A nasal flow measurement system and method based on bilateral nasal resistance

Abstract

The present application discloses a nasal flow test system and method based on bilateral nasal resistance, which relates to the technical field of medical equipment and solves the technical problem of inaccurate nasal resistance test results caused by environmental influences; the system and method include: a data acquisition module: acquiring test environment data and the measured nasal resistance of a person under test; a central processing module: acquiring historical data, grouping the historical data to obtain a test data group, and generating a standard nasal resistance range for each age group according to the test data group; acquiring the age data and test environment data of the person under test, and inputting the age data and the test environment data into an environmental influence model to obtain an adjustment factor; continuously acquiring the measured nasal resistance of the person under test; generating a real nasal resistance according to the measured nasal resistance and the adjustment factor; generating a warning signal according to the real nasal resistance and the standard nasal resistance range; analyzing the actually measured nasal resistance to obtain the real nasal resistance after removing the environmental influence, so as to increase the accuracy of subsequent nasal resistance analysis.

Description

Technical Field

[0001] The present application belongs to the field of medical equipment technology and relates to medical equipment technology, specifically a nasal flow testing system and method based on bilateral nasal resistance. Background Art

[0002] Since the application of nasal resistance detection technology, the diagnosis of nasal diseases has mainly relied on manual inspection and analysis and nasal endoscopy. These inspections are complicated and the test results are not accurate enough. With the development of electronic technology, there are now instruments for nasal resistance. The nasal resistance meter can objectively measure nasal breathing resistance, indicate the location and severity of nasal obstruction, and is very helpful for diagnosing nasal threshold lesions. It can be used in snoring and nasal stimulation experiments, and nasal ventilation research. It can effectively evaluate the efficacy of surgery and drug treatment, and is the best instrument for measuring nasal ventilation.

[0003] If nasal resistance is measured in cold air, the cold air will cause the nasal mucosal blood vessels to dilate and lead to increased nasal resistance, resulting in a gap between the measured nasal resistance and the actual nasal resistance of the person being measured. The existing technology often directly measures the nasal resistance of the person being measured through a nasal resistance measuring device. Due to environmental reasons, there is a gap between the directly measured nasal resistance and the actual nasal resistance of the person being measured, which may lead to erroneous analysis of the nasal resistance in the subsequent analysis.

[0004] Therefore, a nasal flow rate testing system and method based on bilateral nasal resistance is needed. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a nasal flow testing system and method based on bilateral nasal resistance, which is used to solve the technical problem of inaccurate nasal resistance detection results caused by environmental influences. The present application solves the above-mentioned problem by analyzing historical data, establishing a mapping relationship between nasal resistances between different test environments, and converting the measured nasal resistance into a nasal resistance closest to the true value according to the mapping relationship.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a nasal flow rate testing system and method based on bilateral nasal resistance, comprising: a central processing module, and a data acquisition module and a database connected thereto;

[0007] The data acquisition module: acquires the detection environment data through the data acquisition device connected thereto; acquires the measured nasal resistance of the tested person through the bilateral nasal resistance testing device connected thereto;

[0008] The central processing module: obtains historical data through a database, groups the historical data to obtain a detection data group, and generates a standard nasal resistance range for each age group according to the detection data group; and obtains the age data and detection environment data of the person being tested, and inputs the age data and the detection environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the person being tested; generates a real nasal resistance according to the measured nasal resistance and the adjustment factor; generates a warning signal according to the real nasal resistance and the standard nasal resistance range; wherein the environmental impact model is obtained through artificial intelligence model training.

[0009] The present application obtains historical data; groups the historical data to obtain test data groups, and generates standard nasal resistance ranges for various age groups based on the test data groups; obtains age data and test environment data of the person being tested, and inputs the age data and the test environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the person being tested, and generates a real nasal resistance based on the measured nasal resistance and the adjustment factor; generates a warning signal based on the real nasal resistance and the standard nasal resistance range; analyzes the actually measured nasal resistance to obtain the real nasal resistance with the environmental impact removed, so as to increase the accuracy of subsequent nasal resistance analysis.

[0010] Preferably, grouping the historical data to obtain the detection data group includes:

[0011] Extract several historical test data from the historical data; extract historical test age and historical environment data from the historical test data; group the historical test data according to the historical test age to generate age groups; extract the historical environment data in each age group, group the historical test data in the age group according to the historical environment data to generate a test data group; wherein the historical test data includes historical test age, historical environment data and historical nasal resistance data.

[0012] Preferably, generating the standard nasal resistance range for each age group according to the detection data set includes:

[0013] Extract several historical nasal resistance data from each test data group; calculate the variance and mean of the historical nasal resistance data in each test data group; and normalize the variance and mean corresponding to several test data groups in the same age group, and mark the normalized variance as GFi and the normalized mean as GPi; wherein i is the number of each test data group in the same age group;

[0014] The priority coefficient YXi of the corresponding test data group is calculated by the formula YXi=α1×GFi-α2×GPi; wherein i=1, 2, ..., nj; nj is the total number of test data groups in the age group numbered j; α1 and α2 are weight coefficients;

[0015] Acquire several historical nasal resistance data from the detection data group with the largest priority selection coefficient in the same age group; take the historical nasal resistance data with the largest value as the upper limit of the standard nasal resistance; take the historical nasal resistance data with the smallest value as the lower limit of the standard nasal resistance; record the numerical range between the upper limit of the standard nasal resistance and the lower limit of the standard nasal resistance as the standard nasal resistance range for the corresponding age group, and record the age range corresponding to the age group as the age group corresponding to the standard nasal resistance range.

[0016] Since the nasal structures of persons of different ages are different, the normal range of nasal resistance of persons of different ages is also different. The present application generates a test data group corresponding to each age group by classifying and counting the historical data according to the test age, and further analyzes the test data group to obtain the normal range of nasal resistance corresponding to different age groups. This enables the subsequent determination of whether the nasal resistance of the person being tested is abnormal to be more in line with the actual situation of the person being tested.

[0017] Preferably, the environmental impact model is obtained by training an artificial intelligence model, including:

[0018] Acquire historical environmental data and historical nasal resistance data in several detection data groups in several age groups, and generate a nasal resistance range corresponding to the historical environmental data using the maximum and minimum values ​​of the historical nasal resistance data;

[0019] Generate training data and test data based on the age range and historical environmental data corresponding to the age group, as well as the corresponding nasal resistance range and standard nasal resistance range;

[0020] Use training data to train the artificial intelligence model; use test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally, an environmental impact model with age data and test environment data as input and adjustment factors as output is obtained;

[0021] Among them, the adjustment factor is the mapping relationship between the nasal resistance range corresponding to the detection environment data and the standard nasal resistance range corresponding to the detection age; the detection environment data includes temperature data, humidity data and air quality data; the artificial intelligence model includes a BP neural network model or a RBF neural network model.

[0022] Preferably, generating the real nasal resistance according to the measured nasal resistance and the adjustment factor comprises:

[0023] The measured nasal resistance of the measured person is continuously obtained, and when the measured nasal resistance is stable, the corresponding adjustment factor is obtained; and the stabilized measured nasal resistance is converted into the corresponding real nasal resistance within the standard nasal resistance range according to the adjustment factor.

[0024] Preferably, generating the warning signal according to the actual nasal resistance and the standard nasal resistance range includes:

[0025] Obtaining the actual nasal resistance and the standard nasal resistance range corresponding to the age group corresponding to the detected age; determining whether the actual nasal resistance is within the standard nasal resistance range;

[0026] If yes, a normal signal is generated;

[0027] No, when the actual nasal resistance is lower than the lower limit of the standard nasal resistance range, abnormal signal one is generated, otherwise, abnormal signal two is generated; wherein, the warning signal includes abnormal signal one and abnormal signal two.

[0028] The second aspect of the present application provides a bilateral nasal resistance and nasal flow testing method, comprising the following steps:

[0029] Step 1: Obtain historical data; group the historical data to obtain a test data group, and generate a standard nasal resistance range for each age group based on the test data group;

[0030] Step 2: The age data of the tested person and the test environment data are input into the environmental impact model to obtain the adjustment factor;

[0031] Step 3: continuously obtain the measured nasal resistance of the person being measured, and generate the real nasal resistance according to the measured nasal resistance and the adjustment factor;

[0032] Step 4: Generate a warning signal based on the actual nasal resistance and the standard nasal resistance range.

[0033] Compared with the prior art, the beneficial effects of this application are:

[0034] 1. The present application obtains historical data; groups the historical data to obtain a test data group, and generates a standard nasal resistance range for each age group based on the test data group; obtains the age data and test environment data of the person being tested, and inputs the age data and the test environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the person being tested, and generates a real nasal resistance based on the measured nasal resistance and the adjustment factor; generates a warning signal based on the real nasal resistance and the standard nasal resistance range; analyzes the actual measured nasal resistance to obtain the real nasal resistance with the environmental impact removed, so as to increase the accuracy of subsequent nasal resistance analysis.

[0035] 2. The present application generates test data groups corresponding to each age group by classifying and counting historical data according to the test age, and further analyzes the test data groups to obtain the normal range of nasal resistance corresponding to different age groups; so that the subsequent judgment range corresponding to whether the nasal resistance of the tested person is abnormal is more in line with the actual situation of the tested person, and thus the judgment result is more in line with the actual situation of the tested person. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a schematic diagram of the principle of this application;

[0038] Figure 2 A diagram of the method steps in this application. DETAILED DESCRIPTION

[0039] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0040] See also Figure 1-Figure 2 , the first aspect of the present application provides a nasal flow test system and method based on bilateral nasal resistance, including: a central processing module, and a data acquisition module and a database connected thereto;

[0041] The data acquisition module: acquires the detection environment data through the data acquisition device connected thereto; acquires the measured nasal resistance of the person being tested through the bilateral nasal resistance test device connected thereto; the data acquisition device includes a temperature collector, a humidity collector, an air quality collector, etc.; the bilateral nasal resistance test device can be the bilateral nasal resistance test device recorded in the utility model patent with announcement number CN212394919U;

[0042] The central processing module: obtains historical data through a database, groups the historical data to obtain a detection data group, and generates a standard nasal resistance range for each age group according to the detection data group; and obtains the age data and detection environment data of the person being tested, and inputs the age data and the detection environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the person being tested; generates a real nasal resistance according to the measured nasal resistance and the adjustment factor; generates a warning signal according to the real nasal resistance and the standard nasal resistance range; wherein the environmental impact model is obtained through training of an artificial intelligence model, and the measured nasal resistance includes a measured left nasal resistance and a measured right nasal resistance.

[0043] The present application obtains historical data; groups the historical data to obtain test data groups, and generates standard nasal resistance ranges for various age groups based on the test data groups; obtains age data and test environment data of the person being tested, and inputs the age data and the test environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the person being tested, and generates a real nasal resistance based on the measured nasal resistance and the adjustment factor; generates a warning signal based on the real nasal resistance and the standard nasal resistance range; analyzes the actually measured nasal resistance to obtain the real nasal resistance with the environmental impact removed, so as to increase the accuracy of subsequent nasal resistance analysis.

[0044] The historical data are grouped to obtain the detection data group, including:

[0045] Extract several historical test data from the historical data; extract historical test age and historical environment data from the historical test data; group the historical test data according to the historical test age to generate age groups; extract the historical environment data in each age group, group the historical test data in the age group according to the historical environment data to generate a test data group; wherein the historical test data includes historical test age, historical environment data and historical nasal resistance data.

[0046] Since the nasal structures of persons of different ages are different, the normal range of nasal resistance of persons of different ages is also different. The present application generates a test data group corresponding to each age group by classifying and counting the historical data according to the test age, and further analyzes the test data group to obtain the normal range of nasal resistance corresponding to different age groups. This enables the subsequent determination of whether the nasal resistance of the person being tested is abnormal to be more in line with the actual situation of the person being tested.

[0047] Based on the test data set, standard nasal resistance ranges for various age groups are generated, including:

[0048] Extract several historical nasal resistance data from each test data group; calculate the variance and mean of the historical nasal resistance data in each test data group; and normalize the variance and mean corresponding to several test data groups in the same age group, and mark the normalized variance as GFi and the normalized mean as GPi; wherein i is the number of each test data group in the same age group;

[0049] The preferred selection coefficient YXi of the corresponding test data group is calculated by the formula YXi=α1×GFi-α2×GPi; wherein i=1, 2, ..., nj; nj is the total number of test data groups in the age group numbered j; α1 and α2 are weight coefficients; α1 and α2 are set according to experience;

[0050] Acquire several historical nasal resistance data from the detection data group with the largest priority selection coefficient in the same age group; take the historical nasal resistance data with the largest value as the upper limit of the standard nasal resistance; take the historical nasal resistance data with the smallest value as the lower limit of the standard nasal resistance; record the numerical range between the upper limit of the standard nasal resistance and the lower limit of the standard nasal resistance as the standard nasal resistance range for the corresponding age group, and record the age range corresponding to the age group as the age group corresponding to the standard nasal resistance range.

[0051] The present application obtains test data groups in each age group with relatively stable nasal resistance data and smaller nasal resistance values, and generates a standard nasal resistance range corresponding to the age group according to the maximum and minimum values ​​of the nasal resistance in the corresponding test data group. This allows each age group to use nasal resistance data with less environmental impact to generate a standard nasal resistance range, thereby increasing the reliability and accuracy of subsequent judgments on whether the nasal resistance is abnormal.

[0052] The environmental impact model is obtained through artificial intelligence model training, including:

[0053] Acquire historical environmental data and historical nasal resistance data in several detection data groups in several age groups, and generate a nasal resistance range corresponding to the historical environmental data using the maximum and minimum values ​​of the historical nasal resistance data;

[0054] Generate training data and test data based on the age range and historical environmental data corresponding to the age group, as well as the corresponding nasal resistance range and standard nasal resistance range;

[0055] Use training data to train the artificial intelligence model; use test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally, an environmental impact model with age data and test environment data as input and adjustment factors as output is obtained;

[0056] Among them, the adjustment factor is the mapping relationship between the nasal resistance range corresponding to the detection environment data and the standard nasal resistance range corresponding to the detection age; the detection environment data includes temperature data, humidity data, air quality data, etc.; it can be understood that the data types in the detection environment data and the historical environment data are the same, that is, the historical environment data also includes temperature data, humidity data, air quality data, etc.; the artificial intelligence model includes a BP neural network model or a RBF neural network model.

[0057] The present application obtains an environmental impact model by training an artificial intelligence model, and obtains an adjustment factor between the test environment data and the standard nasal resistance range corresponding to the test age by inputting the test environment data into the trained environmental impact model.

[0058] Generating the actual nasal resistance according to the measured nasal resistance and the adjustment factor includes:

[0059] Continuously obtain the measured nasal resistance of the person being measured, and when the measured nasal resistance is stable, obtain the corresponding adjustment factor; convert the stabilized measured nasal resistance into the corresponding real nasal resistance within the standard nasal resistance range according to the adjustment factor;

[0060] Specifically, extracting the adjustment factor, obtaining the adjustment sub-factor corresponding to the measured nasal resistance, and generating the real nasal resistance according to the adjustment sub-factor and the measured nasal resistance;

[0061] It can be understood that the adjustment factor is the mapping relationship between the nasal resistance range corresponding to the detection environment data and the standard nasal resistance range corresponding to the detection age; the adjustment factor should theoretically be the mapping relationship between the nasal resistance range and the corresponding standard nasal resistance range; by measuring the nasal resistance and its corresponding mapping relationship, the nasal resistance in the detection environment corresponding to the standard nasal resistance range can be obtained; because the standard nasal resistance range is obtained from the nasal resistance data with less environmental impact in each age group, the measured nasal resistance data is converted into the detection environment corresponding to the standard nasal resistance, so that the converted nasal resistance is closer to the real value that is not affected by the environment; thereby ensuring the accuracy of the nasal resistance.

[0062] The warning signal is generated according to the actual nasal resistance and the standard nasal resistance range, including:

[0063] Obtaining the actual nasal resistance and the standard nasal resistance range corresponding to the age group corresponding to the detected age; determining whether the actual nasal resistance is within the standard nasal resistance range;

[0064] If yes, a normal signal is generated;

[0065] No, when the actual nasal resistance is lower than the lower limit of the standard nasal resistance range, abnormal signal one is generated, otherwise, abnormal signal two is generated; wherein, the warning signal includes abnormal signal one and abnormal signal two.

[0066] Abnormal signal 1 is that the actual nasal resistance value is lower than the lower limit of the standard nasal resistance range, and the doctor can conduct corresponding examinations on the person being tested based on this; abnormal signal 2 is that the actual nasal resistance value is higher than the upper limit of the standard nasal resistance range, and the doctor can conduct corresponding examinations on the person being tested based on this;

[0067] It can be understood that in this embodiment, different abnormal signals can also be generated according to the size of the gap between the actual nasal resistance and the standard nasal resistance range. Only two are given in this embodiment. The more abnormal signals are set, the finer the division of nasal resistance is, and it is more convenient for doctors to conduct targeted examinations.

[0068] The second aspect of the present application provides a bilateral nasal resistance and nasal flow testing method, comprising the following steps:

[0069] Step 1: Obtain historical data; group the historical data to obtain a test data group, and generate a standard nasal resistance range for each age group based on the test data group;

[0070] Step 2: The age data of the tested person and the test environment data are input into the environmental impact model to obtain the adjustment factor;

[0071] Step 3: continuously obtain the measured nasal resistance of the person being measured, and generate the real nasal resistance according to the measured nasal resistance and the adjustment factor;

[0072] Step 4: Generate a warning signal based on the actual nasal resistance and the standard nasal resistance range.

[0073] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0074] How this application works:

[0075] The present application obtains historical data; groups the historical data to obtain test data groups, and generates standard nasal resistance ranges for various age groups based on the test data groups; obtains age data and test environment data of the person being tested, and inputs the age data and the test environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the person being tested, and generates a real nasal resistance based on the measured nasal resistance and the adjustment factor; generates a warning signal based on the real nasal resistance and the standard nasal resistance range; analyzes the actually measured nasal resistance to obtain the real nasal resistance with the environmental impact removed, so as to increase the accuracy of subsequent nasal resistance analysis.

[0076] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A bilateral nasal resistance and nasal flow testing system, comprising: The central processing module, and the data acquisition module and database connected thereto are characterized in that: The data acquisition module: acquires the detection environment data through the data acquisition device connected thereto; acquires the measured nasal resistance of the tested person through the bilateral nasal resistance testing device connected thereto; The central processing module: obtains historical data through a database, groups the historical data to obtain a test data group, generates a standard nasal resistance range for each age group according to the test data group; and obtains age data and test environment data of the tested person, inputs the age data and the test environment data into an environmental impact model to obtain an adjustment factor; continuously obtains the measured nasal resistance of the tested person; generates a real nasal resistance according to the measured nasal resistance and the adjustment factor; generates a warning signal according to the real nasal resistance and the standard nasal resistance range; wherein the environmental impact model is obtained by training an artificial intelligence model; The historical data is grouped to obtain the detection data group, including: Extract several historical test data from the historical data; extract historical test age and historical environment data from the historical test data; group the historical test data according to the historical test age to generate age groups; extract the historical environment data in each age group, group the historical test data in the age group according to the historical environment data to generate a test data group; wherein the historical test data includes historical test age, historical environment data and historical nasal resistance data.

2. A bilateral nasal resistance and nasal flow testing system according to claim 1, characterized in that: The standard nasal resistance range for each age group is generated according to the detection data group, including: Extract several historical nasal resistance data from each test data group; calculate the variance and mean of the historical nasal resistance data in each test data group; and normalize the variance and mean corresponding to several test data groups in the same age group, and mark the normalized variance as GFi and the normalized mean as GPi; wherein i is the number of each test data group in the same age group; The priority coefficient YXi of the corresponding test data group is calculated by the formula YXi=α1×GFi-α2×GPi; where i=1, 2, …, nj; nj is the total number of test data groups in the age group numbered j; α1 and α2 are weight coefficients; Acquire several historical nasal resistance data from the detection data group with the largest priority selection coefficient in the same age group; take the historical nasal resistance data with the largest value as the upper limit of the standard nasal resistance; take the historical nasal resistance data with the smallest value as the lower limit of the standard nasal resistance; record the numerical range between the upper limit of the standard nasal resistance and the lower limit of the standard nasal resistance as the standard nasal resistance range for the corresponding age group, and record the age range corresponding to the age group as the age group corresponding to the standard nasal resistance range.

3. A bilateral nasal resistance and nasal flow testing system according to claim 2, characterized in that: The environmental impact model is obtained through artificial intelligence model training, including: Acquire historical environmental data and historical nasal resistance data in several detection data groups in several age groups, and generate a nasal resistance range corresponding to the historical environmental data using the maximum and minimum values ​​of the historical nasal resistance data; Generate training data and test data based on the age range and historical environmental data corresponding to the age group, as well as the corresponding nasal resistance range and standard nasal resistance range; Use training data to train the artificial intelligence model; use test data to test the trained artificial intelligence model, and adjust the artificial intelligence model according to the test results; finally, an environmental impact model with age data and test environment data as input and adjustment factors as output is obtained; Among them, the adjustment factor is the mapping relationship between the nasal resistance range corresponding to the detection environment data and the standard nasal resistance range corresponding to the detection age; the detection environment data includes temperature data, humidity data and air quality data; the artificial intelligence model includes a BP neural network model or a RBF neural network model.

4. A bilateral nasal resistance and nasal flow testing system according to claim 1, characterized in that: Generating the actual nasal resistance according to the measured nasal resistance and the adjustment factor includes: The measured nasal resistance of the measured person is continuously obtained, and when the measured nasal resistance is stable, the corresponding adjustment factor is obtained; and the stabilized measured nasal resistance is converted into the corresponding real nasal resistance within the standard nasal resistance range according to the adjustment factor.

5. A bilateral nasal resistance and nasal flow testing system according to claim 1, characterized in that: The warning signal is generated according to the actual nasal resistance and the standard nasal resistance range, including: Obtaining the actual nasal resistance and the standard nasal resistance range corresponding to the age group corresponding to the detected age; determining whether the actual nasal resistance is within the standard nasal resistance range; If yes, a normal signal is generated; No, when the actual nasal resistance is lower than the lower limit of the standard nasal resistance range, abnormal signal one is generated, otherwise, abnormal signal two is generated; wherein, the warning signal includes abnormal signal one and abnormal signal two.

6. A bilateral nasal resistance and nasal flow test method, based on any one of claims 1 to 5 of the bilateral nasal resistance and nasal flow test system, characterized in that: The following steps are involved: Step 1: Obtain historical data; group the historical data to obtain a test data group, and generate a standard nasal resistance range for each age group based on the test data group; Step 2: The age data of the tested person and the test environment data are input into the environmental impact model to obtain the adjustment factor; Step 3: continuously obtain the measured nasal resistance of the person being measured, and generate the real nasal resistance according to the measured nasal resistance and the adjustment factor; Step 4: Generate a warning signal based on the actual nasal resistance and the standard nasal resistance range.

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