Detection system and method based on fabric evaporation rate

Abstract

The invention relates to the technical field of fabric performance, in particular to a detection system and method based on the fabric evaporation rate, and the method comprises the steps: obtaining a surface heat video of each tested fabric surface and a surrounding environment in a detection process, and determining a plurality of corresponding continuous temperature distribution diagrams and feature distribution diagrams according to the surface heat video; according to the color depth of the fabric surface of the feature distribution map, determining whether the feature distribution map has a local temperature abnormal condition or not; and according to the proportion of the number of the feature distribution maps without the local abnormal condition, determining the fabric consistency trend of the corresponding fabric. And determining a fabric characterization trend according to the fabric surface area and the surrounding environment area of the characteristic distribution diagram, and determining a fabric characterization state according to the fabric consistency trend and the fabric characterization trend so as to determine a production state of the corresponding fabric. According to the invention, the detection efficiency is improved, and the fabric production process is guided.

Description

Technical Field

[0001] The present invention relates to the technical field of fabric performance, and particularly to a detection system and method based on the evaporation rate of fabrics. Background Art

[0002] The evaporation rate of fabrics is a key indicator to measure their quick-drying performance. According to GB / T21655.1-2023 "Evaluation of moisture absorption and quick-drying properties of textiles - Part 1: Single combination test method", the evaporation rate detection mainly relies on manual operation, and this manual detection method has many drawbacks: during the process of repeatedly weighing the mass of the test sample and hanging the test sample, human factors are very likely to interfere with water evaporation. For example, waving the test sample during operation will change the ambient wind speed and accelerate water evaporation, resulting in the accuracy and stability of the test results being affected, and the obtained evaporation rate being higher than the actual situation. At the same time, in order to prevent the balance from being corroded by water, other loading vessels are needed when weighing the mass of the test sample. For test samples with poor water absorption or permeability, the condensed water on the surface may be transferred to the loading vessel during the weighing operation instead of evaporating, thus affecting the accuracy and reliability of the test results and causing the measured value of the evaporation rate to deviate from the true value. In addition, when the inspector weighs the mass of multiple test samples one by one at regular time intervals, although a certain time interval deviation is allowed, this deviation has a great impact on the test results. If the interval time is too long or too short each time, the total evaporation time will increase or decrease accordingly, resulting in the obtained evaporation amount and evaporation rate being higher or lower than the measured value at the standard interval time.

[0003] Chinese Patent Publication No. CN222167032U discloses a device for measuring the water evaporation amount of fabrics, belonging to the technical field of textile devices, including a box body. Inside the box body, there is a turntable for driving the main shaft. At the bottom of the turntable, there is a clamping device for clamping and rotating the fabric. The clamping device includes a support component with connection and support functions, a hanging component with the function of hanging the fabric, and a clamping component for clamping the fabric. In this way, when detection is required, the fabric is installed by controlling the clamping component. The hanging component provides a space for the fabric to evaporate water. Under the action of the humidifying structure, rapid automatic water addition to the fabric is realized. It rotates with the main shaft under the support of the support component and undergoes water evaporation. After the detection is completed, the clamping component is controlled to release the fabric. Under the action of gravity, the fabric drops and the movement direction of the fabric is adjusted by the auxiliary piece. Finally, the fabric falls to a place where it is easy to pick up through the blanking structure.

[0004] Therefore, the prior art has the following problems: The problem that the detection accuracy is easily interfered due to human factors or single environmental simulation when detecting the evaporation rate of fabrics, resulting in low detection efficiency. Summary of the Invention

[0005] To this end, the present invention provides a detection system and method based on fabric evaporation rate, so as to overcome the problem that the detection accuracy is easily disturbed due to human factors or single environmental simulation when detecting the fabric evaporation rate in the prior art, thereby resulting in low detection efficiency.

[0006] To achieve the above object, on the one hand, the present invention provides a detection system based on fabric evaporation rate, comprising: An information collection module, comprising an information search unit and an information collection unit, wherein the information search unit is used to obtain a production target scenario of a fabric and determine a production inspection standard of the corresponding fabric according to the production target scenario, and the information collection unit is used to obtain exercise metabolism data of different testers under each production target scenario; Wherein, the exercise metabolism data includes temperature data, sweating data and sweat composition data; A simulation detection module, which is connected to the information acquisition module and is used to construct a sports metabolism model according to the sports metabolism data, and formulate detection parameters according to the constructed sports detection model; A data acquisition module, which is connected to the simulation detection module, and is used to obtain real-time detection data of each test fabric and surface thermal video of each test fabric surface and the surrounding environment during the detection process, determine a corresponding number of continuous temperature distribution graphs based on each of the surface thermal videos, and determine a characteristic distribution graph of a single test fabric according to a preset time interval and the similarity of two adjacent temperature distribution graphs; A data processing module, which is connected to the data acquisition module, is used to obtain the test data obtained by the simulation detection module and determine the data characterization trend of the corresponding fabric according to each group of test data so as to determine the quality characterization state of the fabric in combination with the production test standard; An image analysis module, which is connected to the data acquisition module, is used to determine whether the characteristic distribution map has a local temperature anomaly according to the surface color depth of the characteristic distribution map, and determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local anomalies, determine the fabric characterization trend according to the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine the fabric characterization state according to the fabric consistency trend and the fabric characterization trend; a production control module, which is connected to the data processing module and the image analysis module respectively, and is used to determine the production status based on the quality characterization status and the fabric characterization status; The data characterization trend includes a data consistency trend and a data discrepancy trend; The quality characterization status includes a qualified quality status and an unqualified quality status; The fabric consistency trend includes a uniform trend and an uneven trend, the fabric characterization trend includes a benign evaporation trend and a malignant evaporation trend, and the fabric characterization state includes a qualified performance state and an unqualified performance state.

[0007] As a preferred technical solution for the detection system based on fabric evaporation rate, the production target scenes include outdoor scenes, sports scenes and non-sports scenes.

[0008] As a preferred technical solution of the detection system based on fabric evaporation rate, the data acquisition module includes: Infrared monitoring unit, used to obtain surface thermal video of each test fabric surface and surrounding environment during fabric monitoring; The video cutting unit is used to convert the surface thermal video into a continuous temperature distribution map.

[0009] As a preferred technical solution of the detection system based on fabric evaporation rate, the data processing module obtains the detection data of each detected fabric, and determines the corresponding average deviation and average value according to the detection data values at the same time, and determines the data fluctuation parameter according to the ratio of the average deviation to the average value to determine the data characterization trend of the corresponding fabric; The data characterization trend includes a data consistency trend and a data discrepancy trend.

[0010] As a preferred technical solution of the detection system based on fabric evaporation rate, the data processing module determines the quality characterization state of the fabric according to the determination result of the data characterization trend combined with the production inspection standard, including: Based on the determination result of the consistent trend of the data, the quality characterization status is determined according to the comparison result between the average value and the production inspection standard.

[0011] As a preferred technical solution for the detection system based on the evaporation rate of fabric, the data acquisition module determines that the temperature distribution map with a later time is the characteristic distribution map according to the judgment result that the similarity between any two adjacent temperature distribution maps is less than the preset similarity, and selects the corresponding characteristic distribution map for the second time according to the preset time interval according to the judgment result that the time interval between the two characteristic distribution maps is greater than the preset time interval.

[0012] As a preferred technical solution of the detection system based on fabric evaporation rate, the image analysis module includes: The intelligent analysis unit is used to receive each characteristic distribution map and determine whether there is a local temperature anomaly in each characteristic distribution map, to divide each characteristic distribution map into a fabric surface area and a surrounding environment area, and to send the determination result and the division result to the image determination unit.

[0013] As a preferred technical solution of the detection system based on the evaporation rate of the fabric, the image analysis module further includes: A first analysis unit for determining that the consistent trend of the fabric is a uniform trend according to the determination result that the proportion of the number of characteristic distribution maps without local temperature anomalies is greater than or equal to a preset proportion; A second analysis unit for determining surface temperature data and ambient temperature data based on the fabric surface area and the surrounding environment area of the characteristic distribution map respectively, and determining the fabric characterization trend according to the magnitude relationship between the temperature data difference between the surface temperature data and the ambient temperature data and a preset temperature data difference; An image determination unit, which is respectively connected to the first analysis unit and the second analysis unit, for receiving the division result and the determination result of the intelligent analysis unit, and determining the proportion of the number of characteristic distribution maps without local temperature anomalies based on the determination result, and determining that the fabric characterization state is a qualified performance state according to the uniform trend and the benign evaporation trend.

[0014] As a preferred technical solution of the detection system based on the evaporation rate of the fabric, the production control module determines the production state according to the quality characterization state and the fabric characterization trend, including: Determining that the production state of the corresponding fabric is normal according to the determination results of the quality qualified state and the performance qualified state; Determining that the production state of the corresponding fabric is abnormal according to the determination results of the quality unqualified state and / or the performance unqualified state, and optimizing the production process according to the abnormal reasons.

[0015] On the other hand, the present invention also provides a detection method based on the evaporation rate of the fabric, including: Obtaining the production target scenario of the fabric, and determining the production detection standard corresponding to the fabric according to the production target scenario; Obtaining the exercise metabolism data of different testers in each production target scenario to construct an exercise detection model and formulate detection parameters; Obtaining the detection data detected by the simulation detection module and determining the data characterization trend of the corresponding fabric according to each group of detection data; Determining the quality characterization state of the fabric according to the determination result of the data characterization trend in combination with the production detection standard; Obtaining the surface thermal videos of the surfaces and the surrounding environments of each test fabric during the detection process, determining a corresponding number of consecutive temperature distribution maps based on each of the surface thermal videos, and determining the characteristic distribution map of a single test fabric according to a preset time interval and the similarity between two adjacent temperature distribution maps; Determining the characteristic distribution map of a single test fabric according to a preset time interval and the similarity between two adjacent temperature distribution maps; Determine whether there is a local temperature anomaly in each characteristic distribution map according to the surface color depth of the characteristic distribution map, and determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local anomalies; Determine the fabric characterization trend according to the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine the fabric characterization state in combination with the fabric consistency trend; Determine the production state based on the quality characterization state and the fabric characterization state.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows. The detection system based on the fabric evaporation rate provided by the present invention can, in adapting to production requirements, enable the information acquisition module to obtain the production target scenario and determine the corresponding detection standard, avoiding "one-size-fits-all". At the same time, it collects the exercise metabolism data of different testers, and the simulation detection module constructs a model and formulates parameters accordingly, making the detection more in line with the actual use scenario and providing a reliable basis for R & D and production; In particular, in evaluating the fabric performance, the data acquisition module collects real-time detection data, surface thermal videos and temperature distribution maps in multiple dimensions. The data processing module determines the data characterization trend in combination with the detection data, and the image analysis module analyzes from the perspectives of local temperature anomalies, consistency trends and characterization trends. The two complement each other to comprehensively and accurately evaluate the fabric performance; In particular, in optimizing the production process and quality control, the production control module determines the production state based on the quality characterization state and the fabric characterization trend, can feedback quality problems in real time, adjust process parameters in a timely manner, avoid unqualified products from flowing into the market, reduce production costs and risks, and can also optimize the process, improve efficiency and ensure quality stability by analyzing the fabric consistency trend; In particular, in promoting fabric R & D and innovation, the comprehensive and accurate performance data provided by the system provides a reference for R & D personnel, helps them improve and innovate fabrics targeted, and can quickly evaluate performance, shorten the R & D cycle, speed up the launch of new products, and enhance the market competitiveness of enterprises. In short, the system has a positive impact on fabric production and R & D from multiple dimensions, helping enterprises improve product quality, reduce costs and enhance market competitiveness. Description of the Drawings

[0017] Figure 1 Connection diagram of the detection system based on the fabric evaporation rate according to the embodiment of the present invention; Figure 2 Flow chart of the data processing module determining the quality characterization state according to the embodiment of the present invention; Figure 3 Flow chart of the image analysis module determining the fabric characterization state according to the embodiment of the present invention; Figure 4 Step diagram of the detection method based on the fabric evaporation rate according to the embodiment of the present invention; Detailed Embodiments

[0018] In order to make the objectives and advantages of the present invention more clearly understood, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0020] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0021] In addition, it should also be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0022] Please refer to Figure 1 as shown, which is a detection system based on the evaporation rate of fabrics according to an embodiment of the present invention. An embodiment of the present invention provides a detection system based on the evaporation rate of fabrics, including: An information collection module, including an information search unit and an information collection unit. The information search unit is used to obtain the production target scenarios of fabrics and determine the production detection standards corresponding to the fabrics according to the production target scenarios. The information collection unit is used to obtain the exercise metabolism data of different testers in each production target scenario; Among them, the exercise metabolism data includes temperature data, sweating data, and sweat composition data; It can be understood that fabric production enterprises will regularly control a number of (more than 100 people) testers to exercise in different production target scenarios and continuously obtain the exercise metabolism data of each tester during the exercise process; A simulation detection module, which is connected to the information collection module, is used to construct an exercise metabolism model according to the exercise metabolism data and formulate detection parameters according to the constructed exercise detection model; In implementation, a machine learning model can be used to extract the characteristics of the exercise metabolism data of each tester, construct a corresponding exercise metabolism model based on these characteristics, and determine the detection parameters of the production fabric during detection according to the design use target of the production fabric and this model; A data acquisition module, which is connected to the simulation detection module, is used to obtain the real-time detection data of each test fabric and the surface thermal video of the surface and surrounding environment of each test fabric during the detection process, determine a corresponding number of consecutive temperature distribution maps based on each of the surface thermal videos, and determine the characteristic distribution map of a single test fabric according to the preset time interval and the similarity of two adjacent temperature distribution maps; It can be understood that a single video is composed of several frame images, and one frame image corresponds to a temperature distribution map. Therefore, a single surface thermal video corresponds to a set of consecutive temperature distribution maps; It can be understood that infrared thermography is a technology that uses infrared radiation to detect the surface temperature distribution of an object and converts it into a visual image. Its working principle is based on the laws of thermal radiation (Planck's law, Stefan-Boltzmann law, etc.). Infrared radiation emitted by the object is received by an infrared sensor (such as a microbolometer), and after processing, a thermogram is generated. Different colors or grayscales represent different temperature distributions; A data processing module, which is connected to the data acquisition module, is used to obtain the detection data detected by the simulation detection module and determine the data characterization trend of the corresponding fabric according to each group of detection data to determine the quality characterization state of the fabric in combination with the production detection standard; It can be understood that for fabrics produced in the same batch, several samples will be selected for sampling detection; The present invention makes a horizontal comparison of the detection data of the same batch of fabrics to determine the quality of this batch of fabrics (i.e., the data characterization trend); At the same time, when it is determined that the quality of this batch of fabrics is relatively uniform (i.e., the data consistency trend), a comparison is made between the detection data of this batch of fabrics and the production standard to determine that this batch of fabrics meets the production standard while the quality is unified; An image analysis module, which is connected to the data acquisition module, is used to determine whether there is a local temperature anomaly in the characteristic distribution map according to the surface color depth of the characteristic distribution map, determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local anomalies, determine the fabric characterization trend according to the fabric surface area and the surrounding environment area of each of the characteristic distribution maps, and determine the fabric characterization state according to the fabric consistency trend and the fabric characterization trend; It is understandable that the test fabric should have the same air permeability and evaporation during the test process, so the surface color depth of its thermal imaging image (characteristic distribution map) can determine whether the air permeability / evaporation of the fabric is uniform; in practice, if there is a local abnormality on the surface of the fabric in the characteristic distribution map, it means that the corresponding test fabric has uneven air permeability / evaporation, which means that there may be problems in the preparation process of the fabric, and the uneven performance of the fabric may be caused by deviation in the weaving density; it is understandable that the local unevenness of a single or several characteristic distribution maps may be accidental errors in the test process, so it is necessary to determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local abnormalities, so as to avoid accidental errors in the test process; It is understandable that when the water on the surface of the fabric evaporates, it absorbs heat from the surrounding environment (including the fabric itself), resulting in a decrease in the temperature of the evaporation area; infrared thermal images can capture this temperature difference, and by analyzing the temperature distribution and changes, the evaporation rate of the fabric can be indirectly inferred; in practice, spray the same amount of water on two pieces of fabric with the same material but different thickness or surface treatment, and then observe them with an infrared thermal imager. If the evaporation rate of one piece of fabric is faster, then in the same period of time, the temperature drop of the water evaporation area on the fabric will be more obvious, and it will appear as a more obvious low temperature area in the infrared thermal image; It is understandable that the purpose of the image analysis module is to verify the evaporation and breathability of the fabric based on the infrared thermal imaging video obtained by the infrared camera; a production control module, which is connected to the data processing module and the image analysis module respectively, and is used to determine the production status based on the quality characterization status and the fabric characterization status; The data characterization trend includes a data consistency trend and a data discrepancy trend; The quality characterization status includes a qualified quality status and an unqualified quality status; The fabric consistency trend includes a uniform trend and an uneven trend, the fabric characterization trend includes a benign evaporation trend and a malignant evaporation trend, and the fabric characterization state includes a qualified performance state and an unqualified performance state.

[0023] In implementation, the production target scenarios of outdoor sportswear, such as mountaineering, hiking, cycling, etc., are input through the system. According to this scenario information, the information search unit retrieves the corresponding production detection standards from the database, including requirements for indicators such as evaporation rate and breathability. The information collection unit invites testers with different body types and different exercise habits in the early stage, allows them to exercise in simulated outdoor sport scenarios (such as using a treadmill to simulate mountaineering), and collects the exercise metabolism data of the testers through wearing professional sensor devices, including data such as body temperature changes, sweating volume, and the salt content and pH value of sweat.

[0024] In implementation, the simulation detection module constructs an exercise metabolism model based on the collected exercise metabolism data, analyzes the sweating pattern and body temperature change trend of the testers under different exercise intensities, and formulates detection parameters based on this, such as the speed, duration, environmental temperature and humidity of the simulated exercise. Then, according to the formulated detection parameters, the fabric sample to be tested is placed in the simulation detection device for testing, simulating the heat and moisture exchange process between the human body and the fabric during outdoor sports, and determining the simulation parameters during the simulation process according to the simulated heat and moisture exchange process.

[0025] It is understandable that the present invention obtains the target fabric production scenarios through the information search unit and determines the corresponding production detection standards accordingly, making the detection more targeted and scientific, and capable of accurately evaluating whether the performance of the fabric meets the standards in different usage scenarios; the information collection unit of the information collection module is used to obtain the exercise metabolism data (temperature data, sweating data, and sweat composition data) of a large number of testers in each production target scenario, and the simulation detection module constructs an exercise metabolism model based on this, and then formulates reasonable detection parameters, which helps to more realistically simulate the actual use situation of the fabric and improve the accuracy of detection; the data collection module can not only obtain the real-time detection data of each test fabric during the detection process, but also obtain the surface thermal video of the surface and the surrounding environment, and determine the continuous temperature distribution map based on the video, and then obtain the characteristic distribution map. This comprehensive data collection method provides rich information for subsequent analysis and helps to more deeply understand the performance of the fabric; the data processing module extracts samples from the same batch of fabrics for sampling detection, determines the data representation trend by horizontally comparing the detection data, and then compares with the production standard when the quality is relatively uniform, which can accurately evaluate whether the fabric quality meets the requirements and provides a strong basis for the production quality control; the image analysis module determines whether there is a local temperature anomaly according to the surface color depth of the characteristic distribution map, and determines the fabric consistency trend by the proportion of the number of characteristic distribution maps without local anomalies. At the same time, it determines the fabric representation trend according to the fabric surface area and the surrounding environment area, and finally determines the fabric representation state. This module indirectly proves the evaporation and breathability performance of the fabric from the perspective of infrared thermal imaging, making the evaluation of the fabric performance more comprehensive and intuitive, and avoiding the errors that may be brought by a single detection method; the production control module comprehensively determines the production state based on the quality representation state obtained by the data processing module and the fabric representation trend obtained by the image analysis module, which helps the production enterprise to timely understand the fabric quality situation during the production process, reasonably adjust and optimize the production process, and improve the production efficiency and product quality stability.

[0026] Specifically, the production target scenarios include outdoor scenarios, sports scenarios, and non-sports scenarios.

[0027] Specifically, the data collection module includes: An infrared monitoring unit for obtaining the surface thermal video of the surface and the surrounding environment of each test fabric during the fabric monitoring process; in practice, the infrared monitoring unit is usually an infrared sensor; each frame image in the surface thermal video includes the surface partial image of the test fabric and the surrounding environment partial image; A video cutting unit for converting the surface thermal video into a continuous temperature distribution map.

[0028] In implementation, during the detection process, the data acquisition module obtains in real time the detection data such as the evaporation rate and air permeability of each test fabric, and at the same time uses an infrared thermal imager to capture the surface thermal video of the fabric surface and the surrounding environment, and generates a continuous temperature distribution map based on these videos, recording the temperature change of the fabric surface at different detection time points to illustrate the evaporation and air permeability performance of the fabric from the side.

[0029] Please refer to Figure 2 As shown, it is a flowchart for the data processing module in the embodiment of the present invention to determine the quality characterization state. Specifically, the data processing module obtains the detection data of each detected fabric, determines the corresponding average deviation and average value according to the detection data values at the same time, and determines the data fluctuation parameter according to the ratio of the average deviation to the average value to determine the data characterization trend of the corresponding fabric.

[0030] It can be understood that the calculations of the average deviation and the average value are both prior arts, so they will not be elaborated here; data fluctuation parameter = average deviation ÷ average value, and the data fluctuation parameter represents the degree of dispersion of a set of data. If this value is too large, it means that the set of data is relatively dispersed. If this value is small, it means that the set of data is relatively average; in implementation, the preset fluctuation parameter is usually within 0.1. The smaller the preset fluctuation parameter, the closer the data values in the data set with a consistent data trend are; in implementation, the preset fluctuation parameter usually takes 0.07; In implementation, the data characterization trend of the corresponding fabric is determined according to the magnitude relationship between the data fluctuation parameter and the preset fluctuation parameter, including: if the data fluctuation parameter is greater than the preset fluctuation parameter, it is determined that the data characterization trend of the corresponding fabric is a data consistent trend; if the data fluctuation parameter is less than or equal to the preset fluctuation parameter, it is determined that the data characterization trend of the corresponding fabric is a data uneven trend.

[0031] It can be understood that the data processing module can quantitatively evaluate the discreteness of the data by calculating the average deviation and average value of the test data of each tested fabric at the same time, and further obtaining the data fluctuation parameter. This quantitative evaluation method makes the analysis of fabric test data more objective and accurate, and avoids the errors that may be caused by subjective judgment; the data processing module can judge the data characterization trend of the corresponding fabric based on the size relationship between the data fluctuation parameter and the preset fluctuation parameter, which helps the production enterprise to quickly understand the overall situation of the test data of the same batch of fabrics, and provide a favorable basis for subsequent quality control and production adjustments; the data processing module helps the production enterprise to promptly discover possible problems in the production process by determining the data characterization trend. For example, when the data characterization trend is a data uneven trend, it may mean that there are certain unstable factors in the production process, resulting in fluctuations in fabric performance. At this time, the production enterprise can investigate and improve these problems, thereby improving the level of production quality control; the setting of the preset fluctuation parameter enables the data processing module to flexibly adjust the requirements for data consistency according to different production needs and standards.

[0032] Specifically, the data processing module determines the quality characterization state of the fabric according to the determination result of the data characterization trend in combination with the production inspection standard, including: Based on the determination result of the data consistency trend, the quality characterization state is determined according to the comparison result of the average value and the production inspection standard, including: if the average value of each test data is greater than or equal to the corresponding production inspection standard, the quality characterization state is determined to be a qualified quality state; if the average value of any monitoring data is less than the corresponding production inspection standard, the quality characterization state is determined to be an unqualified quality state; Based on the determination result of the data variance trend, the quality characterization state is determined to be a quality failure state.

[0033] In the implementation, the data processing module obtains the test data of the simulation test module, such as the evaporation rate value of the fabric in different time periods; through the data analysis software, the curve of the evaporation rate changing with time is drawn to determine the data characterization trend of the fabric. If the curve shows a steady upward trend, it means that the evaporation performance of the fabric is gradually stable during the test process; if there is a fluctuation, the cause needs to be further analyzed. In addition, the data characterization trend is compared with the production test standard to preliminarily judge whether the quality of the fabric meets the requirements. If the evaporation rate is lower than the standard requirement, it is considered that there may be problems with the fabric quality.

[0034] Specifically, the data acquisition module determines that the temperature distribution map with a later time is a characteristic distribution map according to the judgment result that the similarity between any two adjacent temperature distribution maps is less than a preset similarity, and selects the corresponding characteristic distribution map for a second time according to the preset time interval according to the judgment result that the time interval between the two characteristic distribution maps is greater than the preset time interval.

[0035] It is understandable that any method in the prior art can be used to determine the similarity between two adjacent temperature distribution diagrams.

[0036] In implementation, the steps of determining the characteristic distribution diagram have a sequence, that is: (1) First, determine whether there is a characteristic distribution diagram based on the similarity between adjacent temperature distribution diagrams: if the similarity is less than the preset similarity, it is determined that there is a characteristic distribution diagram and the characteristic distribution diagram is the temperature distribution diagram with a later time; (2) Obtain the timestamps of all characteristic distribution diagrams, and determine the time interval between two adjacent characteristic distribution diagrams according to the timestamps. If the time interval is greater than the preset time interval, continue to select characteristic distribution diagrams from these two characteristic distribution diagrams, and the number of selected characteristic distribution diagrams = time interval ÷ preset time interval (rounded down); In implementation, the preset similarity ∈ [90%, 100%), and preferably 90% is taken. It is understandable that the smaller the preset similarity, the greater the difference between the two characteristic distribution diagrams selected according to step (1) above, and the more representative they are; In implementation, the preset time interval is [0.1s, 1s), and preferably 0.5s is taken. It is understandable that the smaller the preset time interval, the more characteristic distribution diagrams are selected according to step (2) above, and the more accurate the calculation is, but the more computing resources are required; It is understandable that: (1) Under the consistent data trend, by comparing the average value of each detection data with the production detection standard, it is possible to accurately determine whether the fabric quality is qualified. When the average value of each detection data is greater than or equal to the corresponding production detection standard, it is determined to be in a qualified quality state; if the average value of any one detection data is less than the corresponding production detection standard, it is determined to be in an unqualified quality state; this determination method is direct and clear, which helps production enterprises quickly understand whether the fabric quality meets the requirements; (2) Under the inconsistent data trend, it is directly determined that the quality representation state is an unqualified quality state, which avoids misjudgment that may be caused by large data fluctuations and improves the accuracy of quality determination; (3) For fabrics determined to be in an unqualified quality state, the analysis result of the data processing module provides a quality improvement direction for production enterprises (whether it is the data difference between fabrics or not meeting the data standard), so that enterprises can analyze the reasons for unqualified quality based on specific detection data and production detection standards, and then take corresponding improvement measures to improve fabric quality; (4) The accurate determination of fabric quality by the data processing module helps production enterprises optimize the production process. For example, if it is found that the quality of a certain batch of fabrics is unqualified during the production process, the production process or raw materials can be adjusted in time to avoid the recurrence of similar problems, thereby improving production efficiency and product quality.

[0037] In addition, in combination with the way of determining the characteristic distribution map by the data acquisition module, the data processing module can further improve the accuracy of data analysis and reduce the consumption of computing resources: (1) By determining the later temperature distribution map as the characteristic distribution map according to the determination result that the similarity between adjacent temperature distribution maps is less than the preset similarity, and secondarily selecting the corresponding characteristic distribution map according to the determination result that the time interval between two characteristic distribution maps is greater than the preset time interval, a more representative characteristic distribution map can be obtained, thereby improving the accuracy of data analysis; (2) By reasonably setting the preset similarity and the preset time interval, the data processing module can reduce the consumption of computing resources while ensuring the accuracy of data analysis; for example, the smaller the preset similarity, the greater the difference between the selected characteristic distribution maps, the more representative they are, but at the same time, the calculation amount will also increase; by optimizing the values of the preset similarity and the preset time interval, a balance point can be found between the two to achieve the efficient use of computing resources.

[0038] Please refer to Figure 3 as shown, which is a flowchart of the image analysis module of the embodiment of the present invention for determining the fabric characterization state. Specifically, the image analysis module includes: An intelligent analysis unit, configured to receive each characteristic distribution map and determine whether there is a local temperature anomaly in each characteristic distribution map, configured to divide each of the characteristic distribution maps into a fabric surface area and a surrounding environment area, and send the determination result and the division result to the image determination unit.

[0039] It can be understood that the intelligent analysis unit is linked with a trained machine learning model, and determines whether there is a local temperature anomaly in the characteristic distribution map through this machine learning model; It can be understood that the intelligent analysis unit can accurately identify whether there is a local temperature anomaly in the characteristic distribution map by using a trained machine learning model. This identification method based on machine learning greatly improves the accuracy and efficiency of anomaly detection, and helps to timely discover possible problems in the fabric production or detection process.

[0040] Specifically, the image analysis module further includes: A first analysis unit, configured to determine that the consistent trend of the fabric is a uniform trend according to the determination result that the proportion of the number of characteristic distribution maps without local temperature anomalies is greater than or equal to the preset proportion; It can be understood that the consistent trend of the fabric is determined to be a non-uniform trend according to the determination result that the proportion of the number of characteristic distribution maps without local temperature anomalies is less than the preset proportion; It can be understood that the first analysis unit determines the fabric consistency trend as a uniform trend or a non-uniform trend by calculating the proportion of the number of characteristic distribution maps without local temperature anomalies, and based on this, this objective evaluation method avoids the errors that may be brought by subjective judgment and provides a reliable quality evaluation basis for production enterprises; A second analysis unit is used to respectively determine surface temperature data and ambient temperature data according to the fabric surface area and the surrounding environment area of the characteristic distribution map, and determine the fabric characterization trend according to the magnitude relationship between the temperature data difference between the surface temperature data and the ambient temperature data and a preset temperature data difference; In implementation, determining the fabric characterization trend according to the magnitude relationship between the temperature data difference between the surface temperature data and the ambient temperature data and the preset temperature data difference includes: if the temperature data difference is less than or equal to the preset temperature data difference, it is determined that the fabric characterization trend is a benign evaporation trend; if the temperature data difference is greater than the preset temperature data difference, it is determined that the fabric characterization trend is a malignant evaporation trend; It can be understood that after the second analysis unit obtains the division result, it respectively estimates the corresponding temperature data values according to the fabric surface area and the surrounding environment area, determines the temperature data difference according to the difference between the temperature data values of the two areas, and compares it with the preset temperature data difference to determine the fabric characterization trend; It can be understood that the color application on the thermal imaging image represents the temperature level. According to the mapping relationship between the color and the temperature, the temperature gradient of the thermal imaging image and the temperature values of each position point can be basically determined, and thus the average temperature of the thermal imaging image can be determined, and the average temperature is used to replace the temperature data value of the image; It can be understood that the preset temperature data difference is determined according to the simulation parameters and is determined by the temperature applied to the fabric and the ambient temperature during the simulation detection process; in implementation, the preset temperature data difference = the temperature applied to the fabric - the ambient temperature - the preset temperature value. Generally, the preset temperature value is 1°C to 5°C, and preferably it is set to 3°C; It can be understood that the air permeability of the fabric will affect the air flow inside it, and the air flow will take away the heat on the fabric surface, thereby affecting the temperature distribution: for a fabric with good air permeability, air can pass through more smoothly and take away more heat, making the fabric surface temperature relatively low; for a fabric with poor air permeability, the air flow is restricted and heat accumulates, making the fabric surface temperature relatively high; in addition, when the moisture on the fabric surface evaporates, it will absorb heat from the surrounding environment (including the fabric itself), resulting in a decrease in the temperature of the evaporation area. Therefore, the air permeability and evaporability of the corresponding fabric can be determined through the preset temperature data difference, and the larger the preset temperature value, the higher the judgment requirement for the fabric; It can be understood that the second analysis unit compares the temperature data difference between the fabric surface area and the surrounding environment area in the characteristic distribution map with a preset temperature data difference, and scientifically determines whether the evaporation trend of the fabric is a benign evaporation trend or a malignant evaporation trend. This determination method takes into account the thermodynamic principle in the fabric evaporation process, making the evaluation of the fabric evaporation performance more scientific and accurate; An image determination unit, which is respectively connected to the first analysis unit and the second analysis unit, is used to receive the division result and determination result of the intelligent analysis unit, and determine the proportion of the number of characteristic distribution maps without local temperature anomalies based on the determination result, and determine the fabric characterization status as a qualified performance status according to the uniform trend and the benign evaporation trend (determine the fabric characterization status as an unqualified performance status according to the non-uniform trend and / or the malignant evaporation trend); In implementation, the proportion of the number of characteristic distribution maps without local temperature anomalies = the number of characteristic distribution maps without local temperature anomalies ÷ the total number of characteristic distribution maps × 100%, and the total number of characteristic distribution maps = the number of characteristic distribution maps without local temperature anomalies + the number of characteristic distribution maps with local temperature anomalies; It can be understood that the smaller the proportion of the number of characteristic distribution maps without local temperature anomalies, the more it indicates that the existence of local temperature anomalies is not accidental, and the greater the possibility caused by the fabric itself. Therefore, in implementation, the preset proportion is usually set to about 95%; It can be understood that the image determination unit comprehensively determines the fabric characterization status as a qualified performance status or an unqualified performance status based on the determination results of the first analysis unit and the second analysis unit, as well as the division result and determination result of the intelligent analysis unit; This comprehensive determination method ensures the comprehensiveness and accuracy of the fabric performance evaluation, helps the production enterprise to timely understand the fabric quality situation, and take corresponding measures for improvement; Among them, the fabric consistency trend includes a uniform trend and a non-uniform trend, the fabric characterization trend includes a benign evaporation trend and a malignant evaporation trend, and the fabric characterization status includes a qualified performance status and an unqualified performance status.

[0041] In implementation, analyze the temperature difference between the fabric surface area and the surrounding environment area in the characteristic distribution map to determine the fabric characterization trend; If the fabric surface temperature is always lower than the surrounding environment temperature, it indicates that the fabric has good heat dissipation performance; Otherwise, there may be problems with poor heat dissipation.

[0042] During implementation, the fabric consistency trend and fabric characterization trend are comprehensively considered to further determine the fabric characterization status; if the fabric consistency trend is good and the characterization trend shows excellent heat dissipation performance, the fabric quality is considered to be high; through accurate judgment of the fabric consistency trend and evaporation trend, and comprehensive determination of the fabric characterization status, the data processing module provides a powerful quality control tool for manufacturers. Manufacturers can adjust the production process or raw materials in time according to these judgment results, optimize the production process, and improve fabric quality and production efficiency; accurate fabric performance evaluation helps manufacturers avoid producing unqualified products, thereby reducing production costs and risks, and further enhances the company's market competitiveness by timely discovering and solving problems in the production process.

[0043] Specifically, the production control module determines the production status according to the quality characterization status and the fabric characterization trend, including: According to the determination results of the qualified quality status and the qualified performance status, determining that the production status of the corresponding fabric is normal; According to the determination result of the unqualified quality state and / or the unqualified performance state, determining that the production state of the corresponding fabric is abnormal, and optimizing the production process according to the abnormal cause (unqualified quality state or unqualified performance state); In practice, if the quality is unqualified, the data such as evaporation rate and air permeability can be intuitively explained by the data, which may be unreasonable in the product design stage before production. The design data can be re-simulated or modified. If the performance is unqualified, the thermal imaging image can be used to indirectly illustrate the evaporation and air permeability. It may be caused by the unevenness of the fabric during the production process. The fabric weaving machine can be adjusted according to the specific situation.

[0044] During implementation, the production control module comprehensively judges the production status based on the quality characterization status determined by the data processing module and the fabric characterization trend determined by the image analysis module; if the fabric quality meets the requirements, the production department is notified to continue production according to the current process; if performance problems are found in the fabric, such as the evaporation rate does not meet the standard or the consistency is poor, the production process is adjusted in time, such as changing the fabric structure of the fabric, adjusting the finishing process, etc.; in addition, in the subsequent production process, the detection system is continuously used to conduct random inspections of products to ensure stable product quality, and at the same time, the production process is continuously optimized according to the test results to improve the overall performance and market competitiveness of the product.

[0045] See also Figure 2 As shown, it is a step diagram of a method for detecting the evaporation rate of a fabric according to an embodiment of the present invention. An embodiment of the present invention also provides a method for detecting the evaporation rate of a fabric, comprising: Step S1, obtaining a production target scenario of a fabric, and determining a production inspection standard of the corresponding fabric according to the production target scenario; Step S2: Obtain the exercise metabolism data of different testers in each production target scenario to construct an exercise detection model and formulate detection parameters; Step S311: Obtain the detection data detected by the simulation detection module and determine the data representation trend of the corresponding fabric according to each group of detection data; Step S312: Determine the quality representation status of the fabric according to the determination result of the data representation trend in combination with the production detection standard; Step S321: Obtain the surface thermal videos of the surfaces and surrounding environments of each test fabric during the detection process, determine corresponding several consecutive temperature distribution maps based on each of the surface thermal videos, and determine the characteristic distribution map of a single test fabric according to the preset time interval and the similarity between two adjacent temperature distribution maps; Step S322: Determine the characteristic distribution map of a single test fabric according to the preset time interval and the similarity between two adjacent temperature distribution maps; Step S323: Determine whether there is a local temperature anomaly in the characteristic distribution map according to the surface color depth of each of the characteristic distribution maps, and determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local anomalies; Step S324: Determine the fabric representation trend according to the fabric surface area and the surrounding environment area of each of the characteristic distribution maps, and determine the fabric representation status in combination with the fabric consistency trend; Step S4: Determine the production status based on the quality representation status and the fabric representation status.

[0046] So far, the technical solution of the present invention has been described in combination with the preferred embodiments shown in the drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the protection scope of the present invention.

[0047] The above are only the preferred embodiments of the present invention and are not used to limit the present invention; for those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A detection system based on the evaporation rate of fabric, characterized in that, include: An information collection module, comprising an information search unit and an information collection unit, wherein the information search unit is used to obtain a production target scenario of a fabric and determine a production inspection standard of the corresponding fabric according to the production target scenario, and the information collection unit is used to obtain exercise metabolism data of different testers under each production target scenario; Wherein, the exercise metabolism data includes temperature data, sweating data and sweat composition data; A simulation detection module, which is connected to the information acquisition module and is used to construct a sports metabolism model according to the sports metabolism data, and formulate detection parameters according to the constructed sports detection model; A data acquisition module, which is connected to the simulation detection module, and is used to obtain real-time detection data of each test fabric and surface thermal video of each test fabric surface and the surrounding environment during the detection process, determine a corresponding number of continuous temperature distribution graphs based on each of the surface thermal videos, and determine a characteristic distribution graph of a single test fabric according to a preset time interval and the similarity of two adjacent temperature distribution graphs; A data processing module, which is connected to the data acquisition module, is used to obtain the test data obtained by the simulation detection module and determine the data characterization trend of the corresponding fabric according to each group of test data so as to determine the quality characterization state of the fabric in combination with the production test standard; An image analysis module, which is connected to the data acquisition module, is used to determine whether the characteristic distribution map has a local temperature anomaly according to the surface color depth of the characteristic distribution map, and determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local anomalies, determine the fabric characterization trend according to the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine the fabric characterization state according to the fabric consistency trend and the fabric characterization trend; a production control module, which is connected to the data processing module and the image analysis module respectively, and is used to determine the production status based on the quality characterization status and the fabric characterization status; The data characterization trend includes a data consistency trend and a data discrepancy trend; The quality characterization status includes a qualified quality status and an unqualified quality status; The fabric consistency trend includes a uniform trend and an uneven trend, the fabric characterization trend includes a benign evaporation trend and a malignant evaporation trend, and the fabric characterization state includes a qualified performance state and an unqualified performance state.

2. The detection system based on the evaporation rate of the fabric according to claim 1, characterized in that The production target scenes include outdoor scenes, sports scenes and non-sports scenes.

3. The detection system based on the evaporation rate of the fabric according to claim 1, wherein The data acquisition module comprises: Infrared monitoring unit, used to obtain surface thermal video of each test fabric surface and surrounding environment during fabric monitoring; The video cutting unit is used to convert the surface thermal video into a continuous temperature distribution map.

4. The detection system based on the evaporation rate of the fabric according to claim 1, characterized in that, The data processing module obtains the detection data of each detected fabric, and determines the corresponding average deviation and average value according to the detection data values at the same time, and determines the data fluctuation parameter according to the ratio of the average deviation to the average value to determine the data characterization trend of the corresponding fabric.

5. The detection system based on the evaporation rate of the fabric according to claim 4, wherein The data processing module determines the quality characterization status of the fabric according to the determination result of the data characterization trend in combination with the production inspection standard, including: Based on the determination result of the data consistency trend, determine the quality characterization status according to the comparison result between the average value and the production detection standard.

6. The detection system based on the evaporation rate of the fabric according to claim 1, wherein, The data acquisition module determines the temperature distribution map with a later time as the characteristic distribution map according to the determination result that the similarity between any two adjacent temperature distribution maps is less than the preset similarity, and secondarily selects the corresponding characteristic distribution map according to the preset time interval based on the determination result that the time interval between two characteristic distribution maps is greater than the preset time interval.

7. The detection system based on the evaporation rate of the fabric according to claim 1, characterized in that The image analysis module includes: An intelligent analysis unit, configured to receive each characteristic distribution map and determine whether there is a local temperature anomaly in each characteristic distribution map, divide each of the characteristic distribution maps into a fabric surface area and a surrounding environment area, and send the determination result and the division result to the image determination unit.

8. The detection system based on the evaporation rate of the fabric according to claim 7, characterized in that, The image analysis module further includes: A first analysis unit, configured to determine that the fabric consistency trend is a uniform trend according to the determination result that the proportion of the number of characteristic distribution maps without local temperature anomalies is greater than or equal to the preset proportion; A second analysis unit, configured to determine surface temperature data and ambient temperature data according to the fabric surface area and the surrounding environment area of the characteristic distribution map respectively, and determine the fabric characterization trend according to the magnitude relationship between the temperature data difference between the surface temperature data and the ambient temperature data and the preset temperature data difference; An image determination unit, which is respectively connected to the first analysis unit and the second analysis unit, configured to receive the division result and the determination result of the intelligent analysis unit, determine the proportion of the number of characteristic distribution maps without local temperature anomalies based on the determination result, and determine that the fabric characterization status is a qualified performance status according to the uniform trend and the benign evaporation trend.

9. The detection system based on the evaporation rate of the fabric according to claim 1, characterized in that The production control module determines the production status according to the quality characterization status and the fabric characterization trend, including: Determine that the production status of the corresponding fabric is normal according to the determination results of the quality qualified status and the performance qualified status; According to the determination results of the quality unqualified status and / or the performance unqualified status, determine that the production status of the corresponding fabric is abnormal, and optimize the production process according to the cause of the abnormality.

10. A detection method based on the evaporation rate of fabric using the detection system based on the evaporation rate of fabric according to any one of claims 1-9, characterized in that, Including: Obtain the production target scenario of the fabric, and determine the production detection standard for the corresponding fabric according to the production target scenario; Obtain the exercise metabolism data of different testers in each production target scenario to construct an exercise detection model and formulate detection parameters; Obtain the detection data detected by the simulation detection module and determine the data characterization trend of the corresponding fabric according to each group of detection data; Determine the quality characterization status of the fabric according to the determination result of the data characterization trend in combination with the production detection standard; Obtain the surface thermal videos of the surfaces and the surrounding environments of each test fabric during the detection process, determine a corresponding number of consecutive temperature distribution maps based on each of the surface thermal videos, and determine the characteristic distribution map of a single test fabric according to the preset time interval and the similarity between two adjacent temperature distribution maps; Determine the characteristic distribution map of a single test fabric according to the preset time interval and the similarity between two adjacent temperature distribution maps; Determine whether there is a local temperature anomaly in each characteristic distribution map according to the surface color depth of the characteristic distribution map, and determine the fabric consistency trend of the corresponding fabric according to the proportion of the number of characteristic distribution maps without local anomalies; Determine the fabric characterization trend according to the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine the fabric characterization state in combination with the fabric consistency trend; Determine the production state based on the quality characterization state and the fabric characterization state.

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