Detection system and method based on fabric evaporation rate

Abstract

The present invention relates to the technical field of fabric performance, and more particularly to a fabric evaporation rate-based detection system and method. The present invention obtains surface thermal images of each test fabric surface and its surrounding environment during the detection process and determines corresponding continuous temperature distribution maps and characteristic distribution maps based thereon. The system determines whether local temperature anomalies exist in the characteristic distribution map based on the color depth of the fabric surface in the characteristic distribution map, and determines the fabric consistency trend of the corresponding fabric based on the proportion of characteristic distribution maps without local anomalies. The system also determines the fabric characterization trend based on the fabric surface area and the surrounding environment area in the characteristic distribution map, and determines the fabric characterization state based on the fabric consistency trend and the fabric characterization trend to determine the production state of the corresponding fabric. The present invention improves detection efficiency and provides production guidance for the fabric production process.

Description

Technical Field

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

[0002] The evaporation rate of a fabric is a key indicator of its quick-drying properties. According to GB / T 21655.1-2023, "Evaluation of the Moisture Absorption and Quick-Drying Properties of Textiles - Part 1: Single Item Combined Test Method," evaporation rate testing primarily relies on manual labor, which has numerous drawbacks. The repeated weighing and hanging of samples can easily interfere with evaporation. For example, waving the sample during operation can change the ambient wind speed, accelerating evaporation. This can affect the accuracy and stability of the test results, and the resulting evaporation rate may be higher than the actual value. Furthermore, to prevent moisture from corroding the balance, the sample must be weighed using a separate container. For samples with poor water absorption or permeability, condensed water may transfer to the container during weighing rather than evaporate, affecting the accuracy and reliability of the test results and causing the measured evaporation rate to deviate from the true value. Furthermore, while some time interval deviation is permitted when the inspector weighs multiple samples individually at specified intervals, this deviation can significantly impact the test results. If each interval is too long or too short, 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 values at the standard interval.

[0003] Chinese patent publication number CN222167032U discloses a device for measuring the amount of moisture evaporation from a fabric, belonging to the field of textile device technology, comprising a housing, a turntable for driving a main shaft arranged inside the housing, a clamping device for clamping and rotating the fabric at the bottom of the turntable, the clamping device comprising a support assembly with a connecting support function, a suspension assembly with a function of suspending the fabric, and a clamping assembly for clamping the fabric. Through the above-mentioned method, when it is necessary to perform a test, the fabric is installed by controlling the clamping assembly, a space for evaporating moisture is provided for the fabric through the suspension assembly, and water is quickly and automatically added to the fabric under the action of the humidification structure. Under the support of the support assembly, the fabric rotates with the main shaft and evaporates moisture. After the test is completed, the clamping assembly is controlled to release the fabric, and the fabric descends under the action of gravity and the movement direction of the fabric is adjusted by the auxiliary piece. Finally, the fabric is dropped into a place where it is easy to pick up through the drop structure.

[0004] It can be seen that the prior art has the following problems:

[0005] When testing the evaporation rate of fabrics, the detection accuracy is easily disturbed due to human factors or single environmental simulation, which leads to low detection efficiency. Summary of the Invention

[0006] To this end, the present invention provides a detection system and method based on fabric evaporation rate, which is used to overcome the problem in the prior art that when detecting fabric evaporation rate, the detection accuracy is easily interfered with due to human factors or single environmental simulation, thereby resulting in low detection efficiency.

[0007] To achieve the above objectives, the present invention provides a fabric evaporation rate detection system, comprising:

[0008] 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 for a fabric and determine a production inspection standard for the corresponding fabric based on the production target scenario; and the information collection unit is used to obtain exercise metabolism data of different testers under each production target scenario;

[0009] The exercise metabolism data includes temperature data, sweat data and sweat composition data;

[0010] a simulation detection module connected to the information acquisition module, configured to construct a motion metabolism model based on the motion metabolism data and formulate detection parameters based on the constructed motion detection model;

[0011] a data acquisition module connected to the simulation detection module, configured to obtain real-time detection data of each test fabric and a surface thermal video of the surface of each test fabric and its surrounding environment during the detection process, determine a corresponding plurality of continuous temperature distribution maps based on each of the surface thermal videos, and determine a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps;

[0012] A data processing module, 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 based on each set of test data to determine the quality characterization status of the fabric in combination with the production test standard;

[0013] an image analysis module connected to the data acquisition module, configured to determine whether a local temperature anomaly exists in the characteristic distribution map based on the surface color depth of the characteristic distribution map, determine a fabric consistency trend of the corresponding fabric based on the proportion of the characteristic distribution maps without local anomalies, determine a fabric characterization trend based on the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine a fabric characterization state based on the fabric consistency trend and the fabric characterization trend;

[0014] a production control module, connected to the data processing module and the image analysis module respectively, for determining a production status based on the quality representation status and the fabric representation status;

[0015] The data characterization trend includes a data consistency trend and a data discrepancy trend;

[0016] The quality characterization status includes a qualified quality status and an unqualified quality status;

[0017] 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.

[0018] 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.

[0019] As a preferred technical solution for the detection system based on fabric evaporation rate, the data acquisition module includes:

[0020] Infrared monitoring unit, used to obtain surface thermal video of each test fabric surface and surrounding environment during fabric monitoring;

[0021] The video cutting unit is used to convert the surface thermal video into a continuous temperature distribution map.

[0022] As a preferred technical solution for the fabric evaporation rate detection system, the data processing module obtains the detection data of each detected fabric, and determines the corresponding average deviation and average value based on the detection data values at the same time, and determines the data fluctuation parameter based on the ratio of the average deviation to the average value to determine the data characterization trend of the corresponding fabric;

[0023] The data characterization trend includes a data consistency trend and a data discrepancy trend.

[0024] As a preferred technical solution of the detection system based on fabric evaporation rate, the data processing module determines the quality characterization status of the fabric according to the judgment result of the data characterization trend combined with the production inspection standard, including:

[0025] Based on the determination result of the data consistency trend, the quality characterization status is determined according to the comparison result of the average value and the production inspection standard.

[0026] As a preferred technical solution for the detection system based on the evaporation rate of the fabric, the data acquisition module determines that the temperature distribution map with a later time is the characteristic distribution map based on 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 based on the preset time interval based on the judgment result that the time interval between the two characteristic distribution maps is greater than the preset time interval.

[0027] As a preferred technical solution for the detection system based on fabric evaporation rate, the image analysis module includes:

[0028] The intelligent analysis unit is used to receive each feature distribution map and determine whether there is a local temperature anomaly in each feature distribution map, to divide each feature 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.

[0029] As a preferred technical solution of the detection system based on fabric evaporation rate, the image analysis module also includes:

[0030] A first analysis unit is configured to determine that the fabric consistency trend is a uniform trend based on a determination result that a proportion of the number of characteristic distribution graphs without local temperature anomalies is greater than or equal to a preset proportion;

[0031] a second analyzing unit, configured to determine surface temperature data and ambient temperature data respectively based on the fabric surface area and the surrounding environment area of the characteristic distribution diagram, and determine a fabric characterization trend based on a relationship between a temperature data difference between the surface temperature data and the ambient temperature data and a preset temperature data difference;

[0032] An image determination unit is connected to the first analysis unit and the second analysis unit, respectively, and is used to receive the division result and the determination result of the intelligent analysis unit, and determine the proportion of the characteristic distribution graphs without local temperature anomalies based on the determination result, and determine that the fabric characterization state is a qualified performance state based on the uniform trend and the benign evaporation trend.

[0033] As a preferred technical solution of the fabric evaporation rate-based detection system, the production control module determines the production status according to the quality characterization status and the fabric characterization trend, including:

[0034] Determining 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;

[0035] According to the determination results of the unqualified quality state and / or the unqualified performance state, it is determined that the production state of the corresponding fabric is abnormal, and the production process is optimized according to the cause of the abnormality.

[0036] On the other hand, the present invention also provides a method for detecting the evaporation rate of fabrics, comprising:

[0037] Obtaining a target production scenario for the fabric, and determining a production testing standard for the corresponding fabric based on the target production scenario;

[0038] Obtain exercise metabolism data from different testers in various production target scenarios to build exercise detection models and formulate detection parameters;

[0039] Acquire the test data obtained by the simulation detection module and determine the data representation trend of the corresponding fabric based on each set of test data;

[0040] Determining the quality characterization status of the fabric based on the determination result of the data characterization trend in combination with the production inspection standard;

[0041] Acquire surface thermal videos of the surface of each test fabric and its surroundings during the test process, determine a number of corresponding continuous temperature distribution maps based on each of the surface thermal videos, and determine a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps;

[0042] Determining a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps;

[0043] Determining whether there is a local temperature anomaly in the characteristic distribution map according to the surface color depth of each characteristic distribution map, and determining the fabric consistency trend of the corresponding fabric according to the proportion of the characteristic distribution maps without local anomalies;

[0044] Determining a fabric characterization trend based on the fabric surface area and the surrounding environment area of each of the characteristic distribution maps and determining a fabric characterization state in combination with the fabric consistency trend;

[0045] A production status is determined based on the quality characterization status and the fabric characterization status.

[0046] Compared with the existing technology, the present invention has the following advantages: the fabric evaporation rate detection system provided by the present invention can adapt to production needs. The information collection module can obtain the production target scenario and determine the corresponding detection standard, avoiding a "one-size-fits-all" approach. At the same time, the information of different testers' exercise metabolism data is collected, and the simulation detection module constructs a model and formulates parameters based on the data, making the detection more suitable for actual use scenarios and providing a reliable basis for research and development and production.

[0047] In particular, when evaluating fabric performance, the data acquisition module collects real-time detection data, surface thermal videos, and temperature distribution maps from multiple dimensions. The data processing module combines the detection data to determine data characterization trends. The image analysis module analyzes from the perspectives of local temperature anomalies, consistent trends, and characterization trends. The two complement each other to comprehensively and accurately evaluate fabric performance.

[0048] In particular, in terms of optimizing production processes and quality control, the production control module determines the production status based on the quality characterization status and fabric characterization trends. It can provide real-time feedback on quality issues and promptly adjust process parameters to prevent substandard products from entering the market, reducing production costs and risks. It can also optimize processes, improve efficiency, and ensure stable quality by analyzing fabric consistency trends.

[0049] In particular, the system promotes fabric R&D and innovation by providing comprehensive and accurate performance data, enabling researchers to make targeted improvements and innovations to fabrics. It also enables rapid performance evaluation, shortens R&D cycles, accelerates new product launches, and enhances companies' market competitiveness. In short, the system has a positive impact on fabric production and R&D from multiple perspectives, helping companies improve product quality, reduce costs, and enhance their market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a connection diagram of a detection system based on fabric evaporation rate according to an embodiment of the present invention;

[0051] Figure 2 A flow chart showing how a data processing module determines a quality characterization state according to an embodiment of the present invention;

[0052] Figure 3 Flowchart of determining fabric characterization status by an image analysis module according to an embodiment of the present invention;

[0053] Figure 4 This is a step diagram of a method for detecting fabric evaporation rate according to an embodiment of the present invention; DETAILED DESCRIPTION

[0054] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0055] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0056] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the 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. Therefore, it cannot be understood as a limitation on the present invention.

[0057] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0058] See also Figure 1 As shown, it is a detection system based on the evaporation rate of fabric according to an embodiment of the present invention. The embodiment of the present invention provides a detection system based on the evaporation rate of fabric, including:

[0059] 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 for a fabric and determine a production inspection standard for the corresponding fabric based on the production target scenario; and the information collection unit is used to obtain exercise metabolism data of different testers under each production target scenario;

[0060] The exercise metabolism data includes temperature data, sweat data and sweat composition data;

[0061] It is understandable that fabric manufacturers will regularly control a number of testers (more than 100 people) to exercise in different production target scenarios, and continuously obtain the exercise metabolism data of each tester during the exercise process;

[0062] a simulation detection module connected to the information acquisition module, configured to construct a motion metabolism model based on the motion metabolism data and formulate detection parameters based on the constructed motion detection model;

[0063] In implementation, a machine learning model can be used to extract the features of the exercise metabolism data of each tester, and a corresponding exercise metabolism model can be constructed based on these features. The test parameters of the production fabric during testing can be determined based on the design and use objectives of the production fabric and the model.

[0064] a data acquisition module connected to the simulation detection module to obtain real-time detection data of each test fabric and a surface thermal video of the surface of each test fabric and its surrounding environment during the detection process, determine a corresponding plurality of continuous temperature distribution maps based on each surface thermal video, and determine a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps; it is understood that a single video is composed of a plurality of frames, each frame corresponding to a temperature distribution map, and thus a single surface thermal video corresponds to a set of continuous temperature distribution maps;

[0065] It is understood that infrared thermal imaging is a technology that uses infrared radiation to detect the temperature distribution on the surface 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 an object is received by an infrared sensor (such as a microbolometer) and processed to generate a thermal image (thermogram). Different colors or grayscales represent different temperature distributions.

[0066] A data processing module, 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 based on each set of test data to determine the quality characterization status of the fabric in combination with the production test standard;

[0067] It is understood that for fabrics produced in the same batch, several samples will be sampled for sampling and testing; the present invention conducts a horizontal comparison of the test data of the same batch of fabrics to determine the quality of the batch of fabrics (i.e., the data representation trend); at the same time, when it is determined that the quality of the batch of fabrics is relatively uniform (i.e., the data is consistent), the test data of the batch of fabrics is compared with the production standards to determine that the batch of fabrics has uniform quality and meets the production standards;

[0068] an image analysis module connected to the data acquisition module, configured to determine whether a local temperature anomaly exists in the characteristic distribution map based on the surface color depth of the characteristic distribution map, determine a fabric consistency trend of the corresponding fabric based on the proportion of the characteristic distribution maps without local anomalies, determine a fabric characterization trend based on the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine a fabric characterization state based on the fabric consistency trend and the fabric characterization trend;

[0069] It is understood that the fabric under test should have the same air permeability and evaporation properties during the test process. Therefore, the uniformity of the air permeability / evaporation of the fabric can be determined based on the surface color depth of its thermal imaging image (characteristic distribution map). In practice, if there are local abnormalities on the surface of the fabric in the characteristic distribution map, it means that the corresponding test fabric has uneven air permeability / evaporation, which indicates that there may be problems in the fabric preparation process, such as a deviation in the weaving density, resulting in uneven fabric performance. It is understood that the presence of local unevenness in a single or several characteristic distribution maps may be due to accidental errors in the test process. Therefore, it is necessary to determine the fabric consistency trend of the corresponding fabric based on the proportion of characteristic distribution maps without local abnormalities to avoid accidental errors in the test process.

[0070] It's understandable that when water evaporates from a fabric's surface, it absorbs heat from the surrounding environment (including the fabric itself), causing the temperature of the evaporation area to drop. Infrared thermal images can capture this temperature difference, and by analyzing the temperature distribution and changes, the fabric's evaporation rate can be indirectly inferred. In practice, an equal amount of water is sprayed on two pieces of fabric made of the same material but with different thicknesses or surface treatments, and then observed with an infrared thermal imager. If one piece of fabric has a faster evaporation rate, then over the same period of time, the temperature drop in the evaporation area on that piece of fabric will be more obvious, appearing as a more pronounced low-temperature area in the infrared thermal image.

[0071] 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;

[0072] a production control module, connected to the data processing module and the image analysis module respectively, for determining a production status based on the quality representation status and the fabric representation status;

[0073] The data characterization trend includes a data consistency trend and a data discrepancy trend;

[0074] The quality characterization status includes a qualified quality status and an unqualified quality status;

[0075] 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.

[0076] During implementation, the system inputs the target production scenarios of outdoor sportswear, such as mountaineering, hiking, cycling, etc. The information search unit retrieves the corresponding production inspection standards from the database based on these scenario information, including requirements for indicators such as evaporation rate and breathability; the information collection unit invites testers of different body shapes and different exercise habits in the early stage, and asks them to exercise in simulated outdoor sports scenarios (such as treadmill simulating mountaineering), and collects the testers' exercise metabolic data by wearing professional sensor equipment, including body temperature changes, sweat volume, and sweat salt content, pH value and other composition data.

[0077] During implementation, the simulation testing module constructs an exercise metabolism model based on collected exercise metabolism data, analyzing the tester's sweating patterns and body temperature trends at different exercise intensities. Based on this model, testing parameters such as simulated exercise speed, duration, ambient temperature, and humidity are developed. Then, according to these parameters, the fabric sample to be tested is placed in the simulation testing equipment for testing, simulating the heat and moisture exchange between the human body and the fabric during outdoor exercise. The simulation parameters are then determined based on this simulated heat and moisture exchange process.

[0078] It can be understood that the present invention obtains the target production scenario of the fabric through the information search unit, and determines the corresponding production inspection standard accordingly, so that the inspection is more targeted and scientific, and can accurately evaluate whether the performance of the fabric in different usage scenarios meets the standard; the information acquisition unit of the information acquisition module is used to obtain a large number of motion metabolism data (temperature data, sweating data and sweat composition data) of the testers in each production target scenario, and the simulation detection module constructs a motion metabolism model based on this, and then formulates reasonable detection parameters, which helps to more realistically simulate the situation of the fabric in actual use and improve the accuracy of the inspection; the data acquisition module can not only obtain the real-time detection data of each test fabric during the inspection 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, which helps to have a deeper understanding of the performance of the fabric; the data processing module extracts samples from the same batch of fabrics for sampling inspection By comparing the test data horizontally to determine the data characterization trend, and comparing it with the production standard when the quality is relatively uniform, it can accurately evaluate whether the fabric quality meets the requirements and provide a strong basis for production quality control; the image analysis module determines whether there is a local temperature anomaly based on the surface color depth of the feature distribution map, and determines the fabric consistency trend by the proportion of the feature distribution maps without local anomalies. At the same time, the fabric characterization trend is determined based on the fabric surface area and the surrounding environment area, and finally the fabric characterization status is determined. This module verifies the evaporation and breathability of the fabric from the perspective of infrared thermal imaging, making the evaluation of fabric performance more comprehensive and intuitive, and avoiding the errors that may be caused by a single detection method; the production control module integrates the quality characterization status obtained by the data processing module and the fabric characterization trend obtained by the image analysis module to determine the production status, which helps production companies to timely understand the fabric quality in the production process, reasonably adjust and optimize the production process, and improve production efficiency and product quality stability.

[0079] Specifically, the production target scenes include outdoor scenes, sports scenes and non-sports scenes.

[0080] Specifically, the data acquisition module includes:

[0081] An infrared monitoring unit is used to obtain a surface thermal video of the surface of each test fabric and its surrounding environment during fabric monitoring. In practice, the infrared monitoring unit is usually an infrared sensor. Each frame of the surface thermal video includes a partial image of the surface of the test fabric and a partial image of the surrounding environment.

[0082] The video cutting unit is used to convert the surface thermal video into a continuous temperature distribution map.

[0083] During implementation, the data acquisition module obtains the evaporation rate, air permeability and other test data of each test fabric in real time during the detection process. At the same time, it uses an infrared thermal imager to shoot surface thermal videos of the fabric surface and the surrounding environment, and generates continuous temperature distribution maps based on these videos. The temperature changes of the fabric surface at different detection time points are recorded to indirectly illustrate the evaporation and air permeability performance of the fabric.

[0084] See also Figure 2 FIG2 is a flowchart of a data processing module determining a quality characterization state according to an embodiment of the present invention. Specifically, the data processing module obtains test data for each test fabric, determines the corresponding average deviation and average value based on the test data values at the same time, and determines a data fluctuation parameter based on the ratio of the average deviation to the average value to determine the data characterization trend of the corresponding fabric.

[0085] It is understandable that the calculation of average deviation and average value are both existing technologies and will not be described in detail here. Data fluctuation parameter = average deviation ÷ average value. Data fluctuation parameter represents the degree of dispersion of a set of data. If the value is too large, it means that the set of data is relatively dispersed. If the value is small, it means that the set of data is relatively average. In practice, the preset fluctuation parameter is usually within 0.1. The smaller the preset fluctuation parameter, the closer the data values in the data set are in terms of consistent data trends. In practice, the preset fluctuation parameter is usually 0.07.

[0086] During implementation, the data characterization trend of the corresponding fabric is determined based on the size relationship between the data fluctuation parameter and the preset fluctuation parameter, including: if the data fluctuation parameter is greater than the preset fluctuation parameter, then the data characterization trend of the corresponding fabric is determined to be a data consistent trend; if the data fluctuation parameter is less than or equal to the preset fluctuation parameter, then the data characterization trend of the corresponding fabric is determined to be a data discrepancy trend.

[0087] It can be understood that the data processing module calculates the average deviation and average value of the test data of each tested fabric at the same time, and further obtains the data fluctuation parameter to quantitatively evaluate the degree of discreteness of the data. 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.

[0088] Specifically, the data processing module determines the quality characterization status of the fabric based on the determination result of the data characterization trend in combination with the production inspection standard, including:

[0089] Based on the determination result of the data consistency trend, the quality characterization status 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, then the quality characterization status is determined to be a qualified quality state; if the average value of any monitoring data is less than the corresponding production inspection standard, then the quality characterization status is determined to be an unqualified quality state;

[0090] Based on the determination result of the data discrepancy trend, the quality characterization state is determined to be a quality unqualified state.

[0091] During implementation, the data processing module acquires test data from the simulation testing module, such as the fabric's evaporation rate over different time periods. Using data analysis software, it plots a curve showing the evaporation rate over time to determine the fabric's data trend. If the curve shows a steady upward trend, it indicates that the fabric's evaporation performance has gradually stabilized during the testing process. If fluctuations occur, further analysis is required. Furthermore, the data trend is compared with production testing standards to preliminarily determine whether the fabric meets quality requirements. If the evaporation rate is lower than the standard, it is considered that there may be quality issues with the fabric.

[0092] Specifically, the data acquisition module determines that the temperature distribution map with a later time period is the characteristic distribution map based on 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 based on the preset time interval based on the judgment result that the time interval between the two characteristic distribution maps is greater than the preset time interval.

[0093] It is understandable that any method in the prior art may be used to determine the similarity between two adjacent temperature distribution graphs.

[0094] In implementation, the steps of determining the characteristic distribution map have a sequential order, namely: (1) first determine whether there is a characteristic distribution map between the two maps based on the similarity of adjacent temperature distribution maps: if the similarity is less than the preset similarity, it is determined that there is a characteristic distribution map and the characteristic distribution map is a temperature distribution map with a later time; (2) obtain the timestamps of all characteristic distribution maps, and determine the time interval between the two maps based on the timestamps of the two adjacent characteristic distribution maps. If the time interval is greater than the preset time interval, continue to select characteristic distribution maps from the two characteristic distribution maps, and the number of selected characteristic distribution maps = time interval ÷ preset time interval (rounded down);

[0095] In the implementation, the preset similarity ∈ [90%, 100%), preferably 90%. It can be understood that the smaller the preset similarity, the greater the difference between the two feature distribution maps selected according to the above step (1), and the more representative they are;

[0096] In the implementation, the preset time interval is [0.1s, 1s), preferably 0.5s. It can be understood that the smaller the preset time interval is, the more characteristic distribution graphs are selected according to the above step (2), and the more accurate the calculation is, but more computing resources are required;

[0097] It can be understood that (1) by comparing the average value of each test data with the production test standard under the data consistency trend, it is possible to accurately determine whether the fabric quality is qualified. When the average value of each test data is greater than or equal to the corresponding production test standard, it is determined to be a qualified quality state; if the average value of any test data is less than the corresponding production test standard, it is determined to be an unqualified quality state. This judgment method is direct and clear, which helps production companies quickly understand whether the fabric quality meets the requirements; (2) directly judging the quality characterization state as an unqualified quality state under the data discrepancy trend avoids misjudgment caused by large data fluctuations and improves the quality judgment. (3) For fabrics judged to be of unqualified quality, the analysis results of the data processing module provide the production enterprise with a direction for quality improvement (whether it is the data difference between fabrics or the failure to meet the data standards), so that the enterprise can analyze the reasons for the unqualified quality based on the specific test data and production test standards, and then take corresponding improvement measures to improve the quality of the fabrics; (4) The accurate judgment of fabric quality by the data processing module helps the production enterprise optimize the production process. If it is found that the quality of a 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.

[0098] In addition, by combining the method 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 temperature distribution map with a later time as the characteristic distribution map based on the judgment result that the similarity of adjacent temperature distribution maps is less than the preset similarity, and selecting the corresponding characteristic distribution map for the second time based on the judgment result that the time interval between the 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 amount of calculation 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 efficient use of computing resources.

[0099] See also Figure 3 As shown in FIG, it is a flow chart of determining the fabric characterization state by the image analysis module according to an embodiment of the present invention. Specifically, the image analysis module includes:

[0100] The intelligent analysis unit is used to receive each feature distribution map and determine whether there is a local temperature anomaly in each feature distribution map, to divide each feature 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.

[0101] It is understandable that the intelligent analysis unit is linked to a trained machine learning model, which is used to determine whether there is a local temperature anomaly in the characteristic distribution graph;

[0102] It is understandable that the intelligent analysis unit uses a trained machine learning model to accurately identify whether there are local temperature anomalies in the feature distribution map. This machine learning-based identification method greatly improves the accuracy and efficiency of anomaly detection, and helps to promptly discover possible problems in the fabric production or testing process.

[0103] Specifically, the image analysis module further includes:

[0104] A first analysis unit is configured to determine that the fabric consistency trend is a uniform trend based on a determination result that a proportion of the number of characteristic distribution graphs without local temperature anomalies is greater than or equal to a preset proportion;

[0105] It can be understood that, according to the determination result that the proportion of the number of characteristic distribution graphs without local temperature anomalies is less than the preset proportion, the fabric uniform trend is determined to be an uneven trend;

[0106] It can be understood that the first analysis unit calculates the proportion of characteristic distribution graphs without local temperature anomalies and determines whether the fabric consistency trend is uniform or uneven based on this. This objective evaluation method avoids the errors that may be caused by subjective judgment and provides a reliable quality assessment basis for manufacturers.

[0107] a second analyzing unit, configured to determine surface temperature data and ambient temperature data respectively based on the fabric surface area and the surrounding environment area of the characteristic distribution diagram, and determine a fabric characterization trend based on a relationship between a temperature data difference between the surface temperature data and the ambient temperature data and a preset temperature data difference;

[0108] In implementation, the fabric characterization trend is determined based on the relationship between the temperature data difference between the surface temperature data and the ambient temperature data and the preset temperature data difference, including: if the temperature data difference is less than or equal to the preset temperature data difference, then the fabric characterization trend is determined to be a benign evaporation trend; if the temperature data difference is greater than the preset temperature data difference, then the fabric characterization trend is determined to be a malignant evaporation trend;

[0109] It is understood that after obtaining the division result, the second analysis unit estimates the corresponding temperature data values according to the fabric surface area and the surrounding environment area respectively, and 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;

[0110] It is understood that the colors on the thermal image represent the high and low temperatures. Based on the mapping relationship between color and temperature, the temperature gradient of the thermal image and the temperature value of each position point can be basically determined. In this way, the average temperature of the thermal image can be determined, and the average temperature is used to replace the temperature data value of the image.

[0111] It is understood that the preset temperature data difference is determined according to the simulation parameters, and is determined by the temperature applied to the fabric during the simulation test and the ambient temperature. In practice, 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 is preferably set to 3°C.

[0112] It's understandable that a fabric's breathability affects air flow within it, and air flow in turn removes heat from the fabric's surface, thus affecting temperature distribution: Fabrics with good breathability allow air to flow more freely, removing more heat and resulting in a relatively low surface temperature. Fabrics with poor breathability restrict air flow, accumulating heat and causing a relatively high surface temperature. Furthermore, when moisture on the fabric's surface evaporates, it absorbs heat from the surrounding environment (including the fabric itself), causing the temperature in the evaporation area to drop. Therefore, the permeability and evaporation properties of a given fabric can be determined by the difference in preset temperature data. The higher the preset temperature value, the higher the fabric's quality requirements.

[0113] It can be understood that the second analysis unit compares the temperature data difference between the fabric surface area and the surrounding area in the characteristic distribution diagram with the preset temperature data difference to scientifically determine whether the fabric's evaporation trend is a benign evaporation trend or a malignant evaporation trend. This determination method takes into account the thermodynamic principles of the fabric's evaporation process, making the evaluation of the fabric's evaporation performance more scientific and accurate.

[0114] an image determination unit, connected to the first analysis unit and the second analysis unit, respectively, for receiving the division result and determination result of the intelligent analysis unit, and determining, based on the determination result, a proportion of characteristic distribution graphs in which no local temperature anomaly exists, and determining, based on a uniform trend and a benign evaporation trend, that the fabric characterization state is a qualified performance state (and, based on a non-uniform trend and / or a malignant evaporation trend, that the fabric characterization state is an unqualified performance state);

[0115] In practice, the percentage of feature distribution graphs without local temperature anomalies = the number of feature distribution graphs without local temperature anomalies ÷ the total number of feature distribution graphs × 100%, and the total number of feature distribution graphs = the number of feature distribution graphs without local temperature anomalies + the number of feature distribution graphs with local temperature anomalies. It is understandable that the smaller the percentage of feature distribution graphs without local temperature anomalies, the less likely the local temperature anomaly is to be caused by chance, and the greater the likelihood that it is caused by the fabric itself. Therefore, in practice, the preset percentage is usually set to approximately 95%.

[0116] It can be understood that the image determination unit integrates the determination results of the first analysis unit and the second analysis unit, as well as the classification and determination results of the intelligent analysis unit, to comprehensively and comprehensively determine whether the fabric is characterized as a qualified performance state or an unqualified performance state. This comprehensive determination method ensures the comprehensiveness and accuracy of the fabric performance evaluation, helping manufacturers to timely understand the fabric quality status and take corresponding measures to improve it.

[0117] 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.

[0118] During implementation, the temperature difference between the fabric surface area and the surrounding environment in the characteristic distribution map is analyzed to determine the fabric characterization trend; if the fabric surface temperature is always lower than the surrounding environment temperature, it means that the fabric has good heat dissipation performance; otherwise, there may be a problem of poor heat dissipation.

[0119] 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 the accurate judgment of the fabric consistency trend and evaporation trend, as well as the comprehensive determination of the fabric characterization status, the data processing module provides a powerful quality control tool for manufacturers. According to these judgment results, manufacturers can adjust the production process or raw materials in time, 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 enhance the market competitiveness of enterprises by promptly discovering and solving problems in the production process.

[0120] Specifically, the production control module determines the production status according to the quality characterization status and the fabric characterization trend, including:

[0121] Determining 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;

[0122] Determining that the production status of the corresponding fabric is abnormal based on the determination results of the unqualified quality status and / or the unqualified performance status, and optimizing the production process based on the cause of the abnormality (unqualified quality status or unqualified performance status);

[0123] 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 indirectly illustrate the quality of evaporation and air permeability. It may be caused by the unevenness of the fabric during the production process. The production fabric weaving machine can be adjusted according to the specific situation.

[0124] 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 a timely manner, 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. At the same time, the production process is continuously optimized based on the test results to improve the overall performance and market competitiveness of the product.

[0125] See also Figure 2 , which is a step diagram of a method for detecting fabric evaporation rate according to an embodiment of the present invention. The embodiment of the present invention also provides a method for detecting fabric evaporation rate, comprising:

[0126] Step S1, obtaining a production target scenario of the fabric, and determining a production inspection standard for the corresponding fabric according to the production target scenario;

[0127] Step S2, obtaining exercise metabolism data of different testers in various production target scenarios to build an exercise detection model and formulate detection parameters;

[0128] Step S311, acquiring the detection data obtained by the simulation detection module and determining the data representation trend of the corresponding fabric according to each set of detection data;

[0129] Step S312, determining the quality characterization status of the fabric based on the determination result of the data characterization trend and the production inspection standard;

[0130] Step S321: obtaining a surface thermal video of each test fabric surface and its surrounding environment during the detection process, determining a corresponding plurality of continuous temperature distribution maps based on each surface thermal video, and determining a characteristic distribution map of a single test fabric based on a preset time interval and a similarity between two adjacent temperature distribution maps;

[0131] Step S322, determining a characteristic distribution map of a single test fabric according to a preset time interval and the similarity between two adjacent temperature distribution maps;

[0132] Step S323, determining whether there is a local temperature anomaly in the characteristic distribution map according to the surface color depth of each characteristic distribution map, and determining the fabric consistency trend of the corresponding fabric according to the proportion of the characteristic distribution maps without local anomalies;

[0133] Step S324, determining a fabric characterization trend based on the fabric surface area and the surrounding environment area of each of the characteristic distribution maps and determining a fabric characterization state in combination with the fabric consistency trend;

[0134] Step S4: determining a production status based on the quality characterization status and the fabric characterization status.

[0135] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

[0136] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that the present invention is susceptible to various modifications and variations. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A detection system based on fabric evaporation rate, 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 for a fabric and determine a production inspection standard for the corresponding fabric based on the production target scenario; and the information collection unit is used to obtain exercise metabolism data of different testers under each production target scenario; The exercise metabolism data includes temperature data, sweat data and sweat composition data; a simulation detection module connected to the information acquisition module, configured to construct a motion metabolism model based on the motion metabolism data and formulate detection parameters based on the constructed motion detection model; a data acquisition module connected to the simulation detection module, configured to obtain real-time detection data of each test fabric and a surface thermal video of the surface of each test fabric and its surrounding environment during the detection process, determine a corresponding plurality of continuous temperature distribution maps based on each of the surface thermal videos, and determine a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps; A data processing module, 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 based on each set of test data to determine the quality characterization status of the fabric in combination with the production test standard; an image analysis module connected to the data acquisition module, configured to determine whether a local temperature anomaly exists in the characteristic distribution map based on the surface color depth of the characteristic distribution map, determine a fabric consistency trend of the corresponding fabric based on the proportion of the characteristic distribution maps without local anomalies, determine a fabric characterization trend based on the fabric surface area and the surrounding environment area of each characteristic distribution map, and determine a fabric characterization state based on the fabric consistency trend and the fabric characterization trend; a production control module, connected to the data processing module and the image analysis module respectively, for determining a production status based on the quality representation status and the fabric representation 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 fabric evaporation rate detection system according to claim 1, characterized in that: The production target scenes include outdoor scenes, sports scenes and non-sports scenes.

3. The fabric evaporation rate detection system according to claim 1, characterized in that: 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.

4. The fabric evaporation rate detection system according to claim 1, characterized in that: The data processing module obtains the test data of each test fabric, and determines the corresponding average deviation and average value according to the test 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 fabric evaporation rate detection system according to claim 4, characterized in that: The data processing module determines the quality characterization status of the fabric based on 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 status is determined according to the comparison result of the average value and the production inspection standard.

6. The fabric evaporation rate detection system according to claim 1, characterized in that: The data acquisition module determines that the temperature distribution map with a later time is a characteristic distribution map based on 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 based on the preset time interval based on the judgment result that the time interval between the two characteristic distribution maps is greater than the preset time interval.

7. The fabric evaporation rate detection system according to claim 1, characterized in that: The image analysis module includes: The intelligent analysis unit is used to receive each feature distribution map and determine whether there is a local temperature anomaly in each feature distribution map, to divide each feature 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.

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

9. The fabric evaporation rate detection system 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: Determining 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 unqualified quality state and / or the unqualified performance state, it is determined that the production state of the corresponding fabric is abnormal, and the production process is optimized according to the cause of the abnormality.

10. A method for detecting fabric evaporation rate using the fabric evaporation rate detection system according to any one of claims 1 to 9, characterized in that: include: Obtaining a target production scenario for the fabric, and determining a production testing standard for the corresponding fabric based on the target production scenario; Obtain exercise metabolism data from different testers in various production target scenarios to build exercise detection models and formulate detection parameters; Acquire the test data obtained by the simulation detection module and determine the data representation trend of the corresponding fabric based on each set of test data; Determining the quality characterization status of the fabric based on the determination result of the data characterization trend in combination with the production inspection standard; Acquire surface thermal videos of the surface of each test fabric and its surroundings during the test process, determine a number of corresponding continuous temperature distribution maps based on each of the surface thermal videos, and determine a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps; Determining a characteristic distribution map of a single test fabric based on a preset time interval and the similarity between two adjacent temperature distribution maps; Determining whether there is a local temperature anomaly in the characteristic distribution map according to the surface color depth of each characteristic distribution map, and determining the fabric consistency trend of the corresponding fabric according to the proportion of the characteristic distribution maps without local anomalies; Determining a fabric characterization trend based on the fabric surface area and the surrounding environment area of each of the characteristic distribution maps and determining a fabric characterization state in combination with the fabric consistency trend; A production status is determined based on the quality characterization status and the fabric characterization status.

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