Rapid evaluation method of flax fiber quality based on constant voltage and constant current dual-mode airflow method

Abstract

The application belongs to the technical field of textile fiber quality detection, and provides a flax fiber quality rapid evaluation method based on a constant pressure and constant flow dual-mode airflow method, which comprises the following steps: obtaining an original fineness value according to a constant pressure method, collecting and recording the change of the instantaneous flow difference of the two ends of a fiber sample with time to obtain a dynamic flow decay curve according to a constant flow method, extracting and constructing a correlation feature vector through the dynamic flow decay curve, inputting the correlation feature vector into a maturity prediction model to obtain a predicted maturity value, constructing a two-dimensional evaluation matrix, and obtaining a comprehensive evaluation and grading of the fiber sample according to the original fineness value and the predicted maturity value. The flax fiber quality rapid evaluation method based on the constant pressure and constant flow dual-mode airflow method can realize the detection and determination of the fineness and maturity of the fiber sample at the same time in a single sampling, improve the detection efficiency and shorten the detection period, can generate intuitive comprehensive evaluation and grading of the fiber sample, and is convenient for rapid evaluation of the inherent quality of the fiber.

Description

Technical Field

[0001] This invention belongs to the field of textile fiber quality testing technology, and specifically relates to a rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method, and a rapid evaluation system for flax fiber quality based on constant pressure and constant flow dual-mode airflow method. Background Technology

[0002] Flax fiber is primarily used as a natural textile raw material in textile processing. The intrinsic quality and spinnability of flax fiber are typically assessed using indicators such as fineness and maturity. However, the quality and spinnability of flax fiber are often difficult to reflect and evaluate using only a single indicator. For example, analyzing only the fineness of flax fiber cannot fully reflect performance parameters characterizing flax fiber strength, cohesion, and uniformity. Therefore, current flax fiber quality assessment processes often require independent testing to measure the fineness and maturity of the flax fiber separately, and then combining both measurements for a comprehensive evaluation. However, in actual testing, on the one hand, using two independent tests cannot guarantee the consistency of fiber samples. In some tests, to improve efficiency, different fiber samples are used to simultaneously test fineness and maturity. This results in inconsistent and unreliable test results even though the fiber samples are from the same batch, because the two tests are performed on different fiber samples. Using the same fiber samples would inevitably prolong the testing cycle and affect efficiency. On the other hand, traditional maturity testing usually uses microscopy, slicing, or chemical dissolution methods, which are cumbersome, complex, and time-consuming, requiring specialized personnel. This makes maturity testing time-consuming, labor-intensive, and costly. Furthermore, existing fineness and maturity tests often need to be conducted in a standard atmospheric environment. Fluctuations or changes in ambient temperature and relative humidity during testing significantly affect air density and viscosity. Especially in the process of measuring the fineness of flax fibers using the airflow method, changes in air density and viscosity can cause deviations in air resistance measurements, thus affecting the accuracy and stability of the test results.

[0003] Therefore, it is necessary to design a rapid evaluation method for flax fiber quality that can at least solve some of the above problems and defects. Summary of the Invention

[0004] To address the above technical problems, this invention proposes a rapid evaluation method for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method. This method can simultaneously detect and determine the fineness and maturity of fiber samples in a single sample loading, improving detection efficiency, shortening the detection cycle, and generating intuitive comprehensive evaluation and grading of fiber samples, facilitating rapid assessment of the intrinsic quality of fibers.

[0005] The technical solution of this invention is:

[0006] This invention proposes a rapid evaluation method for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method, comprising the following steps:

[0007] The pretreated fiber sample was measured using the constant pressure method. The original fineness value of the fiber sample was collected, and the constant pressure mode temperature and constant pressure mode relative humidity were collected and recorded simultaneously during the measurement process.

[0008] The pretreated fiber sample was measured using the constant flow method. The dynamic flow decay curve was obtained by collecting and recording the change of the instantaneous flow difference between the two ends of the fiber sample over time. The constant flow mode temperature and constant flow mode relative humidity were collected and recorded simultaneously.

[0009] The correlation feature parameters, including the flow decay rate and the steady-state flow fluctuation variance, are extracted from the dynamic flow decay curve, and a correlation feature vector corresponding to the fiber sample is constructed based on the correlation feature parameters.

[0010] A maturity prediction model is constructed based on the support vector regression algorithm. The associated feature vector is input into the trained maturity prediction model to obtain the predicted maturity value.

[0011] A two-dimensional evaluation matrix is ​​constructed based on the fineness and maturity grading of flax fibers. The comprehensive evaluation and grading of the fiber sample is obtained according to the corresponding positions of the original fineness value and the predicted maturity value in the two-dimensional evaluation matrix.

[0012] Preferably, the formula for calculating the predicted maturity value obtained by the maturity prediction model is as follows:

[0013] ;

[0014] ;

[0015] ;

[0016] In the formula, To predict maturity values, The total number of support vectors, These are the Lagrange multiplier coefficients corresponding to the support vectors. For bias terms, For the first The standardized associated feature vector of each support vector. For kernel width parameters greater than 0, For associated feature vectors, For flow decay rate, The variance of steady-state flow fluctuation is given.

[0017] Preferably, the steps for extracting and calculating the flow attenuation rate are as follows:

[0018] The dynamic flow decay curve is filtered and denoised to remove unstable and excessive data in the initial segment of the curve.

[0019] Calculate the average value of the flow difference within the stable segment at the tail end of the dynamic flow decay curve, and denot it as the steady-state flow difference.

[0020] An exponential decay model is established based on the decay segment data of the dynamic flow decay curve, and the flow decay rate is solved by least squares linear regression.

[0021] Preferably, the steps for extracting and calculating the variance of the steady-state flow fluctuation are as follows:

[0022] Calculate the average value of the flow difference within the analysis segment at the tail end of the dynamic flow decay curve and the number of corresponding sampling points;

[0023] The fluctuation of the flow rate difference at each sampling point is calculated based on the sample variance formula, and the steady-state flow rate fluctuation variance is finally obtained.

[0024] Preferably, the rapid evaluation method for flax fiber quality based on the constant pressure and constant flow dual-mode airflow method provided by the present invention further includes the following steps:

[0025] The original fineness value is compensated by constant pressure mode temperature and constant pressure mode relative humidity to obtain a corrected fineness value. The predicted maturity value is compensated by constant flow mode temperature and constant flow mode relative humidity to obtain a corrected maturity value. The comprehensive evaluation and grading of the fiber sample is obtained based on the corresponding positions of the corrected fineness value and the corrected maturity value in the two-dimensional evaluation matrix.

[0026] Preferably, the formula for calculating the corrected fineness value is as follows:

[0027] ;

[0028] In the formula, To correct the fineness value, This is the original fineness value. Temperature in constant pressure mode. Standard ambient temperature, Relative humidity in constant pressure mode. For standard ambient relative humidity, and These are the corresponding compensation coefficients.

[0029] Preferably, the formula for calculating the corrected maturity value is as follows:

[0030] ;

[0031] In the formula, To correct the maturity value, To predict maturity values, Temperature in constant current mode. Standard ambient temperature, Relative humidity in constant flow mode. For standard ambient relative humidity, and These are the corresponding compensation coefficients.

[0032] Preferably, the fineness grading includes grades of very fine, fine, medium, and coarse, and the maturity grading includes grades of immature, under-mature, mature, and very mature.

[0033] Preferably, the present invention also provides a rapid evaluation system for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method, comprising:

[0034] The airflow measurement module includes a standard sample cylinder for placing fiber samples and a mode switching unit connected to the standard sample cylinder via an air path, so as to control the air path to switch to constant pressure mode or constant flow mode through the mode switching unit.

[0035] The flow acquisition module is arranged at both ends of the standard sample tube and connected to it. It is used to collect and record the change of the instantaneous flow difference between the two ends of the fiber sample over time to obtain the dynamic flow decay curve.

[0036] The environmental acquisition module is used to collect and record the constant pressure mode temperature and constant pressure mode relative humidity during the constant pressure method measurement process, as well as the constant flow mode temperature and constant flow mode relative humidity during the constant flow method measurement process.

[0037] The integrated processing module is connected to the airflow measurement module, flow acquisition module, and environmental acquisition module. It has a built-in maturity prediction model and a two-dimensional evaluation matrix. It is used to receive and process the data sent by the airflow measurement module, flow acquisition module, and environmental acquisition module and output the comprehensive evaluation and grading of the fiber sample.

[0038] The present invention has the following advantages and effects compared with the prior art:

[0039] (1) The fineness and maturity of the fiber sample were measured by two modes: constant pressure method and constant flow method. Both methods were based on the constant flow method and constant pressure method mentioned in the national standard document GB / T17260—2025 "Determination of Fineness of Flax Fiber by Airflow Method". This method can realize the detection and determination of fineness and maturity at the same time in one sample loading, which improves detection efficiency, shortens detection cycle, and reduces detection cost, making it easy to promote and use.

[0040] (2) The original fineness value and the predicted maturity value are compensated by the environmental parameters of temperature and relative humidity during the detection process to obtain the corrected fineness value and the corrected maturity value, so as to eliminate the air resistance deviation and the deviation of the detection results caused by environmental factors, improve the accuracy and reliability of the detection results, and at the same time enable the overall evaluation method to be carried out in non-standard environments, reduce the requirements of the detection environment, and improve the applicability in non-standard environments.

[0041] (3) A two-dimensional evaluation matrix based on the fineness and maturity grading of flax fiber is adopted. The two-dimensional evaluation matrix can provide an intuitive and easy-to-understand comprehensive evaluation grade. Compared with professional fineness and maturity values, the comprehensive evaluation grade can intuitively reflect the overall intrinsic quality and spinnability of flax fiber. The rapid intrinsic quality assessment of flax fiber can be completed without the need for professionals. Attached Figure Description

[0042] Figure 1 This is a flowchart illustrating the rapid evaluation method for flax fiber quality based on the constant pressure and constant flow dual-mode airflow method in Embodiment 1 of the present invention.

[0043] Figure 2 This is a schematic diagram of the dynamic flow rate decay curve in the rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method in Embodiment 1 of the present invention. The red line represents low-maturity flax fiber, and the blue line represents high-maturity flax fiber.

[0044] Figure 3 This is a schematic diagram of the framework of the rapid evaluation system for flax fiber quality based on the constant pressure and constant flow dual-mode airflow method in Embodiment 2 of the present invention. Detailed Implementation

[0045] To enable those skilled in the art to better understand the present invention, specific embodiments will now be described in further detail. It should be understood that the specific embodiments described herein are for illustrative and explanatory purposes only and are not intended to limit the scope of the invention.

[0046] Example 1:

[0047] like Figure 1 As shown, this invention provides a rapid evaluation method for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method, which specifically includes the following steps:

[0048] Step S1: The pretreated fiber sample is measured using the constant pressure method to obtain the original fineness value. The constant pressure mode temperature and constant pressure mode relative humidity are collected and recorded simultaneously during the measurement process.

[0049] It should be noted that the constant pressure method here specifically refers to the constant pressure method test in Chapter 7 of GB / T17260—2025 "Determination of Fineness of Flax Fibers - Airflow Method". The specific test principle, equipment, sample preparation, and procedures are detailed in the standard and will not be elaborated upon here. Furthermore, the pretreatment here includes carding, loosening, sampling, weighing under standard atmospheric conditions, and equilibration of the sample. Detailed procedures for these steps can also be found in the aforementioned standard document and will not be repeated here.

[0050] Step S2: The pretreated fiber sample is measured using the constant flow method. The curve of the instantaneous flow difference between the two ends of the fiber sample over time is collected and recorded. Figure 2 As shown, a dynamic flow rate decay curve is obtained, and the temperature and relative humidity in constant flow mode are simultaneously collected and recorded. Specifically, in this embodiment, a high-frequency flow sensor is used to continuously collect and record the change in the flow rate difference between the two ends of the fiber sample over time at a sampling rate of not less than 200Hz, until the flow rate difference between the two ends of the fiber sample enters the quasi-steady-state fluctuation stage.

[0051] Step S3: Extract the correlation feature parameters, including the flow decay rate and the steady-state flow fluctuation variance, from the dynamic flow decay curve, and construct the correlation feature vector corresponding to the fiber sample based on the correlation feature parameters.

[0052] Further reference Figure 2 As shown, the flow rate decay rate is used to characterize the rate at which the internal airflow channels of a fiber open under constant airflow. Immature fiber cells have thin walls and loose fiber bonds, making them more prone to relaxation and expansion under airflow pressure. This results in faster opening and formation of airflow channels, leading to a rapid decrease in airflow resistance, manifested as a larger flow rate decay rate. Figure 2 (Middle red line); while mature fibers, due to their stable structure, show little change in the gaps after being subjected to airflow impact pressure, and the opening of airflow channels is relatively slow, resulting in a gentle attenuation and a smaller flow rate attenuation rate ( Figure 2 (Middle blue line).

[0053] Specifically, in this embodiment, the steps for calculating and obtaining the flow attenuation rate are as follows:

[0054] Step S311: The dynamic flow decay curve is filtered and denoised to remove unstable transitional data in the initial segment. That is, the signal of the original dynamic flow decay curve is subjected to moving average filtering (the window width is preferably 5-10 sampling points) to remove high-frequency noise and remove unstable transitional data at the moment when the airflow switches from constant pressure mode to crossflow mode.

[0055] Step S312: Calculate the average value of the flow difference within the stable segment at the tail of the dynamic flow decay curve, and record it as the steady-state flow difference, i.e., calculate the last stable segment at the tail of the curve. The arithmetic mean of the data within the time interval is used as the steady-state flow rate. The settings can be adjusted, but it is necessary to ensure that it is within a stable segment that has already entered a macroscopically stable state;

[0056] Step S313: Establish an exponential decay model based on the attenuation segment signal data of the dynamic flow attenuation curve:

[0057] ;

[0058] In the formula, The steady-state flow rate obtained from the above calculations, For flow decay rate, To fit the initial flow difference, i.e., the value calculated through model fitting. The theoretical flow difference at any given time;

[0059] Step S314: The flow rate decay rate is obtained by using least squares linear regression.

[0060] .

[0061] Furthermore, in combination Figure 2 As shown, the steady-state flow fluctuation variance is used to characterize the intensity of micro-pulsations after the airflow channel stabilizes. Immature fiber surfaces are rough and naturally curved and irregular, leading to continuous micro-changes in the cross-section of the airflow channel, resulting in obvious micro-turbulence phenomena, manifested as a larger steady-state flow fluctuation variance value. Figure 2 (Middle red line); while for mature fibers, the surface is uniform, the airflow channels formed are stable, and the steady-state flow fluctuation variance is small ( Figure 2 (Middle blue line).

[0062] Specifically, in this embodiment, the steps for calculating and obtaining the steady-state flow fluctuation variance are as follows:

[0063] Step S321: Calculate and obtain the average value of the flow difference within the analysis segment of the tail section of the dynamic flow attenuation curve and the corresponding number of sampling points, that is, obtain the last segment of the curve. Data within a time interval is used as an analysis segment, and is set as follows: For the corresponding The number of sampling points included in the stability analysis segment of the time interval, the time interval The settings can be adjusted, but it is necessary to ensure that it is within the stable segment that has entered a macroscopically stable state, and calculate the arithmetic mean of the flow difference data of all sampling points within the stable analysis segment. ;

[0064] Step S322: Calculate the steady-state flow fluctuation variance based on the sample variance formula. :

[0065] .

[0066] Specifically, a flow decay rate is constructed based on the associated feature parameters. and steady-state flow fluctuation variance The resulting associated feature vectors are shown below: .

[0067] Step S4: Construct a maturity prediction model based on the support vector regression algorithm, and input the associated feature vectors into the trained maturity prediction model to obtain the predicted maturity value.

[0068] The regression function for the maturity prediction model is:

[0069] ;

[0070] The RBF kernel function for the maturity prediction model is:

[0071] ;

[0072] In the formula, To predict maturity values, The total number of support vectors, Let Lagrange multipliers be the coefficients of the support vectors, and satisfy the following conditions: (C is the penalty parameter) For bias terms, For the first The standardized associated feature vector of each support vector. The kernel width parameter is greater than 0.

[0073] It should be noted that the associated feature vector Standardization preprocessing (such as Z-score standardization) is performed during training and subsequent prediction using the input model. Given that this is a mature existing technology, it will not be elaborated upon further here. Furthermore, after the maturity prediction model is trained, its model file internally saves the corresponding support vector set, corresponding coefficients, bias terms, and corresponding parameter coefficients, meaning it can be used directly.

[0074] Understandably, the model needs to be trained on a training set of calibration samples before use. These calibration samples cover fiber specimens at different maturity levels (e.g., over-mature, immature). The maturity reference value for each calibration sample can be obtained through microscopic analysis or standard chemical staining methods to complete the sample calibration. Furthermore, the maturity prediction model based on the support vector regression algorithm specifically uses the Gaussian RBF kernel function, and the penalty parameter... Core width parameter Insensitive bandwidth Hyperparameters can be determined through grid search or obtained using heuristics built into the standard SVR implementation library, which will not be elaborated further here.

[0075] After training, the model's accuracy needs to be evaluated using an independent validation set. The validation set consists of independent samples that were not used in training. Evaluation metrics include, but are not limited to, the coefficient of determination (R²). 2 Root mean square error (RMSE) and mean relative error (MRE) are used to ensure that the model meets the accuracy and reliability requirements of practical applications.

[0076] Step S5: The original fineness value is compensated based on the constant pressure mode temperature and constant pressure mode relative humidity to obtain the corrected fineness value; the predicted maturity value is compensated based on the constant flow environment temperature and constant flow environment relative humidity to obtain the corrected maturity value.

[0077] It is understandable that changes in the temperature and relative humidity of the testing environment will alter the air density and annual density, thus causing deviations in air resistance measurements. This leads to discrepancies between the obtained raw fineness value and the predicted maturity value. Therefore, correction and compensation are needed to eliminate the influence of environmental factors on the testing results.

[0078] Specifically, in this embodiment, the fineness correction value is calculated using the following formula:

[0079] ;

[0080] In the formula, To correct the fineness value, This is the original fineness value. Temperature in constant pressure mode. The standard ambient temperature is 20℃. Relative humidity in constant pressure mode. The standard ambient relative humidity is 65% RH. and These are the corresponding compensation coefficients.

[0081] It should be noted here that... and These two compensation coefficients are specifically calibrated through controlled environment testing. Multiple fiber samples covering different fineness ranges are selected and their baseline fineness values ​​are measured under standard conditions. Then, the temperature and humidity of the testing environment are changed to cover multiple temperature and relative humidity levels in the actual use range. After fully balancing under each set environmental condition, constant pressure mode measurement is performed. By calculating the measurement deviations under different environmental conditions, a linear or nonlinear relationship between fineness measurement deviation and temperature deviation and relative humidity deviation is established. Finally, the compensation coefficients are obtained by solving multiple regression.

[0082] Furthermore, the revised maturity value is calculated using the following formula:

[0083] ;

[0084] In the formula, To correct the maturity value, To predict maturity values, Temperature in constant current mode. The standard ambient temperature is 20℃. Relative humidity in constant flow mode. The standard ambient relative humidity is 65% RH. and These are the corresponding compensation coefficients.

[0085] It's important to note that temperature primarily affects the rate of pressure drop by influencing air viscosity, exhibiting an exponential response. Humidity, on the other hand, affects air density and the trace moisture absorption state of fibers, showing an approximately linear effect. By selecting multiple fiber samples with stable maturity values ​​under known standard conditions, baseline maturity values ​​were obtained by measuring them under standard environmental conditions. Then, while maintaining a relative humidity close to the standard value, the ambient temperature was varied, and maturity values ​​were measured at different temperatures. By calculating the measurement deviation under different temperature conditions and analyzing its relationship with the temperature ratio, the compensation coefficient used for temperature correction was determined. Subsequently, measurements were taken under multiple different humidity conditions. The residuals after eliminating the temperature effect were regressed against the humidity difference to determine the compensation coefficient used for humidity correction. .

[0086] Step S6: Construct a two-dimensional evaluation matrix based on the fineness and maturity grading of flax fibers. Obtain the comprehensive evaluation and grading of the fiber sample according to the corresponding positions of the corrected fineness value and corrected maturity value in the two-dimensional evaluation matrix.

[0087] Specifically, in this embodiment, the two-dimensional evaluation matrix is ​​shown in the table below:

[0088] As can be seen from the above, the fineness grading includes four levels: extremely fine, relatively fine, medium, and coarse. The maturity grading includes four levels: immature, under-mature, mature, and very mature. The fineness grading and maturity grading of the fiber sample are determined by obtaining the corrected fineness value and corrected maturity value, respectively. This allows for the determination of its actual position in the two-dimensional evaluation matrix, and thus the determination of the comprehensive evaluation grade of the fiber sample, such as excellent, superior, or good, so as to clearly and intuitively understand the intrinsic quality and grade of the fiber sample.

[0089] It should be noted that the above-mentioned granularity and maturity grading are for illustrative purposes only. In actual applications, the number of gradings and the corresponding value ranges for different gradings can be set according to the usage requirements, or the value ranges for the gradings can be adjusted to adapt to different detection or evaluation application scenarios. For example, a maturity value of not less than 0.90 or not less than 0.80 can be modified to be considered very mature, and a maturity value of not more than 0.70 or not more than 0.60 can be modified to be considered immature.

[0090] Example 2:

[0091] Embodiment 2 of the present invention is illustrated in the accompanying drawings. Figure 3 Please provide an explanation.

[0092] like Figure 3 As shown, the present invention relates to a rapid evaluation system for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method, which specifically includes: an airflow measurement module, a flow rate acquisition module, an environmental acquisition module, and an integrated processing module.

[0093] The airflow measurement module includes a standard sample cylinder for holding the fiber sample and a mode switching unit connected to the standard sample cylinder via an air path. The mode switching unit controls the air path to switch between constant pressure mode (constant pressure method) and constant flow mode (constant flow rate method). It should be noted that the mode switching unit may specifically include a proportional pressure valve (to achieve constant pressure) and a flow controller (to achieve constant flow), using a high-speed solenoid valve assembly to switch between different air path modes. Furthermore, the airflow measurement module in this embodiment can fully utilize the flax fiber fineness airflow meter mentioned in GB / T17260—2025 "Determination of Flax Fiber Fineness by Airflow Method" (see the schematic diagram of the simplified constant flow rate method instrument in the standard document), therefore its structure and principle will not be elaborated upon here.

[0094] The flow acquisition module is positioned at both ends of the standard sample cylinder and connected to it. It is used to collect and record the change of the instantaneous flow difference between the two ends of the fiber sample over time to obtain a dynamic flow decay curve. Specifically, the flow acquisition module includes high-frequency flow sensors positioned at both ends of the standard sample cylinder to record the instantaneous flow difference between the two ends of the fiber sample inside the standard sample cylinder in real time.

[0095] The environmental acquisition module is used to collect and record the temperature and relative humidity in constant pressure mode during the constant pressure method measurement process, and the temperature and relative humidity in constant flow mode during the constant flow method measurement process. Specifically, the environmental acquisition module includes integrated high-precision temperature and humidity sensors, and it can calibrate the collected data in different measurement processes to distinguish between environmental data in constant pressure mode and constant flow mode.

[0096] The integrated processing module is connected to the airflow measurement module, flow rate acquisition module, and environmental acquisition module. It incorporates a maturity prediction model and a two-dimensional evaluation matrix to receive, process, and output the comprehensive evaluation rating of the fiber sample from these modules. It should be noted that the integrated processing module can specifically employ an embedded industrial computer or other programmable controller; given these are mature existing technologies, further details are omitted here. Furthermore, the specific data processing flow of the integrated processing module can be found in the method descriptions of the above embodiments, and will not be repeated here.

[0097] In summary, the rapid evaluation method and system for flax fiber quality based on constant pressure and constant flow dual-mode airflow method provided by this invention can simultaneously detect and determine the fineness and maturity of fiber samples in a single sample loading, thereby improving detection efficiency, shortening the detection cycle, and generating intuitive comprehensive evaluation and grading of fiber samples, which facilitates rapid evaluation of the intrinsic quality of fibers.

[0098] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. All equivalent changes and modifications made within the scope of the present invention should still fall within the scope of the present invention.

Claims

1. A rapid evaluation method for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method, characterized in that, Includes the following steps: The pretreated fiber sample was measured using the constant pressure method. The original fineness value of the fiber sample was collected, and the constant pressure mode temperature and constant pressure mode relative humidity were collected and recorded simultaneously during the measurement process. The pretreated fiber sample was measured using the constant flow method. The dynamic flow decay curve was obtained by collecting and recording the change of the instantaneous flow difference between the two ends of the fiber sample over time. The constant flow mode temperature and constant flow mode relative humidity were collected and recorded simultaneously. The correlation feature parameters, including the flow decay rate and the steady-state flow fluctuation variance, are extracted from the dynamic flow decay curve, and a correlation feature vector corresponding to the fiber sample is constructed based on the correlation feature parameters. A maturity prediction model is constructed based on the support vector regression algorithm. The associated feature vector is input into the trained maturity prediction model to obtain the predicted maturity value. A two-dimensional evaluation matrix is ​​constructed based on the fineness and maturity grading of flax fibers. The comprehensive evaluation and grading of the fiber sample is obtained according to the corresponding positions of the original fineness value and the predicted maturity value in the two-dimensional evaluation matrix.

2. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 1, characterized in that, The formula for calculating the predicted maturity value obtained by the maturity prediction model is as follows: ； ； ； In the formula, To predict maturity values, The total number of support vectors, These are the Lagrange multiplier coefficients corresponding to the support vectors. For bias terms, For the first The standardized associated feature vector of each support vector. For kernel width parameters greater than 0, For associated feature vectors, For flow decay rate, The variance of steady-state flow fluctuation is given.

3. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 2, characterized in that, The steps for extracting and calculating the flow attenuation rate are as follows: The dynamic flow decay curve is filtered and denoised to remove unstable and excessive data in the initial segment of the curve. Calculate the average value of the flow difference within the stable segment at the tail end of the dynamic flow decay curve, and denot it as the steady-state flow difference. An exponential decay model is established based on the decay segment data of the dynamic flow decay curve, and the flow decay rate is solved by least squares linear regression.

4. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 2, characterized in that: The steps for extracting and calculating the variance of the steady-state flow fluctuation are as follows: Calculate the average value of the flow difference within the analysis segment at the tail end of the dynamic flow decay curve and the number of corresponding sampling points; The fluctuation of the flow rate difference at each sampling point is calculated based on the sample variance formula, and the steady-state flow rate fluctuation variance is finally obtained.

5. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 1, characterized in that, It also includes the following steps: The original fineness value is compensated by constant pressure mode temperature and constant pressure mode relative humidity to obtain a corrected fineness value. The predicted maturity value is compensated by constant flow mode temperature and constant flow mode relative humidity to obtain a corrected maturity value. The comprehensive evaluation and grading of the fiber sample is obtained based on the corresponding positions of the corrected fineness value and the corrected maturity value in the two-dimensional evaluation matrix.

6. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 5, characterized in that, The formula for calculating the corrected fineness value is as follows: ； In the formula, To correct the fineness value, This is the original fineness value. Temperature in constant pressure mode. Standard ambient temperature, Relative humidity in constant pressure mode. For standard ambient relative humidity, and These are the corresponding compensation coefficients.

7. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 5, characterized in that, The formula for calculating the corrected maturity value is as follows: ； In the formula, To correct the maturity value, To predict maturity values, Temperature in constant current mode. Standard ambient temperature, Relative humidity in constant flow mode. For standard ambient relative humidity, and These are the corresponding compensation coefficients.

8. The rapid evaluation method for flax fiber quality based on constant pressure and constant flow dual-mode airflow method according to claim 1, characterized in that: The fineness grading includes levels of very fine, fine, medium, and coarse, and the maturity grading includes levels of immature, under-mature, mature, and very mature.

9. A rapid evaluation system for flax fiber quality based on a constant pressure and constant flow dual-mode airflow method, characterized in that, include: The airflow measurement module includes a standard sample cylinder for placing fiber samples and a mode switching unit connected to the standard sample cylinder via an air path, so as to control the air path to switch to constant pressure mode or constant flow mode through the mode switching unit. The flow acquisition module is arranged at both ends of the standard sample tube and connected to it. It is used to collect and record the change of the instantaneous flow difference between the two ends of the fiber sample over time to obtain the dynamic flow decay curve. The environmental acquisition module is used to collect and record the constant pressure mode temperature and constant pressure mode relative humidity during the constant pressure method measurement process, as well as the constant flow mode temperature and constant flow mode relative humidity during the constant flow method measurement process. The integrated processing module is connected to the airflow measurement module, flow acquisition module, and environmental acquisition module. It has a built-in maturity prediction model and a two-dimensional evaluation matrix. It is used to receive and process the data sent by the airflow measurement module, flow acquisition module, and environmental acquisition module and output the comprehensive evaluation and grading of the fiber sample.

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