Data processing method, readable storage medium, electronic device, and program product

Abstract

The present application relates to the technical field of terminals, and particularly to a data processing method, a readable storage medium, an electronic device, and a program product. In the method, an electronic device can evaluate a high uric acid content risk level of a user by means of a pre-trained risk evaluation model on the basis of a plurality of types of data related to uric acid content in blood, and alert the user on the basis of the evaluated high uric acid content risk level, wherein the plurality of types of data may include multiple pieces of data among the following data: physiological feature data such as a pulse wave, an electrocardiogram signal, a sleep audio, and vital signs (e.g., a heart rate, a blood oxygen level, blood pressure and the like); joint data such as a joint image and joint pain data; and user information such as age, height, weight, waist circumference, hip circumference, and gender. On the basis of the described manner, the high uric acid content risk level of the user is obtained by means of the risk evaluation model on the basis of the plurality of types of data related to the uric acid content in blood, thereby being conducive to improving the accuracy of the obtained high uric acid content risk level.

Description

Data processing methods, readable storage media, electronic devices and program products

[0001] This application claims priority to Chinese Patent Application No. 202411837392.7, filed on December 13, 2024, entitled “Data Processing Method, Readable Storage Medium, Electronic Device and Program Product”, the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of terminal technology, and in particular to a data processing method, a readable storage medium, an electronic device, and a program product. Background Technology

[0003] High uric acid levels in the body can negatively impact health, potentially leading to inflammation, gout, kidney dysfunction, vascular abnormalities, and cardiovascular disease. Therefore, monitoring uric acid levels is essential to provide timely alerts when levels are elevated.

[0004] However, uric acid testing is currently generally performed by analyzing human blood, which is an invasive test and not suitable for long-term, frequent testing. Summary of the Invention

[0005] In view of the above, this application provides a data processing method, a readable storage medium, an electronic device, and a program product.

[0006] In a first aspect, a data processing method is provided, applied to a first electronic device. The method includes: acquiring physiological characteristic data of a user within a first time period, wherein the physiological characteristic data includes multiple of the following: pulse wave, electrocardiogram signal, sleep audio, and vital signs, and the vital signs include one or more of heart rate, blood pressure, and blood oxygen; based on the physiological characteristic data, assessing the user's high uric acid risk level within the first time period using a risk assessment model to obtain a first assessment result, the first assessment result including the user's first high uric acid risk level within the first time period; and displaying the first assessment result.

[0007] In this method, the first electronic device can process multiple physiological characteristic data of the user within a first time period (or the corresponding data features) using a pre-trained risk assessment model to obtain an assessment result of the user's high uric acid risk level. Since the input data of the risk assessment model includes multiple physiological characteristic data, all of which reflect the uric acid content in the user's blood, this improves the accuracy of the first assessment result. Furthermore, because the aforementioned physiological characteristic data can be obtained non-invasively, it facilitates long-term and frequent non-invasive monitoring of the user's high uric acid levels.

[0008] In some implementations, the first time period can be at least a portion of a time period within an evaluation cycle, or it can be a portion of a time period within an evaluation cycle and at least a portion of a time period within at least one evaluation cycle preceding that evaluation cycle.

[0009] In some implementations, the high uric acid level risk rating indicates the likelihood that a user's blood uric acid levels are too high; the higher the risk rating, the greater the likelihood that the user's blood uric acid levels are too high.

[0010] In some implementations, the physiological characteristic data may be collected entirely by the first electronic device, or a portion may be collected by the first electronic device and another portion may be obtained by the first electronic device from other electronic devices, or all of the physiological characteristic data may be obtained by the first electronic device from other electronic devices.

[0011] In one possible implementation of the first aspect above, the first assessment result is obtained by assessing the user's high uric acid risk level in a first time period based on physiological characteristic data and through a risk assessment model. This includes: obtaining the first assessment result based on data characteristics of physiological characteristic data and through a risk assessment model.

[0012] In this implementation, the data input into the risk assessment model may not be the physiological characteristic data itself, but rather the data characteristics of the collected physiological characteristic data.

[0013] In one possible implementation of the first aspect above, the data characteristics of the pulse wave include at least one of the following characteristics: the diastolic duration of the pulse wave, the systolic duration of the pulse wave, the energy of the pulse wave during diastole, and the energy of the pulse wave during systole; the data characteristics of the electrocardiogram signal include at least one of the following characteristics: the duration of the ST segment of the electrocardiogram signal and the energy of the electrocardiogram signal during the ST segment; the data characteristics of the sleep audio include at least one of the following characteristics: the user's respiratory rate during sleep, the frequency of the user's snoring during sleep, the volume of the user's breathing sound during sleep, and the volume of the user's snoring sound during sleep; the data characteristics of vital signs include at least one of the following characteristics: the value of heart rate, the value of blood pressure, and the value of blood oxygen.

[0014] In one possible implementation of the first aspect above, displaying the first assessment result includes: displaying a prompt message corresponding to the first high uric acid risk level.

[0015] In some implementations, the information may include dietary recommendations, exercise recommendations, and medical advice corresponding to the first high uric acid risk level.

[0016] In one possible implementation of the first aspect above, the method further includes: when the first high uric acid risk level indicates that the user's high uric acid risk is greater than a preset risk level, acquiring joint images of at least one joint of the user and / or pain data of at least one joint; based on the joint images and / or pain data, and physiological characteristic data, obtaining a second assessment result through a risk assessment model, the second assessment result including the user's second high uric acid risk level within a first time period; and displaying the second assessment result.

[0017] In this implementation, when the first high uric acid risk level is high (indicating a greater likelihood of high uric acid levels in the user's blood), the first electronic device can acquire more data related to the user's blood uric acid levels (e.g., joint images, joint pain data, pulse waves, electrocardiogram signals, sleep audio, and vital signs data not used in generating the first assessment result). This acquired data is then processed using a risk assessment model to obtain the user's second assessment result. This results in a more accurate second assessment.

[0018] In this implementation, the first electronic device can process joint images and physiological feature data, or pain data and physiological feature data, or joint images, pain data and physiological feature data, through a risk assessment model to obtain a second assessment result.

[0019] In one possible implementation of the first aspect above, the second assessment result obtained by using a risk assessment model based on joint images and / or pain data, as well as physiological characteristic data, includes: obtaining the second assessment result by using a risk assessment model based on data characteristics of joint images and / or pain data, as well as data characteristics of physiological characteristic data.

[0020] In this implementation, the first electronic device can process the data features of joint images and physiological features, or the data features of pain data and physiological features, or the data features of joint images, pain data and physiological features, through a risk assessment model to obtain a second assessment result.

[0021] In some implementations, the data features of pain data can include the degree of pain, while the data features of joint images can include the degree of swelling and deformity of the joint.

[0022] In one possible implementation of the first aspect above, the method further includes: when a first high uric acid risk level indicates that the user's high uric acid risk is greater than a preset risk level, displaying a first interface, the first interface including a first control, the first control being used to trigger an enhanced assessment of the user's high uric acid risk level.

[0023] In this implementation, when the first high uric acid risk level indicates that the user's high uric acid risk is greater than a preset risk level, the first electronic device can display a first interface to prompt the user whether to perform an enhanced assessment. The first control in the first interface (such as the enhanced assessment control described below) can be used to trigger an enhanced assessment of the user's high uric acid risk level.

[0024] In some implementations, the first interface may also include a prompt message asking the user whether to perform an enhanced evaluation.

[0025] In one possible implementation of the first aspect described above, the method further includes: in response to a user's selection operation on the first control, acquiring a joint image of at least one joint of the user and / or pain data of at least one joint; based on the joint image and / or pain data, and physiological characteristic data, performing an enhanced assessment of the user's hyperuricemia risk level using a risk assessment model, and obtaining and displaying a second assessment result; wherein the second assessment result includes a second hyperuricemia risk level output by the risk assessment model, or the highest risk level among the first hyperuricemia level and the second hyperuricemia risk level, or a third hyperuricemia risk level obtained based on the first hyperuricemia risk level and the second hyperuricemia risk level.

[0026] In this implementation, the first electronic device can acquire joint images of at least one joint and / or pain data of at least one joint only when the user confirms the enhanced assessment (e.g., when the user selects a first control), and perform the enhanced assessment based on the acquired data. This avoids automatically performing the enhanced assessment from interfering with the user's use of the first electronic device.

[0027] In this implementation, the second high uric acid risk level can be obtained by the first electronic device processing joint images and / or pain data, as well as physiological characteristic data, based on a risk assessment model.

[0028] In one possible implementation of the first aspect above, the acquisition of joint images of at least one joint of the user and / or pain data of at least one joint includes: capturing images of at least one joint to obtain joint images, or acquiring joint images captured by a second electronic device; and / or acquiring pain data by collecting skin conductance signals of at least one joint through a skin conductance sensor of the first electronic device, or acquiring pain data of at least one joint collected by the second electronic device.

[0029] In this implementation, the first electronic device can capture joint images using its own camera, or it can acquire joint images of the user using the camera of another electronic device (such as the second electronic device). The first electronic device can also acquire pain data of the user's joints using its own electrodermal sensor, or it can acquire pain data of the user's joints using the electrodermal sensor of another electronic device (such as the second electronic device). Thus, even if the first electronic device does not have the capability to acquire joint images and pain data, auxiliary acquisition can be performed using other electronic devices.

[0030] In one possible implementation of the first aspect above, the data features based on physiological characteristic data are used to obtain a first assessment result through a risk assessment model, including: the data features based on physiological characteristic data and the user's user information are used to obtain a first assessment result through a risk assessment model, wherein the user information includes one or more of the following: gender, age, height, weight, waist circumference, and hip circumference.

[0031] Users with the same uric acid level in their blood may exhibit different physiological characteristics due to differences in age, gender, body type, weight, waist circumference, or hip circumference. Using user information as input to the risk assessment model can further improve the accuracy of the initial assessment results.

[0032] In one possible implementation of the first aspect described above, the assessment of a user's high uric acid risk level within a first time period based on physiological characteristic data and using a risk assessment model includes: assessing the user's high uric acid risk level within a first time period using a risk assessment model when at least one of the following detection conditions is met: detecting that the user triggers an operation to assess the high uric acid risk level; reaching a preset detection cycle; detecting that the user wears the first electronic device for a duration greater than or equal to a first duration threshold during the daytime in a second time period, and the user wears the first electronic device for a duration greater than or equal to a second duration threshold at night in the second time period; detecting that the user wears the first electronic device for a duration greater than or equal to a third duration threshold in the second time period; detecting that the user wears the first electronic device for a duration greater than or equal to a fourth duration in a non-sleep state in the second time period, and the user wears the first electronic device for a duration greater than or equal to a fifth duration threshold in a sleep state in the second time period.

[0033] In this implementation, the first electronic device can automatically assess the user's high uric acid risk level when certain conditions are met, or it can assess the user's high uric acid risk level in response to the user's active assessment.

[0034] In some implementations, the second time period can be an evaluation cycle.

[0035] In one possible implementation of the first aspect above, the assessment of a user's high uric acid risk level within a first time period based on physiological characteristic data and a risk assessment model to obtain a first assessment result includes: assessing a user's high uric acid risk level within a first time period based on physiological characteristic data and the user's joint data and a risk assessment model to obtain a first assessment result, wherein the joint data includes joint images of at least one joint of the user and / or pain data of at least one joint.

[0036] In this implementation, the first electronic device can also use the user's joint data (or the data characteristics of the joint data) as input to the risk assessment model during the generation of the first assessment result. This can further improve the accuracy of the first assessment result.

[0037] In one possible implementation of the first aspect described above, the method further includes: sending the first evaluation result to the second electronic device.

[0038] In this implementation, after obtaining the first evaluation result, the first electronic device can send the first evaluation result to other electronic devices so that the other electronic devices can record the first evaluation result and provide prompts to the user based on the first evaluation result.

[0039] In a second aspect, a readable storage medium is provided, the readable storage medium including one or more programs, which, when executed on an electronic device, cause the electronic device to implement the data processing method provided in the first aspect and any possible implementation of the first aspect.

[0040] Thirdly, an electronic device is provided, comprising: a memory for storing one or more programs; and a processor for executing the one or more programs to cause the electronic device to implement the data processing method provided in the first aspect and any possible implementation of the first aspect.

[0041] In some implementations, electronic devices may include multiple sensors for collecting users’ physiological data or joint data.

[0042] Fourthly, a program product is provided that, when executed on an electronic device, enables the electronic device to implement the data processing method provided in the first aspect and any possible implementation of the first aspect.

[0043] It should be noted that the beneficial effects of the second to fourth aspects mentioned above can be referred to the content of the first aspect, and will not be repeated here. Attached Figure Description

[0044] Figure 1 illustrates a schematic diagram of assessing a user's high uric acid risk level based on a risk assessment model, according to some embodiments of this application.

[0045] Figure 2A shows a schematic diagram of a pulse wave according to some embodiments of this application.

[0046] Figure 2B shows a schematic diagram of an ECG signal according to some embodiments of this application.

[0047] Figure 3 illustrates a process for activating uric acid health functions according to some embodiments of this application.

[0048] Figure 4A illustrates a schematic diagram of a high-risk warning message according to some embodiments of this application.

[0049] Figure 4B illustrates a schematic diagram of a warning message corresponding to medium risk, according to some embodiments of this application.

[0050] Figure 4C illustrates a schematic diagram of a low-risk warning message according to some embodiments of this application.

[0051] Figure 4D illustrates a schematic diagram of a prompt message in the event of insufficient wearing time, according to some embodiments of this application.

[0052] Figure 5 illustrates a schematic diagram showing historical results according to some embodiments of this application.

[0053] Figure 6A illustrates a process for acquiring ECG signals based on user-initiated evaluation, according to some embodiments of this application.

[0054] Figure 6B illustrates a process for collecting joint data based on user-initiated assessment and evaluating the user's high uric acid risk level, according to some embodiments of this application.

[0055] Figure 7 illustrates a schematic diagram of a watch 10 acquiring joint images of a user based on a mobile phone 20, according to some embodiments of this application.

[0056] Figure 8 illustrates a schematic diagram of a watch 10 displaying multiple evaluation results within an evaluation cycle in response to user operation, according to some embodiments of this application.

[0057] Figure 9A illustrates, according to some embodiments of this application, a schematic diagram of a mobile phone 20 displaying a high uric acid risk level and acquiring ECG signals and joint images based on user operation.

[0058] Figure 9B illustrates a schematic diagram of a mobile phone 20 collecting joint pain data based on user operation, according to some embodiments of this application.

[0059] Figure 10A shows a schematic diagram of a mobile phone 20 displaying historical results according to some embodiments of this application.

[0060] Figure 10B illustrates a schematic diagram of medium-risk historical results displayed on a mobile phone 20 according to some embodiments of this application.

[0061] Figure 11 illustrates a flowchart of a data processing method for users to proactively assess their risk level of high uric acid, according to some embodiments of this application.

[0062] Figure 12 illustrates a flowchart of another data processing method for assessing a user's high uric acid risk level in response to user actions, according to some embodiments of this application.

[0063] Figure 13 illustrates a schematic diagram of a watch 10 prompting a user to perform an enhanced assessment, according to some embodiments of this application.

[0064] Figure 14 illustrates a flowchart of a data processing method for automatically assessing a user's high uric acid risk level using an electronic device, according to some embodiments of this application.

[0065] Figure 15 illustrates a flowchart of a data processing method according to some embodiments of this application.

[0066] Figure 16 shows a schematic diagram of the structure of a watch 10 according to some embodiments of this application.

[0067] Figure 17 shows a schematic diagram of the structure of a mobile phone 20 according to some embodiments of this application. Detailed Implementation

[0068] The illustrative embodiments of this application include, but are not limited to, data processing methods, readable storage media, electronic devices, and program products.

[0069] Since invasive testing is not suitable for long-term and frequent assessment of a user's uric acid levels, in some embodiments, non-invasive testing can be used to assess a user's uric acid levels and alert the user if the assessment indicates that the user's uric acid levels may be high.

[0070] For example, persistently high uric acid levels in the blood can cause vascular inflammation and trigger cardiac abnormalities. This can lead to physical abnormalities such as arrhythmia, decreased blood oxygenation, and elevated blood pressure. Based on this, a machine learning model can be trained to determine the correlation between a user's heart rate, blood oxygenation, blood pressure, and uric acid levels. This allows the machine learning model to process the user's heart rate, blood oxygenation, and blood pressure data to determine their uric acid level.

[0071] However, parameters such as heart rate, blood oxygen, and blood pressure are not only related to the uric acid level in a user's blood, but also strongly correlated with other factors. For example, when a user is exercising, stressed, startled, or has insufficient sleep, their heart rate, blood oxygen, and blood pressure can fluctuate significantly. Therefore, the accuracy of machine learning models in determining a user's blood uric acid level based solely on their heart rate, blood oxygen, and blood pressure is usually low.

[0072] It should be noted that, in addition to vital signs such as heart rate, blood oxygen, and blood pressure, other physiological characteristics of users are also related to the uric acid content in the blood.

[0073] For example, uric acid adhering to the blood vessel walls can cause them to thicken, thus affecting the blood vessels' ability to contract and dilate. Therefore, the pulse waves of users with different blood uric acid levels will exhibit different characteristics (e.g., duration, energy) in the signals corresponding to the vasoconstriction and vasodilation phases. Thus, the duration of the vasoconstriction phase, the duration of the vasodilation phase, the energy of the waveform during vasoconstriction, and the energy of the waveform during vasodilation in a user's pulse wave can reflect the user's blood uric acid level.

[0074] For example, a high level of uric acid in the respiratory epithelial fluid can affect a user's lung function, leading to changes in breathing frequency and volume, as well as excessive snoring during sleep. Therefore, the breathing frequency, snoring frequency, volume of breathing, and snoring reflected in audio data during sleep (hereinafter referred to as sleep audio) can also reflect the uric acid level in a user's blood.

[0075] For example, persistently high uric acid levels in the blood can lead to cardiac abnormalities such as sinus tachycardia and arrhythmia. In these cases, the ST segment of the electrocardiogram (ECG) signal will show abnormalities (e.g., prolonged or shortened duration, increased or decreased signal value). Therefore, the characteristics of the ST segment of the ECG signal (e.g., duration and energy) can also reflect the uric acid level in the user's blood.

[0076] For example, when uric acid levels in the blood are consistently high, uric acid can crystallize in the joints, leading to redness, swelling, deformity, and pain. Therefore, the degree of redness, swelling, deformity, and pain in the joints can also reflect the level of uric acid in a user's blood.

[0077] In view of this, embodiments of this application provide a data processing method. This method uses a pre-trained risk assessment model to obtain the correspondence between various data related to blood uric acid levels (e.g., physiological characteristic data such as pulse waves, electrocardiogram signals, sleep audio, vital signs such as heart rate, blood oxygen, and blood pressure; joint data such as joint images and joint pain data; and user information such as age, height, weight, waist circumference, hip circumference, and gender) and the user's blood uric acid levels. During the assessment of a user's high uric acid risk level, the electronic device can use this risk assessment model to evaluate the user's high uric acid risk level (e.g., high risk, medium risk, low risk) based on various data related to blood uric acid levels (e.g., physiological characteristic data, joint data, and user information), and provide risk alerts based on the assessed high uric acid risk level.

[0078] Thus, because the risk assessment model can learn the correlation between a user's physiological characteristics, joint data, user information, and uric acid levels in the blood, even when a change in one data point is caused by factors other than uric acid, the risk assessment model can still obtain a highly accurate assessment result based on other data, which helps improve the accuracy of the obtained high uric acid risk level. Furthermore, the aforementioned physiological characteristics and joint data can be obtained non-invasively, making it suitable for long-term, frequent assessments of a user's high uric acid risk.

[0079] It should be noted that the high uric acid risk level indicates the likelihood of a user having excessively high uric acid levels in their blood. The higher the high uric acid risk level, the greater the likelihood of excessively high uric acid levels in the user's blood. For example, the high uric acid risk level can be divided into three levels: high risk, medium risk, and low risk. Among them, high risk indicates that the user's likelihood of having excessively high uric acid levels is very high (e.g., the probability of excessively high uric acid levels in the blood is greater than the first threshold, such as 80%, 85%, 90%, etc.), medium risk indicates that the user's likelihood of having excessively high uric acid levels in the blood is moderate (e.g., the probability of excessively high uric acid levels in the blood is less than the first threshold but greater than the second threshold, for example, the second threshold can be 40%, 45%, 50%, etc.), and low risk indicates that the user's likelihood of having excessively high uric acid levels in the blood is relatively low (e.g., the probability of excessively high uric acid levels in the blood is less than the second threshold).

[0080] It should be noted that risk reminders may include, but are not limited to, a high uric acid risk level, along with corresponding dietary, exercise, and medical advice. For example, when the high uric acid risk level is moderate (e.g., medium risk), users may be advised to exercise moderately and reduce their intake of high-purine foods; when the high uric acid risk level is high (e.g., high risk), users may be advised to seek medical attention promptly if they feel unwell; and when the low uric acid risk level is low (e.g., low risk), users may be advised to continue monitoring and exercise moderately.

[0081] It should be noted that the values ​​of the first threshold and the second threshold mentioned above are only examples. In other embodiments, the first threshold and the second threshold can also be any other values ​​that satisfy the condition that the first threshold is greater than the second threshold. This is not limited here.

[0082] It should be noted that in some embodiments, the risk level of high uric acid may be divided into more risk levels. This application does not limit the number or specific form of the high uric acid risk level classification. For example, it may be divided into high risk, medium-high risk, medium risk, medium-low risk, and low risk. Alternatively, the high uric acid risk level can also be directly the probability (e.g., likelihood) of a user having excessively high uric acid levels in their blood. For ease of description, the following will describe the high uric acid risk level as including three levels: high risk, medium risk, and low risk.

[0083] To facilitate understanding, we will first introduce the input data, output data, and training methods of the risk assessment model.

[0084] For example, Figure 1 illustrates a schematic diagram of assessing a user's high uric acid risk level based on a risk assessment model, according to some embodiments of this application.

[0085] As shown in Figure 1, the input data for the risk assessment model can include multiple data sources such as sleep audio, electrocardiogram (ECG) signals, pulse waves, joint pain data, joint images, vital signs, and user information. The output data of the risk assessment model can be the user's high uric acid risk level. Among these:

[0086] The data characteristics of a pulse wave may include one or more of the following: diastolic duration, diastolic energy, systolic duration, systolic energy, average diastolic energy, and average systolic energy. In some embodiments, the pulse wave can be acquired by a pulse wave sensor, such as a photoplethysmography (PPG) sensor.

[0087] In some embodiments, referring to Figure 2A, the pulse wave can be a periodic signal within a short period of time. Within one period (e.g., from time t0 to time t2, or from time t2 to time t4), it can be divided into two parts: one part is the signal collected during the user's vasodilation (hereinafter referred to as the diastolic signal), and the other part is the signal collected during the user's vasoconstriction (hereinafter referred to as the systolic signal). The duration of diastole refers to the duration of the diastolic signal (e.g., t1-t0 or t3-t2 in Figure 2A), and the duration of systole refers to the duration of the systolic signal (e.g., t2-t1 or t4-t3 in Figure 2A). The energy of diastole can be either the total energy of the diastolic signal or the average energy of the diastolic signal (hereinafter, the total energy of the diastolic signal is used as an example). The energy of systole can be either the total energy of the systolic signal or the average energy of the systolic signal (hereinafter, the total energy of the systolic signal is used as an example).

[0088] In some embodiments, the energy during diastole and the energy during systole can be obtained by integration. For example, when the amplitude of the pulse wave changes with time as y = f(t), for the case shown in Figure 2A, the energy P of the diastolic phase (from time t0 to time t1) corresponds to the diastolic phase. 舒张 The energy P corresponding to the contraction period (from time t1 to time t2) 收缩 It can be expressed as formula (1) and formula (2) respectively.

[0089] In some embodiments, when the pulse wave signal is a discrete signal, the diastolic energy P 舒张 and contraction energy P 收缩 They can also be expressed as formulas (3) and (4) respectively.

[0090] It should be noted that in other embodiments, the diastolic energy and systolic energy can also be determined in other ways, which are not limited here.

[0091] It should be noted that in other embodiments, the data characteristics of the pulse wave may also include other characteristics, which are not limited here.

[0092] In some embodiments, the data characteristics of the pulse wave may include the duration of the diastolic phase, the energy of the diastolic phase, the duration of the systolic phase, and the energy of the systolic phase in each cycle of the pulse wave of multiple cycles.

[0093] In some embodiments, the data characteristics of the pulse wave include the duration of diastole (which may include the average (and / or standard deviation, maximum, minimum) of the duration of diastole of multiple pulse wave cycles), the energy of diastole (which may include the average (and / or standard deviation, maximum, minimum) of the energy of diastole of multiple pulse wave cycles), the duration of systole (which may include the average (and / or standard deviation, maximum, minimum) of the duration of systole of multiple pulse wave cycles), and the energy of systole (which may include the average (and / or standard deviation, maximum, minimum) of the energy of systole of multiple pulse wave cycles).

[0094] The data characteristics of an ECG signal may include one or more of the ST segment duration and ST segment energy. The ECG signal can be acquired using an electrocardiogram (ECG) sensor. For example, Figure 2B shows a schematic diagram of an ECG signal according to some embodiments of this application.

[0095] As shown in Figure 2B, an ECG signal within one cycle can sequentially include the P wave, PR segment, QRS complex, ST segment, and T wave. The P wave indicates atrial depolarization (or atrial contraction); the PR segment, the signal between the end of the P wave and the beginning of the QRS complex, indicates atrial repolarization; the QRS complex indicates ventricular depolarization (or ventricular contraction); the ST segment, the signal between the end of the QRS complex and the beginning of the T wave, indicates the early stages of ventricular repolarization; and the T wave indicates ventricular repolarization (e.g., the return of ventricular electrical activity to its resting state). The duration of the ST segment is the same as the duration between the end of the QRS complex and the beginning of the T wave in the ECG signal, and the energy of the ST segment is the same as the energy of the ST segment in the ECG signal. The energy of the ST segment can be either the total energy of the ST segment in the ECG signal or the average energy of the ST segment in the ECG signal (the following explanation uses the example of the ST segment energy being the total energy of the ST segment in the ECG signal).

[0096] In some embodiments, the energy of the ST segment can be obtained by integration. For example, when the amplitude of the ECG signal changes with time as u = g(t), the energy P of the ST segment... ST It can be expressed as the following formula (5).

[0097] In some embodiments, when the ECG signal is a discrete signal, the energy P of the ST segment... ST It can also be expressed as formula (6) and formula (4) below.

[0098] In formulas (5) and (6), t S Indicates the start time of segment ST, t T Indicates the end time of the ST segment.

[0099] It should be noted that in other embodiments, the energy of the ST segment can also be determined in other ways, which are not limited here.

[0100] In some embodiments, the data characteristics of an ECG signal may include the duration of the ST segment and the energy of the ST segment in each cycle of an ECG signal with multiple cycles.

[0101] In some embodiments, the duration of the ST segment in the data characteristics of the ECG signal may include the average (and / or standard deviation, maximum, minimum) of the duration of the ST segment in multiple ECG cycles, and the energy of the ST segment may include the average (and / or standard deviation, maximum, minimum) of the energy of the ST segment in multiple ECG cycles.

[0102] In some embodiments, the ECG signal can be the ECG signal when the user is not moving (e.g., at rest or with minimal movement). Since the characteristics of the ECG signal change drastically when the user moves, especially during vigorous movement, using the ECG signal when the user is not moving can improve the accuracy of the risk assessment model's results. Optionally, the electronic device can determine whether the user is moving based on data collected by motion sensors (e.g., accelerometers, gyroscopes, etc.) and collect the ECG signal when it is determined that the user is not moving.

[0103] The data characteristics of sleep audio can include one or more of the following: respiratory rate, snoring rate, breathing volume (the loudness of breathing sounds), and snoring volume (the loudness of snoring sounds) during sleep. Specifically, respiratory rate refers to the number of breaths a user takes per unit time during sleep, snoring rate refers to the number of snoring sounds a user makes per unit time during sleep, breathing volume refers to the volume of breathing sounds in the sleep audio, and snoring volume refers to the volume of snoring sounds in the sleep audio.

[0104] In some embodiments, the data features of sleep audio may include respiratory rate, snoring rate, breathing volume, and snoring volume over multiple time periods.

[0105] In some embodiments, the data features of sleep audio may include the average (and / or standard deviation, maximum, minimum) of respiratory frequency over multiple time periods, the average (and / or standard deviation, maximum, minimum) of snoring frequency over multiple time periods, the average (and / or standard deviation, maximum, minimum) of breathing volume over multiple time periods, and the average (and / or standard deviation, maximum, minimum) of snoring volume over multiple time periods.

[0106] In some embodiments, snoring frequency can be the ratio of the number of snoring sounds in a sleep audio recording to the duration of the user's sleep.

[0107] In some embodiments, the breathing rate can be the ratio of the number of breathing sounds in a sleep audio recording to the duration of the user's sleep.

[0108] In some embodiments, the electronic device can identify the snoring and breathing sounds in a user's sleep audio based on a pre-trained machine learning model, thereby obtaining the number of breathing sounds and the number of snoring sounds in the sleep audio.

[0109] In some embodiments, the electronic device may determine the snoring volume based on the amplitude of the audio signal of the snoring portion of the sleep audio, and the breathing volume based on the amplitude of the audio signal of the breathing portion of the sleep audio.

[0110] In some embodiments, the electronic device may first denoise and filter the sleep audio to obtain a more accurate sleep audio (hereinafter referred to as processed sleep audio). Then, the electronic device may perform frequency domain transformation on the processed sleep audio and extract the snoring and breathing sounds from the processed sleep audio based on the frequency domain transformed audio signal. Finally, the electronic device may obtain the snoring frequency, breathing frequency, snoring volume, and breathing volume based on the snoring and breathing sounds from the processed sleep audio.

[0111] It should be noted that in other embodiments, the data features of sleep audio may also include other features, and the above-mentioned data features in sleep audio may be obtained based on other methods, which are not limited here.

[0112] The data characteristics of joint pain data can be the degree of pain in a user's joints. For example, joint pain data can be the electrical signals on the skin at the joint detected by a skin conductance sensor (e.g., electrical signals generated by the skin conductance response). The degree of pain can be quantified based on the electrical signals detected by the skin conductance sensor.

[0113] The data features of joint images may include, but are not limited to, the degree of swelling and distortion of the user's joints. In some embodiments, the joint image may be an image of one or more of the user's first metatarsophalangeal joint, second metatarsophalangeal joint, third metatarsophalangeal joint, metacarpophalangeal joint, interphalangeal joint, ankle joint, knee joint, wrist joint, etc. Alternatively, the joint image may also be an image of a joint in which the user has a history of gout, or an image of a joint in which the user is experiencing pain. Joint images can be acquired using a camera on an electronic device.

[0114] In some embodiments, the degree of swelling can be determined based on the color of the region where the joint is located in the joint image. For example, the color of the region where the joint is located in the joint image can be compared with reference colors for various degrees of swelling, and the degree of swelling with the highest similarity between the reference color and the color of the region where the joint is located in the joint image can be taken as the degree of swelling corresponding to the joint image. Alternatively, a machine learning model for determining the degree of joint swelling can be pre-trained, which can obtain the degree of swelling of the joint based on the input joint image.

[0115] In some embodiments, the degree of swelling may also be indicated based on the size of the swollen area in the joint image, or the proportion of the swollen area in the joint image (the region where the joint is located in the joint image).

[0116] In some embodiments, the degree of distortion can be determined based on joint images to identify joint contours, and then compared with standard joint contours (e.g., by calculating similarity). The higher the similarity between the determined joint contour and the standard joint contour, the lower the degree of joint distortion. Alternatively, a machine learning model can be pre-trained to determine the degree of joint distortion based on the input joint image.

[0117] The data characteristics of vital signs can be numerical values ​​of vital signs (such as blood pressure, blood oxygen, heart rate, or one or more), such as the average (and / or standard deviation, maximum, minimum) of various vital signs over a period of time, or multiple values ​​of various vital signs collected at multiple time points. Among them, blood pressure can be collected by a blood pressure sensor, blood oxygen can be collected by a blood oxygen saturation sensor, and heart rate can be obtained based on pulse wave signals or ECG signals.

[0118] User information, such as height, weight, waist circumference, hip circumference, gender, and age (or one or more of these). Because user information differs, the reference value for high uric acid in the blood varies, and even users with the same uric acid level may exhibit different physiological characteristics. Using user information as input data for a risk assessment model can improve the accuracy of the model's output.

[0119] It should be noted that, in other embodiments, the input to the risk assessment model can also be sleep audio, ECG pulse wave, joint pain data, joint images, or vital signs themselves. Furthermore, the risk assessment model can process the above data to obtain the corresponding data features for each data point.

[0120] It should be noted that when electronic devices assess the risk level of high uric acid based on a risk assessment model, the types of input data entered into the model each time can be the same or different. The more types of input data are entered into the risk assessment model, the more accurate the high uric acid risk level obtained by the model.

[0121] The training process of the risk assessment model is described below.

[0122] In some embodiments, the risk assessment model can be a holistic model that directly determines a user's high uric acid risk level based on the aforementioned input data. In this case, the training process for the risk assessment model may include:

[0123] S11, Obtain training data. The training data includes multiple sample data, each of which may correspond to part or all of the above input data, and each sample data may have a corresponding reference high uric acid risk level (i.e., the label corresponding to the sample data).

[0124] In some embodiments, a sample data point can be the aforementioned input data of a user, and the reference high uric acid risk level corresponding to this sample data can be determined based on the actual measured uric acid content in the user's blood. For example, the normal range for uric acid in male users is typically 150 to 416 parts per million (μmol / L). If a user's uric acid content is greater than or equal to 416 μmol / L (or other values), the reference high uric acid risk level corresponding to the user's sample data can be determined as high; if a user's uric acid content is less than 416 μmol / L but greater than 300 μmol / L (or other values), the reference high uric acid risk level corresponding to the user's sample data can be determined as medium; and if a user's uric acid content is less than 300 μmol / L (or other values), the reference high uric acid risk level corresponding to the user's sample data can be determined as low.

[0125] It should be noted that the above-described method for processing sample data labels is merely an example and not a limitation. In other embodiments, labels for each sample data may be obtained based on other methods.

[0126] S12, input each sample data into the risk assessment model to obtain the inferred high uric acid risk level corresponding to each sample data.

[0127] It should be noted that the inferred high uric acid risk level is the high uric acid risk level output by the risk assessment model based on the input sample data.

[0128] S13, Based on the reference high uric acid risk level of each sample data and the inferred high uric acid risk level of each sample data, adjust the model parameters of the risk assessment model until the termination condition is met.

[0129] In some embodiments, termination conditions may include: the loss function obtained from the reference high uric acid risk level and the inferred high uric acid risk level of each sample data converges; and the proportion of the inferred high uric acid risk level output by the risk assessment model being the same as the corresponding reference high uric acid risk level is greater than a preset proportion (i.e., the accuracy of the inferred high uric acid risk level output by the risk assessment model is greater than the accuracy corresponding to the preset proportion).

[0130] In some embodiments, the risk assessment model may include multiple sub-models. A sub-model may obtain a user’s high uric acid risk level based on one or more of the above-mentioned input data (hereinafter, the high uric acid risk level output by the sub-model is referred to as the intermediate high uric acid risk level); then the risk assessment model may output an inferred high uric acid risk level (e.g., the probability of having high uric acid risk) based on the multiple intermediate high uric acid risk levels output by the multiple sub-models.

[0131] For example, the risk assessment model may include multiple of the following sub-models: a first sub-model with pulse wave or pulse wave data features as input data; a second sub-model with sleep audio or sleep audio data features as input data; a third sub-model with ECG signal or ECG signal data features as input data; a fourth sub-model with user vital sign data as input data; a fifth sub-model with user information as input data; a sixth sub-model with joint pain data or joint pain data features as input data; a seventh sub-model with joint image or joint image data features as input data; and an eighth sub-model that fuses the input results of the first to seventh sub-models.

[0132] It should be noted that when the risk assessment model only includes some sub-models from the first to the seventh sub-model and the eighth sub-model, the eighth sub-model is used to infer the high uric acid risk level based on the intermediate high uric acid risk level generated by the partial sub-model.

[0133] The following section uses the risk assessment model, including the first to eighth sub-models, as an example to illustrate the training process of the risk assessment model.

[0134] When the risk assessment model includes sub-models one through eight, the training process of the risk assessment model may include:

[0135] S21. Based on the sub-sample data corresponding to each of the first to seventh sub-models and the reference high uric acid risk level corresponding to each sub-sample data, the first to seventh sub-models are trained respectively to obtain the trained first to seventh sub-models.

[0136] For example, the sub-sample data corresponding to the first sub-model can be the pulse wave or pulse wave data features in the sample data of the training data mentioned above, and the reference high uric acid risk level corresponding to a certain sub-sample data can be the label of the sample data to which it belongs. Similarly, the sub-sample data corresponding to the second sub-model can be the sleep audio or sleep audio data features in the sample data of the training data mentioned above, and the reference high uric acid risk level corresponding to a certain sub-sample data can be the label of the sample data to which it belongs, and so on. The training process of each sub-model can refer to the training process of S11 to S13 mentioned above, and will not be repeated here.

[0137] It should be noted that if the risk assessment model only includes some of the sub-models from the first to the seventh sub-model, then in S21, only those sub-models can be trained.

[0138] S22. Based on the trained first to seventh sub-models and the untrained eighth sub-model, the inferred high uric acid risk level of each of the above sample data is obtained. The eighth sub-model is then trained based on the inferred high uric acid risk level and the reference high uric acid risk level of each sample data until the termination condition is met.

[0139] It should be noted that if the risk assessment model only includes some of the sub-models from the first to the seventh sub-model, in S22, the eighth sub-model can be trained based on the trained sub-models and the untrained eighth sub-model.

[0140] It should be noted that the above process of training the risk assessment model is only an example. In other embodiments, the risk assessment model can also be trained in other ways, which are not limited here.

[0141] The risk assessment model obtained in the above manner, when processing the input data (such as the data characteristics of users' physiological characteristics, joint data, user information, vital signs, etc.), can obtain multiple intermediate assessment results (such as multiple probability values ​​of high uric acid levels in users' blood) based on at least some of the pre-trained sub-models from the first to the seventh sub-models that correspond to the input data. Then, the eighth sub-model merges these multiple intermediate assessment results and outputs a single assessment result (such as the high uric acid risk level, or the probability of high uric acid levels in the user's blood).

[0142] For example, the assessment results output by the risk assessment model can be expressed as the following formula (7).

[0143] In formula (7), FX represents the assessment result output by the risk assessment model (high uric acid risk level, or the probability of high uric acid levels in the user's blood); fxi This represents the intermediate evaluation result obtained by the i-th (1≤i≤7) sub-model based on the corresponding input data. This indicates that the eighth sub-model fuses multiple intermediate evaluation results (e.g., indicating the input data (f) of the eighth sub-model). x1 f x2 f x3 f x4 f x5 f x6 f x7 The correspondence between f and the output data (FX); where f xi =G i (x i ), G i This represents the input data (x) of the i-th sub-model. i ) and output data (intermediate evaluation results, f xi The correspondence between the first and seventh sub-models is as follows: The first to seventh sub-models are respectively the first to seventh sub-models mentioned above.

[0144] It should be noted that in formula (7), if some sub-models have no input data, the intermediate evaluation results corresponding to those sub-models can be empty. That is to say, the eighth sub-model can fuse different numbers of intermediate evaluation results to obtain the final evaluation result.

[0145] It should be noted that in the above-mentioned first to seventh sub-models, some sub-models can be merged into one sub-model, and the corresponding intermediate evaluation results in formula (7) can also be merged. For example, the sixth and seventh sub-models can be integrated into one sub-model, the second and fourth sub-models can be merged into one sub-model, etc., without limitation here.

[0146] In some embodiments, the risk assessment model can be a tree model, such as a decision tree model, a random forest model, a boosting regression tree model, a gradient boosting tree model, etc. Alternatively, the risk assessment model can be an improved model based on the above-mentioned tree models, or other models that include the above-mentioned tree models.

[0147] In other embodiments, the risk assessment model may also be other forms of machine learning models, neural network models, deep learning models, or other models.

[0148] In some embodiments, the risk assessment model may include one or more neural networks, such as convolutional neural networks, recurrent neural networks, long short-term memory networks, generative adversarial networks, autoencoders, graph neural networks, artificial neural networks, activation networks, and perceptrons.

[0149] It should be noted that the above risk assessment model can run on any electronic device, and the data processing methods provided in the embodiments of this application can also be applied to any electronic device. For example, electronic devices may include, but are not limited to, mobile stations (MS) and mobile terminals (MT). For example, electronic devices may be mobile phones, wearable devices (such as wristbands, earphones, rings, glasses), tablets (Pads), laptops, desktop computers, servers, in-vehicle systems, smart home devices, smart medical devices, etc. The embodiments of this application do not limit the specific form of the electronic device. For ease of description, the following uses a watch 10 as an example to introduce the technical solution of this application.

[0150] Enable function

[0151] In some embodiments, when a user first runs the innovative research application (for the user) on the watch 10 or when the watch 10 is first powered on, the watch 10 may prompt the user to activate the "Uric Acid Health" function. After this function is activated, the watch 10 may automatically or based on user actions collect the data required for high uric acid risk assessment, such as the aforementioned sleep audio, ECG signal, pulse wave, joint images, joint pain data, vital sign data, etc.

[0152] For example, Figure 3 illustrates a schematic diagram of activating uric acid health function according to some embodiments of this application.

[0153] Referring to Figure 3, watch 10 can display interface P11 in response to the user's first activation of the innovative research application (e.g., clicking icon U11). Interface P11 includes options for health management functions provided by the innovative research application (e.g., option U12 for uric acid health function) and a "Join Research" control U13. Watch 10 can also display user information input interface P12 in response to the user selecting uric acid health option U12 and then clicking the "Join Research" control U13. User information input interface P12 includes options for entering user information such as age, gender, height, weight, waist circumference, and hip circumference, as well as a "Confirm" control U14. Watch 10 can also display permission management interface P13 in response to the user entering user information and then clicking the "Confirm" control U14, prompting the user that the uric acid health function requires sensor permission to be enabled. Watch 10 can also display wearing reminder interface P14 in response to the user clicking the "Enable" control U15 in the permission management interface P13, prompting the user for the required wearing time of the uric acid health function (e.g., 2 hours during the day and 4 hours at night). After a preset duration (e.g., 10 seconds) of the wearing reminder interface P14, the watch 10 can display a high uric acid risk interface P15. This interface P15 can include records of the user's high uric acid risk over multiple dates, as well as the assessment progress for that day (e.g., 0%). The assessment progress increases with the duration the user wears the watch within an assessment cycle (e.g., 12 hours, 1 day, 2 days, etc.).

[0154] It should be noted that the user information entered by the user in the user information input interface P12 may include at least some of the user information indicated in the user information input interface P12, such as age, gender, height, weight, waist circumference, and hip circumference. Users may also choose not to enter user information in the user information input interface P12, which is not limited here.

[0155] It should be noted that an evaluation period can be of any length, such as 12 hours, 24 hours (1 day), 48 hours (2 days), etc. The specific length of the evaluation period is not limited in the embodiments of this application. For ease of description, the following description uses an evaluation adjustment period of 1 day as an example.

[0156] It should be noted that the above process for activating the uric acid health function is just an example. The order of some steps can be changed, and some steps can be added or removed. The uric acid health function can also be enabled by default, which is not limited here.

[0157] After the uric acid health function is activated, the watch 10 can collect one or more physiological characteristic data from the user during wear, such as sleep audio, ECG signal, pulse wave signal, and vital sign data, and obtain the data characteristics of the collected physiological characteristic data. If the user's wearing time meets the assessment conditions (or in response to user operation), the watch 10 can process the user's physiological characteristic data and user information through the aforementioned risk assessment model to obtain and display the user's high uric acid risk level.

[0158] In some embodiments, the evaluation criteria may include the daytime wearing duration being greater than or equal to a first duration threshold within an evaluation period (e.g., within a day), and the nighttime wearing duration within an evaluation period (e.g., within a day) being greater than or equal to a second duration threshold. For example, the first duration threshold may be 2 hours, 3 hours, 4 hours, or other durations, and the second duration threshold may be 4 hours, 5 hours, 6 hours, or other durations. This application embodiment does not limit the specific values ​​of the first and second duration thresholds.

[0159] In some embodiments, the evaluation criteria may include the total duration of wear by the user within an evaluation period being greater than or equal to a third duration threshold.

[0160] In some embodiments, the evaluation criteria may include the user wearing the device for a duration greater than or equal to a fourth duration threshold during non-sleep periods within an evaluation cycle (e.g., within a day), and the user wearing the device for a duration greater than or equal to a fifth duration during sleep periods within a day. For example, the first duration threshold may be 2.5 hours, 3.5 hours, 4.5 hours, or other durations, and the second duration threshold may be 3 hours, 4 hours, 5 hours, or other durations. This application embodiment does not limit the specific values ​​of the fourth and fifth duration thresholds.

[0161] High uric acid risk level reminder

[0162] In some embodiments, after determining the user's high uric acid risk level for the day, the watch 10 can output corresponding prompt information (e.g., displaying prompt information and / or playing prompt audio) based on the determined high uric acid risk level. For example, Figures 4A to 4D, according to some embodiments of this application, show prompt information corresponding to different high uric acid risk levels.

[0163] Referring to Figure 4A, if the watch 10 determines that the user's risk level of high uric acid on May 24 is high based on the risk assessment model, it can prompt the user that "the risk of high uric acid is high. If you feel unwell, please seek medical attention in time."

[0164] Referring to Figure 4B, if the watch 10 determines that the user's risk level of high uric acid on May 25 is medium based on the risk assessment model, it can prompt the user that "there may be a risk of high uric acid. If you feel unwell, please seek medical attention in time."

[0165] In some embodiments, when the risk level of high or medium uric acid is indicated, watch 10 can also provide precautions for high uric acid levels. For example, watch 10 can prompt the user to provide more data (such as joint images, joint pain data, etc.) for enhanced assessment to obtain more accurate results. As another example, watch 10 can provide dietary precautions for high uric acid levels, such as reminding the user to reduce the intake of high-purine foods.

[0166] Referring to Figure 4C, if the watch 10 determines that the user's risk level for high uric acid on May 26 is low based on the risk assessment model, it can prompt the user with "No obvious abnormalities found, please continue to wear it".

[0167] Referring to Figure 4D, if the user's wearing time of watch 10 on May 28 is insufficient (e.g., not meeting the aforementioned evaluation conditions), watch 10 can prompt the user with "Insufficient wearing time, no evaluation result, please continue wearing." Optionally, if the wearing time is insufficient, watch 10 can also prompt the user to obtain the evaluation result through active measurement.

[0168] It should be noted that the above-described notification method is only an example. In other embodiments, the watch 10 may also use other methods to notify the user of the high uric acid risk level and corresponding notification information, which is not limited here. For example, the watch 10 may transmit the high uric acid risk level to other electronic devices of the user, and the other electronic devices may then notify the user. Alternatively, the watch 10 may also notify the user through audio, vibration, images, etc., which is not limited here.

[0169] In some embodiments, watch 10 can record the user's daily high uric acid risk level and display it on the display interface. For example, referring to Figure 5, watch 10 can display a history results interface P16 in response to the user clicking the history option in the aforementioned high uric acid risk interface P15. The history results interface P16 can include the user's high uric acid risk level within one month. In some embodiments, the labels corresponding to different high uric acid risk levels can be different. For example, in the case shown in Figure 5, dates with a high uric acid risk level are displayed with a black background, dates with a medium uric acid risk level are displayed with a diagonal stripe background from the upper left to the lower right, dates with a low uric acid risk level are displayed with a diagonal stripe background from the upper right to the lower left, and dates with no data (e.g., dates with insufficient wearing time) are displayed with a white background.

[0170] It should be noted that in some other embodiments, the watch 10 may also indicate the date of different high uric acid risk levels in other ways, such as through different colored backgrounds, different text colors, different text fonts, different shaped backgrounds, etc., which are not limited here.

[0171] It should be noted that, in some other embodiments, the watch 10 may respond to user operation by displaying the high uric acid risk level within a week, a quarter, a year, or other time periods.

[0172] It should be noted that, in some other embodiments, the watch 10 can also respond to user operations by displaying statistical results of high uric acid risk levels over a period of time (e.g., one day, one week, one month, one quarter, half a year, one year, etc.). For example, the statistical results may include data such as the number of times and the percentage of different high uric acid risk levels within that period of time.

[0173] User-initiated evaluation

[0174] In some embodiments, in addition to automatically triggering the assessment of the user's high uric acid risk level when the above assessment conditions are met, the watch 10 may also assess the user's high uric acid risk level in response to user operation.

[0175] For example, Figures 6A and 6B, according to some embodiments of this application, illustrate a process in which a watch 10 assesses a user's high uric acid risk level based on a user-initiated assessment.

[0176] Refer to Figure 6A:

[0177] Watch 10 can respond to the user's swipe up on the aforementioned high uric acid risk interface P15 and display the high uric acid risk interface P21. Compared to the high uric acid risk interface P15, the high uric acid risk interface P21 adds wearing instructions and active assessment options.

[0178] Watch 10 can respond to the user clicking the "Active Assessment" option on the high uric acid risk interface P21, displaying the high uric acid risk assessment interface P22, collecting the user's ECG signal, and prompting the user "Collecting ECG signal, please remain still, remaining time: 60 seconds". The high uric acid risk assessment interface P22 also includes pause and return options. Watch 10 can pause ECG signal collection in response to the user selecting the pause option; or stop the active assessment in response to the user selecting the return option.

[0179] After collecting the user's ECG signal, watch 10 can display the high uric acid risk assessment interface P23 to prompt the user that "ECG signal acquisition is complete. The next step is to acquire a joint image of the first metatarsophalangeal joint." The high uric acid risk assessment interface P23 may include a capture control and a skip control. Watch 10 can trigger the acquisition of the user's joint image in response to the user's selection of the capture control, or watch 10 can skip the acquisition of the user's joint image in response to the user's selection of the skip control.

[0180] Refer to Figure 6B:

[0181] After detecting the user's click on the shooting control in the high uric acid risk assessment interface P23, watch 10 can display the shooting interface P24 shown in Figure 6B. Shooting interface P24 can include shooting control U21 and prompt message U22 (used to prompt the user "Please take a picture of the front of the first metatarsophalangeal joint of the right foot"). After the user points the camera of watch 10 at the front of the first metatarsophalangeal joint of the right foot, they can click on the shooting control U21. In response to the user's click on shooting control U21, watch 10 can capture a frontal image of the user's first metatarsophalangeal joint of the right foot and display shooting interface P25. Shooting interface P25 can include shooting control U23 and prompt message U24 (used to prompt the user "Please take a picture of the 45° side view of the first metatarsophalangeal joint of the right foot").

[0182] After the user points the camera of watch 10 at the 45° side of the first metatarsophalangeal joint of the right foot, they can click the shooting control U23. Watch 10 responds to the user's click of shooting control U23, capturing a 45° side image of the user's right first metatarsophalangeal joint and displaying the shooting interface P26. The shooting interface P26 may include shooting control U25 and prompt message U26 (used to prompt the user "Please take a picture of the front of the first metatarsophalangeal joint of the left foot").

[0183] After the user points the camera of watch 10 at the front of the first metatarsophalangeal joint of the left foot, they can click the shooting control U25. Watch 10 responds to the user's click of the shooting control U25, capturing a frontal image of the user's first metatarsophalangeal joint of the left foot and displaying the shooting interface P27. The shooting interface P27 may include the shooting control U27 and a prompt message U28 (used to prompt the user "Please take a 45° side view of the first metatarsophalangeal joint of the left foot").

[0184] After the user points the camera of watch 10 at a 45° angle to the first metatarsophalangeal joint of the left foot, they can click the shooting control U27. Watch 10 can then respond to the user's click of the shooting control U27 and capture an image of the user's left first metatarsophalangeal joint at a 45° angle.

[0185] After acquiring a 45° lateral image of the user's left first metatarsophalangeal joint, the joint image acquisition is complete, and the high uric acid risk assessment interface P28 can be displayed on the watch 10. On the high uric acid risk assessment interface P28, the watch 10 can prompt the user, "Joint image acquisition complete, next step is to acquire pain data of the first metatarsophalangeal joint." The high uric acid risk assessment interface P28 can include acquisition controls and skip controls. The watch 10 can trigger the acquisition of the user's first metatarsophalangeal joint pain data in response to the user's selection of the acquisition control, or the watch 10 can skip the acquisition of the user's first metatarsophalangeal joint pain data in response to the user's selection of the skip control.

[0186] It should be noted that the watch 10 may have a skin conductance sensor installed on its body, strap, or other components. When this sensor comes into contact with the skin of a user's joint, it can collect the user's skin conductance signals, thereby obtaining pain data for the joint. The area where the skin conductance sensor is installed on the watch 10 can be determined by… (or other symbols or words) to identify.

[0187] Referring again to Figure 6B, after detecting the user's click on the data collection control on the high uric acid risk assessment interface P28, watch 10 can display the high uric acid risk assessment interface P29. The high uric acid risk assessment interface P29 can display a prompt message (to remind the user "Please remove the watch strap..."). After aligning the marked area close to the first metatarsophalangeal joint, click "Start Data Collection" and the Start Data Collection control.

[0188] After placing the area of ​​the skin conduction sensor on watch 10 close to the first metatarsophalangeal joint, the user can click the "Start Data Collection" control on the high uric acid risk assessment interface P29. Watch 10 responds to the user's click by collecting pain data via the skin conduction sensor and displays the uric acid risk assessment interface P30 as shown in Figure 6B. The high uric acid risk assessment interface P30 may include a prompt message to the user: "Pain data is being collected. Please do not move watch 10. Remaining time: 60 seconds."

[0189] Referring again to Figure 6B, after collecting pain data from the user's first metatarsophalangeal joint, watch 10 can run the aforementioned risk assessment model based on the collected data to assess the user's high uric acid risk level and display the assessment interface P31 to indicate the progress. After obtaining the user's high uric acid risk level through the risk assessment model, watch 10 can display the assessment result interface P32 to indicate that the user's high uric acid risk level on May 28th was high risk, along with the corresponding warning information.

[0190] It should be noted that the order in which the joint images of the first metatarsophalangeal joint are captured can be adjusted, and images of the first metatarsophalangeal joint from more or fewer directions can be captured. Furthermore, joint images of more joints or other joints can also be captured; no limitation is made here. For example, watch 10 can instruct the user to capture joint images of joints experiencing pain or joints with a history of gout.

[0191] It should be noted that the above-described method of first acquiring ECG signals, then acquiring joint images, and finally acquiring joint pain data is just one example. In other embodiments, the order in which different data are acquired can be interchanged or carried out in parallel, and this is not limited here.

[0192] In some embodiments, the watch 10 may not have a camera. Instead, the watch 10 can acquire joint images of the user through other electronic devices that have established a communication connection with it. For example, if the watch 10 does not have a camera, it can display the shooting device selection interface P33 shown in Figure 7 in response to the user clicking the shooting control on the high uric acid risk assessment interface P23. The shooting device selection interface P33 may include the watch 10's associated device (e.g., mobile phone 20) and a new device addition control U29. The watch 10 can send an auxiliary shooting request to the mobile phone 20 in response to the user clicking the mobile phone 20. Referring again to Figure 7, the mobile phone 20 can display a confirmation box U30 in response to the auxiliary shooting request, prompting the user to "accept the auxiliary shooting request from the watch 10." The confirmation box U30 also includes an accept control and a reject control. The mobile phone 20 can activate its camera and guide the user to capture joint images in response to the user clicking the accept control in the confirmation box U30.

[0193] It should be noted that the associated devices of watch 10 may include electronic devices that have established a communication connection with watch 10, or electronic devices that have established a trust relationship.

[0194] In some embodiments, the watch 10 may not be equipped with a skin conductance sensor, but instead the user's joint pain data may be collected through other devices that establish a communication connection with the watch 10.

[0195] In some embodiments, when multiple assessment results exist within an assessment period (e.g., within a day), the watch 10 can synthesize these multiple assessment results into a single assessment result as the unique assessment result for that assessment period. For example, the assessment result with the highest risk level among the multiple assessment results can be used as the unique assessment result for that assessment period; or the assessment result with the largest proportion of high uric acid risk levels among the multiple assessment results can be used as the unique assessment result for that assessment period; or the multiple assessment results can be merged into a single unique assessment result through weighted calculation or other methods. These multiple assessment results may include at least one assessment result automatically obtained by the watch 10 under certain assessment conditions, and at least one assessment result obtained by the watch 10 in response to a user's active assessment.

[0196] In some embodiments, if multiple assessment results exist within an assessment period (e.g., within a day), watch 10 can also store and display these multiple assessment results. For example, referring to Figure 8, if multiple assessment results exist on May 19th, watch 10 can respond to the user's action of clicking "19" on the aforementioned historical results interface P16 and display the high uric acid risk interface P34. The high uric acid risk interface P34 may include the overall assessment result for May 19th as "high risk" and corresponding prompt information. The high uric acid risk interface P34 may also include a prompt message to the user that "multiple assessment results exist, please swipe to view." Watch 10 can respond to the user's swiping operation (e.g., swiping left or up) and display the multiple assessment results for May 19th. For example, in response to the user's swiping operation on the high uric acid risk interface P34, watch 10 can display the assessment results automatically performed by watch 10 under the condition of meeting the assessment criteria (assessment result 1 / 3 (indicating a total of 3 assessment results), medium risk) on the high uric acid risk interface P35. Watch 10 can respond to the user's swipe gesture on the high uric acid risk interface P35, displaying the second of three assessment results (2 / 3, medium risk, obtained through user-initiated assessment) on the high uric acid risk interface P36. Watch 10 can also respond to the user's swipe gesture on the high uric acid risk interface P36, displaying the third of three assessment results (3 / 3, high risk, obtained through user-initiated assessment) on the high uric acid risk interface P37.

[0197] Mobile 20 responds to user-initiated evaluation

[0198] In some embodiments, besides the watch 10 described above which has the function of collecting ECG signals and pain data, other electronic devices that do not have the function of collecting ECG signals and pain data (such as mobile phone 20) can also implement the data processing method provided in this application embodiment. For example, watch 10 can transmit the user's determined high uric acid risk level to mobile phone 20. Mobile phone 20 can display the user's historical high uric acid risk level based on the received data. As another example, mobile phone 20 can also determine the user's high uric acid risk level through a risk assessment model based on physiological characteristic data collected by wearable devices such as watch 10, joint images / joint pain data collected by mobile phone 20 itself or other devices.

[0199] For example, Figures 9A and 9B, according to some embodiments of this application, illustrate a schematic diagram of a mobile phone 20 displaying a high uric acid risk level and assessing a user's high uric acid risk level based on user operations.

[0200] As shown in Figure 9A, the mobile phone 20 can display user health-related data, such as heart health, blood oxygen saturation, sleep duration, and high uric acid risk, on the display interface P41 of the sports and health application. In the high uric acid risk option, the mobile phone 20 can display the user's high uric acid risk level for the day, determined by itself or obtained from the watch 10.

[0201] Referring again to Figure 9A, the mobile phone 20 can respond to the user's click on the high uric acid risk option in the display interface P41, displaying the high uric acid risk interface P42. The high uric acid risk interface P42 can include more information related to high uric acid risk. For example, the mobile phone 20 can display data characteristics used to assess the user's high uric acid risk, such as: continuous wearing time (24 hours), snoring frequency (10 times per minute, indicating the user is not snoring), respiratory rate (10 breaths per minute), mean systolic blood pressure (the average systolic blood pressure during the user's wearing period, 142 mmHg), foot joint risk (low), etc. The high uric acid risk interface P42 may also include a history control (used to trigger the mobile phone 20 to display the user's historical high uric acid risk level) and an active assessment control (the user triggers the mobile phone 20 to assess the user's high uric acid risk based on the user's actions).

[0202] It should be noted that foot joint risk can refer to the degree of redness and swelling, the degree of pain in the foot joints, or the high uric acid risk level obtained by the risk assessment model based on joint data.

[0203] In some embodiments, in response to a user clicking the active assessment control on the high uric acid risk interface P42, the mobile phone 20 can collect the user's ECG signal based on a wearable device (such as the aforementioned watch 10, bracelet, earphones, smart glasses, smart ring, etc.) connected to the mobile phone 20. For example, continuing to refer to Figure 9A, the mobile phone 20 can collect the user's ECG signal based on the watch 10 connected to the mobile phone 20 and display the ECG signal acquisition interface P43 to prompt the user "Connected to watch 10, collecting ECG signal, please remain still, 60 seconds remaining." The ECG signal acquisition interface P43 also includes a pause option and a return option. The mobile phone 20 can pause the ECG signal acquisition in response to the user selecting the pause option; or stop the active assessment in response to the user selecting the return option.

[0204] After acquiring the ECG signal, the mobile phone 20 can display the joint image acquisition interface P44 and prompt the user that "ECG signal acquisition is complete. The next step is to acquire the joint image of the first metatarsophalangeal joint." The joint image acquisition interface P44 may include a shooting control and a skip control. The mobile phone 20 can trigger the acquisition of the user's joint image in response to the user's selection of the shooting control, or the mobile phone 20 can skip the acquisition of the user's joint image in response to the user's selection of the skip control. It should be noted that the process of the mobile phone 20 acquiring the user's joint image based on the user's operation can refer to the aforementioned content on the acquisition of the user's joint image by the watch 10, and will not be repeated here.

[0205] After acquiring the user's joint images, the mobile phone 20 can display the pain data acquisition interface P45 shown in Figure 9B to prompt the user, "Joint image acquisition complete, next step is to acquire pain data of the first metatarsophalangeal joint." The pain data acquisition interface P45 may include acquisition controls and skip controls. The mobile phone 20 can connect to other devices (such as the mobile phone 20 or other wearable devices) to acquire the user's first metatarsophalangeal joint pain data in response to the user's selection of the acquisition control; or the mobile phone 20 can skip acquiring the user's first metatarsophalangeal joint pain data in response to the user's selection of the skip control. For example, continuing to refer to Figure 9B, the mobile phone 20 can display the pain data acquisition interface P46 in response to the user clicking the acquisition control, and display the prompt message "Connected to watch 10, please remove the strap..." After placing the marked area close to the first metatarsophalangeal joint, click "Start Data Acquisition". After placing the area containing the skin conduction sensor on watch 10 close to the first metatarsophalangeal joint, the user can click the "Start Data Acquisition" control on the pain data acquisition interface P46. The mobile phone 20 can respond to the user's click of the "Start Data Acquisition" control and acquire the pain data collected by the skin conduction sensor on watch 10.

[0206] After acquiring pain data at the user's first metatarsophalangeal joint, the mobile phone 20 can use a risk assessment model to process the user's historically collected sleep audio (or sleep audio data characteristics), the user's pulse wave (or pulse wave data characteristics), and the ECG signal, joint images, and joint pain data collected based on the processes in Figures 9A and 9B, to generate and display the user's high uric acid risk level.

[0207] It should be noted that in some implementations, sleep audio can also be collected by the phone itself.

[0208] In some embodiments, the mobile phone 20 can also store historical results showing the user's high uric acid risk level. For example, referring to Figure 10A, the mobile phone 20 can also display a historical results interface P47 in response to the user clicking the history control on the high uric acid risk interface P42. The historical results interface P47 includes the user's high uric acid risk level within one month (May), wherein dates with a high uric acid risk level are displayed with a black background, dates with a medium uric acid risk level are displayed with a diagonal stripe background from the upper left to the lower right, dates with a low uric acid risk level are displayed with a diagonal stripe background from the upper right to the lower left, and dates with no data (e.g., dates with insufficient wearing time) are displayed with a white background. The historical results interface P47 can also include monthly, quarterly, and yearly options, and the mobile phone 20 can display the high uric acid risk level within one month, one quarter, or one year based on the user's operation on the monthly, quarterly, or yearly options, respectively.

[0209] In some embodiments, the mobile phone 20 may also respond to a user's click on a date in the historical results interface P47 to display detailed information on the high uric acid risk level corresponding to that date, such as the data characteristics used to assess the user's high uric acid risk on that date, the high uric acid risk level on that date, and related prompts.

[0210] For example, mobile phone 20 can respond to the user's action of clicking "24" on the historical results interface P47 (corresponding to May 24th) and display the high uric acid risk interface P48 shown in Figure 10A. The high uric acid risk interface P48 may include the high uric acid risk level (high risk) corresponding to May 24th, prompt information, and data characteristics used to assess the user's high uric acid risk, such as "continuous wearing time (19 hours), snoring frequency (12 times per minute), respiratory rate (5 times per minute), mean systolic blood pressure (average systolic blood pressure during the user's wearing process, 160 mmHg)", foot joint risk (low), etc.

[0211] For example, mobile phone 20 can respond to the user's action of clicking "25" on the historical results interface P47 (corresponding to May 25th) and display the high uric acid risk interface P49 as shown in Figure 10B. The high uric acid risk interface P49 may include the high uric acid risk level (medium risk) corresponding to May 25th, prompt information, and data characteristics used to assess the user's high uric acid risk, such as "continuous wearing time (19 hours), snoring frequency (10 times per minute), respiratory rate (5 times per minute), mean systolic blood pressure (average systolic blood pressure during the user's wearing period, 160 mmHg)", foot joint risk (low), etc.

[0212] It should be noted that the methods by which watch 10 and mobile phone 20 collect users' physiological characteristic data, joint data, vital signs, etc., in the above embodiments are only examples. In other embodiments, the data used by watch 10 or mobile phone 20 (or other electronic devices) to assess the user's high uric acid risk level based on a risk assessment model can be collected entirely by one electronic device or jointly by multiple electronic devices, and this is not limited here.

[0213] The technical solution of this application will now be described in conjunction with the risk assessment model shown in Figure 1 and the risk assessment process or result display method shown in Figures 3 to 10B.

[0214] First, we will introduce the technical solution for user-initiated evaluation.

[0215] For example, Figure 11 illustrates a flowchart of a data processing method for a user to actively assess the risk level of high uric acid, according to some embodiments of this application. The method can be executed by a first electronic device, and as shown in Figure 11, the method includes:

[0216] S1101, a user-triggered high uric acid risk assessment operation was detected.

[0217] When the first electronic device detects that a user has triggered an operation that induces a high uric acid risk assessment, it may trigger the data processing method provided in the embodiments of this application.

[0218] For example, in the case where the first electronic device is a watch 10, referring to Figure 6A above, the operation that triggers the high uric acid risk assessment can be the user clicking the active assessment control in the high uric acid risk interface P15.

[0219] For example, in the case where the first electronic device is a mobile phone 20, referring to Figure 9A, the user can trigger the high uric acid risk assessment by clicking the active assessment control in the high uric acid risk interface P42.

[0220] S1102 acquires ECG signals and joint data based on user operations.

[0221] After detecting that the user has triggered a high uric acid risk assessment, the first electronic device can guide the user to collect ECG signals, as well as joint data such as joint images and joint pain data.

[0222] For example, in the case where the first electronic device is a watch 10, the process by which the watch 10 guides the user to collect ECG signals, joint images, and joint pain data can be referred to the embodiments in Figures 6A, 6B, and 7, and will not be described in detail here.

[0223] For example, in the case where the first electronic device is a mobile phone 20, the process of the mobile phone 20 guiding the user to collect ECG signals, joint images and joint pain data can be referred to the embodiments in Figures 9A and 9B, which will not be described in detail here.

[0224] It should be noted that, in the process of acquiring ECG signals and joint data based on user operation, the first electronic device may acquire all data from ECG signals, joint images, and joint pain data in response to user operation, or it may acquire only a portion of the data from ECG signals, joint images, and joint pain data.

[0225] For example, in the scenarios shown in Figures 6A and 9A, if the user selects to skip collecting joint images in the high uric acid risk assessment interface P23 or the joint image acquisition interface P44, the watch 10 or the mobile phone 20 will not be able to acquire joint images; in the scenarios shown in Figures 6B and 9B, if the user selects to skip collecting pain data in the high uric acid risk assessment interface P28 or the pain data acquisition interface P45, the watch 10 or the mobile phone 20 will not be able to acquire joint pain data.

[0226] It should be noted that in some embodiments, the ECG signal may also be the ECG signal collected by the first electronic device without the user's awareness, without requiring the user to collect it.

[0227] It should be noted that the ECG signal and joint data can be collected by the first electronic device itself or by the first electronic device through other electronic devices, and there is no limitation here. For example, if the first electronic device is watch 10, the joint image can be collected by mobile phone 20 (as a second electronic device); if the first electronic device is mobile phone 20, the ECG signal and joint pain data can be collected by watch 10 (as a second electronic device).

[0228] S1103, based on the data characteristics of the acquired data, as well as the data characteristics of pulse waves, sleep audio data, and at least some data characteristics of vital signs, obtains the user's first high uric acid risk level through a risk assessment model.

[0229] After acquiring ECG signals and joint data based on user operations, the first electronic device can determine the user's first high uric acid risk level through a risk assessment model based on the data characteristics of the acquired data, as well as pulse wave data characteristics, sleep audio data characteristics, at least some data characteristics of vital signs, and user information. For example, the first electronic device can input the data characteristics of the acquired data, as well as pulse wave data characteristics, sleep audio data characteristics, and at least some data characteristics of vital signs, into the risk assessment model, and process the input data through the risk assessment model to obtain the first high uric acid risk level.

[0230] In some embodiments, the data characteristics of the acquired data vary depending on the data acquired in S1102. For example, if the acquired data only includes ECG signals, the data characteristics of the acquired data may include the data characteristics of ECG signals; if the acquired data only includes joint images, the data characteristics of the acquired data may include the data characteristics of joint images; if the acquired data only includes joint pain data, the data characteristics of the acquired data may include the data characteristics of joint pain data; if the acquired data includes multiple data such as ECG signals, joint images, and joint pain data, the data characteristics of the acquired data may include the data characteristics corresponding to those multiple data.

[0231] In some embodiments, if the duration of time the user wears the wearable device (e.g., watch 10) meets the aforementioned assessment conditions during the current assessment period (e.g., the assessment period in which the user triggers the operation of high uric acid risk assessment), the data features of the pulse wave and the data features of the sleep audio can be respectively the data features of the user's pulse wave during the current assessment period (e.g., within the day) and the data features of the user's sleep audio during the current assessment period.

[0232] In some embodiments, if the duration of time a user wears the wearable device in the current evaluation period does not meet the aforementioned evaluation conditions, the pulse wave data features and the sleep audio data features can be the data features corresponding to the user's pulse wave in the previous evaluation period and the data features corresponding to the user's sleep audio in the previous evaluation period, respectively.

[0233] In some embodiments, if the duration of time the user wears the wearable device within the current evaluation period does not meet the aforementioned evaluation conditions, the pulse wave data characteristics may be the data characteristics of pulse waves that meet the corresponding collection duration (e.g., the aforementioned first duration threshold, or the sum of the aforementioned first duration threshold and the second duration threshold, or other durations) before the current moment, or the data characteristics of pulse waves within the duration corresponding to the previous evaluation period. In this case, the pulse wave data characteristics may include the data characteristics of pulse waves collected within the current evaluation period, and / or the data characteristics of pulse waves collected within at least one evaluation period before the current evaluation period.

[0234] In some embodiments, if the duration of time the user wears the wearable device within the current evaluation period does not meet the aforementioned evaluation conditions, the sleep audio data features may be the data features corresponding to sleep audio collected within a specified collection duration (e.g., the aforementioned second duration threshold, or other durations) prior to the current moment, or the data features corresponding to sleep audio within the duration corresponding to the previous evaluation period. In this case, the sleep audio data features may include the data features of sleep audio collected within the current evaluation period, and / or the data features of sleep audio collected within at least one evaluation period prior to the current evaluation period.

[0235] In some embodiments, if the acquired data includes joint images and / or joint pain data, and the user's wearing time of the wearable device within the current assessment period does not meet the aforementioned assessment conditions, the pulse wave data characteristics and sleep audio data characteristics can be respectively the user's pulse wave data characteristics within the current assessment period (e.g., within the same day) and the user's sleep audio data characteristics within the current assessment period. If the acquired data does not include joint images and joint pain data, and the user's wearing time of the wearable device within the current assessment period does not meet the aforementioned assessment conditions, the pulse wave data characteristics and sleep audio data characteristics can be respectively the pulse wave data characteristics within the duration corresponding to the previous assessment period at the current moment and the sleep audio data characteristics within the duration corresponding to the previous assessment period at the current moment.

[0236] In some embodiments, the first electronic device may obtain the first high uric acid risk level by running a risk assessment model itself, or the first electronic device may send the data characteristics of the acquired data, as well as the data characteristics of the pulse wave, the data characteristics of the sleep audio, and at least some of the data characteristics of the vital signs, to other electronic devices (such as the second electronic device) outside the first electronic device, and the other electronic devices may obtain the user's first high uric acid risk level through the risk assessment model.

[0237] It should be noted that, when the first electronic device is a wearable device, at least some of the data in the ECG signal, pulse wave, sleep audio, and vital signs can be automatically collected by the first electronic device while the user is wearing it. After collecting the at least some data, the first electronic device can store the at least some data, or process the at least some data to obtain and store the data features of the at least some data.

[0238] S1104, in response to meeting the enhanced assessment conditions, obtains the user's second high uric acid risk level through a risk assessment model.

[0239] In some embodiments, after obtaining a first high uric acid risk level, the first electronic device can obtain a second high uric acid risk level for the user through a risk assessment model when enhanced assessment conditions are met. For example, the first electronic device can guide the user to collect more physiological characteristic data or joint data, and based on the data characteristics of the collected data and the data characteristics used in the aforementioned S1103, enhance the assessment of the user's high uric acid risk level through a risk assessment model to obtain the user's second high uric acid risk level.

[0240] In some embodiments, the first electronic device can guide the user to collect data not acquired in the aforementioned S1102 (at least some of the data from ECG signals, joint images, and joint pain data), and process the data characteristics of the acquired data and the data characteristics used in the aforementioned S1103 based on a risk assessment model to obtain a second high uric acid risk, etc. The process by which the first electronic device guides the user to collect ECG signals, joint images, and joint pain data can be referred to the embodiments shown in Figures 6A to 9B above, and will not be described in detail here.

[0241] It should be noted that when the first electronic device performs an enhanced assessment of the user's high uric acid risk level based on the risk assessment model, the amount of input data into the risk assessment model is greater than the amount of input data into the risk assessment model when generating the first high uric acid risk level. Therefore, the accuracy of the second high uric acid risk level is higher than the accuracy of the first high uric acid risk level.

[0242] In some embodiments, enhanced assessment conditions may include the failure to obtain joint images of the user and / or the failure to obtain joint pain data of the user, and at least one of the following conditions: a first high uric acid risk level is higher than a preset risk level; the user's wearing time in the current assessment period does not meet the aforementioned assessment conditions (e.g., the wearing time during the day in the current assessment period is less than a first duration threshold, and / or the wearing time at night in the current assessment period is less than a second duration threshold, and / or the wearing time in the non-sleep state in the current assessment period is less than a fourth duration threshold, and / or the wearing time in the sleep state in the current assessment period is less than a fifth duration threshold).

[0243] Based on the above-mentioned enhanced assessment conditions, the first electronic device can further enhance the assessment of the user's high uric acid risk level by acquiring more data based on the user's operation when the obtained first high uric acid risk level is high or when the collected data is limited (e.g., the user's wearing time does not meet the aforementioned assessment conditions within the current assessment period), which can further improve the accuracy of the assessment results.

[0244] In some embodiments, the preset risk level can be low risk, that is, when the first high uric acid risk level is high risk or medium risk, the first high uric acid risk level is higher than the preset risk level.

[0245] In other embodiments, the preset risk level can also be medium risk, that is, when the first high uric acid risk level is high risk, the first high uric acid risk level is higher than the preset risk level.

[0246] In some embodiments, if the enhanced assessment conditions are determined to be met, the first electronic device may also display a prompt message asking the user whether to perform an enhanced assessment. The first electronic device may execute S1104 if the enhanced assessment conditions are met and the user confirms the enhanced assessment; or if the enhanced assessment conditions are met and the user cancels the enhanced assessment, or if the enhanced assessment conditions are not met, it may proceed to S1106, displaying a first high uric acid risk level.

[0247] S1105 indicates the second highest uric acid risk level.

[0248] After obtaining the user's second highest uric acid risk level, the first electronic device can display the second highest uric acid risk level.

[0249] In some embodiments, the first electronic device may also store a second high uric acid risk level or send the second high uric acid risk level to other electronic devices.

[0250] In some embodiments, the first electronic device may also fuse the second and first high uric acid risk levels to obtain a third high uric acid risk level, and display the third high uric acid risk level. For example, if the probability that a user corresponding to the first high uric acid risk level has a high uric acid risk is a first probability, and the probability that a user corresponding to the first high uric acid risk level has a high uric acid risk is a second probability, the first electronic device may perform a weighted sum of the first and second probabilities to obtain a third probability, and determine the third high uric acid risk level based on the relationship between the third probability and the aforementioned first and second thresholds.

[0251] In some embodiments, the first electronic device may also display the higher risk level among the first and second hyperuricemia risk levels. Exemplarily, when the first and second hyperuricemia risk levels include high risk and low risk, high risk and medium risk, low risk and high risk, medium risk and high risk, or both high risk, the higher risk level can be high risk. When the first and second hyperuricemia risk levels include medium risk and low risk, low risk and medium risk, or both medium risk, the higher risk level can be medium risk. When both the first and second hyperuricemia risk levels are low risk, the higher risk level can be low risk.

[0252] In some embodiments, the first electronic device may also display a prompt message corresponding to the higher risk level between the first and second high uric acid risk levels.

[0253] In some embodiments, the first electronic device may simultaneously record a first high uric acid risk level and a second high uric acid risk level, and display the first high uric acid risk level and / or the second high uric acid risk level. The first electronic device may also display prompt information corresponding to the first high uric acid risk level and / or the second high uric acid risk level.

[0254] In some embodiments, S1104 and S1105 are optional. Without executing S1104 and S1105, the first electronic device can execute S1106 after obtaining a first high uric acid risk level.

[0255] In some embodiments, the first electronic device may also send a first high uric acid risk level, or a second high uric acid risk level, or the aforementioned third high uric acid risk level to other electronic devices.

[0256] S1106, indicating a first-degree high uric acid risk due to failure to meet enhanced assessment criteria.

[0257] After obtaining the first high uric acid risk level, the first electronic device can display the first high uric acid risk level even if the enhanced assessment conditions are not met. Optionally, the first electronic device can also display a prompt message corresponding to the first high uric acid risk level. The specific content of the prompt message can be referred to the embodiments in Figures 4A to 4C above, and will not be repeated here.

[0258] In some embodiments, the first electronic device may also store a first high uric acid risk level and / or send the first high uric acid risk level to other electronic devices.

[0259] Based on the above method, the first electronic device can respond to user operations and, based on various data related to uric acid levels in the blood, use a risk assessment model to determine the user's high uric acid risk level and alert the user, thus achieving an accurate assessment of the user's high uric acid risk. Furthermore, when the obtained uric acid level is high, more data can be obtained based on user operations to enhance the assessment, further improving the accuracy of the assessment results.

[0260] This application embodiment also provides another data processing method for assessing a user's hyperuricemia risk level in response to user operation. The difference from the embodiment in Figure 11 is that, after detecting a user's triggering of a hyperuricemia risk assessment, the first electronic device can first assess the user's hyperuricemia risk level based on collected physiological characteristic data (which may also include user information) to obtain a first hyperuricemia risk level. If the first hyperuricemia risk level is high (e.g., high or medium risk), the first electronic device can guide the user to collect more data for enhanced assessment; if the first hyperuricemia risk level is low, the first electronic device can directly display the first hyperuricemia risk level. Based on the above method, the user does not need to collect joint data every time they perform an assessment, which improves the user experience.

[0261] For example, Figure 12 illustrates a flowchart of another data processing method for assessing a user's high uric acid risk level in response to user operation, according to some embodiments of this application. The method can be executed by a first electronic device (e.g., the aforementioned watch 10 or mobile phone 20), and as shown in Figure 12, the method includes:

[0262] S1201, a user-triggered high uric acid risk assessment operation was detected.

[0263] When the first electronic device detects that a user has triggered an operation to assess the risk of high uric acid, it can trigger the data processing method provided in this application embodiment. The specific form of the user's operation to trigger the risk assessment of high uric acid can be referred to in step S1101, and will not be elaborated here.

[0264] S1202, based on physiological characteristic data, uses a risk assessment model to determine the user's first high uric acid risk level. The physiological characteristic data includes multiple data such as ECG signal, pulse wave, sleep audio, and vital signs.

[0265] When a user triggers an operation that triggers a high uric acid risk assessment, the first electronic device can input the data characteristics of physiological features into the risk assessment model, and use the risk assessment model to process the input data to obtain the user's first high uric acid risk level.

[0266] In some embodiments, the data input into the risk assessment model may also include user information.

[0267] In some embodiments, the ECG signal may be collected historically by the first electronic device or after detecting a user's action that triggers a high uric acid risk assessment.

[0268] In some embodiments, the physiological characteristic data may be collected by a first electronic device, or at least partially by other electronic devices (e.g., a second electronic device). For example, if the first electronic device is a watch 10, joint images and sleep audio may be collected by a mobile phone 20 (as a second electronic device); if the first electronic device is a mobile phone 20, at least some of the joint pain data, pulse wave, ECG signal, sleep audio, and vital signs may be collected by the watch 10 (as a second electronic device).

[0269] In some embodiments, the pulse wave data characteristics may include the pulse wave data characteristics of the user in the current assessment period, and / or the pulse wave data characteristics of the user in at least one assessment period prior to the current assessment period.

[0270] In some embodiments, the data features of sleep audio may include the data features of the user's sleep audio during the current assessment period, and / or the data features of the user's sleep audio during at least one assessment period prior to the current assessment period.

[0271] S1203, in response to the first high uric acid risk level being higher than the preset risk level, prompts the user to perform an enhanced assessment.

[0272] After obtaining the first high uric acid risk level, the first electronic device can prompt the user to conduct an enhanced assessment if the first high uric acid risk level is higher than the preset risk level.

[0273] In some embodiments, the preset risk level can be low risk. In this case, the first electronic device can prompt the user to perform an enhanced assessment when the first high uric acid risk level is high or medium risk. Alternatively, the preset risk level can be medium risk. In this case, the first electronic device can prompt the user to perform an enhanced assessment when the first high uric acid risk level is high risk. For example, referring to FIG13, the first electronic device (taking watch 10 as an example) can display the prompt message "High uric acid risk level is high, do you want to perform an enhanced assessment?" in the prompt box U41. The prompt box U41 may also include a cancel control and an enhanced assessment control. Watch 10 can perform an enhanced assessment in response to the user clicking the enhanced assessment control or cancel the enhanced assessment in response to the user clicking the cancel control, and proceed to execute S1207.

[0274] S1204, in response to user confirmation, performs enhanced evaluation and acquires joint data based on user operation.

[0275] Upon detecting that the user has confirmed the operation of enhancing the assessment, the first electronic device can guide the user to obtain joint data such as joint images and joint pain data. The method by which the first electronic device guides the user to obtain joint data such as joint images and joint pain data can be referred to the embodiments in Figures 6A, 6B, 9A, and 9B above, and will not be described in detail here.

[0276] For example, after detecting that the user clicks the enhancement assessment control in the interface shown in Figure 13, the first electronic device can determine that the user has confirmed the enhancement assessment.

[0277] It should be noted that in some other embodiments, the first electronic device may determine that the user has confirmed the enhancement assessment under other circumstances, which is not limited here. For example, the first electronic device may determine that the user has confirmed the enhancement assessment upon receiving a voice command, gesture, or other confirmation from the user.

[0278] S1205, based on physiological and joint data, uses a risk assessment model to determine the user's second-highest uric acid risk level.

[0279] After acquiring joint data, the first electronic device can determine the user's second high uric acid risk level based on the data characteristics of the joint data, the data characteristics of the physiological characteristics data, and user information through a risk assessment model. Since the input data used to generate the second high uric acid risk level is more extensive than the input data used to generate the first high uric acid risk level, the accuracy of the second high uric acid risk level output by the risk assessment model can be improved.

[0280] S1206 indicates the second highest uric acid risk level.

[0281] After obtaining the second high uric acid risk level, the first electronic device can display the second high uric acid risk level, or display both the first and second high uric acid risk levels, or display the higher risk level between the first and second high uric acid risk levels, or display a third high uric acid risk level derived from the first and second high uric acid risk levels. For details, please refer to the aforementioned S1105, which will not be elaborated upon here.

[0282] In some embodiments, the first electronic device may also store a first high uric acid risk level, or a second high uric acid risk level, or the aforementioned third high uric acid risk level.

[0283] In some embodiments, the first electronic device may also send a first high uric acid risk level, or a second high uric acid risk level, or the aforementioned third high uric acid risk level to other electronic devices.

[0284] S1207 indicates the highest risk level for high uric acid.

[0285] When the first high uric acid risk level is lower than or equal to the preset risk level, or when the user cancels the enhanced assessment, the first electronic device may display the first high uric acid risk level.

[0286] In some embodiments, the first electronic device may also send a first high uric acid risk level to other electronic devices.

[0287] Based on the above method, the first electronic device can respond to user operations and, based on various data related to uric acid levels in the blood, use a risk assessment model to determine the user's high uric acid risk level and provide prompts, thus achieving an accurate assessment of the user's high uric acid risk. When the initial high uric acid risk level is high (e.g., high or medium risk), the first electronic device can guide the user to collect more data for enhanced assessment; conversely, when the initial high uric acid risk level is low, the first electronic device can directly display the initial high uric acid risk level. Based on this method, users do not need to collect joint data every time they undergo an assessment, which improves the user experience.

[0288] The following describes the technical solution for the first electronic device to automatically assess the user's risk of high uric acid.

[0289] For example, Figure 14 illustrates a flowchart of a data processing method for automatically assessing a user's high uric acid risk level using a first electronic device, according to some embodiments of this application. As shown in Figure 14, the method includes:

[0290] S1401, the evaluation conditions have been met.

[0291] When the first electronic device detects that the evaluation conditions are met, it can trigger the data processing method provided in the embodiments of this application.

[0292] In some embodiments, the evaluation conditions may include at least one of the following conditions: the duration of wearing the wearable device during the daytime within an evaluation period is greater than or equal to a first duration threshold, and the duration of wearing it at night within the same evaluation period is greater than or equal to a second duration threshold; the duration of wearing the wearable device within an evaluation period is greater than a third duration threshold; a preset detection period is reached; it is detected that the duration of wearing the wearable device in a non-sleep state within an evaluation period is greater than or equal to a fourth duration, and the duration of wearing the wearable device in a sleep state within an evaluation period is greater than or equal to a fifth duration threshold. The wearable device may be a first electronic device or other electronic devices besides the first electronic device.

[0293] It should be noted that in other embodiments, the evaluation conditions may also be other conditions, which are not limited here.

[0294] S1402, based on physiological characteristic data, uses a risk assessment model to determine the user's first high uric acid risk level. The physiological characteristic data includes multiple data such as ECG signal, pulse wave, sleep audio, and vital signs.

[0295] If the assessment conditions are met, the first electronic device can input the data characteristics of physiological features into the risk assessment model, and use the risk assessment model to process the input data to obtain the user's first high uric acid risk level.

[0296] In some embodiments, the data input into the risk assessment model may also include user information.

[0297] In some embodiments, the ECG signal may be historically acquired by the first electronic device or acquired after the evaluation conditions are detected.

[0298] In some embodiments, the physiological characteristic data may be collected by a first electronic device or at least partially by other electronic devices, without limitation.

[0299] In some embodiments, the pulse wave data features may include the user's pulse wave data features during the current assessment period, and the sleep audio data features may include the user's sleep audio data features during the current assessment period.

[0300] S1403 indicates the highest risk level for high uric acid.

[0301] After obtaining the first high uric acid risk level, the first electronic device can record the first high uric acid risk level, for example, by storing it in the first electronic device or sending it to other electronic devices. For example, if the first electronic device is a watch 10, the watch 10 can store the first high uric acid risk level in the watch 10, or it can send the first high uric acid risk level to a mobile phone 20 or a server.

[0302] In some embodiments, after obtaining a first high uric acid risk level, the first electronic device may, in response to a user's operation to view the high uric acid risk level or a user control to turn on the screen of the first electronic device, display the first high uric acid risk level and corresponding prompt information. Optionally, the first electronic device may also directly display the first high uric acid risk level and corresponding prompt information after obtaining it.

[0303] In some embodiments, the first electronic device may also store a first high uric acid risk level or send the first high uric acid risk level to other electronic devices.

[0304] S1404, in response to the first high uric acid risk level being higher than the preset risk level, prompts the user to perform an enhanced assessment.

[0305] If the first high uric acid risk level is higher than the preset risk level (e.g., medium or low risk), the first electronic device may prompt the user to perform an enhanced assessment if the first high uric acid risk level is higher than the preset risk level (the method by which the first electronic device prompts the user can be referred to the embodiment in Figure 13, and will not be described in detail here). The first electronic device may execute S1405 to S1407 in response to the user confirming the enhanced assessment; or it may terminate the process in response to the user canceling the enhanced assessment (or no user operation is detected).

[0306] S1405, in response to user confirmation, performs enhanced evaluation and acquires joint data based on user operation.

[0307] S1406, based on physiological and joint data, uses a risk assessment model to determine the user's second-highest uric acid risk level.

[0308] S1407 indicates the second highest uric acid risk level.

[0309] It should be noted that S1405 to S1407 are the same as S1204 to S1206 mentioned above, and will not be repeated here.

[0310] Based on the above method, the first electronic device can automatically determine the user's high uric acid risk level and alert the user based on various data related to uric acid levels in the blood, using a risk assessment model, provided the assessment conditions are met. If the automatically determined high uric acid risk level is high (e.g., high or medium risk), the first electronic device can guide the user to collect more data for enhanced assessment.

[0311] Based on the foregoing embodiments, this application also provides a data processing method applied to a first electronic device. Exemplarily, FIG15 shows a flowchart of a data processing method according to some embodiments of this application. As shown in FIG15, the method includes:

[0312] S1501, acquire the user's physiological characteristic data within the first time period, including pulse wave, electrocardiogram signal, sleep audio, and multiple vital signs.

[0313] The first electronic device can acquire various physiological characteristic data of the user within a first time period, such as pulse wave, electrocardiogram signal, sleep audio, and vital signs. The method by which the first electronic device acquires physiological characteristic data can refer to the input data of the risk assessment model in Figure 1 above, and the method by which the watch 10 and mobile phone 20 collect the user's physiological characteristic data, which will not be elaborated here.

[0314] In some embodiments, the physiological characteristic data may be collected entirely by the first electronic device, or a portion may be collected by the first electronic device and another portion may be obtained by the first electronic device from other electronic devices (e.g., the second electronic device), or all of the physiological characteristic data may be obtained by the first electronic device from other electronic devices.

[0315] In some embodiments, the first electronic device can also acquire the user's joint data, such as joint images, joint pain data, etc. The method by which the first electronic device acquires the user's joint data can be referred to the contents of Figures 6A, 6B, 9A, 9B, S1102, S1104, S1204, and S1405 above, and will not be repeated here.

[0316] In some embodiments, the first time period may be an evaluation cycle, or it may include a portion of an evaluation cycle and at least a portion of at least one evaluation cycle preceding that evaluation cycle. For details, please refer to the aforementioned S1103, which will not be repeated here.

[0317] In some embodiments, the first electronic device may be the aforementioned watch 10 or mobile phone 20.

[0318] S1502, based on the acquired physiological characteristic data, uses a risk assessment model to assess the user's risk level of high uric acid in the first time period, and obtains the first assessment result.

[0319] For example, the first electronic device can process the acquired physiological characteristic data through a risk assessment model to obtain a first assessment result of the user's high uric acid risk level in a first time period.

[0320] In some embodiments, the first electronic device may also process the data characteristics of the acquired physiological characteristic data through a risk assessment model to obtain a first assessment result of the user's high uric acid risk level in a first time period.

[0321] In some embodiments, the first electronic device may also process the data characteristics (or the physiological characteristic data itself) and user information of the acquired physiological characteristic data through a risk assessment model to obtain a first assessment result of the user's high uric acid risk level in a first time period.

[0322] In some embodiments, the first electronic device may also process the data characteristics of the acquired physiological characteristic data (or the physiological characteristic data itself), the data characteristics of the user's joint data (or the joint data itself), and user information through a risk assessment model to obtain a first assessment result of the user's high uric acid risk level in a first time period.

[0323] In some embodiments, the first electronic device can assess the user's high uric acid risk level within a first time period using a risk assessment model when at least one of the following detection conditions is met: detecting that the user triggers an operation to assess the high uric acid risk level; reaching a preset detection cycle; detecting that the user wears the first electronic device for a duration greater than or equal to a first duration threshold during the daytime and for a duration greater than or equal to a second duration threshold during the nighttime in the second time period; detecting that the user wears the first electronic device for a duration greater than or equal to a third duration threshold during the second time period; detecting that the user wears the first electronic device for a duration greater than or equal to a fourth duration during the non-sleep state in the second time period and for a duration greater than or equal to a fifth duration threshold during the sleep state in the second time period. In other words, the first electronic device can automatically assess the user's high uric acid risk level when certain conditions are met (see the embodiment in Figure 14 above), or it can assess the user's high uric acid risk level in response to the user triggering an operation to assess the high uric acid risk level (see the embodiments in Figures 12 and 13 above).

[0324] In some embodiments, the second time period can be an evaluation cycle.

[0325] S1503 shows the first evaluation result.

[0326] After obtaining the initial assessment result, the first electronic device can display that result. For example, the first electronic device can display the first high uric acid risk level, along with corresponding prompts (such as dietary recommendations, exercise recommendations, and medical advice).

[0327] In some embodiments, if the first electronic device displays a first interface (e.g., the interface shown in Figure 13 above) prompting the user to perform an enhanced assessment when the first high uric acid risk level is greater than a preset risk level, and displays a first control (e.g., the aforementioned enhanced assessment control) for triggering the enhanced assessment in the first interface. In response to the user's selection of the first control, the first electronic device can acquire joint images and joint pain data of at least one of the user's joints, and based on the acquired joint data and the physiological characteristic data in S1501 above, process the data through a risk assessment model to obtain and display a second assessment result. Because more data is input into the risk assessment model, the accuracy of the first assessment result is higher. For details, please refer to the contents of S1104 and S1105 above, or the contents of S1203 to S1206 above, or the contents of S1404 to S1407 above, which will not be elaborated upon here.

[0328] In some embodiments, the second assessment result may include a second high uric acid risk level obtained by the first electronic device based on the acquired joint data and the physiological characteristic data in the aforementioned S1501 through a risk assessment model, or the higher risk level between the first high uric acid risk level and the second high uric acid risk level, or a third high uric acid risk level obtained based on the first high uric acid risk level and the second high uric acid risk level.

[0329] In some embodiments, the first electronic device may acquire joint images and / or joint pain data itself, or it may acquire joint images and / or joint pain data based on other electronic devices. For details, please refer to the aforementioned content regarding the acquisition of joint images and joint pain data by the watch 10 and mobile phone 20, which will not be repeated here.

[0330] In some embodiments, the first electronic device may also send the first evaluation result or the second evaluation result to other electronic devices, so that the other electronic devices can display the first evaluation result and the second evaluation result, and provide risk warnings to the user based on the first evaluation result and / or the second evaluation result.

[0331] Based on the above method, the first electronic device can process multiple physiological characteristic data (or the corresponding data features) of the user within a first time period using a pre-trained risk assessment model to obtain an assessment result of the user's high uric acid risk level. Since the input data of the risk assessment model includes multiple physiological characteristic data, all of which reflect the uric acid content in the user's blood, this helps improve the accuracy of the first assessment result. Furthermore, because the aforementioned physiological characteristic data can all be obtained non-invasively, it facilitates long-term and frequent non-invasive monitoring of the user's high uric acid levels.

[0332] Furthermore, Figure 16 shows a schematic diagram of the structure of a watch 10 according to some embodiments of this application.

[0333] As shown in Figure 16, the watch 10 may include a processor 101, a memory 102, an audio module 103, a display screen 104, a communication module 105, a camera 106, a power supply module 107, and a sensor module 108.

[0334] The processor 101 is used to execute instructions to realize the relevant functions of the watch 10, such as executing instructions to obtain physiological characteristic data, joint data, and user information, and executing instructions to obtain the user's high uric acid risk level through a risk assessment model based on physiological characteristic data, joint data, and user information.

[0335] In some embodiments, processor 101 may include one or more processing units. For example, processor 101 may include one or more of the following: central processing unit (CPU), modem processor, graphics processing unit (GPU), image signal processor (ISP), microcontroller unit (MCU), video codec, digital signal processor (DSP), baseband processor, neural network processing unit (NPU), and field-programmable gate array (FPGA). In some embodiments, different processing units may be independent devices or integrated into one or more processors.

[0336] The memory 102 is used to store data and instructions. For example, the memory 102 can be used to store instructions for the data processing methods involved in the embodiments of this application, such as instructions for storing risk assessment models; the memory 102 can also be used for data generated, collected or received during the operation of the watch 10, such as physiological characteristic data, joint data, vital signs, user information, data characteristics of physiological characteristic data, data characteristics of joint data, historical assessment results, etc.

[0337] The audio module 103 can be used to acquire or output audio. In some embodiments, the audio module 103 can be used to acquire audio while the user is sleeping, or to play alert audio.

[0338] In some embodiments, the audio module 103 may include a speaker, a receiver, a microphone, etc.

[0339] The display screen 104 is used to display images, videos, etc. The display screen 104 may include a display panel. In some embodiments, the display screen 104 may be used to display the user interface of the operating system or application of the watch 10 (such as the interface of the aforementioned innovative application), or it may be used to display the high uric acid risk level and corresponding prompts.

[0340] The communication module 105 may include wireless local area networks (WLAN) (such as Wi-Fi), Bluetooth (BT), global navigation satellite system (GNSS), near field communication (NFC), infrared (IR), near link (NL), and other wireless communication solutions. It may also include mobile communication solutions such as 2G / 3G / 4G / 5G / 6G to enable communication between the watch 10 and other electronic devices. For example, the communication module 105 can be used to receive joint images and joint pain data sent by other electronic devices, or to send physiological characteristic data, joint data, and high uric acid risk assessment results to other electronic devices.

[0341] Camera 106 is used to capture still images or videos. For example, camera 106 can be used to capture images of a user's joints.

[0342] The power supply module 107 is used to supply power to the processor 101, memory 102, audio module 103, display screen 104, communication module 105, camera 106, sensor module 108, etc.

[0343] The sensor module 108 may include an electrocardiogram sensor 1081, a blood pressure sensor 1082, a blood oxygen sensor 1083, a pulse wave sensor 1084, an acceleration sensor 1085, a skin conductance signal sensor 1086, etc.

[0344] The ECG sensor 1081 can be used to acquire signals from the fluctuations generated by the heart contraction to obtain physiological characteristics such as the user's electrocardiogram and heart rate.

[0345] The blood pressure sensor 1082 can be used to measure blood pressure. For example, the blood pressure sensor may include a micropump, an air bladder, and a pressure sensing unit. The micropump can inflate or deflate the air bladder; the air bladder is used to compress the user's blood vessels; and the pressure sensing unit is used to detect changes in air pressure. The watch 10 can measure the user's blood pressure based on the micropump, air bladder, and pressure sensing unit.

[0346] The 1083 blood oxygen sensor is used to detect a user's blood oxygen saturation.

[0347] The pulse wave sensor 1084 is used to collect a user's pulse wave. For example, the pulse wave sensor 1084 can be used to collect pulse waves through the fluctuations generated by vasoconstriction to obtain physiological characteristic data such as the user's pulse wave, heart rate, blood oxygenation, and respiratory rate. Optionally, the pulse wave sensor 1084 can be a PPG sensor.

[0348] The accelerometer 1085 can detect the magnitude of the acceleration of the watch 10 in various directions (generally three axes). The processor 101 can determine whether the user is stationary, whether the user is in motion, or whether the user is asleep based on the data collected by the accelerometer 1085. Optionally, the accelerometer 1085 can be an accelerometer, gyroscope, or other sensors.

[0349] The galvanic skin sensor 1086 is a sensor used to measure the galvanic skin response. The processor 101 can determine the degree of pain in the user's joints based on the data (pain data) collected by the galvanic skin sensor 1086.

[0350] It should be noted that the sensor module 108 may also include more or fewer sensors, such as temperature sensors, etc., which is not limited here.

[0351] In some embodiments, while the user is wearing the watch 10, the watch 10 can collect physiological characteristic data such as the user's pulse wave, ECG signal, sleep audio, and vital signs based on the sensor module 108. The watch 10 can store the collected physiological characteristic data itself, or it can process the physiological characteristic data to obtain and store the data features of the physiological characteristic data. The watch 10 can also upload the collected physiological characteristic data and / or the data features of the physiological characteristic data to the account associated with the watch 10 on the server.

[0352] It is understood that the structure of the watch 10 shown in the embodiments of this application does not constitute a specific limitation on the electronic device. In other embodiments of this application, the electronic device may include more or fewer components than shown, or combine some components, or split some components, or have different component arrangements. The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0353] Furthermore, Figure 17 shows a schematic diagram of the structure of a mobile phone 20 according to some embodiments of this application.

[0354] As shown in Figure 17, the mobile phone 20 may include a processor 210, an external memory interface 220, an internal memory 221, a universal serial bus (USB) interface 230, a charging management module 240, a power management module 241, a battery 242, an antenna 1, an antenna 2, a mobile communication module 250, a wireless communication module 260, an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone jack 270D, a sensor module 280, buttons 290, a motor 291, an indicator 292, a camera 293, a display screen 294, and a subscriber identification module (SIM) card interface 295, etc. The sensor module 280 may include a pressure sensor 280A, a gyroscope sensor 280B, a barometric pressure sensor 280C, a magnetic sensor 280D, an accelerometer sensor 280E, a distance sensor 280F, a proximity light sensor 280G, a fingerprint sensor 280H, a temperature sensor 280J, a touch sensor 280K, an ambient light sensor 280L, a bone conduction sensor 280M, etc.

[0355] Processor 210 may include one or more processing units, such as a CPU, modem processor, GPU, ISP, MCU, video codec, DSP, baseband processor, NPU, or FPGA. In some embodiments, different processing units may be independent devices or integrated into one or more processors.

[0356] In some embodiments, the processor 210 may be used to execute one or more programs / instructions corresponding to the data processing methods provided in the foregoing embodiments.

[0357] The charging management module 240 receives charging input from the charger. While charging the battery 242, the charging management module 240 can also supply power to the electronic device through the power management module 241.

[0358] The power management module 241 is used to connect the battery 242, the charging management module 240, and the processor 210. The power management module 241 receives input from the battery 242 and / or the charging management module 240 to power the processor 210, internal memory 221, display 294, camera 293, mobile communication module 250, and wireless communication module 260, etc.

[0359] The wireless communication function of mobile phone 20 can be realized through antenna 1, antenna 2, mobile communication module 250, wireless communication module 260, modem processor and baseband processor.

[0360] Antenna 1 and antenna 2 are used to transmit and receive electromagnetic wave signals.

[0361] The mobile communication module 250 can provide solutions for wireless communication applications including 2G / 3G / 4G / 5G / 6G on the mobile phone 20. The mobile communication module 250 may include at least one filter, switch, power amplifier, low noise amplifier (LNA), etc. The mobile communication module 250 can receive electromagnetic waves via antenna 1, and perform filtering, amplification, and other processing on the received electromagnetic waves before transmitting them to a modem processor for demodulation. The mobile communication module 250 can also amplify the signal modulated by the modem processor and convert it into electromagnetic waves for radiation via antenna 1. In some embodiments, at least some functional modules of the mobile communication module 250 may be housed in the processor 210. In some embodiments, at least some functional modules of the mobile communication module 250 and at least some modules of the processor 210 may be housed in the same device.

[0362] The wireless communication module 260 can provide solutions for wireless communication applications on the mobile phone 20, including WLAN, BT, GNSS, NFC, IR, NL, etc. The wireless communication module 260 can be one or more devices integrating at least one communication processing module. The wireless communication module 260 receives electromagnetic waves via antenna 2, performs frequency modulation and filtering of the electromagnetic wave signals, and sends the processed signal to processor 210. The wireless communication module 260 can also receive signals to be transmitted from processor 210, perform frequency modulation and amplification, and convert them into electromagnetic waves for radiation via antenna 2.

[0363] In some embodiments, the wireless communication module 260 can be used for communication with wearable devices (such as watch 10). For example, mobile phone 20 can obtain data such as the user's vital signs, pulse wave, ECG signal, sleep audio, joint pain data, joint images, etc. (or the data characteristics of the above data) from wearable devices or other devices based on the wireless communication module 260, or send the collected joint images to wearable devices through the wireless communication module 260.

[0364] The mobile phone 20 implements display functions through a GPU, a display screen 294, and an application processor. The GPU is a microprocessor for image processing, connected to the display screen 294 and the application processor. The GPU is used to perform mathematical and geometric calculations and for graphics rendering. The processor 210 may include one or more GPUs, which execute program instructions to generate or modify display information.

[0365] Display screen 294 is used to display images. For example, display screen 294 can be used to display an interface, such as a desktop interface, an always-on display interface, a lock screen interface, and other interfaces. In some embodiments, display screen 294 can be used to display the risk level of high uric acid and corresponding prompts.

[0366] Camera 293 is used to capture still images or videos. For example, camera 293 can be used to capture images of a user's joints.

[0367] The external memory interface 220 can be used to connect an external memory card. The external memory card communicates with the processor 210 through the external memory interface 220 to perform data storage functions. For example, application data can be stored on the external memory card.

[0368] Internal memory 221 can be used to store one or more programs and corresponding data. Internal memory 221 may include a program storage area and a data storage area. In some embodiments, the program storage area may store the operating system, at least one application required for a function, such as programs / instructions corresponding to the data processing methods provided in the foregoing embodiments, such as the instructions of the foregoing risk assessment model. The data storage area may store data created during the use of mobile phone 20, such as user physiological characteristic data, data features of physiological characteristic data, user joint data, and data features of user joint data. In addition, internal memory 221 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, general flash memory, etc. Processor 210 executes various functional applications of mobile phone 20 by running one or more programs stored in internal memory 221 and / or one or more programs stored in memory disposed in processor 210.

[0369] The mobile phone 20 can implement audio functions through an audio module 270, a speaker 270A, a receiver 270B, a microphone 270C, a headphone jack 270D, and an application processor. For example, the audio module 270 can be used to play alerts to inform the user of their high uric acid risk level.

[0370] Audio module 270 is used to convert digital audio information into analog audio signal output, and also to convert analog audio input into digital audio signal. Audio module 270 can also be used for encoding and decoding audio signals.

[0371] The speaker 270A, also known as a "loudspeaker," is used to convert audio electrical signals into sound signals. In some embodiments, the speaker 270A can be used to play cue audio.

[0372] The receiver 270B, also known as the "earpiece", is used to convert audio electrical signals into sound signals.

[0373] The microphone 270C, also known as a "microphone" or "voice transducer," is used to convert sound signals into electrical signals. For example, the microphone 270C can be used to capture sleep audio from a user's sleep.

[0374] The headphone jack 270D is used to connect wired headphones.

[0375] Pressure sensor 280A is used to sense pressure signals and convert them into electrical signals. In some embodiments, pressure sensor 280A may be disposed on display screen 294. There are many types of pressure sensors 280A, 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 pressure sensor 280A, the capacitance between the electrodes changes. Mobile phone 20 determines the pressure intensity based on the change in capacitance. When a touch operation is applied to display screen 294, mobile phone 20 detects the touch operation intensity based on pressure sensor 280A. Mobile phone 20 can also calculate the touch position based on the detection signal from pressure sensor 280A.

[0376] The barometric pressure sensor 280C can be used to detect the air pressure at the location of the mobile phone 20.

[0377] The accelerometer 280E can detect the magnitude of acceleration in various directions (typically three axes) of the mobile phone 20. When the mobile phone 20 is stationary, it can detect the magnitude and direction of gravity. The data collected by the accelerometer 280E can also be used to determine whether the user is stationary, whether the user is in motion, or whether the user is asleep.

[0378] The ambient light sensor 280L is used to detect ambient light levels. The phone 20 can adaptively adjust the brightness of its display 294 based on the detected ambient light. The ambient light sensor 280L can also be used to automatically adjust the white balance when taking photos. The ambient light sensor 280L can also work in conjunction with the proximity sensor 280G to detect whether the phone 20 is in a pocket, preventing accidental touches.

[0379] The fingerprint sensor 280H is used to collect fingerprints. The phone 20 can then use the collected fingerprints to unlock the device, access app locks, take photos with the fingerprint, and answer calls with the fingerprint.

[0380] Touch sensor 280K, also known as a "touch device," can be located on display screen 294. The touch sensor 280K and display screen 294 together form a touchscreen, also known as a "touchscreen." Touch sensor 280K detects touch operations applied to or near it. The touch sensor can transmit the detected touch operation to the application processor to determine the type of touch event. Visual output related to the touch operation can be provided through display screen 294. In other embodiments, touch sensor 280K may also be located on the surface of mobile phone 20, in a different position than display screen 294.

[0381] Buttons 290 include a power button, volume buttons, etc. Buttons 290 can be mechanical buttons or touch buttons. Mobile phone 20 can receive button input and generate key signal inputs related to user settings and function control of mobile phone 20.

[0382] Motor 291 can generate vibration alerts. Indicator 292 can be an indicator light, used to indicate charging status, battery level changes, or messages and notifications. In some embodiments, mobile phone 20 can alert the user by controlling the vibration of motor 291.

[0383] The SIM card interface 295 is used to connect a SIM card or eSIM.

[0384] It is understood that the structure of the mobile phone 20 shown in the embodiments of this application does not constitute a specific limitation on the mobile phone 20. In other embodiments of this application, the electronic device may include more or fewer components than shown, or combine some components, or split some components, or have different component arrangements. The components shown may be implemented in hardware, software, or a combination of software and hardware.

[0385] This application also provides a computer program product, which may be a software or program product including instructions, capable of running on a computing device or stored on any usable medium. When the computer program product is run on at least one computing device, it causes the at least one computing device to implement the data processing method provided in this application.

[0386] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any storage medium (e.g., magnetic medium, optical medium, semiconductor medium, etc.) capable of storing and / or retrieving data by a computing device. The computer-readable storage medium includes instructions that direct the computing device to implement the data processing method provided in this application.

[0387] It should be noted that in the embodiments of this application, "or" describes the relationship between related objects, indicating that there can be two relationships. For example, A or B can mean either A or B, where A or B can be singular or plural.

[0388] It should be noted that the term "connection" used in the embodiments of this application describes the connection relationship between two objects and can represent two kinds of connection relationships. For example, the connection between A and B can represent two situations: A is directly connected to B, and A is connected to B through C.

[0389] It should be noted that in the embodiments of this application, terms such as "for example," "in some embodiments," "in another embodiment," "in yet another embodiment," and "exemplarily" are used to indicate examples, illustrations, or descriptions. Any embodiment or design scheme described as an "example" in this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the term "example" is intended to present concepts in a concrete manner.

[0390] It should be noted that the terms "first" and "second" used in the embodiments of this application are only used for descriptive purposes and should not be construed as indicating or implying relative importance or order. The term "equal to" in the embodiments of this application can be used with "greater than" to apply to technical solutions used when "greater than," and can also be used with "less than" to apply to technical solutions used when "less than." It should be noted that when "equal to" is used with "greater than," it is not used with "less than," and vice versa.

[0391] In the accompanying drawings, some structural or methodological features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be necessary. Rather, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. Furthermore, the inclusion of structural or methodological features in a particular figure does not imply that such features are required in all embodiments, and in some embodiments, these features may be omitted or may be combined with other features.

[0392] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A data processing method applied to a first electronic device, characterized in that, The method includes: Acquire the user's physiological characteristic data within a first time period, wherein the physiological characteristic data includes multiple of the following: pulse wave, electrocardiogram signal, sleep audio, vital signs, and the vital signs include one or more of heart rate, blood pressure, and blood oxygen; Based on the physiological characteristic data, the user's high uric acid risk level during the first time period is assessed using a risk assessment model to obtain a first assessment result, which includes the user's first high uric acid risk level during the first time period. The results of the first evaluation are displayed.

2. The method according to claim 1, characterized in that, Based on the physiological characteristic data, the risk assessment model is used to assess the user's high uric acid risk level during the first time period, resulting in a first assessment result, including: Based on the data characteristics of the physiological characteristic data, the first assessment result is obtained through the risk assessment model; The data characteristics of the pulse wave include at least one of the following characteristics: the diastolic duration of the pulse wave, the systolic duration of the pulse wave, the energy of the pulse wave during the diastolic phase, and the energy of the pulse wave during the systolic phase. The data characteristics of the electrocardiogram (ECG) signal include at least one of the following characteristics: the duration of the ST segment of the ECG signal, the energy of the ECG signal in the ST segment, The data features of the sleep audio include at least one of the following features: the user's breathing rate during sleep, the frequency of the user's snoring during sleep, the volume of the user's breathing sounds during sleep, and the volume of the user's snoring sounds during sleep. The data characteristics of the vital signs include at least one of the following characteristics: the heart rate value, the blood pressure value, and the blood oxygen value.

3. The method according to claim 1, characterized in that, The display of the first evaluation result includes: Displays a prompt message corresponding to the first high uric acid risk level.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: If the first high uric acid risk level indicates that the user's high uric acid risk is greater than a preset risk level, obtain joint images of at least one joint of the user and / or pain data of at least one joint. Based on the joint images and / or the pain data, as well as the physiological characteristic data, a second assessment result is obtained through the risk assessment model. The second assessment result includes the user's second high uric acid risk level during the first time period. The second evaluation result is displayed.

5. The method according to any one of claims 1 to 3, characterized in that, The method further includes: When the first high uric acid risk level indicates that the user's high uric acid risk is greater than a preset risk level, a first interface is displayed. The first interface includes a first control, which is used to trigger an enhanced assessment of the user's high uric acid risk level.

6. The method according to claim 5, characterized in that, The method further includes: In response to the user's selection operation on the first control, obtain joint images of at least one joint of the user and / or pain data of at least one joint; Based on the joint images and / or the pain data, as well as the physiological characteristic data, the user's high uric acid risk level is enhanced by the risk assessment model to obtain and display a second assessment result; The second assessment result includes the second high uric acid risk level output by the risk assessment model, or the highest risk level among the first high uric acid level and the second high uric acid risk level, or the third high uric acid risk level obtained based on the first high uric acid risk level and the second high uric acid risk level.

7. The method according to any one of claims 4 to 6, characterized in that, The acquisition of joint images of at least one joint of the user and / or pain data of at least one joint includes: The image of the joint is obtained by photographing the at least one joint, or the image of the joint is obtained by photographing the at least one joint by a second electronic device; And / or, The pain data can be obtained by acquiring the skin conductance signal of at least one joint through the skin conductance sensor of the first electronic device, or by acquiring the pain data of at least one joint acquired by the second electronic device.

8. The method according to any one of claims 1 to 7, characterized in that, Based on the data characteristics of the physiological feature data, and through the risk assessment model, the first assessment result is obtained, including: Based on the data characteristics of the physiological characteristic data and the user information, the first assessment result is obtained through the risk assessment model, wherein the user information includes one or more of the following: gender, age, height, weight, waist circumference, and hip circumference.

9. The method according to any one of claims 1 to 8, characterized in that, The assessment of the user's high uric acid risk level during the first time period based on the physiological characteristic data and using a risk assessment model includes: The user's risk level of high uric acid during the first time period is assessed using a risk assessment model if at least one of the following detection conditions is met: The system detected that the user triggered an action that would assess the risk level of high uric acid. The preset detection cycle has been reached; The system detects that the user wears the first electronic device for a duration greater than or equal to a first duration threshold during the daytime in the second time period, and that the user wears the first electronic device for a duration greater than or equal to a second duration threshold at night during the second time period. The system detects that the duration for which the user wears the first electronic device during the second time period is greater than or equal to a third duration threshold. The system detects that the user wears the first electronic device for a duration greater than or equal to the fourth duration during the second time period while not asleep, and that the user wears the first electronic device for a duration greater than or equal to the fifth duration threshold during the second time period while asleep.

10. The method according to any one of claims 1 to 9, characterized in that, Based on the physiological characteristic data, the risk assessment model is used to assess the user's high uric acid risk level during the first time period, resulting in a first assessment result, including: Based on the physiological characteristic data and the user's joint data, the user's high uric acid risk level during the first time period is assessed using a risk assessment model to obtain the first assessment result. The joint data includes joint images of at least one joint of the user and / or pain data of the at least one joint.

11. The method according to any one of claims 1 to 9, characterized in that, The method further includes: The first evaluation result is sent to the second electronic device.

12. A readable storage medium, characterized in that, The readable storage medium includes one or more programs that, when executed on an electronic device, cause the electronic device to perform the data processing method according to any one of claims 1 to 11.

13. An electronic device, characterized in that, include: Memory, used to store one or more programs; A processor for executing the one or more programs to cause the electronic device to implement the data processing method of any one of claims 1 to 11.

14. A program product, characterized in that, When the program product is executed on an electronic device, it causes the electronic device to implement the data processing method according to any one of claims 1 to 11.

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