Visual analysis method for flaws of spunlace non-woven fabric

Abstract

The invention relates to a spunlace non-woven fabric defect visual analysis method, and relates to the field of spunlace non-woven fabric defect inspection, and the method comprises the following steps: responding to a cloth in-place signal to obtain an overlook infrared thermal imaging graph, the cloth in-place signal being a signal for placing a to-be-detected spunlace non-woven fabric on a preset defect detection device; analyzing the temperature distribution and the average temperature of the water-thorn non-woven fabric through an overlooking infrared thermal image; comparing the temperature distribution with the average temperature to obtain an abnormal temperature region and a corresponding abnormal temperature; based on the abnormal temperature and the average temperature, flaws and flaw types corresponding to the flaws are analyzed; and forming a flaw signal from the abnormal temperature region and the flaw category and outputting the flaw signal. The non-woven fabric flaw detection method has the advantages that whether the non-woven fabric has flaws or not can be efficiently judged, and the flaw types can be accurately analyzed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water-jet non-woven fabric flaw inspection, in particular to a water-jet non-woven fabric flaw visual analysis method. BACKGROUND

[0002] At present, water-jet non-woven fabric is widely used in medical protection, health care, industrial filtration and home cleaning fields due to its softness, air permeability, strong moisture absorption, environmental protection and degradability, and becomes an indispensable basic material in modern life. However, it is found in actual use that some products have problems such as pore blockage leading to water permeability decrease and surface residual water marks affecting the aesthetic degree, so quality inspection is needed for finished products to reduce the defect rate of products.

[0003] At present, the flaw detection of water-jet non-woven fabric mainly detects non-woven fabric defects manually. The operator visually scans the surface of the fabric under natural light or standard light source to identify obvious flaws such as holes, dirt and fiber agglomeration visible to the naked eye.

[0004] For the related art in the above, the worker relies on naked eye observation of surface defects, which has problems such as low efficiency, easy influence by subjective factors, high missed detection rate, and especially difficult to find hidden defects such as internal pore blockage. Moreover, the whole process relies on individual sensory acuity and experience accumulation, is easy to be affected by fatigue, light change and subjective judgment difference, and is difficult to quantify the problems of small pore structure or deep fiber distribution. SUMMARY

[0005] In order to more scientifically and accurately identify the flaws of water-jet non-woven fabric, the present application provides a water-jet non-woven fabric flaw visual analysis method.

[0006] The present application provides a water-jet non-woven fabric flaw visual analysis method, which adopts the following technical scheme: A water-jet non-woven fabric flaw visual analysis method, characterized in that it comprises: Step 1: obtaining an overhead infrared thermal imaging diagram in response to a fabric in-place signal, the fabric in-place signal being a signal of placing the water-jet non-woven fabric to be detected on a preset flaw detection device, the flaw detection device comprising a conveying wheel for placing the water-jet non-woven fabric and a nozzle for spraying hot gas, there being a space for the water-jet non-woven fabric to pass between the upper and lower conveying wheels, the nozzle being arranged on the lower side of the water-jet non-woven fabric and facing the water-jet non-woven fabric, the hot gas being uniformly sprayed to the water-jet non-woven fabric, and the flaw detection device further comprising an infrared detector arranged above the water-jet non-woven fabric; Step 2: analyzing the temperature distribution and average temperature of the water-jet non-woven fabric through the overhead infrared thermal imaging diagram; Step 3: comparing the temperature distribution with the average temperature to obtain an abnormal temperature zone and corresponding abnormal temperature; Step 4: analyzing the defect and its corresponding defect category based on the abnormal temperature and the average temperature; Step 5: outputting the abnormal temperature zone and the defect category to form a defect signal.

[0007] By adopting the above technical solution, the high-efficiency detection of the spunlace non-woven fabric defect is successfully realized by using the hot gas dynamic penetration and infrared thermal imaging technology. The core is to use the hot gas to uniformly spray the spunlace non-woven fabric, combine the infrared detector to capture the material temperature distribution in real time, compare through the coarse and fine grid segmentation, determine the multi-level temperature interval, and analyze the thermodynamic characteristics of the defect area, accurately identify the defect types such as pore blockage, hole, water mark and the like. The method takes the scientific and quantitative temperature difference as the criterion, replaces the subjectivity and low efficiency of the traditional manual visual inspection, quickly locates the abnormal temperature zone through the thermal imaging diagram and classifies the defects, not only improves the detection efficiency and coverage, but also penetrates the surface to identify the internal structural defects, effectively solves the problem that the traditional method is difficult to balance the efficiency, accuracy and comprehensiveness, and provides objective and reliable technical support for the non-woven fabric quality control.

[0008] Optionally, the specific method of comparing the temperature distribution with the average temperature to obtain the abnormal temperature zone comprises: Step 30: dividing the overhead infrared thermal imaging diagram into multiple intervals according to a preset coarse grid segmentation method, and defining the divided area as a coarse interval; Step 31: dividing the temperature distribution according to the coarse interval to obtain a coarse interval average temperature; Step 32: calculating a coarse interval temperature interval based on the average temperature and a preset coarse fluctuation parameter; Step 33: defining the coarse interval in which the coarse interval average temperature does not fall into the coarse interval temperature interval as a coarse abnormal temperature interval; Step 34: dividing the coarse abnormal temperature interval into multiple intervals according to a preset fine grid segmentation method, and defining the divided interval as a fine interval; Step 35: dividing the temperature distribution according to the fine interval to obtain a fine interval average temperature; Step 36: obtaining a fine interval temperature interval based on the average temperature and a preset fine fluctuation parameter; Step 37: defining the fine interval in which the fine interval average temperature does not fall into the fine interval temperature interval as the abnormal temperature zone and outputting.

[0009] By adopting the technical scheme, the coarse-fine grid segmentation strategy is used to realize rapid screening and accurate positioning of the abnormal temperature area. The coarse grid segmentation method is used to determine the approximate defect position, and then the fine grid segmentation method is used to further segment the coarse abnormal area, so as to accurately lock the defect area caused by the micro temperature difference, and solve the problem that the single segmentation method cannot accurately obtain the defect position and is prone to cause over-segmentation or under-segmentation. Through the coarse-fine segmentation method, the detection reliability and engineering practicability can be significantly improved.

[0010] Optionally, the specific method for analyzing the defect and the defect category corresponding to the defect based on the abnormal temperature and the average temperature comprises: Step 40: traversing all the abnormal temperature areas to form a defect interval group, and any two defect intervals in the defect interval group exist at least one path connecting them through adjacent defect intervals; Step 41: trimming the defect interval group through the overhead infrared thermal imaging diagram to obtain a complete defect area; Step 42: dividing the temperature distribution according to the complete defect area to obtain a defect area average temperature; Step 43: expanding the complete defect area outward by a preset heat diffusion width to obtain a surrounding defect area; Step 44: dividing the temperature distribution according to the surrounding defect area to obtain a surrounding area average temperature; Step 440: when the surrounding area average temperature exceeds the subdivision interval temperature interval and the defect area average temperature is lower than the subdivision interval temperature interval, defining the defect as a blockage defect, and outputting a blockage defect signal; Step 441: when the surrounding area average temperature falls within the subdivision interval temperature interval and the defect area average temperature is lower than the subdivision interval temperature interval, defining the defect as a hole defect, and outputting a hole defect signal; Step 442: when the surrounding area average temperature is lower than the subdivision interval temperature interval and the defect area average temperature is lower than the subdivision interval temperature interval, defining the defect as a water mark defect, and outputting a water mark defect signal.

[0011] By adopting the technical scheme, the temperature difference between the abnormal temperature area and the surrounding environment is systematically analyzed, and the precise classification of the spunlace non-woven fabric defect is realized. First, the discrete abnormal temperature area is integrated into a connected defect complete area, and the average temperature is calculated, then a surrounding area is obtained by expanding outward by a preset width, and the corresponding temperature is calculated, finally, through the double-area temperature comparison combined with the subdivision temperature interval rule, the defect is qualitatively classified as a blockage, a hole or a water mark defect. This solves the defect of pore blockage which cannot be efficiently identified by methods such as light analysis and machine learning. The method improves the scientificity and objectivity of defect classification, provides data support for targeted process optimization, and significantly reduces the risk of processing errors caused by confusion of defect types.

[0012] Optionally, when the average temperature of the surrounding area exceeds the subdivision temperature interval and the average temperature of the defect area is lower than the subdivision temperature interval, a method for not outputting a blockage defect signal is further included, and the method comprises the following steps. Step 4400: calculating a blockage area temperature difference by the average temperature of the defect area and the average temperature of the surrounding area; Step 4401: obtaining a blockage degree value from a preset temperature blockage degree table according to the blockage area temperature difference; Step 4402: obtaining a defect area based on the defect complete area; Step 4403: obtaining a local blockage degree based on the defect area and the blockage degree value; Step 44030: when the local blockage degree is higher than a preset blockage threshold, defining the defect complete area corresponding to the blockage degree value as a complete blockage area, and outputting a blockage defect signal; Step 44031: when the local blockage degree is lower than the blockage threshold, defining the defect complete area corresponding to the blockage degree value as a partial blockage area, and not outputting a blockage defect signal.

[0013] By adopting the technical scheme, the blockage degree is quantified according to the temperature difference between the defect area and the surrounding area, and the local blockage degree is comprehensively evaluated by combining the defect area, and finally the complete or partial blockage area is automatically distinguished according to the threshold. The core solves the problem that the traditional manual detection cannot accurately quantify the blockage severity, and through the data of temperature difference and area, the grading determination of blockage defect is realized. The method eliminates subjective judgment deviation by using a standardized temperature blockage comparison table, and balances the relationship between local blockage degree and influence range through a local blockage degree model.

[0014] Optionally, it further comprises: Step 4404: obtaining the number of all the partial blockage areas, defined as the number of partial blockage areas; Step 4405: Obtain all the partial blockage area corresponding to the blockage degree value, defined as partial blockage degree value; Step 4406: Calculate the spunlace nonwoven air permeability value according to the partial blockage degree value and the number of partial blockage areas; Step 4407: If the spunlace nonwoven air permeability value is lower than the preset air permeability threshold, form an overall air permeability defect signal and output.

[0015] By adopting the above technical scheme, by counting the number of partial blockage areas and their corresponding blockage degree values, a quantitative index is established and the overall air permeability value of the spunlace nonwoven fabric is calculated, and finally it is determined whether it is lower than the preset air permeability threshold. This solves the problem of air permeability evaluation deviation caused by ignoring the cumulative effect of partial blockage in traditional detection. Through the mathematical model, the discrete partial blockage defects are converted into systematic air permeability performance index, and the accurate identification of material defects is realized.

[0016] Optionally, when the average temperature of the surrounding area falls into the subdivided temperature interval and the average temperature of the defect area is lower than the subdivided temperature interval, the method can not output the hole defect signal, and the method comprises: Step 4410: Obtain the perimeter of the defect complete area from the overhead infrared thermal image, which is defined as the defect perimeter; Step 4411: Obtain the pore circularity of the defect complete area based on the defect area and the defect perimeter; Step 4412: When the pore circularity is greater than the preset circularity threshold, the corresponding defect complete area is defined as a normal pore area, and the hole defect signal can not be output.

[0017] By adopting the above technical scheme, by calculating the pore circularity of the defect area, the abnormal area with regular shape is screened out, and when the circularity exceeds the threshold, it is determined as a normal pore rather than a hole defect. This solves the problem of misjudging regular pores as holes due to temperature anomalies. Through geometric feature analysis, the process inherent pores and the real damage defects are effectively distinguished, which significantly reduces the false positive rate while ensuring the hole detection rate, and improves the result reliability of the quality inspection system.

[0018] Optionally, it further comprises: Step 4413: Obtain all the pore core coordinates of the normal pore area, wherein the pore core coordinates refer to the coordinates of the geometric center of the normal pore area; Step 4414: Arrange all the pore core coordinates to form a pore core coordinate group; Step 4415: calculate the lateral distance and longitudinal coordinate distance between any two adjacent pore core coordinates in the pore core coordinate set, and define as pore lateral coordinate interval and pore longitudinal coordinate interval; Step 4416: obtain the standard pore lateral interval based on the statistics of all the pore lateral coordinate intervals; Step 4417: obtain the standard pore longitudinal interval based on the statistics of all the pore longitudinal coordinate intervals; Step 4418: construct a pore arrangement grid with any of the pore core coordinates, the standard pore lateral interval and the standard pore longitudinal interval; Step 4419: obtain an arrangement matching degree according to the pore core coordinates and the pore arrangement grid; Step 4420: when the arrangement matching degree is lower than a preset matching degree threshold, define the corresponding normal pore region as an abnormal pore region, define the defect as an abnormal pore defect, and output a preset abnormal pore defect signal.

[0019] By using the above technical solution, by extracting the core coordinates of normal pores and analyzing the arrangement regularity, a standard pore grid model is constructed, the matching degree of actual pore distribution and ideal arrangement is quantified, and structural abnormalities caused by pore disorder are identified. This scheme solves the problem of identifying abnormal round holes as standard round holes, establishes a reference grid by statistically analyzing the lateral and longitudinal standard intervals, determines the arrangement disorder area combined with the matching degree threshold, and effectively captures the hidden structural defects caused by process fluctuations.

[0020] Optionally, the method can not output the abnormal temperature area, and the method comprises: Step 370: obtain an edge correction temperature parameter based on the average temperature of the subdivision interval; Step 371: obtain an edge subdivision area temperature interval according to the edge correction temperature parameter and the subdivision interval temperature interval; Step 372: when the defect interval coordinate falls into a preset edge coordinate set, define the corresponding subdivision area average temperature as an edge average temperature; Step 3720: if the edge average temperature falls into the edge subdivision area temperature interval, the defect signal can not be output; Step 3721: if the edge average temperature does not fall into the edge subdivision area temperature interval, continue to output the defect signal.

[0021] By adopting the technical scheme, the flaw determination logic of the edge area is optimized in a targeted manner by introducing the edge correction temperature parameter and the edge subdivision area temperature interval. This solves the false alarm problem caused by uneven edge heat dissipation, environmental interference and other factors in traditional detection, while retaining the strict detection standard of the core area, and adopting a more relaxed determination threshold for the edge area, which avoids invalid flaw signals caused by the natural gradient of the edge temperature, ensures that real defects can still be effectively identified, and significantly improves the anti-interference ability and result reliability of the quality inspection system.

[0022] Optionally, the method of outputting the flaw signal further comprises: Step 50: Obtain the number of all the coarse division abnormal temperature intervals, defined as the number of coarse division abnormal intervals; Step 51: When the number of coarse division abnormal intervals exceeds a preset abnormal interval threshold, define the flaw as an overall quality flaw, and output a preset overall quality flaw signal; Step 52: Perform standard deviation analysis on the temperature of the overhead infrared thermography of the coarse division interval to obtain the corresponding coarse division area standard deviation; Step 53: When the coarse division area standard deviation exceeds a preset abnormal standard deviation threshold, the flaw is also defined as the overall quality flaw, and the overall quality flaw signal is output.

[0023] By adopting the technical scheme, the number of coarse division abnormal temperature intervals is counted and the standard deviation of the temperature is analyzed, and the flaw is determined from two dimensions of abnormal distribution density and dispersion degree. When the number of abnormal intervals exceeds the threshold or the standard deviation exceeds the abnormal range, it indicates that there are multiple small area flaws in the cloth or the cloth has obvious thickness unevenness problem, that is, the overall quality flaw signal is triggered. This solves the misjudgment problem caused by a single indicator in traditional detection, for example, small area anomalies may accumulate to form a systematic risk, or a high temperature dispersion degree reflects a material uniformity defect.

[0024] Optionally, the method of outputting the overall quality flaw signal further comprises: Step 54: Obtain the standard abnormal area number and the standard sample standard deviation threshold based on a preset standard overhead infrared spectrum; Step 55: When the number of coarse division abnormal areas is greater than the number of standard abnormal areas or the coarse division area standard deviation is greater than the standard sample standard deviation threshold, the flaw is defined as the overall quality flaw, and the overall quality flaw signal is output.

[0025] By adopting the technical scheme, the standard overhead infrared spectrum is introduced, the number of standard abnormal regions and the standard sample standard deviation threshold value are obtained as a benchmark, and the coarse division abnormal data in actual detection is compared with the standard value. This solves the problem that the threshold value setting in the traditional method depends on subjective experience, and through the comparison of the quantitative standard and the real-time data, the overall quality defect is objectively determined, the accuracy and consistency of the detection are significantly improved, and the risk of misjudgment or missed detection caused by threshold deviation is reduced.

[0026] To sum up, the present application includes at least one of the following beneficial technical effects: 1. The method can efficiently determine whether the non-woven fabric has defects by infrared thermal imaging technology combined with temperature distribution analysis; the scientific basis is that the temperature field difference generated when hot gas penetrates the material is used for comparison, coarse and fine grid segmentation, and multi-level temperature interval determination to accurately locate the abnormal temperature area, avoiding the subjectivity and inefficiency of traditional manual detection, and realizing objective quantitative analysis based on physical property difference.

[0027] 2. The method can effectively identify pore blockage defects. By comparing the temperature difference between the defect area and the surrounding area and the blockage degree value, combined with the calculation of the air permeability value, it can distinguish between complete blockage and partial blockage, and quantitatively evaluate the influence of blockage on the overall air permeability; this analysis method based on thermal conductivity characteristics and temperature gradient breaks through the technical limitations of traditional visual inspection or permeation test that cannot accurately determine the internal pore state.

[0028] 3. The method can detect both local defects and evaluate overall quality. By counting the number of coarse division abnormal intervals, analyzing the standard deviation, and comparing with the preset standard spectrum, it can determine whether the material has systematic defects; at the same time, through the temperature distribution modification of the fine grid and the calculation of the pore arrangement grid matching degree, it can capture internal fiber structure abnormalities through surface features, realizing multi-level defect identification from microscopic pores to macroscopic texture. BRIEF DESCRIPTION OF DRAWINGS

[0029] Figure 1 is a flowchart of a spunlace non-woven fabric defect visual analysis method in an embodiment of the present application.

[0030] Figure 2 is a schematic diagram of an apparatus for obtaining an overhead infrared thermal imaging image in an embodiment of the present application.

[0031] Figure 3 is a schematic diagram of a defect interval group, a complete defect area, and a surrounding defect area in an embodiment of the present application. DETAILED DESCRIPTION

[0032] The present application will be further described in detail below in combination with the drawings and embodiments.

[0033] The embodiment of the application discloses a spunlace non-woven fabric flaw visual analysis method. Referring to Figure 1 A spunlace non-woven fabric flaw visual analysis method comprises the following steps: A spunlace non-woven fabric flaw visual analysis method comprises the following steps: Step 1: obtaining an overhead infrared thermal imaging map in response to a cloth-in-place signal, the cloth-in-place signal being a signal indicating that the spunlace non-woven fabric to be detected is placed on a preset flaw detection device.

[0034] Referring to Figure 2 The flaw detection device comprises a conveying wheel for placing the spunlace non-woven fabric and a nozzle for spraying hot gas, and a space for the spunlace non-woven fabric to pass between the upper and lower conveying wheels. The nozzle is arranged on the lower side of the spunlace non-woven fabric and faces the spunlace non-woven fabric, and the hot gas is uniformly sprayed onto the spunlace non-woven fabric. The flaw detection device further comprises an infrared detector arranged above the spunlace non-woven fabric for detecting the temperature distribution on the surface of the cloth.

[0035] The cloth-in-place signal refers to an initial signal indicating that the spunlace non-woven fabric to be detected has been placed on the flaw detection device and the subsequent detection process can be triggered.

[0036] The overhead infrared thermal imaging map refers to a thermal imaging image collected vertically downward by the infrared detector arranged above the spunlace non-woven fabric, which includes the surface temperature distribution of the spunlace non-woven fabric after being heated.

[0037] The obtaining process of a complete overhead infrared thermal imaging map is as follows: after the spunlace non-woven fabric is conveyed to the predetermined position to trigger the cloth-in-place signal, the nozzle sprays high-speed hot gas at a preset temperature to the hot gas station, and the thermal imaging map of the spunlace non-woven fabric is collected after a preset duration. The cloth-in-place signal can be emitted by the infrared detector or emitted according to the light sensor and other devices, which is only described here.

[0038] Step 2: analyzing the temperature distribution and average temperature of the spunlace non-woven fabric from the overhead infrared thermal imaging map.

[0039] The temperature distribution refers to the phenomenon of uneven heating of the spunlace non-woven fabric surface caused by flaws, and the temperature distribution can be directly presented as the spatial distribution characteristics of different color temperature regions through the overhead infrared thermal imaging map. The analysis process is a basic method of thermal imaging and temperature conversion, which is well known to those skilled in the art and will not be described here.

[0040] The average temperature is the overall average of the temperature of all pixel points in the thermal imaging map, which is used to represent the comprehensive thermal state of the cloth after being heated. All temperature data are calculated according to the average value calculation method.

[0041] Step 3: comparing the temperature distribution with the average temperature to obtain the abnormal temperature zone and the corresponding abnormal temperature.

[0042] Abnormal temperature zone refers to the area where the temperature value of the local area on the surface of the spunlace nonwoven fabric deviates significantly from the overall average temperature. This deviation is usually caused by defects in the spunlace nonwoven fabric, such as blockage, holes and water marks, etc., thus appearing as "high temperature zone" or "low temperature zone" in the infrared thermal imaging diagram. The method for comparing the temperature distribution with the average temperature is numerical comparison.

[0043] Step 4: Analyze the defect and its corresponding defect category based on the abnormal temperature and the average temperature.

[0044] Defect refers to the local structural abnormal area caused by process abnormalities or material defects in the production process of spunlace nonwoven fabric. Defect category is the type of different defects. It is classified according to the temperature distribution pattern, geometric morphological characteristics and physical causes corresponding to the defect.

[0045] The system first combines adjacent abnormal temperature zones into a connected whole, and eliminates isolated partitions to ensure defect integrity; then it corrects the boundary through temperature gradient fitting to form a continuous area consistent with the actual shape, and calculates the average temperature of the area. At the same time, based on the preset thermal diffusion parameters, the transition zone is expanded outward to obtain the average temperature of the surrounding area. Finally, the defect type is determined by comparing the temperature relationship between the two: if the surrounding area presents high temperature accumulation due to blocked heat conduction and the defect area is low temperature, it is determined as blockage defect; if the surrounding temperature is normal but the defect area is low temperature due to structural loss, it is classified as hole defect; if the surrounding area presents low temperature due to water heat absorption and the defect area presents high specific heat capacity, it is identified as water mark defect.

[0046] Step 5: Form the defect signal by combining the abnormal temperature zone and the defect category.

[0047] Defect signal refers to the information containing the coordinates of the defect and the corresponding defect category. The system automatically collects the coordinates of the abnormal temperature zone, labels the corresponding defect category, and integrates it into the information signal containing the defect location and type. The purpose of integrated output is to realize efficient problem positioning and provide accurate defect identification basis for subsequent quality control by integrating the coordinate positioning of abnormal area and the defect type label.

[0048] The specific method of comparing the temperature distribution with the average temperature to obtain the abnormal temperature zone includes: Step 30: Divide the overhead infrared thermal imaging diagram into multiple intervals according to the preset coarse grid division method, and define the divided areas as coarse intervals.

[0049] Coarse grid division method refers to a uniform grid generation rule based on fixed row and column division to divide the overhead infrared thermal imaging diagram into multiple rectangular areas of equal size or equal proportion.

[0050] The coarse partition interval is a rectangular region divided by the coarse grid division method. The global temperature distribution is discretized into a limited number of intervals by coarse grid division, reducing the complexity of data processing.

[0051] Step 31: Divide the temperature distribution according to the coarse partition interval to obtain the coarse partition interval average temperature.

[0052] The coarse partition interval average temperature refers to the value obtained by performing arithmetic average calculation on the temperature data of all pixel points in each coarse partition interval after dividing the overhead infrared thermal imaging diagram into multiple coarse partition intervals of equal size or equal proportion by the preset coarse grid division method. This value reflects the overall heating state of the spunlace nonwoven fabric surface in the corresponding coarse partition interval, and is a basic index for subsequent comparison with the global average temperature to screen potential abnormal temperature zones.

[0053] Step 32: Calculate the coarse partition interval temperature interval based on the average temperature and the preset coarse fluctuation parameter.

[0054] The coarse fluctuation parameter is a parameter set to obtain the allowable temperature fluctuation range according to the material properties, production process and historical test data of the spunlace nonwoven fabric. The staff sets the upper and lower floating parameters corresponding to the maximum temperature range that can be accepted as the coarse fluctuation parameter after a large number of tests on the spunlace nonwoven fabric.

[0055] The coarse partition interval temperature interval is a temperature range calculated based on the global average temperature and the coarse fluctuation parameter, used to determine whether the coarse partition interval average temperature belongs to normal fluctuation. The coarse partition interval temperature interval includes the coarse partition interval temperature upper limit and the coarse partition interval temperature lower limit, wherein the coarse partition interval temperature upper limit is equal to the coarse partition interval average temperature plus the coarse partition interval average temperature multiplied by the coarse fluctuation parameter, and the coarse partition interval temperature lower limit is equal to the coarse partition interval average temperature minus the coarse partition interval average temperature multiplied by the coarse fluctuation parameter.

[0056] Step 33: Define the coarse partition interval whose coarse partition interval average temperature does not fall into the coarse partition interval temperature interval as a coarse abnormal temperature interval.

[0057] The coarse abnormal temperature interval refers to the coarse partition interval whose average temperature value exceeds the preset coarse partition interval temperature interval.

[0058] When the coarse partition interval average temperature does not fall into the coarse partition interval temperature interval, it means that the coarse partition interval temperature is significantly abnormal, which is preliminarily determined as possible material defects such as holes, blockage, etc., which need to be further positioned accurately by the fine grid division method.

[0059] Step 34: Divide the coarse abnormal temperature interval into multiple intervals according to the preset fine grid division method, and define the divided intervals as fine partition intervals.

[0060] The fine grid division method is a high-density grid generation rule for further dividing the coarsely divided abnormal temperature interval, which divides the coarsely divided abnormal region into smaller equal-size rectangular regions by increasing the number of rows and columns.

[0061] The fine interval is a rectangular region divided by the fine grid division method, which locates the specific boundary and coordinates of the defect through a smaller interval size.

[0062] Step 35: Divide the temperature distribution according to the fine interval to obtain the fine interval average temperature.

[0063] The fine interval average temperature refers to the arithmetic average of the temperature data of all pixel points in a single fine interval.

[0064] Step 36: Obtain the fine interval temperature interval based on the average temperature and the preset fine fluctuation parameter.

[0065] The fine fluctuation parameter is a parameter set according to the material properties, production process, and historical detection data of the spunlace nonwoven fabric to obtain the allowable temperature fluctuation range under the fine condition. Its acquisition method is the same as step 32, which is not repeated here.

[0066] The fine interval temperature interval is a temperature range calculated based on the global average temperature and the fine fluctuation parameter, which includes the upper and lower limits of the fine interval temperature. The specific calculation method is the same as step 32, which is not repeated here.

[0067] Step 37: Define the fine interval whose fine interval average temperature does not fall within the fine interval temperature interval as an abnormal temperature zone and output it.

[0068] The fine interval average temperature not falling within the temperature interval indicates that the region deviates significantly from the normal range, indicating the presence of defects such as holes or abnormal fiber structure, which is marked and output by the algorithm for subsequent analysis.

[0069] Reference Figure 3 Based on the abnormal temperature and the average temperature, the specific method for analyzing the defect and its corresponding defect category includes: Step 40: Traverse all abnormal temperature zones to form a defect interval group, and any two defect intervals in the defect interval group have at least one path connecting them through adjacent defect intervals.

[0070] The defect interval group is a connected set composed of all abnormal temperature zones, which satisfies that any two abnormal temperature zones in the group can be connected through adjacent abnormal temperature zones. It is used to ensure that all abnormal temperature zones in the group form a single connected region through adjacent relationships such as up, down, left, right, or diagonal adjacency, without isolated partitions. At the same time, the defect interval group contains all local abnormal temperature zones belonging to the same macroscopic defect, avoiding the fragmentation of defects caused by grid division.

[0071] Step 41: The flaw complete area is obtained by modifying the flaw interval group through the overhead infrared thermal imaging map.

[0072] The flaw complete area is a continuous and closed area obtained by morphological modification of the flaw interval group. Its boundary is determined by the temperature gradient or curve fitting algorithm in the thermal imaging map, rather than relying solely on the original grid division. By identifying the temperature mutation boundary between the flaw area and the normal area, the points with temperature difference exceeding the preset threshold in the adjacent grid are connected into a continuous isotherm, and the closed isotherm finally forms a complete area consistent with the actual flaw shape, and the boundary is composed of critical points with significant temperature difference inside and outside.

[0073] Step 42: The temperature distribution is divided according to the flaw complete area to obtain the flaw area average temperature.

[0074] The flaw area average temperature refers to the arithmetic mean of the temperature values of all pixel points in the flaw complete area after dividing the temperature distribution data in the infrared thermal imaging map.

[0075] Step 43: The flaw complete area is expanded outward by a preset thermal diffusion width to obtain the surrounding flaw area.

[0076] The thermal diffusion width is a preset fixed distance parameter used to expand outward from the boundary of the flaw complete area to form the surrounding flaw area. Its value is set by the staff based on the material thermal conductivity characteristics, heating process parameters and detection equipment accuracy, and is used to estimate the shape and position of the surrounding flaw area based on the flaw complete area.

[0077] The surrounding flaw area is a ring-shaped area formed by uniformly expanding the flaw complete area outward by the thermal diffusion width, which includes the transition zone affected by heat conduction around the flaw. The surrounding flaw area does not include the flaw complete area.

[0078] Step 44: The surrounding area average temperature is obtained by dividing the temperature distribution according to the surrounding flaw area.

[0079] The surrounding area average temperature is the result of arithmetic mean calculation of the temperature values of all pixel points in the surrounding flaw area. This temperature value is used to assist in judging the flaw type, such as blockage, hole or water mark defects.

[0080] Step 440: When the surrounding area average temperature exceeds the subdivision interval temperature interval and the flaw area average temperature is lower than the subdivision interval temperature interval, the flaw is defined as a blockage flaw, and a blockage flaw signal is output.

[0081] The blockage defect refers to a heat conduction obstruction defect of the spunlace nonwoven fabric caused by fiber agglomeration, impurity accumulation or local high density. The blockage defect signal is an alarm information output by the system after detecting the blockage defect, including defect type identification, position information and associated data such as average temperature of the defect area, average temperature of the surrounding area and temperature interval comparison result of the subdivided interval.

[0082] When the average temperature of the surrounding area exceeds the subdivided interval temperature interval and the average temperature of the defect area is lower than the subdivided interval temperature interval, it indicates that the surrounding area is in a state of heat accumulation, and the defect area is in a state of heat conduction obstruction. The reason why the surrounding area is high in temperature is that the blockage area hinders the normal heat conduction path, forcing the heat to detour and accumulate in the surrounding area, and the reason why the defect area is high in temperature is that the fiber is dense or foreign matter exists, so the heat conduction efficiency is low, and therefore the surface temperature rises slowly when heated.

[0083] Step 441: when the average temperature of the surrounding area falls into the subdivided interval temperature interval and the average temperature of the defect area is lower than the subdivided interval temperature interval, the defect is defined as a hole defect, and a hole defect signal is output.

[0084] The hole defect refers to a penetrating defect of the spunlace nonwoven fabric caused by fiber breakage, structure loss or local low density. The hole defect signal is an alarm information output by the system after detecting the hole defect, including defect type identification, position information and associated data.

[0085] When the average temperature of the surrounding area falls into the subdivided interval temperature interval and the average temperature of the defect area is lower than the subdivided interval temperature interval, it indicates that the material has local heat capacity anomaly caused by fiber breakage or hole, which is defined as a hole defect by the system. The reason why the defect area is high in temperature is that the hole area cannot retain heat when heated due to structure loss, resulting in no rise in surface temperature. The reason why the surrounding area of the hole is normal in temperature is that the structure of the surrounding area of the hole is complete, and the heat conduction path is not obviously disturbed.

[0086] Step 442: when the average temperature of the surrounding area is lower than the subdivided interval temperature interval and the average temperature of the defect area is lower than the subdivided interval temperature interval, the defect is defined as a water mark defect, and a water mark defect signal is output.

[0087] The water mark defect refers to a local thermal property anomaly defect of the spunlace nonwoven fabric caused by water residue, such as humid environment, incomplete drying after cleaning or water stain penetration in the production process. The water mark defect signal is an alarm information output by the system after detecting the water mark defect, including defect type identification, position information and associated data.

[0088] When the average temperature of the surrounding area is lower than the temperature interval of the subinterval and the average temperature of the defect area is lower than the temperature interval of the subinterval, it indicates that the material has local thermal property changes caused by moisture residue, and the system defines it as a water mark defect. The water mark area has a high water content and a larger heat capacity than the non-water material, so the temperature rise is lower when the same heat is absorbed, and the temperature of the surrounding area is lower than the temperature interval of the subinterval because the heat is taken away by the surrounding area of the defect.

[0089] The method also includes not outputting a clogging defect signal when the average temperature of the surrounding area is higher than the temperature interval of the subinterval and the average temperature of the defect area is lower than the temperature interval of the subinterval, and the method comprises: Step 4400: Calculate the clogging area temperature difference based on the average temperature of the defect area and the average temperature of the surrounding area.

[0090] The clogging area temperature difference is the difference between the average temperature of the surrounding area and the average temperature of the defect area, which can be obtained by subtracting the average temperature of the defect area from the average temperature of the surrounding area. The clogging area temperature difference is a quantitative measure of the local heat accumulation effect caused by the clogging defect, and the larger the difference, the more serious the heat conduction obstruction caused by the clogging.

[0091] Step 4401: Obtain the clogging degree value from the preset temperature clogging degree table according to the clogging area temperature difference.

[0092] The temperature clogging degree table is a preset mapping table used to convert the clogging area temperature difference into discrete clogging degree values. The temperature clogging degree table is obtained by a large number of experiments by the staff, by analyzing a large number of clogging area temperature differences of water-jet textile fabrics with different clogging degrees, and by corresponding the different clogging degrees with the clogging area temperature differences, and finally storing the corresponding relationship in the table to obtain the temperature clogging degree table.

[0093] The clogging degree value is a discrete numerical value obtained from the temperature clogging degree table, which represents the defect severity level corresponding to the current clogging area temperature difference. The clogging degree value can be directly obtained from the temperature clogging degree table by the clogging area temperature difference.

[0094] Step 4402: Obtain the defect area based on the defect complete area.

[0095] The defect area refers to the area of the defect complete area obtained by infrared thermal imaging, with the unit of square millimeters or square centimeters.

[0096] Step 4403: Obtain the local clogging degree based on the defect area and the clogging degree value.

[0097] The local clogging degree is a comprehensive index used to quantify the influence of the defect on the function of the material, and its value is calculated by weighting the defect area and the clogging degree value through a preset formula.

[0098] ;

[0099] wherein S 瑕疵 : flaw area; V 堵塞 : clogging degree value; K: correction coefficient. The correction coefficient is obtained by the workers through the design of different flaw area and clogging degree combination experiments, simulating the defect distribution in actual production, testing the changes of key performance such as material air permeability and strength, and analyzing the synergistic effect of the two on function. The workers obtain the most suitable parameters that can correlate flaw area, clogging degree and local clogging degree after a large number of experiments, which are used as correction coefficients.

[0100] Step 44030: When the local clogging degree is higher than the preset clogging threshold value, the flaw complete area corresponding to the clogging degree value is defined as a completely clogged area, and a clogging flaw signal is output.

[0101] The completely clogged area refers to an area whose blocking condition has significantly affected the material function, such as air permeability and strength.

[0102] The clogging threshold value is a value set by the workers to measure the clogging degree. When the local clogging degree is higher than the preset clogging threshold value, it means that the combined effect of flaw area and clogging degree has exceeded the process allowable range, which may lead to material functional failure, so the clogging signal needs to be sent out.

[0103] Step 44031: When the local clogging degree is lower than the clogging threshold value, the flaw complete area corresponding to the clogging degree value is defined as a partially clogged area, and no clogging flaw signal is output.

[0104] The partially clogged area refers to an area whose clogging is within an acceptable range. When the local clogging degree is lower than the preset clogging threshold value, it means that the flaw is within a controllable range and has a small effect on material performance, so it can be allowed to continue production or be left for subsequent sampling inspection.

[0105] Further comprising: Step 4404: Obtain the number of all partially clogged areas, defined as the number of partially clogged areas.

[0106] The number of partially clogged areas refers to the total number of flaw areas that are determined to be "partially clogged areas" in the detection process.

[0107] Step 4405: Obtain the clogging degree value corresponding to all partially clogged areas, defined as the partially clogging degree value.

[0108] The partially clogging degree value is a collection of clogging degree values corresponding to all partially clogged areas. The clogging degree value of each partially clogged area is obtained from the temperature clogging degree table, which represents the heat conduction obstruction caused by clogging in that area. The specific operation has been introduced in step 4401 and will not be repeated here.

[0109] Step 4406: Calculate the spunlace nonwoven fabric air permeability value according to the partial blockage degree value and the number of partial blockage areas.

[0110] The spunlace nonwoven fabric air permeability value is a comprehensive index calculated by a pre-set algorithm based on the number of partial blockage areas and the partial blockage degree value, which is used to quantify the overall air permeability of the material. The spunlace nonwoven fabric air permeability value can be obtained by subtracting the sum of the local blockage degree values of all the local blockage areas from the reference air permeability value. The reference air permeability value is set by the staff.

[0111] Step 4407: If the spunlace nonwoven fabric air permeability value is lower than the pre-set air permeability threshold value, form an overall air permeability defect signal and output it.

[0112] The air permeability threshold value is a pre-set critical value used to determine whether the spunlace nonwoven fabric air permeability value is qualified. The air permeability threshold value is obtained by the staff through a large number of experiments, by simulating different degrees of blockage defects, and finally obtaining the lowest value that can represent the qualified air permeability value, which is used as the air permeability threshold value.

[0113] When the spunlace nonwoven fabric air permeability value is lower than the pre-set air permeability threshold value, it indicates that the cumulative effect of the partial blockage area is significant. Unlike traditional local defects, if the air permeability value is higher than the threshold value, it indicates that the defect has developed from an isolated problem to a systematic risk affecting the overall performance, and the production batch needs to be thoroughly investigated or the process parameters need to be adjusted.

[0114] The overall air permeability defect signal refers to the overall defect alarm signal output by the system when the spunlace nonwoven fabric air permeability value is lower than the air permeability threshold value.

[0115] When the average temperature of the surrounding area falls into the subdivided temperature interval and the average temperature of the defect area is lower than the subdivided temperature interval, the method of not outputting the hole defect signal can be used, which includes: Step 4410: Obtain the perimeter of the complete defect area from the overhead infrared thermal image, and define it as the defect perimeter.

[0116] The defect perimeter refers to the closed curve length of the boundary of the complete defect area. The defect perimeter can be obtained by using the contour extraction algorithm of the computer to obtain the contour size of the complete defect area, and then obtaining the perimeter of the complete defect area.

[0117] Step 4411: Obtain the hole circularity of the complete defect area based on the defect area and the defect perimeter.

[0118] The hole circularity is a dimensionless parameter used to quantify the closeness of the shape of the defect area to an ideal circle. Its calculation formula is: ; The value of the hole circularity is between 0 and 1, and the larger the hole circularity value, the closer the shape is to a circle.

[0119] Step 4412: When the circularity of the pore is greater than the preset circularity threshold, the corresponding flaw complete area is defined as a normal pore area, and no hole flaw signal can be output.

[0120] The normal pore area refers to an area that is a normal pore rather than a hole flaw. The circularity threshold is set by the staff, representing the value that the flaw complete area can be considered as a uniform hole formed by the process.

[0121] When the pore circularity is higher than the preset threshold, it means that the flaw area is close to a circle, which meets the basic characteristics of the process formed pores, so the system determines that it is a normal pore and does not output a hole flaw signal, avoiding false positives.

[0122] Also includes: Step 4413: Obtain the pore core coordinates of all normal pore areas. The pore core coordinates refer to the coordinates of the geometric center of the normal pore area.

[0123] The pore core coordinates are the geometric center coordinates of the normal pore area, usually represented by two-dimensional plane coordinates (x, y). For each normal pore area, the minimum circumscribed rectangle is extracted by image processing algorithm, and the coordinates of the center of the rectangle are calculated as the pore core coordinates. The core coordinates can also be obtained by the centroid calculation method of the binary image.

[0124] Step 4414: Arrange all the pore core coordinates to form a pore core coordinate group.

[0125] The pore core coordinate group is a collection of all normal pore core coordinates, usually stored in the form of a list or array. All pore core coordinates are arranged in column priority, with x coordinates from left to right, and y coordinates in the same column from top to bottom.

[0126] Step 4415: Calculate the horizontal distance and vertical coordinate distance between any two adjacent pore core coordinates in the pore core coordinate group, and define them as pore horizontal coordinate spacing and pore vertical coordinate spacing.

[0127] The pore horizontal coordinate spacing refers to the x-direction coordinate difference between the two adjacent pore core coordinates in the coordinate group. The pore vertical coordinate spacing refers to the y-direction coordinate difference between the two adjacent pore core coordinates in the coordinate group.

[0128] Step 4416: Based on all the pore horizontal coordinate spacings, the standard pore horizontal spacing is obtained.

[0129] The standard pore transverse interval is the mode of the longitudinal distance of all adjacent pore core coordinates, used to describe the ideal uniform distribution interval of pores in the vertical direction. The standard pore transverse interval is obtained by analyzing the transverse distance of all adjacent pore core coordinates, and the value with a clear concentration trend in the statistical distribution is taken as the standard pore transverse interval.

[0130] Step 4417: Obtain the standard pore longitudinal interval based on the statistical distance of all pore longitudinal coordinates.

[0131] The standard pore longitudinal interval is the mode of the longitudinal distance of all adjacent pore core coordinates, used to describe the ideal uniform distribution interval of pores in the vertical direction. The standard pore transverse interval is obtained by analyzing the transverse distance of all adjacent pore core coordinates, and the value with a clear concentration trend in the statistical distribution is taken as the standard pore transverse interval.

[0132] Step 4418: Construct a pore arrangement grid based on any pore core coordinate, standard pore transverse interval, and standard pore longitudinal interval.

[0133] The pore arrangement grid is a regular two-dimensional grid constructed based on the standard transverse interval, the standard longitudinal interval, and any pore core coordinate, used to simulate the ideal uniform distribution of pore positions.

[0134] First, an optional pore core coordinate is selected as the grid origin, usually the first or central position of the pore, and then expanded in the transverse and longitudinal directions according to the standard pore transverse interval and the standard pore longitudinal interval to generate regular grid points. During the expansion process, the expansion range is controlled according to the size of the thermal image to ensure that the grid covers all pore core coordinates.

[0135] Step 4419: Obtain the arrangement matching degree based on the pore core coordinate and the pore arrangement grid.

[0136] The arrangement matching degree is a quantitative indicator of the deviation of the actual pore core coordinate from the ideal grid point, used to evaluate the regularity of the pore distribution. After matching each actual pore core coordinate with the nearest grid point, the offset is calculated, such as the Euclidean distance. Then, all the obtained Euclidean distances are substituted into the root mean square error to calculate the value, which is taken as the arrangement matching degree.

[0137] Step 4420: When the arrangement matching degree is lower than the preset matching degree threshold, define the corresponding normal pore region as an abnormal pore region, define the defect as an abnormal pore defect, and output the preset abnormal pore defect signal.

[0138] The abnormal pore defect refers to the phenomenon that the distribution regularity of the normal pore region in the material is destroyed. When the actual arrangement of the pore core coordinates deviates significantly from the regular grid constructed based on the standard interval, it indicates that the pore distribution in this region does not conform to the expected uniformity or regularity, and is thus defined as an abnormal pore defect.

[0139] The abnormal hole defect signal is the early warning or identification information output by the system after detecting the abnormal hole defect, including defect type identification, position information and associated data.

[0140] The low arrangement matching degree below the preset threshold value indicates that the deviation of the actual pore core coordinate distribution from the ideal regular grid exceeds the acceptable range, reflecting the disorder or unevenness of the pore arrangement. It is indicated that the regular hole is not intentionally designed, and therefore an abnormal hole defect signal needs to be issued.

[0141] The method includes a method that can not output an abnormal temperature zone, the method comprising: Step 370: Obtain the edge correction temperature parameter based on the average temperature of the subdivision interval.

[0142] The edge correction temperature parameter refers to a temperature correction parameter calculated based on the difference in thermal conductivity characteristics of the edge region of the cloth, used to correct the temperature measurement deviation caused by the edge heat dissipation effect. The edge correction temperature parameter is obtained by looking up a preset correction temperature parameter table. The correction temperature parameter table is obtained by a large number of experiments by the staff, and the staff associates the average temperature of the cloth with the edge correction temperature parameter through the thermal conductivity characteristics of the edge region of the cloth, and saves the corresponding relationship in the correction temperature parameter table.

[0143] Step 371: Obtain the edge subdivision region temperature interval according to the edge correction temperature parameter and the subdivision interval temperature interval.

[0144] The edge subdivision region temperature interval refers to the reasonable range of temperature after superimposing the edge correction on the basis of the subdivision interval temperature interval. The edge subdivision region temperature interval includes the upper and lower limits of the temperature of the edge subdivision region. The specific calculation method is the same as step 32, which is not repeated here.

[0145] Step 372: When the defect interval coordinates fall into the preset edge coordinate set, define the corresponding subdivision region average temperature as the edge average temperature.

[0146] The edge average temperature refers to the average temperature value of the defect region re-calculated when the defect coordinates fall into the edge region. The edge coordinate set is a specific region located at the physical edge of the cloth preset by the staff according to the actual size of the cloth, and its width is 5% of the total width of the cloth, used to identify abnormal temperature zones that may be affected by the thermal conductivity characteristics of the edge.

[0147] When the defect interval coordinates fall into the edge coordinate set, it indicates that the detected abnormal temperature zone is located at the physical edge of the cloth, and therefore in order to prevent misjudgment caused by the difference in thermal conductivity characteristics of the edge region of the cloth, the temperature of the corresponding region needs to be analyzed and then judged.

[0148] Step 3720: If the edge average temperature falls into the edge subdivision region temperature interval, the defect signal can not be output.

[0149] If the edge average temperature falls into the edge subdivision region temperature interval, it means that the abnormal temperature zone is located at the physical edge of the cloth, but the temperature deviation belongs to the normal fluctuation range caused by the edge heat conduction characteristics, such as the edge heat dissipation effect. At this time, it is determined that there is no substantial defect in the region, and no defect signal needs to be output.

[0150] Step 3721: If the edge average temperature does not fall into the edge subdivision region temperature interval, continue to output the defect signal.

[0151] If the edge average temperature does not fall into the edge subdivision region temperature interval, it means that the deviation of the abnormal temperature zone exceeds the normal range allowed by the edge heat conduction characteristics, indicating that there is a substantial defect, such as a hole, a blockage, a contaminant, etc. At this time, the defect signal needs to be output, prompting that the region is a real defect.

[0152] Also includes: Step 50: Obtain the number of all coarse division abnormal temperature intervals, defined as the number of coarse division abnormal intervals.

[0153] The number of coarse division abnormal intervals refers to the total number of abnormal temperature intervals obtained by coarse division segmentation in the infrared thermal imaging image. Specifically, it refers to the number of independent regions whose temperature distribution exceeds the normal range, such as being higher or lower than the temperature interval of the subdivision interval Step 51: When the number of coarse division abnormal intervals exceeds the preset abnormal interval threshold, define the defect as an overall quality defect, and output the preset overall quality defect signal.

[0154] Overall quality defect refers to a comprehensive quality problem caused by widespread defects in materials or products, rather than a single local defect. The overall quality defect signal is the alarm information output by the system when it detects an overall quality defect, including defect type identification, location information, and associated data. Workers collect the temperature distribution of spunlace non-woven fabric under normal production conditions to obtain the minimum threshold that can represent the spunlace non-woven fabric without overall defects, which is used as the abnormal interval threshold.

[0155] The abnormal interval threshold refers to the critical value used to determine whether the number of coarse division abnormal intervals exceeds the normal fluctuation range, which serves to distinguish between accidental local defects and systematic overall quality defects.

[0156] When the number of coarse division abnormal intervals exceeds the preset threshold, it means that the distribution density of abnormal temperature zones in the material or product has exceeded the acceptable range, indicating that the production process is unstable or the equipment parameter deviation has caused widespread defects. Therefore, the overall quality defect signal is needed.

[0157] Step 52: Calculate the standard deviation of the temperature of the rough partition interval of the overhead infrared thermal image.

[0158] Standard deviation analysis is a statistical method used to measure the degree of dispersion of a data set. In infrared thermal imaging detection, the standard deviation of the temperature distribution of the rough partition region is calculated to evaluate the uniformity of the temperature field. The larger the standard deviation, the more uneven the temperature distribution, indicating the presence of thermal conduction abnormalities or structural defects.

[0159] The rough partition region standard deviation refers to the standard deviation value calculated after statistical analysis of the temperature of the rough partition region of the infrared thermal image. The rough partition region standard deviation is calculated by extracting the temperature values of all pixel points in the rough partition region, calculating the average of these temperature values, and then calculating the standard deviation based on the difference between each temperature value and the average.

[0160] Step 53: When the rough partition region standard deviation exceeds the preset abnormal standard deviation threshold, the defect is also defined as a whole quality defect, and a whole quality defect signal is output.

[0161] When the rough partition region standard deviation exceeds the preset abnormal standard deviation threshold, it indicates that the temperature distribution on the spunlace nonwoven fabric is extremely uneven.

[0162] The method of outputting the whole quality defect signal further comprises: Step 54: Obtain the standard abnormal region quantity and the standard sample standard deviation threshold based on the preset standard overhead infrared spectrum.

[0163] The standard overhead infrared spectrum refers to the infrared image of a flawless product as a reference, containing normal temperature distribution.

[0164] The standard abnormal region quantity refers to the maximum number threshold of normal abnormal regions allowed in the preset standard overhead infrared spectrum. Workers analyze a large number of historical infrared spectra of flawless products to statistically determine the number of abnormal regions caused by allowable deviations, and set it as the standard abnormal region quantity.

[0165] The standard sample standard deviation threshold refers to the critical value of the standard deviation of the temperature of the rough abnormal region, and is considered as a whole quality abnormality if it exceeds. Workers calculate the standard deviation of the characteristic value of the normal region in the standard infrared spectrum, set the lowest threshold that can be accepted according to the allowable fluctuation range of the process, and set it as the standard sample standard deviation threshold.

[0166] Step 55: When the rough abnormal region quantity is greater than the standard abnormal region quantity or the rough region standard deviation is greater than the standard sample standard deviation threshold, the defect is defined as a whole quality defect, and a whole quality defect signal is output.

[0167] When the number of the coarse division abnormal area is greater than the number of the standard abnormal area, it indicates that the number of the actual detected abnormal area exceeds the normal fluctuation range, and it indicates that the flaw may be universal rather than isolated point, and thus the whole quality flaw signal needs to be sent out.

[0168] When the standard deviation of the coarse division area is greater than the standard sample standard deviation threshold, it indicates that the temperature distribution of the abnormal area is too high in dispersion degree, and exceeds the normal random noise range, and it indicates that the whole may have a large area of flaws, and thus the whole quality flaw signal needs to be sent out.

[0169] The above only describes the preferred embodiments of the present application, and the protection scope of the present application is not limited to the above-described embodiments only, and any technical solution falling within the idea of the present application falls within the protection scope of the present application. It should be noted that, for ordinary skilled in the art, some improvements and refinements without departing from the principles of the present application, these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for visual analysis of defects in spunlace nonwoven fabric, characterized in that, include: Step 1: In response to the fabric arrival signal, acquire a top-view infrared thermal image. The fabric arrival signal is the signal that the spunlace nonwoven fabric to be inspected is placed on a preset defect detection device. The defect detection device includes a conveyor wheel for placing the spunlace nonwoven fabric and a nozzle for spraying hot air. There is a space between the upper and lower conveyor wheels for the spunlace nonwoven fabric to pass through. The nozzle is located on the lower side of the spunlace nonwoven fabric and faces the spunlace nonwoven fabric, and sprays hot air evenly onto the spunlace nonwoven fabric. The defect detection device also includes an infrared detector located above the spunlace nonwoven fabric. Step 2: Analyze the temperature distribution and average temperature of the spunlace nonwoven fabric using the top-view infrared thermal imaging image; Step 3: Compare the temperature distribution with the average temperature to obtain the abnormal temperature zone and the corresponding abnormal temperature; Step 4: Analyze the defects and their corresponding defect categories based on the abnormal temperature and the average temperature; Step 5: Output the defect signal by forming a defect signal from the abnormal temperature zone and defect category.

2. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 1, characterized in that, The specific method for determining the abnormal temperature zone by comparing the temperature distribution with the average temperature includes: Step 30: Divide the top-view infrared thermal image into multiple intervals according to the preset coarse grid segmentation method, and define the divided areas as coarse intervals; Step 31: Divide the temperature distribution according to the coarse interval to obtain the average temperature of the coarse interval; Step 32: Calculate the coarse-division interval temperature range based on the average temperature and the preset coarse-division fluctuation parameters; Step 33: Define the coarse interval whose average temperature does not fall within the coarse interval temperature range as the coarse interval abnormal temperature range; Step 34: Divide the coarse-divided abnormal temperature range into multiple ranges according to the preset fine-grid segmentation method, and define the divided ranges as subdivided ranges; Step 35: Divide the temperature distribution according to the subdivided intervals to obtain the average temperature of the subdivided intervals; Step 36: Obtain the subdivided temperature range based on the average temperature and the preset subdivided fluctuation parameters; Step 37: Define the subdivided interval whose average temperature does not fall within the subdivided interval temperature range as the abnormal temperature zone and output it.

3. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 1, characterized in that, The specific method for analyzing the defect and its corresponding defect category based on the abnormal temperature and the average temperature includes: Step 40: Traverse all the abnormal temperature zones to form defect interval groups. Within each defect interval group, there is at least one path connecting any two defect intervals through adjacent defect intervals. Step 41: The defect area group is modified using the top-view infrared thermal image to obtain the complete defect area; Step 42: Divide the temperature distribution according to the intact area of ​​the defect to obtain the average temperature of the defect area; Step 43: Expand the defective area outward by a predetermined heat diffusion width to obtain a surrounding defect area; Step 44: Divide the surrounding defect area according to the temperature distribution to obtain the average temperature of the surrounding area; Step 440: When the average temperature of the surrounding area exceeds the temperature range of the subdivision interval and the average temperature of the defect area is lower than the temperature range of the subdivision interval, the defect is defined as a blockage defect and a blockage defect signal is output. Step 441: When the average temperature of the surrounding area falls within the temperature range of the subdivision interval and the average temperature of the defective area is lower than the temperature range of the subdivision interval, the defect is defined as a hole defect and a hole defect signal is output. Step 442: When the average temperature of the surrounding area is lower than the temperature range of the subdivision interval and the average temperature of the defect area is lower than the temperature range of the subdivision interval, the defect is defined as a watermark defect and a watermark defect signal is output.

4. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 3, characterized in that, It also includes a method for not outputting a blockage defect signal when the average temperature of the surrounding area exceeds the subdivision interval temperature range and the average temperature of the defect area is lower than the subdivision interval temperature range, the method comprising: Step 4400: Calculate the temperature difference of the blocked area by using the average temperature of the defective area and the average temperature of the surrounding area; Step 4401: Obtain the blockage degree value from the preset temperature blockage degree table based on the temperature difference of the blockage area; Step 4402: Obtain the defect area based on the complete defect area; Step 4403: Obtain the degree of local blockage based on the defect area and the blockage degree value; Step 44030: When the local blockage degree is higher than the preset blockage threshold, the defective complete area corresponding to the blockage degree value is defined as the completely blocked area, and a blockage defect signal is output. Step 44031: When the local blockage level is lower than the blockage threshold, the defective complete area corresponding to the blockage level value is defined as a partially blocked area, and no blockage defect signal is output.

5. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 4, characterized in that, Also includes: Step 4404: Obtain the total number of all the partially blocked areas, defined as the number of partially blocked areas; Step 4405: Obtain the blockage degree value corresponding to all the partially blocked areas, and define it as the partial blockage degree value; Step 4406: Calculate the air permeability value of the spunlace nonwoven fabric based on the partial blockage degree value and the number of partial blockage areas; Step 4407: If the air permeability value of the spunlace nonwoven fabric is lower than the preset air permeability threshold, an overall air permeability defect signal is generated and output.

6. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 3, characterized in that, A method for not outputting a hole defect signal when the average temperature of the surrounding area falls within the subdivided temperature range and the average temperature of the defect area is lower than the subdivided temperature range, the method comprising: Step 4410: Obtain the perimeter of the complete area of ​​the defect based on the top-view infrared thermal image, and define it as the defect perimeter; Step 4411: Obtain the porosity of the intact area of ​​the defect based on the defect area and the defect perimeter; Step 4412: When the roundness of the pore is greater than the preset roundness threshold, the corresponding intact area of ​​the defect is defined as a normal pore area, and the hole defect signal may not be output.

7. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 6, characterized in that, Also includes: Step 4413: Obtain the pore core coordinates of all the normal pore regions, where the pore core coordinates refer to the coordinates of the geometric center of the normal pore region; Step 4414: Arrange all the pore core coordinates to form a pore core coordinate group; Step 4415: Calculate the lateral distance and longitudinal distance between any two adjacent pore core coordinates in the pore core coordinate group, and define them as the pore lateral coordinate spacing and the pore longitudinal coordinate spacing. Step 4416: Obtain the standard pore transverse spacing based on the statistical analysis of all the pore transverse coordinate spacings; Step 4417: Obtain the standard pore longitudinal spacing based on the statistical analysis of all the pore longitudinal coordinate spacings; Step 4418: Construct a pore arrangement grid using the coordinates of any of the pore cores, the standard pore lateral spacing, and the standard pore longitudinal spacing; Step 4419: Obtain the alignment matching degree based on the coordinates of the pore core and the pore arrangement grid; Step 4420: When the alignment matching degree is lower than the preset matching degree threshold, the corresponding normal pore area is defined as an abnormal pore area, the defect is defined as an abnormal pore defect, and a preset abnormal pore defect signal is output.

8. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 2, characterized in that, This includes a method that does not output the abnormal temperature region, the method comprising: Step 370: Obtain edge correction temperature parameters based on the average temperature of the subdivided intervals; Step 371: Obtain the edge subdivision region temperature range based on the edge correction temperature parameters and the subdivision interval temperature range; Step 372: When the defect interval coordinates fall into the preset edge coordinate set, the average temperature of the corresponding subdivided area is defined as the edge average temperature; Step 3720: If the average edge temperature falls within the temperature range of the edge subdivision region, the defect signal may not be output. Step 3721: If the average edge temperature does not fall within the temperature range of the edge subdivision region, then continue to output the defect signal.

9. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 1, characterized in that, The method for outputting the defect signal further includes: Step 50: Obtain the total number of all the coarse-division abnormal temperature intervals, defined as the number of coarse-division abnormal intervals; Step 51: When the number of coarse-divided abnormal intervals exceeds the preset abnormal interval threshold, the defect is defined as an overall quality defect, and a preset overall quality defect signal is output. Step 52: Perform standard deviation analysis on the temperature of the top-view infrared thermal image between the coarse divisions, and calculate the corresponding standard deviation of the coarse division regions; Step 53: When the standard deviation of the coarse segmentation region exceeds the preset abnormal standard deviation threshold, the defect is also defined as the overall quality defect, and the overall quality defect signal is output.

10. The method for visual analysis of defects in spunlace nonwoven fabric according to claim 9, characterized in that, The method for outputting the overall quality defect signal further includes: Step 54: Obtain the number of standard abnormal regions and the standard deviation threshold of standard samples based on the preset standard top-down infrared spectrum; Step 55: If the number of coarse-divided abnormal regions is greater than the number of standard abnormal regions or the standard deviation of the coarse-divided regions is greater than the standard deviation threshold of the standard sample, the defect is defined as the overall quality defect, and the overall quality defect signal is output.

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