A flash steam nonwoven fabric uniformity detection method and device

Abstract

The present application discloses a method and device for detecting the uniformity of non-woven fabrics produced by a flash vaporization method, which relates to the field of fabric production detection and can solve the technical problem of large delay in existing flash vaporization non-woven fabric uniformity testing methods. The method comprises the following steps: obtaining rolling pressure data and light transmittance intensity data of a test sample; performing time series analysis and processing on the rolling pressure data and light transmittance intensity data to obtain a pressure change curve and a light transmittance change curve; obtaining a uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve based on an interpolation algorithm; marking outliers of the uniformity curve of the test sample based on a maximum likelihood estimation method to obtain an outlier set; obtaining a cluster analysis result of the outlier set based on a density-based clustering algorithm, and obtaining a uniformity test result of the test sample based on the cluster analysis result, so as to adjust process parameters according to the uniformity test result.

Description

Technical Field

[0001] The present application relates to the field of fabric production testing, and in particular to a method and device for testing the uniformity of flash-evaporated nonwoven fabrics. Background Art

[0002] Flash-spun nonwovens are high-performance materials that combine the advantages of paper, film, and cloth, offering excellent physical properties such as high strength, tear resistance, and excellent waterproof and breathable properties. However, during the production process, the uniformity of flash-spun nonwovens is affected by various factors, and this uniformity directly impacts their physical properties. Therefore, it is necessary to test the uniformity of flash-spun nonwovens to ensure consistent product quality.

[0003] Existing flash-steamed nonwoven fabric uniformity testing methods typically use various measuring tools to measure and calculate physical parameters such as thickness, mass, and density at different locations of the flash-steamed nonwoven fabric to assess its uniformity. The disadvantages of these methods are that they rely on a single testing method and require sampling and testing after product production is complete, which introduces a certain amount of latency and prevents timely test results. Timely data is crucial for decision-making during the production process. Therefore, reducing the latency and improving the timeliness of flash-steamed nonwoven fabric uniformity testing remains a pressing issue in existing technologies. Summary of the Invention

[0004] The present application provides a method and device for detecting uniformity of nonwoven fabrics produced by a flash vaporization process, so as to solve the technical problem of large delay in the existing method for detecting uniformity of nonwoven fabrics produced by a flash vaporization process.

[0005] According to a first aspect of the embodiment of the present application, a method for detecting uniformity of a flash-steamed nonwoven fabric is provided, comprising:

[0006] Obtain rolling pressure data and light transmittance intensity data of the test sample; wherein the rolling pressure data is the surface pressure collected when the test sample is rolled.

[0007] The rolling pressure data and light transmittance intensity data are subjected to time series analysis and processing to obtain pressure change curves and light transmittance change curves.

[0008] According to the pressure change curve and the light transmittance change curve, the uniformity curve of the test sample is obtained based on the interpolation algorithm.

[0009] Based on the maximum likelihood estimation method, the outliers of the uniformity curve of the test sample are marked to obtain an outlier set; wherein the outliers are points whose uniformity outlier probability is greater than a preset threshold.

[0010] According to the density-based clustering algorithm, a cluster analysis result of the outlier set is obtained, and a uniformity test result of the test sample is obtained according to the cluster analysis result, so as to adjust the process parameters according to the uniformity test result.

[0011] In certain embodiments of the present application, the time series analysis and processing of the rolling pressure data and the light transmittance intensity data to obtain the pressure change curve and the light transmittance change curve specifically includes:

[0012] The rolling pressure data is subjected to Fourier transform to obtain first pressure frequency domain data, and the first pressure frequency domain data is filtered according to a preset vibration frequency threshold to obtain second pressure frequency domain data.

[0013] The light transmission intensity data are decomposed by wavelet transform to obtain a plurality of first light transmission frequency domain data, and the plurality of first light transmission frequency domain data are filtered based on a median filtering algorithm to obtain a plurality of second light transmission frequency domain data.

[0014] The second pressure frequency domain data is subjected to inverse Fourier transform to obtain a pressure variation curve, and the plurality of second light transmission frequency domain data are subjected to inverse wavelet transform synthesis to obtain a light transmission variation curve.

[0015] This application first performs Fourier transform on the rolling pressure data, and then filters it based on a preset vibration frequency threshold to obtain second pressure frequency domain data, which can filter out the noise caused by mechanical vibration within a specific frequency range; at the same time, the transmittance intensity data is subjected to wavelet decomposition transform, and then filtered based on the median filtering algorithm to obtain multiple second transmittance frequency domain data, which can filter out the spike noise caused by the optical signal within a specific frequency range; and then the second pressure frequency domain data and the multiple second transmittance frequency domain data are synthesized by Fourier inverse transform and wavelet inverse transform to obtain a pressure change curve and a transmittance change curve, which can improve data accuracy and reduce data errors.

[0016] In certain embodiments of the present application, obtaining the uniformity curve of the test sample based on the pressure change curve and the transmittance change curve and an interpolation algorithm specifically includes:

[0017] The pressure change curve and the transmittance change curve are aligned according to the timestamps, and according to the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve, the pressure change curve and the transmittance change curve are divided and combined according to a plurality of preset partitions to obtain a plurality of regional continuous point sets.

[0018] Based on the interpolation algorithm, multiple uniformity curves corresponding to multiple preset partitions are obtained according to the pressure and transmittance of each point in the continuous point set of each area.

[0019] Multiple uniformity curves are spliced ​​in the order of timestamps to obtain the uniformity curve of the test sample.

[0020] This application first aligns the pressure change curve and the transmittance change curve according to the timestamp, and combines the pressure change curve and the transmittance change curve according to the first transmission speed, the second transmission speed and the preset multiple partition divisions to obtain multiple regional continuous point sets, which can divide the detection area into multiple sub-areas, improve detection efficiency, and reduce detection errors. Then, based on the interpolation algorithm, multiple uniformity curves corresponding to the preset multiple partitions are obtained, thereby obtaining the uniformity curve of the detection sample. The interpolation algorithm can be used to calculate and smooth the uniformity curve, reduce curve burrs, and then reduce data errors and ensure data accuracy.

[0021] In certain embodiments of the present application, the method of marking the outliers of the uniformity curve of the detection sample based on the maximum likelihood estimation method to obtain the outlier set specifically includes:

[0022] Based on the preset sampling interval, the uniformity curve of the test sample is sampled to obtain a sampling point set.

[0023] Based on the maximum likelihood estimation method, the outlier probability of each point in the sampling point set is calculated, and the outlier points are marked according to the outlier probability threshold to obtain the outlier point set.

[0024] This application first samples the uniformity curve of the test sample based on a preset sampling interval to obtain a sampling point set, and calculates the outlier probability of each point in the sampling point set based on the maximum likelihood estimation method, and then marks and obtains the outlier point set. The calculation of the outlier probability through the maximum likelihood estimation method is more accurate and simple, and can improve the calculation speed while ensuring accuracy, thereby improving the detection efficiency.

[0025] In certain embodiments of the present application, the method of obtaining a cluster analysis result of an outlier set according to a density-based clustering algorithm and obtaining a uniformity test result of the test sample according to the cluster analysis result specifically includes:

[0026] According to the density-based clustering algorithm, all outlier points in the outlier set are clustered to obtain multiple clusters.

[0027] According to the number of outliers in each of the multiple clusters and a preset outlier number threshold, cluster analysis results of the multiple clusters are determined, and cluster analysis results of the outlier set are obtained according to the cluster analysis results of the multiple clusters.

[0028] According to the cluster analysis results, the uniformity test results of the test samples are determined.

[0029] This application first obtains multiple clusters through clustering based on a density-based clustering algorithm, and can classify all outliers according to the distribution of the fabric area, and then determine the clustering analysis results based on a preset outlier number threshold. It can intuitively reflect the uniformity change through the number of outliers and determine the uniformity analysis monitoring results. Compared with the complex calculations of the existing technology, it is simpler, thereby improving the detection efficiency.

[0030] According to a second aspect of the embodiment of the present application, a flash vaporization nonwoven fabric uniformity detection device is provided, comprising a data acquisition module, a time series processing module, an interpolation processing module, a likelihood estimation module and a cluster analysis module.

[0031] The data acquisition module is used to obtain the rolling pressure data and light transmittance intensity data of the test sample; wherein the rolling pressure data is the surface pressure collected when the test sample is rolled.

[0032] The time series processing module is used to perform time series analysis on the rolling pressure data and the light transmittance intensity data to obtain a pressure change curve and a light transmittance change curve.

[0033] The interpolation processing module is used to obtain the uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve and the interpolation algorithm.

[0034] The likelihood estimation module is used to mark the outliers of the uniformity curve of the detection sample based on the maximum likelihood estimation method to obtain an outlier set; wherein the outlier is a point whose uniformity outlier probability is greater than a preset threshold.

[0035] The cluster analysis module is used to obtain cluster analysis results of the outlier set based on the density-based clustering algorithm, and obtain the uniformity test results of the test samples based on the cluster analysis results, so as to adjust the process parameters according to the uniformity test results.

[0036] In certain embodiments of the present application, the timing processing module includes a pressure processing unit, a light intensity processing unit, and a transformation processing unit.

[0037] The pressure processing unit is used to perform Fourier transform on the rolling pressure data to obtain first pressure frequency domain data, and filter the first pressure frequency domain data according to a preset vibration frequency threshold to obtain second pressure frequency domain data.

[0038] The light intensity processing unit is used to perform wavelet transform decomposition on the light transmission intensity data to obtain multiple first light transmission frequency domain data, and filter the multiple first light transmission frequency domain data based on a median filtering algorithm to obtain multiple second light transmission frequency domain data.

[0039] The transformation processing unit is used to perform inverse Fourier transformation on the second pressure frequency domain data to obtain a pressure change curve, and perform inverse wavelet transformation synthesis on multiple second light transmission frequency domain data to obtain a light transmission change curve.

[0040] In some embodiments of the present application, the interpolation processing module includes an alignment processing unit, an interpolation processing unit, and a splicing processing unit.

[0041] An alignment processing unit is used to align the pressure change curve and the transmittance change curve according to the timestamp, and divide and combine the pressure change curve and the transmittance change curve according to a plurality of preset partitions according to the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve, to obtain a plurality of regional continuous point sets.

[0042] The interpolation processing unit is used to obtain a plurality of uniformity curves corresponding to a plurality of preset partitions based on the pressure and transmittance of each point in the continuous point set of each region based on an interpolation algorithm.

[0043] The splicing processing unit is used to splice multiple uniformity curves in the order of timestamps to obtain the uniformity curve of the test sample.

[0044] In certain embodiments of the present application, the likelihood estimation module includes a curve sampling unit and a likelihood estimation unit.

[0045] The curve sampling unit is used to sample the uniformity curve of the detection sample based on a preset sampling interval to obtain a sampling point set.

[0046] The likelihood estimation unit is used to calculate the outlier probability of each point in the sampling point set based on the maximum likelihood estimation method, and mark the outlier points according to the outlier probability threshold to obtain the outlier point set.

[0047] In certain embodiments of the present application, the cluster analysis module includes a density clustering unit, a cluster analysis unit, and a uniformity detection unit.

[0048] The density clustering unit is used to cluster all outlier points in the outlier point set according to a density-based clustering algorithm to obtain multiple clusters.

[0049] The cluster analysis unit is used to determine the cluster analysis results of the multiple clusters according to the number of outliers in each of the multiple clusters and a preset outlier number threshold, and obtain the cluster analysis results of the outlier set according to the cluster analysis results of the multiple clusters.

[0050] The uniformity detection unit is used to determine the uniformity detection result of the detection sample according to the cluster analysis result.

[0051] The present application first obtains the rolling pressure data and light transmittance data of the test sample through the pressure sensing array and the light intensity detection array, which can improve the data acquisition speed and reduce the detection delay, and then performs time series analysis and processing on the rolling pressure data and the light transmittance data to obtain the pressure change curve and the light transmittance change curve, and then obtains the uniformity curve of the test sample based on the interpolation algorithm, while considering the pressure exerted on the flash steamed non-woven fabric during rolling and the light transmittance after rolling. Compared with the existing technology that only considers a certain parameter, it is more comprehensive and can improve the detection confidence and accuracy, and then the outlier point set is marked based on the maximum likelihood estimation method, and the clustering analysis result is obtained based on the density clustering algorithm to obtain the uniformity detection result of the test sample. Direct analysis and statistics are performed through mathematical statistical methods, which can further reduce the detection delay, thereby improving the efficiency of uniformity detection while ensuring the uniformity detection accuracy of flash steamed non-woven fabrics. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 : A schematic flow chart of a method for detecting uniformity of a flash-evaporated nonwoven fabric according to certain embodiments of the present application;

[0053] Figure 2 : A module structure diagram of a flash-evaporation nonwoven fabric uniformity detection device shown in certain embodiments of the present application. DETAILED DESCRIPTION

[0054] The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below in conjunction with the accompanying drawings are exemplary and are only used to explain some embodiments of the present application and should not be understood as limiting the embodiments of the present application. Based on the embodiments shown in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0055] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly indicate the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. In the description of this application, unless otherwise clearly specified, "multiple" and "several" mean two or more.

[0056] It should be understood that the "uniformity" mentioned in this application refers specifically to the uniformity of the nonwoven fabric, and more specifically refers to the uniformity of the fiber web distribution of the nonwoven fabric.

[0057] See Figure 1The embodiment of the present application provides a method for detecting uniformity of a nonwoven fabric produced by flash vaporization, comprising steps S101 to S105, each of which is specifically as follows:

[0058] Step S101: Acquire rolling pressure data and light transmittance intensity data of the test sample; wherein the rolling pressure data is the surface pressure collected when the test sample is rolled.

[0059] In certain embodiments of the present application, rolling pressure data is acquired by collecting the surface pressure of the test sample during rolling using a pressure sensing array, and light intensity data is acquired by collecting the intensity of light transmitted through the test sample using a light intensity detection array. The number of rows, columns, and arrangement of the pressure sensing array are the same as those of the light intensity detection array. Specifically, the number of rows and columns of the pressure sensing array and the light intensity detection array can be set to 10 rows and 10 columns. The pressure sensing array and the light intensity detection array can be used to divide the test sample into multiple regions of at least 10×10 according to a preset frequency and preset speed, and can acquire time-series rolling pressure data and light intensity data at preset time intervals.

[0060] Step S102: performing time series analysis on the rolling pressure data and the light transmittance data to obtain a pressure change curve and a light transmittance change curve.

[0061] In certain embodiments of the present application, the time series analysis and processing of the rolling pressure data and the light transmittance intensity data to obtain the pressure change curve and the light transmittance change curve specifically includes:

[0062] The rolling pressure data is subjected to Fourier transform to obtain first pressure frequency domain data, and the first pressure frequency domain data is filtered according to a preset vibration frequency threshold to obtain second pressure frequency domain data.

[0063] The light transmission intensity data are decomposed by wavelet transform to obtain a plurality of first light transmission frequency domain data, and the plurality of first light transmission frequency domain data are filtered based on a median filtering algorithm to obtain a plurality of second light transmission frequency domain data.

[0064] The second pressure frequency domain data is subjected to inverse Fourier transform to obtain a pressure variation curve, and the plurality of second light transmission frequency domain data are subjected to inverse wavelet transform synthesis to obtain a light transmission variation curve.

[0065] In certain embodiments of the present application, the preferred solution for the preset vibration frequency threshold is 60 Hz. The first pressure frequency domain data is filtered according to the preset vibration frequency threshold to obtain the second pressure frequency domain data, specifically:

[0066] The data greater than a preset vibration frequency threshold in the first pressure frequency domain data is filtered out to obtain the second pressure frequency domain data.

[0067] In certain embodiments of the present application, the wavelet transform decomposition is based on a wavelet basis function, and the preferred solution is the db4 wavelet with 5 decomposition levels.

[0068] In certain embodiments of the present application, the plurality of first light transmission frequency domain data are filtered based on the median filtering algorithm to obtain the plurality of second light transmission frequency domain data, specifically:

[0069] Data greater than the median transmittance value in the plurality of first light transmission frequency domain data are filtered out to obtain a plurality of second light transmission frequency domain data; wherein the median transmittance value is the median value of a sequence formed by all data in the plurality of first light transmission frequency domain data in order of data size.

[0070] This application first performs Fourier transform on the rolling pressure data, and then filters it based on a preset vibration frequency threshold to obtain second pressure frequency domain data, which can filter out the noise caused by mechanical vibration within a specific frequency range; at the same time, the transmittance intensity data is subjected to wavelet decomposition transform, and then filtered based on the median filtering algorithm to obtain multiple second transmittance frequency domain data, which can filter out the spike noise caused by the optical signal within a specific frequency range; and then the second pressure frequency domain data and the multiple second transmittance frequency domain data are synthesized by Fourier inverse transform and wavelet inverse transform to obtain a pressure change curve and a transmittance change curve, which can improve data accuracy and reduce data errors.

[0071] Step S103: Obtaining a uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve and an interpolation algorithm.

[0072] In certain embodiments of the present application, obtaining the uniformity curve of the test sample based on the pressure change curve and the transmittance change curve and an interpolation algorithm specifically includes:

[0073] The pressure change curve and the transmittance change curve are aligned according to the timestamps, and according to the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve, the pressure change curve and the transmittance change curve are divided and combined according to a plurality of preset partitions to obtain a plurality of regional continuous point sets.

[0074] Based on the interpolation algorithm, multiple uniformity curves corresponding to multiple preset partitions are obtained according to the pressure and transmittance of each point in the continuous point set of each area.

[0075] Multiple uniformity curves are spliced ​​in the order of timestamps to obtain the uniformity curve of the test sample.

[0076] In certain embodiments of the present application, the pressure change curve and the transmittance change curve are divided and combined according to a plurality of preset partitions based on the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve to obtain a plurality of regional continuous point sets, specifically:

[0077] According to the first transmission speed corresponding to the pressure sensing array, the pressure change curve is divided into a plurality of preset partitions to obtain a plurality of regional continuous pressure point sets.

[0078] According to the second transmission speed corresponding to the light intensity detection array, the light transmission change curve is divided into a plurality of preset partitions to obtain a plurality of regional continuous light intensity point sets.

[0079] The points of the multiple regional continuous pressure point sets and the multiple regional continuous light intensity point sets are combined in a one-to-one correspondence to obtain multiple regional continuous point sets.

[0080] In certain embodiments of the present application, the interpolation algorithm includes but is not limited to Lagrange interpolation, Newton interpolation and cubic spline interpolation.

[0081] In certain embodiments of the present application, the plurality of uniformity curves corresponding to the plurality of preset partitions are obtained based on the pressure and transmittance of each point in the continuous point set of each region, specifically:

[0082] The uniformity of each point in the continuous point set of each region is obtained according to the weighted sum of the logarithm of the pressure and the logarithm of the transmittance of each point in the continuous point set of each region.

[0083] According to the uniformity of each point in the continuous point set of each region, a plurality of uniformity curves corresponding to the preset plurality of partitions are obtained.

[0084] In certain embodiments of the present application, a preferred implementation of the weighted sum of the logarithm of pressure and the logarithm of transmittance is: the weight of the logarithm of pressure is 0.5, and the weight of the logarithm of transmittance is 0.5.

[0085] This application first aligns the pressure change curve and the transmittance change curve according to the timestamp, and combines the pressure change curve and the transmittance change curve according to the first transmission speed, the second transmission speed and the preset multiple partition divisions to obtain multiple regional continuous point sets, which can divide the detection area into multiple sub-areas, improve detection efficiency, and reduce detection errors. Then, based on the interpolation algorithm, multiple uniformity curves corresponding to the preset multiple partitions are obtained, thereby obtaining the uniformity curve of the detection sample. The interpolation algorithm can be used to calculate and smooth the uniformity curve, reduce curve burrs, and then reduce data errors and ensure data accuracy.

[0086] Step S104: Based on the maximum likelihood estimation method, outlier points of the uniformity curve of the test sample are marked to obtain an outlier point set; wherein the outlier points are points whose uniformity outlier probability is greater than a preset threshold.

[0087] In certain embodiments of the present application, the method of marking the outliers of the uniformity curve of the detection sample based on the maximum likelihood estimation method to obtain the outlier set specifically includes:

[0088] Based on the preset sampling interval, the uniformity curve of the test sample is sampled to obtain a sampling point set.

[0089] Based on the maximum likelihood estimation method, the outlier probability of each point in the sampling point set is calculated, and the outlier points are marked according to the outlier probability threshold to obtain the outlier point set.

[0090] In certain embodiments of the present application, a preferred implementation of the preset sampling interval is to sample once every five points.

[0091] In certain embodiments of the present application, the outlier probability of each point in the sampling point set is calculated based on the maximum likelihood estimation method, and the outlier points are marked according to the outlier probability threshold to obtain the outlier point set, specifically:

[0092] The mean and standard deviation of all sampling points in the sampling point set are calculated, and the outlier probability of each point in the sampling point set is calculated based on the maximum likelihood estimation method according to the mean and standard deviation.

[0093] Mark all sampling points in the sampling point set whose outlier probability is greater than the outlier probability threshold to obtain the outlier point set.

[0094] In certain embodiments of the present application, the preferred value of the outlier probability threshold is 0.6.

[0095] This application first samples the uniformity curve of the test sample based on a preset sampling interval to obtain a sampling point set, and calculates the outlier probability of each point in the sampling point set based on the maximum likelihood estimation method, and then marks and obtains the outlier point set. The calculation of the outlier probability through the maximum likelihood estimation method is more accurate and simple, and can improve the calculation speed while ensuring accuracy, thereby improving the detection efficiency.

[0096] Step S105: obtaining a cluster analysis result of the outlier set according to a density-based clustering algorithm, and obtaining a uniformity test result of the test sample according to the cluster analysis result, so as to adjust the process parameters according to the uniformity test result.

[0097] In certain embodiments of the present application, the method of obtaining a cluster analysis result of an outlier set according to a density-based clustering algorithm and obtaining a uniformity test result of the test sample according to the cluster analysis result specifically includes:

[0098] According to the density-based clustering algorithm, all outlier points in the outlier set are clustered to obtain multiple clusters.

[0099] According to the number of outliers in each of the multiple clusters and a preset outlier number threshold, cluster analysis results of the multiple clusters are determined, and cluster analysis results of the outlier set are obtained according to the cluster analysis results of the multiple clusters.

[0100] According to the cluster analysis results, the uniformity test results of the test samples are determined.

[0101] In certain embodiments of the present application, the preset outlier number threshold is preferably set to 10.

[0102] In certain embodiments of the present application, the cluster analysis results of the multiple clusters are determined based on the number of outliers in each of the multiple clusters and a preset outlier number threshold, specifically:

[0103] If the number of outliers in multiple clusters exceeds the preset outlier threshold, the cluster analysis result of the corresponding cluster is "unqualified uniformity".

[0104] In certain embodiments of the present application, the cluster analysis results of the outlier set obtained based on the cluster analysis results of the multiple clusters are specifically:

[0105] If the cluster analysis result of any cluster among the multiple clusters is "uniformity unqualified", the cluster analysis result of the outlier set is "uniformity unqualified" and the area corresponding to the cluster with the cluster analysis result of "uniformity unqualified" among the multiple clusters.

[0106] In certain embodiments of the present application, the uniformity test result of the test sample is a cluster analysis result of an outlier set.

[0107] This application first obtains multiple clusters through clustering based on a density-based clustering algorithm, and can classify all outliers according to the distribution of the fabric area, and then determine the clustering analysis results based on a preset outlier number threshold. It can intuitively reflect the uniformity change through the number of outliers and determine the uniformity analysis monitoring results. Compared with the complex calculations of the existing technology, it is simpler, thereby improving the detection efficiency.

[0108] Compared with the existing technology, the present application first obtains the rolling pressure data and light transmittance data of the test sample through the pressure sensing array and the light intensity detection array, which can improve the data acquisition speed and reduce the detection delay, and then performs time series analysis and processing on the rolling pressure data and the light transmittance data to obtain the pressure change curve and the light transmittance change curve, and then obtains the uniformity curve of the test sample based on the interpolation algorithm, while considering the pressure exerted on the flash steamed non-woven fabric during rolling and the light transmittance after rolling. Compared with the existing technology that only considers a certain parameter, it is more comprehensive and can improve the detection confidence and accuracy, and then marks the outlier point set based on the maximum likelihood estimation method, and obtains the clustering analysis result based on the density clustering algorithm to obtain the uniformity detection result of the test sample. Direct analysis and statistics are performed through mathematical statistical methods, which can further reduce the detection delay, thereby improving the efficiency of uniformity detection while ensuring the uniformity detection accuracy of flash steamed non-woven fabrics.

[0109] Corresponding to the above method, see Figure 2 The embodiment of the present application provides a flash vaporization nonwoven fabric uniformity detection device, including a data acquisition module 210, a time series processing module 220, an interpolation processing module 230, a likelihood estimation module 240 and a cluster analysis module 250.

[0110] The data acquisition module 210 is used to obtain the rolling pressure data and light transmission intensity data of the test sample; wherein the rolling pressure data is the surface pressure collected when the test sample is rolled; the light transmission intensity data is obtained by detecting the test sample through the light intensity detection array.

[0111] The time series processing module 220 is used to perform time series analysis on the rolling pressure data and the light transmittance data to obtain a pressure change curve and a light transmittance change curve.

[0112] The interpolation processing module 230 is used to obtain a uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve and an interpolation algorithm.

[0113] The likelihood estimation module 240 is used to mark the outliers of the uniformity curve of the test sample based on the maximum likelihood estimation method to obtain an outlier set; wherein the outliers are points whose uniformity outlier probability is greater than a preset threshold.

[0114] The cluster analysis module 250 is used to obtain cluster analysis results of the outlier set based on a density-based clustering algorithm, and obtain uniformity test results of the test sample based on the cluster analysis results, so as to adjust the process parameters according to the uniformity test results.

[0115] In some embodiments of the present application, the timing processing module 220 includes a pressure processing unit, a light intensity processing unit, and a transformation processing unit.

[0116] The pressure processing unit is used to perform Fourier transform on the rolling pressure data to obtain first pressure frequency domain data, and filter the first pressure frequency domain data according to a preset vibration frequency threshold to obtain second pressure frequency domain data.

[0117] The light intensity processing unit is used to perform wavelet transform decomposition on the light transmission intensity data to obtain multiple first light transmission frequency domain data, and filter the multiple first light transmission frequency domain data based on a median filtering algorithm to obtain multiple second light transmission frequency domain data.

[0118] The transformation processing unit is used to perform inverse Fourier transformation on the second pressure frequency domain data to obtain a pressure change curve, and perform inverse wavelet transformation synthesis on multiple second light transmission frequency domain data to obtain a light transmission change curve.

[0119] In some embodiments of the present application, the interpolation processing module 230 includes an alignment processing unit, an interpolation processing unit, and a splicing processing unit.

[0120] An alignment processing unit is used to align the pressure change curve and the transmittance change curve according to the timestamp, and divide and combine the pressure change curve and the transmittance change curve according to a plurality of preset partitions according to the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve, to obtain a plurality of regional continuous point sets.

[0121] The interpolation processing unit is used to obtain a plurality of uniformity curves corresponding to a plurality of preset partitions based on the pressure and transmittance of each point in the continuous point set of each region based on an interpolation algorithm.

[0122] The splicing processing unit is used to splice multiple uniformity curves in the order of timestamps to obtain the uniformity curve of the test sample.

[0123] In some embodiments of the present application, the likelihood estimation module 240 includes a curve sampling unit and a likelihood estimation unit.

[0124] The curve sampling unit is used to sample the uniformity curve of the detection sample based on a preset sampling interval to obtain a sampling point set.

[0125] The likelihood estimation unit is used to calculate the outlier probability of each point in the sampling point set based on the maximum likelihood estimation method, and mark the outlier points according to the outlier probability threshold to obtain the outlier point set.

[0126] In some embodiments of the present application, the cluster analysis module 250 includes a density clustering unit, a cluster analysis unit, and a uniformity detection unit.

[0127] The density clustering unit is used to cluster all outlier points in the outlier point set according to a density-based clustering algorithm to obtain multiple clusters.

[0128] The cluster analysis unit is used to determine the cluster analysis results of the multiple clusters according to the number of outliers in each of the multiple clusters and a preset outlier number threshold, and obtain the cluster analysis results of the outlier set according to the cluster analysis results of the multiple clusters.

[0129] The uniformity detection unit is used to determine the uniformity detection result of the detection sample according to the cluster analysis result.

[0130] The present application first obtains the rolling pressure data and light transmittance data of the test sample through the pressure sensing array and the light intensity detection array, which can improve the data acquisition speed and reduce the detection delay, and then performs time series analysis and processing on the rolling pressure data and the light transmittance data to obtain the pressure change curve and the light transmittance change curve, and then obtains the uniformity curve of the test sample based on the interpolation algorithm, while considering the pressure exerted on the flash steamed non-woven fabric during rolling and the light transmittance after rolling. Compared with the existing technology that only considers a certain parameter, it is more comprehensive and can improve the detection confidence and accuracy, and then the outlier point set is marked based on the maximum likelihood estimation method, and the clustering analysis result is obtained based on the density clustering algorithm to obtain the uniformity detection result of the test sample. Direct analysis and statistics are performed through mathematical statistical methods, which can further reduce the detection delay, thereby improving the efficiency of uniformity detection while ensuring the uniformity detection accuracy of flash steamed non-woven fabrics.

[0131] It should be understood that the device provided in the embodiment of the present application corresponds to the aforementioned method, and the flash-evaporation nonwoven fabric uniformity detection device provided in the embodiment of the present application can implement the flash-evaporation nonwoven fabric uniformity detection method provided in any embodiment of the present application.

[0132] Adaptively, the embodiments of the present application further provide a computer device and a computer-readable storage medium.

[0133] The computer device includes: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor; wherein, when the processor executes the computer program, a flash steaming nonwoven fabric uniformity detection method of the present application is implemented.

[0134] The computer-readable storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor to execute a flash-evaporated nonwoven fabric uniformity detection method of the present application.

[0135] The above description is a partial embodiment of the present application, which further describes the purpose, technical solutions, and beneficial effects of the present application in detail. It should be understood that the above description of the partial embodiment of the present application is not to be construed as limiting the present application. In particular, it is pointed out that for those skilled in the art, any changes, modifications, equivalent substitutions, and variations made within the spirit and principles of the present application should be included within the scope of protection of the present application.

Claims

1. A method for detecting uniformity of flash-evaporated nonwoven fabrics, characterized in that: include: Acquiring rolling pressure data and light transmittance intensity data of the test sample; wherein the rolling pressure data is the surface pressure collected when the test sample is rolled; Performing time series analysis on the rolling pressure data and the light transmittance data to obtain a pressure change curve and a light transmittance change curve; Obtaining a uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve and an interpolation algorithm; Based on the maximum likelihood estimation method, outliers of the uniformity curve of the test sample are marked to obtain an outlier set; wherein the outliers are points whose uniformity outlier probability is greater than a preset threshold; Obtaining a cluster analysis result of the outlier set according to a density-based clustering algorithm, and obtaining a uniformity test result of the test sample according to the cluster analysis result, so as to adjust the process parameters according to the uniformity test result; Obtaining the uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve and an interpolation algorithm specifically includes: The pressure change curve and the transmittance change curve are aligned according to the timestamps, and according to the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve, the pressure change curve and the transmittance change curve are divided and combined according to a plurality of preset partitions to obtain a plurality of regional continuous point sets; based on the interpolation algorithm, according to the pressure and transmittance of each point in each regional continuous point set, a plurality of uniformity curves corresponding to the preset plurality of partitions are obtained; the plurality of uniformity curves are spliced ​​in the order of the timestamps to obtain the uniformity curve of the test sample.

2. The method for detecting uniformity of flash-evaporated nonwoven fabrics according to claim 1, wherein: The performing time series analysis on the rolling pressure data and the light transmission intensity data to obtain a pressure change curve and a light transmission change curve specifically includes: Performing Fourier transform on the rolling pressure data to obtain first pressure frequency domain data, and filtering the first pressure frequency domain data according to a preset vibration frequency threshold to obtain second pressure frequency domain data; Performing wavelet transform decomposition on the light transmission intensity data to obtain a plurality of first light transmission frequency domain data, and filtering the plurality of first light transmission frequency domain data based on a median filter algorithm to obtain a plurality of second light transmission frequency domain data; The second pressure frequency domain data is subjected to inverse Fourier transformation to obtain a pressure variation curve, and the plurality of second light transmission frequency domain data are subjected to inverse wavelet transformation synthesis to obtain a light transmission variation curve.

3. The method for detecting uniformity of flash-evaporated nonwoven fabrics according to claim 1, wherein: The method of marking the outliers of the uniformity curve of the test sample based on the maximum likelihood estimation method to obtain an outlier set specifically includes: Based on a preset sampling interval, sampling the uniformity curve of the test sample to obtain a sampling point set; Based on the maximum likelihood estimation method, the outlier probability of each point in the sampling point set is calculated, and the outlier points are marked according to the outlier probability threshold to obtain an outlier point set.

4. The method for detecting uniformity of flash-steamed nonwoven fabrics according to claim 1, wherein: Obtaining the cluster analysis result of the outlier set according to the density-based clustering algorithm, and obtaining the uniformity test result of the test sample according to the cluster analysis result, specifically includes: Clustering all outlier points in the outlier point set according to a density-based clustering algorithm to obtain a plurality of clusters; Determining cluster analysis results of the multiple clusters according to the number of outliers in each of the multiple clusters and a preset outlier number threshold, and obtaining a cluster analysis result of the outlier set according to the cluster analysis results of the multiple clusters; According to the cluster analysis result, the uniformity test result of the test sample is determined.

5. A flash steam nonwoven fabric uniformity detection device, characterized in that: It includes data acquisition module, time series processing module, interpolation processing module, likelihood estimation module and cluster analysis module; The data acquisition module is used to acquire rolling pressure data and light transmittance intensity data of the test sample; wherein the rolling pressure data is the surface pressure collected when the test sample is rolled; The time series processing module is used to perform time series analysis on the rolling pressure data and the light transmittance data to obtain a pressure change curve and a light transmittance change curve; The interpolation processing module is used to obtain the uniformity curve of the test sample based on the pressure change curve and the light transmittance change curve and an interpolation algorithm; The likelihood estimation module is used to mark the outliers of the uniformity curve of the test sample based on the maximum likelihood estimation method to obtain an outlier set; wherein the outliers are points whose uniformity outlier probability is greater than a preset threshold; The cluster analysis module is used to obtain a cluster analysis result of the outlier set according to a density-based clustering algorithm, and obtain a uniformity test result of the test sample according to the cluster analysis result, so as to adjust the process parameters according to the uniformity test result; The interpolation processing module includes an alignment processing unit, an interpolation processing unit and a splicing processing unit; The alignment processing unit is used to align the pressure change curve and the transmittance change curve according to the timestamp, and divide and combine the pressure change curve and the transmittance change curve according to a plurality of preset partitions according to the first transmission speed corresponding to the pressure change curve and the second transmission speed corresponding to the transmittance change curve, so as to obtain a plurality of regional continuous point sets; the interpolation processing unit is used to obtain a plurality of uniformity curves corresponding to the preset plurality of partitions according to the pressure and transmittance of each point in each regional continuous point set based on an interpolation algorithm; the splicing processing unit is used to splice the plurality of uniformity curves in the order of the timestamps to obtain the uniformity curve of the detection sample.

6. The flash-steamed nonwoven fabric uniformity detection device according to claim 5, characterized in that: The timing processing module includes a pressure processing unit, a light intensity processing unit and a transformation processing unit; The pressure processing unit is configured to perform Fourier transform on the rolling pressure data to obtain first pressure frequency domain data, and filter the first pressure frequency domain data according to a preset vibration frequency threshold to obtain second pressure frequency domain data; The light intensity processing unit is configured to perform wavelet transform decomposition on the light transmission intensity data to obtain a plurality of first light transmission frequency domain data, and filter the plurality of first light transmission frequency domain data based on a median filtering algorithm to obtain a plurality of second light transmission frequency domain data; The transformation processing unit is configured to perform inverse Fourier transformation on the second pressure frequency domain data to obtain a pressure change curve, and perform inverse wavelet transformation synthesis on the plurality of second light transmission frequency domain data to obtain a light transmission change curve.

7. The flash-steamed nonwoven fabric uniformity detection device according to claim 5, characterized in that: The likelihood estimation module includes a curve sampling unit and a likelihood estimation unit; The curve sampling unit is used to sample the uniformity curve of the test sample based on a preset sampling interval to obtain a sampling point set; The likelihood estimation unit is used to calculate the outlier probability of each point in the sampling point set based on the maximum likelihood estimation method, and mark the outlier points according to the outlier probability threshold to obtain the outlier point set.

8. The flash-steamed nonwoven fabric uniformity detection device according to claim 5, characterized in that: The cluster analysis module includes a density clustering unit, a cluster analysis unit and a uniformity detection unit; The density clustering unit is used to cluster all outliers in the outlier set according to a density-based clustering algorithm to obtain a plurality of clusters; The cluster analysis unit is used to determine the cluster analysis results of the multiple clusters according to the number of outliers in each of the multiple clusters and a preset outlier number threshold, and obtain the cluster analysis result of the outlier set according to the cluster analysis results of the multiple clusters; The uniformity detection unit is used to determine the uniformity detection result of the detection sample according to the cluster analysis result.

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