Method and system for evaluating antibacterial performance of fabric

Abstract

The application discloses a fabric antibacterial performance evaluation method and system, and relates to the technical field of antibacterial data processing. The fabric antibacterial performance evaluation method comprises the following steps: obtaining a bacterial data set of a sample fabric to be evaluated after bacterial culture; performing primary evaluation on the sample fabric to be evaluated based on the bacterial data set, and screening out primary evaluation qualified primary screening sample fabrics; obtaining physical characteristic data of the primary screening sample fabrics, and analyzing and screening out finally qualified sample fabrics based on the physical characteristic data; and determining a fabric antibacterial evaluation qualified rate based on the ratio of the sample fabrics to the sample fabric to be evaluated, performing preliminary screening, and preliminarily determining the antibacterial effect of the fabric. Then, the physical characteristic data of the primary screening sample fabrics are obtained, the finally qualified sample fabrics are screened out through physical characteristic data analysis, and the fabric antibacterial evaluation qualified rate is determined. The method effectively solves the problems of incomplete data and insufficient accuracy in the process of evaluating the antibacterial effect of the fabric, and enhances the scientificity and objectivity of the antibacterial performance evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of antibacterial data processing, and in particular to a method and system for evaluating the antibacterial performance of fabrics. Background Art

[0002] Textile fabrics, especially clothing, bedding, and towels, often come into direct contact with the human body, making their antimicrobial properties particularly important for human health. Traditionally, the antimicrobial properties of textile fabrics have been primarily assessed manually, such as by visual culture plate methods to detect and count bacteria. These traditional methods are often time-consuming and labor-intensive, and can be affected by the operator's skill, experience, and judgment, leading to inconsistent results.

[0003] The Chinese patent application with publication number CN117368470A discloses a textile antibacterial detection and quality assessment system, which includes a data acquisition module, a data processing module, a microbial culture and identification module, a detection module, an evaluation module and an output module; wherein, the data acquisition module is responsible for acquiring the physical and chemical property data of the textile, and the data processing module performs preprocessing; the microbial culture and identification module cultivates the microorganisms present in the textile and identifies their types; the detection module analyzes and determines the antibacterial properties of the textile; the evaluation module receives the test results and generates a textile quality assessment report based on preset evaluation criteria; the output module is responsible for displaying the assessment report, which not only improves the speed and efficiency of detection, but also provides more accurate and scientific antibacterial performance judgment through advanced microbial identification algorithms and antibacterial index calculations.

[0004] However, although the existing technology combines physical and chemical property data for evaluation, there are problems with incomplete data and insufficient accuracy in the evaluation of the antibacterial effect of fabrics. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a method and system for evaluating the antibacterial properties of fabrics, which solves the problems of incomplete data and insufficient accuracy in the evaluation process of the antibacterial effect of fabrics in the existing technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a method for evaluating the antibacterial properties of fabrics, comprising the following steps: obtaining a bacterial dataset after bacterial culture of a sample fabric to be evaluated; performing an initial evaluation of the sample fabric to be evaluated based on the bacterial dataset, and screening out preliminary screening sample fabrics that pass the initial evaluation; obtaining physical characteristic data of the preliminary screening sample fabrics, and screening out finally qualified sample fabrics based on the physical characteristic data analysis; and determining the fabric antibacterial evaluation pass rate based on the ratio of the sample fabric to the sample fabric to be evaluated.

[0007] Furthermore, the bacterial data set includes the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species. The number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species serve as an analytical basis for screening out preliminary screening sample fabrics that pass the initial evaluation.

[0008] Furthermore, the process of screening out the primary screening sample fabrics that pass the initial evaluation is as follows: extracting the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species and the average bacterial activity of each surviving bacterial species in the bacterial data set; obtaining the primary screening evaluation index based on the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species and the average bacterial activity of each surviving bacterial species; judging whether the primary screening evaluation index is greater than the primary screening evaluation threshold set in the database: if the primary screening evaluation index is greater than the primary screening evaluation threshold set in the database, the primary screening fails; if the primary screening evaluation index is not greater than the primary screening evaluation threshold set in the database, the primary screening passes, and the primary screening sample fabrics that pass the initial evaluation are obtained.

[0009] Furthermore, the calculation formula of the initial screening evaluation index is as follows:

[0010] ;

[0011] Where, is the initial screening assessment index, is the number of the surviving bacterial species, , is the total number of surviving bacterial species, For the The bacterial survival rate of surviving bacterial species, For the The average bacterial activity of surviving bacterial species, is a natural constant.

[0012] Furthermore, the physical characteristic data includes permeability data and strength data, and the permeability data and strength data serve as analysis basis for screening the final qualified sample fabrics.

[0013] Furthermore, the screening of the final qualified sample fabric based on the analysis of physical property data includes: extracting permeability data, and obtaining the permeability coefficient of the preliminary screened sample fabric based on the permeability data analysis, wherein the permeability data includes the air permeability of the preliminary screened sample fabric and the water permeability of the preliminary screened sample fabric; extracting strength data, and obtaining the strength coefficient of the preliminary screened sample fabric based on the strength data analysis, wherein the strength coefficient includes the tear strength of the preliminary screened sample fabric, the bursting strength of the preliminary screened sample fabric and the wear resistance of the preliminary screened sample fabric; obtaining the physical characteristic evaluation index of the preliminary screened sample fabric based on the permeability coefficient and the strength coefficient; judging whether the physical characteristic evaluation index is greater than the physical characteristic evaluation threshold set in the database: if the physical characteristic evaluation index is greater than the physical characteristic evaluation threshold set in the database, the evaluation is qualified, and the final qualified sample fabric is obtained; if the physical characteristic evaluation index is not greater than the physical characteristic evaluation threshold set in the database, the evaluation is unqualified.

[0014] Furthermore, the calculation formula of the physical characteristic evaluation index is as follows:

[0015] ;

[0016] Where, is the physical characteristics evaluation index, is the permeability coefficient, is the weight factor of the permeability coefficient stored in the database, is the strength coefficient, is the weighting factor of the intensity coefficient stored in the database.

[0017] Furthermore, before determining the fabric antibacterial evaluation pass rate based on the ratio of the sample fabric to the sample fabric to be evaluated, the method further includes: obtaining a bacterial data set and physical characteristic data of the finally qualified sample fabric; obtaining a preliminary screening evaluation index based on the bacterial data set of the finally qualified sample fabric; obtaining a physical characteristic evaluation index based on the physical characteristic data of the finally qualified sample fabric; obtaining a comprehensive evaluation index of the finally qualified sample fabric based on the preliminary screening evaluation index and the physical characteristic evaluation index; and comparing the comprehensive evaluation index with a grade classification threshold stored in a database: if the comprehensive evaluation index is greater than or equal to the grade classification threshold stored in the database, the corresponding finally qualified sample fabric is classified as a secondary fabric; if the comprehensive evaluation index is less than the grade classification threshold stored in the database, the corresponding finally qualified sample fabric is classified as a high-grade fabric.

[0018] Furthermore, the comprehensive evaluation index is obtained as follows: taking the inverse of the physical characteristic evaluation index; performing weighted summation on the inverse of the initial screening evaluation index and the physical characteristic evaluation index to obtain the comprehensive evaluation index.

[0019] The application discloses a fabric antibacterial performance evaluation system, which is used for the above-mentioned fabric antibacterial performance evaluation system and comprises a bacterial data set acquisition module, a preliminary screening evaluation module, a final screening evaluation module and a qualified rate determination module.

[0020] The application has the following beneficial effects:

[0021] (1) The fabric antibacterial performance evaluation method can preliminarily determine the antibacterial effect of the fabric by culturing bacteria on the sample fabric to be evaluated to obtain a bacterial data set and performing preliminary screening based on the data set, then further screening the finally qualified sample fabric by analyzing the physical characteristic data of the sample fabric after preliminary screening. Finally, the qualified rate of the fabric antibacterial evaluation is determined based on the ratio of the sample fabric to the sample fabric to be evaluated, so that the evaluation result is more comprehensive, accurate and reliable, the problem of incomplete data and insufficient accuracy in the process of evaluating the antibacterial effect of the fabric is solved, and the scientificity and objectivity of the antibacterial performance evaluation are enhanced.

[0022] (2) The fabric antibacterial performance evaluation method can better allocate resources by accurate fabric evaluation, for example, more resources are input into the production of high-grade fabric, and the input on the secondary fabric is reduced, so that the production cost is optimized and the efficiency is improved.

[0023] Of course, any product implementing the application does not necessarily need to achieve all the advantages mentioned above at the same time. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The application discloses a fabric antibacterial performance evaluation method flow chart.

[0025] Figure 2 The application discloses a fabric antibacterial performance evaluation method flow chart.

[0026] Figure 3 The application discloses a fabric antibacterial performance evaluation system flow chart. DETAILED DESCRIPTION

[0027] The application embodiment solves the problems of incomplete data and insufficient accuracy in the process of evaluating the antibacterial effect of the fabric by combining bacterial monitoring and physical monitoring, and enhances the scientificity and objectivity of the antibacterial performance evaluation.

[0028] The general idea of the problem in the application embodiment is as follows:

[0029] The bacterial data set is obtained by culturing the sample fabric to be evaluated, and the antibacterial effect of the fabric is preliminarily determined based on the data set. Then, the physical characteristic data of the preliminary screening sample fabric is obtained, and the finally qualified sample fabric is further analyzed and screened out through the physical characteristic data. Finally, the fabric antibacterial evaluation pass rate is determined based on the ratio of the sample fabric to the sample fabric to be evaluated, so as to ensure that the evaluation result is more comprehensive, accurate and reliable.

[0030] Please refer to Figure 1 The application embodiment provides a technical solution: a fabric antibacterial performance evaluation method, comprising the following steps: obtaining the bacterial data set of the sample fabric to be evaluated after bacterial culture; performing primary evaluation on the sample fabric to be evaluated based on the bacterial data set, and screening out the preliminary screening sample fabric that passes the primary evaluation; obtaining the physical characteristic data of the preliminary screening sample fabric, and analyzing and screening the finally qualified sample fabric based on the physical characteristic data; and determining the fabric antibacterial evaluation pass rate based on the ratio of the sample fabric to the sample fabric to be evaluated.

[0031] After bacterial culture of the sample fabric to be evaluated, the bacterial data set is collected, the sample fabric is preliminarily evaluated based on the bacterial data set, and the preliminary screening sample fabric that passes the primary evaluation is screened out. The physical characteristic data of the preliminary screening sample fabric is collected, and the finally qualified sample fabric is screened out based on the physical characteristic data. The pass rate of the fabric antibacterial evaluation is determined through the ratio of the sample fabric to the sample fabric to be evaluated.

[0032] By combining bacterial data and physical characteristic data, the evaluation result is more comprehensive, avoiding the limitations brought by a single data source. The sample with antibacterial performance is screened out through preliminary bacterial monitoring, and the reliability of the screened sample fabric in actual application is ensured through further analysis of the physical characteristic data.

[0033] Specifically, the bacterial data set includes the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the bacterial average activity of each surviving bacterial species, which are used as analysis basis for screening out the preliminary screening sample fabric that passes the primary evaluation.

[0034] The number of surviving bacterial species is obtained by recording the number of different bacterial species surviving on the sample fabric through testing after bacterial culture. The bacterial survival of each surviving bacterial species is measured by quantitative detection methods, such as the plate count method. The average bacterial activity of each surviving bacterial species is evaluated by the activity staining method, which specifically involves culturing the bacterial sample to be tested on an appropriate culture medium and using specific dyes such as MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) or CCK-8 (cell counting kit-8). These dyes can enter living cells and are converted into colored products by enzymes within the cells. The staining intensity is measured by microscopic observation or using a spectrophotometer. The staining intensity is proportional to the metabolic activity of the bacteria. The staining results are quantified into specific values ​​to evaluate the average bacterial activity.

[0035] The process of screening out the sample fabrics that have passed the initial evaluation is as follows: extracting the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species in the bacterial data set; obtaining a preliminary screening evaluation index based on the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species; judging whether the preliminary screening evaluation index is greater than the preliminary screening evaluation threshold set in the database: if the preliminary screening evaluation index is greater than the preliminary screening evaluation threshold set in the database, the preliminary screening is unqualified; if the preliminary screening evaluation index is not greater than the preliminary screening evaluation threshold set in the database, the preliminary screening is qualified, and the preliminary screening sample fabrics that have passed the initial evaluation are obtained.

[0036] The initial screening evaluation threshold needs to be determined through experimental data and statistical analysis to ensure the scientificity and rationality of the screening criteria. A large number of fabric samples are collected for bacterial culture experiments to obtain bacterial data sets. The collected data are statistically analyzed, and the initial screening evaluation index of each sample is calculated. The initial screening evaluation index of all samples is statistically analyzed, and a frequency distribution graph is drawn. The mean (μ) and standard deviation (σ) of the initial screening evaluation index are calculated. Using the normal distribution theory, an appropriate standard deviation range is selected according to the distribution of the samples. Assuming 1.96 standard deviations (i.e., 95% confidence interval) are selected, the initial screening evaluation threshold can be set to μ+1.96σ.

[0037] In this embodiment, the number of surviving bacterial species, the bacterial survival rate of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species are extracted from the bacterial data set. Based on the number of surviving bacterial species, the bacterial survival rate of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species, a preliminary screening evaluation index is calculated. The preliminary screening evaluation index is compared with a preliminary screening evaluation threshold set in the database. If the preliminary screening evaluation index is greater than the threshold, the sample fails the preliminary screening. If the preliminary screening evaluation index is not greater than the threshold, the sample passes the preliminary screening, thereby obtaining a preliminary screening sample fabric that passes the initial evaluation.

[0038] Evaluation through specific bacterial data (number of species, survival rate, average activity) makes the screening process more accurate. Quantitative analysis is performed using specific initial screening evaluation indexes and set thresholds, reducing subjective judgments and increasing the objectivity and scientific nature of the evaluation. Through clear evaluation indicators and thresholds, initial screening samples that meet the requirements can be quickly screened out, saving time and resources.

[0039] Specifically, the calculation formula for the initial screening evaluation index is as follows (calculate each parameter for scalar processing and remove the unit):

[0040] ;

[0041] Where, is the initial screening assessment index, is the number of the surviving bacterial species, , is the total number of surviving bacterial species, For the The bacterial survival rate of surviving bacterial species, For the The average bacterial activity of surviving bacterial species, is a natural constant.

[0042] In this embodiment, data of different orders of magnitude can be smoothed through logarithmic transformation, so that large values ​​will not have too much impact on the results, thereby ensuring the stability of the evaluation. The formula takes into account two parameters: bacterial survival and average bacterial activity. Through multiplication and average calculation, the evaluation results can comprehensively reflect the antibacterial effects of various bacterial species. The final power operation of the natural exponent makes the evaluation index more sensitive to changes in bacterial survival and activity, thereby improving the accuracy of the evaluation.

[0043] Specifically, the physical characteristic data includes permeability data and strength data, and the permeability data and strength data serve as analysis basis for screening the final qualified sample fabrics.

[0044] Extract permeability and strength data from the initial screening sample fabrics. Permeability data includes air permeability and water permeability, while strength data refers to the mechanical strength of the fabric. Based on the permeability coefficient and strength data, the final qualified sample fabrics are screened. The performance of the sample fabrics is comprehensively evaluated based on these two data to ensure their reliability and practicality in actual applications.

[0045] The method of screening the final qualified sample fabric based on the analysis of physical property data includes: extracting permeability data, and obtaining the permeability coefficient of the pre-screened sample fabric based on the permeability data analysis, wherein the permeability data includes the air permeability and water permeability of the pre-screened sample fabric.

[0046] For the initial screening sample fabric, the air permeability of the fabric is evaluated by measuring the amount of air passing through the sample fabric within a certain period of time: the fabric sample is fixed on the air permeability tester, and the air flow through the sample is measured under constant airflow. According to the standard (such as ASTM D737), the air permeability value (unit: L / m 2 / s or other). For the initial screening sample fabric water permeability, the water permeability of the fabric is evaluated by measuring the amount of water passing through the sample fabric within a certain period of time: the fabric sample is fixed on the water permeability tester, and the amount of water passing through the sample is measured under constant water pressure. The water permeability value (unit: mm or other) is calculated according to the standard (such as AATCC 127).

[0047] Based on the permeability data (air permeability and water permeability), the permeability coefficient of the initial screening sample fabric is calculated, and the permeability coefficient is used as an evaluation standard to determine the permeability performance of the sample fabric.

[0048] The calculation formula of the permeability coefficient is as follows (each parameter is processed as a scalar before calculation and the unit is removed):

[0049] ;

[0050] Where, is the permeability coefficient, To initially screen the air permeability of the sample fabric, It is the air permeability benchmark value of the initial screening sample fabric stored in the database. is the air permeability weight factor stored in the database, To initially screen the water permeability of the sample fabric, is the water permeability benchmark value of the initial screening sample fabric stored in the database, is the permeability weight factor stored in the database.

[0051] By using benchmark values, the air and water permeability of each sample is standardized, making comparisons between different samples more scientific and reasonable. The weighting factors a and b can be used to adjust the influence ratio of air and water permeability, combining these two important parameters into a single permeability coefficient Tx, making the evaluation results more comprehensive.

[0052] The air permeability benchmark value and water permeability benchmark value are obtained by the following method: a group of standard samples with known good performance are selected as benchmark samples, and the air permeability and water permeability of the benchmark samples are tested to obtain the air permeability benchmark value. and permeability benchmark values If multiple reference samples are used, the average of their air and water permeability test results is taken as the reference value. and .

[0053] For the air permeability weight factor and the water permeability weight factor in the database, historical test data of a large number of fabric samples are collected, including the air permeability, the water permeability and the overall performance evaluation result (such as user feedback, actual use effect, etc.) of each sample, a multiple linear regression analysis method is used, the overall performance evaluation result is set as the dependent variable, the air permeability and the water permeability are set as the independent variables, a multiple linear regression model is established, and the optimal air permeability regression coefficient and the optimal water permeability regression coefficient are obtained through multiple model constructions, which respectively represent the influence degree of the air permeability and the water permeability on the overall performance evaluation result, and the optimal air permeability regression coefficient and the optimal water permeability regression coefficient are respectively used as the air permeability weight factor and the water permeability weight factor.

[0054] The strength data are extracted, and the strength coefficients of the pre-screened sample fabric are obtained based on the analysis of the strength data, including the tear strength of the pre-screened sample fabric, the bursting strength of the pre-screened sample fabric and the wear resistance strength of the pre-screened sample fabric.

[0055] For the tear strength of the pre-screened sample fabric, the sample is cut according to the standard (such as ASTM D2261 or ISO 13937), usually a right-angle or tongue-shaped sample, a tear strength tester is used, such as Elmendorf tear tester, the sample is fixed on the tester, a force is applied to make the sample tear, and the force required for tearing is recorded as the tear strength.

[0056] For the bursting strength of the pre-screened sample fabric, a circular or square sample is prepared according to the standard (such as ASTM D3786 or ISO 13938), a hydraulic or pneumatic bursting strength tester is used, the sample is fixed on the tester, a uniform pressure is applied until the sample breaks, and the pressure at the time of breaking is recorded as the bursting strength.

[0057] For the wear resistance strength of the pre-screened sample fabric, the sample is prepared according to the standard (such as ASTM D4966 or ISO 12947), a wear resistance tester is used, such as Martindale wear tester or Taber wear tester, the sample is fixed on the tester, the friction medium (such as sandpaper or standard fabric) is set, the test is started, and the number of friction times when the sample is damaged is recorded as the wear resistance strength.

[0058] The calculation formula of the strength coefficient is as follows (the pure quantity of each parameter is processed before calculation, and the unit is removed):

[0059] ;

[0060] In the formula, is the strength coefficient, is the tear strength of the pre-screened sample fabric, is the bursting strength of the pre-screened sample fabric, is the wear resistance strength of the pre-screened sample fabric, is a natural constant.

[0061] By taking the logarithmic average of tear strength, bursting strength, and abrasion resistance, the three strength indicators are comprehensively considered to ensure a comprehensive assessment. The logarithmic transformation can narrow the wide range of values ​​to a smaller range, reducing the impact of outliers and making the results more stable and reliable. The final exponential transformation can amplify the differences between different samples, making the distinction between high-performance and low-performance samples more obvious.

[0062] By taking the average value of the logarithms, the balance of tear strength, bursting strength and abrasion resistance is comprehensively considered to avoid the excessive influence of a single strength indicator on the final evaluation results.

[0063] Based on the permeability coefficient and the strength coefficient, the physical characteristic evaluation index of the initial screening sample fabric is obtained; it is judged whether the physical characteristic evaluation index is greater than the physical characteristic evaluation threshold set in the database: if the physical characteristic evaluation index is greater than the physical characteristic evaluation threshold set in the database, the evaluation is qualified, and the final qualified sample fabric is obtained; if the physical characteristic evaluation index is not greater than the physical characteristic evaluation threshold set in the database, the evaluation is unqualified.

[0064] The permeability coefficient Tx and the strength coefficient Q are combined to calculate the physical characteristic evaluation index of the initial screening sample fabric. The physical characteristic evaluation index is calculated and compared with the physical characteristic evaluation threshold set in the database. If the physical characteristic evaluation index is greater than the threshold, the sample evaluation is qualified; otherwise, the evaluation is unqualified.

[0065] For the physical characteristic evaluation threshold set in the database, a large amount of historical data is collected, including the permeability coefficient, strength coefficient and their corresponding actual performance (such as user feedback, actual usage effects, etc.), and the collected data is statistically analyzed. A distribution map of the physical characteristic evaluation index is drawn, and a reasonable confidence interval (such as a 95% confidence interval) is selected. The threshold is set and the quantile method is used to select 95% of the sample values ​​as the threshold based on the distribution of the physical characteristic evaluation index.

[0066] By collecting and analyzing historical data, setting physical characteristic evaluation thresholds, and conducting a comprehensive evaluation based on the permeability coefficient and strength coefficient, we can comprehensively and accurately screen out sample fabrics that meet the requirements.

[0067] By combining the two important parameters of permeability and strength, the comprehensiveness and accuracy of the evaluation results are ensured.

[0068] The calculation formula of the physical characteristics evaluation index is as follows:

[0069] ;

[0070] Where, is the physical characteristics evaluation index, is the permeability coefficient, is the weight factor of the permeability coefficient stored in the database, is the strength coefficient, is the weighting factor of the intensity coefficient stored in the database.

[0071] For the weight factors of the permeability coefficient and the strength coefficient in the database, a large number of historical test data of fabric samples were collected, including the permeability coefficient, strength coefficient and physical performance evaluation results of each sample (such as user feedback, actual usage effect, etc.). The multiple linear regression analysis method was used, and the physical performance evaluation results were set as the dependent variables, the permeability coefficient and the strength coefficient. A multiple linear regression model was established to obtain the permeability coefficient regression coefficient and the strength coefficient regression coefficient, which respectively represent the degree of influence of the permeability coefficient and the strength coefficient on the physical performance evaluation results. The permeability coefficient regression coefficient and the strength coefficient regression coefficient were used as the permeability coefficient weight factor and the strength coefficient weight factor, respectively.

[0072] Table 1 Calculation data of physical characteristics evaluation index

[0073]

[0074] In the table, the permeability and strength of sample 1 are both low, and the physical characteristics evaluation index is 1.32, indicating that its comprehensive physical properties are relatively low. The permeability and strength of sample 2 are slightly improved, and the physical characteristics evaluation index is 1.62, indicating that its comprehensive physical properties have improved. The permeability and strength of sample 3 are further improved, and the physical characteristics evaluation index reaches 1.88, indicating that its comprehensive physical properties are good. The permeability of sample 4 is higher than that of sample 3, but the strength is slightly lower, and the physical characteristics evaluation index is still 1.88, indicating that its comprehensive physical properties are comparable to those of sample 3. The permeability of sample 5 is slightly lower than that of sample 4, but the strength is higher, and the physical characteristics evaluation index is 1.90, indicating that its comprehensive physical properties are the best among these groups of data.

[0075] Figure 2 This is the causal relationship between the physical characteristic evaluation index (indicated by the "-" line) and the permeability coefficient (indicated by the " / " line) and the strength coefficient (indicated by the "\" line). It can be seen from the figure that the permeability of sample 5 is slightly lower than that of sample 4, but the strength is higher. The physical characteristic evaluation index is 1.90, indicating that its comprehensive physical properties are the best among these sets of data.

[0076] Specifically, before determining the fabric antibacterial evaluation pass rate based on the ratio of the sample fabric to the sample fabric to be evaluated, the method further includes: obtaining a bacterial data set and physical characteristic data of the finally qualified sample fabric; obtaining a preliminary screening evaluation index based on the bacterial data set of the finally qualified sample fabric; obtaining a physical characteristic evaluation index based on the physical characteristic data of the finally qualified sample fabric; obtaining a comprehensive evaluation index of the finally qualified sample fabric based on the preliminary screening evaluation index and the physical characteristic evaluation index; and comparing the comprehensive evaluation index with a grade classification threshold stored in a database: if the comprehensive evaluation index is greater than or equal to the grade classification threshold stored in the database, the corresponding finally qualified sample fabric is classified as a secondary fabric; if the comprehensive evaluation index is less than the grade classification threshold stored in the database, the corresponding finally qualified sample fabric is classified as a high-grade fabric.

[0077] The comprehensive evaluation index is obtained as follows: taking the inverse of the physical characteristic evaluation index; performing weighted summation on the inverse of the initial screening evaluation index and the physical characteristic evaluation index to obtain the comprehensive evaluation index.

[0078] Obtain the bacterial data set and physical characteristic data of the final qualified samples, use these data to calculate the initial screening evaluation index and physical characteristic evaluation index, calculate the inverse of the physical characteristic evaluation index, combine it with the initial screening evaluation index, and calculate the comprehensive evaluation index through weighted summation. The weight factors in the database can be adjusted according to the actual application and requirements of the fabric.

[0079] The calculated comprehensive evaluation index is compared with the grade classification threshold stored in the database, and the sample fabric is classified as secondary or high-grade fabric based on the comparison result.

[0080] By combining the evaluation of bacterial data and physical characteristic data, we ensure that the antibacterial performance and physical properties of the fabric meet the standards. The use of inverse and weighted summation methods can adjust the influence of each indicator in the final evaluation and increase the accuracy of the evaluation. The grade division threshold is an objective standard, which provides a clear basis for the classification of fabrics and improves the transparency and replicability of the operation.

[0081] For the grading threshold, collect comprehensive evaluation index data for a large number of fabric samples and select an appropriate quantile as the grading threshold. For example, the 50th percentile (median) can be selected as the dividing point between inferior and superior fabrics. Based on the sorted comprehensive evaluation index data, calculate the selected quantile value and use it as the grading threshold.

[0082] The calculation formula for the comprehensive evaluation index is as follows:

[0083] ;

[0084] Where, is a comprehensive evaluation index. Stored in the database The weight factor, Stored in the database The weight factor of .

[0085] for and ,By analyzing the relationship between the initial screening evaluation index and ,physical characteristic evaluation index in the historical data set and the ,final performance of the product, correlation analysis or regression ,analysis can be used to determine the degree of influence of these ,indices on the final result, and the regression coefficient can be directly used as ,a weighting factor.

[0086] Table 2 Calculation data of comprehensive evaluation index

[0087]

[0088] Calculation of the comprehensive evaluation index showed that sample 3 had the highest evaluation index, meaning it performed worst in terms of bacterial resistance and physical characteristics, and sample 2 had the lowest evaluation index, meaning it performed best in terms of bacterial resistance and physical characteristics.

[0089] A fabric antibacterial performance evaluation system is used for the above-mentioned fabric antibacterial performance evaluation system, such as Figure 3 As shown, it includes a bacterial data set acquisition module, a primary screening evaluation module, a final screening evaluation module and a pass rate determination module, wherein: the bacterial data set acquisition module is used to obtain a bacterial data set after bacterial culture of the sample fabric to be evaluated; the primary screening evaluation module is used to perform an initial evaluation of the sample fabric to be evaluated based on the bacterial data set, and screen out the primary screening sample fabric that passes the initial evaluation; the final screening evaluation module is used to obtain the physical characteristic data of the primary screening sample fabric, and screen the final qualified sample fabric based on the physical characteristic data analysis; the pass rate determination module is used to determine the pass rate of the fabric antibacterial evaluation based on the ratio of the sample fabric to the sample fabric to be evaluated.

[0090] The bacterial dataset acquisition module is responsible for acquiring bacterial datasets of samples to be evaluated that have undergone bacterial culture, including key indicators such as bacterial species, quantity, and growth rate, to provide basic data for the initial evaluation. The initial screening evaluation module conducts an initial evaluation based on the bacterial dataset, assessing the basic antimicrobial effect of the fabric, screening out fabrics that meet basic antimicrobial standards, and providing candidate samples for further detailed analysis. The final screening evaluation module conducts a more in-depth analysis of the physical properties of the samples after the initial screening, such as strength, breathability, and wear resistance. This step is to ensure that the selected fabrics also meet high standards in terms of physical properties. The pass rate determination module determines the pass rate of the fabric's antimicrobial performance based on the ratio of the comprehensive evaluation results (including bacterial resistance and physical properties) to existing standards or samples to be evaluated. This ratio reflects the relative performance of the new fabric compared to the standard or existing product.

[0091] An electronic device includes: a processor and a memory, wherein computer program instructions are stored in the memory, and when the computer program instructions are executed by the processor, the processor executes the above-mentioned fabric antibacterial performance evaluation method.

[0092] A computer-readable storage medium is used to store a program, which, when executed by a processor, implements the above-mentioned method for evaluating the antibacterial performance of fabrics.

[0093] In summary, this application has at least the following effects:

[0094] By culturing the sample fabrics to be evaluated to obtain a bacterial dataset, and conducting a preliminary screening based on this dataset, the fabric's antimicrobial effect can be preliminarily determined. The physical characteristic data of the pre-screened sample fabrics is then obtained, and further analysis and screening are conducted based on this physical characteristic data to ultimately select qualified sample fabrics. Finally, the fabric antimicrobial evaluation pass rate is determined based on the ratio of the sample fabric to the sample fabric to be evaluated, ensuring a more comprehensive, accurate, and reliable evaluation result. This effectively addresses the issues of incomplete data and insufficient accuracy in the fabric antimicrobial effect evaluation process, and enhances the scientific nature and objectivity of the antimicrobial performance evaluation.

[0095] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0096] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0097] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0098] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A method for evaluating the antibacterial properties of fabrics, characterized in that: The following steps are involved: Obtain a bacterial dataset after bacterial culture of the sample fabric to be evaluated; Conduct an initial evaluation of the sample fabrics to be evaluated based on the bacterial dataset, and select the initial screening sample fabrics that pass the initial evaluation; The process of screening out the initial sample fabrics that pass the initial evaluation is as follows: Extract the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species in the bacterial data set; The primary screening evaluation index was obtained based on the number of surviving bacterial species, the bacterial survival of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species; Determine whether the initial screening evaluation index is greater than the initial screening evaluation threshold set in the database: If the initial screening evaluation index is greater than the initial screening evaluation threshold set in the database, the initial screening fails; If the multiple initial screening evaluation index is not greater than the initial screening evaluation threshold set in the database, the initial screening is qualified, and the initial screening sample fabric that has passed the initial evaluation is obtained; The calculation formula of the initial screening evaluation index is as follows: ； Where, is the initial screening assessment index, is the number of the surviving bacterial species, , is the total number of surviving bacterial species, For the The bacterial survival rate of surviving bacterial species, For the The average bacterial activity of surviving bacterial species, is a natural constant; Obtain the physical characteristic data of the initial screening sample fabrics, and screen the final qualified sample fabrics based on the physical characteristic data analysis; The screening of the final qualified sample fabric based on the analysis of physical property data includes: Extracting permeability data, and obtaining a permeability coefficient of the primary screening sample fabric based on the permeability data analysis, wherein the permeability data includes air permeability and water permeability of the primary screening sample fabric; Extracting strength data, and obtaining a strength coefficient of the pre-screened sample fabric based on the strength data analysis, wherein the strength coefficient includes the tearing strength of the pre-screened sample fabric, the bursting strength of the pre-screened sample fabric, and the abrasion resistance of the pre-screened sample fabric; The physical characteristic evaluation index of the primary screening sample fabric is obtained based on the permeability coefficient and strength coefficient; Determine whether the physical feature evaluation index is greater than the physical feature evaluation threshold set in the database: If the physical feature evaluation index is greater than the physical feature evaluation threshold set in the database, the evaluation is qualified and the final qualified sample fabric is obtained; If the physical feature evaluation index is not greater than the physical feature evaluation threshold set in the database, the evaluation is unqualified; The calculation formula of the physical characteristic evaluation index is as follows: ; Where, is the physical characteristics evaluation index, is the permeability coefficient, is the weight factor of the permeability coefficient stored in the database, is the strength coefficient, is the weight factor of the intensity coefficient stored in the database; The pass rate of the fabric antibacterial evaluation is determined based on the ratio of the sample fabric to the sample fabric to be evaluated.

2. The method for evaluating the antibacterial properties of fabrics according to claim 1, characterized in that: The bacterial data set includes the number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species. The number of surviving bacterial species, the bacterial survival amount of each surviving bacterial species, and the average bacterial activity of each surviving bacterial species serve as the analysis basis for screening out the initial screening sample fabrics that pass the initial evaluation.

3. The method for evaluating the antibacterial properties of fabrics according to claim 1, wherein: The physical characteristic data include permeability data and strength data, and the permeability data and strength data serve as analysis basis for screening the final qualified sample fabrics.

4. The method for evaluating the antibacterial properties of fabrics according to claim 1, wherein: Before determining the fabric antibacterial evaluation qualification rate based on the ratio of the sample fabric to the sample fabric to be evaluated, the method further includes: Obtain the bacterial data set and physical characteristic data of the final qualified sample fabric; Obtaining a preliminary screening evaluation index based on the bacterial data set of the final qualified sample fabric; Obtaining a physical characteristic evaluation index based on the physical characteristic data of the final qualified sample fabric; Based on the initial screening evaluation index and physical characteristics evaluation index, the final comprehensive evaluation index of qualified sample fabrics is obtained; Compare the comprehensive evaluation index with the grade classification thresholds stored in the database: If the comprehensive evaluation index is greater than or equal to the grade classification threshold stored in the database, the corresponding final qualified sample fabric is classified as secondary fabric; If the comprehensive evaluation index is less than the grade classification threshold stored in the database, the corresponding final qualified sample fabric is classified as high-grade fabric.

5. The method for evaluating the antibacterial properties of fabrics according to claim 4, characterized in that: The comprehensive evaluation index is obtained as follows: Take the inverse of the physical characteristics evaluation index; The weighted sum of the initial screening evaluation index and the inverse of the physical characteristic evaluation index is taken to obtain the comprehensive evaluation index.

6. A fabric antibacterial performance evaluation system, used in a fabric antibacterial performance evaluation method according to any one of claims 1 to 5, characterized in that: It includes a bacterial data set acquisition module, a primary screening evaluation module, a final screening evaluation module, and a pass rate determination module, among which: The bacterial data set acquisition module is used to obtain a bacterial data set after bacterial culture of the sample fabric to be evaluated; The primary screening evaluation module is used to perform a primary evaluation on the sample fabrics to be evaluated based on the bacterial data set, and screen out the primary screening sample fabrics that pass the initial evaluation; The final screening evaluation module is used to obtain the physical characteristic data of the initial screening sample fabrics, and screen the final qualified sample fabrics based on the physical characteristic data analysis; The qualified rate determination module is used to determine the qualified rate of the fabric antibacterial evaluation based on the ratio of the sample fabric to the sample fabric to be evaluated.

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