A method and system for testing the performance of thermal and moisture comfort fiber fabrics

Abstract

The present invention is applicable to the field of fabric performance testing, and provides a method and system for testing the performance of thermal and wet comfort fiber fabrics, the system comprising: a data recording and detection module, an antibacterial performance calculation module, a performance change model module, a barrier effectiveness analysis module, a comfort performance simulation module, and a performance report generation module. By introducing a dynamic humidity simulation environment, the method can truly restore the actual use scenarios of fiber fabrics under different hot and humid conditions, and use the humidity simulation environment as a core variable in each step to comprehensively analyze the impact of humidity on microbial activity, the regulatory effect of odor release, and the role of humidity in barrier effectiveness and comprehensive performance evaluation. In addition, the method innovatively introduces the antibacterial effectiveness index and the barrier effectiveness index, combines antibacterial performance with odor release control ability, and comprehensively evaluates the actual effect of the fabric as a microbial barrier.

Description

Technical Field

[0001] The invention belongs to the field of fabric performance testing, and in particular relates to a method and system for testing the performance of thermal and moisture comfort fiber fabrics. Background Art

[0002] The current research and development of thermal and wet comfort fiber fabrics aims to improve the comfort and functionality of fabrics under hot and wet conditions. This field focuses on the selection of fiber materials and the optimization of weaving processes to achieve better breathability, moisture absorption and perspiration capacity, and antibacterial properties, so that the fabrics can provide a comfortable wearing experience under different climatic conditions. These fiber fabrics are widely used in sportswear, outdoor equipment, and daily wear to meet consumers' dual needs for comfort and functionality.

[0003] Testing technology for thermal and moisture comfort fabrics includes testing of antibacterial properties, breathability, moisture management capabilities, odor control effects, and other aspects of the fabric. The goal of this field is to provide accurate data support through scientific testing methods to help improve fabric design and manufacturing processes and ensure the efficiency and reliability of products in actual use. Testing technology not only serves the R&D and production links, but also provides an important quality assurance basis for market promotion.

[0004] At present, most methods only focus on a single performance indicator, lacking comprehensive analysis of multi-dimensional performance such as antimicrobial performance, moisture management ability, breathability and odor control. The evaluation results are often one-sided and difficult to fully reflect the overall performance of the fabric. Secondly, traditional methods mostly rely on qualitative descriptions or single-point data, lacking scientific quantitative standards, making it difficult to accurately compare and analyze fabric performance. More importantly, in current technology, antimicrobial performance and odor release control are usually studied as independent evaluation directions, and the two have not been combined, making it impossible to effectively evaluate the comprehensive effectiveness of fabrics as microbial barriers. Summary of the invention

[0005] The purpose of the present invention is to provide a method for testing the performance of thermal and moisture comfort fiber fabrics, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0006] The present invention is achieved by a method for testing the performance of a thermal and moist comfort fiber fabric, the method comprising:

[0007] Record the initial data of the sample and set several humidity simulation environments, measure the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments, and detect the current activity of the microorganisms;

[0008] The inhibition rate was calculated by combining the initial antimicrobial performance data and the microbial activity results;

[0009] Establish a management analysis model, use the obtained microbial activity results and antimicrobial performance data to calculate the antimicrobial efficacy index, and predict the performance changes in different humidity simulated environments based on the initial sample data;

[0010] Monitor the odor release data in different humidity simulated environments, and combine the odor release data with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier;

[0011] Combined with the initial data of the samples, the antimicrobial efficacy index, the performance of the samples in different humidity environments and the barrier efficacy index were integrated to establish a comprehensive performance model, and the comfort performance of the samples in different environments was simulated through the comprehensive performance model;

[0012] The simulation results are synthesized to generate a performance report describing how the sample performs in different environments.

[0013] As a further solution of the present invention, the recording of the initial data of the sample and setting of several humidity simulation environments, measuring the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments, and detecting the current activity of the microorganisms, specifically includes:

[0014] Record the initial surface humidity, temperature and sample state of the sample surface;

[0015] Measure and record the initial air permeability and water vapor transmission rate of the sample;

[0016] Set several humidity conditions, build a humidity simulation environment according to different humidity conditions, and record the information of the microorganisms inoculated in each humidity simulation environment. At the same time, measure the microbial activity at preset intervals through fluorescence detection.

[0017] As a further solution of the present invention, the combination of the initial antibacterial performance data and the microbial activity results to calculate the antibacterial rate specifically includes:

[0018] Establish a data set to integrate sample initial data, microbial information, and microbial metabolic activity at different time points;

[0019] The initial antimicrobial properties of the samples were quantified based on the metabolic activity of the microorganisms at different time points:

[0020] ;

[0021] in, is the inhibition rate, is the initial number of microorganisms in the control group, is the initial number of microorganisms in the experimental group, is the number of microorganisms in the control group after the experiment.

[0022] As a further solution of the present invention, the management analysis model is established, and the antimicrobial efficacy index is calculated using the obtained microbial activity results and antimicrobial performance data, and the performance changes in different humidity simulated environments are predicted based on the initial sample data, specifically including:

[0023] Set the interval time and record the colony forming units on the sample at each interval time point ;

[0024] Construct a management analysis model based on initial data, microbial activity results, and antimicrobial performance data to calculate the antimicrobial efficacy index;

[0025] Specific:

[0026] ;

[0027] in, Represents the antimicrobial efficacy index, represents the initial colony-forming unit, Indicates a specific point in time The colony forming units, is the environmental impact coefficient, is the temperature, is the relative humidity, is the water vapor transmission rate, which indicates the moisture management ability of the fabric. is a constant, set according to the unit and test conditions, is the oxygen permeability, indicating the breathability of the fabric;

[0028] Extract a specific time point Antimicrobial efficacy index , colony forming unit changes and volatile organic compounds changes ;

[0029] Simulate the performance changes based on the antimicrobial efficacy index and initial data under different humidity environments, specifically:

[0030] ;

[0031] in, For a specific time and relative humidity Under the performance, For a specific time and relative humidity The antimicrobial efficacy index under is the initial water vapor transmission rate, For humidity The influence coefficient of is the initial oxygen permeability, For humidity The influence coefficient of For temperature The influence coefficient of Microbial active factors, It is the odor releasing factor;

[0032] The simulation results are used to predict the performance of samples in different humidity environments.

[0033] As a further solution of the present invention, the use of simulation results to predict the performance of the sample in different humidity environments specifically includes:

[0034] Recording fabric surface temperature and ambient temperature , water vapor concentration on fabric surface and water vapor concentration in the environment ;

[0035] Calculating the thermal conductivity of fabrics :

[0036] ;

[0037] in, is the thermal conductivity of the fabric, which indicates the material's ability to transfer heat. is the surface area of ​​the fabric in contact with the air, is the thickness of the fabric;

[0038] Diffusion efficiency of water vapor in fabrics :

[0039] ;

[0040] in, is the vapor diffusion coefficient, which indicates the diffusion capacity of water vapor through the material;

[0041] ;

[0042] Specific time and relative humidity The material properties under is the porosity of the fabric, which affects the flow of air and water vapor. It is the fabric efficiency factor, which takes into account the fabric's moisture absorption and comfort.

[0043] As a further solution of the present invention, the odor release data is combined with the microbial activity results to analyze the effect of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier, specifically including:

[0044] Set up several different simulated humidity environments and record the real-time humidity data, temperature data and odor release in each simulated humidity environment;

[0045] Read the microbial activity results under different humidity environments, and match the odor release data with the microbial activity results at the corresponding time points to evaluate the impact of microbial activity on odor release;

[0046] Calculate the barrier effectiveness index of the samples under different humidity environments.

[0047] As a further solution of the present invention, the barrier effectiveness index of the sample under different humidity environments is calculated, specifically:

[0048] ;

[0049] in, is the barrier effectiveness index, For the changes in volatile organic compounds, is the humidity influencing factor.

[0050] As a further solution of the present invention, the initial data of the sample is combined with the comprehensive antibacterial efficacy index, the performance of the sample in different humidity environments and the barrier efficacy index to establish a comprehensive performance model, and the comfort performance of the sample in different environments is simulated by the comprehensive performance model, specifically including:

[0051] Extract sample initial data, antimicrobial efficacy index, sample performance in different humidity environments, and barrier efficacy index;

[0052] Establish a comprehensive performance model to analyze the performance of samples under different environmental variables:

[0053] ;

[0054] in, Indicates the comprehensive performance of the sample. represents the model coefficient, Subscript the index;

[0055] Compare performance under different environmental variables and identify optimal and suboptimal conditions.

[0056] Another object of the present invention is to provide a thermal and moisture comfort fiber fabric performance detection system, the system comprising:

[0057] The data recording and detection module is used to record the initial data of the sample and set a number of humidity simulation environments to measure the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments and detect the current activity of the microorganisms;

[0058] The antibacterial performance calculation module is used to calculate the antibacterial rate by combining the initial antibacterial performance data and the microbial activity results;

[0059] The performance change model module is used to establish a management analysis model, use the obtained microbial activity results and antimicrobial performance data to calculate the antimicrobial efficacy index, and predict the performance changes in different humidity simulation environments based on the initial sample data;

[0060] The barrier effectiveness analysis module is used to monitor the odor release data in different humidity simulation environments, and combine the odor release data with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier;

[0061] The comfort performance simulation module is used to combine the initial data of the sample, comprehensively consider the antimicrobial efficacy index, the performance of the sample in different humidity environments and the barrier efficacy index, establish a comprehensive performance model, and simulate the comfort performance of the sample in different environments through the comprehensive performance model;

[0062] The performance report generation module is used to integrate the simulation results and generate performance reports to describe the performance of the samples in different environments.

[0063] The beneficial effects of the present invention are:

[0064] By introducing a dynamic humidity simulation environment, this method can truly restore the actual usage scenarios of fiber fabrics under different hot and humid conditions, and use the humidity simulation environment as the core variable in each step to comprehensively analyze the impact of humidity on microbial activity, the regulatory effect of odor release, and the role of humidity in barrier effectiveness and comprehensive performance evaluation.

[0065] This method enables accurate quantification and comparative analysis of fabric performance. These models can not only measure the performance of fabrics in multiple dimensions such as antibacterial, moisture management and odor control, but also flexibly adapt to different application scenarios by adjusting the weight factors in the model.

[0066] In addition, the method innovatively introduces the antimicrobial efficacy index and barrier efficacy index, combining antimicrobial performance with odor release control ability to comprehensively evaluate the actual effect of fabrics as microbial barriers. By analyzing the dynamic relationship between microbial activity and volatile organic compounds, this method can quantify the dual effectiveness of fabrics in odor control and antimicrobial protection, providing a reliable basis for the scientific evaluation and optimization of fabric performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 A flow chart of a method for detecting the performance of a thermal and moist comfort fiber fabric provided by an embodiment of the present invention;

[0068] Figure 2 A flow chart for recording initial sample data provided by an embodiment of the present invention;

[0069] Figure 3 A flowchart for calculating the antibacterial rate provided by an embodiment of the present invention;

[0070] Figure 4 A flow chart for predicting performance changes in different humidity simulation environments provided by an embodiment of the present invention;

[0071] Figure 5 A flow chart of evaluating the barrier effectiveness index of a sample as a microbial barrier provided by an embodiment of the present invention;

[0072] Figure 6 A flow chart of simulating the comfort performance of a sample in different environments through a comprehensive performance model provided by an embodiment of the present invention;

[0073] Figure 7 A structural block diagram of a thermal and moisture comfort fiber fabric performance detection system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0075] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0076] Figure 1 A flow chart of a method for detecting the performance of a thermal and moist comfort fiber fabric provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0077] S100, recording the initial data of the sample, and setting a number of humidity simulation environments, measuring the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments, and detecting the current activity of the microorganisms;

[0078] The initial surface humidity, temperature and overall sample state of the sample surface need to be accurately recorded to ensure accurate simulation of the subsequent experimental environment. The initial state description of the sample includes the overall shape of the fabric, fiber arrangement, etc. These details will affect the air permeability and water vapor permeability of the fabric.

[0079] The initial air permeability and water vapor transmission rate of the sample are two indicators that are crucial to evaluating the comfort and moisture management capabilities of the fabric. The air permeability test is performed using an air flow tester, while the water vapor transmission rate is established using a water vapor transmission rate tester.

[0080] In the experimental design, several humidity conditions are set and humidity simulation environments are built based on these conditions. These humidity conditions will include multiple stages from dry to extremely humid to fully simulate the conditions that fabrics may encounter in different environments. In each humidity simulation environment, standard microorganisms are inoculated onto the fabrics, which are usually common skin flora, to simulate the possible microbial contact of the human body during wearing.

[0081] In order to monitor the activity changes of microorganisms in real time, the activity of these microorganisms is measured at preset intervals by fluorescence detection. This method can provide fast and accurate microbial activity data, providing instant feedback for subsequent performance testing and antimicrobial efficacy analysis.

[0082] like Figure 2 As shown, the initial data of the sample is recorded, and several humidity simulation environments are set, and the information of the standard microorganisms inoculated on the fabric is measured in different humidity simulation environments, and the current activity of the microorganisms is detected, specifically including:

[0083] S110, recording the initial surface humidity, temperature and sample state of the sample surface;

[0084] S120, measuring and recording the initial air permeability and water vapor transmission rate of the sample;

[0085] S130, setting a number of humidity conditions, building a humidity simulation environment according to different humidity conditions, and recording the information of the microorganisms inoculated in each humidity simulation environment, and at the same time measuring the activity of the microorganisms at preset intervals through fluorescence detection.

[0086] S200, combining the initial antimicrobial performance data and microbial activity results to calculate the inhibition rate;

[0087] This step integrates the initial surface data of the sample (such as temperature, humidity, air permeability, etc.), the standard microbial information of the inoculation (including microbial species, initial number, etc.), and the microbial metabolic activity data at different times under different humidity simulation environments by establishing a data set. This integrated data set ensures the comprehensiveness of subsequent analysis and the accuracy of the data.

[0088] Based on the microbial metabolic activity at different time points, the initial antibacterial properties of the samples are quantitatively analyzed, which can scientifically evaluate the antibacterial ability of the samples under different humidity conditions, especially by comparing the differences in microbial activity between the experimental group and the control group, so as to derive the inhibitory strength of the samples on microorganisms.

[0089] This step integrates multi-dimensional data, including initial sample performance, microbial information, and dynamic activity data, to ensure that the calculation results of the antibacterial rate can fully reflect the actual performance of the sample under different environments. Compared with traditional single time point or static analysis methods, this step monitors and quantifies the dynamic microbial metabolic activity, which can capture the changing trend of the sample's antibacterial performance and provide more timely and scientific results.

[0090] At the same time, the use of a formulaic data quantification method can provide a clear and comparable indicator for antibacterial performance. This indicator can not only intuitively reflect the antibacterial effect of the sample, but also be compared horizontally with other samples, providing a scientific basis for the screening of highly effective antibacterial materials. Traditional technologies may rely more on subjective judgment or single test data, while this method greatly improves the objectivity and accuracy of the analysis through multi-point time monitoring and data modeling.

[0091] This step is designed specifically to analyze the antibacterial properties under humidity conditions. This technical method combined with the humidity simulation environment significantly enhances the evaluation of the applicability of samples in actual application scenarios. Through the correlation analysis of microbial activity and antibacterial efficacy in a dynamic humidity environment, this step can provide a more reliable prediction of the durability and stability of the antibacterial properties of samples under different humidity conditions. Compared with traditional single-environment experiments, this method is more in line with actual usage scenarios, such as the evaluation of the antibacterial effect and comfort of fabrics in high humidity environments.

[0092] like Figure 3 As shown, the combination of the initial antibacterial performance data and the microbial activity results to calculate the inhibition rate specifically includes:

[0093] S210, establish a data set to integrate sample initial data, microbial information, and microbial metabolic activity at different time points;

[0094] S220, quantification of the initial antimicrobial performance of the samples based on the metabolic activity of the microorganisms at different time points:

[0095] ;

[0096] in, is the inhibition rate, is the initial number of microorganisms in the control group, is the initial number of microorganisms in the experimental group, is the number of microorganisms in the control group after the experiment.

[0097] S300, establish a management analysis model, use the obtained microbial activity results and antimicrobial performance data to calculate the antimicrobial efficacy index, and predict the performance changes in different humidity simulation environments based on the initial sample data;

[0098] This step combines the microbial activity data, antimicrobial performance data, and sample initial data through a management analysis model, accurately calculates the antimicrobial efficacy index through a mathematical formula, and predicts the performance changes of the sample in different humidity environments. This is the core step of the entire method, and its focus is on using multi-dimensional data and formulaic analysis methods to comprehensively predict the antimicrobial efficacy and comfort performance of fabrics under dynamic humidity conditions.

[0099] First, set the interval time and record the colony forming units on the sample at each interval time point, which is the basic data source of antibacterial performance. By comparing the initial colony forming units with the colony forming units at a specific moment, combined with environmental factors such as relative humidity and temperature, as well as the water vapor permeability and oxygen permeability of the fabric, the antibacterial efficacy index is calculated through a formula. This formula comprehensively considers the humidity management ability and breathability of the sample, and adjusts the impact of temperature and humidity on antibacterial performance through the environmental impact coefficient, making the antibacterial efficacy index a dynamic and quantifiable expression.

[0100] Then, based on the antimicrobial efficacy index and initial data, the performance changes of the samples under specific time and humidity conditions were further calculated, and the parameters such as humidity, air permeability, water vapor transmission rate, and changes in the number of colonies and odor release were combined to quantify the performance of the samples. The innovation of this formula is that it fully considers the dynamic changes in humidity and air permeability in the environment to regulate the antimicrobial efficacy, and also introduces the influencing factors of odor release to make the performance evaluation more in line with the actual usage scenario.

[0101] In addition, in order to further simulate the comfort performance of samples in a humid environment, the surface temperature of the fabric and the ambient temperature, as well as the water vapor concentration, are recorded to calculate the heat conduction efficiency and water vapor diffusion efficiency, and the hygroscopicity, porosity and diffusion capacity of the fabric are combined to obtain a material performance model. This model combines the heat and moisture transfer performance and hygroscopicity of the fabric, so that the final prediction results can fully reflect the comfort and microbial barrier performance of the fabric in a dynamic humidity environment.

[0102] This step uses multi-dimensional data fusion and dynamic modeling methods to integrate and analyze complex factors such as initial antibacterial data, dynamic changes in microbial activity, environmental humidity and temperature conditions. In particular, the antibacterial efficacy index combines key performance indicators such as temperature and humidity, air permeability and water vapor transmission rate, making the evaluation of antibacterial performance more comprehensive and accurate. This dynamic modeling method makes up for the shortcomings of single-point evaluation or ignoring environmental changes in traditional technologies.

[0103] At the same time, dynamic factors such as humidity, temperature, water vapor and oxygen permeability are introduced to enable the antimicrobial efficacy index and sample performance model to respond to environmental changes in real time. Compared with the static evaluation method commonly used in the prior art, this dynamic adjustment mechanism can more accurately predict the actual performance of samples in a changing environment.

[0104] Combining the changes in the number of colonies with the dynamic release of volatile organic compounds truly achieves a unified evaluation of antibacterial performance and odor control. This is of great significance for the actual use value of fabrics in high humidity environments. In addition, combined with the calculation of heat conduction efficiency and water vapor diffusion efficiency, the comfort of fabrics can also be predicted.

[0105] By dynamically simulating a variety of humidity environments, an environmental response model of fabric performance was established, making the test results closer to actual usage scenarios. Traditional methods are mostly limited to result evaluation under static laboratory conditions, while this method can directly provide the actual performance of fabrics in different humidity environments, providing a reliable basis for fabric design and optimization.

[0106] like Figure 4 As shown, the management analysis model is established, and the antimicrobial efficacy index is calculated using the obtained microbial activity results and antimicrobial performance data, and the performance changes in different humidity simulation environments are predicted based on the initial sample data, specifically including:

[0107] S310, set the interval time and record the colony forming units on the sample at each interval time point ;

[0108] S320, constructing a management analysis model based on initial data, microbial activity results, and antimicrobial performance data, and calculating the antimicrobial efficacy index;

[0109] Specific:

[0110] ;

[0111] in, Represents the antimicrobial efficacy index, represents the initial colony-forming unit, Indicates a specific point in time The colony forming units, is the environmental impact coefficient, is the temperature, is the relative humidity, is the water vapor transmission rate, which indicates the moisture management ability of the fabric. is a constant, set according to the unit and test conditions, is the oxygen permeability, indicating the breathability of the fabric;

[0112] S330, extracting a specific time point Antimicrobial efficacy index , colony forming unit changes and volatile organic compounds changes ;

[0113] S340 simulates the performance changes based on the antimicrobial efficacy index and initial data under different humidity conditions. Specifically:

[0114] ;

[0115] in, For a specific time and relative humidity Under the performance, For a specific time and relative humidity The antimicrobial efficacy index under is the initial water vapor transmission rate, For humidity The influence coefficient of is the initial oxygen permeability, For humidity The influence coefficient of For temperature The influence coefficient of Microbial active factors, It is the odor releasing factor;

[0116] S350, using simulation results to predict the performance of samples in different humidity environments.

[0117] In this step, the use of simulation results to predict the performance of the sample in different humidity environments specifically includes:

[0118] Recording fabric surface temperature and ambient temperature , water vapor concentration on fabric surface and water vapor concentration in the environment ;

[0119] Calculating the thermal conductivity of fabrics :

[0120] ;

[0121] in, is the thermal conductivity of the fabric, which indicates the material's ability to transfer heat. is the surface area of ​​the fabric in contact with the air, is the thickness of the fabric;

[0122] Diffusion efficiency of water vapor in fabrics :

[0123] ;

[0124] in, is the vapor diffusion coefficient, which indicates the diffusion capacity of water vapor through the material;

[0125] ;

[0126] Specific time and relative humidity The material properties under is the porosity of the fabric, which affects the flow of air and water vapor. It is the fabric efficiency factor, which takes into account the fabric's moisture absorption and comfort.

[0127] S400, monitors the odor release data in different humidity simulated environments, and combines the odor release data with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier;

[0128] In this step, several different humidity conditions (such as 20%, 50%, 80%, etc.) will be set, and the humidity and temperature data will be monitored in real time in these simulated humidity environments. The recording of these basic data can accurately reflect the performance of the fabric in different humidity environments, and provide a scientific basis for subsequent analysis. In these environments, detecting the odor release characteristics of the fabric is a key link. Odor release is usually monitored through changes in the concentration of volatile organic compounds. Sensors can collect and record these data at different time points to ensure that the dynamic changes in odor release are fully captured.

[0129] At the same time, the results of microbial activity under the corresponding humidity environment (such as changes in colony forming units or microbial metabolic activity data) are directly read, and these data are matched and analyzed with the odor release data at the corresponding time point. Through correlation analysis, the direct impact of microbial activity on odor release can be evaluated. For example, higher microbial activity may lead to more obvious volatile organic compound release, while lower activity may inhibit odor diffusion. This process not only reveals the relationship between microbial activity and odor release, but also provides a scientific basis for further performance optimization.

[0130] Finally, the barrier effectiveness index of the samples under different humidity environments was calculated, which fully considered the dynamic correlation between microbial activity and odor release, as well as the regulatory effect of humidity on this process. This can comprehensively quantify the performance of the fabric as a microbial barrier.

[0131] This step reveals the multiple effects of humidity environment on fabric performance by accurately matching real-time humidity, odor release and microbial activity data. By analyzing the dynamic relationship between microbial activity and changes in organic compounds, odor release is included in the scope of antimicrobial performance evaluation, innovatively combining antibacterial and comfort. By calculating the barrier effectiveness index, it can not only evaluate the antibacterial durability of the fabric, but also reflect its actual effect on odor control, providing a more comprehensive reference for fabric design.

[0132] Dynamic quantitative evaluation can also help fabric manufacturers better optimize product performance for specific application scenarios (such as sportswear, outdoor equipment in humid environments, etc.). By analyzing the impact of odor release and humidity control on fabric performance, manufacturers can make targeted material selection and treatment technology improvements to improve the use of fabrics in specific scenarios.

[0133] like Figure 5 As shown, the odor release data is combined with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier, including:

[0134] S410, setting a number of different simulated humidity environments, and recording real-time humidity data, temperature data, and odor release in each simulated humidity environment;

[0135] S420, reading the microbial activity results under different humidity environments, and matching the odor release data with the microbial activity results at corresponding time points to evaluate the effect of microbial activity on odor release;

[0136] S430, calculating the barrier effectiveness index of the sample under different humidity environments.

[0137] In this step, the barrier effectiveness index of the sample under different humidity environments is calculated, specifically:

[0138] ;

[0139] in, is the barrier effectiveness index, For the changes in volatile organic compounds, is the humidity influencing factor.

[0140] S500, combined with the initial data of the sample, comprehensively considers the antimicrobial efficacy index, the performance of the sample in different humidity environments and the barrier efficacy index, establishes a comprehensive performance model, and simulates the comfort performance of the sample in different environments through the comprehensive performance model;

[0141] This step integrates and evaluates multi-dimensional data such as antimicrobial performance, breathability, water vapor management capabilities, and environmental factors (such as temperature and humidity). Through the comprehensive performance model, it is possible to simulate the performance of samples under different environmental variables, and by analyzing and comparing the comprehensive performance under different environments, identify the optimal and suboptimal adaptation of fabrics in various conditions, such as odor control capabilities in high humidity environments or microbial barrier effectiveness in low humidity environments.

[0142] This step achieves comprehensive integration of multi-dimensional data through a comprehensive performance model, combining antibacterial properties, breathability and dynamic changes in hot and humid environments, and quantifying the comprehensive performance of fabrics under real use conditions. Compared with traditional static evaluation methods, dynamic modeling introduces temperature and humidity environmental variables, making the evaluation results of fabric performance more scientific and close to reality. For example, through dynamic analysis, it can be revealed whether the sample can effectively inhibit the spread of microorganisms under high humidity conditions while maintaining moisture management capabilities and comfort. Especially when comparing the comprehensive performance under different environmental variables, the system can quickly identify the optimal performance of fabrics in different scenarios, helping R&D personnel optimize the direction of material design.

[0143] like Figure 6 As shown, the initial data of the combined sample, the comprehensive antibacterial efficacy index, the performance of the sample in different humidity environments and the barrier efficacy index are integrated to establish a comprehensive performance model, and the comfort performance of the sample in different environments is simulated by the comprehensive performance model, specifically including:

[0144] S510, extracting sample initial data, antimicrobial efficacy index, sample performance in different humidity environments, and barrier efficacy index;

[0145] S520, establish a comprehensive performance model to analyze the performance of samples under different environmental variables:

[0146] ;

[0147] in, Indicates the comprehensive performance of the sample. represents the model coefficient, Subscript the index;

[0148] S530, compares the performance under different environmental variables and identifies the optimal and suboptimal conditions.

[0149] S600, synthesizes simulation results and generates performance reports describing how the sample performs in different environments.

[0150] Figure 7 A structural block diagram of a thermal and moisture comfort fiber fabric performance detection system provided by an embodiment of the present invention, such as Figure 7 As shown, the system comprises:

[0151] The data recording and detection module 100 is used to record the initial data of the sample, and set a number of humidity simulation environments, measure the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments, and detect the current activity of the microorganisms;

[0152] The antibacterial performance calculation module 200 is used to calculate the antibacterial rate by combining the initial antibacterial performance data and the microbial activity results;

[0153] The performance change model module 300 is used to establish a management analysis model, calculate the antimicrobial efficacy index using the obtained microbial activity results and antimicrobial performance data, and predict the performance changes in different humidity simulation environments based on the sample initial data;

[0154] The barrier effectiveness analysis module 400 is used to monitor the odor release data in different humidity simulated environments, and combine the odor release data with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier;

[0155] The comfort performance simulation module 500 is used to combine the initial data of the sample, comprehensively consider the antimicrobial efficacy index, the performance of the sample in different humidity environments and the barrier efficacy index, establish a comprehensive performance model, and simulate the comfort performance of the sample in different environments through the comprehensive performance model;

[0156] The performance report generation module 600 is used to integrate the simulation results and generate a performance report describing the performance of the sample in different environments.

[0157] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0158] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0159] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0160] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0161] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for testing the performance of thermal and moisture comfort fiber fabrics, characterized in that: The method comprises: Record the initial data of the sample, set several humidity simulation environments, measure the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments, and detect the current activity of the microorganisms; The inhibition rate was calculated by combining the initial antimicrobial performance data and the microbial activity results; Establish a management analysis model, use the obtained microbial activity results and antimicrobial performance data to calculate the antimicrobial efficacy index, and predict the performance changes in different humidity simulated environments based on the initial sample data; Monitor the odor release data in different humidity simulated environments, and combine the odor release data with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier; Combined with the initial data of the samples, the antimicrobial efficacy index, the performance of the samples in different humidity environments and the barrier efficacy index were integrated to establish a comprehensive performance model, and the comfort performance of the samples in different environments was simulated through the comprehensive performance model; Synthesize simulation results and generate performance reports describing how the sample performs in different environments; The method of combining the initial antibacterial performance data and the microbial activity results to calculate the antibacterial rate specifically includes: Establish a data set to integrate sample initial data, microbial information, and microbial metabolic activity at different time points; The initial antimicrobial properties of the samples were quantified based on the metabolic activity of the microorganisms at different time points: ； in, is the inhibition rate, is the initial number of microorganisms in the control group, is the initial number of microorganisms in the experimental group, is the number of microorganisms after the experiment in the control group; The obtained microbial activity results and antimicrobial performance data are used to calculate the antimicrobial efficacy index, and based on the initial sample data, the performance changes in different humidity simulated environments are predicted, including: Set the interval time and record the colony forming units on the sample at each interval time point ; Construct a management analysis model based on initial data, microbial activity results, and antimicrobial performance data to calculate the antimicrobial efficacy index; Specific: ； in, Represents the antimicrobial efficacy index, represents the initial colony-forming unit, Indicates a specific point in time The colony forming units, is the environmental impact coefficient, is the temperature, is the relative humidity, is the water vapor transmission rate, which indicates the moisture management ability of the fabric. is a constant, set according to the unit and test conditions, is the oxygen permeability, indicating the breathability of the fabric; Extract a specific time point Antimicrobial efficacy index , colony forming unit changes and volatile organic compounds changes ; Simulate the performance changes based on the antimicrobial efficacy index and initial data under different humidity environments, specifically: ； in, For a specific time and relative humidity Under the performance, For a specific time and relative humidity The antimicrobial efficacy index under is the initial water vapor transmission rate, For humidity The influence coefficient of is the initial oxygen permeability, For humidity The influence coefficient of For temperature The influence coefficient of Microbial active factors, It is the odor releasing factor; Use simulation results to predict how samples will perform in different humidity environments; The odor release data was combined with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier, including: Set up several different simulated humidity environments and record the real-time humidity data, temperature data and odor release in each simulated humidity environment; Read the microbial activity results under different humidity environments, and match the odor release data with the microbial activity results at the corresponding time points to evaluate the impact of microbial activity on odor release; Calculate the barrier effectiveness index of samples under different humidity environments; The barrier effectiveness index of the sample under different humidity environments is calculated as follows: ； in, is the barrier effectiveness index, For the changes in volatile organic compounds, is the humidity influencing factor.

2. The method according to claim 1, characterized in that The recording of the initial data of the sample and setting of several humidity simulation environments, measuring the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments, and detecting the current activity of the microorganisms, specifically includes: Record the initial surface humidity, temperature and sample state of the sample surface; Measure and record the initial air permeability and water vapor transmission rate of the sample; Set several humidity conditions, build a humidity simulation environment according to different humidity conditions, and record the information of the microorganisms inoculated in each humidity simulation environment. At the same time, measure the microbial activity at preset intervals through fluorescence detection.

3. The method according to claim 1, characterized in that The method of using simulation results to predict the performance of samples in different humidity environments specifically includes: Recording fabric surface temperature and ambient temperature , water vapor concentration on fabric surface and water vapor concentration in the environment ; Calculating the thermal conductivity of fabrics : ； in, is the thermal conductivity of the fabric, which indicates the material's ability to transfer heat. is the surface area of ​​the fabric in contact with the air, is the thickness of the fabric; Diffusion efficiency of water vapor in fabrics : ； in, is the vapor diffusion coefficient, which indicates the diffusion capacity of water vapor through the material; ； Specific time and relative humidity The material properties under is the porosity of the fabric, which affects the flow of air and water vapor. It is the fabric efficiency factor, which takes into account the fabric's moisture absorption and comfort.

4. The method according to claim 2, characterized in that: The initial data of the samples are combined with the comprehensive antimicrobial efficacy index, the performance of the samples in different humidity environments and the barrier efficacy index to establish a comprehensive performance model, and the comfort performance of the samples in different environments is simulated by the comprehensive performance model, specifically including: Extract sample initial data, antimicrobial efficacy index, sample performance in different humidity environments, and barrier efficacy index; Establish a comprehensive performance model to analyze the performance of samples under different environmental variables: ； in, Indicates the comprehensive performance of the sample. represents the model coefficient, Subscript the index; Compare performance under different environmental variables and identify optimal and suboptimal conditions.

5. A thermal and moisture comfort fiber fabric performance testing system for implementing the method as claimed in claim 1, characterized in that: The system comprises: The data recording and detection module is used to record the initial data of the sample and set a number of humidity simulation environments to measure the information of the standard microorganisms inoculated on the fabric in different humidity simulation environments and detect the current activity of the microorganisms; The antibacterial performance calculation module is used to calculate the antibacterial rate by combining the initial antibacterial performance data and the microbial activity results; The performance change model module is used to establish a management analysis model, use the obtained microbial activity results and antimicrobial performance data to calculate the antimicrobial efficacy index, and predict the performance changes in different humidity simulation environments based on the initial sample data; The barrier effectiveness analysis module is used to monitor the odor release data in different humidity simulation environments, and combine the odor release data with the microbial activity results to analyze the impact of microbial activity on odor release and evaluate the barrier effectiveness index of the sample as a microbial barrier; The comfort performance simulation module is used to combine the initial data of the sample, comprehensively consider the antimicrobial efficacy index, the performance of the sample in different humidity environments and the barrier efficacy index, establish a comprehensive performance model, and simulate the comfort performance of the sample in different environments through the comprehensive performance model; The performance report generation module is used to integrate the simulation results and generate performance reports to describe the performance of the samples in different environments.

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