive concept, this application also provides an early warning device for respiratory diseases. Figure 18 This is a schematic diagram of the structure of a respiratory disease early warning device 1800 provided in an embodiment of this application. The device includes:

[0180] The acquisition module 1801 is used to acquire at least one of the following: heartbeat data, respiratory data, and posture data of a living organism;

[0181] The detection module 1802 is used to detect a target event corresponding to a respiratory disease based on at least one of the acquired heartbeat data, respiratory data, and posture data.

[0182] The determination module 1803 is used to determine first risk data based on the target event. The first risk data is used to describe the risk of a living organism having a respiratory disease.

[0183] Optionally, the acquisition module 1801 is further configured to acquire physiological sound data of a living organism;

[0184] The determination module 1803 is specifically used to determine the frequency of the target event detected within a first time period; if it is determined based on the frequency of the target event that the living body has symptoms corresponding to respiratory diseases, then the first risk data is determined based on the physiological sound data of the living body.

[0185] Optionally, the determining module 1803 is also used for:

[0186] Based on weather data and the frequency of target events, a second risk data point is determined;

[0187] If the second risk data indicates that the living organism has symptoms corresponding to a respiratory disease, then it is determined that the living organism has symptoms corresponding to a respiratory disease.

[0188] Optionally, the device further includes:

[0189] The first prompt module is used to issue a first prompt message if the frequency of the target event is greater than or equal to a first frequency threshold. The first prompt message is used to indicate the risk of the living organism developing symptoms corresponding to respiratory diseases.

[0190] The determining module 1803 is further configured to: if a first feedback message is received based on a first prompt message, and the first feedback message is a message confirming that a living person has symptoms corresponding to a respiratory disease, then determine that the living person has symptoms corresponding to a respiratory disease.

[0191] Optionally, module 1803 is specifically used for:

[0192] Based on physiological sound data, weather data, and the frequency of target events, the first risk data is determined.

[0193] Optionally, the physiological sound data includes at least one of breath sound data, speech sound data, and cough sound data.

[0194] Optionally, the target event includes at least one of a deep breathing event, a coughing event, and a nose blowing event.

[0195] Optionally, the device further includes a second prompting module for:

[0196] When it is determined that a living person has symptoms corresponding to a respiratory disease, a second prompt message is issued. The second prompt message is used to indicate that the living person has symptoms corresponding to a respiratory disease.

[0197] The determination module 1803 is specifically used to determine the first risk data based on the physiological sound data of the living organism if a second feedback message is received based on the second prompt message and the second feedback message is a message agreeing to determine the first risk data.

[0198] Optionally, the device further includes a third prompting module for:

[0199] A third alert message is issued, which is used to alert the living person to the risk of having a respiratory disease.

[0200] It is understood that the respiratory disease early warning device 1800 provided in this application embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, so it will not be described again here.

[0201] Based on the same inventive concept, this application also provides a terminal device. Figure 19 This is a schematic diagram of the structure of the terminal device 1900 provided in the embodiments of this application, as shown below. Figure 19 As shown, the terminal device 1900 provided in this embodiment includes: a memory 1910 and a processor 1920. The memory 1910 is used to store computer programs; the processor 1920 is used to execute the method described in the above method embodiment when the computer program is invoked.

[0202] The terminal device 1900 provided in this embodiment can execute the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0203] Based on the same inventive concept, this application also provides a chip system. The chip system includes a processor coupled to a memory, which executes a computer program stored in the memory to implement the methods described in the above-described method embodiments.

[0204] The chip system can be a single chip or a chip module composed of multiple chips.

[0205] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the methods described in the above-described method embodiments.

[0206] This application also provides a computer program product that, when run on a terminal device, enables the terminal device to implement the methods described in the above-described method embodiments.

[0207] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable storage medium can include at least: any entity or device capable of carrying computer program code to a photographic device / terminal device, a recording medium, a computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0208] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0209] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0210] In the embodiments provided in this application, it should be understood that the disclosed apparatus / devices and methods can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0211] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0212] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0213] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0214] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0215] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0216] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them; although this application has been described in detail with reference to the foregoing embodiments, modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A respiratory disease early warning device, characterized by, include: The acquisition module is used to acquire at least one of the heartbeat data, breathing data and posture data of a living organism, and to acquire the physiological sound data of the living organism when it is determined that the living organism has symptoms corresponding to a respiratory disease. The detection module is used to detect a target event corresponding to the respiratory disease based on at least one of the acquired heartbeat data, breathing data, and posture data. The determination module is used to determine the frequency at which the target event is detected within a first time period; Based on weather data and the frequency of the target events, a second risk data is determined; If the second risk data indicates that the living organism has symptoms corresponding to the respiratory disease, a first risk data is determined based on the physiological sound data. The first risk data is used to indicate the risk that the living organism has the respiratory disease.

2. The apparatus of claim 1, wherein, The device further includes: The first prompting module is used to issue a first prompting message if the frequency of the target event is greater than or equal to a first frequency threshold. The first prompting message is used to indicate the risk that the living organism will develop symptoms corresponding to the respiratory disease. The determining module is further configured to: if a first feedback message is received based on the first prompt message, and the first feedback message is a message confirming that the living organism has symptoms corresponding to the respiratory disease, determine the first risk data based on the physiological sound data.

3. The apparatus of claim 1 or 2, wherein, The determining module is specifically used for: The first risk data is determined based on the physiological sound data, weather data, and the frequency of the target event.

4. The apparatus of claim 3, wherein, The physiological sound data includes at least one of breath sound data, speech sound data, and cough sound data.

5. The apparatus of any of claims 1, 2, and 4, wherein, The device further includes a second prompting module, used to issue a second prompting message when it is determined that the living body has the symptoms corresponding to the respiratory disease. The second prompting message is used to prompt that the living body has the symptoms corresponding to the respiratory disease. The determining module is specifically used to determine the first risk data based on the physiological sound data of the living organism if a second feedback message is received based on the second prompt message, and the second feedback message is a message agreeing to determine the first risk data.

6. The apparatus of any of claims 1, 2, and 4, wherein, The device further includes a third notification module for: A third alert message is issued, which is used to alert the living organism to the risk of having a respiratory disease.

7. The apparatus of any of claims 1, 2, and 4, wherein, The target event includes at least one of a deep breathing event, a coughing event, and a nose blowing event.

8. A terminal device, comprising: include: A memory and a processor, the memory being used to store a computer program; the processor being used to execute a method performed by the apparatus as described in any one of claims 1 to 7 when the computer program is invoked.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by the processor, it implements the method performed by the apparatus as described in any one of claims 1 to 7.

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