Respiratory parameter dynamic measurement method, system, storage medium and program product

Abstract

The application discloses a kind of respiratory parameter dynamic measurement method, system, storage medium and program product.The method is applied to the processing module in respiratory parameter dynamic measurement system, by controlling excitation module to generate and multiple preset frequency respectively corresponding target oscillation wave;Obtain the integrated gas signal corresponding to each preset frequency;For each preset frequency, based on the integrated gas signal corresponding to each preset frequency at each time, generate the target respiratory parameter corresponding to each preset frequency and time corresponding relationship;For each target time, based on the integrated gas signal corresponding to each target time of multiple preset frequency respectively, generate the target respiratory parameter corresponding to each target time and frequency corresponding relationship;Based on the target respiratory parameter corresponding to multiple preset frequency respectively and time corresponding relationship and the target respiratory parameter corresponding to multiple target time respectively and frequency corresponding relationship generate target respiratory parameter-frequency-time relationship data, realizes respiratory parameter dynamic measurement.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical devices, and in particular to a respiratory parameter dynamic measurement method, system, storage medium and program product. BACKGROUND

[0002] Pulmonary function test is an important means for diagnosing respiratory system obstruction, asthma and other diseases. Among them, the respiratory oscillation measurement technology is not suitable for severe patients or the elderly, compared with the common pulmonary function test, such as spirometer, peak flow meter, etc. which requires the patient to actively cooperate with the test to exhale, inhale or hold breath, etc. The oscillation measurement technology does not require the cooperation of the subject, so the technology has important application value in clinical practice.

[0003] At present, the oscillation method pulmonary function detection is a new type of pulmonary function detection technology based on forced oscillation technology to measure the respiratory impedance of the human body. It does not require the active cooperation of the subject, and can distinguish the resistance changes of the inspiration phase and the expiration phase, identify the respiratory system lesions caused by smoking in the early stage, and has been recommended by the European Respiratory Society as a recommended pulmonary function detection method. The principle of the technology is to regard the human respiratory system as an electrical circuit system composed of resistance, inductance and capacitance, generate mechanical waves of different frequencies by an external generator, and then monitor the flow rate and pressure signals of the subject's normal breathing. The human respiratory impedance is calculated by referring to the impedance and voltage current relationship in the circuit. And combined with the respiratory impedance model analysis, the characteristic indexes under different frequency excitation signals are extracted.

[0004] However, the existing oscillation pulmonary function detection is to detect the overall parameters of the respiratory process, and the obtained respiratory parameters are limited, which can only represent the static characteristics of the respiratory system at some time, and is not conducive to the comprehensive analysis of the function of the respiratory system. SUMMARY

[0005] The present application provides a respiratory parameter dynamic measurement method, system, storage medium and program product to solve the problem of the respiratory system dynamic characteristics that cannot be extracted by the existing oscillation pulmonary function detection, realizes the dynamic measurement of the respiratory parameters, and improves the dynamic measurement efficiency and accuracy.

[0006] According to one aspect of the present application, a respiratory parameter dynamic measurement method is provided, which is applied to a processing module in a respiratory parameter dynamic measurement system, and the method comprises:

[0007] The excitation module generates target oscillation waves corresponding to a plurality of preset frequencies; the target oscillation waves corresponding to the plurality of preset frequencies are sequentially transmitted to the airway opening of the target object through the sound guide tube; and the target oscillation wave corresponding to any preset frequency is generated based on the pulse excitation signal and the sine excitation signal corresponding to the preset frequency.

[0008] acquire a comprehensive gas signal corresponding to each preset frequency; wherein the comprehensive gas signal corresponding to the preset frequency is acquired during the transmission of the target oscillation wave corresponding to the preset frequency in the sound guide tube;

[0009] For each preset frequency, generate a target respiratory parameter-time corresponding relationship corresponding to each preset frequency based on the comprehensive gas signal corresponding to the preset frequency at each time; for each target time, generate a target respiratory parameter-frequency corresponding relationship corresponding to each target time based on the comprehensive gas signal corresponding to each preset frequency at each target time;

[0010] Generate a target respiratory parameter-frequency-time relationship data based on the target respiratory parameter-time corresponding relationship corresponding to each preset frequency and the target respiratory parameter-frequency corresponding relationship corresponding to each target time.

[0011] According to another aspect of the present application, a respiratory parameter dynamic measurement system is provided, comprising: an excitation module, a processing module, a gas signal acquisition module and a sound guide tube; the excitation module is in communication connection with the processing module; the gas signal acquisition module is arranged on the inner surface of the sound guide tube, and the gas signal acquisition module is in communication connection with the processing module; one end of the sound guide tube is in physical connection with the excitation module; wherein,

[0012] The processing module is used to control the excitation module to generate target oscillation waves corresponding to a plurality of preset frequencies; the target oscillation wave corresponding to any preset frequency is generated based on the pulse excitation signal and the sine excitation signal corresponding to the preset frequency;

[0013] The excitation module is used to generate target oscillation waves corresponding to a plurality of preset frequencies in response to the control signal of the processing module, and the target oscillation waves corresponding to the plurality of preset frequencies are sequentially transmitted to the airway opening of the target object through the sound guide tube;

[0014] The gas signal acquisition module is used to acquire a comprehensive gas signal in the sound guide tube; the comprehensive gas signal includes a comprehensive gas signal corresponding to a plurality of preset frequencies in the test phase and a comprehensive gas signal not in the test phase;

[0015] The processing module is further configured to generate a target respiratory parameter-time relationship corresponding to each preset frequency based on the comprehensive gas signal corresponding to each preset frequency; for each preset frequency, generate a target respiratory parameter-time relationship corresponding to each preset frequency based on the comprehensive gas signal corresponding to the preset frequency at each time; for each target time, generate a target respiratory parameter-frequency relationship corresponding to each target time based on the comprehensive gas signals corresponding to the multiple preset frequencies at each target time; and generate the target respiratory parameter-frequency-time relationship data based on the target respiratory parameter-time relationships corresponding to the multiple preset frequencies and the target respiratory parameter-frequency relationships corresponding to the multiple target times.

[0016] According to another aspect of the present application, an electronic device is provided, which comprises:

[0017] at least one processor; and

[0018] a memory in communication connection with the at least one processor; wherein

[0019] the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the respiratory parameter dynamic measurement method of any one of the embodiments of the present application.

[0020] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to perform the respiratory parameter dynamic measurement method of any one of the embodiments of the present application when executed by the processor.

[0021] According to another aspect of the present application, a computer program product is provided, which comprises a computer program for implementing the respiratory parameter dynamic measurement method of any one of the embodiments of the present application when executed by a processor.

[0022] The technical solution of the embodiments of the present application generates the target respiratory parameter-frequency-time relationship data based on the target respiratory parameter-time relationships corresponding to the multiple preset frequencies and the target respiratory parameter-frequency relationships corresponding to the multiple target times, instead of only determining the target respiratory parameter-time relationship corresponding to any preset frequency or the target respiratory parameter-frequency relationship corresponding to any target time, thereby solving the problem of the respiratory system dynamic characteristics that cannot be extracted by the existing oscillatory lung function detection, achieving respiratory parameter dynamic measurement, and improving the dynamic measurement efficiency and accuracy.

[0023] It is to be understood that the details set forth herein do not limit the scope of the embodiments of the application to the specific embodiments described. The foregoing detailed description has been presented for purposes of clarity and description. It is BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0025] Figure 1 is a flow chart of a respiratory parameter dynamic measurement method provided by the first embodiment of the present application;

[0026] Figure 2A is a schematic diagram of a pulse excitation signal provided by the first embodiment of the present application;

[0027] Figure 2B is a schematic diagram of a sine excitation signal corresponding to a preset frequency provided by the first embodiment of the present application;

[0028] Figure 2C is a schematic diagram of a synthesized excitation signal corresponding to a preset frequency provided by the first embodiment of the present application;

[0029] Figure 3 is a flow chart of a respiratory parameter dynamic measurement method provided by the second embodiment of the present application;

[0030] Figure 4A is a schematic diagram of a comprehensive gas pressure signal waveform separation provided by the second embodiment of the present application;

[0031] Figure 4B is a schematic diagram of a comprehensive gas flow signal waveform separation provided by the second embodiment of the present application;

[0032] Figure 5 is a schematic diagram of a target respiratory parameter-time corresponding relationship and a target respiratory parameter-frequency corresponding relationship generation process provided by the second embodiment of the present application;

[0033] Figure 6A is a schematic diagram of a respiratory impedance-frequency-time relationship data generation process provided by the second embodiment of the present application;

[0034] Figure 6B is a schematic diagram of a respiratory reactance-frequency-time relationship data generation process provided by the second embodiment of the present application;

[0035] Figure 7is a structural schematic diagram of a respiratory parameter dynamic measurement system provided by embodiment three of the present application;

[0036] Figure 8 is a structural schematic diagram of a respiratory parameter dynamic measurement system provided by embodiment three of the present application;

[0037] Figure 9 is a structural schematic diagram of an electronic device for implementing a respiratory parameter dynamic measurement method. DETAILED DESCRIPTION

[0038] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should fall within the scope of protection of the present application.

[0039] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0040] Embodiment one

[0041] Figure 1 is a flowchart of a respiratory parameter dynamic measurement method provided by embodiment one of the present application. The present embodiment can be applicable to the extraction of dynamic characteristics of the respiratory system of a target object. The method can be executed by a processing module, which can be realized in the form of hardware and / or software. The processing module can be configured in a respiratory parameter dynamic measurement system.

[0042] In the present embodiment, the target object is a human body or other animal body whose respiratory parameters change over time. The respiratory parameters are parameters representing the function of the respiratory system of the target object. For example, the respiratory parameters include, but are not limited to, respiratory impedance and respiratory reactance.

[0043] It should be noted that the target object has dynamic changes in the respiratory system during the breathing process, for example, the movement or opening and closing state changes of the tongue root, glottis and other respiratory system structures will affect the detection of the respiratory parameter. By dynamically measuring the respiratory parameter, the dynamic function of the respiratory system of the target object can be determined.

[0044] As shown in the method applied to the processing module in the respiratory parameter dynamic measurement system, comprising: Figure 1

[0045] S110, control the excitation module to generate a target oscillation wave corresponding to a plurality of preset frequencies; the target oscillation wave corresponding to the plurality of preset frequencies is transmitted to the airway of the target object through the sound guide tube in turn; the target oscillation wave corresponding to any preset frequency is generated based on the pulse excitation signal and the sine excitation signal corresponding to the preset frequency.

[0046] In this embodiment, the excitation module is a module for generating a target oscillation wave in the respiratory parameter dynamic measurement system, which can be realized in the form of hardware and / or software. The target oscillation wave is an oscillation wave used to impact the respiratory system of the target object with sound waves, so as to measure the respiratory parameter based on the target oscillation wave.

[0047] Specifically, the control signal is a signal for controlling the excitation module to generate a target oscillation wave, and the control signal includes one or more preset frequencies. For example, the preset frequency includes but is not limited to 5 Hz, 10 Hz and 20 Hz. In the process of generating the target oscillation wave, the processing module generates the control signal and communicates the control signal to the excitation module, so that the excitation module analyzes the control signal to obtain at least one preset frequency, and the excitation module continuously generates a synthesized excitation signal corresponding to the preset frequency based on each preset frequency, wherein the synthesized excitation signal corresponding to the preset frequency includes a pulse excitation signal and a sine excitation signal corresponding to the preset frequency. The excitation module generates a target oscillation wave corresponding to the preset frequency based on the synthesized excitation signal corresponding to the preset frequency.

[0048] In some embodiments, the control signal includes one preset frequency, and the processing module can sequentially transmit a plurality of control signals to the excitation module, so that the excitation module sequentially traverses the plurality of control signals and generates a target oscillation wave corresponding to the preset frequency included in the plurality of control signals in turn. In some embodiments, the control signal includes a plurality of preset frequencies, and the processing module can transmit one control signal to the excitation module, so that the excitation module analyzes the plurality of preset frequencies included in the control signal, and generates a target oscillation wave corresponding to the plurality of preset frequencies in turn by traversing the plurality of preset frequencies in turn.

[0049] ​It should be noted that the control signal includes a preset duration, the preset duration is the duration of the excitation module continuously generating the synthesized excitation signal, that is, the duration of the target oscillation wave continuously impacting the respiratory system, the control signals corresponding to the plurality of preset frequencies include the same preset duration, for example, the preset duration is 15 seconds. Taking the preset frequency corresponding to the currently generated target oscillation wave as the current preset frequency, in the case that the difference between the current time and the starting generation time of the currently generated target oscillation wave is greater than the preset duration, the control excitation module stops generating the target oscillation wave corresponding to the current preset frequency, and starts generating the target oscillation wave corresponding to the next preset frequency. Wherein, in the case that the control signal includes one preset frequency, the next preset frequency is the preset frequency included in the next control signal including the current control signal, and the current control signal includes the current preset frequency; in the case that the control signal includes a plurality of preset frequencies, the next preset frequency is the next preset frequency of the current preset frequency.

[0050] Taking any preset frequency as an example, Figure 2A is a schematic diagram of a pulse excitation signal provided by an embodiment of the present application, Figure 2B is a schematic diagram of a sinusoidal excitation signal corresponding to a preset frequency provided by an embodiment of the present application, Figure 2C is a schematic diagram of a synthesized excitation signal corresponding to a preset frequency provided by an embodiment of the present application, as shown in Figure 2C , the synthesized excitation signal corresponding to the preset frequency is, as shown in Figure 2A , the pulse excitation signal and the sinusoidal excitation signal corresponding to the preset frequency as shown in Figure 2B are superimposed and synthesized.

[0051] The sound guide pipe is a pipeline for conducting the target oscillation wave in the respiratory parameter dynamic measurement system. The plurality of target oscillation waves generated by the excitation module in turn are conducted along the sound guide pipe from the excitation module to the airway port of the target object, and the respiratory system of the target object is subjected to sound wave impact. Exemplarily, the sound guide pipe can be a hose or a hard pipe, and the present embodiment does not limit this.

[0052] S120, acquiring a comprehensive gas signal corresponding to each preset frequency respectively; wherein the comprehensive gas signal corresponding to the preset frequency is obtained by collecting the target oscillation wave corresponding to the preset frequency in the sound guide pipe during the transmission process.

[0053] In the present embodiment, the comprehensive gas signal is a signal for characterizing the gas characteristics in the sound guide pipe, for example, the gas characteristics include gas flow and gas pressure.

[0054] Specifically, during the transmission of the target oscillation wave in the acoustic conduit, the gas signal acquisition module acquires in real time a signal representing a characteristic of the gas in the acoustic conduit, to obtain a comprehensive gas signal at the current time. The gas signal acquisition module is a module for acquiring a comprehensive gas signal in the respiratory parameter dynamic measurement system, for example, the gas signal acquisition module includes a gas pressure sensor and a gas flow sensor. The processing module reads in real time the comprehensive gas signal acquired by the gas signal acquisition module. By corresponding the comprehensive gas signal at the current time to the preset frequency corresponding to the target oscillation wave currently transmitted in the acoustic conduit, the comprehensive gas signal corresponding to the preset frequency is obtained, and the comprehensive gas signals at multiple times corresponding to each preset frequency are recorded in time sequence.

[0055] For example, the processing module records the comprehensive gas signal read in real time as the comprehensive gas signal corresponding to the preset frequency f1 during the transmission of the target oscillation wave corresponding to the preset frequency f1 in the acoustic conduit.

[0056] S130, for each preset frequency, based on the comprehensive gas signal at each time corresponding to the preset frequency, a target respiratory parameter corresponding to each preset frequency is generated respectively corresponding to the time; for each target time, based on the comprehensive gas signal at each target time corresponding to multiple preset frequencies respectively, a target respiratory parameter corresponding to each target time is generated corresponding to the frequency.

[0057] In this embodiment, the target respiratory parameter corresponding to each preset frequency and the time corresponds to the relationship between the target respiratory parameter of each preset frequency and the change of time. The target respiratory parameter is any respiratory parameter that can be extracted from the comprehensive gas signal, for example, the target respiratory parameter is the respiratory impedance or the respiratory reactance. The target time is the time corresponding to the target respiratory parameter in the target respiratory parameter and frequency correspondence.

[0058] Specifically, for each preset frequency, the processing module respectively processes the comprehensive gas signals at multiple times corresponding to the preset frequency to obtain the target respiratory parameters at multiple times corresponding to the preset frequency. For each target time, the processing module respectively processes the comprehensive gas signals at the target time corresponding to multiple preset frequencies to obtain the target respiratory parameters at the target time corresponding to multiple preset frequencies.

[0059] The target respiratory parameter corresponding to each preset frequency is generated based on the target respiratory parameter corresponding to the time point of each preset frequency, with the time point as the independent variable and the target respiratory parameter corresponding to the preset frequency as the dependent variable, to generate a target respiratory parameter-time corresponding relationship corresponding to the target time point; the target respiratory parameter corresponding to each preset frequency is generated based on the target respiratory parameter corresponding to the time point of each preset frequency, with the frequency as the independent variable and the target respiratory parameter corresponding to the target time point as the dependent variable, to generate a target respiratory parameter-frequency corresponding relationship corresponding to the target time point.

[0060] For example, assuming that the target respiratory parameter is respiratory impedance (or, respiratory reactance), the target respiratory parameter-time corresponding relationship can be represented by a respiratory impedance (or, respiratory reactance) curve changing with time, and the target respiratory parameter-frequency corresponding relationship can be represented by a respiratory impedance (or, respiratory reactance) curve changing with frequency.

[0061] It should be noted that the comprehensive gas signal includes three signal components, namely, a respiratory signal component, a sinusoidal oscillation wave signal component, and a pulse oscillation signal component. Optionally, the waveform separation processing is performed on the comprehensive gas signal corresponding to each preset frequency to obtain a first comprehensive gas signal corresponding to each preset frequency and a second comprehensive gas signal corresponding to each preset frequency; the frequency component of the first comprehensive gas signal corresponding to each preset frequency is the same as the frequency component of the sinusoidal excitation signal corresponding to the preset frequency; the target respiratory parameter corresponding to each preset frequency is generated based on the first comprehensive gas signal corresponding to the time point of each preset frequency; and correspondingly, for each target time point, the target respiratory parameter-frequency corresponding relationship corresponding to each target time point is generated based on the second comprehensive gas signal corresponding to the target time point of each preset frequency.

[0062] In this embodiment, the first comprehensive gas signal is a signal corresponding to the sinusoidal oscillation wave signal component, and the second comprehensive gas signal is a signal corresponding to the pulse oscillation wave signal component.

[0063] Specifically, in the process of performing waveform separation processing on the comprehensive gas signal corresponding to each preset frequency respectively: the comprehensive gas signal is subjected to Fourier transformation to obtain a comprehensive gas conversion signal; the comprehensive gas signal is subjected to filtering processing based on a preset frequency threshold, and a waveform component obtained by filtering and having a frequency less than the preset frequency threshold is determined as a respiratory signal corresponding to the preset frequency, wherein the preset frequency threshold is a frequency threshold used for determining a respiratory signal, and the respiratory signal is a signal corresponding to a respiratory signal component; the waveform component obtained by separation and having a frequency greater than or equal to the preset frequency threshold is subjected to filtering processing based on a frequency component of the sinusoidal excitation signal corresponding to the preset frequency, and a waveform component obtained by filtering and identical to the frequency component of the sinusoidal excitation signal corresponding to the preset frequency is determined as a first comprehensive gas signal corresponding to the preset frequency, and a waveform component obtained by filtering and not identical to the frequency component of the sinusoidal excitation signal corresponding to the preset frequency is determined as a second comprehensive gas signal corresponding to the preset frequency. For example, the preset frequency threshold is 0.5 Hz. For each preset frequency, the processing module performs respiratory parameter calculation processing on the first comprehensive gas signal corresponding to the preset frequency at multiple time points respectively to obtain target respiratory parameters corresponding to the preset frequency at the multiple time points. For each target time point, the processing module performs respiratory parameter calculation processing on the second comprehensive gas signal corresponding to the preset frequency at the target time point respectively to obtain target respiratory parameters corresponding to the preset frequency at the target time point. Based on the target respiratory parameters corresponding to the preset frequency at the multiple time points, a target respiratory parameter corresponding to the preset frequency and time corresponding relationship is generated. Based on the target respiratory parameters corresponding to the preset frequency at the target time point, a target respiratory parameter corresponding to the target time point and frequency corresponding relationship is generated.

[0064] For example, the first comprehensive gas signal or the second comprehensive gas signal is input into a preset respiratory parameter calculation formula to obtain a calculation result, wherein the calculation result includes a real part and an imaginary part, in the case where the target respiratory parameter is respiratory impedance, the real part included in the calculation result is determined as the target respiratory parameter; in the case where the target respiratory parameter is respiratory reactance, the imaginary part included in the calculation result is determined as the target respiratory parameter.

[0065] The technical scheme of the embodiment can obtain the first comprehensive gas signal corresponding to each preset frequency and the second comprehensive gas signal corresponding to each preset frequency by performing waveform separation processing on the comprehensive gas signal corresponding to each preset frequency; generate the target respiratory parameter corresponding to each preset frequency and time corresponding relationship based on the first comprehensive gas signal corresponding to each preset frequency; and generate the target respiratory parameter corresponding to each target moment and frequency corresponding relationship based on the second comprehensive gas signal corresponding to the target moment of each preset frequency for each target moment, which can reduce the mutual interference between the first comprehensive gas signal and the second comprehensive gas signal, improve the reliability of the target respiratory parameter and time corresponding relationship and the target respiratory parameter and frequency corresponding relationship, and help improve the accuracy of dynamic measurement of the respiratory parameter.

[0066] S140, generate the target respiratory parameter-frequency-time relationship data based on the target respiratory parameter and time corresponding relationship corresponding to each preset frequency and the target respiratory parameter and frequency corresponding relationship corresponding to each target moment.

[0067] Specifically, the first target respiratory parameter-frequency-time relationship data is generated based on the target respiratory parameter and time corresponding relationship corresponding to each preset frequency, with the time and the preset frequency as independent variables and the target respiratory parameter as a dependent variable; the second target respiratory parameter-frequency-time relationship data is generated based on the target respiratory parameter and frequency corresponding relationship corresponding to each target moment, with the time and the preset frequency as independent variables and the target respiratory parameter as a dependent variable; and the target respiratory parameter-frequency-time relationship data is obtained by performing fusion processing on the first target respiratory parameter-frequency-time relationship data and the second target respiratory parameter-frequency-time relationship data, for example, the fusion processing can be weighted summation processing.

[0068] In some embodiments, the target respiratory parameter-frequency-time relationship data is obtained by surface fitting processing. Optionally, the first fitting surface is obtained by performing surface fitting processing on the target respiratory parameter and time corresponding relationship corresponding to each preset frequency; the second fitting surface is obtained by performing surface fitting processing on the target respiratory parameter and frequency corresponding relationship corresponding to each target moment; and the target respiratory parameter-frequency-time relationship data is generated based on the first fitting surface and the second fitting surface.

[0069] In this embodiment, the target respiratory parameter-time relationship corresponding to each preset frequency is in a curve form, that is, in the process of generating the target respiratory parameter-time relationship corresponding to each preset frequency, the target respiratory parameters of the multiple time points corresponding to the preset frequency are subjected to curve fitting processing to obtain the target respiratory parameter-time relationship corresponding to the preset frequency in a curve form. Similarly, the target respiratory parameter-frequency relationship corresponding to each target time is also in a curve form, that is, in the process of generating the target respiratory parameter-frequency relationship corresponding to each target time, the target respiratory parameters of the multiple preset frequencies corresponding to the target time are subjected to curve fitting processing to obtain the target respiratory parameter-frequency relationship corresponding to the target time in a curve form.

[0070] Specifically, the target respiratory parameter-time relationships corresponding to the multiple preset frequencies are subjected to curve interpolation processing based on a preset interpolation algorithm to obtain the target respiratory parameter-time relationship corresponding to any frequency. The target respiratory parameter-time relationships corresponding to the multiple frequencies are stacked along the frequency dimension to obtain a first fitting surface. The target respiratory parameter-frequency relationships corresponding to the multiple target times are subjected to curve interpolation processing based on the preset interpolation algorithm to obtain the target respiratory parameter-frequency relationship corresponding to any time. The target respiratory parameter-frequency relationships corresponding to the multiple times are stacked along the time dimension to obtain a second fitting surface. The first fitting surface and the second fitting surface are subjected to fusion processing to obtain a fusion fitting surface, and the fusion fitting surface is determined as the relationship data of the target respiratory parameter-frequency-time.

[0071] For example, the preset interpolation algorithm includes, but is not limited to, a nearest neighbor interpolation algorithm and a bicubic interpolation algorithm. The fusion fitting surface is a weighted average of the first fitting surface and the second fitting surface, and the weight of the first fitting surface and the weight of the second fitting surface are both 1.

[0072] The technical solution of this embodiment can obtain reliable relationship data of the target respiratory parameter-frequency-time by respectively performing surface fitting processing on the target respiratory parameter-time relationships corresponding to the multiple preset frequencies and the target respiratory parameter-frequency relationships corresponding to the multiple target times, which helps to improve the accuracy of respiratory parameter dynamic measurement.

[0073] In some embodiments, the relationship data of the target respiratory parameter-frequency-time is obtained by processing through a deep learning algorithm. Optionally, the target respiratory parameter-time relationships corresponding to the multiple preset frequencies and the target respiratory parameter-frequency relationships corresponding to the multiple target times are input into a surface generation model constructed based on a preset deep learning algorithm to obtain the relationship data of the target respiratory parameter-frequency-time.

[0074] In the embodiment, the preset deep learning algorithm is an algorithm for mapping the target respiratory parameter-time correspondence and the target respiratory parameter-frequency correspondence into the target respiratory parameter-frequency-time relationship data, the input of the surface generation model is the target respiratory parameter-time correspondence corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency correspondence corresponding to the plurality of target time points, and the output of the surface generation model is the target respiratory parameter-frequency-time relationship data.

[0075] Specifically, the processing module calls the pre-trained surface generation model, and performs inference processing on the input target respiratory parameter-time correspondence corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency correspondence corresponding to the plurality of target time points based on the surface generation model, to obtain the target respiratory parameter-frequency-time relationship data.

[0076] For example, the surface generation model is a convolutional neural network model.

[0077] The technical scheme of the embodiment obtains the target respiratory parameter-frequency-time relationship data through the surface generation model, can comprehensively analyze the target respiratory parameter-time correspondence corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency correspondence corresponding to the plurality of target time points, and further improves the accuracy of the target respiratory parameter-frequency-time relationship data.

[0078] In some embodiments, the dynamic measurement of the target respiratory parameter is realized by dynamically extracting the target respiratory parameter-time correspondence and / or the target respiratory parameter-frequency correspondence from the target respiratory parameter-frequency-time relationship data. Optionally, the target respiratory parameter-time correspondence corresponding to the target frequency is determined based on the target respiratory parameter-frequency-time relationship data and the target frequency; and the target respiratory parameter-frequency correspondence corresponding to the target time point is determined based on the target respiratory parameter-frequency-time relationship data and the target time point.

[0079] Specifically, the target respiratory parameter-time correspondence corresponding to the target frequency is obtained by extracting the relationship data with the target frequency from the target respiratory parameter-frequency-time relationship data, the target respiratory parameter-time correspondence corresponding to any frequency can be obtained by changing the target frequency, which is helpful to determine the characteristics of all parts of the airway in the respiratory system of the target object changing over time. The target respiratory parameter-frequency correspondence corresponding to the target time point is obtained by extracting the relationship data with the target time point from the target respiratory parameter-frequency-time relationship data, the target respiratory parameter-frequency correspondence corresponding to any time point can be obtained by changing the target time point, which is helpful to determine the respiratory resistance characteristics of different parts of the airway in the respiratory system of the target object.

[0080] Taking the target respiratory parameter-frequency-time relationship data in the form of a surface as an example, a section of the target respiratory parameter-frequency-time relationship data is processed based on a target frequency, to obtain a target respiratory parameter-time curve corresponding to the target frequency, which represents the corresponding relationship between the target respiratory parameter and time corresponding to the target frequency; the corresponding relationship between the target respiratory parameter and time corresponding to a target time point is the same, and a section of the target respiratory parameter-frequency-time relationship data is processed based on the target time point, which will not be described herein.

[0081] Taking the target respiratory parameter-frequency-time relationship data in the form of a function as an example, the frequency in the target respiratory parameter-frequency-time relationship data is set as a target frequency, to obtain the corresponding relationship between the target respiratory parameter and time corresponding to the target frequency; the time in the target respiratory parameter-frequency-time relationship data is set as a target time point, to obtain the corresponding relationship between the target respiratory parameter and frequency corresponding to the target time point.

[0082] The technical scheme of the embodiment generates the target respiratory parameter-frequency-time relationship data based on the corresponding relationship between the target respiratory parameter and time corresponding to a plurality of preset frequencies and the corresponding relationship between the target respiratory parameter and frequency corresponding to a plurality of target time points, rather than determining the corresponding relationship between the target respiratory parameter and time corresponding to any preset frequency or the corresponding relationship between the target respiratory parameter and frequency corresponding to any target time point, thereby solving the problem of respiratory system dynamic characteristics that cannot be extracted by the existing oscillatory lung function detection, achieving dynamic measurement of respiratory parameters, and improving the dynamic measurement efficiency and accuracy.

[0083] Embodiment Two

[0084] Figure 3 is a flowchart of a respiratory parameter dynamic measurement method provided by Embodiment Two of the present application. The technical scheme of the embodiment is improved on the basis of the above-mentioned embodiments. The explanations of the same or corresponding terms as in the above-mentioned embodiments will not be repeated here. As shown in Figure 3 the method comprises:

[0085] S210, in the case of not entering the test stage, obtaining a comprehensive gas signal collected in an acoustic coupler; based on the comprehensive gas signal, controlling an excitation module to generate target oscillation waves corresponding to a plurality of preset frequencies; wherein, in the case of not entering the test stage, the target object is in a normal breathing state; and in the test stage based on the target oscillation wave, obtaining a comprehensive gas signal collected in the process of conducting the target oscillation wave corresponding to a preset frequency in the acoustic coupler; based on the comprehensive gas signal, controlling the excitation module to generate target oscillation waves corresponding to a plurality of preset frequencies.

[0086] Specifically, in the case of not entering the test phase, the processing module does not control the excitation module to generate the target oscillation wave, and the target oscillation wave is not conducted in the sound guide tube. The target object is in a normal breathing state, and the airway opening of the target object is connected to the sound guide tube, so that the gas in the sound guide tube flows. The gas signal acquisition module acquires the signal representing the characteristics of the gas in the sound guide tube in real time to obtain a comprehensive gas signal. The processing module reads the comprehensive gas signal obtained in real time, determines the pulse generation time based on the comprehensive gas signal, and generates a control signal based on the pulse generation time and the preset frequency, wherein the pulse generation time is the starting time of the pulse with the preset pulse width in the pulse excitation signal generated by the excitation module, for example, the preset pulse width can be 10*40 milliseconds. The excitation module generates the target oscillation wave corresponding to the preset frequency in response to the control signal.

[0087] In the case of the test phase based on the target oscillation wave, the target oscillation wave corresponding to the preset frequency is conducted in the sound guide tube, the target object is in a breathing state in which the respiratory system is impacted by the target oscillation wave, and the airway opening of the target object is connected to the sound guide tube, so that the gas in the sound guide tube flows. For any preset frequency, during the continuous propagation of the target oscillation wave corresponding to the preset frequency, the gas signal acquisition module acquires the signal representing the characteristics of the gas in the sound guide tube in real time to obtain a comprehensive gas signal. Similarly, the processing module controls the excitation module to generate the target oscillation wave corresponding to each of the plurality of preset frequencies based on the comprehensive gas signal, which will not be described here.

[0088] In some examples, the target time is the pulse generation time. Optionally, a plurality of target times are determined based on the comprehensive gas signal; the excitation module is controlled to generate a pulse excitation signal corresponding to each target time based on each target time; the excitation module is controlled to generate a sinusoidal excitation signal corresponding to each preset frequency based on each preset frequency; and the target oscillation wave corresponding to each preset frequency is generated based on the pulse excitation signal and the sinusoidal excitation signal corresponding to each preset frequency.

[0089] Specifically, based on the comprehensive gas signals at multiple time points, a comprehensive gas signal-time correspondence is determined, and based on the comprehensive gas signal-time correspondence, multiple target time points are determined. A control signal is generated based on one or more target time points. For example, when the control signal includes multiple target time points, the processing module sends a control signal to the excitation module every preset time interval, so that the excitation module obtains the multiple target time points by analyzing the control signal, and generates a pulse in the pulse excitation signal at the target time point. It should be noted that the control signal includes at least part of the multiple target time points located between the time points of adjacent two times of sending the control signal; in the case that the control signal includes one target time point, the processing module sends a control signal to the excitation module at the target time point, so that the excitation module generates a pulse in the pulse excitation signal at the target time point. Optionally, the comprehensive gas signal includes a comprehensive gas flow signal and a comprehensive gas pressure signal; accordingly, a first time point corresponding to a zero point state of the comprehensive gas flow signal in the test stage is determined; multiple second time points are determined based on the multiple first time points and a preset time interval, and the first time point and the second time point are determined as the target time point.

[0090] Wherein, the zero point state includes a zero point state in which the signal value in the comprehensive gas flow signal changes from positive to negative, and a zero point state in which the signal value changes from negative to positive.

[0091] Wherein, the preset time interval is determined based on the target object's breathing cycle. The determination method of the target object's breathing cycle includes: performing Fourier transform on the comprehensive gas flow signal and the comprehensive gas pressure signal in the test stage to obtain flow conversion data and pressure conversion data; determining the breathing cycle based on the maximum value in the flow conversion data and the maximum value in the pressure conversion data.

[0092] In this embodiment, the gas signal acquisition module includes a gas pressure sensor and a gas flow sensor, the comprehensive gas flow signal is a gas signal acquired based on the gas flow sensor, and the comprehensive gas pressure signal is a gas signal acquired based on the gas pressure sensor.

[0093] Specifically, by inputting the comprehensive gas flow signal and the comprehensive gas pressure signal in the test stage into a preset Fourier transform formula respectively, flow conversion data and pressure conversion data are obtained respectively, wherein the flow conversion data is a frequency domain comprehensive gas flow signal, and the pressure conversion data is a frequency domain comprehensive gas pressure signal. By inputting the maximum value in the flow conversion data and the maximum value in the pressure conversion data into a breathing cycle calculation formula, the breathing cycle is obtained. Wherein, the breathing cycle is the cycle period of the breathing signal.

[0094] Exemplarily, the respiratory cycle calculation formula is represented as T0=(fmp0+fmq0) / 2, where T0 represents the respiratory cycle, fmp0 represents the maximum value in the pressure conversion data, and fmq0 represents the maximum value in the flow conversion data.

[0095] The preset time interval is obtained by multiplying the respiratory cycle by a preset coefficient. Exemplarily, the preset coefficient is 0.5, and the preset time interval is represented as T0 / 2. It can be understood that the preset time interval is the time interval between two adjacent first time points corresponding to the comprehensive gas flow signal in the case where the test stage has not been entered. A second time point is obtained by summing each first time point with the preset time interval, and the plurality of first time points and the plurality of second time points are all determined as target time points.

[0096] Exemplarily, the first time points include tz i and tf i , i≥1, where tz i represents the time point at which the signal value in the comprehensive gas flow signal changes from positive to negative through zero point state, and tf i represents the time point at which the signal value in the comprehensive gas flow signal changes from negative to positive through zero point state. Assuming that the preset time interval is represented as T0 / 2, the second time points include tz i +T0 / 2 and tf i +T0 / 2.

[0097] The technical solution of the embodiment determines the first time points corresponding to the zero point state in the comprehensive gas flow signal in the case where the test stage has not been entered or in the test stage; determines the plurality of second time points based on the plurality of first time points and the preset time interval, and determines the first time points and the second time points as target time points, so as to ensure that the target time points are the time points at which the target respiratory parameter changes with time, and to help improve the reliability of the target respiratory parameter-frequency-time relationship data generation.

[0098] Specifically, the processing module sends a control signal to the excitation module. The excitation module analyzes the control signal to obtain at least one target time point and at least one preset frequency. In the case where the current time point is any target time point, the excitation module generates a pulse in the pulse excitation signal with a preset pulse width. A current preset frequency is determined from the at least one preset frequency, and a sinusoidal excitation signal corresponding to the current preset frequency is generated based on the current preset frequency. The current time point is obtained by fusing the pulse excitation signal and the sinusoidal excitation signal at the current time point, and a target oscillation wave corresponding to the current preset frequency is generated based on the current time point.

[0099] The technical solution of this embodiment determines multiple target times based on comprehensive gas signals; the control excitation module generates a pulse excitation signal corresponding to each target time based on each target time, which can ensure that the target oscillation wave generated at the target time has an oscillation wave component corresponding to the pulse excitation signal, which helps to improve the accuracy of dynamic measurement of respiratory parameters.

[0100] S220. Acquire the comprehensive gas signal corresponding to each preset frequency; wherein, the comprehensive gas signal corresponding to the preset frequency is acquired during the propagation of the target oscillation wave corresponding to the preset frequency in the acoustic duct.

[0101] S230. For each preset frequency, based on the comprehensive gas signal corresponding to the preset frequency at each time, generate the target respiratory parameter and time correspondence corresponding to each preset frequency; for each target time, based on the comprehensive gas signal corresponding to multiple preset frequencies at each target time, generate the target respiratory parameter and frequency correspondence corresponding to each target time.

[0102] For example, assume that the composite gas signal includes a composite gas pressure signal. Figure 4A This is a schematic diagram of integrated gas pressure signal waveform separation provided in Embodiment 2 of the present invention. Figure 4A As shown, the combined gas pressure signal is as follows: Figure 4A The signal diagram in the middle of the far left is shown; the gas pressure signal in the respiratory signal is as follows: Figure 4A The signal diagram at the top right is shown; the gas pressure signal belonging to the first comprehensive gas signal in the comprehensive gas pressure signal is as follows: Figure 4A As shown in the middle rightmost signal diagram, the gas pressure signal belonging to the second comprehensive gas signal in the comprehensive gas pressure signal is as follows: Figure 4A The signal diagram is shown at the very bottom right.

[0103] For example, assume that the composite gas signal includes a composite gas flow rate signal. Figure 4B This is a schematic diagram of integrated gas flow signal waveform separation provided in Embodiment 2 of the present invention. Figure 4B As shown, the combined gas flow signal is as follows: Figure 4B The signal diagram in the middle of the far left is shown; the gas flow signal in the respiratory signal is as follows: Figure 4B The signal diagram at the top right is shown; the gas flow signals belonging to the first comprehensive gas signal in the comprehensive gas flow signal are as follows: Figure 4B As shown in the middle rightmost signal diagram, the gas flow signals belonging to the second comprehensive gas signal in the comprehensive gas flow signal are as follows: Figure 4B The signal diagram is shown at the very bottom right.

[0104] For example, Figure 5is a schematic diagram of a target respiratory parameter-time relationship and a target respiratory parameter-frequency relationship generation process provided by Embodiment Two of the present application. As shown in Figure 5 each preset frequency respectively corresponds to a target respiratory parameter-time relationship, which is generated based on the gas pressure signal and the gas flow signal belonging to the first comprehensive gas signal in the comprehensive gas flow signal respectively corresponding to the preset frequency, that is, the target respiratory parameter-time relationship corresponding to the preset frequency f j is represented as R3(f j ,t), the preset frequency f j is represented as X3(f j ,t), j≥1. Each target time respectively corresponds to a target respiratory parameter-frequency relationship, which is generated based on the gas pressure signal and the gas flow signal belonging to the second comprehensive gas signal in the comprehensive gas flow signal respectively corresponding to the target time, that is, the respiratory impedance-frequency relationship corresponding to the target time tm is represented as R4(f, t i ), and the respiratory reactance-frequency relationship corresponding to the target time tm is represented as X4(f, t i ), wherein t i =tz i ,tz i +T0 / 2, tf i ,tf i +T0 / 2.

[0105] S240, generating target respiratory parameter-frequency-time relationship data based on the target respiratory parameter-time relationship respectively corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency relationship respectively corresponding to the plurality of target times.

[0106] For example, assuming that the target respiratory parameter-frequency-time relationship data is in the form of a surface, Figure 6A is a schematic diagram of a respiratory impedance-frequency-time relationship data generation process provided by Embodiment Two of the present application, as shown in Figure 6A The respiratory impedance-frequency-time relationship data is generated based on the respiratory impedance-time relationship R3(f j ,t) respectively corresponding to the plurality of preset frequencies f j and the respiratory impedance-frequency relationship R4(f, t i ) respectively corresponding to the plurality of target times t i , and the generated respiratory impedance-frequency-time surface R(f, t).

[0107] For example, assuming that the target respiratory parameter-frequency-time relationship data is in the form of a surface, Figure 6Bis a schematic diagram of a respiratory reactance-frequency-time relationship data generation process provided by Embodiment Two of the present application, as shown in Figure 6B The respiratory impedance-frequency-time relationship data is based on a plurality of preset frequencies f j The corresponding respiratory reactance and time corresponding relationship is represented as X3(f j ,t) and a plurality of target time t i The corresponding respiratory reactance and frequency corresponding relationship is represented as X4(f,t i ), and the respiratory impedance-frequency-time surface X(f,t) is generated.

[0108] The technical solution of the present embodiment, by acquiring the comprehensive gas signal collected in the acoustic coupler without entering the test phase, and acquiring the comprehensive gas signal collected in the acoustic coupler during the transmission of the target oscillation wave corresponding to the preset frequency based on the target oscillation wave in the test phase based on the target oscillation wave; and based on the comprehensive gas signal, the excitation module generates the target oscillation wave corresponding to the plurality of preset frequencies without entering the test phase or the test phase based on the target oscillation wave, so that the processing module can control the excitation module to generate the target oscillation wave corresponding to the comprehensive gas signal acquired at the current time, so that the target oscillation wave can adapt to the change of the comprehensive gas signal, which helps to improve the stability and accuracy of the dynamic measurement of the respiratory parameter.

[0109] Embodiment Three

[0110] Figure 7 is a structural schematic diagram of a respiratory parameter dynamic measurement system provided by Embodiment Three of the present application. The present embodiment can be applicable to the case of dynamically measuring the respiratory parameter of the measured sample, and the respiratory parameter dynamic measurement system can be realized in the form of hardware and / or software. Optionally, it is realized by an electronic device, which can be a mobile terminal, a PC terminal or a server, etc.

[0111] As shown in Figure 7 , the respiratory parameter dynamic measurement system 300 can specifically include: an excitation module 310, a processing module 320, a gas signal acquisition module 330 and an acoustic coupler 340; the excitation module 310 is in communication connection with the processing module 320; the gas signal acquisition module 330 is arranged on the inner surface of the acoustic coupler 340, and the gas signal acquisition module 330 is in communication connection with the processing module 320; one end of the acoustic coupler 340 is in physical connection with the excitation module 310.

[0112] The processing module 320 is configured to control the excitation module 310 to generate a target oscillation wave corresponding to a plurality of preset frequencies; the target oscillation wave corresponding to any preset frequency is generated based on a pulse excitation signal and a sine excitation signal corresponding to the preset frequency;

[0113] The excitation module 310 is configured to generate a plurality of target oscillation waves corresponding to a plurality of preset frequencies respectively in response to a control signal of the processing module 320, and the plurality of target oscillation waves corresponding to the plurality of preset frequencies respectively are sequentially conducted to the airway opening of the target object through the sound guide tube 340.

[0114] The gas signal acquisition module 330 is configured to acquire a comprehensive gas signal in the sound guide tube 340, and the comprehensive gas signal includes a plurality of comprehensive gas signals corresponding to the plurality of preset frequencies in the test phase and a comprehensive gas signal not in the test phase.

[0115] The processing module 320 is further configured to generate a target respiratory parameter-time corresponding relationship corresponding to each preset frequency based on the comprehensive gas signal corresponding to each preset frequency, generate a target respiratory parameter-time corresponding relationship corresponding to each preset frequency based on the preset frequency corresponding comprehensive gas signal at each time, generate a target respiratory parameter-frequency corresponding relationship corresponding to each target time based on the comprehensive gas signal corresponding to the plurality of preset frequencies at each target time, and generate the target respiratory parameter-frequency-time relationship data based on the target respiratory parameter-time corresponding relationship corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency corresponding relationship corresponding to the plurality of target times.

[0116] It should be noted that the continuous acquisition duration of the gas signal acquisition module 330 is greater than the product of the preset continuous duration and the preset frequency number. Assuming that the preset continuous duration is 15 seconds and the preset frequency number is 3, the continuous acquisition duration of the gas signal acquisition module 330 can be 50 seconds.

[0117] An exemplary, Figure 8 is a structural schematic diagram of a respiratory parameter dynamic measurement system 300 provided by an embodiment of the present application. As Figure 8The processing module 320 is a computer as shown. The excitation module 310 includes an excitation source 311, a synthesizer 312, and a loudspeaker 313; a first excitation channel 3101 and a second excitation channel 3102 are arranged in parallel between the excitation source 311 and the synthesizer 312; the excitation source 311 is configured to simultaneously generate a sinusoidal excitation signal at a preset frequency and a pulse excitation signal in response to a control signal, and transmit the sinusoidal excitation signal at the preset frequency to the synthesizer 312 through the first excitation channel 3101 and transmit the pulse excitation signal to the synthesizer 312 through the second excitation channel 3102. The synthesizer 312 is configured to superimpose process the sinusoidal excitation signal at the preset frequency and the pulse excitation signal to generate a synthesized excitation signal corresponding to the preset frequency, and drive the loudspeaker 313 based on the synthesized excitation signal. The loudspeaker 313 is configured to generate a target oscillation wave corresponding to the preset frequency based on the synthesized excitation signal in response to the driving of the synthesizer 312. The sound guide tube 340 is connected to the airway opening of the target object through the mask 350. The gas signal acquisition module 330 further includes a gas pressure sensor 331, a gas flow sensor 332, an amplifier 333, a filter 334, and an acquisition device 335. The amplifier 333 is configured to amplify the signals acquired by the gas pressure sensor 331 and / or the gas flow sensor 332, the filter 334 is configured to filter out noise in the amplified signals, and the acquisition device 335 is configured to acquire the filtered signals to obtain a comprehensive gas signal. It should be noted that, because Figure 8 The sound guide tube 340 is narrow, so the gas pressure sensor 331 and the gas flow sensor 332 are shown outside the sound guide tube 340, and in actual fact, the gas pressure sensor 331 and the gas flow sensor 332 are arranged on the inner surface of the sound guide tube 340.

[0118] The technical scheme of the embodiment generates the target respiratory parameter-frequency-time relationship data based on the target respiratory parameter-time relationship corresponding to a plurality of preset frequencies respectively and the target respiratory parameter-frequency relationship corresponding to a plurality of target instants respectively, rather than determining only the target respiratory parameter-time relationship corresponding to any preset frequency respectively or the target respiratory parameter-frequency relationship corresponding to any target instant respectively, thereby solving the problem that the respiratory system dynamic characteristics cannot be extracted in the existing oscillatory lung function detection, achieving dynamic measurement of respiratory parameters, and improving the dynamic measurement efficiency and accuracy.

[0119] On the basis of the above-mentioned embodiments, the processing module 320 is specifically configured to: in the case of not entering the test stage, acquire the comprehensive gas signal collected in the acoustic conduit 340; control the excitation module 310 to generate the target oscillation wave corresponding to each of the plurality of preset frequencies based on the comprehensive gas signal; wherein in the case of not entering the test stage, the target object is in a normal breathing state; and in the test stage based on the target oscillation wave, acquire the comprehensive gas signal collected in the process of conduction of the target oscillation wave corresponding to each of the plurality of preset frequencies in the acoustic conduit 340; control the excitation module 310 to generate the target oscillation wave corresponding to each of the plurality of preset frequencies based on the comprehensive gas signal.

[0120] On the basis of the above-mentioned embodiments, the processing module 320 is specifically configured to: determine a plurality of target instants based on the comprehensive gas signal; control the excitation module 310 to generate a pulse excitation signal corresponding to each target instant based on each target instant; control the excitation module 310 to generate a sinusoidal excitation signal corresponding to each preset frequency based on each preset frequency; and generate the target oscillation wave corresponding to each preset frequency based on the pulse excitation signal and the sinusoidal excitation signal corresponding to each preset frequency.

[0121] On the basis of the above-mentioned embodiments, the comprehensive gas signal includes a comprehensive gas flow signal and a comprehensive gas pressure signal; accordingly, the processing module 320 is specifically configured to: determine a first instant corresponding to a zero point state in the comprehensive gas flow signal in the case of not entering the test stage or in the test stage, wherein the zero point state includes a zero point state in which the signal value in the comprehensive gas flow signal changes from positive to negative and a zero point state in which the signal value changes from negative to positive; determine a plurality of second instants based on the plurality of first instants and a preset time interval, respectively, and determine the first instant and the second instant as the target instant; wherein the preset time interval is determined based on the breathing period of the target object; accordingly, the processing module 320 is further configured to: perform Fourier transform on the comprehensive gas flow signal and the comprehensive gas pressure signal in the case of not entering the test stage to obtain flow conversion data and pressure conversion data; and determine the breathing period based on the maximum value in the flow conversion data and the maximum value in the pressure conversion data.

[0122] On the basis of the above-mentioned embodiments, optionally, the processing module 320 is specifically configured to: perform waveform separation processing on the comprehensive gas signals corresponding to each of the preset frequencies respectively to obtain first comprehensive gas signals corresponding to each of the preset frequencies and second comprehensive gas signals corresponding to each of the preset frequencies; wherein the frequency components of the first comprehensive gas signals corresponding to each of the preset frequencies are the same as the frequency components of the sinusoidal excitation signals corresponding to the preset frequencies; generate target respiratory parameters corresponding to each of the preset frequencies and time corresponding relationships based on the first comprehensive gas signals corresponding to each of the preset frequencies; and correspondingly, for each target moment, generate a third comprehensive gas signal corresponding to the target moment based on the signal value of the second comprehensive gas signal corresponding to each of the preset frequencies at the target moment; and generate target respiratory parameters corresponding to each target moment and frequency corresponding relationships based on the third comprehensive gas signals corresponding to each target moment respectively.

[0123] On the basis of the above-mentioned embodiments, optionally, the processing module 320 is specifically configured to: perform surface fitting processing on the target respiratory parameters corresponding to each of the preset frequencies and time corresponding relationships to obtain a first fitting surface; perform surface fitting processing on the target respiratory parameters corresponding to each of the target moments and frequency corresponding relationships to obtain a second fitting surface; generate target respiratory parameter-frequency-time relationship data based on the first fitting surface and the second fitting surface; or input the target respiratory parameters corresponding to each of the preset frequencies and time corresponding relationships and the target respiratory parameters corresponding to each of the target moments and frequency corresponding relationships into a surface generation model constructed based on a preset deep learning algorithm to obtain the target respiratory parameter-frequency-time relationship data.

[0124] The processing module in the respiratory parameter dynamic measurement system provided in the embodiments of the present application can execute the respiratory parameter dynamic measurement method provided in any of the embodiments of the present application, and has the function modules and beneficial effects corresponding to the execution method.

[0125] Embodiment four

[0126] Figure 9 is a structural schematic diagram of an electronic device for implementing the respiratory parameter dynamic measurement method of the embodiments of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections, and relationships, and their functions, are merely examples and are not intended to limit the implementations of the present application described and / or claimed herein.

[0127] AsFigure 9 As shown, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., communicatively connected to the at least one processor 11, where the memory stores a computer program executable by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded into the random access memory (RAM) 13 from the storage unit 18. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0128] Various components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc., an output unit 17, such as various types of displays, a speaker, etc., a storage unit 18, such as a magnetic disk, an optical disk, etc., and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0129] The processor 11 can be various general and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the respiratory parameter dynamic measurement method.

[0130] In some embodiments, the respiratory parameter dynamic measurement method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the respiratory parameter dynamic measurement method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the respiratory parameter dynamic measurement method by any other appropriate means, such as by means of firmware.

[0131] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system on a chip systems (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0132] Computer programs used to implement the respiratory parameter dynamic measurement method of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program running on the processor implements the functions / operations specified in the flow charts and / or block diagrams. The computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on a remote machine or server.

[0133] Embodiment Five

[0134] Embodiment five of the present application also provides a computer readable storage medium, the computer readable storage medium storing computer instructions, the computer instructions being used for causing a processor to execute a respiratory parameter dynamic measurement method, the method being applied to a processing module in a respiratory parameter dynamic measurement system, and comprising:

[0135] The control excitation module generates target oscillation waves corresponding to a plurality of preset frequencies; the target oscillation waves corresponding to the plurality of preset frequencies are sequentially conducted to the airway opening of the target object through the sound guide tube; the target oscillation wave corresponding to any preset frequency is generated based on the pulse excitation signal and the sine excitation signal corresponding to the preset frequency; a comprehensive gas signal corresponding to each preset frequency is acquired; wherein the comprehensive gas signal corresponding to the preset frequency is acquired during the transmission of the target oscillation wave corresponding to the preset frequency in the sound guide tube; for each preset frequency, a target respiratory parameter corresponding to each preset frequency and time is generated based on the comprehensive gas signal corresponding to the preset frequency at each time; for each target time, a target respiratory parameter corresponding to each target time and frequency is generated based on the comprehensive gas signals corresponding to the plurality of preset frequencies at each target time; and a target respiratory parameter-frequency-time relationship data is generated based on the target respiratory parameter-time relationship corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency relationship corresponding to the plurality of target times.

[0136] In the context of the present application, a computer readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium can be a machine readable signal medium. More specific examples of the machine readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0137] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0138] The systems and techniques described herein can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described herein), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), blockchain network, and the Internet.

[0139] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. A server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0140] It should be understood that the various forms of flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be executed in parallel, executed in sequence, or executed in different orders, as long as the desired results of the technical solutions of the present disclosure are achieved, and the present disclosure is not limited herein.

[0141] Embodiment six

[0142] The embodiment six of the present application further provides a computer program product comprising a computer program which, when executed by a processor, implements the method for dynamically measuring a breathing parameter according to any one of the embodiments of the present application.

[0143] The above detailed description does not constitute a limitation on the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method of dynamic measurement of respiratory parameters, characterized in that, The application relates to a processing module applied to a respiratory parameter dynamic measurement system, and the method comprises the following steps: The control excitation module generates target oscillation waves corresponding to a plurality of preset frequencies; the target oscillation waves corresponding to the plurality of preset frequencies are sequentially conducted to the airway opening of a target object through an acoustic guide tube; the target oscillation wave corresponding to any preset frequency is generated based on a pulse excitation signal and a sine excitation signal corresponding to the preset frequency; A comprehensive gas signal corresponding to each preset frequency is acquired; wherein the comprehensive gas signal corresponding to the preset frequency is acquired during the conduction of the target oscillation wave corresponding to the preset frequency in the acoustic guide tube; For each preset frequency, a target respiratory parameter corresponding to each preset frequency and time is generated based on the comprehensive gas signal corresponding to the preset frequency at each moment; for each target moment, a target respiratory parameter corresponding to each target moment and frequency is generated based on the comprehensive gas signals corresponding to the plurality of preset frequencies at each target moment; A target respiratory parameter-frequency-time relationship data is generated based on the target respiratory parameter-time relationship corresponding to the plurality of preset frequencies and the target respiratory parameter-frequency relationship corresponding to the plurality of target moments.

2. The method of claim 1, wherein, The control excitation module generates target oscillation waves corresponding to a plurality of preset frequencies, which comprises the following steps: In the case of not entering the test stage, a comprehensive gas signal acquired in the acoustic guide tube is acquired; the excitation module is controlled to generate target oscillation waves corresponding to a plurality of preset frequencies based on the comprehensive gas signal; wherein in the case of not entering the test stage, the target object is in a normal breathing state; And in the test stage based on the target oscillation wave, a comprehensive gas signal acquired during the conduction of the target oscillation wave corresponding to the preset frequency in the acoustic guide tube is acquired; the excitation module is controlled to generate target oscillation waves corresponding to a plurality of preset frequencies based on the comprehensive gas signal.

3. The method of claim 2, wherein, The control excitation module generates target oscillation waves corresponding to a plurality of preset frequencies based on the comprehensive gas signal, which comprises the following steps: A plurality of target moments are determined based on the comprehensive gas signal; The excitation module is controlled to generate a pulse excitation signal corresponding to each target moment; The excitation module is controlled to generate a sine excitation signal corresponding to each preset frequency; The target oscillation wave corresponding to each preset frequency is generated based on the pulse excitation signal and the sine excitation signal corresponding to each preset frequency.

4. The method of claim 3, wherein, The comprehensive gas signal comprises a comprehensive gas flow signal and a comprehensive gas pressure signal; The plurality of target moments are determined based on the comprehensive gas signal, which comprises the following steps: A first moment corresponding to a zero point state in the comprehensive gas flow signal in the case of not entering the test stage or the test stage is determined, wherein the zero point state comprises a zero point state in which the signal value in the comprehensive gas flow signal changes from positive to negative and a zero point state in which the signal value changes from negative to positive; Determine a plurality of second time instants based on the plurality of first time instants and a preset time interval, and determine the first time instants and the second time instants as the target time instants; The preset time interval is determined based on a breathing cycle of the target object, and the target object is determined in the following manner: Perform Fourier transform on the comprehensive gas flow signal and the comprehensive gas pressure signal in the case that the test phase is not entered to obtain flow conversion data and pressure conversion data, and determine the breathing cycle based on a maximum value in the flow conversion data and a maximum value in the pressure conversion data.

5. The method of claim 1, wherein, For each preset frequency, generate a target breathing parameter corresponding to each preset frequency and a time corresponding relationship based on the comprehensive gas signal corresponding to each preset frequency at each time instant, including: Perform waveform separation processing on the comprehensive gas signal corresponding to each preset frequency to obtain a first comprehensive gas signal corresponding to each preset frequency and a second comprehensive gas signal corresponding to each preset frequency, wherein the frequency component of the first comprehensive gas signal corresponding to each preset frequency is the same as the frequency component of the sinusoidal excitation signal corresponding to the preset frequency. Generate a target breathing parameter corresponding to each preset frequency and a time corresponding relationship based on the first comprehensive gas signal at a plurality of time instants corresponding to each preset frequency. Correspondingly, for each target time instant, generate a target breathing parameter corresponding to each target time instant and a frequency corresponding relationship based on the comprehensive gas signal corresponding to each preset frequency at each target time instant, including: For each target time instant, generate a target breathing parameter corresponding to each target time instant and a frequency corresponding relationship based on the second comprehensive gas signal corresponding to each preset frequency at each target time instant.

6. The method of claim 1, wherein, Generate target breathing parameter-frequency-time relationship data based on the target breathing parameter corresponding to each preset frequency and the time corresponding relationship and the target breathing parameter corresponding to each target time instant and the frequency corresponding relationship, including: Perform surface fitting processing on the target breathing parameter corresponding to each preset frequency and the time corresponding relationship to obtain a first fitting surface, and perform surface fitting processing on the target breathing parameter corresponding to each target time instant and the frequency corresponding relationship to obtain a second fitting surface, and generate the target breathing parameter-frequency-time relationship data based on the first fitting surface and the second fitting surface. Or, input the target breathing parameter corresponding to each preset frequency and the time corresponding relationship and the target breathing parameter corresponding to each target time instant and the frequency corresponding relationship into a surface generation model constructed based on a preset deep learning algorithm to obtain the target breathing parameter-frequency-time relationship data.

7. The method of claim 1, wherein, After generating the target breathing parameter-frequency-time relationship data, further comprising: Determine a target breathing parameter corresponding to a target frequency and a time corresponding relationship based on the target breathing parameter-frequency-time relationship data and the target frequency. Determine a target breathing parameter corresponding to a target frequency and a time corresponding relationship based on the target breathing parameter-frequency-time relationship data and the target frequency. Determine a target respiratory parameter-frequency relationship corresponding to the target time point based on the target respiratory parameter-frequency-time relationship data and the target time point.

8. A respiratory parameter dynamic measurement system, characterized by, Comprise: The excitation module, the processing module, the gas signal acquisition module and the acoustic conduit; the excitation module is connected with the processing module in communication; the gas signal acquisition module is arranged on the inner surface of the acoustic conduit, and the gas signal acquisition module is connected with the processing module in communication; one end of the acoustic conduit is physically connected with the excitation module; wherein, The processing module is used for controlling the excitation module to generate target oscillation waves corresponding to a plurality of preset frequencies respectively; the target oscillation wave corresponding to any preset frequency is generated based on a pulse excitation signal and a sine excitation signal corresponding to the preset frequency; The excitation module is used for generating a plurality of target oscillation waves corresponding to a plurality of preset frequencies respectively in response to the control signal of the processing module, and the target oscillation waves corresponding to the plurality of preset frequencies are sequentially conducted to the airway opening of the target object through the acoustic conduit; The gas signal acquisition module is used for acquiring a comprehensive gas signal in the acoustic conduit; the comprehensive gas signal includes a plurality of comprehensive gas signals corresponding to a plurality of preset frequencies in a test phase and a comprehensive gas signal not in the test phase; The processing module is further used for generating a target respiratory parameter-time relationship corresponding to each of the preset frequencies based on the comprehensive gas signal corresponding to each of the preset frequencies; for each of the preset frequencies, a target respiratory parameter-time relationship corresponding to each of the preset frequencies is generated based on the comprehensive gas signal corresponding to the preset frequency at each time; for each target time, a target respiratory parameter-frequency relationship corresponding to each of the target times is generated based on a plurality of comprehensive gas signals corresponding to a plurality of preset frequencies at each of the target times; and target respiratory parameter-frequency-time relationship data is generated based on a plurality of target respiratory parameter-time relationships corresponding to a plurality of preset frequencies and a plurality of target respiratory parameter-frequency relationships corresponding to a plurality of target times.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions, and the computer instructions are used to make the processor execute the respiratory parameter dynamic measurement method in any one of claims 1-7.

10. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is used to make the processor execute the respiratory parameter dynamic measurement method in any one of claims 1-7 when executed.

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