Preparation method of anti-ultraviolet fabric and anti-ultraviolet fabric

Abstract

According to the preparation method, a time queue of dye bath condition data (a temperature value and a PH value) of a monitored dye bath is acquired through a sensor group, and data integration and feature extraction are performed on the dye bath condition data by using a data analysis and coding mode based on deep learning; therefore, the dye bath PH value is intelligently recommended according to the double-view attention interaction response representation between the dye bath temperature time sequence characteristics and the dye bath PH value time sequence characteristics. According to the technical scheme, the change of dye bath conditions can be monitored in real time, and the pH value is correspondingly adjusted so as to be kept within the optimal range. Meanwhile, according to the scheme, the time sequence characteristics of the dye bath temperature and the pH value can be accurately analyzed, the subtle and complex interaction between the dye bath temperature and the pH value can be captured, and therefore it is guaranteed that the dye can play the best effect under different temperature conditions, and the dyeing quality and the overall performance of the fabric are improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent preparation, and more specifically, to a preparation method of anti-ultraviolet fabric and anti-ultraviolet fabric. Background Art

[0002] With the improvement of people's quality of life and the increase of outdoor activities, the demand for anti-ultraviolet (UV) protection is also growing. Traditional textiles are prone to sunburn, aging and even skin cancer when exposed to ultraviolet rays for a long time. Therefore, the development of fabrics with high-efficiency anti-UV function has become an important research direction in the textile industry.

[0003] Patent CN109056181B proposes a method for preparing UV-resistant fabrics and UV-resistant fabrics, which firstly mix silicon nitride or aluminum nitride ceramic particles with hydroxyl-containing polymers for surface modification, then dry and perform in-situ polymerization to generate UV-resistant polyester fibers. The grey fabric is woven using a knitting machine, dyed using a high-temperature and high-pressure overflow dyeing machine, the pH value is adjusted, and the fabric is processed in sections. Finally, an anti-UV finishing agent is added to enhance the effect, and after finishing steps such as shaping and napping, the dimensional stability and soft feel of the fabric are ensured.

[0004] In the preparation process of UV-resistant fabrics, the dyeing step is a crucial link, which can give the fabric a uniform and lasting color while ensuring its excellent UV resistance and overall quality. However, the pH adjustment in the patent mainly relies on the addition of a fixed amount of regulator (such as glacial acetic acid) at a pre-set time point. This method cannot respond to changes in dye bath conditions in real time and may cause the pH value to deviate from the ideal range. In addition, the sensitivity of some dyes or finishing agents to pH values ​​varies with temperature. If the relationship between temperature and pH value is ignored, the pH value may not be suitable for the optimal performance of the dye under specific temperature conditions, thereby affecting the dyeing effect and fabric quality.

[0005] Therefore, a preparation scheme of UV-resistant fabric is desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method for preparing UV-resistant fabrics and UV-resistant fabrics, which collects the time queue of the dye bath condition data (temperature value and pH value) of the monitored dye bath through a sensor group, and uses a data analysis and encoding method based on deep learning to integrate and extract features of the dye bath condition data, so as to intelligently recommend the dye bath pH value based on the dual-perspective attention interaction response representation between the dye bath temperature time series characteristics and the dye bath pH value time series characteristics. This technical solution can monitor the changes in the dye bath conditions in real time and adjust the pH value accordingly to ensure that it remains within the optimal range. At the same time, the solution can also accurately analyze the time series characteristics of the dye bath temperature and pH value, and capture the subtle and complex interactions between them, so as to ensure that the dye can play the best effect under different temperature conditions, thereby improving the dyeing quality and the overall performance of the fabric.

[0007] According to one aspect of the present application, a method for preparing an anti-ultraviolet fabric is provided, which comprises: mixing ceramic particles with a hydroxyl-containing polymer to perform surface modification on the ceramic particles to obtain a modified mixture; drying, esterifying and polycondensing the modified mixture, spinning the obtained polyester to prepare an anti-ultraviolet fiber; weaving the anti-ultraviolet fiber to obtain a grey cloth; dyeing the grey cloth to obtain a dye; preparing an anti-ultraviolet finishing agent, and adding the anti-ultraviolet finishing agent to the dye in a predetermined proportion to perform anti-ultraviolet finishing on the dye to obtain an anti-ultraviolet fabric; shaping, napping and applying a soft finishing to the anti-ultraviolet fabric to obtain a finished anti-ultraviolet fabric; wherein the grey cloth is dyed to obtain a dye, comprising: collecting a time queue of dye bath condition data of a monitored dye bath through a sensor group, wherein , the dye bath condition data includes a temperature value and a pH value; data is sorted and time series feature extraction is performed on the time queue of the dye bath condition data to obtain a dye bath temperature time series feature and a dye bath pH value time series feature; the dye bath temperature time series feature and the dye bath pH value time series feature are subjected to a feature-to-feature interactive response guided by a dual-perspective attention field to obtain a dye bath temperature-PH value time series interactive response coding feature, including: constructing a positive and negative dye bath temperature-PH value dual-perspective attention balance field between the dye bath temperature time series feature and the dye bath pH value time series feature; based on the positive and negative dye bath temperature-PH value dual-perspective attention balance field, the dye bath temperature time series feature and the dye bath pH value time series feature are modulated and interactively responded to obtain the dye bath temperature-PH value time series interactive response coding feature; based on the dye bath temperature-PH value time series interactive response coding feature, a recommended value of the dye bath pH value is obtained.

[0008] According to another aspect of the present application, an anti-ultraviolet fabric is provided, and the anti-ultraviolet fabric is prepared by the preparation method of the anti-ultraviolet fabric as described above.

[0009] Compared with the prior art, the present application provides a method for preparing an anti-ultraviolet fabric and an anti-ultraviolet fabric, which collects the time queue of the dye bath condition data (temperature value and pH value) of the monitored dye bath through a sensor group, and uses a data analysis and encoding method based on deep learning to integrate and extract features of the dye bath condition data, so as to intelligently recommend the dye bath pH value based on the dual-perspective attention interactive response representation between the dye bath temperature time series characteristics and the dye bath pH value time series characteristics. This technical solution can monitor the changes in the dye bath conditions in real time and adjust the pH value accordingly to ensure that it remains within the optimal range. At the same time, the solution can also accurately analyze the time series characteristics of the dye bath temperature and pH value, and capture the subtle and complex interactions between them, so as to ensure that the dye can play the best effect under different temperature conditions, thereby improving the dyeing quality and the overall performance of the fabric. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0011] Figure 1 The present invention is a flowchart of a method for preparing an anti-ultraviolet fabric according to an embodiment of the present application.

[0012] Figure 2 Schematic diagram of data flow of a method for preparing UV-resistant fabric according to an embodiment of the present application.

[0013] Figure 3 This is a flow chart of sub-step S4 of the method for preparing the anti-ultraviolet fabric according to an embodiment of the present application.

[0014] Figure 4 This is a flow chart of sub-step S43 of the method for preparing the anti-ultraviolet fabric according to an embodiment of the present application. DETAILED DESCRIPTION

[0015] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0016] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0017] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0018] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0019] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0020] In the technical solution of the present application, a method for preparing an anti-ultraviolet fabric is proposed. Figure 1 The present invention is a flowchart of a method for preparing an anti-ultraviolet fabric according to an embodiment of the present application. Figure 2 FIG. 1 is a data flow diagram of a method for preparing an anti-ultraviolet fabric according to an embodiment of the present application. Figure 1 and Figure 2 As shown, the preparation method of the anti-ultraviolet fabric according to the embodiment of the present application includes the following steps: S1, mixing ceramic particles with a hydroxyl-containing polymer to perform surface modification on the ceramic particles to obtain a modified mixture; S2, drying, esterifying and polycondensing the modified mixture, and spinning the obtained polyester to prepare an anti-ultraviolet fiber; S3, weaving the anti-ultraviolet fiber to obtain a grey cloth; S4, dyeing the grey cloth to obtain a dye; S5, preparing an anti-ultraviolet finishing agent, and adding the anti-ultraviolet finishing agent to the dye in a predetermined proportion to perform anti-ultraviolet finishing on the dye to obtain an anti-ultraviolet fabric; S6, shaping, napping and applying a soft finishing to the anti-ultraviolet fabric to obtain a finished anti-ultraviolet fabric.

[0021] In particular, in S1, ceramic particles are mixed with a hydroxyl-containing polymer to perform surface modification on the ceramic particles to obtain a modified mixture. In one example, first, ceramic particles with high efficiency in shielding ultraviolet rays are selected as the core component of anti-ultraviolet rays. The ceramic particles can be selected from silicon nitride (Si 3 N 4 ) or aluminum nitride (AlN), which has excellent optical properties and chemical stability, and can effectively reflect and absorb ultraviolet rays. In order to ensure that the ceramic particles can be evenly distributed in the fiber and exert their maximum efficiency, their surface needs to be modified. Next, a polymer containing hydroxyl groups is selected as a modifier. Commonly used polymers include ethylene glycol polymers, propylene glycol polymers or butanediol polymers. These polymer molecules contain abundant hydroxyl functional groups, which can be combined with the surface of ceramic particles through hydrogen bonds or other weak interactions, thereby changing the surface properties of ceramic particles, making them easier to disperse in aqueous or organic phases, and enhancing their compatibility with the polyester matrix. The mass proportion of hydroxyl-containing polymers should be controlled between 0.1% and 3% of the mass of ceramic particles to achieve the best modification effect without affecting the subsequent process. In specific operations, the selected ceramic particles are fully mixed with an appropriate amount of hydroxyl-containing polymers. In order to ensure uniform mixing of the two, this process is usually carried out in a liquid medium, for example, using an appropriate solvent such as water or a low-boiling point organic solvent to dissolve the polymer, and then adding the ceramic particles. During the mixing process, mechanical stirring, ultrasonic dispersion and other methods can be used to promote the contact and reaction between the two. In addition, the ceramic particles can be further refined by liquid phase grinding or gas phase grinding to keep their size within the average particle size range of 50 to 500 nanometers, which helps to improve the dispersibility and active surface area of ​​the ceramic particles.

[0022] In particular, in S2, after drying, esterification and polycondensation of the modified mixture, the obtained polyester is spun to make an anti-ultraviolet fiber. In one example, first, the modified mixture is dried to remove excess water and other volatile components. The drying temperature is usually set between 80 and 120 degrees Celsius, and the specific selection depends on the thermal stability and solvent characteristics of the material used. Within this temperature range, moisture can be effectively removed without destroying the structural integrity of the ceramic particles or hydroxyl-containing polymers. It is worth mentioning that the drying process can be achieved by an oven, a rotary evaporator or other appropriate equipment to ensure that the mixture is evenly heated and thoroughly dried. Then, the dried modified mixture is introduced into a polymerization kettle, where esterification and polycondensation reactions occur to generate polyester with anti-ultraviolet effect. Finally, the obtained anti-ultraviolet polyester is converted into fibers by melt spinning or solution spinning, the polymerized polyester is heated to a molten state, and then a thin stream is extruded through a spinneret, and rapidly cooled and solidified into fibers. During the spinning process, the spinneret aperture, spinning speed and cooling conditions need to be controlled to obtain the ideal fiber morphology and physical properties. In the technical solution of this application, the single fiber fineness can be controlled between 0.5 and 5D to ensure that the fiber has both good mechanical strength and sufficient surface area for carrying ceramic particles.

[0023] In particular, in S3, the anti-ultraviolet fiber is woven to obtain a grey cloth. In particular, in a specific example of the present application, in order to achieve efficient weaving and ensure that the anti-ultraviolet fiber is evenly distributed in the fabric structure, a high-speed tricot knitting machine is selected for weaving. This machine can quickly weave a complex knitted structure while maintaining high production efficiency. The tricot knitting machine is suitable for producing grey cloth with high density and flatness, and is very suitable for manufacturing anti-ultraviolet fabrics.

[0024] In particular, the step S4 is to dye the grey cloth to obtain a dye. Specifically, in a specific example of the present application, Figure 3 As shown, the S4 includes: S41, collecting a time queue of dye bath condition data of the monitored dye bath through a sensor group, wherein the dye bath condition data includes a temperature value and a pH value; S42, sorting the time queue of the dye bath condition data and extracting time series features to obtain a dye bath temperature time series feature and a dye bath pH value time series feature; S43, performing a dual-view attention field guided feature-to-feature interaction response on the dye bath temperature time series feature and the dye bath pH value time series feature to obtain a dye bath temperature-PH value time series interaction response coding feature; S44, obtaining a recommended value of the dye bath pH value based on the dye bath temperature-PH value time series interaction response coding feature.

[0025] Specifically, the S41 collects the time queue of the dye bath condition data of the monitored dye bath through the sensor group, wherein the dye bath condition data includes temperature value and pH value. In particular, in the selection and installation process of the sensor, in order to accurately monitor the dye bath conditions, it is first necessary to select a high-precision, fast-response and durable sensor. Common sensor types include PT100 platinum resistance thermometer or thermocouple for temperature measurement, and glass electrode pH sensor or composite electrode for pH measurement. These sensors should be installed in key positions of the dye bath to ensure full coverage of the entire dye bath area and avoid being affected by external factors (such as bubbles, stirring). Typically, the temperature sensor is directly immersed in the dye bath, while the pH sensor may need to be installed in a protective sleeve to prevent mechanical damage.

[0026] Specifically, the S42, the time queue of the dye bath condition data is data sorted and the time series feature is extracted to obtain the dye bath temperature time series feature and the dye bath pH value time series feature. That is, in the technical solution of the present application, first, the time queue of the dye bath condition data is data sorted according to the dye bath parameter sample dimension to obtain the time queue of the temperature value and the time queue of the pH value. Considering that different dye bath parameters (such as temperature and pH value) have their own unique change rules and interaction patterns, in order to understand and analyze these characteristics more clearly, the present application separates the time queue of the dye bath condition data according to the parameter dimension, and generates the time queue of the temperature value and the time queue of the pH value respectively. In this way, it is possible to focus on the time series feature analysis of each parameter, deeply understand its inherent change pattern and trend, and carefully explore the relationship between the two. That is, by accurately capturing the dynamic characteristics of each parameter and its complex interaction, a solid foundation is provided for optimizing the dyeing process, which helps to achieve more sophisticated process control, improve dyeing quality and production efficiency. Furthermore, considering that in the dye bath process, the temperature value and the pH value are both time series data that change dynamically over time, and the dye bath effect at the current moment and the subsequent recommended adjustments often depend on the previous temperature and pH value changes, and there is a clear causal relationship. Based on this, in order to better capture the temporal dynamic change trend of temperature and pH values ​​in different time ranges and the implicit temporal associations between each other, the present application extracts features of the time queue of the temperature value and the time queue of the pH value through a dye bath condition temporal feature extractor based on a void causal convolutional network to obtain a dye bath temperature temporal feature vector and a dye bath pH value temporal feature vector. It is worth mentioning that the void causal convolutional network is specifically designed to effectively process this type of sequence data with time sequence. Compared with traditional convolutional networks, it can expand the receptive field without losing temporal information, better capture the changing trend of temperature and pH values ​​in a longer time range and the implicit temporal dependencies between each other, so it is suitable for extracting the temporal features of dye bath conditions. Moreover, the atrous causal convolutional network has the characteristics of causality. It only uses the input information of the past and the current moment to generate output, which conforms to the causal relationship of the dye bath condition data in the time dimension, avoids the unreasonable introduction of future information, and can better meet the logical requirements of data processing in the actual production process. In this way, deep feature information hidden in the original temperature and pH value time series data that is difficult to observe directly by the naked eye can be excavated. For example, a small fluctuation in temperature in a specific time period may indicate the key stage of the dye bath reaction, thereby more comprehensively understanding the internal laws of the dye bath process.

[0027] Specifically, the S43 performs a feature-to-feature interaction response guided by a dual-view attention field on the dye bath temperature timing characteristics and the dye bath pH value timing characteristics to obtain a dye bath temperature-PH value timing interaction response coding feature. Considering that in the dye bath process, the temperature and pH value do not change independently of each other, there is a complex dynamic interaction between them, and this interaction will have an important impact on the dyeing effect and the quality of the final UV-resistant fabric. However, traditional linear or simple nonlinear models are difficult to fully capture the subtle changes in these interactions. Based on this, the present application introduces a feature-to-feature interaction response method guided by a dual-view attention field to interact with the dye bath temperature timing characteristics and the dye bath pH value timing characteristics to obtain a dye bath temperature-PH value timing interaction response coding feature. That is, the method comprehensively examines the temporal interaction between temperature and pH value from both forward and reverse perspectives, focuses on the relationship between the two, and focuses on the most valuable interaction features. This enables the system to capture subtle and complex interaction information that is difficult to detect with conventional analysis methods, and accurately portrays the interactive response characteristics between the two to improve the control of subsequent dyeing conditions. In a specific example of the present application, such as Figure 4 As shown, the S43 includes: S431, constructing a positive and negative dye bath temperature-PH value dual-perspective attention balance field between the dye bath temperature timing characteristics and the dye bath pH value timing characteristics; S432, based on the positive and negative dye bath temperature-PH value dual-perspective attention balance field, modulating the dye bath temperature timing characteristics and the dye bath pH value timing characteristics to interactively respond to obtain the dye bath temperature-PH value timing interactive response coding characteristics.

[0028] More specifically, the S431 constructs a positive and negative dye bath temperature-PH value dual-perspective attention balance field between the dye bath temperature time series feature and the dye bath pH value time series feature. That is, in a specific example of the application, first, the dye bath temperature time series feature vector and the dye bath pH value time series feature vector are subjected to homography projection transformation to obtain a dye bath temperature time series feature homography projection coding vector and a dye bath pH value time series feature homography projection coding vector. Since the dye bath temperature time series feature vector and the dye bath pH value time series feature vector may have different scales or distributions, direct comparison or combination may lead to inaccurate results. By mapping the dye bath temperature time series feature vector and the dye bath pH value time series feature vector to a new feature space, the two feature vectors can be compared or interacted in the same space, so as to capture the potential relationship between the two. It is helpful to more accurately identify the association pattern between the dye bath temperature and the dye bath pH value in the subsequent process. More specifically, the dye bath temperature time series feature vector and the dye bath pH value time series feature vector are homographically projected to obtain a dye bath temperature time series feature homographic projection coding vector and a dye bath pH value time series feature homographic projection coding vector using the following homographic projection transformation formula; wherein the homographic projection transformation formula is: ;in, is the dye bath temperature time series characteristic vector, is the time series characteristic vector of the dye bath pH value, is the dyebath temperature mapping homography matrix, is the dyebath pH mapping homography matrix, is the homography projection encoding vector of the dye bath temperature time series feature, It is the homography projection encoding vector of the dye bath pH value time series feature.

[0029] Next, based on the dye bath temperature time series feature homography projection coding vector and the dye bath pH value time series feature homography projection coding vector, a forward dye bath temperature-PH value attention score field and a reverse dye bath temperature-PH value attention score field are constructed. That is, in a specific example of the present application, first, the forward dye bath temperature-PH value attention score field of the dye bath temperature time series feature homography projection coding vector relative to the dye bath pH value time series feature homography projection coding vector is calculated. Then, the reverse dye bath temperature-PH value attention score field of the dye bath pH value time series feature homography projection coding vector relative to the dye bath temperature time series feature homography projection coding vector is calculated. Here, by introducing the attention mechanism in the machine learning algorithm, the dynamic analysis of the relationship between the dye bath conditions (temperature and pH value) can be realized. Specifically, the specific step of calculating the forward dye bath temperature-PH value attention score field of the dye bath temperature time series feature homography projection code vector relative to the dye bath PH value time series feature homography projection code vector includes: after calculating the product of the dye bath temperature time series feature homography projection code vector and the transposed vector of the dye bath PH value time series feature homography projection code vector, the obtained dye bath temperature-PH value interaction matrix is ​​divided by the square root of the length of the dye bath PH value time series feature homography projection code vector by position to obtain the dye bath temperature-PH value interaction correlation matrix as the forward dye bath temperature-PH value attention score field. Then, the forward and reverse dye bath temperature-PH value dual-view attention balance field between the forward dye bath temperature-PH value attention score field and the reverse dye bath temperature-PH value attention score field is constructed. More specifically, based on the dye bath temperature temporal feature homography projection coding vector and the dye bath pH value temporal feature homography projection coding vector, the forward dye bath temperature-PH value attention score field and the reverse dye bath temperature-PH value attention score field are constructed using the following attention score field construction formula; wherein the attention score field construction formula is: ;in, yes The transposed vector of yes The transposed vector of is matrix multiplication, yes Length, yes Length, It is the forward dye bath temperature-pH value attention score field, It is the reverse dye bath temperature-pH value attention score field.

[0030] Furthermore, the forward and reverse dye bath temperature-PH value dual-perspective attention balance field is constructed between the forward dye bath temperature-PH value attention score field and the reverse dye bath temperature-PH value attention score field. In particular, by constructing the forward dye bath temperature-PH value attention score field and the reverse dye bath temperature-PH value attention score field, a two-way analysis of the mutual influence between the dye bath temperature and the pH value can be achieved. This means that not only how the temperature affects the pH value (positive) is considered, but also how the change in pH value in turn affects the temperature (reverse). This enables the system to be adjusted within a more comprehensive and dynamic framework to ensure that the dye bath conditions are always in the best state to adapt to the different sensitivities of different dyes or finishing agents to pH and temperature. More specifically, the forward and reverse dye bath temperature-PH value dual-perspective attention balance field between the forward dye bath temperature-PH value attention score field and the reverse dye bath temperature-PH value attention score field is constructed using the following dual-perspective attention balance field construction formula; wherein the dual-perspective attention balance field construction formula is: ;in, is the feature concatenation operation, is a convolutional code with a convolution kernel of 3×3. It is the positive and reverse dye bath temperature-pH value dual perspective attention balance field.

[0031] More specifically, in S432, based on the positive and negative dye bath temperature-PH value dual-view attention balance field, the dye bath temperature time series feature and the dye bath PH value time series feature are modulated and interactively responded to obtain the dye bath temperature-PH value time series interactive response coding feature. That is, in a specific example of the application, first, the dye bath temperature time series feature homography projection coding vector and the dye bath PH value time series feature homography projection coding vector are respectively mapped to the positive and negative dye bath temperature-PH value dual-view attention balance field to obtain the dye bath temperature time series feature homography projection attention modulation coding vector and the dye bath PH value time series feature homography projection attention modulation coding vector; further, the dye bath temperature time series feature homography projection attention modulation coding vector and the dye bath PH value time series feature homography projection attention modulation coding vector are calculated by dividing the position point by the dye bath temperature time series feature homography projection attention modulation coding vector and the dye bath PH value time series feature homography projection attention modulation coding vector to obtain the dye bath temperature-PH value time series interactive response coding vector as the dye bath temperature-PH value time series interactive response coding feature. Here, by dividing by position points, the homography projection attention modulation coding vector of the dye bath temperature time series feature and the homography projection attention modulation coding vector of the dye bath pH value time series feature are compared to reveal the new relationship between the two feature vectors after their respective modulations. This deep feature comparison and integration reveals the subtle proportional relationship between the dye bath temperature-PH value features, providing a solid foundation for achieving accurate recommendation of the dye bath pH value. More specifically, based on the positive and negative dye bath temperature-PH value dual-perspective attention balance field, the dye bath temperature time series feature and the dye bath pH value time series feature are modulated and interactively responded to obtain the dye bath temperature-PH value time series interactive response coding feature using the following modulation interactive response formula; wherein, the modulation interactive response formula is: ;in, is the homography projection attention modulation encoding vector of the dye bath temperature temporal feature, is the homography projection attention modulation encoding vector of the dye bath pH value temporal feature, is the dye bath temperature-PH value time series interaction response encoding vector.

[0032] Specifically, the S44 obtains the recommended value of the dye bath pH value based on the dye bath temperature-PH value time series interactive response coding feature. That is, in the technical solution of the present application, the dye bath temperature-PH value time series interactive response coding vector is passed through a decoder-based pH value adjuster to obtain the recommended decoded value of the dye bath pH value as the recommended value of the dye bath pH value. That is, the dye bath temperature-PH value time series interactive response coding feature obtained by performing a dual-view response using the dye bath temperature time series feature vector and the dye bath pH value time series feature vector is decoded, so as to use the decoder to accurately identify the meaning of the temperature and pH value interaction level represented by each element in the coding vector, and then use its internal mapping mechanism to decode the corresponding reasonable dye bath pH value recommendation. More specifically, the dye bath temperature-PH value time series interactive response coding vector is passed through a decoder-based PH value adjuster to obtain a recommended decoded value of the dye bath PH value, including: multiplying the decoding weight matrix of the decoder with the dye bath temperature-PH value time series interactive response coding vector to obtain a decoded dye bath temperature-PH value time series interactive response coding vector, and accumulating the characteristic values ​​of each position in the decoded dye bath temperature-PH value time series interactive response coding vector position by position to obtain the recommended decoded value of the dye bath PH value. In one example, during the dyeing process of anti-ultraviolet fabrics, the control system automatically adjusts the amount of pH regulator added according to the recommended value provided by the decoder, so that the dye bath pH value smoothly transitions to the new recommended value, and establishes a closed-loop feedback mechanism, that is, after each adjustment, the system will detect the actual pH value and temperature again, and compare them with the target value. If there is a deviation, continue to make fine adjustments until the ideal dye bath conditions are reached.

[0033] Preferably, in one example, the step of passing the dye bath temperature-PH value time series interaction response coding vector through a decoder-based PH value adjuster to obtain a recommended decoded value of the dye bath PH value comprises: arranging the characteristic values ​​of the dye bath temperature-PH value time series interaction response coding vector in ascending order to form a dye bath temperature-PH value time series interaction response sequence coding vector; The eigenvalue and The absolute value of the difference between the eigenvalues ​​is less than or equal to the distance difference hyperparameter , calculate the The eigenvalues ​​are similar to the The weighted sum between the eigenvalues ​​is the optimized The eigenvalues ​​are: ; In response to the dye bath temperature-PH value time series interaction response sequence encoding vector The eigenvalue and The absolute value of the difference between the eigenvalues ​​is greater than the distance difference hyperparameter , calculate the square root of the sum of the squares of all eigenvalues ​​of the dye bath temperature-PH value time series interaction response encoding vector, multiply it by 2, and then divide it by the square of the length of the dye bath temperature-PH value time series interaction response encoding vector to obtain the dye bath temperature-PH value time series interaction response space primitive value , and multiplying the dye bath temperature-PH value time series interactive response space primitive value by the first After the eigenvalue, calculate the product with the first The weighted reduction between the eigenvalues ​​is the optimized The eigenvalues ​​are: ; Combining the optimization of the dye bath temperature-PH value time series interaction response order encoding vector The optimized dye bath temperature-PH value timing interaction response coding vector is obtained by using a decoder-based PH value adjuster to obtain a recommended decoded value of the dye bath pH value.

[0034] Here, in the case where the dye bath temperature time series feature vector and the dye bath pH value time series feature vector represent the time series correlation features of the dye bath temperature value and the dye bath pH value respectively, when performing an interactive response based on the feature positive and negative attention fields, the complexity of the positive and negative attention field mechanism under the differences in the temporal features of heterogeneous data will lead to insufficient representation of the long-distance interactive response of the dye bath temperature-PH value time series interactive response coding vector, thereby reducing the expression effect of the dye bath temperature-PH value time series interactive response coding vector and affecting the accuracy of the recommended decoded value of the dye bath pH value obtained by the decoder-based PH value adjuster.

[0035] Therefore, in order to address the problem that the feature set of the dye bath temperature-PH value temporal interactive response coding vector is insufficient in the global interactive response representation capability due to the long distance exceeding the predetermined local distribution interval threshold under the predetermined eigenvalue sequential distribution, the high-dimensional feature space primitive representation of the dye bath temperature-PH value temporal interactive response coding vector based on self-inner product fusion is used to capture the complex structure of the global network interaction of its eigenvalues, thereby reconstructing the interactive response relationship between the eigenvalues ​​of the dye bath temperature-PH value temporal interactive response coding vector by simulating the scale-based high-dimensional feature space potential primitives, so as to realize the coding reconstruction of the real sequence distribution behavior of the dye bath temperature-PH value temporal interactive response coding vector under the long distance, improve the interactive response expression effect of the dye bath temperature-PH value temporal interactive response coding vector, and improve the accuracy of the recommended decoding value of the dye bath pH value obtained by the decoder-based PH value adjuster.

[0036] In particular, in S5, an anti-ultraviolet finishing agent is prepared, and the anti-ultraviolet finishing agent is added to the dye in a predetermined proportion to perform anti-ultraviolet finishing on the dye to obtain an anti-ultraviolet fabric. Specifically, first, in the process of preparing the anti-ultraviolet finishing agent, 2-(2'-hydroxy-5'-methylphenyl)benzotriazole (UV-328) can be used as the main ultraviolet absorber. In order to improve the dispersibility and stability of this absorber in the dyeing and finishing process, propylene glycol and its dendritic polymer are used as dispersants. These dispersants can not only encapsulate and stabilize the ultraviolet absorber, but also form a complex structure with the dye molecules during the dyeing process, so that the dye molecules enter the fabric fiber through the integration of dyeing and finishing, thereby enhancing its washing resistance. Next, the selected components are mixed together in a predetermined proportion and dissolved in a suitable solvent, such as water or an organic solvent. In this process, it is necessary to ensure that all components are evenly dispersed to avoid the formation of precipitation or agglomerates. Tools such as a stirrer and an ultrasonic device can be used to promote dissolution and mixing to ensure that the final anti-ultraviolet finishing agent solution is uniform and stable. In order to ensure that the finishing agent remains stable during storage and does not delaminate, crystallize or deteriorate, the mixed solution is usually stabilized. Then, the anti-UV finishing agent is added to the dye in a predetermined proportion. The specific proportion depends on the type of fabric, the expected anti-UV effect and the requirements of the production process. Generally speaking, the amount of anti-UV finishing agent is 0.5% to 3% owf (on weight offabric) of the weight of the anti-UV fabric. The amount added within this range can ensure sufficient anti-UV protection without excessively affecting the feel or other physical properties of the fabric. In actual production, the anti-UV finishing agent is applied to the grey fabric by dipping, coating, spraying, etc. In one example, the anti-UV finishing agent can be directly added to the dye bath during the dyeing process, so that the finishing agent penetrates into the fiber during the dyeing process, further enhancing the anti-UV performance of the fabric.

[0037] In particular, in S6, the anti-ultraviolet fabric is shaped, napped, and subjected to a soft finishing to obtain a finished anti-ultraviolet fabric. That is, the anti-ultraviolet fabric is shaped, napped, and subjected to a soft finishing to ensure the dimensional stability and soft feel of the fabric. Specifically, shaping is an important link in fabric production, and its main purpose is to fix the shape of the fabric, prevent shrinkage and deformation, and give the fabric a certain dimensional stability and mechanical strength. When shaping the anti-ultraviolet fabric, a heat setting process is usually used, with the temperature set between 150 and 160 degrees Celsius and the speed controlled at 25 to 30 meters per minute. This process can make the internal structure of the fiber more compact, reduce the shrinkage rate in subsequent use, and also help to improve the adhesion effect of the anti-ultraviolet finishing agent. Through the shaping process, the fabric can obtain more stable physical properties, such as flatness, thickness and elasticity, thereby improving the overall quality of the product. The napping process is intended to treat the surface of the fabric to form a soft and uniform fluff layer. Napping not only increases the softness and warmth retention of the fabric, but also improves the feel of the fabric, making it more comfortable to wear. In addition, the fluff layer can also increase the reflectivity of the fabric surface to ultraviolet rays, further enhancing its anti-ultraviolet effect. The softening process is a process to provide the fabric with a certain dimensional stability and soft feel. This treatment method not only improves the touch of the fabric, but also enhances its wrinkle resistance and wear resistance, and prolongs its service life. In addition, the ternary copolymer amino silicone oil can form a protective film on the fiber surface, which helps to maintain the effect of the anti-ultraviolet finishing agent and prevent the loss of effective ingredients during washing. Post-finishing refers to the use of the ironing machine and the shearing machine to cooperate with each other to further comb and trim the fluff after raising. This step not only improves the aesthetics of the fabric, but also increases the reflection efficiency of the fabric surface when exposed to ultraviolet rays and reduces the ultraviolet penetration rate. In addition, post-finishing can also remove surface impurities and improve the cleanliness and hygienic properties of the fabric.

[0038] According to the preparation method of the anti-ultraviolet fabric of the embodiment of the present application, a sensor group is used to collect the time queue of the dye bath condition data (temperature value and pH value) of the monitored dye bath, and the data analysis and encoding method based on deep learning is used to integrate and extract features of the dye bath condition data, so as to intelligently recommend the dye bath pH value based on the dual-perspective attention interaction response representation between the dye bath temperature time series characteristics and the dye bath pH value time series characteristics. This technical solution can monitor the changes in the dye bath conditions in real time and adjust the pH value accordingly to ensure that it remains within the optimal range. At the same time, the solution can also accurately analyze the time series characteristics of the dye bath temperature and pH value, and capture the subtle and complex interactions between them, so as to ensure that the dye can play the best effect under different temperature conditions, thereby improving the dyeing quality and the overall performance of the fabric.

[0039] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.

Claims

1. A method for preparing an anti-ultraviolet fabric, comprising: mixing ceramic particles with a hydroxyl-containing polymer to modify the surface of the ceramic particles to obtain a modified mixture; After drying, esterification and polycondensation of the modified mixture, the obtained polyester is spun to prepare UV-resistant fiber; Weaving the anti-ultraviolet fiber to obtain a grey cloth; Performing dyeing treatment on the grey cloth to obtain dye; Preparing an anti-ultraviolet finishing agent, and adding the anti-ultraviolet finishing agent to the dye in a predetermined ratio to perform anti-ultraviolet finishing on the dye to obtain an anti-ultraviolet fabric; The anti-ultraviolet fabric is shaped, napped, and subjected to a soft finishing to obtain a finished anti-ultraviolet fabric; characterized in that the grey cloth is dyed to obtain a dye, including: collecting a time queue of dye bath condition data of a monitored dye bath through a sensor group, wherein the dye bath condition data includes a temperature value and a pH value; sorting the time queue of the dye bath condition data and extracting time series features to obtain a dye bath temperature time series feature and a dye bath pH value time series feature; performing dual-viewing angle attention field guidance on the dye bath temperature time series feature and the dye bath pH value time series feature The interactive response between features is used to obtain a dye bath temperature-PH value time series interactive response coding feature, including: constructing a positive and negative dye bath temperature-PH value dual-perspective attention balance field between the dye bath temperature time series feature and the dye bath pH value time series feature; based on the positive and negative dye bath temperature-PH value dual-perspective attention balance field, modulating the dye bath temperature time series feature and the dye bath pH value time series feature to interactively respond to obtain the dye bath temperature-PH value time series interactive response coding feature; based on the dye bath temperature-PH value time series interactive response coding feature, obtaining a recommended value of the dye bath pH value.

2. The method for preparing the anti-ultraviolet fabric according to claim 1, characterized in that: The time queue of the dye bath condition data is sorted and time series features are extracted to obtain the dye bath temperature time series features and the dye bath pH value time series features, including: sorting the time queue of the dye bath condition data according to the dye bath parameter sample dimension to obtain the temperature value time queue and the pH value time queue; passing the temperature value time queue and the pH value time queue through a dye bath condition time series feature extractor based on a void causal convolutional network to obtain a dye bath temperature time series feature vector as the dye bath temperature time series feature and a dye bath pH value time series feature vector as the dye bath pH value time series feature.

3. The method for preparing the anti-ultraviolet fabric according to claim 2, characterized in that: Constructing a forward and reverse dye bath temperature-PH value dual-perspective attention balance field between the dye bath temperature time series feature and the dye bath pH value time series feature, comprising: performing homography projection transformation on the dye bath temperature time series feature vector and the dye bath pH value time series feature vector to obtain a dye bath temperature time series feature homography projection coding vector and a dye bath pH value time series feature homography projection coding vector; constructing a forward dye bath temperature-PH value attention score field and a reverse dye bath temperature-PH value attention score field based on the dye bath temperature time series feature homography projection coding vector and the dye bath pH value time series feature homography projection coding vector; constructing the forward and reverse dye bath temperature-PH value dual-perspective attention balance field between the forward dye bath temperature-PH value attention score field and the reverse dye bath temperature-PH value attention score field.

4. The method for preparing the anti-ultraviolet fabric according to claim 3, characterized in that: Based on the dye bath temperature temporal feature homography projection coding vector and the dye bath PH value temporal feature homography projection coding vector, constructing a forward dye bath temperature-PH value attention score field and a reverse dye bath temperature-PH value attention score field, including: calculating the forward dye bath temperature-PH value attention score field of the dye bath temperature temporal feature homography projection coding vector relative to the dye bath PH value temporal feature homography projection coding vector; calculating the reverse dye bath temperature-PH value attention score field of the dye bath PH value temporal feature homography projection coding vector relative to the dye bath temperature temporal feature homography projection coding vector.

5. The method for preparing the anti-ultraviolet fabric according to claim 4, characterized in that: Calculating the forward dye bath temperature-PH value attention score field of the dye bath temperature time series feature homography projection coding vector relative to the dye bath PH value time series feature homography projection coding vector, including: calculating the product of the dye bath temperature time series feature homography projection coding vector and the transposed vector of the dye bath PH value time series feature homography projection coding vector, and then dividing the obtained dye bath temperature-PH value interaction matrix by the square root of the length of the dye bath PH value time series feature homography projection coding vector by position to obtain a dye bath temperature-PH value interaction correlation matrix as the forward dye bath temperature-PH value attention score field.

6. The method for preparing the anti-ultraviolet fabric according to claim 5, characterized in that: Based on the positive and negative dye bath temperature-PH value dual-view attention balance field, the dye bath temperature time series characteristics and the dye bath pH value time series characteristics are modulated and interactively responded to obtain the dye bath temperature-PH value time series interactive response coding characteristics, including: mapping the dye bath temperature time series characteristic homography projection coding vector and the dye bath pH value time series characteristic homography projection coding vector to the positive and negative dye bath temperature-PH value dual-view attention balance field respectively to obtain the dye bath temperature time series characteristic homography projection attention modulation coding vector and the dye bath pH value time series characteristic homography projection attention modulation coding vector; calculating the position point division between the dye bath temperature time series characteristic homography projection attention modulation coding vector and the dye bath pH value time series characteristic homography projection attention modulation coding vector to obtain the dye bath temperature-PH value time series interactive response coding vector as the dye bath temperature-PH value time series interactive response coding characteristic.

7. The method for preparing the anti-ultraviolet fabric according to claim 6, characterized in that: Based on the dye bath temperature-PH value time series interactive response coding feature, a recommended value of the dye bath pH value is obtained, including: passing the dye bath temperature-PH value time series interactive response coding vector through a decoder-based pH value adjuster to obtain a recommended decoded value of the dye bath pH value as the recommended value of the dye bath pH value.

8. The method for preparing the anti-ultraviolet fabric according to claim 7, characterized in that: The dye bath temperature-PH value timing interaction response coding vector is passed through a decoder-based PH value adjuster to obtain a recommended decoded value of the dye bath PH value, including: multiplying a decoding weight matrix of the decoder with the dye bath temperature-PH value timing interaction response coding vector to obtain a decoded dye bath temperature-PH value timing interaction response coding vector, and accumulating the characteristic values ​​of each position in the decoded dye bath temperature-PH value timing interaction response coding vector position by position to obtain the recommended decoded value of the dye bath PH value.

9. An anti-ultraviolet fabric, characterized in that: The anti-ultraviolet fabric is prepared by the preparation method of the anti-ultraviolet fabric according to any one of claims 1 to 8.

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