CN117470657B - Textile mattress fabric inspection data acquisition method and device and computing equipment - Google Patents
Textile mattress fabric inspection data acquisition method and device and computing equipment Download PDFInfo
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Abstract
The invention provides a method, a device and a computing device for acquiring textile mattress fabric inspection data, and relates to the technical field of fabric inspection, wherein the method comprises the following steps: arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the plurality of pressure sensors are used for detecting pressure distribution of different parts; placing the sample to be detected on a test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by a pressure sensor when the pressure is loaded; according to the pressure data, calculating to obtain a pressure distribution diagram and a pressure peak value of a sample to be detected; and judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value. According to the invention, the plurality of pressure sensors can be arranged on the surface of the textile mattress fabric sample, pressure data of different parts are collected, and accurate quantitative judgment on the softness and rebound resilience of the textile mattress fabric is realized according to pressure distribution and peak value.
Description
Technical Field
The invention relates to the technical field of fabric inspection, in particular to a method and a device for acquiring fabric inspection data of a textile mattress and computing equipment.
Background
The softness and rebound resilience of the textile mattress fabric are important indexes for judging the quality of the textile mattress fabric.
The traditional method for testing the softness and rebound resilience of the textile mattress fabric is manually carried out, and is judged through hand feeling and experience, so that the accuracy and the repeatability are poor.
At present, some testing equipment and methods can detect pressure distribution and pressure peaks of different parts of the textile mattress fabric, but the detection accuracy and repeatability are not high, and quantitative judgment cannot be realized.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method, a device and a computing device for acquiring textile mattress fabric inspection data, wherein a plurality of pressure sensors can be arranged on the surface of a textile mattress fabric sample to acquire pressure data of different parts, and the accurate quantitative judgment on the softness and rebound resilience of the textile mattress fabric is realized according to pressure distribution and peak value.
In order to solve the technical problems, the technical scheme of the invention is as follows:
in a first aspect, a method for acquiring textile mattress fabric inspection data, the method comprising:
cutting a textile mattress fabric sample to be tested into a detection sheet;
Arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the plurality of pressure sensors are used for detecting pressure distribution of different parts;
Placing the sample to be detected on a test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by a pressure sensor when the pressure is loaded;
according to the pressure data, calculating to obtain a pressure distribution diagram and a pressure peak value of a sample to be detected;
and judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value.
Further, cut the textile mattress fabric sample to be tested into test pieces, comprising:
selecting representative positions from raw materials of the textile mattress fabric, and marking out 5 samples by using a circular proofing device;
Cutting the sample by using a laser cutting machine, and cutting the sample into square or round shapes;
Randomly measuring the length and the width of each sample by using a vernier caliper at 3 points, and recording measured values;
Using a thickness gauge to measure 3-point thickness at the center position and the edge position of each sample, and recording thickness measurement values;
And calculating the average value and standard deviation of the length, width and thickness of each sample, and re-cutting if the size of the sample is not satisfactory.
Further, a plurality of pressure sensors are disposed on the surface of the detection sheet to obtain a sample to be detected, including:
Measuring the length, width and thickness of the detection sheet, and calculating the area of the planar shape;
Estimating the number of sensors to be set according to the size of the area so as to detect the pressure distribution of the whole area;
determining an arrangement mode of the sensor according to the shape of the detection sheet;
According to the arrangement mode of the sensors, key points are selected from the center position, the peripheral edges and the middle position of the detection sheet, and the pressure sensor is fixedly installed by using double-sided adhesive tape;
Detecting the installation state of each sensor by using a multi-meter surface contact measuring instrument, checking the contact area between the sensor and the surface of the detection sheet, numbering each sensor, and marking the corresponding position of the detection sheet.
Further, the sample to be detected is placed on a test bench, and pressure is loaded, when the pressure is loaded, the pressure sensor detects and collects pressure data of different parts in real time, and the method comprises the following steps:
pre-marking the placement position of the detection sample on the test bench;
moving a sample to be detected to a test bench placing position, wherein the surface of the sample is in complete contact with the bench surface;
Checking the connection state of each pressure sensor and the data acquisition module, setting the rate of loading pressure and the maximum pressure value, and gradually loading the pressure to the set pressure value by using the rate of loading pressure of the pressure loading device;
The pressure sensor detects the surface pressure in real time, and pressure and time data in the loading process are collected;
Pressure data acquired by each sensor is acquired, and coordinate position information of each sensor is identified.
Further, according to the pressure data, a pressure distribution diagram and a pressure peak value of a sample to be detected are calculated, including:
acquiring pressure and time primary data acquired by a pressure sensor;
Preprocessing the original data to obtain preprocessed data;
according to the coordinate position information of each sensor, mapping the pressure value onto a sample plan to obtain a pressure distribution image;
Smoothing and color mapping are carried out on the pressure distribution image to obtain a pressure distribution chromatogram;
marking a region with a pressure value larger than or equal to a preset threshold value on the chromatogram, and extracting a specific pressure value of the region;
and analyzing the pressure distribution curves of different areas, and determining the coordinate positions of the pressure peak points.
Further, judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value, including:
Analyzing pressure uniformity over the pressure profile;
Comparing the pressure peak values of different samples, and setting a pressure peak value rating standard;
obtaining a softness evaluation grade according to the peak value result of each sample and the pressure peak value rating standard;
analyzing the pressure and time curves, and calculating the curve slope of the pressure loading and unloading process;
The rebound rating was obtained from the slope of the curve.
Further, after judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value, the method further comprises:
Comparing the softness rating result of the test sample with the standard sample, and calculating a softness difference value between the sample and the standard;
comparing the rebound resilience evaluation result of the test sample with a standard elastic material, and calculating the rebound resilience difference value between the sample and the standard material;
And calculating the comprehensive score of the sample according to the softness degree difference value and the rebound resilience difference value and the weight proportion of the softness degree difference value and the rebound resilience difference value.
In a second aspect, a textile mattress fabric inspection data acquisition device includes:
The acquisition module is used for cutting the textile mattress fabric sample to be tested into detection pieces; arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the plurality of pressure sensors are used for detecting pressure distribution of different parts;
The processing module is used for placing the sample to be detected on the test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by the pressure sensor when the pressure is loaded; according to the pressure data, calculating to obtain a pressure distribution diagram and a pressure peak value of a sample to be detected; and judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value.
In a third aspect, a computing device includes:
one or more processors;
And a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement the above-described methods.
In a fourth aspect, a computer readable storage medium stores a program that when executed by a processor implements the above method.
The scheme of the invention at least comprises the following beneficial effects:
According to the scheme, the pressure distribution of different parts of the textile mattress fabric can be quantitatively detected, the pressure distribution map is obtained, the pressure peak value data of the different parts can be intuitively reflected, the softness and rebound resilience of the textile mattress fabric can be quantitatively judged, and the plurality of pressure sensors are arranged to detect the different parts, so that more comprehensive and accurate pressure data can be obtained.
Drawings
Fig. 1 is a schematic flow chart of a method for acquiring inspection data of textile mattress fabric according to an embodiment of the invention.
Fig. 2 is a schematic diagram of a textile mattress fabric inspection data acquisition device according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described more closely below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
As shown in fig. 1, an embodiment of the present invention proposes a method for acquiring inspection data of textile mattress fabric, the method comprising:
step 11, cutting a textile mattress fabric sample to be tested into a detection sheet;
step 12, arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the pressure sensors are used for detecting pressure distribution of different parts;
Step 13, placing the sample to be detected on a test bench, loading pressure, and detecting and collecting pressure data of different parts by a pressure sensor in real time when the pressure is loaded;
step 14, calculating a pressure distribution diagram and a pressure peak value of a sample to be detected according to the pressure data;
And step 15, judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value.
In the embodiment of the invention, the pressure distribution of different parts of the textile mattress fabric can be quantitatively detected to obtain a pressure distribution map, the pressure peak value data of different parts can be detected and obtained, the softness and rebound resilience of the textile mattress fabric can be quantitatively judged, and more comprehensive and accurate pressure data can be obtained by arranging a plurality of pressure sensors to detect different parts.
In a preferred embodiment of the present invention, the step 11 may include:
Step 111, selecting representative positions from the raw materials of the textile mattress fabric, and marking out 5 samples by using a circular sampler;
Step 112, cutting the sample by using a laser cutting machine, and cutting the sample into square or round shapes;
Step 113, randomly measuring 3 points of the length and the width of each sample by using a vernier caliper, and recording measured values;
step 114, measuring the thickness of 3 points at the center position and the edge position of each sample by using a thickness meter, and recording thickness measurement values;
In step 115, the mean and standard deviation of the length, width and thickness of each sample are calculated, and if the sample size is not satisfactory, the sample is re-cut.
In the embodiment of the invention, samples are selected from different representative positions, so that the performance of the raw materials can be comprehensively reflected. And a round proofing device is adopted for proofing, so that the consistency of the shape and the size of all samples is ensured, and the comparability of subsequent tests is facilitated. Cutting accuracy is guaranteed by cutting with a laser cutting machine, and errors caused by manual operation are avoided. The length, width and thickness data of each sample are measured and recorded in detail, the mean value and standard deviation are calculated, and the consistency among samples can be evaluated. And (3) re-cutting samples which do not meet the requirements, so that all the detection samples reach the size requirements, and the reliability of the subsequent test results is ensured. The standardized sample preparation process ensures the repeatability and comparability of the detection result, and provides a sample with controllable quality for subsequent detection. The detailed record of the sample preparation parameters provides a basis for analysis of test results, and is convenient for tracing the root cause of the problem. The standardized flow simplifies the operation and improves the sample preparation efficiency.
In another preferred embodiment of the present invention, the step 112 may include:
Step 1121, flatly fixing a textile mattress fabric sample on a workbench of a laser cutting machine, drawing a cutting pattern according to a preset cutting shape, and inputting cutting parameters including parameters such as cutting shape size, laser power, cutting speed and the like;
Step 1122, the laser cutting machine calls cutting path information of a cutting pattern through a control system, uses an infrared laser beam to cut a sample according to a cutting path planned in advance, monitors the cutting process in real time through a visual monitoring system on the laser cutting machine in the cutting process, processes the monitored image, feeds back a processed image signal to the control system, and adjusts laser power and cutting speed parameters by combining visual feedback information to realize closed-loop control, and accurately controls the cutting path, wherein the function of the cutting path is q (x, y), wherein x and y are coordinates of a cutting head on a workbench, and the actual cutting path obtained by image post-processing is q' (x, y); the cutting path deviation function is e (x, y) =q (x, y) -q' (x, y), if |e (x, y) | epsilon, the cutting deviation is judged to exceed the allowable range, wherein epsilon is an allowable deviation threshold, and e (x, y) is the cutting deviation; by the direction and the magnitude of the cutting deviation e (x, y) An adjustment amount deltau of the integrated control amount is calculated, where deltau=f (deltav, θ, deltaq), deltaq=k p e (x, y), Deltav=k v | (x, y) |, θ is the laser beam angle adjustment, deltaq is the power adjustment, k p is the proportional gain, deltav is the adjustment of the cutting head speed v, k v is the speed control gain,Representing the partial derivative of the e (x, y) function with respect to x,Representing the partial derivative of the e (x, y) function with respect to y, t being time.
In a preferred embodiment of the present invention, the step 12 may include:
Step 121, measuring the length, width and thickness of the detection sheet, and calculating the area of the planar shape;
step 122, estimating the number of sensors to be set according to the size of the area so as to detect the pressure distribution of the whole area;
step 123, determining an arrangement mode of the sensor according to the shape of the detection sheet;
step 124, selecting key points at the center position, the peripheral edges and the middle position of the detection sheet according to the arrangement mode of the sensors, and fixedly mounting the pressure sensors by using double-sided adhesives;
And step 125, detecting the installation state of each sensor by using a multi-meter surface contact measuring instrument, checking the contact area between the sensor and the surface of the detection sheet, numbering each sensor, and marking the corresponding position of the detection sheet.
In the embodiment of the invention, the size of the detection sheet is measured, the specific area of the sensor to be arranged can be calculated, the number of the sensors is conveniently determined, the distribution density of the sensors can be preliminarily ensured according to the number of the area estimation sensors, the requirement of collecting pressure data of different parts is met, the sensors are arranged according to the shape of the detection sheet, the sensors can be more reasonably distributed, the key position sensors are more densely arranged at key positions, key information of the pressure distribution can be detected, the sensors are fixed by using double-sided adhesive tape, good contact between the sensors and the detection sheet can be ensured, the contact area of each sensor is detected, the sensor with poor contact can be eliminated, each sensor is numbered, the identification and the processing of the collected data are facilitated, the repeatability and the comparability of the test can be ensured due to the standardization of the sensor arrangement, and the number and the position of the sensors are recorded, so that the data analysis and the problem can be traced.
In a preferred embodiment of the present invention, the step 121 may include: the length and the width of the detection sheet are automatically measured by using a numerical control vernier caliper in a multipoint mode, the detection sheet is scanned by using a laser scanner, the boundary outline of the detection sheet is obtained, the outline is analyzed by using an image processing method, and the plane shape area of the detection sheet is calculated. The step 122 may include: and (3) according to the number of the sensors required by the calculation of the area of the plane shape, establishing a finite element model of sensor distribution, simulating a sensor arrangement scheme, and optimizing the sensor distribution to ensure that the density of pressure detection points in each area meets the requirement. The step 123 may include: analyzing the geometry of the test patch, determining a rectangular or circular sensor array, determining dense and sparse areas of the sensor layout according to finite element analysis, and generating an accurate placement scheme of the sensors. The step 124 may include: the coordinates of each sensor are marked in the designed arrangement scheme, the installation position of the sensor is accurately positioned by using a machine vision system, and a mechanical arm automatically grabs the sensor and is accurately fixed on the surface of the detection sheet. The step 125 may include: the multipoint surface contact measuring instrument automatically moves, the contact area of each sensor is measured point by point, the contact pressure distribution of the sensors is detected by utilizing the pressure sensitive film, and the positions of the sensors are automatically adjusted by the manipulator according to the measurement result, so that good contact is ensured.
In a preferred embodiment of the present invention, the step 121 may include: automatically measuring the length and the width of a detection sheet by using a numerical control vernier caliper in a multipoint manner, using a laser scanner with the resolution of 0.1mm, wherein the scanning speed is 10mm/s, the scanning line spacing is 0.5mm, and obtaining a point cloud data set p 0＝{p1,p2,…,pN }, wherein p i＝(xi,yi,zi) represents the three-dimensional coordinate of the ith point, and N is the total point number; calculating an original point cloud through P= { (P i,ni) }, and removing outliers to obtain a processed point cloud, wherein n i is the normal line of an ith point; according to the processed point cloud, throughA light-smoothened surface is obtained, where n represents the normal vector on the reconstruction surface S, n represents the whole normal vector field, n * represents the measurement normal of the original point cloud,Is a Laplace operator, represents the sum of second partial derivatives, S represents a three-dimensional surface to be reconstructed, and lambda represents regularization parameters; triangulating the reconstruction surface according to C= { (x (s 1),y(s1)),s1 epsilon [0, L ]) to extract the contour line, wherein L is the total length of the contour line, C represents the extracted three-dimensional surface contour line, x (s 1),y(s1) represents the coordinate of a point on the contour line, s 1 is the curve parameter of the contour line, and the three-dimensional surface contour line is obtained according to the following stepsAn area S 2 within the closed contour is calculated, where R represents the closed region enclosed by contour line C and A represents the tiny bin within region R. The step 122 may include: calculating the number of sensors according to the area of the detection chip, and establishing a three-dimensional finite element model of the detection chip through omega e = { (x, y, z) |x epsilon [0, a ], y epsilon [0, a ], z epsilon [0, h ] }, wherein the sensor unit is rectangular, the side length is a, the height is h, and omega e represents the area of the e-th finite element unit; according to the three-dimensional finite element model, a sensor arrangement scheme is simulated, sensor distribution is optimized, and the density of pressure detection points in each area meets the requirements.
In a preferred embodiment of the present invention, the step 13 may include:
Step 131, pre-marking the placement position of the detection sample on the test bench;
step 132, moving the sample to be detected to a test bench placing position, wherein the surface of the sample is in full contact with the table top;
Step 133, checking the connection state of each pressure sensor and the data acquisition module, setting the rate of loading pressure and the maximum pressure value, and gradually loading the pressure to the set pressure value by using the rate of loading pressure of the pressure loading device;
step 134, the pressure sensor detects the surface pressure in real time, and collects the pressure and time data in the loading process;
And step 135, acquiring pressure data acquired by each sensor and identifying coordinate position information of each sensor.
In the embodiment of the invention, the sample placement position is marked in advance, the repeatability of the test can be ensured, the sample is ensured to be in full contact with the test bench, the poor contact effect on the test result is avoided, the connection state of the sensor is checked, the accuracy of data acquisition is ensured, the loading pressure parameter is set, standardized pressure loading can be carried out, the pressure is loaded step by step, the pressure distribution change condition in the whole loading process can be detected, the pressure sensor acquires the pressure data in the loading process in real time, the complete test data is ensured to be acquired, the data of each sensor is acquired, the coordinates are marked, the pressure distribution condition of different parts can be distinguished, the repeatability of the result can be improved in the standardized loading and acquisition process, the complete and accurate pressure-time data is obtained, a reliable basis is provided for subsequent analysis modeling, and data support is provided for the pressure distribution comparison analysis under different loading pressure conditions.
In a preferred embodiment of the present invention, the step 14 may include:
step 141, acquiring pressure and time raw data acquired by a pressure sensor;
Step 142, preprocessing the original data to obtain preprocessed data;
Step 143, mapping the pressure value onto a sample plan according to the coordinate position information of each sensor to obtain a pressure distribution image;
Step 144, performing smoothing and color mapping processing on the pressure distribution image to obtain a pressure distribution chromatogram;
step 145, marking a region with a pressure value greater than or equal to a preset threshold value on the chromatogram, and extracting a specific pressure value of the region;
And step 146, analyzing the pressure distribution curves of different areas, and determining the coordinate positions of the pressure peak points.
In the embodiment of the invention, the original pressure data is acquired, the reliability of subsequent analysis is ensured, the original data is preprocessed, the data quality can be improved, the pressure value is mapped to a sample plan, the pressure distribution condition can be intuitively reflected, the pressure distribution image is processed, the visual effect can be enhanced, a key high-pressure area is identified, the pressure concentration part can be rapidly positioned, the specific value of the high-pressure area is extracted, the pressure curves of different areas can be quantitatively analyzed, the coordinates of the pressure peak value can be judged, the imaged pressure distribution analysis can intuitively reflect the softness distribution of the sample, and the pressure distribution characteristics can be accurately judged through the combination of image processing and quantitative analysis.
In another preferred embodiment of the present invention, the step 142 may include: processing the collected pressure raw data P (x, y) by P '(x, y) =w -1[dj WP (x, y) ], to obtain processed pressure data P' (x, y), wherein W represents a wavelet transform operation, W -1 represents wavelet inverse transform, and d j is a coefficient; constructing an automatic encoder to remove abnormal values according to the processed pressure data P '(x, y) and through P' (x, y) =f θ (P '(x, y)), wherein P' (x, y) is the pressure data from which the abnormal values are removed, and f θ represents a trained automatic encoder model; by removing abnormal pressure data P' (x, y)Performing regression prediction of the pressure field, wherein P' "(x, y) is the predicted pressure field, h i (x, y) is a basis function, and omega i is a corresponding weight coefficient; according to the predicted pressure field P '"(x, y), performing optical flow motion compensation on the predicted pressure field through P (x,y) = P'" (x+u, y+v), wherein P (x,y) is the compensated pressure field, and u and v are motion vectors estimated by the optical flow method.
In a preferred embodiment of the present invention, the step 15 may include:
Step 151, analyzing pressure uniformity on the pressure distribution map;
step 152, comparing the pressure peak values of different samples, and setting a pressure peak value rating standard;
Step 153, obtaining a softness evaluation level according to the peak value result of each sample and the pressure peak value rating standard;
Step 154, analyzing the pressure and time curves, and calculating the curve slope of the pressure loading and unloading process;
at step 155, a resiliency rating is obtained from the slope of the curve.
In the embodiment of the invention, the uniformity of pressure is analyzed, the uniformity of softness of samples can be judged, peak results are compared, the softness of different samples can be quantitatively judged, a peak rating standard is set, the rating scale of different samples can be unified, the softness grade is judged according to the peak results, a standardized rating result can be obtained, the slope of a pressure curve is analyzed, the rebound resilience of the samples can be quantitatively judged, the slope of a loading and unloading curve can be calculated, the symmetry of the rebound resilience can be evaluated, the rebound resilience grade is obtained according to the slope of the curve, the rating result can be standardized, the rating standardization can enable the comparability of the results to be strong, the comparison of different samples is convenient, the subjective influence can be reduced by a quantitative rating mode, the reliability of the results is improved, and the final rating result is visual and standard, thereby providing reference for quality control and production optimization.
In another preferred embodiment of the present invention, the step 151 may include: and carrying out Gaussian smoothing denoising treatment on the pressure distribution image, so that an edge detection algorithm extracts pressure distribution contour lines, calculates the mean value and variance of pressure gradient among the contour lines, and statistically judges the uniformity of the pressure distribution according to the mean value and variance. The step 152 may include: and obtaining a plurality of groups of sample pressure peak value data, labeling corresponding subjective softness grades, establishing a mapping model of the pressure peak value and the softness grade by using a support vector machine algorithm, and optimizing model parameters through cross verification to minimize a prediction grade error. The step 153 may include: and carrying out data preprocessing on the pressure peak value of the new sample, inputting the preprocessed pressure peak value into the established support vector machine model, and outputting a softness rating result predicted by the SVM model. The step 154 may include: and carrying out wavelet denoising treatment on the pressure-time curve, fitting the pressure curve by adopting a cubic spline function, and obtaining the first derivative of the fitted curve as the pressure change rate. The step 155 may include: and setting a grading standard of the elasticity rating according to the pressure change rate, substituting the pressure change rate of the sample into the grading standard, and determining the elasticity rating of the sample.
In another preferred embodiment of the present invention, in the step 151, the pressure distribution image I (x, y) is subjected to gaussian filtering by I '(x, y) =g (x, y) ×i (x, y) to obtain a filtered pressure distribution image I' (x, y), where G (x, y) is a gaussian kernel; by passing through Detecting edges of the filtered pressure distribution image I' (x, y) to obtain an edge image E (x, y); from the edge image E (x, y), an isobar C i is extracted by processing C i = { (x, y) |e (x, y) > τ } where τ is a threshold value; according to isobar C i, byCalculating the mean value of the pressure gradient between adjacent isobars C i and C i+1 By means ofCalculating the variance sigma of the pressure gradient between adjacent isobars C i and C i+1; according to the mean value of the pressure gradientAnd the variance sigma of the pressure gradient determines the uniformity of the pressure distribution.
In a preferred embodiment of the present invention, after the step 15, the method may further include:
Step 16, comparing the softness rating result of the test sample with the standard sample, and calculating a softness difference value between the sample and the standard;
step 17, comparing the rebound resilience evaluation result of the test sample with a standard elastic material, and calculating the rebound resilience difference value between the sample and the standard material;
And step 18, calculating the comprehensive score of the sample according to the softness degree difference value and the rebound resilience difference value and the weight proportion of the softness degree difference value and the rebound resilience difference value.
In the embodiment of the invention, the test sample result is compared with the standard sample, the performance of the test sample can be more objectively and accurately evaluated, the difference value of the softness degree and the rebound resilience is calculated, the quantitative evaluation result can be obtained, the difference between the test sample and the standard is conveniently judged, the weight proportion of the softness degree and the rebound resilience is set, the importance of two indexes can be flexibly determined according to actual needs, the comprehensive score is calculated, an integral and quantitative index can be obtained, the reference is provided for the optimization of the test sample, and compared with the direct judgment according to the grade, the evaluation result can be finer and more accurate by calculating the difference value and the comprehensive score.
In another preferred embodiment of the present invention, the step 16 may include: obtaining standard sample data { Z 1,Z2,…,Zn }, wherein Z i represents pressure image data of an ith sample; a corresponding standard sample softness rating { R 1,R2,…,Rn }, where R i represents the softness level of the i-th sample, is obtained. Assuming that the convolutional neural network comprises convolutional layers l=1, 2, …, L and fully-connected layers m=1, 2, …, M; for the first layer, let the input feature map be X l-1 and the convolution kernel beBias b l, activation function f, output characteristic diagram X l of the first layer isWhere i and j represent pixel coordinates in the feature map and k represents the channel dimension. For the m-th layer, setting the input as X m-1, the weight matrix as W m and the bias as b m, and setting the output of the m-th layer as Z m＝WmXm-1+bm,Xm＝f(Zm); Finally outputting a layer classification result according to R 1＝WMXM-1+bM, wherein R 1 is a softness prediction result; predicting a pressure image Z t of the test sample to obtain a softness level R t; The difference D s＝∣Rt-Rs | was calculated from the softness level R s of the standard sample. The step 17 may include: obtaining standard material data { K 1,K2,...,Km }, wherein K i represents pressure time series data of an ith material; Obtaining a corresponding standard material resilience index { E 1,E2,...,Em }, wherein E i represents the resilience index of the ith material; establishing an LSTM network resilience prediction model, inputting the model into a pressure sequence K, and outputting the model into a resilience index E, wherein E=g (K; W x,Wh, b), wherein g represents the calculation flow of the LSTM network, and W x,Wh, b represents the parameters of the network; Predicting a pressure sequence K t of the test sample to obtain a rebound resilience index E t; calculated difference from the rebound resilience index E s of the standard material: The step 18 may include: a composite score was calculated from s=w s×Ds+we×De, where w s and w e represent the weight coefficients of softness and resilience, respectively.
As shown in fig. 2, an embodiment of the present invention further provides a textile mattress fabric inspection data acquisition device 20, comprising:
An acquisition module 21 for cutting a textile mattress fabric sample to be tested into detection pieces; arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the plurality of pressure sensors are used for detecting pressure distribution of different parts;
The processing module 22 is used for placing the sample to be detected on a test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by the pressure sensor when the pressure is loaded; according to the pressure data, calculating to obtain a pressure distribution diagram and a pressure peak value of a sample to be detected; and judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value.
Optionally, cutting the textile mattress fabric sample to be tested into test pieces, including:
selecting representative positions from raw materials of the textile mattress fabric, and marking out 5 samples by using a circular proofing device;
Cutting the sample by using a laser cutting machine, and cutting the sample into square or round shapes;
Randomly measuring the length and the width of each sample by using a vernier caliper at 3 points, and recording measured values;
Using a thickness gauge to measure 3-point thickness at the center position and the edge position of each sample, and recording thickness measurement values;
And calculating the average value and standard deviation of the length, width and thickness of each sample, and re-cutting if the size of the sample is not satisfactory.
Optionally, a plurality of pressure sensors are disposed on a surface of the detection sheet to obtain a sample to be detected, including:
Measuring the length, width and thickness of the detection sheet, and calculating the area of the planar shape;
Estimating the number of sensors to be set according to the size of the area so as to detect the pressure distribution of the whole area;
determining an arrangement mode of the sensor according to the shape of the detection sheet;
According to the arrangement mode of the sensors, key points are selected from the center position, the peripheral edges and the middle position of the detection sheet, and the pressure sensor is fixedly installed by using double-sided adhesive tape;
Detecting the installation state of each sensor by using a multi-meter surface contact measuring instrument, checking the contact area between the sensor and the surface of the detection sheet, numbering each sensor, and marking the corresponding position of the detection sheet.
Optionally, the sample to be detected is placed on a test bench, and pressure is loaded, and when the pressure is loaded, the pressure sensor detects and collects pressure data of different parts in real time, including:
pre-marking the placement position of the detection sample on the test bench;
moving a sample to be detected to a test bench placing position, wherein the surface of the sample is in complete contact with the bench surface;
Checking the connection state of each pressure sensor and the data acquisition module, setting the rate of loading pressure and the maximum pressure value, and gradually loading the pressure to the set pressure value by using the rate of loading pressure of the pressure loading device;
The pressure sensor detects the surface pressure in real time, and pressure and time data in the loading process are collected;
Pressure data acquired by each sensor is acquired, and coordinate position information of each sensor is identified.
Optionally, according to the pressure data, a pressure distribution diagram and a pressure peak value of the sample to be detected are obtained through calculation, including:
acquiring pressure and time primary data acquired by a pressure sensor;
Preprocessing the original data to obtain preprocessed data;
according to the coordinate position information of each sensor, mapping the pressure value onto a sample plan to obtain a pressure distribution image;
Smoothing and color mapping are carried out on the pressure distribution image to obtain a pressure distribution chromatogram;
marking a region with a pressure value larger than or equal to a preset threshold value on the chromatogram, and extracting a specific pressure value of the region;
and analyzing the pressure distribution curves of different areas, and determining the coordinate positions of the pressure peak points.
Optionally, judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value, including:
Analyzing pressure uniformity over the pressure profile;
Comparing the pressure peak values of different samples, and setting a pressure peak value rating standard;
obtaining a softness evaluation grade according to the peak value result of each sample and the pressure peak value rating standard;
analyzing the pressure and time curves, and calculating the curve slope of the pressure loading and unloading process;
The rebound rating was obtained from the slope of the curve.
Optionally, after judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value, the method further comprises:
Comparing the softness rating result of the test sample with the standard sample, and calculating a softness difference value between the sample and the standard;
comparing the rebound resilience evaluation result of the test sample with a standard elastic material, and calculating the rebound resilience difference value between the sample and the standard material;
And calculating the comprehensive score of the sample according to the softness degree difference value and the rebound resilience difference value and the weight proportion of the softness degree difference value and the rebound resilience difference value.
It should be noted that the apparatus is an apparatus corresponding to the above method, and all implementation manners in the above method embodiment are applicable to this embodiment, so that the same technical effects can be achieved.
Embodiments of the present invention also provide a computing device comprising: a processor, a memory storing a computer program which, when executed by the processor, performs the method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Embodiments of the present invention also provide a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method as described above. All the implementation manners in the method embodiment are applicable to the embodiment, and the same technical effect can be achieved.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk, etc.
Furthermore, it should be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. Also, the steps of performing the series of processes described above may naturally be performed in chronological order in the order of description, but are not necessarily performed in chronological order, and some steps may be performed in parallel or independently of each other. It will be appreciated by those of ordinary skill in the art that all or any of the steps or components of the methods and apparatus of the present invention may be implemented in hardware, firmware, software, or a combination thereof in any computing device (including processors, storage media, etc.) or network of computing devices, as would be apparent to one of ordinary skill in the art after reading this description of the invention.
The object of the invention can thus also be achieved by running a program or a set of programs on any computing device. The computing device may be a well-known general purpose device. The object of the invention can thus also be achieved by merely providing a program product containing program code for implementing said method or apparatus. That is, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. It is apparent that the storage medium may be any known storage medium or any storage medium developed in the future. It should also be noted that in the apparatus and method of the present invention, it is apparent that the components or steps may be disassembled and/or assembled. Such decomposition and/or recombination should be considered as equivalent aspects of the present invention. The steps of executing the series of processes may naturally be executed in chronological order in the order described, but are not necessarily executed in chronological order. Some steps may be performed in parallel or independently of each other.
While the foregoing is directed to the preferred embodiments of the present invention, it will be appreciated by those skilled in the art that various modifications and adaptations can be made without departing from the principles of the present invention, and such modifications and adaptations are intended to be comprehended within the scope of the present invention.
Claims (4)
1. A method for acquiring inspection data of textile mattress fabric, the method comprising:
cutting a textile mattress fabric sample to be tested into a detection sheet;
Arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the plurality of pressure sensors are used for detecting pressure distribution of different parts;
Placing the sample to be detected on a test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by a pressure sensor when the pressure is loaded;
according to the pressure data, calculating to obtain a pressure distribution diagram and a pressure peak value of a sample to be detected;
judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value;
Cutting a textile mattress fabric sample to be tested into test pieces, comprising:
selecting representative positions from raw materials of the textile mattress fabric, and marking out 5 samples by using a circular proofing device;
Cutting the sample by using a laser cutting machine, and cutting the sample into square or round shapes;
Randomly measuring the length and the width of each sample by using a vernier caliper at 3 points, and recording measured values;
Using a thickness gauge to measure 3-point thickness at the center position and the edge position of each sample, and recording thickness measurement values;
Calculating the average value and standard deviation of the length, width and thickness of each sample, and if the size of the sample does not meet the requirements, re-cutting;
a plurality of pressure sensors are arranged on the surface of the detection sheet to obtain a sample to be detected, comprising:
Measuring the length, width and thickness of the detection sheet, and calculating the area of the planar shape;
Estimating the number of sensors to be set according to the size of the area so as to detect the pressure distribution of the whole area;
determining an arrangement mode of the sensor according to the shape of the detection sheet;
According to the arrangement mode of the sensors, key points are selected from the center position, the peripheral edges and the middle position of the detection sheet, and the pressure sensor is fixedly installed by using double-sided adhesive tape;
Detecting the installation state of each sensor by using a multi-meter surface contact measuring instrument, checking the contact area between the sensor and the surface of the detection sheet, numbering each sensor, and marking the corresponding position of the detection sheet;
Placing the sample to be detected on a test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by a pressure sensor when loading the pressure, wherein the method comprises the following steps of:
pre-marking the placement position of the detection sample on the test bench;
moving a sample to be detected to a test bench placing position, wherein the surface of the sample is in complete contact with the bench surface;
Checking the connection state of each pressure sensor and the data acquisition module, setting the rate of loading pressure and the maximum pressure value, and gradually loading the pressure to the set pressure value by using the rate of loading pressure of the pressure loading device;
The pressure sensor detects the surface pressure in real time, and pressure and time data in the loading process are collected;
acquiring pressure data acquired by each sensor and identifying coordinate position information of each sensor;
according to the pressure data, calculating a pressure distribution diagram and a pressure peak value of a sample to be detected, wherein the pressure distribution diagram and the pressure peak value comprise:
acquiring pressure and time primary data acquired by a pressure sensor;
Preprocessing the original data to obtain preprocessed data;
according to the coordinate position information of each sensor, mapping the pressure value onto a sample plan to obtain a pressure distribution image;
Smoothing and color mapping are carried out on the pressure distribution image to obtain a pressure distribution chromatogram;
marking a region with a pressure value larger than or equal to a preset threshold value on the chromatogram, and extracting a specific pressure value of the region;
analyzing pressure distribution curves of different areas, and determining coordinate positions of pressure peak points;
Judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value, wherein the method comprises the following steps:
Analyzing pressure uniformity over the pressure profile;
Comparing the pressure peak values of different samples, and setting a pressure peak value rating standard;
obtaining a softness evaluation grade according to the peak value result of each sample and the pressure peak value rating standard;
analyzing the pressure and time curves, and calculating the curve slope of the pressure loading and unloading process;
obtaining a rebound resilience rating according to the slope of the curve;
after judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value, the method further comprises the following steps:
Comparing the softness rating result of the test sample with the standard sample, and calculating a softness difference value between the sample and the standard;
comparing the rebound resilience evaluation result of the test sample with a standard elastic material, and calculating the rebound resilience difference value between the sample and the standard material;
calculating the comprehensive score of the sample according to the difference value of the softness and the difference value of the rebound resilience and the weight proportion of the difference value of the softness and the difference value of the rebound resilience;
Measuring the length, width and thickness of the test piece, calculating the area of the planar shape, comprising: the length and the width of the detection sheet are automatically measured by using a numerical control vernier caliper in a multipoint manner, a laser scanner with the resolution of 0.1mm is used, the scanning speed is 10mm/s, the scanning line spacing is 0.5mm, and a point cloud data set is obtained Wherein, p i=(xi,yi,zi) represents the three-dimensional coordinates of the ith point, and N is the total point number; removing outliers from the original point cloud to obtain a processed point cloud, wherein n i is the normal line of the ith point; according to the processed point cloud, throughA light-smoothened surface is obtained, where n represents the normal vector on the reconstruction surface S,Representing the entire normal vector field,A measurement normal representing the original point cloud,Is the Laplace operator, represents the sum of the second partial derivatives,Representing a three-dimensional surface to be reconstructed,Representing regularization parameters; according toCalculating the area within a closed contourWherein R represents a closed region surrounded by a contour line C, and a represents a minute bin within the region R.
2. A textile mattress fabric inspection data acquisition device, characterized in that it is applied in claim 1, comprising:
The acquisition module is used for cutting the textile mattress fabric sample to be tested into detection pieces; arranging a plurality of pressure sensors on the surface of the detection sheet to obtain a sample to be detected, wherein the plurality of pressure sensors are used for detecting pressure distribution of different parts;
The processing module is used for placing the sample to be detected on the test bench, loading pressure, and detecting and collecting pressure data of different parts in real time by the pressure sensor when the pressure is loaded; according to the pressure data, calculating to obtain a pressure distribution diagram and a pressure peak value of a sample to be detected; and judging the softness and rebound resilience of the sample to be detected according to the pressure distribution condition and the peak value.
3. A computing device, comprising:
one or more processors;
Storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method of claim 1.
4. A computer readable storage medium, characterized in that the computer readable storage medium has stored therein a program which, when executed by a processor, implements the method according to claim 1.
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