Data processing method and system

Abstract

This specification provides a data processing method and system, wherein the data processing method includes: receiving audio data of a user collected by a mobile terminal device through an audio acquisition component, and determining audio correction parameters based on the user's user data; determining audio energy intensity based on the audio data, and determining the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data; and calculating the user's lung function parameters based on the expiratory energy and the audio correction parameters.

Description

Technical Field

[0001] The embodiments in this specification relate to the field of data processing technology, and in particular to data processing methods and systems. Background Technology

[0002] In the field of pulmonary function assessment, traditional methods rely on specialized medical equipment (such as spirometers) for testing. While these methods offer high accuracy, they suffer from drawbacks such as high equipment cost, complex operation, and limited application scenarios, making them unsuitable for routine health monitoring or large-scale screening. In recent years, some studies have attempted to use mobile devices to collect users' exhalation or blowing audio, converting the audio into air pressure values, and then into vital capacity values ​​to analyze pulmonary function indicators. However, these methods generally ignore the influence of individual physiological differences on the acoustic characteristics of exhalation, resulting in significant biases in the test results. Therefore, a more effective data processing method is urgently needed to address these issues. Summary of the Invention

[0003] In view of the above, embodiments of this specification provide a data processing method. One or more embodiments of this specification also relate to a data processing system, a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, to address the technical deficiencies existing in the prior art.

[0004] According to a first aspect of the embodiments of this specification, a data processing method is provided, comprising: Receive audio data from a user collected by a mobile terminal device through an audio acquisition component, and determine audio correction parameters based on the user's data; The audio energy intensity is determined based on the audio data, and the user's exhalation energy is determined based on the audio energy intensity and the audio acquisition duration corresponding to the audio data. The user's lung function parameters are calculated based on the expiratory energy and the audio correction parameters.

[0005] Optionally, the receiving mobile terminal device collects the user's audio data through the audio acquisition component, including: Receive the motion start audio data of the user at the start time of motion, which is collected by the mobile terminal device through the audio acquisition component; Receive the motion termination audio data of the user at the motion termination time, which is collected by the mobile terminal device through the audio acquisition component; The motion start audio data and the motion end audio data are used as the audio data.

[0006] Optionally, after calculating the user's lung function parameters based on the expiratory energy and the audio correction parameters, the method further includes: Based on the lung function parameters, determine the exercise initiation lung function parameters corresponding to the exercise initiation audio data and the exercise termination lung function parameters corresponding to the exercise termination audio data; The exercise recovery parameters of the user are determined based on the exercise initiation lung function parameters and the exercise termination lung function parameters. The exercise recovery parameters are used to assess the user's lung function and to provide the user with exercise and expiratory advice information.

[0007] Optionally, determining the audio correction parameters based on the user's user data includes: Determine device factors, and determine user attribute factors of the user based on the user data; The audio correction parameters are calculated based on the device factor and / or the user attribute factor.

[0008] Optionally, determining the audio energy intensity based on the audio data includes: The audio acquisition duration and at least one audio sample value are determined based on the audio data; The audio energy intensity is calculated based on the energy intensity calculation rules, the audio acquisition duration, and the at least one audio sample value.

[0009] Optionally, determining the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data includes: Determine the audio acquisition duration corresponding to the audio data, and the time slice corresponding to the audio energy intensity; The user's expiratory energy is calculated based on the expiratory capacity calculation rules, the audio energy intensity, and the time slice.

[0010] Optionally, the receiving mobile terminal device collects the user's audio data through the audio acquisition component, including: The system receives initial audio data from a user collected by a mobile terminal device through an audio acquisition component, and removes noise from the initial audio data to obtain the audio data.

[0011] Optionally, before receiving the user's audio data collected by the mobile terminal device through the audio acquisition component, the method further includes: In response to the user's test command, a test preparation interface is displayed in the interface display component of the mobile terminal device. The test preparation interface includes test distance prompt information and test environment prompt information. In response to the test start command, the test preparation interface is updated to the test start interface.

[0012] According to a second aspect of the embodiments of this specification, a data processing system is provided, including a client and a server, comprising: The client is used to determine the user's audio data collected by the audio acquisition component of the mobile terminal device, and send the audio data to the server. The server is configured to determine audio correction parameters based on the user's user data; determine audio energy intensity based on the audio data, and determine the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data; calculate the user's lung function parameters based on the expiratory energy and the audio correction parameters, and send the lung function parameters to the client.

[0013] According to a third aspect of the embodiments of this specification, a data processing apparatus is provided, comprising: The receiving module is configured to receive audio data of a user collected by a mobile terminal device through an audio acquisition component, and to determine audio correction parameters based on the user's data. The determining module is configured to determine the audio energy intensity based on the audio data, and to determine the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data; The calculation module is configured to calculate the user's lung function parameters based on the expiratory energy and the audio correction parameters.

[0014] According to a fourth aspect of the embodiments of this specification, a computing device is provided, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0015] According to a fifth aspect of the embodiments of this specification, a computer-readable storage medium is provided that stores computer-executable instructions, which, when executed by a processor, implement the steps of the data processing method described above.

[0016] According to a sixth aspect of the embodiments of this specification, a computer program product is provided, including a computer program or instructions that, when executed by a processor, implement the steps of the data processing method described above.

[0017] This specification provides a data processing method in one embodiment that receives audio data from a user collected by a mobile terminal device through an audio acquisition component, and determines audio correction parameters based on the user's data. The method determines the audio energy intensity based on the audio data, and determines the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration. The method calculates the user's lung function parameters based on the expiratory energy and the audio correction parameters. During lung function testing, the audio acquisition component of the mobile terminal device is used to collect audio data, and combined with the user's data, the audio data is converted into the user's lung function parameters. This allows lung function testing to be performed not only using professional lung function testing equipment, but also using a mobile terminal device, increasing the difficulty of lung function testing. Combining user data with the conversion of audio data into lung function parameters improves the accuracy of lung function testing. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a data processing method provided in one embodiment of this specification; Figure 2 This is a schematic diagram of audio data acquisition for a data processing method provided in one embodiment of this specification; Figure 3 This is a flowchart illustrating the processing procedure of a data processing method provided in one embodiment of this specification. Figure 4 This is a schematic diagram of the structure of a data processing system provided in one embodiment of this specification; Figure 5 This is a schematic diagram of the structure of a data processing device provided in one embodiment of this specification; Figure 6 This is a structural block diagram of a computing device provided in one embodiment of this specification. Detailed Implementation

[0019] Many specific details are set forth in the following description to provide a full understanding of this specification. However, this specification can be implemented in many other ways than those described herein, and those skilled in the art can make similar extensions without departing from the spirit of this specification. Therefore, this specification is not limited to the specific implementations disclosed below.

[0020] The terminology used in one or more embodiments of this specification is for the purpose of describing particular embodiments only and is not intended to be limiting of the one or more embodiments of this specification. The singular forms “a,” “described,” and “the” as used in one or more embodiments of this specification and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used in one or more embodiments of this specification refers to and includes any or all possible combinations of one or more associated listed items.

[0021] It should be understood that although the terms first, second, etc., may be used to describe various information in one or more embodiments of this specification, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from one another. For example, first may also be referred to as second without departing from the scope of one or more embodiments of this specification, and similarly, second may also be referred to as first. Depending on the context, the word "if" as used herein may be interpreted as "when," "when," or "in response to a determination."

[0022] Furthermore, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0023] First, the terms and concepts used in one or more embodiments of this specification will be explained.

[0024] FVC (Forced Vital Capacity): Forced vital capacity refers to the total amount of air a subject can exhale with maximum effort and speed after inhaling to their total lung capacity (TLC) until all air is exhaled.

[0025] SVC (Slow Vital Capacity): Slow vital capacity (or resting vital capacity) refers to the total amount of air a subject can exhale slowly, steadily, and without restriction after inhaling to their total lung capacity (TLC) until all the air is exhaled.

[0026] Zero-crossing rate (ZCR) is a fundamental and important time-domain characteristic in digital signal processing (DSP) and audio analysis. It represents the number of times a signal waveform crosses the zero axis (from positive to negative or vice versa) per unit time (or per frame of signal).

[0027] This specification provides a data processing method, and also relates to a data processing system, a data processing apparatus, a computing device, a computer-readable storage medium, and a computer program product, which will be described in detail in the following embodiments.

[0028] See Figure 1 , Figure 1A flowchart of a data processing method according to an embodiment of this specification is shown, which specifically includes the following steps.

[0029] Step 102: Receive the user's audio data collected by the mobile terminal device through the audio acquisition component, and determine the audio correction parameters based on the user's data.

[0030] Specifically, the mobile terminal device can be a user-held mobile terminal, including but not limited to mobile phones, tablets, smartwatches, and other terminal devices containing audio acquisition components. The audio acquisition component can be a microphone or other component with audio acquisition capabilities installed or connected to the mobile terminal device. The audio data can be audio generated by the user through a target action, which can be a blowing action performed at a preset distance from the audio acquisition component. The user's data represents the user's attributes, including but not limited to attributes that affect lung function, such as gender, height, and age. Audio correction parameters are used to correct predicted lung function parameters when testing the user's lung function.

[0031] Therefore, in lung function testing scenarios, a user's lung function parameters can be determined by analyzing the audio data collected by the audio acquisition component of a mobile terminal device. The process involves receiving the user's audio data collected by the mobile terminal device through the audio acquisition component. Audio correction parameters are then determined based on the user's data. This allows for subsequent analysis of the user's lung function based on the audio correction parameters and the audio data, yielding the lung function parameters corresponding to the user's specific lung function test. The audio correction parameters are used to accommodate users with different user attributes, ensuring the accuracy of the lung function test.

[0032] Furthermore, considering the differences in user attributes among different users and the device differences among different mobile terminal devices, user attribute factors and device factors can be determined separately for these differences. Audio correction parameters are then calculated based on these user attribute factors and device factors, as specifically implemented below: Determine the device factor and the user attribute factor of the user based on the user data; calculate the audio correction parameter based on the device factor and / or the user attribute factor.

[0033] Specifically, device factors can be device-related parameters determined based on differences in recording data between different devices. User attribute factors represent the difference parameters corresponding to individual attribute differences among different users. User attribute factors include, but are not limited to, factors such as height, gender, and age that affect lung capacity values.

[0034] Based on this, device factors are determined, and user attribute factors such as height, gender, and age are determined based on user data. Audio correction parameters are calculated based on device factors and / or user attribute factors, which can be determined by multiplying the device factors and user attribute factors.

[0035] For example, considering the potential differences in recording data between different mobile devices, a device factor can be set. Different users exhibit individual differences such as gender and age, so user attribute factors such as height, gender, and age can be set. The height factor can be represented as a numerical height, and the gender correction factor could be 1.0 for males and 0.85 for females. The audio correction parameter can be determined by multiplying the user attribute factor and the device factor; this audio correction parameter is the calibration coefficient K.

[0036] In summary, by calculating audio correction parameters based on device factors and / or user attribute factors, and by taking device factors and user attribute factors as influencing the calculation of user vital capacity parameters, the accuracy of subsequent calculations of vital capacity parameters can be improved.

[0037] Furthermore, considering that the initial audio data collected from users using mobile terminal devices is collected in a natural environment, the initial audio data inevitably contains environmental noise. Before directly processing the initial audio data, it is necessary to remove noise to obtain usable audio data. The specific implementation is as follows: The system receives initial audio data from a user collected by a mobile terminal device through an audio acquisition component, and removes noise from the initial audio data to obtain the audio data.

[0038] Based on this, the system receives initial audio data from the user collected by the mobile terminal device through its audio acquisition component. This initial audio data is the audio generated by the user blowing air onto the mobile terminal device. The user's blowing operation must meet the specifications for lung capacity testing, i.e., exhaling all air after a maximum inhalation. The blowing operation may include: posture preparation (standing or sitting upright with a straight back and feet naturally shoulder-width apart); deep inhalation (taking 1-2 normal breaths to adjust the rhythm, then inhaling deeply through both mouth and nose simultaneously, or inhaling deeply through only the mouth); maintaining a consistent distance between the mouth and the audio acquisition point on the mobile terminal device; and even exhalation (blowing at a moderate speed and evenly). The process stops when the displayed value on the mobile terminal device stops increasing or all air is exhaled. Noise removal is then performed on the initial audio data to obtain the final audio data. Methods for noise removal include, but are not limited to, frequency band filtering, noise spectrum estimation of silent segments, and the use of noise reduction models to process the initial audio data and filter out environmental noise.

[0039] In practical applications, the spectrum of noise (such as human speech or clapping) overlaps with that of blowing air, and simple filtering cannot remove it; logical judgment is required. A dual decision-making process using zero-crossing rate (ZCR) and energy is employed: Blowing air is typical turbulent noise, characterized by high energy and a high ZCR (waveform densely crossing the zero axis). If the energy is high but the ZCR is low, it may be a low-frequency boom or a speech vowel, and should be discarded. If the energy is low but the ZCR is high, it may be a high-frequency hiss, and should also be discarded. Only when both the energy and ZCR are high is it considered valid blowing air, and the frame is retained; otherwise, the energy of the frame is set to zero or significantly attenuated. Spectral centroid analysis: The spectral centroid of blowing air is usually stable within a specific range. If the centroid suddenly and drastically drifts (such as someone screaming), it can be identified as interference, and the segment should be discarded.

[0040] In summary, noise removal is performed on the initial audio data to obtain the final audio data, thereby removing environmental noise from the initial audio data and ensuring the audio quality of the final audio data.

[0041] Furthermore, considering that the audio quality of the audio data is related to the distance between the user and the mobile terminal device, as well as the user's environment, the user can be visually guided to prepare for the audio data collection test before the audio data is collected, in order to ensure the quality of the collected audio data. The specific implementation is as follows: In response to the user's test command, a test preparation interface is displayed in the interface display component of the mobile terminal device. The test preparation interface includes test distance prompts and test environment prompts. In response to the test start command, the test preparation interface is updated to the test start interface.

[0042] Specifically, the mobile terminal device includes a visual interface display component, such as a display screen. The test preparation interface can be the audio capture interface of a lung function testing application installed on the mobile terminal device. Alternatively, it can be the audio capture interface of a lung function testing webpage accessible through the mobile terminal device. Test distance and test environment prompts can be displayed on the test preparation interface, and these prompts can be played via audio playback. The test distance prompts remind the user to maintain a certain distance between their head and the mobile terminal device during audio capture. The test environment prompts remind the user to conduct audio capture in a quiet environment, or provide a timer to indicate the start of audio capture.

[0043] Based on this, in response to the user's test command, a test preparation interface is displayed on the interface display component of the mobile terminal device. This interface is shown to the user. The test preparation interface includes test distance and test environment prompts. The test distance prompts remind the user to maintain a certain distance between their head and the mobile terminal device during audio capture. The test environment prompts the user to conduct audio capture in a quiet environment, or provides a timer to indicate the start and duration of audio capture. In response to the test start command, the test preparation interface is updated to the test start interface, and the test begins, initiating audio capture for the user.

[0044] Continuing with the previous example, such as Figure 2 As shown in (a), this is the test preparation interface. The interface will prompt the user, through text or illustrations, to maintain a distance of 5-10 cm from the mobile terminal device (e.g., a mobile phone). It can also provide voice prompts to maintain an appropriate distance. The interface can prompt the user to check for ambient noise and can automatically detect ambient noise. If the ambient noise meets the audio acquisition requirements, the interface will prompt the user to begin audio acquisition. The user can submit the test start command and begin audio acquisition by triggering the "Start Test" control. Figure 2 As shown in (b), the lung capacity value (corresponding to the green cylinder) will continue to increase as the user continues to blow air until the blowing stops.

[0045] In summary, the test preparation interface includes test distance and test environment prompts to provide users with information on the test distance and environment before audio acquisition, assisting users in audio acquisition and ensuring the quality of the acquired audio data.

[0046] Step 104: Determine the audio energy intensity based on the audio data, and determine the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data.

[0047] Specifically, after receiving the user's audio data from the mobile terminal device via the audio acquisition component, and determining the audio correction parameters based on the user's data, the audio energy intensity can be determined based on the audio data. Furthermore, the user's expiratory energy can be determined based on the audio energy intensity and the corresponding audio acquisition duration. Audio energy intensity represents the average intensity of sound energy over a period of time, reflecting the average level of energy carried by the sound wave over that period. Sound is a mechanical wave, and its energy is proportional to the square of its amplitude. The higher the energy intensity, the louder the loudness perceived by the human ear is usually. The audio acquisition duration represents the duration of the audio data. The user's expiratory energy represents the total expiratory energy of the user corresponding to the audio data, which can be determined by time integration of the audio energy intensity over the time period corresponding to the audio acquisition duration.

[0048] Based on this, after receiving the user's audio data from the mobile terminal device via the audio acquisition component, and determining the audio correction parameters based on the user's data, the audio energy intensity is determined based on the audio data. Audio energy intensity represents the average intensity of sound energy over a period of time, reflecting the average level of energy carried by sound waves over that period. The user's expiratory energy is then determined based on the audio energy intensity and the corresponding audio acquisition duration, converting the user's audio data into their expiratory energy.

[0049] Furthermore, considering that audio data has a certain duration, and that different moments in the audio data may correspond to different energy intensities, it is necessary to calculate the audio energy intensity of the audio data based on the audio acquisition duration, and to determine the average intensity of the user's voice energy within this audio acquisition period. The specific implementation is as follows: The audio acquisition duration and at least one audio sample value are determined based on the audio data; the audio energy intensity is calculated according to the energy intensity calculation rules, the audio acquisition duration, and the at least one audio sample value.

[0050] Specifically, the audio sample value can be the audio value corresponding to the audio sample point determined after determining the audio sampling point based on the audio acquisition duration, and the value range can be (-1, 1). The energy intensity calculation rule is used to calculate the average intensity of the sound energy corresponding to the audio data, that is, the audio energy intensity.

[0051] Based on this, the audio acquisition duration and the corresponding audio sample values ​​at at least one audio sampling moment within the audio acquisition duration are determined using audio data. The audio energy intensity is then calculated according to the energy intensity calculation rules, the audio acquisition duration, and at least one audio sample value. Audio energy intensity represents the average intensity of sound energy within the audio acquisition duration, that is, the average level of energy carried by the sound wave.

[0052] Following the previous example, audio energy intensity is represented by RMS. After determining the audio acquisition duration of 10 seconds corresponding to the audio data, at least one audio acquisition moment within the audio acquisition duration can be determined. The audio acquisition moment can be determined at 1-second intervals, that is, the audio sample value corresponding to 1 second, the audio sample value corresponding to 2 seconds, and the audio sample value corresponding to each second are determined. Then, the average value is calculated to obtain the audio energy intensity. The energy intensity calculation rule can be expressed as the following formula (1): RMS=sqrt(sample 2 ) / N(1) Where sample represents the audio sample value corresponding to the audio acquisition time, and the value range can be (-1, 1); N represents the number of audio acquisition times, that is, the number of sampling points within the audio acquisition time range.

[0053] In summary, based on the energy intensity calculation rules, audio acquisition duration, and at least one audio sample value, the audio energy intensity is calculated, the average level of sound wave energy carried within the audio acquisition duration is determined, and the average intensity of sound energy in the audio data is calculated with low computational cost.

[0054] Furthermore, considering that a user's expiratory energy can be correlated with the audio energy intensity of their audio data, the user's expiratory energy can be determined by calculating the audio energy intensity. The specific implementation is as follows: Determine the audio acquisition duration corresponding to the audio data, and the time slice corresponding to the audio energy intensity; calculate the user's expiratory energy based on the expiratory capacity calculation rules, the audio energy intensity, and the time slice.

[0055] Based on this, the audio acquisition duration corresponding to the audio data and the time slice corresponding to the audio energy intensity are determined. The user's expiratory energy is calculated based on the expiratory capacity calculation rules, audio energy intensity, and time slice. The expiratory capacity calculation rules can be integral calculation rules, that is, the expiratory intensity (audio energy intensity) at a certain moment is integrally calculated.

[0056] Following the example above, expiratory energy is represented by S, and expiratory energy can be calculated using the following formula (2): S=∫RMS(t)dt(2) Where RMS(t) represents the expiratory intensity at a certain moment; dt represents a very small time slice. By integrating, the total expiratory energy corresponding to the user's audio data can be obtained, that is, the user's expiratory energy.

[0057] In summary, based on expiratory capacity calculation rules, audio energy intensity, and time slices, a user's expiratory energy can be calculated. This allows the abstract concept of "sound" to be quantified into concrete "user expiratory energy," thereby enabling respiratory-based health monitoring and reducing reliance on specialized equipment.

[0058] Step 106: Calculate the user's lung function parameters based on the expiratory energy and the audio correction parameters.

[0059] Specifically, after determining the audio energy intensity based on the audio data and determining the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration, the user's lung function parameters can be calculated based on the expiratory energy and audio correction parameters. These lung function parameters represent the user's lung function test results and can include vital capacity values. Lung function parameters include, but are not limited to, forced vital capacity and slow vital capacity.

[0060] Based on this, after determining the audio energy intensity based on the audio data and determining the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration, the user's lung function parameters are calculated based on the expiratory energy and audio correction parameters to obtain the user's vital capacity value for this lung function test, thus realizing the conversion of the user's audio data into the user's vital capacity value.

[0061] In practice, after determining the audio correction parameter, i.e., the calibration coefficient K, and the expiratory energy S, the lung function parameter VC can be determined by calculating the product of K and S, i.e., lung function parameter VC = K × S.

[0062] In practical applications, a user's lung function parameters can reflect the user's lung function status. Users can continuously perform lung function tests using mobile terminal devices at fixed intervals, continuously recording their lung function parameters. The stability of the user's lung function can be analyzed by analyzing the continuously recorded lung function parameters.

[0063] Furthermore, considering that user-provided audio data can be affected by the user's own state, when collecting user audio data, the user's exercise start audio data can be collected before the user exercises, and the exercise end audio data can be collected after the user exercises. Using the exercise start audio data and exercise end audio data as audio data, the fluctuation of the user's lung function parameters can be determined by calculating the lung function parameters corresponding to the exercise start audio data and exercise end audio data respectively. The specific implementation is as follows: The system receives motion start audio data collected by the mobile terminal device through the audio acquisition component at the start time of the motion; it also receives motion end audio data collected by the mobile terminal device through the audio acquisition component at the end time of the motion; and uses the motion start audio data and the motion end audio data as the audio data.

[0064] Specifically, the start time of exercise can be any moment before the user begins exercising, provided their breathing is stable. The exercise start audio data is the audio data collected by the user using the mobile device at the start time of exercise. The exercise end time can be the moment the user completes the exercise, i.e., the moment the exercise ends. The exercise end audio data is the audio data collected by the user using the mobile device after the exercise ends.

[0065] Based on this, the system receives the user's exercise start audio data collected by the mobile terminal device via the audio acquisition component at the start time of exercise, and also receives the user's exercise termination audio data collected by the mobile terminal device via the audio acquisition component at the end time of exercise. The exercise start audio data and exercise termination audio data are used as audio data. After collecting the user's exercise start audio data, the corresponding exercise start lung function parameters can be calculated to determine the user's lung function status before exercise. After collecting the user's exercise termination audio data, the corresponding exercise termination lung function parameters can be calculated to determine the user's lung function status after exercise. By comparing the exercise start audio data and exercise termination audio data, a respiratory recovery index can be generated for dynamically assessing changes in the user's cardiopulmonary function.

[0066] Using the previous example, the user maintains a steady breathing state before exercise. At this time, the user opens the lung function test application, triggers the test control on the lung function test page of the application, and blows continuously into the phone microphone according to the test prompts on the lung function test page. After completing a standard lung capacity test blowing operation, the user obtains the exercise start audio data.

[0067] In summary, by collecting audio data at the start and end of exercise, we can obtain audio data before and after a user's exercise. By analyzing this audio data, we can determine the fluctuations in the user's lung function parameters, and subsequently, we can develop a reasonable exercise plan for the user based on these fluctuations.

[0068] Furthermore, after determining the audio data at the start and end of exercise, the user's exercise recovery can be analyzed by converting the audio data at the start and end of exercise. Specifically, the user's exercise recovery can be analyzed based on these parameters. Based on the lung function parameters, determine the exercise initiation lung function parameters corresponding to the exercise initiation audio data and the exercise termination lung function parameters corresponding to the exercise termination audio data; based on the exercise initiation lung function parameters and the exercise termination lung function parameters, determine the user's exercise recovery parameters, which are used to assess the user's lung function and provide the user with exercise and breathing suggestions.

[0069] Specifically, the exercise start time can include at least two exercise start times with a fixed time interval. For example, if the exercise start time is 10:00 and the fixed time interval is 5 minutes, then the exercise start time can include start times such as 10:00, 10:55, and 10:50. Correspondingly, the exercise start audio data includes sub-data corresponding to each exercise start time, and the exercise start pulmonary function parameters include sub-parameters corresponding to each exercise start time. The exercise end time can also include at least two exercise end times with a fixed time interval. For example, if the exercise end time is 11:00 and the fixed time interval is 5 minutes, then the exercise end time can include end times such as 11:00, 11:05, and 11:10. Correspondingly, the exercise end audio data includes sub-data corresponding to each exercise end time, and the exercise end pulmonary function parameters include sub-parameters corresponding to each exercise end time. The user's exercise recovery parameters can be determined by calculating the difference between the exercise start pulmonary function parameters and the exercise end pulmonary function parameters. The user's exercise recovery parameters can include lung function parameters corresponding to each start and end point of exercise. Lung function parameter curves can be plotted in chronological order based on each lung function parameter to visually represent the user's lung function and respiratory recovery after exercise.

[0070] Based on this, the starting lung function parameters corresponding to the exercise initiation audio data and the ending lung function parameters corresponding to the exercise termination audio data are determined according to lung function parameters. The user's exercise recovery parameters are then determined based on these parameters. These recovery parameters are used to assess the user's lung function and provide exercise and expiratory advice. The exercise start time can include at least two start times, and the exercise termination time can include at least two termination times. Correspondingly, a set of exercise audio data is collected for each start and termination time, and the audio data corresponding to each time point is used as the audio data. The starting and ending audio data collected at each start and termination time are transformed to obtain the corresponding lung function parameters, resulting in a set of lung function parameter data arranged chronologically according to exercise time. An exercise recovery curve is plotted based on this set of lung function parameter data arranged chronologically according to exercise time. This provides a clear understanding of the user's respiratory recovery after exercise and their level of fatigue, facilitating better subsequent exercise planning.

[0071] Following the previous example, after determining the user's initial exercise audio data, the server of the lung function testing application can begin analyzing the initial audio data, converting it into the user's lung function parameters, i.e., vital capacity, and recording the data. After the user completes the exercise (such as push-ups), the user triggers the test control on the lung function testing page of the application to complete the collection and submission of the exercise termination audio data. The collected initial and termination audio data are used as audio data. Based on the set of initial and termination lung function parameters corresponding to the initial and termination audio data, a lung function parameter curve is plotted in chronological order. This curve allows analysis of the impact of the user's exercise on lung function. By comparing the initial and termination lung function parameters, a respiratory recovery index is generated to dynamically assess changes in the user's cardiopulmonary function. A respiratory recovery index of 0.8 indicates good respiratory recovery, supporting abdominal training three times a week. If the respiratory rate drops from 20 breaths / min to 15 breaths / min within 10 minutes after training, the lung function parameter curve is smooth, indicating no obvious respiratory delay. This suggests that the fatigue after abdominal training is low and the continuous training rhythm can be maintained.

[0072] In summary, by determining the user's exercise recovery parameters based on the lung function parameters at the start and end of exercise, the user's lung function can be assessed, and exercise and expiratory advice can be provided to ensure that the user adopts a scientific breathing method during exercise and better completes the exercise plan.

[0073] This specification provides a data processing method in one embodiment that receives audio data from a user collected by a mobile terminal device through an audio acquisition component, and determines audio correction parameters based on the user's data. The method determines the audio energy intensity based on the audio data, and determines the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration. The method calculates the user's lung function parameters based on the expiratory energy and the audio correction parameters. During lung function testing, the audio acquisition component of the mobile terminal device is used to collect audio data, and combined with the user's data, the audio data is converted into the user's lung function parameters. This allows lung function testing to be performed not only using professional lung function testing equipment, but also using a mobile terminal device, increasing the difficulty of lung function testing. Combining user data with the conversion of audio data into lung function parameters improves the accuracy of lung function testing.

[0074] The following is in conjunction with the appendix Figure 3 Taking the application of the data processing method provided in this specification in the lung capacity test as an example, the data processing method will be further explained. Figure 3 A flowchart illustrating the processing procedure of a data processing method according to an embodiment of this specification is shown, specifically including the following steps.

[0075] Step 302: In response to the user's test command, display the test preparation interface in the interface display component of the mobile terminal device. The test preparation interface includes test distance prompts and test environment prompts.

[0076] In practical applications, mobile terminal devices can be devices such as mobile phones, tablets, and smartwatches with audio capture capabilities. The interface display component refers to the screen of the mobile terminal device. The test preparation interface can be a page displaying test instructions to the user, or it can be a page from a lung function testing application or webpage. When a lung function test is needed, the user can submit a test start command by triggering the test controls on the test preparation interface.

[0077] Step 304: In response to the test start command, update the test preparation interface to the test start interface, and receive the user's audio data collected by the mobile terminal device through the audio acquisition component.

[0078] After the test begins, the user blows air according to the prescribed actions, and the mobile terminal device can collect the audio data corresponding to the user's blowing process.

[0079] Step 306: Determine the device factor and the user attribute factor based on the user data, and calculate the audio correction parameters based on the device factor and the user attribute factor.

[0080] In practical applications, user attribute factors include, but are not limited to, factors representing individual user attributes such as age, height, and gender. Device factors are set to minimize the impact of differences in recorded data from different devices on the lung capacity test results.

[0081] Step 308: Determine the audio acquisition duration and at least one audio sample value based on the audio data, and calculate the audio energy intensity based on the audio acquisition duration and at least one audio sample value.

[0082] Audio energy intensity can represent the average intensity of sound energy within the acquisition time range corresponding to the audio data.

[0083] Step 310: Determine the audio acquisition duration corresponding to the audio data and the time slice corresponding to the audio energy intensity, and calculate the user's expiratory energy based on the audio energy intensity and the time slice.

[0084] Step 312: Calculate the user's lung function parameters based on expiratory energy and audio correction parameters.

[0085] In practical applications, a user's lung function parameters, i.e., the user's vital capacity, can be determined by multiplying expiratory energy and audio correction parameters.

[0086] Furthermore, based on lung function parameters, the starting lung function parameters corresponding to the exercise audio data and the ending lung function parameters corresponding to the exercise audio data are determined. Based on these starting and ending lung function parameters, the user's exercise recovery parameters are determined. These recovery parameters are used to assess the user's lung function and provide exercise and expiratory advice. Preset time intervals can also be set, and audio data from the user can be collected again at 5 minutes, 10 minutes, etc., after exercise, and converted into vital capacity values ​​to plot an exercise recovery curve. Based on the recovery curve, fatigue can be assessed, and an exercise plan can be developed for the user.

[0087] In summary, the data processing method provided in this embodiment allows users to perform lung capacity tests using their mobile phones. By collecting audio data corresponding to a single lung capacity test, and based on the principle that lung capacity is approximately equal to expiratory flow rate multiplied by expiratory time, the audio data is converted into a lung capacity value. This method can accurately measure a user's lung capacity value without requiring specialized lung capacity testing equipment. Lung capacity values ​​can be measured using mobile terminals such as mobile phones, improving the simplicity and flexibility of lung capacity testing and making it no longer limited to professional equipment. Furthermore, it can perform pre- and post-exercise comparison tests of lung capacity, generating a "respiratory recovery index" to dynamically assess changes in cardiopulmonary function.

[0088] This specification provides a data processing method in one embodiment that receives audio data from a user collected by a mobile terminal device through an audio acquisition component, and determines audio correction parameters based on the user's data. The method determines the audio energy intensity based on the audio data, and determines the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration. The method calculates the user's lung function parameters based on the expiratory energy and the audio correction parameters. During lung function testing, the audio acquisition component of the mobile terminal device is used to collect audio data, and combined with the user's data, the audio data is converted into the user's lung function parameters. This allows lung function testing to be performed not only using professional lung function testing equipment, but also using a mobile terminal device, increasing the difficulty of lung function testing. Combining user data with the conversion of audio data into lung function parameters improves the accuracy of lung function testing.

[0089] Figure 4 A schematic diagram of a data processing system according to an embodiment of this specification is shown. The data processing system 400 includes a client 410 and a server 420. The client 410 is configured to determine audio data of a user collected by an audio acquisition component of a mobile terminal device and send the audio data to the server 420. The server 420 is configured to determine audio correction parameters based on the user's user data; determine audio energy intensity based on the audio data; determine the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data; calculate the user's lung function parameters based on the expiratory energy and the audio correction parameters, and send the lung function parameters to the client 410.

[0090] This specification provides a data processing system according to one embodiment, including a client and a server. The client extends to a mobile terminal device, and the server refers to a server that provides computing and logical processing capabilities to the mobile terminal device. The server can be a local server or a cloud server. The client determines the user's audio data collected by the audio acquisition component of the mobile terminal device and sends the audio data to the server. The server determines the user's audio data and determines audio correction parameters based on the user's data. It determines the audio energy intensity based on the audio data and determines the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration. It calculates the user's lung function parameters based on the expiratory energy and the audio correction parameters. During lung function testing, the audio acquisition component of the mobile terminal device is used to collect audio data, and combined with the user's data, the audio data is converted into the user's lung function parameters. This allows the user's lung function testing to be performed not only using professional lung function testing equipment, but also using a mobile terminal device, increasing the difficulty of lung function testing. Combining user data with the conversion of audio data into lung function parameters improves the accuracy of lung function testing.

[0091] Corresponding to the above method embodiments, this specification also provides data processing apparatus embodiments. Figure 5 A schematic diagram of the structure of a data processing apparatus according to one embodiment of this specification is shown. Figure 5 As shown, the device includes: The receiving module 502 is configured to receive audio data of a user collected by a mobile terminal device through an audio acquisition component, and determine audio correction parameters based on the user's data. The determining module 504 is configured to determine the audio energy intensity based on the audio data, and to determine the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data; The calculation module 506 is configured to calculate the user's lung function parameters based on the expiratory energy and the audio correction parameters.

[0092] In one optional embodiment, the receiving of user audio data collected by the mobile terminal device through the audio acquisition component includes: Receive the motion start audio data of the user at the start time of motion, which is collected by the mobile terminal device through the audio acquisition component; Receive the motion termination audio data of the user at the motion termination time, which is collected by the mobile terminal device through the audio acquisition component; The motion start audio data and the motion end audio data are used as the audio data.

[0093] In an optional embodiment, after calculating the user's lung function parameters based on the expiratory energy and the audio correction parameters, the method further includes: Based on the lung function parameters, determine the exercise initiation lung function parameters corresponding to the exercise initiation audio data and the exercise termination lung function parameters corresponding to the exercise termination audio data; The exercise recovery parameters of the user are determined based on the exercise initiation lung function parameters and the exercise termination lung function parameters. The exercise recovery parameters are used to assess the user's lung function and to provide the user with exercise and expiratory advice information.

[0094] In an optional embodiment, determining the audio correction parameters based on the user's user data includes: Determine device factors, and determine user attribute factors of the user based on the user data; The audio correction parameters are calculated based on the device factor and / or the user attribute factor.

[0095] In an optional embodiment, determining the audio energy intensity based on the audio data includes: The audio acquisition duration and at least one audio sample value are determined based on the audio data; The audio energy intensity is calculated based on the energy intensity calculation rules, the audio acquisition duration, and the at least one audio sample value.

[0096] In an optional embodiment, determining the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data includes: Determine the audio acquisition duration corresponding to the audio data, and the time slice corresponding to the audio energy intensity; The user's expiratory energy is calculated based on the expiratory capacity calculation rules, the audio energy intensity, and the time slice.

[0097] In one optional embodiment, the receiving of user audio data collected by the mobile terminal device through the audio acquisition component includes: The system receives initial audio data from a user collected by a mobile terminal device through an audio acquisition component, and removes noise from the initial audio data to obtain the audio data.

[0098] In an optional embodiment, before receiving the user's audio data collected by the mobile terminal device through the audio acquisition component, the method further includes: In response to the user's test command, a test preparation interface is displayed in the interface display component of the mobile terminal device. The test preparation interface includes test distance prompt information and test environment prompt information. In response to the test start command, the test preparation interface is updated to the test start interface.

[0099] This specification provides a data processing apparatus in one embodiment that receives audio data from a user collected by a mobile terminal device through an audio acquisition component, and determines audio correction parameters based on the user's data. It determines the audio energy intensity based on the audio data, and determines the user's expiratory energy based on the audio energy intensity and the corresponding audio acquisition duration. It calculates the user's lung function parameters based on the expiratory energy and the audio correction parameters. During lung function testing, the audio acquisition component of the mobile terminal device is used to collect audio data, and combined with the user's data, the audio data is converted into the user's lung function parameters. This allows the user's lung function testing to be performed not only using professional lung function testing equipment, but also using a mobile terminal device, increasing the difficulty of lung function testing. Combining user data with the conversion of audio data into lung function parameters improves the accuracy of lung function testing.

[0100] The above is an illustrative scheme of a data processing apparatus according to this embodiment. It should be noted that the technical solution of this data processing apparatus and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the data processing apparatus, please refer to the description of the technical solution of the data processing method described above.

[0101] Figure 6 A structural block diagram of a computing device 600 according to one embodiment of this specification is shown. The components of the computing device 600 include, but are not limited to, a memory 610 and a processor 620. The processor 620 is connected to the memory 610 via a bus 630, and a database 650 is used to store data.

[0102] The computing device 600 also includes an access device 640, which enables the computing device 600 to communicate via one or more networks 660. Examples of these networks include Public Switched Telephone Network (PSTN), Local Area Network (LAN), Wide Area Network (WAN), Personal Area Network (PAN), or combinations of communication networks such as the Internet. The access device 640 may include one or more of any type of wired or wireless network interface (e.g., a network interface card (NIC)), such as an IEEE 802.11 Wireless Local Area Network (WLAN) wireless interface, a Wi-MAX (Worldwide Interoperability for Microwave Access) interface, an Ethernet interface, a Universal Serial Bus (USB) interface, a cellular network interface, a Bluetooth interface, or a Near Field Communication (NFC) interface.

[0103] In one embodiment of this specification, the above-described components of the computing device 600 and Figure 6 Other components, not shown, can also be connected to each other, for example, via a bus. It should be understood that... Figure 6 The block diagram of the computing device shown is for illustrative purposes only and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.

[0104] The computing device 600 can be any type of stationary or mobile computing device, including mobile computers or mobile computing devices (e.g., tablet computers, personal digital assistants, laptop computers, notebook computers, netbooks, etc.), mobile phones (e.g., smartphones), wearable computing devices (e.g., smartwatches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or personal computers (PCs). The computing device 600 can also be a mobile or stationary server.

[0105] The processor 620 is configured to execute the following computer-executable instructions, which, when executed by the processor, implement the steps of the above-described data processing method.

[0106] The above is an illustrative scheme of a computing device according to this embodiment. It should be noted that the technical solution of this computing device and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computing device, please refer to the description of the technical solution of the data processing method described above.

[0107] An embodiment of this specification also provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0108] The above is an illustrative scheme of a computer-readable storage medium according to this embodiment. It should be noted that the technical solution of this storage medium and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the storage medium, please refer to the description of the technical solution of the data processing method described above.

[0109] An embodiment of this specification also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described data processing method.

[0110] The above is an illustrative scheme of a computer program product according to this embodiment. It should be noted that the technical solution of this computer program product and the technical solution of the data processing method described above belong to the same concept. For details not described in detail in the technical solution of the computer program product, please refer to the description of the technical solution of the data processing method described above.

[0111] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0112] The computer instructions include computer program code, which may be in the form of source code, object code, executable file, or certain intermediate forms. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium may be appropriately added or removed according to the requirements of patent practice. For example, in some regions, according to patent practice, computer-readable media may not include electrical carrier signals and telecommunication signals.

[0113] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments in this specification are not limited to the described order of actions, because according to the embodiments in this specification, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the embodiments in this specification.

[0114] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0115] The preferred embodiments disclosed above are merely illustrative of this specification. Optional embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the embodiments described in this specification. These embodiments are selected and specifically described in this specification to better explain the principles and practical applications of the embodiments, thereby enabling those skilled in the art to better understand and utilize this specification.

Claims

1. A data processing method, characterized by, include: Receive audio data from a user collected by a mobile terminal device through an audio acquisition component, and determine audio correction parameters based on the user's data; The audio energy intensity is determined based on the audio data, and the user's exhalation energy is determined based on the audio energy intensity and the audio acquisition duration corresponding to the audio data. The user's lung function parameters are calculated based on the expiratory energy and the audio correction parameters.

2. The data processing method according to claim 1, characterized in that, The audio data of the user collected by the mobile terminal device through the audio acquisition component includes: Receive the motion start audio data of the user at the start time of motion, which is collected by the mobile terminal device through the audio acquisition component; Receive the motion termination audio data of the user at the motion termination time, which is collected by the mobile terminal device through the audio acquisition component; The motion start audio data and the motion end audio data are used as the audio data.

3. The data processing method according to claim 2, characterized in that, After calculating the user's lung function parameters based on the expiratory energy and the audio correction parameters, the method further includes: Based on the lung function parameters, determine the exercise initiation lung function parameters corresponding to the exercise initiation audio data and the exercise termination lung function parameters corresponding to the exercise termination audio data; The exercise recovery parameters of the user are determined based on the exercise initiation lung function parameters and the exercise termination lung function parameters. The exercise recovery parameters are used to assess the user's lung function and to provide the user with exercise and expiratory advice information.

4. The data processing method of claim 1, wherein, The step of determining the audio correction parameters based on the user's user data includes: Determine device factors, and determine user attribute factors of the user based on the user data; The audio correction parameters are calculated based on the device factor and / or the user attribute factor.

5. The data processing method of claim 1, wherein, Determining the audio energy intensity based on the audio data includes: The audio acquisition duration and at least one audio sample value are determined based on the audio data; The audio energy intensity is calculated based on the energy intensity calculation rules, the audio acquisition duration, and the at least one audio sample value.

6. The data processing method of claim 1, wherein, Determining the user's expiratory energy based on the audio energy intensity and the audio acquisition duration corresponding to the audio data includes: Determine the audio acquisition duration corresponding to the audio data, and the time slice corresponding to the audio energy intensity; The user's expiratory energy is calculated based on the expiratory capacity calculation rules, the audio energy intensity, and the time slice.

7. The data processing method of claim 1, wherein, The audio data of the user collected by the mobile terminal device through the audio acquisition component includes: The system receives initial audio data from a user collected by a mobile terminal device through an audio acquisition component, and removes noise from the initial audio data to obtain the audio data.

8. The data processing method of claim 1, wherein, Before receiving the user's audio data collected by the mobile terminal device through the audio acquisition component, the method further includes: In response to the user's test command, a test preparation interface is displayed in the interface display component of the mobile terminal device. The test preparation interface includes test distance prompt information and test environment prompt information. In response to the test start command, the test preparation interface is updated to the test start interface.

9. A data processing system comprising a client and a server, characterized in that, include: The client is used to determine the user's audio data collected by the audio acquisition component of the mobile terminal device, and send the audio data to the server. The server is used to determine audio correction parameters based on the user's user data; The audio energy intensity is determined based on the audio data, and the user's exhalation energy is determined based on the audio energy intensity and the audio acquisition duration corresponding to the audio data. The user's lung function parameters are calculated based on the expiratory energy and the audio correction parameters, and the lung function parameters are sent to the client.

10. A computing device, characterized in that, include: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions, which, when executed by the processor, implement the steps of the data processing method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, It stores computer-executable instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 8.

12. A computer program product, characterised in that, It includes a computer program or instructions that, when executed by a processor, implement the steps of the data processing method according to any one of claims 1 to 8.

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