A demand-oriented dyeing and printing strategy guidance system and method

Abstract

This invention discloses a demand-oriented dyeing and printing strategy guidance system and method, belonging to the field of dyeing and printing engineering technology. The system includes: an environmental constraint acquisition module for acquiring the application environment constraint characteristics of the dyed and printed materials; a process analysis module for obtaining process difference parameters and their distribution; a constraint allocation module for obtaining a constraint map; and a dyeing and printing guidance module for providing guidance using dyeing and printing strategies. This application solves the technical problem in existing dyeing and printing engineering technologies where dyeing and printing design cannot accurately allocate process parameters according to the application environment, leading to a mismatch between the performance of dyed and printed materials and actual needs. It achieves the technical effect of generating accurate dyeing and printing strategies through constraint maps in dyeing and printing engineering technologies, ensuring that the performance of dyed and printed materials matches the needs of the application environment and improving resource utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of printing and dyeing engineering technology, specifically to a demand-oriented printing and dyeing strategy guidance system and method. Background Technology

[0002] In the field of dyeing and printing engineering technology, traditional dyeing and printing process design often focuses on artistic indicators such as color reproduction and pattern expressiveness, lacking a systematic consideration of the application environment. With the diversification of textile applications, outdoor sportswear needs to meet the challenges of colorfastness under extreme weather conditions, medical protective equipment needs to meet high cleanliness and antibacterial performance requirements, and building decoration materials need to balance weather resistance and fire resistance. These differentiated environmental constraints place multi-dimensional performance demands on dyeing and printing processes. The existing technology system has the following core shortcomings: First, the process design process relies heavily on manual experience, lacking a mechanism for quantitatively collecting and dynamically mapping application environment parameters, leading to a mismatch between dyeing and printing strategies and actual usage scenarios; second, the coupling relationship between the complexity of the dyed image and the physical properties of the material is not fully analyzed, resulting in technical bottlenecks such as uneven dye penetration and pattern deformation. Therefore, it is urgent to construct a dyeing and printing strategy guidance mechanism driven by application environment requirements, providing an executable, traceable, and sustainable overall solution for dyeing and printing engineering technology. Summary of the Invention

[0003] This application provides a demand-oriented dyeing and printing strategy guidance system and method, aiming to solve the technical problem in existing dyeing and printing engineering technology that dyeing and printing design cannot accurately allocate process parameters according to the application environment, resulting in a mismatch between the performance of dyed and printed materials and actual needs. The system and method achieve the technical effect of generating accurate dyeing and printing strategies through constraint maps in dyeing and printing engineering technology, so that the performance of dyed and printed materials is consistent with the needs of the application environment and the efficiency of resource utilization is improved.

[0004] In view of the above problems, this application provides a demand-oriented dyeing and printing strategy guidance system and method.

[0005] The first aspect disclosed in this application provides a demand-oriented dyeing and printing strategy guidance system. This system includes: an environmental constraint acquisition module for acquiring application environment constraint characteristics of the dyed and printed materials, wherein the application environment constraint characteristics characterize the performance constraint parameters and constraint thresholds of the dyed and printed materials under the application environment; a process analysis module for performing process analysis on the dyeing and printing design according to the dyeing and printing image and material distribution to obtain process difference parameters and difference distribution; a constraint allocation module for allocating constraint parameters to the process difference parameters and difference distribution based on the application environment constraint characteristics to obtain a constraint map; and a dyeing and printing guidance module for decomposing the dyeing and printing design process based on the constraint map to obtain a dyeing and printing strategy, and using the dyeing and printing strategy to provide dyeing and printing guidance.

[0006] Another aspect of this application discloses a demand-oriented method for guiding printing and dyeing strategies. This method includes: acquiring the application environment constraint characteristics of the printed and dyed materials, wherein the application environment constraint characteristics characterize the performance constraint parameters and constraint thresholds of the printed and dyed materials under the application environment; performing process analysis on the printing and dyeing design according to the printing and dyeing image and material distribution to obtain process difference parameters and difference distribution; allocating constraint parameters to the process difference parameters and difference distribution based on the application environment constraint characteristics to obtain a constraint map; decomposing the printing and dyeing design process based on the constraint map to obtain a printing and dyeing strategy; and using the printing and dyeing strategy to guide printing and dyeing.

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

[0008] This approach utilizes an environmental constraint acquisition module to obtain the application environment constraint characteristics of the dyed and printed materials. These characteristics characterize the performance constraint parameters and thresholds of the dyed and printed materials under the application environment. A process analysis module analyzes the dyeing and printing design according to the dyeing and printing pattern and material distribution to obtain process difference parameters and their distribution. A constraint allocation module allocates constraint parameters based on the application environment constraint characteristics and their distribution to obtain a constraint map. A dyeing and printing guidance module decomposes the dyeing and printing design process based on the constraint map to obtain dyeing and printing strategies, which are then used for dyeing and printing guidance. This solves the technical problem in existing dyeing and printing engineering technologies where dyeing and printing designs cannot accurately allocate process parameters according to the application environment, leading to a mismatch between the performance of the dyed and printed materials and actual needs. It achieves the technical effect of generating accurate dyeing and printing strategies through constraint maps in dyeing and printing engineering technologies, ensuring that the performance of the dyed and printed materials matches the needs of the application environment and improving resource utilization efficiency.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below. Attached Figure Description

[0010] Figure 1 This application provides a schematic diagram of a demand-oriented dyeing and printing strategy guidance system.

[0011] Figure 2 This application provides a flowchart illustrating a demand-oriented dyeing and printing strategy guidance method.

[0012] Figure labeling: Module 11 for environmental constraint acquisition, Module 12 for process analysis, Module 13 for constraint allocation, and Module 14 for dyeing and printing guidance. Detailed Implementation

[0013] This application provides a demand-oriented dyeing strategy guidance system and method, which solves the technical problem in existing dyeing engineering technology that dyeing design cannot accurately allocate process parameters according to the application environment, resulting in a mismatch between the performance of dyed and printed materials and actual needs. It achieves the technical effect of generating accurate dyeing strategies through constraint maps in dyeing engineering technology, making the performance of dyed and printed materials consistent with the needs of the application environment and improving resource utilization efficiency.

[0014] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0015] Example 1, as Figure 1 As shown in the figure, this application provides a demand-oriented dyeing and printing strategy guidance system, which includes:

[0016] The environmental constraint acquisition module 11 is used to acquire the application environment constraint characteristics of the dyed and printed materials. The application environment constraint characteristics characterize the performance constraint parameters and constraint thresholds of the dyed and printed materials under the application environment.

[0017] Specifically, in the environmental constraint acquisition module 11, data is obtained from customer requirement documents and industry standard databases (such as ISO 105, AATCC, etc.). Key performance indicators are extracted from multiple sources of information, including indicators and technical specifications of end products, to construct performance constraint parameters and thresholds for printed and dyed materials under different application environments. For example, if the fabric is used in a medical protective scenario, specific indicators such as the upper limit of sterilization temperature tolerance (e.g., 134℃±2℃), the threshold for the number of chemical disinfectant immersion times (e.g., greater than or equal to 50 times), and the blood osmotic pressure threshold (e.g., greater than or equal to 20 kPa) will be set for each area. By summarizing these constraints under application environments, application environment constraint characteristics are formed, providing quantifiable boundary conditions and decision benchmarks for subsequent process difference analysis and parameter allocation.

[0018] The process analysis module 12 is used to analyze the printing and dyeing design according to the printing and dyeing picture and material distribution, and obtain the process difference parameters and difference distribution.

[0019] Specifically, in the process analysis module 12, the digital printing and dyeing image provided by the designer is first imported into the system terminal. Image analysis technology is then used to calculate the color, texture complexity, and pattern precision of each pixel. For color calculation, a color space conversion is used to transform the color information in the printing and dyeing image from RGB space to CIELAB color space to obtain the chromaticity and luminance values ​​of each pixel. Texture complexity is analyzed through the local texture frequency of the image to evaluate the texture changes in each region. Specifically, the printing and dyeing image is divided into multiple small regions (such as 3×3 or 5×5 pixel windows). For each small region, a local Fourier transform (such as a two-dimensional fast Fourier transform, FFT) is used to transform the printing and dyeing image from the spatial domain to the frequency domain. In the frequency domain, the frequency components of the texture are distributed in low-frequency and high-frequency regions. Low frequencies represent larger, smoother texture structures, while high frequencies represent finer, more complex textures. By summing the amplitude spectra of the low-frequency and high-frequency regions in the frequency domain image, the energy of each region is obtained. Then, the local texture complexity is evaluated based on the ratio of the energy of the high-frequency region to the energy of the low-frequency region. Pattern precision is determined based on design requirements, weighted by the requirements for line fineness and detail clarity. Simultaneously, scanning equipment is used to examine different material areas (such as fiber layers, fabric patches, and laminated areas) to obtain the porosity of each area. By combining these material properties with color, texture, and pattern precision information, and mapping them to the corresponding process parameters (such as dye type, dye concentration, dye adsorption capacity, dyeing temperature, dyeing time, and inkjet pressure) for different areas of the printed image, the process differences and their distribution in each area during the dyeing process are determined. This helps in subsequent process optimization and parameter allocation, providing data support for developing precise dyeing strategies.

[0020] The constraint allocation module 13 is used to allocate constraint parameters to the process difference parameters and difference distribution based on the constraint characteristics of the application environment, and obtain a constraint map.

[0021] Specifically, in the constraint allocation module 13, after obtaining the application environment constraint characteristics and process difference parameters and their distribution, each process difference parameter and its distribution are analyzed to construct a process distribution map structure containing multiple segmented regions. Then, application environment constraint characteristics are allocated to each segmented region in the process distribution map structure to ensure that the process difference parameters match the environmental constraint requirements in different regions. For example, some regions may have more refined color requirements, necessitating the selection of processes with higher dye adsorption capacity. After the process difference parameters and environmental constraints are matched, the process distribution map structure is assigned values ​​based on the matching results, generating a constraint map. This constraint map shows the degree of matching between process difference parameters and environmental constraints in different regions. Each segmented region in the map is assigned different constraint parameters, which describe how to optimally allocate and adjust process differences under specific environmental conditions to ensure that the final dyeing and printing products meet predetermined performance requirements in actual use. This achieves precise alignment between process design and application environment constraints, improving the accuracy and effectiveness of the dyeing and printing process.

[0022] Furthermore, the constraint assignment module 13 includes:

[0023] Based on the process difference parameters and their distribution, a process distribution map structure is constructed, which includes multiple segmented regions. The application environment constraint features are used to perform constraint traversal on each segmented region in the process distribution map structure, and constraint parameters for each segmented region are configured. Constraints are assigned to the process distribution map structure according to the constraint parameters of each segmented region to establish a constraint map for the design distribution map.

[0024] In a preferred embodiment, the dyeing and printing design is first segmented based on the calculated process difference parameters and their distribution. Then, the segmentation results are further segmented based on the penetration difference regions of the material distribution. By superimposing the results of the two segmentations, a process distribution map structure is constructed. This process distribution map structure divides the entire dyeing and printing design area into multiple smaller segments. Each segment represents an area with similar process characteristics in the design and corresponds to a set of process difference parameters. For example, some areas may be segments with relatively uniform color, while others may have more complex textures. Subsequently, the previously obtained application environment constraint features are used to constrain these segmented areas. That is, each segment is matched with the constraint conditions at the corresponding positions in the application environment constraint features, thereby configuring corresponding constraint parameters for each segment, such as penetration depth, number of soaking times, and color fixing process, to ensure that the design area can meet the predetermined performance requirements in a specific application environment. Next, constraint parameters will be assigned to the entire process distribution diagram structure. Specifically, constraint parameters will be mapped to the specific locations of each segmented region within the process distribution diagram structure, thus configuring corresponding constraints for each segmented region. This ensures that the dyeing process in each region meets environmental constraints and maintains consistency throughout the process. After the constraints of each segmented region in each process distribution diagram structure are marked, a constraint map will be created. This map displays the constraint situation of different regions in the entire design distribution diagram, providing detailed guidance for subsequent dyeing and printing processes and ensuring that dyed and printed products achieve optimal performance in various usage scenarios.

[0025] Furthermore, the constraint assignment module 13 includes:

[0026] Based on the difference distribution associated with the process difference parameters and the design screen, region segmentation is performed to obtain a process difference distribution map; based on the difference distribution associated with the material distribution in the process difference distribution map, region segmentation is performed to obtain a penetration difference distribution map; based on the process difference map and the penetration difference map, the process distribution map structure is constructed by overlaying and segmenting.

[0027] In one feasible implementation, the dyeing and printing design is first segmented based on process difference parameters. During this stage, image features such as color, texture complexity, and pattern precision of each pixel in the design are extracted from the process difference parameters. Specific values ​​of these features are obtained from the corresponding difference distribution, and the gradient of these image features is calculated. By analyzing these gradients, adjacent pixels are merged to generate a process difference distribution map. Subsequently, the process difference distribution map is used to further segment the difference distribution related to material distribution. For example, for areas related to silk necklines, due to the special nature of the material, low-pressure inkjet technology (0.15 MPa) is used, meaning that the dye penetration is lower, and the dye's adhesion to silk relies more on the surface layer. For synthetic fiber hems, high-pressure penetration technology (0.8 MPa) is used, requiring the dye to penetrate deeper into the fiber. To achieve these process requirements, the regions of different materials are segmented based on their porosity distribution to identify areas with varying penetration depths. The segmentation results are then marked on the original image (dyeing and printing design screen) corresponding to the process difference distribution map, identifying areas with different penetration depth requirements and forming a penetration difference distribution map. Finally, the process difference distribution map and the penetration difference distribution map are overlaid and segmented through interactive region analysis to construct the final process distribution map structure. In this structure, the process parameters (such as inkjet pressure and penetration pressure) and material characteristics (such as the differences between silk and synthetic fibers) of each region are comprehensively considered, resulting in multiple segmented regions. Each segmented region is assigned specific process requirements and penetration parameters, ensuring that different material regions can be dyed using suitable processes and providing a precise process distribution basis for formulating dyeing and printing strategies.

[0028] Furthermore, the constraint assignment module 13 includes:

[0029] Based on the printing and dyeing design, the process difference gradient of each pixel is calculated, including the color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient. Based on the color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient, a comprehensive process difference gradient is generated for each pixel. A region growing algorithm with sampling gradient constraints merges adjacent pixels to form connected regions, establishes process partitions, and configures weight labels according to the process difference gradient to obtain the process difference distribution map.

[0030] In one optional implementation, the color, texture complexity, and pattern precision of each pixel in the printing and dyeing design image are first extracted from the process difference parameters. By calculating the differences in color, texture complexity, and pattern precision between adjacent pixels, the color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient between adjacent pixels are obtained. These gradient data are then aggregated to form the process difference gradient for each pixel. Subsequently, the three gradient data for each pixel are processed using a maximum-minimum normalization method to ensure that these data are all under the same dimension. Then, the three normalized gradient data for each pixel are weighted and fused using preset weights to obtain the comprehensive process difference gradient for each pixel. This comprehensive process difference gradient can comprehensively reflect the process requirement differences of each pixel in terms of color, texture, and pattern precision, providing a basis for subsequent process optimization. Next, a region growing algorithm with sampling gradient constraints is used to merge adjacent pixels based on the comprehensive process difference gradient. Specifically, the algorithm randomly selects a pixel as the initial pixel and, starting from this initial pixel, incorporates adjacent pixels into the same region according to the similarity of the comprehensive process difference gradient (which can be measured using absolute deviation) and a preset similarity, until all pixels are divided. This divides the printing and dyeing design into multiple connected regions with similar process requirements, and each connected region is considered a process partition. Finally, a weight label is assigned to each region based on the process difference gradient of each process partition. This weight label represents the proportion of the average process difference gradient of the process partition to the total process difference gradient. By assigning weight labels to corresponding positions in the printing and dyeing design, a final process difference distribution map is generated. In this map, each region is marked as a specific process requirement region. The differences in these regions will help in the subsequent printing and dyeing process to accurately select different dyeing processes and parameters, ensuring that the final product meets the design requirements.

[0031] Furthermore, the constraint assignment module 13 also includes:

[0032] By scanning the material, the porosity distribution of the material is identified; based on the surface topology of the material and the porosity distribution, a permeability coefficient matrix is ​​established; and based on the permeability coefficient matrix, regional clustering is performed to generate the permeability difference distribution map.

[0033] In one feasible implementation, the porosity distribution within the material is first obtained from process difference parameters using scanning equipment (such as a high-resolution microscope, X-ray CT scanner, or electron microscope). This porosity distribution marks the pore size, number, and porosity of each point or small region in the material, reflecting its permeability. Subsequently, based on the material surface topology, the points or small regions in the porosity distribution are located, and the permeability coefficient of each point or small region is calculated using the Kozeny-Carman equation. By mapping these permeability coefficients to locations, a permeability coefficient matrix is ​​constructed, where each element represents the permeability coefficient of a specific location or region. Next, region clustering is performed based on the constructed permeability coefficient matrix. Clustering algorithms (such as K-means, DBSCAN, etc.) divide the material into multiple regions based on the similarity of permeability coefficients, with each region exhibiting similar permeability coefficient characteristics. Finally, the clustered regions are mapped onto the dyeing and printing design screen to form a permeability difference distribution map. This map aids in subsequent process design or dyeing process decisions, ensuring that the dye penetration effect in different regions meets design requirements.

[0034] Furthermore, the constraint assignment module 13 also includes:

[0035] Based on the process difference distribution map and the penetration difference distribution map, process interaction area analysis is performed on adjacent segmented regions to determine the cross-influence area, and the process cross-influence area is marked according to the cross-influence characteristics.

[0036] In one optional implementation, after obtaining the process difference distribution map and the penetration difference distribution map, these two maps are superimposed, that is, jointly mapped onto the dyeing and printing design screen to form a superimposed difference distribution map. In this superimposed difference distribution map, the average process difference gradient and average penetration coefficient of each region in adjacent areas are extracted. By identifying the deviation of the average process difference gradient and average penetration coefficient of adjacent areas, that is, by calculating the absolute difference between the average process difference gradient and average penetration coefficient of the two regions, the degree of deviation of the process difference gradient and the degree of deviation of the penetration coefficient between the two regions are obtained. When any one or both of the degree of deviation of the process difference gradient and the degree of deviation of the penetration coefficient exceed the preset deviation level, it indicates that there is a large change in this adjacent area, which may produce cross-influence and lead to inconsistencies in the dyeing process, such as uneven color transition or uneven penetration. To avoid this problem, the deviation exceeding a preset level is divided by the corresponding maximum parameter value (maximum process difference gradient or maximum penetration coefficient). The calculated result is then multiplied by the baseline buffer width to obtain the cross-boundary buffer width. Based on this cross-boundary buffer width, a buffer area is constructed at the cross-boundary as a cross-influence zone. If both exceed the preset deviation level, the results of dividing by the corresponding maximum parameter are added together and then multiplied by the baseline buffer width. Finally, the cross-influence zone is marked on the overlay difference distribution map to form the final process distribution map structure. This allows the system terminal to more accurately control the process and penetration effect during dyeing, avoiding uneven dyeing or color difference problems in the cross-influence zone, thereby improving the quality and consistency of the final dyeing and printing products.

[0037] The dyeing and printing guidance module 14 is used to decompose the dyeing and printing design process based on the constraint map, obtain the dyeing and printing strategy, and use the dyeing and printing strategy to provide dyeing and printing guidance.

[0038] Specifically, in the dyeing and printing guidance module 14, after obtaining the constraint map, the dyeing and printing design is decomposed based on this map. The constraint map includes process parameters, penetration requirements, and performance constraints of the application environment for each region. This information provides specific boundary conditions and constraints for the design of the dyeing and printing process. By using regional environmental protection strategies to maximize and optimize process parameters, specific dyeing and printing strategies adapted to the needs of different regions are formulated. Finally, the generated dyeing and printing strategies are used to provide dyeing and printing guidance for different regions, instructing production personnel on how to adjust process parameters, configure equipment, and arrange production processes in actual production. This ensures that all indicators in the entire dyeing and printing process achieve the expected results, thereby improving the quality and consistency of the final product.

[0039] Furthermore, the dyeing and printing guidance module 14 includes:

[0040] Based on the constraint map, the dyeing and printing design process is decomposed to obtain the set of process parameters for each dyeing and printing design zone; regional environmental protection strategies are matched based on geographic information; according to the regional environmental protection strategies, dyeing and printing process parameters are mapped and analyzed to obtain process environmental protection constraint indicators; according to the process environmental protection constraint indicators, the set of process parameters is searched to obtain the dyeing and printing strategy that satisfies the process environmental protection constraint indicators and maximizes the constraint map.

[0041] In a preferred embodiment, the dyeing and printing design is first decomposed based on an existing constraint map to obtain a set of process parameters for each design zone. Information from the constraint map, such as color requirements and penetration depth, is used to break down the design, ensuring that the process requirements for each area are clearly and in detail allocated. For example, some areas may have more specific color requirements, while other areas may require higher penetration. Based on these requirements, the system assigns initial process parameters such as dye, dyeing time, temperature, and pressure to each area, forming a complete set of process parameters. Subsequently, the geographical information of the dyeing and printing process is located, and local regional environmental protection strategies are matched based on the geographical information. Regional environmental protection strategies typically include emission standards (such as VOC emissions and wastewater discharge), energy use standards, waste disposal standards, and restrictions on the use of certain chemicals (such as environmental requirements for dyes and auxiliaries). Subsequently, key environmental indicators related to the dyeing and printing process are extracted from regional environmental protection strategies. For example, some regions may have specific regulations on dye emissions, requiring the use of low-toxicity or harmless dyes; other regions may stipulate upper limits on the chemical oxygen demand (COD) or total nitrogen content in wastewater; and there may also be regulations regarding the volatile organic compound (VOC) content in exhaust emissions. By mapping the key environmental indicators extracted from regional environmental protection strategies to the specific process parameters of dyeing and printing design—that is, mapping the process parameters directly related to these key environmental indicators—interference from irrelevant process parameters is avoided. Then, based on the environmental constraints of the process, the directly related process parameters in the set of process parameters are searched and optimized. Specifically, the directly related process parameters are input into the simulation model for simulation, and the simulated environmental indicators of the current process parameters are obtained. The current process parameters are taken as the temporary optimal result, and the simulated environmental indicator set is taken as the temporary optimal environmental indicators. Then, according to the preset adjustment step size, these process parameters are adjusted within the constraint map's range, and the adjustment results are input into the simulation model for simulation, obtaining the simulated environmental indicators of the adjusted process parameters. These are compared with the temporary optimal environmental indicators; if they are greater, they are replaced. This process is repeated until the maximum number of iterations is reached. After optimization, the current temporary optimal result is combined with the process parameters that were not optimized to form a dyeing and printing process strategy, which serves as the optimal dyeing and printing strategy for subsequent dyeing and printing guidance, ensuring that the entire dyeing and printing process is both environmentally friendly and efficient.

[0042] Furthermore, the dyeing and printing guidance module 14 includes:

[0043] Based on the dyeing and printing strategy, risk areas and safe areas are identified; based on the risk areas, process gradient control correlation analysis is performed to obtain control identification gradients; a mapping relationship between the safe areas, risk areas and the constraint map is established, and the control identification gradient is used as the control feedback step size to configure the risk areas; based on the constraint map, dyeing and printing guidance is provided according to the dyeing and printing strategy.

[0044] In one optional implementation, a risk assessment is first performed on each area of ​​the dyeing design based on the dyeing strategy, identifying risk areas and safe areas. Risk areas typically refer to those areas where, due to differences in processes, material characteristics, etc., the dyeing effect is prone to instability or exceeding the tolerance range. For example, some areas may experience excessive dye penetration or uneven dyeing due to excessively high color requirements or complex materials. Safe areas, on the other hand, refer to those areas where the dyeing process is relatively stable and process control can easily achieve the expected results. Subsequently, based on the identified risk areas, the process parameters of the risk areas and the corresponding constraints in the constraint map are input into a pre-trained deep neural network. This deep neural network is pre-trained through forward propagation, loss calculation, backpropagation, and parameter optimization of sample data (sample process parameters, sample constraint information, and sample control recognition gradient). After receiving the process parameters and constraint information, the deep neural network calculates the control recognition gradient based on the learned mapping relationship. This control recognition gradient reflects the magnitude and sensitivity of the process parameters that need to be adjusted in different risk areas. Next, the process parameters of the risk area and the safety area are mapped to the constraint map to establish a mapping relationship among the three. The obtained control identification gradient is then used as the control feedback step size and configured in the risk area. Finally, based on the constraint map and the defined dyeing strategy, dyeing guidance is provided for the corresponding areas according to the mapping relationship to ensure that the final product meets design requirements and environmental standards.

[0045] Furthermore, the dyeing and printing guidance module 14 includes:

[0046] Based on the constraint map, the printing and dyeing design position alignment projection is performed. When in the safe zone, continuous printing and dyeing control is performed according to the printing and dyeing strategy. When in the risk zone, the printing and dyeing control results are fed back according to the control feedback step size, and adaptive printing and dyeing guidance is performed based on the feedback results.

[0047] In one optional implementation, the dyeing and printing design is first aligned and projected according to the constraint map. That is, based on the mapping relationship between the constraint map and risk and safe areas, the risk and safe areas are mapped to their corresponding positions in the dyeing and printing design, ensuring that each area in the design corresponds to the correct position on the production line. When the dyeing and printing design is in a safe area, continuous dyeing and printing control is performed according to a preset dyeing and printing strategy. Within the safe area, the dyeing process is relatively stable, and the process parameters can be continuously operated according to the settings of the dyeing and printing strategy without frequent adjustments. The system performs precise control based on the process parameters corresponding to the constraint map and maintains the continuity of these parameters throughout the dyeing process. For example, if the color requirement for the safe area is dark blue, dyeing will be performed continuously according to the set dye concentration and dyeing time, ensuring that the final product color is consistent and meets expectations. When the dyeing and printing design is in a risk area, the dyeing and printing process needs to be fed back and adjusted in real time according to the control feedback step size. Risk areas usually have significant process differences or are more sensitive to process control, therefore requiring more precise monitoring of the dyeing effect and adjustments based on the feedback results. During this process, adjustments are made to the dyeing process based on real-time monitoring data (such as color changes, dye penetration depth, and texture details). If the dyeing effect deviates from expectations, various parameters of the dyeing process are adjusted according to the control feedback step size. For example, the dye penetration time or dye concentration can be increased or adjusted based on feedback information to ensure a more uniform dyeing effect and avoid color differences or texture distortion. Simultaneously, adaptive dyeing guidance is provided based on feedback results. That is, the aforementioned optimization process is re-executed based on real-time feedback, and process parameters are adjusted to ensure that the dyeing quality meets requirements. This not only addresses potential changes during production but also allows for adjustments to the production process based on feedback, ensuring ideal dyeing results even in complex or unstable areas.

[0048] In summary, the demand-oriented dyeing and printing strategy guidance system provided in this application has the following technical effects:

[0049] The environmental constraint acquisition module 11 is used to acquire the application environment constraint characteristics of the dyed and printed materials. These characteristics represent the performance constraint parameters and thresholds of the dyed and printed materials under the application environment. The process analysis module 12 is used to analyze the dyeing and printing design according to the dyeing and printing pattern and material distribution, obtaining process difference parameters and their distribution. The constraint allocation module 13 is used to allocate constraint parameters based on the application environment constraint characteristics and their distribution, obtaining a constraint map. The dyeing and printing guidance module 14 is used to decompose the dyeing and printing design process based on the constraint map, obtaining a dyeing and printing strategy, and using this strategy for guidance. Through these steps, the technical problem in existing dyeing and printing engineering technology—the inability to accurately allocate process parameters according to the application environment, leading to a mismatch between the performance of the dyed and printed materials and actual needs—is solved. This achieves the technical effect of generating accurate dyeing and printing strategies through constraint maps in dyeing and printing engineering technology, ensuring that the performance of the dyed and printed materials matches the requirements of the application environment and improving resource utilization efficiency.

[0050] Example 2, based on the same inventive concept as the demand-oriented dyeing and printing strategy guidance system in the foregoing examples, such as... Figure 2 As shown in the embodiment of this application, a demand-oriented dyeing and printing strategy guidance method is provided, the method comprising:

[0051] The application environment constraint characteristics of the printed and dyed materials are obtained, which characterize the performance constraint parameters and constraint thresholds of the printed and dyed materials under the application environment; the printing and dyeing design is analyzed according to the printing and dyeing pattern and material distribution to obtain process difference parameters and difference distribution; constraint parameters are allocated to the process difference parameters and difference distribution based on the application environment constraint characteristics to obtain a constraint map; the printing and dyeing design process is decomposed based on the constraint map to obtain printing and dyeing strategies, and the printing and dyeing strategies are used to guide printing and dyeing.

[0052] Furthermore, the methods include:

[0053] Based on the constraint map, the dyeing and printing design process is decomposed to obtain the set of process parameters for each dyeing and printing design zone; regional environmental protection strategies are matched based on geographic information; according to the regional environmental protection strategies, dyeing and printing process parameters are mapped and analyzed to obtain process environmental protection constraint indicators; according to the process environmental protection constraint indicators, the set of process parameters is searched to obtain the dyeing and printing strategy that satisfies the process environmental protection constraint indicators and maximizes the constraint map.

[0054] Furthermore, the methods include:

[0055] Based on the process difference parameters and their distribution, a process distribution map structure is constructed, which includes multiple segmented regions. The application environment constraint features are used to perform constraint traversal on each segmented region in the process distribution map structure, and constraint parameters for each segmented region are configured. Constraints are assigned to the process distribution map structure according to the constraint parameters of each segmented region to establish a constraint map for the design distribution map.

[0056] Furthermore, the methods include:

[0057] Based on the difference distribution associated with the process difference parameters and the design screen, region segmentation is performed to obtain a process difference distribution map; based on the difference distribution associated with the material distribution in the process difference distribution map, region segmentation is performed to obtain a penetration difference distribution map; based on the process difference map and the penetration difference map, the process distribution map structure is constructed by overlaying and segmenting.

[0058] Furthermore, the methods include:

[0059] Based on the process difference distribution map and the penetration difference distribution map, process interaction area analysis is performed on adjacent segmented regions to determine the cross-influence area, and the process cross-influence area is marked according to the cross-influence characteristics.

[0060] Furthermore, the methods include:

[0061] Based on the printing and dyeing design, the process difference gradient of each pixel is calculated, including the color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient. Based on the color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient, a comprehensive process difference gradient is generated for each pixel. A region growing algorithm with sampling gradient constraints merges adjacent pixels to form connected regions, establishes process partitions, and configures weight labels according to the process difference gradient to obtain the process difference distribution map.

[0062] Furthermore, the methods include:

[0063] By scanning the material, the porosity distribution of the material is identified; based on the surface topology of the material and the porosity distribution, a permeability coefficient matrix is ​​established; and based on the permeability coefficient matrix, regional clustering is performed to generate the permeability difference distribution map.

[0064] Furthermore, the methods include:

[0065] Based on the dyeing and printing strategy, risk areas and safe areas are identified; based on the risk areas, process gradient control correlation analysis is performed to obtain control identification gradients; a mapping relationship between the safe areas, risk areas and the constraint map is established, and the control identification gradient is used as the control feedback step size to configure the risk areas; based on the constraint map, dyeing and printing guidance is provided according to the dyeing and printing strategy.

[0066] Furthermore, the methods include:

[0067] Based on the constraint map, the printing and dyeing design position alignment projection is performed. When in the safe zone, continuous printing and dyeing control is performed according to the printing and dyeing strategy. When in the risk zone, the printing and dyeing control results are fed back according to the control feedback step size, and adaptive printing and dyeing guidance is performed based on the feedback results.

[0068] In summary, any step of the method described above can be stored as a computer instruction or program in an unrestricted computer memory, and can be called and identified by an unrestricted computer processor to implement any method in the embodiments of this application, without any additional restrictions.

[0069] Furthermore, the "first" or "second" mentioned above may not only represent a sequential relationship, but may also represent a specific concept, and / or refer to the individual or collective selection of multiple elements. Clearly, those skilled in the art can make various modifications and variations to this application without departing from its scope. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application intends to include such modifications and variations.

Claims

1. A demand-oriented dyeing and printing strategy guidance system, characterized in that, include: The environmental constraint acquisition module is used to acquire the application environment constraint characteristics of the dyed and printed materials. The application environment constraint characteristics characterize the performance constraint parameters and constraint thresholds of the dyed and printed materials under the application environment. The process analysis module is used to analyze the printing and dyeing design according to the printing and dyeing picture and material distribution, and obtain the process difference parameters and difference distribution. The constraint allocation module is used to allocate constraint parameters to the process difference parameters and difference distribution based on the constraint characteristics of the application environment, and obtain a constraint map. The dyeing and printing guidance module is used to decompose the dyeing and printing design process based on the constraint map, obtain the dyeing and printing strategy, and use the dyeing and printing strategy to provide dyeing and printing guidance. The dyeing and printing guidance module includes: Based on the constraint map, the dyeing and printing design process is decomposed to obtain the set of process parameters for each dyeing and printing design zone. Regional environmental protection strategies are matched based on geographic information; Based on the aforementioned regional environmental protection strategy, a mapping analysis of dyeing and printing process parameters is conducted to obtain environmental protection constraint indicators for the process. Based on the environmental constraints of the process, the set of process parameters is searched to obtain the dyeing and printing strategy that satisfies the environmental constraints of the process and maximizes the constraint map. Based on the dyeing and printing strategy, risk areas and safe areas are identified; Based on the risk area, a process gradient control correlation analysis is performed to obtain the control identification gradient; Establish a mapping relationship between the safe area, the risk area and the constraint map, and configure the risk area using the control identification gradient as the control feedback step size; Based on the constraint map, provide dyeing guidance according to the dyeing strategy; Based on the constraint map, the printing and dyeing design position is aligned and projected. When it is in the safe area, continuous printing and dyeing control is performed according to the printing and dyeing strategy. When in the risk zone, the dyeing control results are fed back according to the control feedback step size, and adaptive dyeing guidance is provided based on the feedback results.

2. The demand-oriented dyeing and printing strategy guidance system according to claim 1, characterized in that, The constraint assignment module includes: Based on the process difference parameters and their distribution, a process distribution map structure is constructed, which includes multiple segmented regions. The constraint characteristics of the application environment are used to perform constraint traversal on each segmented region in the process distribution diagram structure, and the constraint parameters of each segmented region are configured. The process distribution diagram structure is constrained and assigned values ​​according to the constraint parameters of each segmented region to establish a constraint map of the design distribution diagram.

3. The demand-oriented dyeing and printing strategy guidance system according to claim 2, characterized in that, The constraint assignment module includes: Based on the difference distribution associated with the process difference parameters and the design screen, the region is segmented to obtain a process difference distribution map; Based on the difference distribution associated with the material distribution in the process difference distribution map, the regions are segmented to obtain the penetration difference distribution map; The process distribution map structure is constructed by overlaying and segmenting the process difference distribution map and the penetration difference distribution map.

4. The demand-oriented dyeing and printing strategy guidance system according to claim 3, characterized in that, The constraint assignment module also includes: Based on the process difference distribution map and the penetration difference distribution map, process interaction area analysis is performed on adjacent segmented regions to determine the cross-influence area, and the process cross-influence area is marked according to the cross-influence characteristics.

5. The demand-oriented dyeing and printing strategy guidance system according to claim 3, characterized in that, The constraint assignment module includes: Based on the printing and dyeing design, calculate the process difference gradient for each pixel, including color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient. Based on the color difference gradient, texture complexity gradient, and pattern precision requirement difference gradient, a comprehensive process difference gradient is generated for each pixel. The sampling gradient-constrained region growing algorithm merges adjacent pixels to form connected regions, establishes process partitions, and configures weight labels according to the process difference gradient to obtain the process difference distribution map.

6. The demand-oriented dyeing and printing strategy guidance system according to claim 3, characterized in that, The constraint assignment module includes: By scanning the material, the porosity distribution of the material can be identified; A permeability coefficient matrix is ​​established based on the surface topology of the material and its porosity distribution. Based on the permeability coefficient matrix, regional clustering is performed to generate the permeability difference distribution map.

7. A demand-oriented method for guiding dyeing and printing strategies, characterized in that, The method is executed through a demand-oriented dyeing and printing strategy guidance system as described in any one of claims 1 to 6, comprising: Obtain the application environment constraint characteristics of the printed and dyed materials, wherein the application environment constraint characteristics characterize the performance constraint parameters and constraint thresholds of the printed and dyed materials under the application environment; The printing and dyeing design is analyzed according to the printing and dyeing pattern and material distribution to obtain the process difference parameters and difference distribution; Based on the application environment constraint characteristics, the process difference parameters and difference distribution are assigned constraint parameters to obtain a constraint map; Based on the constraint map, the printing and dyeing design process is decomposed to obtain printing and dyeing strategies, and these strategies are used to guide printing and dyeing.

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