Method for detecting lasting antibacterial performance of antibacterial non-woven fabric

Abstract

The invention discloses a method for detecting the lasting antibacterial performance of an antibacterial non-woven fabric, and relates to the technical field of antibacterial performance detection. The method comprises the following steps: carrying out multi-dimensional data acquisition on the antibacterial non-woven fabric to obtain a multi-dimensional data set; extracting a plurality of bacterial colony image features; performing antibacterial analysis on the surface of the antibacterial non-woven fabric on the plurality of environmental data to generate a plurality of antibacterial correlation coefficients; carrying out antibacterial performance attenuation modeling, constructing an antibacterial attenuation model, carrying out antibacterial performance trend analysis, and drawing an antibacterial performance attenuation trend curve; carrying out antibacterial contribution calculation according to the antibacterial performance attenuation trend curve to obtain an antibacterial contribution degree, carrying out antibacterial durability analysis on the antibacterial non-woven fabric to obtain a durable antibacterial analysis result, and generating an antibacterial suggestion. The technical problem that in the prior art, the evaluation dimension is single in the antibacterial property durability detection process of the antibacterial non-woven fabric, and consequently the accuracy of the detection result is insufficient is solved, and the technical effect of improving the comprehensiveness and accuracy of antibacterial property detection is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of antibacterial performance detection, and in particular to a method for detecting the durable antibacterial performance of an antibacterial nonwoven fabric. Background Art

[0002] As people's requirements for health and hygiene continue to increase, antibacterial non-woven fabrics, as a material with both antibacterial properties and practicality, are widely used in medical, daily life and industrial fields. However, the detection methods for the antibacterial properties of antibacterial non-woven fabrics in the prior art mostly focus on the evaluation of initial performance, and lack a dynamic evaluation of its antibacterial properties over time and environmental changes. This not only leads to insufficient accuracy in the analysis of the persistence of antibacterial properties, but also makes it difficult to provide a scientific basis for the improvement and optimization of materials. In addition, the detection process failed to effectively integrate multidimensional data (such as environmental parameters, colony distribution characteristics, etc.) for comprehensive analysis, and it was impossible to fully reveal the law of antibacterial performance attenuation. Summary of the invention

[0003] The present application provides a method for detecting the durable antibacterial properties of antibacterial nonwoven fabrics, which solves the technical problem in the prior art that the evaluation dimension is single during the detection of the durable antibacterial properties of antibacterial nonwoven fabrics, resulting in insufficient accuracy of the detection results.

[0004] In view of the above problems, the present application provides a method for detecting the durable antibacterial properties of antibacterial non-woven fabrics.

[0005] The present application provides a method for detecting the durable antibacterial performance of an antibacterial nonwoven fabric, the method comprising:

[0006] Multi-dimensional data collection is performed on the antibacterial non-woven fabric through a data sensor network to obtain a multi-dimensional data set, which includes multiple environmental data and multiple surface colony image data of the antibacterial non-woven fabric; based on the multiple environmental data, the multiple surface colony image data are traversed and analyzed to extract multiple colony image features; according to the multiple colony image features, the antibacterial surface of the antibacterial non-woven fabric is analyzed on the multiple environmental data to generate multiple antibacterial correlation coefficients; based on the multiple antibacterial correlation coefficients, antibacterial performance attenuation modeling is performed to construct an antibacterial attenuation model, and the trend analysis of antibacterial performance is performed through the antibacterial attenuation model to draw an antibacterial performance attenuation trend curve; according to the antibacterial performance attenuation trend curve, antibacterial contribution calculation is performed to obtain antibacterial contribution degree, and antibacterial persistence analysis is performed on the antibacterial non-woven fabric according to the antibacterial contribution degree to obtain a persistent antibacterial analysis result, and antibacterial recommendations are generated.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] First, multi-dimensional data collection is performed on the antibacterial non-woven fabric through a data sensing network to obtain a multi-dimensional data set, which contains multiple environmental data and multiple surface colony image data of the antibacterial non-woven fabric. Then, based on the multiple environmental data, the multiple surface colony image data are traversed and analyzed to extract multiple colony image features. Further, the antibacterial analysis of the surface of the antibacterial non-woven fabric is performed on the multiple environmental data according to the multiple colony image features to generate multiple antibacterial correlation coefficients. Then, the antibacterial performance attenuation modeling is performed based on the multiple antibacterial correlation coefficients, and an antibacterial attenuation model is constructed. The trend analysis of the antibacterial performance is performed through the antibacterial attenuation model, and the antibacterial performance attenuation trend curve is drawn. Finally, the antibacterial contribution is calculated according to the antibacterial performance attenuation trend curve to obtain the antibacterial contribution degree, and the antibacterial persistence analysis of the antibacterial non-woven fabric is performed according to the antibacterial contribution degree to obtain the persistent antibacterial analysis result, and the antibacterial suggestion is generated. The technical problem that the evaluation dimension is single in the process of the antibacterial performance persistence detection of the antibacterial non-woven fabric in the prior art, resulting in insufficient accuracy of the detection result, is solved, and the technical effect of improving the comprehensiveness and accuracy of the antibacterial performance detection is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic diagram of a process for detecting the durable antibacterial properties of an antibacterial nonwoven fabric provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of the process of extracting multiple colony image features in the method for detecting the durable antibacterial properties of antibacterial non-woven fabrics provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application solves the technical problem in the prior art that the evaluation dimension is single during the detection of the persistence of antibacterial properties of antibacterial non-woven fabrics, resulting in insufficient accuracy of the detection results, by providing a method for detecting the persistent antibacterial properties of antibacterial non-woven fabrics.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or are inherent to these processes, methods, products or devices.

[0015] Examples, such as Figure 1 As shown, the embodiment of the present application provides a method for detecting the durable antibacterial performance of an antibacterial non-woven fabric, wherein the method comprises:

[0016] Multidimensional data collection is performed on the antibacterial nonwoven fabric through a data sensor network to obtain a multidimensional data set, which contains multiple environmental data and multiple surface colony image data of the antibacterial nonwoven fabric.

[0017] According to the application scenarios of antibacterial non-woven fabrics, appropriate sensors are selected for deployment. These sensors may include temperature sensors, humidity sensors, light sensors, gas sensors, etc., which are used to monitor key parameters such as temperature, humidity, light intensity, gas composition, etc. of the environment in which the antibacterial non-woven fabrics are located; use image acquisition equipment such as high-resolution cameras or microscopes to regularly photograph the surface of the antibacterial non-woven fabrics to obtain colony image data; integrate the collected environmental data and surface colony image data to construct a multidimensional data set. The multidimensional data set contains a variety of data on antibacterial non-woven fabrics at different times and under different environmental conditions, providing a rich information basis for subsequent analysis and evaluation.

[0018] The plurality of surface colony image data are traversed and analyzed based on the plurality of environmental data to extract a plurality of colony image features.

[0019] Based on the collected multiple environmental data, the surface colony image data are analyzed one by one, and multiple colony image features are extracted in combination with the changes in the environmental data. These colony image features include but are not limited to the area, morphology, distribution density, color change, etc. of the colony, which are used to comprehensively characterize the growth state of the colony and its response to environmental conditions, providing key support for the antibacterial performance analysis.

[0020] Furthermore, if Figure 2 As shown, based on the multiple environmental data, the multiple surface colony image data are traversed and analyzed to extract multiple colony image features, and the method includes:

[0021] Cluster analysis is performed on the multiple surface colony image data according to the multiple environmental data to determine multiple colony image groups, each of which corresponds to one environmental data; based on the multiple environmental data, multiple colony image groups are traversed to perform morphological analysis to determine the morphological characteristics of colony distribution; based on the multiple environmental data, multiple colony image groups are traversed to perform density analysis to determine the colony density characteristics; according to the colony distribution morphological characteristics and the colony density characteristics, image enhancement is performed on the multiple surface colony image data to generate multiple surface colony enhanced images; the multiple surface colony enhanced images are traversed to perform feature analysis to obtain multiple feature vectors, and based on the multiple feature vectors, the colony distribution morphological characteristics and the colony density characteristics are integrated by dimensionality reduction to generate the multiple colony image features.

[0022] Specifically, according to the collected multiple environmental data (such as temperature, humidity, light, etc.), a clustering algorithm (such as K-means, hierarchical clustering, etc.) is used to perform cluster analysis on multiple surface colony image data, and the colony images are grouped according to different conditions of the environmental data (such as grouping the colony images according to environmental data such as temperature and humidity), and multiple colony image groups are generated, wherein each colony image group corresponds to a specific environmental data. For example, the colony images are grouped according to humidity, and are divided into high humidity (humidity>80%), medium humidity (humidity 50%~80%), and low humidity (humidity<50%). Traverse multiple colony image groups, perform morphological analysis on the colony images through image processing algorithms (such as edge detection), extract colony distribution morphological features, including but not limited to the boundary morphology, irregularity, ductility, etc. of the colony, so as to quantify the morphological changes of the colony; perform density analysis on the clustered multiple colony image groups, and determine the colony density features of the colonies in the image through statistics, such as the number of colonies per unit area, the degree of colony overlap, etc., so as to evaluate the aggregation degree of the colonies; perform image enhancement processing on multiple surface colony image data according to the extracted colony distribution morphological features and colony density features, and generate multiple surface colony enhanced images using enhancement algorithms (such as edge detection, contrast adjustment, etc.), enhance the detailed features of the images, and make them more suitable for subsequent feature analysis; traverse multiple surface colony enhanced images, extract key features, and form multiple feature vectors; perform dimensionality reduction integration on the extracted colony distribution morphological features and colony density features through feature dimensionality reduction algorithms (such as principal component analysis or t-SNE), remove redundant information, retain important features, and finally generate the multiple colony image features for subsequent antibacterial performance analysis.

[0023] Further, the plurality of surface colony enhanced images are traversed for feature analysis to obtain a plurality of feature vectors, and based on the plurality of feature vectors, the colony distribution morphological features and the colony density features are integrated by dimensionality reduction to generate the plurality of colony image features, the method comprising:

[0024] The plurality of surface colony enhanced images are traversed to perform edge detection, and the plurality of surface colony enhanced images are segmented according to the edge detection results to determine a plurality of colony area images; colony feature combination transformation is performed according to the plurality of colony area images to generate a plurality of feature vectors; the plurality of feature vectors are normalized to generate a standard feature vector; principal component analysis is used in combination with the standard feature vector to reduce the dimension of the colony distribution morphology feature and the colony density feature to generate a colony distribution morphology reduction feature matrix and a colony density reduction feature matrix; principal component features are screened and determined according to the standard feature vector, and the colony distribution morphology reduction feature matrix and the colony density reduction feature matrix are associated and integrated based on the principal component features to obtain the plurality of colony image features.

[0025] Specifically, multiple surface colony enhanced images are traversed, and an edge detection algorithm (such as Canny or Sobel algorithm) is applied to each image to extract the edge features of the colony, that is, to identify the boundary between the colony and the background; according to the edge detection result, the surface colony enhanced image is segmented to generate multiple colony area images, and the independent areas of the colony and their distribution boundaries are clarified; for multiple colony area images, key features (such as morphological features, texture features, color features, etc.) are extracted, and the extracted multiple features are combined and transformed to generate multiple feature vectors, which represent the feature information of each colony area in numerical form; the generated multiple feature vectors are normalized, and the feature values ​​are mapped to a standardized range (such as [0,1] or [-1,1]) to generate standard feature vectors.

[0026] The principal component analysis (PCA) was used to reduce the dimensionality of the standard feature vectors, including calculating the covariance matrix of the feature vectors; extracting eigenvalues ​​and eigenvectors, and selecting principal components with a large contribution rate; reducing the dimensionality of the colony distribution morphology and colony density characteristics based on the principal components, and generating the colony distribution morphology reduction feature matrix and the colony density reduction feature matrix respectively. According to the principal component features generated by the dimensionality reduction and the contribution rate of the standard feature vectors, the principal component features with high representation ability were selected; based on the selected principal component features, the colony distribution morphology reduction feature matrix and the colony density reduction feature matrix were associated and integrated; multiple colony image features were generated through the integration operation to fully reflect the distribution morphology and density characteristics of the colonies.

[0027] The antibacterial analysis of the surface of the antibacterial non-woven fabric is performed on the multiple environmental data according to the multiple colony image features to generate multiple antibacterial correlation coefficients.

[0028] The extracted multiple colony image features are matched with the corresponding environmental data (such as temperature, humidity, light intensity, etc.) to establish a mapping relationship between colony features and environmental conditions; through statistical analysis methods (such as regression analysis, correlation analysis, etc.), the response law of colony image features (such as colony density, morphological distribution, etc.) with changes in environmental data is evaluated, and the impact of environmental conditions on colony changes is quantified; based on the mapping relationship between colony image features and environmental data, correlation calculation methods (such as Pearson correlation coefficient or Spearman correlation coefficient) are used to generate multiple antibacterial correlation coefficients to characterize the intensity of the influence of environmental conditions on the antibacterial properties of antibacterial non-woven fabrics.

[0029] Furthermore, the antibacterial analysis of the surface of the antibacterial non-woven fabric is performed on the multiple environmental data according to the multiple bacterial colony image features to generate multiple antibacterial correlation coefficients, and the method includes:

[0030] According to the colony distribution morphology dimensionality reduction feature matrix and the colony density dimensionality reduction feature matrix, a multivariate regression analysis is performed on the multiple environmental data to obtain multivariate regression coefficients; based on the multivariate regression coefficients, an antibacterial evaluation of the antibacterial non-woven fabric surface is performed on the multiple environmental data to obtain a feature impact score; according to the feature impact score, the multiple environmental data are arranged in descending order to generate an environmental impact sequence; based on the environmental impact sequence, variable screening is performed on the multiple environmental data, and environmental impact variables are determined for antibacterial calculation to generate the multiple antibacterial correlation coefficients.

[0031] Preferably, a multiple regression analysis method (such as linear regression, ridge regression, lasso regression, etc.) is used, and the dimensionality reduction feature matrix of the colony distribution morphology and the dimensionality reduction feature matrix of the colony density are used as independent variables, and the environmental data are used as dependent variables. Regression analysis is performed to calculate the influence of each environmental variable on the colony characteristics, and the multiple regression coefficient is obtained to quantify the explanatory power of the environmental data on the changes in colony characteristics; based on the multiple regression coefficient, the contribution of each environmental variable to the antibacterial performance of the antibacterial non-woven fabric surface is evaluated, that is, by calculating the absolute value of the regression coefficient of each variable or the standardized regression coefficient, the degree of influence of each environmental variable on the antibacterial performance of the antibacterial non-woven fabric surface is obtained, that is, the characteristic impact score; multiple environmental data are arranged in descending order according to the characteristic impact score to generate an environmental impact sequence; based on the environmental impact sequence, the environmental data is screened for variables, and the environmental impact variables that have a significant impact on the antibacterial performance are determined by setting a threshold (such as the characteristic impact score reaches a certain level).

[0032] The antibacterial performance is calculated by using the screened environmental influencing variables in combination with the colony characteristics. Specifically, the screened environmental variables (such as temperature, humidity, etc.) are matched with the colony characteristics (such as the colony distribution morphology dimension reduction feature matrix, the colony density dimension reduction feature matrix) to form an environmental variable-colony feature combination; the antibacterial performance index is defined according to actual needs, such as the inhibition rate (the reduction ratio of the colony area or density); the antibacterial performance calculation model is constructed based on the environmental variable-colony feature combination, and the linear regression method can be used to quantify the influence of environmental variables on the changes in colony characteristics. For example, Among them, P is the antibacterial performance value, x i is the combination of environmental variables and colony characteristics, β i is the linear regression coefficient; the antibacterial performance value is used to perform correlation analysis (such as Pearson correlation coefficient or Spearman correlation coefficient) on each pair of environmental variables and colony characteristics, quantify the impact of environmental variables on colony changes, and generate antibacterial correlation coefficients.

[0033] Based on the multiple antibacterial correlation coefficients, antibacterial performance attenuation modeling is performed to construct an antibacterial attenuation model, and the antibacterial performance trend analysis is performed through the antibacterial attenuation model to draw an antibacterial performance attenuation trend curve.

[0034] According to the antibacterial correlation coefficient, an antibacterial attenuation model is constructed. Specifically, assuming that the antibacterial performance decays with time (or changes in environmental conditions), it can usually be expressed as P(t) = P0e -kt , where P(t) is the antibacterial performance value at time t, P0 is the initial antibacterial performance value, and k is the attenuation coefficient. After the antibacterial attenuation model is constructed, the trend analysis of the antibacterial performance is performed; by simulating the antibacterial performance attenuation in different time periods or under different environmental conditions, the trend of the antibacterial performance changing with time or environment is calculated; based on the antibacterial attenuation model and the results of the trend analysis, the antibacterial performance attenuation trend curve is drawn, which reflects the changing trend of the antibacterial performance changing with time or environmental conditions.

[0035] Furthermore, the antibacterial performance trend analysis is performed using the antibacterial attenuation model to draw an antibacterial performance attenuation trend curve, and the method includes:

[0036] Performing short-term analysis according to the multiple antibacterial correlation coefficients through the antibacterial attenuation model to determine multiple short-term antibacterial groups; performing long-term analysis according to the multiple antibacterial correlation coefficients through the antibacterial attenuation model to determine multiple long-term antibacterial groups; performing attenuation analysis on the multiple short-term antibacterial groups based on the half-life time, and generating a short-term antibacterial performance attenuation trend curve according to the first analysis result; performing attenuation analysis on the multiple long-term antibacterial groups based on the half-life time, and generating a long-term antibacterial performance attenuation trend curve according to the second analysis result; cross-validating the short-term antibacterial performance attenuation trend curve with the long-term antibacterial performance attenuation trend curve, and drawing the antibacterial performance attenuation trend curve according to the validation result.

[0037] Through the antibacterial decay model, the antibacterial performance is analyzed in the short term according to multiple antibacterial correlation coefficients. Specifically, according to the short-term changes in environmental data and colony characteristics, multiple short-term antibacterial groups are determined. These groups represent the changes in antibacterial performance in a short period of time. Short-term analysis can focus on the decay process of antibacterial performance in the initial stage.

[0038] Through the antimicrobial decay model, the antimicrobial performance was analyzed in the long term based on multiple antimicrobial correlation coefficients. At this time, considering the long-term stability of environmental data and colony characteristics and their long-term impact on antimicrobial performance, multiple long-term antimicrobial groups were determined, which represent the trends and laws of antimicrobial performance decay under long-term environmental conditions.

[0039] Based on the half-life time, attenuation analysis was performed on multiple short-term antibacterial groups; the half-life time represents the time required for the antibacterial performance to decay to half of the initial value; by analyzing the decay process of the short-term antibacterial group, a short-term antibacterial performance decay trend curve was generated according to the first analysis result, showing the changing trend of the antibacterial performance in a short period of time.

[0040] Also based on the half-life time, attenuation analysis is performed on multiple long-term antibacterial groups; under long-term conditions, the attenuation of antibacterial performance may be affected by other factors (such as environmental changes, material aging, etc.); based on the second analysis results, a long-term antibacterial performance attenuation trend curve is generated to show the attenuation characteristics of the antibacterial performance during long-term use.

[0041] The short-term antibacterial performance attenuation trend curve is cross-validated with the long-term antibacterial performance attenuation trend curve. Cross-validation can ensure the consistency of short-term and long-term analysis results, and evaluate the accuracy of the antibacterial performance attenuation model by comparing the differences between the two. Finally, based on the validation results, the final antibacterial performance attenuation trend curve is drawn to present the overall trend of antibacterial performance changes over time or environmental conditions.

[0042] The antibacterial contribution is calculated according to the antibacterial performance attenuation trend curve to obtain the antibacterial contribution degree, the antibacterial durability of the antibacterial non-woven fabric is analyzed according to the antibacterial contribution degree to obtain the durable antibacterial analysis result, and the antibacterial recommendation is generated.

[0043] The antibacterial contribution indicates the contribution of the antibacterial performance to the antibacterial effect in a specific time period, and the changes in antibacterial performance under different environmental conditions and time periods can be calculated by integration or accumulation.

[0044] According to the antibacterial contribution, the antibacterial persistence of the antibacterial non-woven fabric is analyzed in order to evaluate the duration and decay rate of the antibacterial performance of the antibacterial non-woven fabric in practical applications; by analyzing the changes in the antibacterial contribution, the persistence of the antibacterial performance is determined, and the decay cycle of the antibacterial performance is predicted; based on the results of the antibacterial persistence analysis, antibacterial recommendations are generated, such as material optimization and improvement of environmental adaptability.

[0045] Furthermore, the antibacterial contribution is calculated according to the antibacterial performance attenuation trend curve to obtain the antibacterial contribution degree, and the method includes:

[0046] Retrieve the historical colony data record log, traverse the historical colony data record log for time series identification, and determine the colony generation time series; extract the initial inhibition rate based on the antibacterial performance attenuation trend curve, perform colony accumulation calculation according to the initial inhibition rate combined with the colony generation time series, and determine the colony accumulation area data; perform time segmentation calculation according to the colony accumulation area data combined with the antibacterial performance attenuation trend curve to obtain the antibacterial efficiency of multiple time periods; integrate the antibacterial efficiency of the multiple time periods according to the colony generation time series to generate the antibacterial contribution of the antibacterial non-woven fabric.

[0047] Specifically, the historical colony data record log is retrieved to obtain the colony growth data at different times. These data usually include information such as colony area and density, and record the specific time of each colony generation; the historical colony data record log is traversed and time-series marked to determine the colony generation time series, that is, the specific time node of each colony growth and the corresponding antibacterial performance; based on the antibacterial performance attenuation trend curve, the initial inhibition rate (that is, the antibacterial ability of the antibacterial non-woven fabric at the initial moment) is extracted, and then, the colony accumulation calculation is performed according to the initial inhibition rate combined with the colony generation time series; according to the colony cumulative area data and the antibacterial performance attenuation trend curve, time segmentation calculation is performed, and the antibacterial efficiency of each time period is calculated by dividing it into multiple time periods (that is, the change of antibacterial performance within the period), and the antibacterial efficiency reflects the degree of inhibition of colony growth by antibacterial performance in each time period; based on the antibacterial efficiency of multiple time periods, the antibacterial contribution is calculated by integrating the colony generation time series. The antibacterial contribution can be obtained by integrating the antibacterial efficiency of each time period.

[0048] Furthermore, the method of performing antibacterial persistence analysis on the antibacterial non-woven fabric according to the antibacterial contribution to obtain a persistent antibacterial analysis result includes:

[0049] A persistent calculation is performed based on the antibacterial contribution of the antibacterial non-woven fabric in combination with the antibacterial efficiency of the multiple time periods to obtain a persistent antibacterial efficiency; a performance calculation is performed based on the persistent antibacterial efficiency to obtain a long-term performance ratio, and a plurality of persistent performance indicators are set based on the long-term performance ratio; the antibacterial non-woven fabrics are performance-classified based on the multiple persistent performance indicators to generate a plurality of performance levels; a persistent antibacterial determination is performed based on the multiple performance levels to generate an antibacterial determination result, and the antibacterial determination result is added to the persistent antibacterial analysis result.

[0050] According to the antibacterial contribution of the antibacterial non-woven fabric, combined with the antibacterial efficiency of multiple time periods, a durable calculation is performed to obtain the durable antibacterial efficiency. Specifically, the durable antibacterial efficiency reflects the changes in the antibacterial performance during long-term use. By integrating and averaging the antibacterial efficiency throughout the entire use cycle, the sustained effect of the antibacterial performance can be quantified. According to the durable antibacterial efficiency, the long-term performance ratio is calculated to indicate the proportion of the antibacterial non-woven fabric with a high-efficiency antibacterial effect during the entire use cycle; according to the long-term performance ratio, multiple durable performance indicators are set, such as antibacterial duration, antibacterial effect stability, and antibacterial efficiency maintenance rate, to evaluate the durable performance of the antibacterial non-woven fabric; according to these durable performance indicators, the performance of the antibacterial non-woven fabric is classified to generate different performance levels (such as excellent, good, and poor) to reflect the strength of its antibacterial persistence; based on the generated performance level, a durable antibacterial judgment is made to determine whether the durable antibacterial ability of the antibacterial non-woven fabric meets the predetermined standards, and the antibacterial judgment results are added to the durable antibacterial analysis results.

[0051] Furthermore, the method for generating the antimicrobial recommendation includes:

[0052] Perform antibacterial analysis based on the historical colony data record log and set an expected antibacterial performance threshold; determine whether the multiple performance levels are greater than or equal to the expected antibacterial performance threshold, and if the multiple performance levels are less than the expected antibacterial performance threshold, generate the antibacterial determination result, and the antibacterial determination result includes an antibacterial performance spatial distribution heat map; calculate the surface antibacterial uniformity data of the antibacterial non-woven fabric according to the antibacterial performance spatial distribution heat map; generate an early warning signal according to the surface antibacterial uniformity data, trigger an antibacterial improvement instruction through the early warning signal, perform optimization analysis in accordance with the antibacterial improvement instruction and the multiple performance levels, and generate the antibacterial recommendation.

[0053] Based on the historical colony data record log, antibacterial performance analysis is performed. By analyzing the historical data, the expected antibacterial performance threshold is set, that is, the minimum standard that the antibacterial performance should reach; it is judged whether multiple performance levels are greater than or equal to the expected antibacterial performance threshold. If the performance level is lower than the expected threshold, the antibacterial judgment result is generated. The antibacterial judgment result includes an antibacterial performance spatial distribution heat map, which shows the distribution of antibacterial effects in different regions; according to the antibacterial performance spatial distribution heat map, the antibacterial uniformity data of the antibacterial non-woven fabric surface is calculated. The antibacterial uniformity data can reflect whether the distribution of antibacterial performance on the non-woven fabric surface is uniform, ensuring that different parts can obtain effective antibacterial effects; based on the surface antibacterial uniformity data, an early warning signal is generated. When the antibacterial effect in some areas does not meet the standard or there is a large fluctuation, the early warning signal will be triggered; according to the early warning signal, the antibacterial improvement instruction is triggered, and optimization analysis is performed in combination with multiple performance levels; through optimization analysis, the improvement plan is determined, such as adjusting the distribution of the antibacterial agent, improving the antibacterial properties of the material, etc., and antibacterial suggestions are obtained to guide the optimization and improvement of the antibacterial non-woven fabric and improve the durability and uniformity of its antibacterial performance.

[0054] In summary, the embodiments of the present application have at least the following technical effects:

[0055] First, multi-dimensional data collection is performed on the antibacterial non-woven fabric through a data sensing network to obtain a multi-dimensional data set, which contains multiple environmental data and multiple surface colony image data of the antibacterial non-woven fabric. Then, based on the multiple environmental data, the multiple surface colony image data are traversed and analyzed to extract multiple colony image features. Further, the antibacterial analysis of the surface of the antibacterial non-woven fabric is performed on the multiple environmental data according to the multiple colony image features to generate multiple antibacterial correlation coefficients. Then, the antibacterial performance attenuation modeling is performed based on the multiple antibacterial correlation coefficients, and an antibacterial attenuation model is constructed. The trend analysis of the antibacterial performance is performed through the antibacterial attenuation model, and the antibacterial performance attenuation trend curve is drawn. Finally, the antibacterial contribution is calculated according to the antibacterial performance attenuation trend curve to obtain the antibacterial contribution degree, and the antibacterial persistence analysis of the antibacterial non-woven fabric is performed according to the antibacterial contribution degree to obtain the persistent antibacterial analysis result, and the antibacterial suggestion is generated. The technical problem that the evaluation dimension is single in the process of the antibacterial performance persistence detection of the antibacterial non-woven fabric in the prior art, resulting in insufficient accuracy of the detection result, is solved, and the technical effect of improving the comprehensiveness and accuracy of the antibacterial performance detection is achieved.

[0056] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0057] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0058] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for detecting the durable antibacterial properties of antibacterial nonwoven fabrics, characterized in that: The method comprises: Performing multi-dimensional data collection on the antibacterial non-woven fabric through a data sensor network to obtain a multi-dimensional data set, wherein the multi-dimensional data set includes a plurality of environmental data and a plurality of surface colony image data of the antibacterial non-woven fabric; Performing traversal analysis on the plurality of surface colony image data based on the plurality of environmental data to extract a plurality of colony image features; Performing antibacterial analysis on the surface of the antibacterial non-woven fabric on the plurality of environmental data according to the plurality of colony image features, and generating a plurality of antibacterial correlation coefficients; Based on the multiple antibacterial correlation coefficients, antibacterial performance attenuation modeling is performed to construct an antibacterial attenuation model, and antibacterial performance trend analysis is performed through the antibacterial attenuation model to draw an antibacterial performance attenuation trend curve; The antibacterial contribution is calculated according to the antibacterial performance attenuation trend curve to obtain the antibacterial contribution degree, the antibacterial durability of the antibacterial non-woven fabric is analyzed according to the antibacterial contribution degree to obtain the durable antibacterial analysis result, and the antibacterial recommendation is generated.

2. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 1, characterized in that: The plurality of surface colony image data are traversed and analyzed based on the plurality of environmental data to extract a plurality of colony image features, the method comprising: Performing cluster analysis on the plurality of surface colony image data according to the plurality of environmental data to determine a plurality of colony image groups, wherein each of the plurality of colony image groups corresponds to one environmental data; Based on the multiple environmental data, multiple colony image groups are traversed to perform morphological analysis to determine the morphological characteristics of colony distribution; Based on the multiple environmental data, multiple colony image groups are traversed to perform density analysis to determine colony density characteristics; Performing image enhancement on the plurality of surface colony image data according to the colony distribution morphology characteristics and the colony density characteristics to generate a plurality of surface colony enhanced images; The plurality of surface colony enhanced images are traversed to perform feature analysis to obtain a plurality of feature vectors, and based on the plurality of feature vectors, the colony distribution morphological features and the colony density features are integrated by dimensionality reduction to generate the plurality of colony image features.

3. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 2, characterized in that: Traversing the plurality of surface colony enhanced images for feature analysis to obtain a plurality of feature vectors, and performing dimension reduction integration on the colony distribution morphological features and the colony density features based on the plurality of feature vectors to generate the plurality of colony image features, the method comprising: Traversing the plurality of surface colony enhanced images to perform edge detection, segmenting the plurality of surface colony enhanced images according to the edge detection results, and determining a plurality of colony region images; Perform colony feature combination transformation according to the multiple colony region images to generate multiple feature vectors; Normalizing the multiple feature vectors to generate a standard feature vector; The colony distribution morphology characteristics and the colony density characteristics are reduced in dimension by using the principal component analysis method in combination with the standard feature vector to generate a colony distribution morphology reduction feature matrix and a colony density reduction feature matrix; The principal component features are determined by screening according to the standard feature vectors, and the colony distribution morphology dimension reduction feature matrix and the colony density dimension reduction feature matrix are associated and integrated based on the principal component features to obtain the multiple colony image features.

4. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 3, characterized in that: According to the multiple colony image features, the multiple environmental data are subjected to antibacterial analysis on the surface of the antibacterial non-woven fabric to generate multiple antibacterial correlation coefficients, and the method includes: Performing a multivariate regression analysis on the plurality of environmental data according to the colony distribution morphology dimension reduction feature matrix and the colony density dimension reduction feature matrix to obtain a multivariate regression coefficient; Based on the multiple regression coefficients, the plurality of environmental data are subjected to an antibacterial evaluation on the surface of the antibacterial nonwoven fabric to obtain a characteristic impact score; Arrange the plurality of environmental data in descending order according to the feature impact scores to generate an environmental impact sequence; The plurality of environmental data are screened for variables based on the environmental impact sequence, environmental impact variables are determined for antibacterial calculation, and the plurality of antibacterial correlation coefficients are generated.

5. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 1, characterized in that: The antibacterial performance trend analysis is performed by using the antibacterial attenuation model to draw an antibacterial performance attenuation trend curve, and the method includes: Performing short-term analysis according to the multiple antibacterial correlation coefficients using the antibacterial attenuation model to determine multiple short-term antibacterial groups; Performing a long-term analysis according to the multiple antibacterial correlation coefficients using the antibacterial attenuation model to determine multiple long-term antibacterial groups; Performing attenuation analysis on the multiple short-term antibacterial groups based on half-life time, and generating a short-term antibacterial performance attenuation trend curve according to the first analysis result; Performing attenuation analysis on the multiple long-term antibacterial groups based on half-life time, and generating a long-term antibacterial performance attenuation trend curve according to the second analysis result; The short-term antibacterial performance attenuation trend curve is cross-validated with the long-term antibacterial performance attenuation trend curve, and the antibacterial performance attenuation trend curve is drawn according to the validation result.

6. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 1, characterized in that: The antibacterial contribution is calculated according to the antibacterial performance attenuation trend curve to obtain the antibacterial contribution degree, and the method includes: Retrieving historical colony data record logs, traversing the historical colony data record logs for time sequence identification, and determining the colony generation time sequence; Extracting the initial inhibition rate based on the antibacterial performance attenuation trend curve, performing colony accumulation calculation according to the initial inhibition rate combined with the colony generation time series, and determining the colony accumulation area data; Perform time segment calculation based on the colony cumulative area data combined with the antibacterial performance attenuation trend curve to obtain the antibacterial efficiency in multiple time periods; The antibacterial efficiency in the plurality of time periods is integrated according to the colony generation time series to generate the antibacterial contribution of the antibacterial nonwoven fabric.

7. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 6, characterized in that: The method of performing antibacterial persistence analysis on the antibacterial non-woven fabric according to the antibacterial contribution to obtain a persistent antibacterial analysis result comprises: Performing a durable calculation based on the antibacterial contribution of the antibacterial non-woven fabric and the antibacterial efficiency in the multiple time periods to obtain a durable antibacterial efficiency; Performing performance calculation according to the long-lasting antibacterial efficiency to obtain a long-lasting performance ratio, and setting a plurality of long-lasting performance indicators according to the long-lasting performance ratio; Classifying the antibacterial nonwoven fabrics according to the multiple durable performance indicators to generate multiple performance grades; A durable antibacterial determination is performed based on the plurality of performance levels, an antibacterial determination result is generated, and the antibacterial determination result is added to the durable antibacterial analysis result.

8. The method for detecting the durable antibacterial performance of the antibacterial nonwoven fabric according to claim 7, characterized in that: Generating the antimicrobial recommendation, the method comprising: Performing antimicrobial analysis based on the historical colony data record log and setting an expected antimicrobial performance threshold; Determine whether the multiple performance levels are greater than or equal to the expected antibacterial performance threshold, and if the multiple performance levels are less than the expected antibacterial performance threshold, generate the antibacterial determination result, wherein the antibacterial determination result includes a heat map of the spatial distribution of antibacterial performance; Calculating surface antibacterial uniformity data of the antibacterial nonwoven fabric according to the antibacterial performance spatial distribution heat map; An early warning signal is generated according to the surface antibacterial uniformity data, an antibacterial improvement instruction is triggered by the early warning signal, and an optimization analysis is performed according to the antibacterial improvement instruction in combination with the multiple performance levels to generate the antibacterial recommendation.

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