A Spirometer Linearity Testing System

Abstract

The present invention relates to the field of linearity testing technology, and specifically to a spirometer linearity testing system, which includes a differential pressure measurement module, a frequency characteristic analysis module, a dynamic performance testing module, and a comprehensive testing module. In the present invention, by measuring the pressure difference of the spirometer under various flow rates and applying fast Fourier transform to the data, a deep analysis of the spirometer performance is achieved, allowing the system to accurately evaluate the performance of the spirometer under extreme breathing frequencies, such as fast or slow breathing patterns. At the same time, by simulating various breathing frequencies to perform dynamic performance tests, the deviation of the spirometer can be effectively identified and corrected, ensuring the repeatability and stability of its test results. In addition, the test parameters are adjusted according to the performance under various environmental conditions, and comprehensive performance tests and parameter optimization are performed to further improve the accuracy and reliability of the linearity test of the spirometer and ensure the accuracy of medical diagnosis.

Description

Technical Field

[0001] The invention relates to the technical field of linearity testing, and in particular to a spirometer linearity testing system. Background Art

[0002] Linearity testing focuses on evaluating and verifying the consistency and accuracy of instrument measurements. In medical devices, linearity testing is used to ensure that the device provides stable and repeatable results throughout its entire measurement range. The test usually includes analyzing the proportional relationship between the instrument response and the actual physical quantity to ensure that the device output accurately reflects the actual value of the measured value regardless of the test conditions. It is not limited to medical devices, but is also widely used in various industrial and scientific research fields. It aims to improve overall operational efficiency and product quality by improving the accuracy and reliability of measurement tools.

[0003] Among them, the spirometer linearity test system is a system specially designed to evaluate and calibrate the performance and accuracy of the spirometer (a medical instrument that measures lung volume). Its main purpose is to ensure that the spirometer can accurately measure lung capacity within its operating range, thereby accurately diagnosing the patient's lung function. By testing the linearity of the spirometer, any deviation or error in the spirometer can be identified and corrected, ensuring the accuracy of medical diagnosis and the effectiveness of patient treatment.

[0004] Although existing technologies focus on the consistency and accuracy of instrument measurements, they fail to fully adapt to the rapidly changing clinical environment, especially when dealing with the accurate measurement of fast or slow breathing patterns. Traditional testing methods often fail to adjust and optimize test parameters in real time to adapt to changes in the environment and patient status, leading to misdiagnosis or delayed diagnosis in clinical emergencies. In addition, these methods often lack the necessary adaptability in the face of environmental changes, resulting in unstable performance of the equipment under different operating conditions, which in turn affects the consistency of diagnosis, increases uncertainty in patient treatment, and affects the overall use of spirometers. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a spirometer linearity testing system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A spirometer linearity testing system comprises:

[0007] The differential pressure measurement module determines the test point of the spirometer, installs a differential pressure sensor at the test point, identifies the pressure change by measuring the pressure difference at the test point under differential flow, and obtains a micro-pressure difference data set;

[0008] The frequency characteristic analysis module performs fast Fourier transform according to the micro-pressure difference data set, analyzes the spirometer signal performance under the difference frequency, establishes the adaptability of the spirometer to fast and slow breathing patterns according to the analysis results, and obtains the frequency characteristic index;

[0009] The dynamic performance test module simulates the differential breathing frequency from the fast breathing mode to the slow breathing mode based on the frequency characteristic index, tests the linearity performance of the spirometer under extreme breathing changes according to the simulation results, and generates a linearity test result;

[0010] The comprehensive test module analyzes the performance of the spirometer under different environmental conditions based on the linearity test results, adjusts and refines the test parameters based on the performance results, matches the breathing rate and pressure changes, ensures the measurement accuracy and consistency of the spirometer, and establishes a comprehensive test result of the spirometer linearity.

[0011] As a further solution of the present invention, the steps for acquiring the micro-pressure difference data set are:

[0012] Select the test point for installing the differential pressure sensor, based on the fluid dynamic characteristics and environmental conditions, using the formula:

[0013] ;

[0014] Calculate the static pressure at each test point and generate preliminary static pressure results, where: represents static pressure, represents the fluid density, represents the acceleration due to gravity, Represents the height difference, is the environmental adjustment factor, used to compensate for environmental impacts;

[0015] Select the sensor based on the preliminary static pressure results, using the formula:

[0016] ;

[0017] Predict pressure differences under differential flow, determine sensor range, and generate sensor configuration results, where represents the expected pressure difference, and Represent the pressure values ​​at high flow and low flow respectively. is the correction factor, is the sensitivity adjustment parameter;

[0018] Perform the differential pressure sensor test, based on the sensor configuration results, using the formula:

[0019] ;

[0020] Verify sensor accuracy and generate verification test results; among them, Represents flow, represents the flow coefficient, is the measured pressure difference, is the fluid density, is the flow response adjustment factor;

[0021] Collect and analyze the verification test results, applying the formula:

[0022]

[0023] Calculate the micro-pressure difference data and obtain the micro-pressure difference data set, where: and are the sensitivity and accuracy parameters adjusted for the difference test points, Represents the actual flow rate, represents the reference flow, Represents the weighted difference between the flow data at the difference test point and the reference flow value.

[0024] As a further embodiment of the present invention, the steps of analyzing the spirometer signal performance at the difference frequency are:

[0025] After screening and cleaning the micro-pressure difference data set, the data integrity and applicability were determined and the formula was used:

[0026]

[0027] in, is the signal strength value, is the DC component, and is the amplitude coefficient, is the fundamental angular frequency, It's time. is the number of harmonic terms, generating a time series signal dataset;

[0028] Based on the time series signal data set, fast Fourier transform is applied to convert the data from time domain to frequency domain, and the formula is applied:

[0029]

[0030] in, is the response value, is the total number of samples, is the frequency index, generating a frequency domain data set;

[0031] Based on the frequency domain data set, identify and label the key frequency components, using the formula:

[0032]

[0033] in, Indicates the frequency The power spectral density at the frequency The energy distribution on the image is used to mark the performance characteristics of the spirometer signal at different frequencies and generate the spirometer frequency analysis results.

[0034] As a further solution of the present invention, the step of obtaining the frequency characteristic index is:

[0035] The spirometer frequency analysis results were integrated to summarize the key frequency response characteristics into a graph using the formula:

[0036]

[0037] in, represents the weighted frequency response value, is the frequency The weight coefficient indicates the importance. Indicates the total number of analysis results and generates a frequency response overview graph;

[0038] Based on the frequency response overview graphs, evaluate the performance of the spirometer in fast and slow breathing patterns, comparing the consistency of response at different breathing rates, and apply the formula:

[0039]

[0040] in, represents the adaptive ratio between fast and slow breathing patterns, and Represents the critical frequencies of fast and slow breathing, determines the adaptability and performance of the spirometer, and generates the adaptability assessment results of the spirometer;

[0041] According to the adaptability evaluation results, the key frequency characteristic indicators of the spirometer are extracted and defined to reveal the performance of the spirometer under different breathing modes, using the formula:

[0042] ;

[0043] in, represents a set of frequency characteristic indicators, is a single indicator, is the corresponding adaptability evaluation result, and Threshold is the indicator extraction threshold function, which is used to determine the significant performance characteristics and generate frequency characteristic indicators.

[0044] As a further solution of the present invention, the simulation step of the differential respiratory frequency is:

[0045] Extract the frequency index of fast and slow breathing patterns from the frequency characteristic index, calculate the average frequency, and apply the formula:

[0046]

[0047] in, Indicates the reference frequency value, Indicates the frequency of fast and slow modes, is the corresponding weight factor, which represents the occurrence frequency of the corresponding pattern and generates the reference frequency.

[0048] Based on the base frequency, set the simulator parameters to reflect the frequency change from fast to slow, and adjust the frequency using the period adjustment function:

[0049]

[0050] in, is the maximum cycle time, Generate a simulation period adjustment scheme for phase adjustment parameters;

[0051] The simulation cycle adjustment scheme is used to simulate the breathing cycle from fast to slow, using the formula:

[0052]

[0053] Data collection is performed, including is the simulated breathing volume value, is the maximum respiratory volume, is the attenuation coefficient, is the time, simulating the natural decay of volume during breathing and generating simulated breathing data.

[0054] As a further solution of the present invention, the steps of obtaining the linearity test results are:

[0055] According to the simulated breathing data, the test parameters of the spirometer are adjusted to capture every change point from the fastest to the slowest breathing rate, using the formula:

[0056]

[0057] in, is the average frequency value calculated based on the spirometry test requirements, is the expected frequency variation range, is the time constant, adjusting the rate of response, is the adjustment constant, which is used to adjust the frequency response sensitivity of the spirometer to obtain the adjustment parameter;

[0058] According to the adjustment parameters, actual data acquisition is performed to record the response to the simulated breathing waveform, using the formula:

[0059]

[0060] Calculate linearity where The linearity percentage reflects the performance accuracy of the spirometer. is the respiratory volume actually recorded by the spirometer, is the expected breathing volume in the simulation data, and the test data are obtained;

[0061] The test data are summarized and the performance of the spirometer under different respiratory rates is analyzed to reveal the adaptability and accuracy of the spirometer under extreme conditions and generate linearity test results.

[0062] As a further aspect of the present invention, the steps of analyzing the performance of the spirometer under different environmental conditions are:

[0063] Integrate all data obtained from the linearity test results to comprehensively evaluate the performance of the spirometer in different test environments using the formula:

[0064]

[0065] Calculate the environmental performance score, where is the weighted score, It is Performance scores in different environments, is the weight of the target environment, using logarithmic enhancement to affect the difference, is the nonlinear coefficient used to enhance the score difference;

[0066] According to the environmental performance scores, an index adjustment method is used to optimize the test parameters to match the different environments:

[0067]

[0068] in, is the new test parameter, are the original test parameters, is the adjustment sensitivity coefficient, which is used to control the adjustment rate. is the target performance score, and the adjusted parameter set is obtained;

[0069] Based on the described adjusted parameter set, the performance of the spirometer under standardized test conditions was evaluated and a new performance benchmark was established:

[0070]

[0071] in, represents the performance benchmark, It is The performance results of the tests are: Represents the total number of tests, and the square root of the square sum average is used to evaluate the consistency of the overall performance to obtain the performance evaluation result.

[0072] As a further solution of the present invention, the steps for obtaining the spirometer linearity comprehensive test result are:

[0073] Based on the performance evaluation results, the spirometer test parameters were refined to match the differences in breathing rate and pressure changes:

[0074]

[0075] in, is the respiratory volume actually recorded by the spirometer, is the expected breathing volume in the simulated data It is an adjustment factor used to optimize the matching effect, avoid zero division errors, and output refined parameters ;

[0076] Verify the adjustment effect in an actual test environment according to the refined parameters to ensure that the spirometer maintains high accuracy and consistency under changing breathing conditions and obtain verification data;

[0077] Based on the validation data, the performance of the spirometer was re-evaluated and a comprehensive spirometer linearity test result was generated:

[0078]

[0079] in, is the total number of tests, represents the average performance index of the spirometer under all test conditions, expressed as a percentage, Indicates The performance results of the tests are: Represents a performance benchmark.

[0080] Compared with the prior art, the advantages and positive effects of the present invention are:

[0081] In the present invention, by measuring the pressure difference of the spirometer at various flow rates and applying fast Fourier transform to the data, an in-depth analysis of the spirometer performance is achieved, allowing the system to accurately evaluate the performance of the spirometer under extreme breathing rates, such as fast or slow breathing patterns. At the same time, by simulating various breathing rates to perform dynamic performance tests, the deviation of the spirometer can be effectively identified and corrected to ensure the repeatability and stability of its test results. In addition, the test parameters are adjusted according to the performance under various environmental conditions, and comprehensive performance testing and parameter optimization are performed to further improve the accuracy and reliability of the spirometer linearity test and ensure the accuracy of medical diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a system flow chart of the present invention;

[0083] Figure 2 The flowchart of obtaining the micro-pressure difference data set of the present invention;

[0084] Figure 3 is a flow chart of analyzing the spirometer signal performance under the difference frequency of the present invention;

[0085] Figure 4 A flowchart for obtaining the frequency characteristic index of the present invention;

[0086] Figure 5 is a simulation flow chart of the differential respiratory frequency of the present invention;

[0087] Figure 6 A flowchart for obtaining the linearity test results of the present invention;

[0088] Figure 7 is a flow chart for analyzing the performance of the spirometer of the present invention under different environmental conditions;

[0089] Figure 8 The present invention is a flowchart for obtaining the comprehensive test results of the spirometer linearity. DETAILED DESCRIPTION

[0090] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0091] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0092] Embodiment 1

[0093] See also Figure 1 , a spirometer linearity testing system comprising:

[0094] The differential pressure measurement module determines the test point of the spirometer, installs a differential pressure sensor at the test point, identifies the pressure change by measuring the pressure difference at the test point under differential flow, and obtains a micro-pressure difference data set;

[0095] The frequency characteristic analysis module performs fast Fourier transform based on the micro-pressure difference data set, analyzes the spirometer signal performance under the difference frequency, and based on the analysis results, establishes the adaptability of the spirometer to fast and slow breathing patterns and obtains the frequency characteristic index;

[0096] The dynamic performance test module simulates the difference in breathing frequency from fast breathing mode to slow breathing mode based on the frequency characteristic index, tests the linearity performance of the spirometer under extreme breathing changes according to the simulation results, and generates linearity test results;

[0097] The comprehensive test module analyzes the performance of the spirometer under different environmental conditions based on the linearity test results. Based on the performance results, it adjusts and refines the test parameters to match the breathing rate and pressure changes, ensure the measurement accuracy and consistency of the spirometer, and establish a comprehensive test result of the spirometer linearity.

[0098] The micro-pressure difference data set includes the test point pressure value, pressure change rate and pressure measurement error value. The frequency characteristic indicators include signal stability, response speed and adaptability score. The linearity test results include test linear deviation analysis results, linear response diagram and linearity rating. The comprehensive spirometer linearity test results include environmental adaptation evaluation results, parameter stability analysis results and consistency measurement records.

[0099] See also Figure 2 , the steps to obtain the micro-pressure difference data set are:

[0100] Select the test point for installing the differential pressure sensor, based on the fluid dynamic characteristics and environmental conditions, using the formula:

[0101] ;

[0102] Calculate the static pressure at each test point and generate preliminary static pressure results, where: represents static pressure, represents the fluid density, represents the acceleration due to gravity, Represents the height difference, is the environmental adjustment factor, used to compensate for environmental impacts;

[0103] Select the sensor based on the preliminary static pressure results, using the formula:

[0104] ;

[0105] Predict the pressure difference at different flow rates, determine the sensor range, and generate sensor configuration results, where: represents the expected pressure difference, and Represent the pressure values ​​at high flow and low flow respectively. is the correction factor, is the sensitivity adjustment parameter;

[0106] To perform the differential pressure sensor test, use the following formula based on the sensor configuration results:

[0107] ;

[0108] Verify sensor accuracy and generate verification test results; among them, Represents flow, represents the flow coefficient, is the measured pressure difference, is the fluid density, is the flow response adjustment factor;

[0109] Collect and analyze the calibration test results, applying the formula:

[0110]

[0111] Calculate the micro-pressure difference data and obtain the micro-pressure difference data set, where: and are the sensitivity and accuracy parameters adjusted for the difference test points, Represents the actual flow rate, represents the reference flow, Represents the weighted difference between the flow data at the difference test point and the reference flow value.

[0112] Assume the following data exists:

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] Substituting into the formula, we get:

[0118] ;

[0119] Calculated static pressure yes , which represents the static pressure value at the test point under given conditions of altitude, fluid density and gravitational acceleration.

[0120] Pressure difference prediction, assuming:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] Substituting into the formula, we get:

[0126] ;

[0127] Calculated expected pressure difference yes It means the predicted pressure change under set high and low flow conditions.

[0128] Flow calculation:

[0129] : Flow rate (cubic meters per second, );

[0130] : Flow coefficient, set to 0.8;

[0131] : The measured pressure difference is obtained using the previous ;

[0132] : Fluid density ;

[0133] : Flow response adjustment factor, set to 2;

[0134] Substituting into the formula, we get:

[0135]

[0136] The traffic obtained yes , which represents the flow value passing through the sensor under a given pressure difference and fluid density.

[0137] Calculation of micro pressure difference data set, assuming:

[0138] ;

[0139] ;

[0140] ;

[0141] (obtained according to the above calculation);

[0142] ;

[0143] ;

[0144] ;

[0145] Substituting into the formula, we get:

[0146]

[0147]

[0148]

[0149]

[0150]

[0151] Calculated The value is . This value represents the weighted difference between the flow data at different test points and the reference flow value, where the weights take into account the sensitivity and accuracy of each test point. This data set can be used to evaluate the performance differences of the sensor under different operating conditions and to conduct a comprehensive analysis of the impact of micro-pressure differential data. This value shows the weighted cumulative impact of flow changes between test points and can be used for further analysis and adjustment of sensor configuration to optimize performance and accuracy.

[0152] See also Figure 3 , the analysis steps of the spirometer signal performance at the difference frequency are:

[0153] After screening and cleaning the micro-pressure difference data set, the data integrity and applicability are determined, and the formula is used:

[0154]

[0155] in, is the signal strength value, is the DC component, and is the amplitude coefficient, is the fundamental angular frequency, It's time. is the number of harmonic terms, generating a time series signal dataset;

[0156] Based on the time series signal data set, apply fast Fourier transform to convert the data from time domain to frequency domain, and apply the formula:

[0157]

[0158] in, is the response value, is the total number of samples, is the frequency index, generating a frequency domain data set;

[0159] Based on the frequency domain data set, identify and label the key frequency components, using the formula:

[0160]

[0161] in, Indicates the frequency The power spectral density at the frequency The energy distribution on the image is used to mark the performance characteristics of the spirometer signal at different frequencies and generate the spirometer frequency analysis results.

[0162] Data collation, assuming the following data exists:

[0163] : DC component, affecting the baseline of the signal, assuming 0.5 represents the average value of the signal;

[0164] and : Amplitude coefficient, which determines the intensity of each harmonic component. Assumptions: , corresponding to the amplitude of the first harmonic;

[0165] : Basic angular frequency, assumed to be rad / s, represents the fundamental frequency period in the signal;

[0166] : Time variable, assuming the observation time is from 0 to 1 second;

[0167] Calculate at t=0.5 seconds Value, considering two harmonic terms (M=2):

[0168] Substituting into the formula, we get:

[0169]

[0170]

[0171]

[0172]

[0173] Calculation results It shows that at 0.5 seconds, the signal strength after sorting is 0.4.

[0174] Fast Fourier Transform:

[0175] : Sample points of time series signal, assuming , corresponding to the signal intensity at two different time points;

[0176] : Total number of samples, here we take 2;

[0177] : Frequency index, will calculate and ,but:

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184] The calculation results show that There is a small negative response (-0.1) in the frequency component of There is a small positive response (0.1) in the frequency component of . This indicates that the signal changes at these two frequency points.

[0185] Frequency Analysis:

[0186]

[0187]

[0188] The calculation results show that the power spectrum density at the two frequency points (0 and 1) is 0.01, indicating that the energy distribution of the signal at these two frequencies is the same, providing basic data for further analysis.

[0189] See also Figure 4 , the steps to obtain the frequency characteristic index are:

[0190] The spirometer frequency analysis results were integrated to summarize the key frequency response characteristics into a graph using the formula:

[0191]

[0192] in, represents the weighted frequency response value, is the frequency The weight coefficient indicates the importance. Indicates the total number of analysis results and generates a frequency response overview graph;

[0193] Based on the rate response overview graph, evaluate the performance of the spirometer in fast and slow breathing patterns, compare the consistency of the response at different breathing rates, and apply the formula:

[0194]

[0195] in, represents the adaptive ratio between fast and slow breathing patterns, and Represents the critical frequencies of fast and slow breathing, determines the adaptability and performance of the spirometer, and generates the adaptability assessment results of the spirometer;

[0196] According to the adaptability evaluation results, the key frequency characteristic indicators of the spirometer are extracted and defined to reveal the performance of the spirometer under different breathing modes, using the formula:

[0197] ;

[0198] in, represents a set of frequency characteristic indicators, is a single indicator, is the corresponding adaptability evaluation result, and Threshold is the indicator extraction threshold function, which is used to determine the significant performance characteristics and generate frequency characteristic indicators.

[0199] Integrate the analysis results:

[0200] : The weight coefficient of each frequency point, which affects the importance of the frequency. Assume that and , weights are 0.6 and 0.4 respectively;

[0201] : The frequency of the above calculation The power spectral density of and ;

[0202] Substituting into the formula, we get:

[0203]

[0204]

[0205]

[0206] Calculation results It shows the response value of the integrated frequency response diagram at frequency point 0, which shows the overall frequency response characteristics after weighted processing and is suitable for further adaptability analysis.

[0207] Adaptability Assessment:

[0208] and : represent the critical frequencies of fast and slow breathing, respectively. Assume and ;

[0209] Substituting into the formula, we get:

[0210]

[0211]

[0212]

[0213] Calculation results showed that the spirometer was twice as adaptable to fast breathing patterns as to slow patterns, indicating that the device was more responsive to rapid changes.

[0214] Frequency characteristic index extraction:

[0215] Threshold is the indicator extraction threshold function, and the threshold function is set to ,but:

[0216]

[0217]

[0218]

[0219] Calculation results It shows that the key performance indicator of the spirometer is 2, which means that the adaptability of the spirometer in the rapid breathing mode is higher than the set threshold, reflecting the high sensitivity of the device to rapid changes.

[0220] See also Figure 5 , the simulation steps of differential respiratory frequency are:

[0221] Extract the frequency index of fast and slow breathing patterns from the frequency characteristic index, calculate the average frequency, and apply the formula:

[0222]

[0223] in, Indicates the reference frequency value, Indicates the frequency of fast and slow modes, is the corresponding weight factor, which represents the occurrence frequency of the corresponding pattern and generates the reference frequency.

[0224] Based on the base frequency, set the simulator parameters to reflect the frequency change from fast to slow. Use the period adjustment function to adjust the frequency:

[0225]

[0226] in, is the maximum cycle time, Generate a simulation period adjustment scheme for phase adjustment parameters;

[0227] Using the simulated cycle adjustment scheme, simulate the breathing cycle from fast to slow, through the formula:

[0228]

[0229] Data collection is performed, including is the simulated breathing volume value, is the maximum respiratory volume, is the attenuation coefficient, is the time, simulating the natural decay of volume during breathing and generating simulated breathing data.

[0230] Assume the following parameters:

[0231] Fast mode frequency times / minute, weight ;

[0232] Slow mode frequency times / minute, weight ;

[0233] Substituting into the formula for calculation, we get:

[0234]

[0235] This indicates that the average frequency of the simulation is 16 breaths per minute, providing an accurate frequency benchmark for simulation settings.

[0236] Simulation parameter settings:

[0237] : Maximum cycle time, assumed to be 60 seconds;

[0238] : Phase adjustment, set to To optimize the changes in simulation cycle;

[0239] Substitute into the formula to calculate:

[0240]

[0241]

[0242] This means that at the beginning of the simulation, each breathing cycle is 30 seconds, and it gradually adjusts from fast mode to slow mode.

[0243] Generate simulated breathing data:

[0244] : Maximum respiratory volume, set to 500mL;

[0245] : Attenuation coefficient, set to 0.1, indicating that the volume decays by 10% per second;

[0246] : time, set to 1s;

[0247] Substituting into the formula, we get:

[0248] ;

[0249] ;

[0250] It shows that at 1 second, the simulated respiratory volume is about 450 mL, providing realistic input data for testing the responsiveness of the spirometer.

[0251] See also Figure 6 , the steps to obtain the linearity test results are:

[0252] According to the simulated breathing data, adjust the test parameters of the spirometer to capture every change point from the fastest to the slowest breathing rate, using the formula:

[0253] ;

[0254] in, is the average frequency value calculated based on the spirometry test requirements, is the expected frequency variation range, is the time constant, adjusting the rate of response, is the adjustment constant, which is used to adjust the frequency response sensitivity of the spirometer to obtain the adjustment parameter;

[0255] According to the adjustment parameters, actual data acquisition is performed to record the response to the simulated breathing waveform, using the formula:

[0256]

[0257] Calculate linearity where The linearity percentage reflects the performance accuracy of the spirometer. is the respiratory volume actually recorded by the spirometer, is the expected breathing volume in the simulation data, and the test data are obtained;

[0258] The test data is summarized and the performance of the spirometer under different respiratory rates is analyzed to reveal the adaptability and accuracy of the spirometer under extreme conditions and generate linearity test results.

[0259] Assumptions:

[0260] The average frequency calculated above is assumed to be 16 times / minute;

[0261] : The frequency range is from 10 to 30 times / minute, so times / minute;

[0262] The time constant is set to 1 minute to adjust the response speed;

[0263] Adjust the constant, assume it is 0.5, to optimize the sensitivity of the frequency regulation;

[0264] Substitute into the formula to calculate:

[0265] ;

[0266] This means that the spirometer will be configured to have a specific sensitivity. Responding to frequency changes ensures a stable performance assessment throughout the test. In this way, it is possible to ensure that the spirometer has an accurate and continuous response to frequency changes from fast to slow breathing patterns.

[0267] set up:

[0268] : The respiratory volume actually measured by the spirometer, assuming that at a specific point in time ;

[0269] : The expected respiratory volume based on the simulation parameters, assuming that the expected volume at the same time point is ;

[0270] but:

[0271] ;

[0272] ;

[0273] ;

[0274] Calculation results In this test, the performance of the spirometer showed a high The high linearity of the spirometer indicates that the spirometer can follow the simulated breathing changes very accurately. This high linearity indicates that the spirometer is extremely accurate and reliable in measuring real breathing patterns.

[0275] See also Figure 7 , the steps for analyzing the performance of the spirometer under different environmental conditions are:

[0276] Integrate all data obtained from the linearity test results to comprehensively evaluate the performance of the spirometer in different test environments using the formula:

[0277]

[0278] Calculate the environmental performance score, where is the weighted score, It is Performance scores in different environments, is the weight of the target environment, using logarithmic enhancement to affect the difference, is the nonlinear coefficient used to enhance the score difference;

[0279] Based on the environmental performance scores, the index adjustment method is used to optimize the test parameters to match the different environments:

[0280] ;

[0281] in, is the new test parameter, are the original test parameters, is the adjustment sensitivity coefficient, which is used to control the adjustment rate. is the target performance score, and the adjusted parameter set is obtained;

[0282] Evaluate the performance of the spirometer under standardized test conditions and establish a new performance benchmark based on the adjusted parameter set:

[0283]

[0284] in, represents the performance benchmark, It is The performance results of the tests are: Represents the total number of tests, and the square root of the square sum average is used to evaluate the consistency of the overall performance to obtain the performance evaluation result.

[0285] Assume there are two environmental conditions, the scores are and , weight ,but:

[0286] ;

[0287] ;

[0288] ;

[0289] ;

[0290] ;

[0291] ;

[0292] result Represents a weighted performance score, with higher values ​​indicating better overall performance. This score reflects the average performance of the device under given environmental conditions, with non-linear weighting to emphasize the importance of excellent performance.

[0293] Adjust test parameters:

[0294] Assuming the original parameters (according to the benchmark setting);

[0295] : Adjust the sensitivity coefficient, set it to 0.05, and determine the reaction speed of parameter adjustment;

[0296] : Target performance score, set to a full score of 100;

[0297] but:

[0298] ;

[0299] ;

[0300] ;

[0301] ;

[0302] Calculation shows the adjusted parameters Extreme reduction indicates a large discrepancy between the original settings and the target performance, which may require reconsidering strategy or replacing equipment.

[0303] Generate a performance benchmark:

[0304] Assume that three tests were performed and the performance results were ,but:

[0305] ;

[0306] result It is an average performance benchmark, representing the average performance of a tuned device under standard conditions, and is used for further analysis and device improvement.

[0307] See also Figure 8 , the steps to obtain the comprehensive test results of spirometer linearity are:

[0308] Based on the performance evaluation results, refine the spirometer test parameters to match the different breathing rates and pressure changes:

[0309]

[0310] in, is the respiratory volume actually recorded by the spirometer, is the expected breathing volume in the simulated data It is an adjustment factor used to optimize the matching effect, avoid zero division errors, and output refined parameters ;

[0311] Verify the adjustment effect in the actual test environment according to the refined parameters to ensure that the spirometer maintains high accuracy and consistency under changing breathing conditions and obtain verification data;

[0312] Based on the validation data, the performance of the spirometer is re-evaluated to generate comprehensive spirometer linearity test results:

[0313]

[0314] in, is the total number of tests, represents the average performance index of the spirometer under all test conditions, expressed as a percentage, Indicates The performance results of the tests are: Represents a performance benchmark.

[0315] Assumptions:

[0316] ;

[0317] ;

[0318] ;

[0319] ;

[0320] Substituting into the formula, we get:

[0321]

[0322] The calculation results show that the adjusted test parameters Small improvements make the spirometer settings more sensitive to match actual measurement conditions, helping to improve measurement accuracy and consistency.

[0323] Assuming that 5 adjusted tests were conducted, the performance results were , and performance benchmarks (obtained from the above calculation), then:

[0324] ;

[0325] ;

[0326] ;

[0327] ;

[0328] result The results showed that the adjusted spirometers exhibited extremely high performance consistency, almost perfectly matching the expected benchmark, demonstrating excellent adaptability and accuracy.

[0329] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A spirometer linearity test system, characterized in that: The system comprises: The differential pressure measurement module determines the test point of the spirometer, installs a differential pressure sensor at the test point, identifies the pressure change by measuring the pressure difference at the test point under differential flow, and obtains a micro-pressure difference data set; The frequency characteristic analysis module performs fast Fourier transform according to the micro-pressure difference data set, analyzes the spirometer signal performance under the difference frequency, establishes the adaptability of the spirometer to fast and slow breathing patterns according to the analysis results, and obtains the frequency characteristic index; The steps for analyzing the spirometer signal performance at the difference frequency are: After screening and cleaning the micro-pressure difference data set, the data integrity and applicability were determined and the formula was used: in, is the signal strength value, is the DC component, and is the amplitude coefficient, is the fundamental angular frequency, It's time. is the number of harmonic terms, generating a time series signal dataset; Based on the time series signal data set, fast Fourier transform is applied to convert the data from time domain to frequency domain, and the formula is applied: in, is the response value, is the total number of samples, is the frequency index, generating a frequency domain data set; Based on the frequency domain data set, identify and label the key frequency components, using the formula: ； in, Indicates the frequency The power spectral density at the frequency The energy distribution on the spirometer is used to mark the performance characteristics of the spirometer signal at the difference frequency and generate the spirometer frequency analysis result; The steps for obtaining the frequency characteristic index are: The spirometer frequency analysis results were integrated to summarize the key frequency response characteristics into a graph using the formula: in, represents the weighted frequency response value, is the frequency The weight coefficient indicates the importance. Indicates the total number of analysis results and generates a frequency response overview graph; Based on the frequency response overview graphs, evaluate the performance of the spirometer in fast and slow breathing patterns, comparing the consistency of response at different breathing rates, and apply the formula: in, represents the adaptive ratio between fast and slow breathing patterns, and Represents the critical frequencies of fast and slow breathing, determines the adaptability and performance of the spirometer, and generates the adaptability assessment results of the spirometer; According to the adaptability evaluation results, the key frequency characteristic indicators of the spirometer are extracted and defined to reveal the performance of the spirometer under different breathing modes, using the formula: ； in, represents a set of frequency characteristic indicators, is a single indicator, is the corresponding adaptability evaluation result, Threshold is the index extraction threshold function, which is used to determine the significant performance characteristics and generate the frequency characteristic index; The dynamic performance test module simulates the differential breathing frequency from the fast breathing mode to the slow breathing mode based on the frequency characteristic index, tests the linearity performance of the spirometer under extreme breathing changes according to the simulation results, and generates a linearity test result; The comprehensive test module analyzes the performance of the spirometer under different environmental conditions based on the linearity test results, adjusts and refines the test parameters based on the performance results, matches the breathing rate and pressure changes, ensures the measurement accuracy and consistency of the spirometer, and establishes a comprehensive test result of the spirometer linearity.

2. The spirometer linearity test system according to claim 1, characterized in that: The steps for obtaining the micro-pressure difference data set are: Select the test point for installing the differential pressure sensor, based on the fluid dynamic characteristics and environmental conditions, using the formula: ； Calculate the static pressure at each test point and generate preliminary static pressure results, where: represents static pressure, represents the fluid density, represents the acceleration due to gravity, Represents the height difference, is the environmental adjustment factor, used to compensate for environmental impacts; Select the sensor based on the preliminary static pressure results, using the formula: ； Predict pressure differences under differential flow, determine sensor range, and generate sensor configuration results, where represents the expected pressure difference, and Represent the pressure values ​​at high flow and low flow respectively. is the correction factor, is the sensitivity adjustment parameter; Perform the differential pressure sensor test, based on the sensor configuration results, using the formula: ； Verify sensor accuracy and generate verification test results; among them, Represents flow, represents the flow coefficient, is the measured pressure difference, is the fluid density, is the flow response adjustment factor; Collect and analyze the verification test results, applying the formula: Calculate the micro-pressure difference data and obtain the micro-pressure difference data set, where: and are the sensitivity and accuracy parameters adjusted for the difference test points, Represents the actual flow rate, represents the reference flow, Represents the weighted difference between the flow data at the difference test point and the reference flow value.

3. The spirometer linearity testing system according to claim 1, characterized in that: The simulation steps of the differential respiratory frequency are: Extract the frequency index of fast and slow breathing patterns from the frequency characteristic index, calculate the average frequency, and apply the formula: in, Indicates the reference frequency value, Indicates the frequency of fast and slow modes, is the corresponding weight factor, which indicates the occurrence frequency of the corresponding pattern and generates the reference frequency; Based on the base frequency, set the simulator parameters to reflect the frequency change from fast to slow, and adjust the frequency using the period adjustment function: in, is the maximum cycle time, Generate a simulation period adjustment scheme for phase adjustment parameters; The simulation cycle adjustment scheme is used to simulate the breathing cycle from fast to slow, using the formula: Data collection is performed, including is the simulated breathing volume value, is the maximum respiratory volume, is the attenuation coefficient, is the time, simulating the natural decay of volume during breathing and generating simulated breathing data.

4. The spirometer linearity testing system according to claim 3, characterized in that: The steps for obtaining the linearity test result are: According to the simulated breathing data, the test parameters of the spirometer are adjusted to capture every change point from the fastest to the slowest breathing rate, using the formula: in, is the average frequency value calculated based on the spirometry test requirements, is the expected frequency variation range, is the time constant, adjusting the rate of response, is the adjustment constant, which is used to adjust the frequency response sensitivity of the spirometer to obtain the adjustment parameter; According to the adjustment parameters, actual data acquisition is performed to record the response to the simulated breathing waveform, using the formula: Calculate linearity where The linearity percentage reflects the performance accuracy of the spirometer. is the respiratory volume actually recorded by the spirometer, is the expected breathing volume in the simulation data, and the test data are obtained; The test data are summarized and the performance of the spirometer under different respiratory rates is analyzed to reveal the adaptability and accuracy of the spirometer under extreme conditions and generate linearity test results.

5. The spirometer linearity testing system according to claim 1, characterized in that: The steps for analyzing the performance of the spirometer under different environmental conditions are: Integrate all data obtained from the linearity test results to comprehensively evaluate the performance of the spirometer in different test environments using the formula: Calculate the environmental performance score, where is the weighted score value, It is Performance scores in different environments, is the weight of the target environment, using logarithmic enhancement to affect the difference, is the nonlinear coefficient used to enhance the score difference; According to the environmental performance scores, an index adjustment method is used to optimize the test parameters to match the different environments: in, is the new test parameter, are the original test parameters, is the adjustment sensitivity coefficient, which is used to control the adjustment rate. is the target performance score, and the adjusted parameter set is obtained; Based on the described adjusted parameter set, the performance of the spirometer under standardized test conditions was evaluated and a new performance benchmark was established: in, represents the performance benchmark, It is The performance results of the tests are: Represents the total number of tests, and the square root of the square sum average is used to evaluate the consistency of the overall performance to obtain the performance evaluation result.

6. The spirometer linearity testing system according to claim 5, characterized in that: The steps for obtaining the spirometer linearity comprehensive test result are: Based on the performance evaluation results, the spirometer test parameters were refined to match the differences in breathing rate and pressure changes: in, is the respiratory volume actually recorded by the spirometer, is the expected breathing volume in the simulated data It is an adjustment factor used to optimize the matching effect, avoid zero division errors, and output refined parameters ; Verify the adjustment effect in an actual test environment according to the refined parameters to ensure that the spirometer maintains high accuracy and consistency under changing breathing conditions and obtain verification data; Based on the validation data, the performance of the spirometer was re-evaluated and a comprehensive spirometer linearity test result was generated: in, is the total number of tests, represents the average performance index of the spirometer under all test conditions, expressed as a percentage, Indicates The performance results of the tests are: Represents a performance benchmark.

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